index
int64 0
100k
| blob_id
stringlengths 40
40
| code
stringlengths 7
7.27M
| steps
listlengths 1
1.25k
| error
bool 2
classes |
|---|---|---|---|---|
99,400 |
58a90ea44db4e27ebfde1cfefffcff2147eba0b0
|
import pandas as pd
import tensorflow as tf
import numpy as np
|
[
"import pandas as pd\nimport tensorflow as tf\nimport numpy as np\n",
"<import token>\n"
] | false |
99,401 |
6ca70a7d59066a017bd328e5c641b27d9d816e4d
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-09-28 19:51
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('courses', '0003_tabla_tablita'),
]
operations = [
migrations.RenameField(
model_name='tablita',
old_name='ciudad',
new_name='pais',
),
]
|
[
"# -*- coding: utf-8 -*-\n# Generated by Django 1.11.5 on 2017-09-28 19:51\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('courses', '0003_tabla_tablita'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='tablita',\n old_name='ciudad',\n new_name='pais',\n ),\n ]\n",
"from __future__ import unicode_literals\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n dependencies = [('courses', '0003_tabla_tablita')]\n operations = [migrations.RenameField(model_name='tablita', old_name=\n 'ciudad', new_name='pais')]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('courses', '0003_tabla_tablita')]\n operations = [migrations.RenameField(model_name='tablita', old_name=\n 'ciudad', new_name='pais')]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,402 |
cae1f513da3b53107459c0ec43a8e4fa1258eb26
|
#zadanie 15
liczba = 10
print("Wartość liczby to {}, a {} to jej druga potęga".format(liczba, liczba**2))
|
[
"#zadanie 15\nliczba = 10\nprint(\"Wartość liczby to {}, a {} to jej druga potęga\".format(liczba, liczba**2))",
"liczba = 10\nprint('Wartość liczby to {}, a {} to jej druga potęga'.format(liczba, \n liczba ** 2))\n",
"<assignment token>\nprint('Wartość liczby to {}, a {} to jej druga potęga'.format(liczba, \n liczba ** 2))\n",
"<assignment token>\n<code token>\n"
] | false |
99,403 |
46f241700f11cba1ecd591bb633c729857ac770a
|
from Classes.Lexema import Lexema
def default_compare(stored_value: object, compared_value: object) -> bool:
"""
Простейшее сравнение.
:param stored_value: Хранимое значение в хеш-таблице.
:param compared_value: Значение, с которым сравнивается stored_value.
:return: True, если stored_value и compared_value совпадают. Иначе - False.
"""
if stored_value is None:
return False
return stored_value == compared_value
def str_object_lexema_compare(stored_value: Lexema, compared_value: str) -> bool:
"""
Сравнение строковой лексемы и объекта-лексемы.
:param stored_value: Лексема-объект.
:param compared_value: Строковая лексема.
:return: True, если лексема-объект содержит строковую лексему в char.
"""
if stored_value is None:
return False
return stored_value.char == compared_value
|
[
"from Classes.Lexema import Lexema\n\n\ndef default_compare(stored_value: object, compared_value: object) -> bool:\n \"\"\"\n Простейшее сравнение.\n :param stored_value: Хранимое значение в хеш-таблице.\n :param compared_value: Значение, с которым сравнивается stored_value.\n :return: True, если stored_value и compared_value совпадают. Иначе - False.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value == compared_value\n\n\ndef str_object_lexema_compare(stored_value: Lexema, compared_value: str) -> bool:\n \"\"\"\n Сравнение строковой лексемы и объекта-лексемы.\n :param stored_value: Лексема-объект.\n :param compared_value: Строковая лексема.\n :return: True, если лексема-объект содержит строковую лексему в char.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value.char == compared_value\n",
"from Classes.Lexema import Lexema\n\n\ndef default_compare(stored_value: object, compared_value: object) ->bool:\n \"\"\"\n Простейшее сравнение.\n :param stored_value: Хранимое значение в хеш-таблице.\n :param compared_value: Значение, с которым сравнивается stored_value.\n :return: True, если stored_value и compared_value совпадают. Иначе - False.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value == compared_value\n\n\ndef str_object_lexema_compare(stored_value: Lexema, compared_value: str\n ) ->bool:\n \"\"\"\n Сравнение строковой лексемы и объекта-лексемы.\n :param stored_value: Лексема-объект.\n :param compared_value: Строковая лексема.\n :return: True, если лексема-объект содержит строковую лексему в char.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value.char == compared_value\n",
"<import token>\n\n\ndef default_compare(stored_value: object, compared_value: object) ->bool:\n \"\"\"\n Простейшее сравнение.\n :param stored_value: Хранимое значение в хеш-таблице.\n :param compared_value: Значение, с которым сравнивается stored_value.\n :return: True, если stored_value и compared_value совпадают. Иначе - False.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value == compared_value\n\n\ndef str_object_lexema_compare(stored_value: Lexema, compared_value: str\n ) ->bool:\n \"\"\"\n Сравнение строковой лексемы и объекта-лексемы.\n :param stored_value: Лексема-объект.\n :param compared_value: Строковая лексема.\n :return: True, если лексема-объект содержит строковую лексему в char.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value.char == compared_value\n",
"<import token>\n<function token>\n\n\ndef str_object_lexema_compare(stored_value: Lexema, compared_value: str\n ) ->bool:\n \"\"\"\n Сравнение строковой лексемы и объекта-лексемы.\n :param stored_value: Лексема-объект.\n :param compared_value: Строковая лексема.\n :return: True, если лексема-объект содержит строковую лексему в char.\n \"\"\"\n if stored_value is None:\n return False\n return stored_value.char == compared_value\n",
"<import token>\n<function token>\n<function token>\n"
] | false |
99,404 |
b3fc5ba1f91da7b58892311c39bb2be79fd15bf1
|
from code_challenge_6.challenge_6 import my_filter
from code_challenge_6.challenge_6 import number_is_less_than_5
from code_challenge_6.challenge_6 import number_is_even
from code_challenge_6.challenge_6 import number_is_odd
from code_challenge_6.challenge_6 import a_dodgy_function
from code_challenge_6.challenge_6 import an_even_more_dodgy_function
def test_filter_less_than_5():
result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)
assert result == [3, 4]
def test_filter_number_is_odd():
result = my_filter([3, 4, 5, 6, 7, 8], number_is_odd)
assert result == [3, 5, 7]
def test_filter_number_is_even():
result = my_filter([3, 4, 5, 6, 7, 8], number_is_even)
assert result == [4, 6, 8]
def test_filter_with_an_invalid_fuction():
result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)
assert result == [3, 4, 5, 6, 7, 8]
def test_filter_with_another_invalid_fuction():
result = my_filter([3, 4, 5, 6, 7, 8], an_even_more_dodgy_function)
assert result == [3, 4, 5, 6, 7, 8]
|
[
"from code_challenge_6.challenge_6 import my_filter\nfrom code_challenge_6.challenge_6 import number_is_less_than_5\nfrom code_challenge_6.challenge_6 import number_is_even\nfrom code_challenge_6.challenge_6 import number_is_odd\nfrom code_challenge_6.challenge_6 import a_dodgy_function\nfrom code_challenge_6.challenge_6 import an_even_more_dodgy_function\n\n\ndef test_filter_less_than_5():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)\n assert result == [3, 4]\n\n\ndef test_filter_number_is_odd():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_odd)\n assert result == [3, 5, 7]\n\n\ndef test_filter_number_is_even():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_even)\n assert result == [4, 6, 8]\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\ndef test_filter_with_another_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], an_even_more_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n",
"<import token>\n\n\ndef test_filter_less_than_5():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)\n assert result == [3, 4]\n\n\ndef test_filter_number_is_odd():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_odd)\n assert result == [3, 5, 7]\n\n\ndef test_filter_number_is_even():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_even)\n assert result == [4, 6, 8]\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\ndef test_filter_with_another_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], an_even_more_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n",
"<import token>\n\n\ndef test_filter_less_than_5():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)\n assert result == [3, 4]\n\n\ndef test_filter_number_is_odd():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_odd)\n assert result == [3, 5, 7]\n\n\n<function token>\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\ndef test_filter_with_another_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], an_even_more_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n",
"<import token>\n\n\ndef test_filter_less_than_5():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)\n assert result == [3, 4]\n\n\ndef test_filter_number_is_odd():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_odd)\n assert result == [3, 5, 7]\n\n\n<function token>\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\n<function token>\n",
"<import token>\n\n\ndef test_filter_less_than_5():\n result = my_filter([3, 4, 5, 6, 7, 8], number_is_less_than_5)\n assert result == [3, 4]\n\n\n<function token>\n<function token>\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n\n\ndef test_filter_with_an_invalid_fuction():\n result = my_filter([3, 4, 5, 6, 7, 8], a_dodgy_function)\n assert result == [3, 4, 5, 6, 7, 8]\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,405 |
1ca948fa1e2877b4732705ee6170d7f0b330a21c
|
from fractions import Fraction
def get_combos(elems, k):
'''Given a list of elements (elems) and a specified combo size k (non-neg int),
Return a set of tuples representing every unique k-length-combo of elements'''
#Base cases
if k == 1:
return set((e,) for e in elems)
#Recursive case
n = len(elems)
output = set()
for i in range(n-k+1):
for subcombo in get_combos(elems[i+1:],k-1):
output.add( tuple( sorted( (elems[i],)+subcombo ) ) )
return output
#t = lambda a, b, skills: Fraction(skills[a],skills[a]+skills[b])
def t(a,b,skills,mem):
if (a,b) in mem:
return mem[(a,b)], mem
output = Fraction(skills[a],skills[a]+skills[b])
mem[(a,b)] = output
return output, mem
def tn(L,n,skills,mem):
a, rest = L[0], L[1:]
if n == 2: # Base case
return t(a, rest[0], skills, mem)
groupings = get_combos(rest, int((n/2)-1))
output = 0
for A_group in groupings:
a_win, mem = tn([a]+list(A_group), n/2, skills, mem)
B_group = [i for i in rest if i not in A_group]
for b in B_group:
B_losers = [j for j in B_group if j != b]
b_win, mem = tn([b]+list(B_losers), n/2, skills, mem)
ab, mem = t(a,b, skills, mem)
output += ab * a_win * b_win
return output/len(groupings), mem
def solution(skills):
#print(f'skills input: {skills}')
if 'Andy' not in skills:
return '01'
rest = [key for key in skills if key != 'Andy']
out = str(tn(['Andy']+rest, len(skills), skills, {})[0]).split('/')
return out[0]+out[1]
print('expect 2940764800, result:', solution({'Andy': 7, 'Novak': 5, 'Roger': 3, 'Rafael': 2}))
|
[
"from fractions import Fraction\r\n\r\ndef get_combos(elems, k):\r\n '''Given a list of elements (elems) and a specified combo size k (non-neg int),\r\n Return a set of tuples representing every unique k-length-combo of elements'''\r\n #Base cases\r\n if k == 1:\r\n return set((e,) for e in elems)\r\n #Recursive case\r\n n = len(elems)\r\n output = set()\r\n for i in range(n-k+1):\r\n for subcombo in get_combos(elems[i+1:],k-1):\r\n output.add( tuple( sorted( (elems[i],)+subcombo ) ) )\r\n return output\r\n\r\n#t = lambda a, b, skills: Fraction(skills[a],skills[a]+skills[b])\r\ndef t(a,b,skills,mem):\r\n if (a,b) in mem:\r\n return mem[(a,b)], mem\r\n output = Fraction(skills[a],skills[a]+skills[b])\r\n mem[(a,b)] = output\r\n return output, mem\r\n\r\ndef tn(L,n,skills,mem):\r\n a, rest = L[0], L[1:]\r\n if n == 2: # Base case\r\n return t(a, rest[0], skills, mem)\r\n groupings = get_combos(rest, int((n/2)-1))\r\n output = 0\r\n for A_group in groupings:\r\n a_win, mem = tn([a]+list(A_group), n/2, skills, mem)\r\n B_group = [i for i in rest if i not in A_group]\r\n for b in B_group:\r\n B_losers = [j for j in B_group if j != b]\r\n b_win, mem = tn([b]+list(B_losers), n/2, skills, mem)\r\n ab, mem = t(a,b, skills, mem)\r\n output += ab * a_win * b_win\r\n return output/len(groupings), mem\r\n\r\ndef solution(skills):\r\n #print(f'skills input: {skills}')\r\n if 'Andy' not in skills:\r\n return '01'\r\n rest = [key for key in skills if key != 'Andy']\r\n out = str(tn(['Andy']+rest, len(skills), skills, {})[0]).split('/')\r\n return out[0]+out[1]\r\n\r\nprint('expect 2940764800, result:', solution({'Andy': 7, 'Novak': 5, 'Roger': 3, 'Rafael': 2}))",
"from fractions import Fraction\n\n\ndef get_combos(elems, k):\n \"\"\"Given a list of elements (elems) and a specified combo size k (non-neg int),\n Return a set of tuples representing every unique k-length-combo of elements\"\"\"\n if k == 1:\n return set((e,) for e in elems)\n n = len(elems)\n output = set()\n for i in range(n - k + 1):\n for subcombo in get_combos(elems[i + 1:], k - 1):\n output.add(tuple(sorted((elems[i],) + subcombo)))\n return output\n\n\ndef t(a, b, skills, mem):\n if (a, b) in mem:\n return mem[a, b], mem\n output = Fraction(skills[a], skills[a] + skills[b])\n mem[a, b] = output\n return output, mem\n\n\ndef tn(L, n, skills, mem):\n a, rest = L[0], L[1:]\n if n == 2:\n return t(a, rest[0], skills, mem)\n groupings = get_combos(rest, int(n / 2 - 1))\n output = 0\n for A_group in groupings:\n a_win, mem = tn([a] + list(A_group), n / 2, skills, mem)\n B_group = [i for i in rest if i not in A_group]\n for b in B_group:\n B_losers = [j for j in B_group if j != b]\n b_win, mem = tn([b] + list(B_losers), n / 2, skills, mem)\n ab, mem = t(a, b, skills, mem)\n output += ab * a_win * b_win\n return output / len(groupings), mem\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\nprint('expect 2940764800, result:', solution({'Andy': 7, 'Novak': 5,\n 'Roger': 3, 'Rafael': 2}))\n",
"<import token>\n\n\ndef get_combos(elems, k):\n \"\"\"Given a list of elements (elems) and a specified combo size k (non-neg int),\n Return a set of tuples representing every unique k-length-combo of elements\"\"\"\n if k == 1:\n return set((e,) for e in elems)\n n = len(elems)\n output = set()\n for i in range(n - k + 1):\n for subcombo in get_combos(elems[i + 1:], k - 1):\n output.add(tuple(sorted((elems[i],) + subcombo)))\n return output\n\n\ndef t(a, b, skills, mem):\n if (a, b) in mem:\n return mem[a, b], mem\n output = Fraction(skills[a], skills[a] + skills[b])\n mem[a, b] = output\n return output, mem\n\n\ndef tn(L, n, skills, mem):\n a, rest = L[0], L[1:]\n if n == 2:\n return t(a, rest[0], skills, mem)\n groupings = get_combos(rest, int(n / 2 - 1))\n output = 0\n for A_group in groupings:\n a_win, mem = tn([a] + list(A_group), n / 2, skills, mem)\n B_group = [i for i in rest if i not in A_group]\n for b in B_group:\n B_losers = [j for j in B_group if j != b]\n b_win, mem = tn([b] + list(B_losers), n / 2, skills, mem)\n ab, mem = t(a, b, skills, mem)\n output += ab * a_win * b_win\n return output / len(groupings), mem\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\nprint('expect 2940764800, result:', solution({'Andy': 7, 'Novak': 5,\n 'Roger': 3, 'Rafael': 2}))\n",
"<import token>\n\n\ndef get_combos(elems, k):\n \"\"\"Given a list of elements (elems) and a specified combo size k (non-neg int),\n Return a set of tuples representing every unique k-length-combo of elements\"\"\"\n if k == 1:\n return set((e,) for e in elems)\n n = len(elems)\n output = set()\n for i in range(n - k + 1):\n for subcombo in get_combos(elems[i + 1:], k - 1):\n output.add(tuple(sorted((elems[i],) + subcombo)))\n return output\n\n\ndef t(a, b, skills, mem):\n if (a, b) in mem:\n return mem[a, b], mem\n output = Fraction(skills[a], skills[a] + skills[b])\n mem[a, b] = output\n return output, mem\n\n\ndef tn(L, n, skills, mem):\n a, rest = L[0], L[1:]\n if n == 2:\n return t(a, rest[0], skills, mem)\n groupings = get_combos(rest, int(n / 2 - 1))\n output = 0\n for A_group in groupings:\n a_win, mem = tn([a] + list(A_group), n / 2, skills, mem)\n B_group = [i for i in rest if i not in A_group]\n for b in B_group:\n B_losers = [j for j in B_group if j != b]\n b_win, mem = tn([b] + list(B_losers), n / 2, skills, mem)\n ab, mem = t(a, b, skills, mem)\n output += ab * a_win * b_win\n return output / len(groupings), mem\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\n<code token>\n",
"<import token>\n<function token>\n\n\ndef t(a, b, skills, mem):\n if (a, b) in mem:\n return mem[a, b], mem\n output = Fraction(skills[a], skills[a] + skills[b])\n mem[a, b] = output\n return output, mem\n\n\ndef tn(L, n, skills, mem):\n a, rest = L[0], L[1:]\n if n == 2:\n return t(a, rest[0], skills, mem)\n groupings = get_combos(rest, int(n / 2 - 1))\n output = 0\n for A_group in groupings:\n a_win, mem = tn([a] + list(A_group), n / 2, skills, mem)\n B_group = [i for i in rest if i not in A_group]\n for b in B_group:\n B_losers = [j for j in B_group if j != b]\n b_win, mem = tn([b] + list(B_losers), n / 2, skills, mem)\n ab, mem = t(a, b, skills, mem)\n output += ab * a_win * b_win\n return output / len(groupings), mem\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\n<code token>\n",
"<import token>\n<function token>\n\n\ndef t(a, b, skills, mem):\n if (a, b) in mem:\n return mem[a, b], mem\n output = Fraction(skills[a], skills[a] + skills[b])\n mem[a, b] = output\n return output, mem\n\n\n<function token>\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\n<code token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n\n\ndef solution(skills):\n if 'Andy' not in skills:\n return '01'\n rest = [key for key in skills if key != 'Andy']\n out = str(tn(['Andy'] + rest, len(skills), skills, {})[0]).split('/')\n return out[0] + out[1]\n\n\n<code token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"
] | false |
99,406 |
09bc5daabee306c485952a93446db3f72ed8e2a3
|
#coding:gb2312
import pygame
from pygame.sprite import Sprite
class Star(Sprite):
def __init__(self,al_setting,screen):
"""ÉèÖÃÐÇÐÇͼ°¸"""
super(Star,self).__init__()
self.screen = screen
self.al_setting = al_setting
self.image = pygame.image.load("xx.bmp")
self.rect = self.image.get_rect()
self.rect.x = self.rect.width
self.rect.y = self.rect.height
#self.x = float(self.rect.x)
def blitme():
self.screen.blit(self.image,self.rect)
|
[
"#coding:gb2312\r\nimport pygame\r\nfrom pygame.sprite import Sprite\r\nclass Star(Sprite):\r\n\tdef __init__(self,al_setting,screen):\r\n\t\t\"\"\"ÉèÖÃÐÇÐÇͼ°¸\"\"\"\r\n\t\tsuper(Star,self).__init__()\r\n\t\tself.screen = screen\r\n\t\tself.al_setting = al_setting\r\n\t\tself.image = pygame.image.load(\"xx.bmp\")\r\n\t\tself.rect = self.image.get_rect()\r\n\t\tself.rect.x = self.rect.width\r\n\t\tself.rect.y = self.rect.height\r\n\t\t#self.x = float(self.rect.x)\r\n\tdef blitme():\r\n\t\tself.screen.blit(self.image,self.rect)\r\n",
"import pygame\nfrom pygame.sprite import Sprite\n\n\nclass Star(Sprite):\n\n def __init__(self, al_setting, screen):\n \"\"\"ÉèÖÃÐÇÐÇͼ°¸\"\"\"\n super(Star, self).__init__()\n self.screen = screen\n self.al_setting = al_setting\n self.image = pygame.image.load('xx.bmp')\n self.rect = self.image.get_rect()\n self.rect.x = self.rect.width\n self.rect.y = self.rect.height\n\n def blitme():\n self.screen.blit(self.image, self.rect)\n",
"<import token>\n\n\nclass Star(Sprite):\n\n def __init__(self, al_setting, screen):\n \"\"\"ÉèÖÃÐÇÐÇͼ°¸\"\"\"\n super(Star, self).__init__()\n self.screen = screen\n self.al_setting = al_setting\n self.image = pygame.image.load('xx.bmp')\n self.rect = self.image.get_rect()\n self.rect.x = self.rect.width\n self.rect.y = self.rect.height\n\n def blitme():\n self.screen.blit(self.image, self.rect)\n",
"<import token>\n\n\nclass Star(Sprite):\n <function token>\n\n def blitme():\n self.screen.blit(self.image, self.rect)\n",
"<import token>\n\n\nclass Star(Sprite):\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,407 |
c9381e455b5c2a1b93ae689d2166d09da0328e3d
|
"""
Dronesmith API
Python Bindings
Author: Geoff Gardner <[email protected]>
Copyright 2016 Dronesmith Technologies
"""
__title__ = 'dronesmith'
__version__ = '1.0.01'
#__build__ = 0x000000
__author__ = 'Geoff Gardner'
#__license__ = ''
__copyright__ = 'Copyright 2016 Dronesmith Technologies'
import requests
import json
import math
import time
class APIError(Exception):
def __init__(self, sstr):
self.val = sstr
def __str__(self):
print ':', self.val
class Dronesmith(object):
"""docstring for Dronesmith."""
def __init__(self, email, key):
super(Dronesmith, self).__init__()
self._link = Link(email, key)
if self.__auth() is False:
raise APIError("Error authenticating")
def drone(self, name=""):
return DroneNode(self._link, name)
def drones(self):
pass
def changeName(self, name):
pass
# self._link.Request()
def deleteDrone(self, name):
pass
def __auth(self):
code, data = self._link.Request()
return bool(code == 204)
class Link(object):
"""docstring for Link."""
def __init__(self, email, key):
super(Link, self).__init__()
self._userEmail = email
self._userKey = key
self._API = 'api.dronesmith.io/api/'
def __createHeader(self):
return {
'user-email': self._userEmail,
'user-key': self._userKey,
'Content-Type': 'application/json'
}
def __createUrl(self, sub=""):
return 'http://' + self._API + sub
def Request(self, method="GET", path="", body=None):
url = self.__createUrl(path)
print url
if method == 'GET':
response = requests.get(url, headers=self.__createHeader())
try:
jsonText = json.loads(response.text)
except:
return response.status_code, None
else:
return response.status_code, jsonText
elif method == 'POST':
compiled = {}
if body != None:
try:
compiled = json.dumps(body)
except:
compiled = {}
response = requests.post(url, data=compiled, headers=self.__createHeader())
print response.text
try:
jsonText = json.loads(response.text)
except:
return response.status_code, None
else:
return response.status_code, jsonText
else:
return None
class DroneNode(object):
"""docstring for DroneNode."""
def __init__(self, link, name=""):
super(DroneNode, self).__init__()
self._link = link
self._name = name
# self.missions = {}
code, obj = self._link.Request('GET', self.__getDroneUrl())
if code == 200:
self.__updateMeta(obj)
else:
code, obj = self._link.Request('POST', self.__getDroneUrl())
if code == 200:
self.__updateMeta(obj)
else:
raise APIError("Could not create drone: " + str(code))
#
# Drone Object
#
def __updateMeta(self, droneObj):
self.name = droneObj["name"]
self.created = droneObj["created"]
self.online = droneObj["online"]
self.type = droneObj["type"]
self.hardwareId = droneObj["firmwareId"]
#
# Telemetry calls
#
def position(self):
obj = self.__telem('position')
if obj != None:
self._position = Position(obj)
return self._position
def attitude(self):
obj = self.__telem('attitude')
if obj != None:
self._attitude = Attitude(obj)
return self._attitude
def takeoff(self, altitude=10):
code, obj = self._link.Request('POST', self.__getDroneUrl('takeoff'), {
"altitude": altitude
})
if code == 200 and obj["Command"] == 22 \
and obj["StatusCode"] == 0:
return True
else:
return False
def goto(self, latitude, longitude, altitude=None):
obj = {}
obj["lat"] = latitude
obj["lon"] = longitude
if altitude != None:
obj["altitude"] = altitude
code, obj = self._link.Request('POST', self.__getDroneUrl('goto'), obj)
if code == 200 and obj["Command"] == 192 \
and obj["Status"] == "Command accepted.":
return True
else:
return False
def land(self):
code, obj = self._link.Request('POST', self.__getDroneUrl('land'), {})
if code == 200 and obj["Command"] == 21 \
and obj["StatusCode"] == 0:
return True
else:
return False
def running(self):
code, obj = self._link.Request('GET', self.__getDroneUrl('status'))
if code != 200:
return False
else:
if obj != None and \
"Online" in obj:
return bool(obj["Online"]) == True
else:
return False
def info(self):
code, obj = self._link.Request('GET', self.__getDroneUrl())
if code == 200:
self.__updateMeta(obj)
return self
def run(self):
code, obj = self._link.Request('POST', self.__getDroneUrl('start'))
if code == 200:
attempts = 60
while not self.Running():
attempts -= 1
if attempts <= 0:
return False
time.sleep(1)
return True
else:
return False
def pause(self):
code, obj = self._link.Request('POST', self.__getDroneUrl('stop'))
if code == 200:
return True
else:
return False
def abort(self):
code, obj = self._link.Request('POST', self.__getDroneUrl('mode'), {
'mode': 'RTL'
})
def __telem(self, name):
code, obj = self._link.Request('GET', self.__getDroneUrl(name))
if code == 200:
return obj
else:
return None
def __getDroneUrl(self, endpoint=""):
return 'drone/' + self._name + '/' + endpoint
class Position(object):
"""docstring for Position."""
def __init__(self, obj):
super(Position, self).__init__()
self.x = obj['X']
self.y = obj['Y']
self.z = obj['Z']
self.latitude = obj['Latitude']
self.longitude = obj['Longitude']
self.altitude = obj['Altitude']
class Attitude(object):
"""docstring for Attitude."""
def __init__(self, obj):
super(Attitude, self).__init__()
self.roll = obj['Roll']
self.pitch = obj['Pitch']
self.yaw = obj['Yaw']
|
[
"\"\"\"\n Dronesmith API\n Python Bindings\n\n Author: Geoff Gardner <[email protected]>\n Copyright 2016 Dronesmith Technologies\n\"\"\"\n\n__title__ = 'dronesmith'\n__version__ = '1.0.01'\n#__build__ = 0x000000\n__author__ = 'Geoff Gardner'\n#__license__ = ''\n__copyright__ = 'Copyright 2016 Dronesmith Technologies'\n\nimport requests\nimport json\nimport math\nimport time\n\nclass APIError(Exception):\n\tdef __init__(self, sstr):\n\t\tself.val = sstr\n\n\tdef __str__(self):\n\t\tprint ':', self.val\n\nclass Dronesmith(object):\n \"\"\"docstring for Dronesmith.\"\"\"\n def __init__(self, email, key):\n super(Dronesmith, self).__init__()\n self._link = Link(email, key)\n if self.__auth() is False:\n raise APIError(\"Error authenticating\")\n\n def drone(self, name=\"\"):\n return DroneNode(self._link, name)\n\n def drones(self):\n pass\n\n def changeName(self, name):\n pass\n # self._link.Request()\n\n def deleteDrone(self, name):\n pass\n\n def __auth(self):\n code, data = self._link.Request()\n return bool(code == 204)\n\nclass Link(object):\n \"\"\"docstring for Link.\"\"\"\n def __init__(self, email, key):\n super(Link, self).__init__()\n self._userEmail = email\n self._userKey = key\n self._API = 'api.dronesmith.io/api/'\n\n def __createHeader(self):\n return {\n 'user-email': self._userEmail,\n 'user-key': self._userKey,\n 'Content-Type': 'application/json'\n }\n\n def __createUrl(self, sub=\"\"):\n return 'http://' + self._API + sub\n\n def Request(self, method=\"GET\", path=\"\", body=None):\n url = self.__createUrl(path)\n print url\n if method == 'GET':\n response = requests.get(url, headers=self.__createHeader())\n try:\n jsonText = json.loads(response.text)\n except:\n return response.status_code, None\n else:\n return response.status_code, jsonText\n elif method == 'POST':\n compiled = {}\n\n if body != None:\n try:\n compiled = json.dumps(body)\n except:\n compiled = {}\n\n response = requests.post(url, data=compiled, headers=self.__createHeader())\n print response.text\n try:\n jsonText = json.loads(response.text)\n except:\n return response.status_code, None\n else:\n return response.status_code, jsonText\n else:\n return None\n\n\nclass DroneNode(object):\n \"\"\"docstring for DroneNode.\"\"\"\n def __init__(self, link, name=\"\"):\n super(DroneNode, self).__init__()\n self._link = link\n self._name = name\n # self.missions = {}\n\n code, obj = self._link.Request('GET', self.__getDroneUrl())\n if code == 200:\n self.__updateMeta(obj)\n else:\n code, obj = self._link.Request('POST', self.__getDroneUrl())\n if code == 200:\n self.__updateMeta(obj)\n else:\n raise APIError(\"Could not create drone: \" + str(code))\n\n #\n # Drone Object\n #\n def __updateMeta(self, droneObj):\n self.name = droneObj[\"name\"]\n self.created = droneObj[\"created\"]\n self.online = droneObj[\"online\"]\n self.type = droneObj[\"type\"]\n self.hardwareId = droneObj[\"firmwareId\"]\n\n #\n # Telemetry calls\n #\n def position(self):\n obj = self.__telem('position')\n if obj != None:\n self._position = Position(obj)\n return self._position\n\n def attitude(self):\n obj = self.__telem('attitude')\n if obj != None:\n self._attitude = Attitude(obj)\n return self._attitude\n\n def takeoff(self, altitude=10):\n code, obj = self._link.Request('POST', self.__getDroneUrl('takeoff'), {\n \"altitude\": altitude\n })\n\n if code == 200 and obj[\"Command\"] == 22 \\\n and obj[\"StatusCode\"] == 0:\n return True\n else:\n return False\n\n def goto(self, latitude, longitude, altitude=None):\n obj = {}\n obj[\"lat\"] = latitude\n obj[\"lon\"] = longitude\n if altitude != None:\n obj[\"altitude\"] = altitude\n\n code, obj = self._link.Request('POST', self.__getDroneUrl('goto'), obj)\n if code == 200 and obj[\"Command\"] == 192 \\\n and obj[\"Status\"] == \"Command accepted.\":\n return True\n else:\n return False\n\n def land(self):\n code, obj = self._link.Request('POST', self.__getDroneUrl('land'), {})\n if code == 200 and obj[\"Command\"] == 21 \\\n and obj[\"StatusCode\"] == 0:\n return True\n else:\n return False\n\n def running(self):\n code, obj = self._link.Request('GET', self.__getDroneUrl('status'))\n if code != 200:\n return False\n else:\n if obj != None and \\\n \"Online\" in obj:\n return bool(obj[\"Online\"]) == True\n else:\n return False\n\n def info(self):\n code, obj = self._link.Request('GET', self.__getDroneUrl())\n if code == 200:\n self.__updateMeta(obj)\n return self\n\n def run(self):\n code, obj = self._link.Request('POST', self.__getDroneUrl('start'))\n if code == 200:\n attempts = 60\n while not self.Running():\n attempts -= 1\n if attempts <= 0:\n return False\n time.sleep(1)\n return True\n else:\n return False\n\n def pause(self):\n code, obj = self._link.Request('POST', self.__getDroneUrl('stop'))\n if code == 200:\n return True\n else:\n return False\n\n def abort(self):\n code, obj = self._link.Request('POST', self.__getDroneUrl('mode'), {\n 'mode': 'RTL'\n })\n\n def __telem(self, name):\n code, obj = self._link.Request('GET', self.__getDroneUrl(name))\n\n if code == 200:\n return obj\n else:\n return None\n\n def __getDroneUrl(self, endpoint=\"\"):\n return 'drone/' + self._name + '/' + endpoint\n\n\nclass Position(object):\n \"\"\"docstring for Position.\"\"\"\n def __init__(self, obj):\n super(Position, self).__init__()\n self.x = obj['X']\n self.y = obj['Y']\n self.z = obj['Z']\n self.latitude = obj['Latitude']\n self.longitude = obj['Longitude']\n self.altitude = obj['Altitude']\n\nclass Attitude(object):\n \"\"\"docstring for Attitude.\"\"\"\n def __init__(self, obj):\n super(Attitude, self).__init__()\n self.roll = obj['Roll']\n self.pitch = obj['Pitch']\n self.yaw = obj['Yaw']\n"
] | true |
99,408 |
70db5d4b2844a9c22ed161b722d01a61ec0e5b9d
|
import cv2 as cv
from aug.algorithms.algorithm import Algorithm
class ORB(Algorithm):
def __init__(self):
super().__init__(cv.ORB_create(), cv.DescriptorMatcher_create(
cv.DescriptorMatcher_BRUTEFORCE_HAMMING), 30)
def __str__(self):
return f"Detector: ORB\nMatcher: Bruteforce Hamming"
|
[
"import cv2 as cv\nfrom aug.algorithms.algorithm import Algorithm\n\n\nclass ORB(Algorithm):\n\n def __init__(self):\n super().__init__(cv.ORB_create(), cv.DescriptorMatcher_create(\n cv.DescriptorMatcher_BRUTEFORCE_HAMMING), 30)\n\n def __str__(self):\n return f\"Detector: ORB\\nMatcher: Bruteforce Hamming\"\n",
"import cv2 as cv\nfrom aug.algorithms.algorithm import Algorithm\n\n\nclass ORB(Algorithm):\n\n def __init__(self):\n super().__init__(cv.ORB_create(), cv.DescriptorMatcher_create(cv.\n DescriptorMatcher_BRUTEFORCE_HAMMING), 30)\n\n def __str__(self):\n return f'Detector: ORB\\nMatcher: Bruteforce Hamming'\n",
"<import token>\n\n\nclass ORB(Algorithm):\n\n def __init__(self):\n super().__init__(cv.ORB_create(), cv.DescriptorMatcher_create(cv.\n DescriptorMatcher_BRUTEFORCE_HAMMING), 30)\n\n def __str__(self):\n return f'Detector: ORB\\nMatcher: Bruteforce Hamming'\n",
"<import token>\n\n\nclass ORB(Algorithm):\n\n def __init__(self):\n super().__init__(cv.ORB_create(), cv.DescriptorMatcher_create(cv.\n DescriptorMatcher_BRUTEFORCE_HAMMING), 30)\n <function token>\n",
"<import token>\n\n\nclass ORB(Algorithm):\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,409 |
0a08b1dd3fb380ad61b9614630038787e79628bc
|
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 05 23:55:12 2018
@author: Kazushige Okayasu, Hirokatsu Kataoka
"""
import sys
import numpy as np
import torch
import torch.nn as nn
# Training
def train(args, model, device, train_loader, optimizer, epoch, iteration):
model.train()
criterion = nn.CrossEntropyLoss(size_average=True) # previous PyTorch ver.
#criterion = nn.CrossEntropyLoss(reduction='sum')
for i_batch, sample_batched in enumerate(train_loader):
data, target = sample_batched["image"].to(device), sample_batched["label"].to(device)
optimizer.zero_grad()
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct = pred.eq(target.view_as(pred)).sum().item()
loss = criterion(output, target)
loss.backward()
optimizer.step()
if i_batch % args.log_interval == 0:
sys.stdout.write("\repoch:{0:>3} iteration:{1:>6} train_loss: {2:.6f} train_accracy: {3:5.2f}%".format(
epoch, iteration, loss.item(), 100.*correct/float(len(sample_batched["label"]))))
sys.stdout.flush()
iteration += 1
# Validation
def val(args, model, device, test_loader, iteration):
model.eval()
criterion = nn.CrossEntropyLoss(size_average=False) # previous PyTorch ver.
#criterion = nn.CrossEntropyLoss(reduction='sum')
test_loss = 0
correct = 0
with torch.no_grad():
for i_batch, sample_batched in enumerate(test_loader):
data, target = sample_batched["image"].to(device), sample_batched["label"].to(device)
output = model(data)
test_loss += criterion(output, target).item()
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= float(len(test_loader.dataset))
correct /= float(len(test_loader.dataset))
print("\nValidation: Accuracy: {0:.2f}% test_loss: {1:.6f}".format(100. * correct, test_loss))
return test_loss, 100. * correct
|
[
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 05 23:55:12 2018\n@author: Kazushige Okayasu, Hirokatsu Kataoka\n\"\"\"\n\nimport sys\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\n\n# Training\ndef train(args, model, device, train_loader, optimizer, epoch, iteration):\n\tmodel.train()\n\tcriterion = nn.CrossEntropyLoss(size_average=True) # previous PyTorch ver.\n\t#criterion = nn.CrossEntropyLoss(reduction='sum')\n\tfor i_batch, sample_batched in enumerate(train_loader):\n\t\tdata, target = sample_batched[\"image\"].to(device), sample_batched[\"label\"].to(device)\n\t\toptimizer.zero_grad()\n\t\toutput = model(data)\n\t\tpred = output.max(1, keepdim=True)[1]\n\t\tcorrect = pred.eq(target.view_as(pred)).sum().item()\n\t\tloss = criterion(output, target)\n\t\tloss.backward()\n\t\toptimizer.step()\n\t\tif i_batch % args.log_interval == 0:\n\t\t\tsys.stdout.write(\"\\repoch:{0:>3} iteration:{1:>6} train_loss: {2:.6f} train_accracy: {3:5.2f}%\".format(\n\t\t\t\t\t\t\tepoch, iteration, loss.item(), 100.*correct/float(len(sample_batched[\"label\"]))))\n\t\t\tsys.stdout.flush()\n\t\titeration += 1\n\n# Validation\ndef val(args, model, device, test_loader, iteration):\n\tmodel.eval()\n\tcriterion = nn.CrossEntropyLoss(size_average=False) # previous PyTorch ver.\n\t#criterion = nn.CrossEntropyLoss(reduction='sum')\n\ttest_loss = 0\n\tcorrect = 0\n\twith torch.no_grad():\n\t\tfor i_batch, sample_batched in enumerate(test_loader):\n\t\t\tdata, target = sample_batched[\"image\"].to(device), sample_batched[\"label\"].to(device)\n\t\t\toutput = model(data)\n\t\t\ttest_loss += criterion(output, target).item()\n\t\t\tpred = output.max(1, keepdim=True)[1]\n\t\t\tcorrect += pred.eq(target.view_as(pred)).sum().item()\n\ttest_loss /= float(len(test_loader.dataset))\n\tcorrect /= float(len(test_loader.dataset))\n\tprint(\"\\nValidation: Accuracy: {0:.2f}% test_loss: {1:.6f}\".format(100. * correct, test_loss))\n\treturn test_loss, 100. * correct\n",
"<docstring token>\nimport sys\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\n\ndef train(args, model, device, train_loader, optimizer, epoch, iteration):\n model.train()\n criterion = nn.CrossEntropyLoss(size_average=True)\n for i_batch, sample_batched in enumerate(train_loader):\n data, target = sample_batched['image'].to(device), sample_batched[\n 'label'].to(device)\n optimizer.zero_grad()\n output = model(data)\n pred = output.max(1, keepdim=True)[1]\n correct = pred.eq(target.view_as(pred)).sum().item()\n loss = criterion(output, target)\n loss.backward()\n optimizer.step()\n if i_batch % args.log_interval == 0:\n sys.stdout.write(\n '\\repoch:{0:>3} iteration:{1:>6} train_loss: {2:.6f} train_accracy: {3:5.2f}%'\n .format(epoch, iteration, loss.item(), 100.0 * correct /\n float(len(sample_batched['label']))))\n sys.stdout.flush()\n iteration += 1\n\n\ndef val(args, model, device, test_loader, iteration):\n model.eval()\n criterion = nn.CrossEntropyLoss(size_average=False)\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for i_batch, sample_batched in enumerate(test_loader):\n data, target = sample_batched['image'].to(device), sample_batched[\n 'label'].to(device)\n output = model(data)\n test_loss += criterion(output, target).item()\n pred = output.max(1, keepdim=True)[1]\n correct += pred.eq(target.view_as(pred)).sum().item()\n test_loss /= float(len(test_loader.dataset))\n correct /= float(len(test_loader.dataset))\n print('\\nValidation: Accuracy: {0:.2f}% test_loss: {1:.6f}'.format(\n 100.0 * correct, test_loss))\n return test_loss, 100.0 * correct\n",
"<docstring token>\n<import token>\n\n\ndef train(args, model, device, train_loader, optimizer, epoch, iteration):\n model.train()\n criterion = nn.CrossEntropyLoss(size_average=True)\n for i_batch, sample_batched in enumerate(train_loader):\n data, target = sample_batched['image'].to(device), sample_batched[\n 'label'].to(device)\n optimizer.zero_grad()\n output = model(data)\n pred = output.max(1, keepdim=True)[1]\n correct = pred.eq(target.view_as(pred)).sum().item()\n loss = criterion(output, target)\n loss.backward()\n optimizer.step()\n if i_batch % args.log_interval == 0:\n sys.stdout.write(\n '\\repoch:{0:>3} iteration:{1:>6} train_loss: {2:.6f} train_accracy: {3:5.2f}%'\n .format(epoch, iteration, loss.item(), 100.0 * correct /\n float(len(sample_batched['label']))))\n sys.stdout.flush()\n iteration += 1\n\n\ndef val(args, model, device, test_loader, iteration):\n model.eval()\n criterion = nn.CrossEntropyLoss(size_average=False)\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for i_batch, sample_batched in enumerate(test_loader):\n data, target = sample_batched['image'].to(device), sample_batched[\n 'label'].to(device)\n output = model(data)\n test_loss += criterion(output, target).item()\n pred = output.max(1, keepdim=True)[1]\n correct += pred.eq(target.view_as(pred)).sum().item()\n test_loss /= float(len(test_loader.dataset))\n correct /= float(len(test_loader.dataset))\n print('\\nValidation: Accuracy: {0:.2f}% test_loss: {1:.6f}'.format(\n 100.0 * correct, test_loss))\n return test_loss, 100.0 * correct\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef val(args, model, device, test_loader, iteration):\n model.eval()\n criterion = nn.CrossEntropyLoss(size_average=False)\n test_loss = 0\n correct = 0\n with torch.no_grad():\n for i_batch, sample_batched in enumerate(test_loader):\n data, target = sample_batched['image'].to(device), sample_batched[\n 'label'].to(device)\n output = model(data)\n test_loss += criterion(output, target).item()\n pred = output.max(1, keepdim=True)[1]\n correct += pred.eq(target.view_as(pred)).sum().item()\n test_loss /= float(len(test_loader.dataset))\n correct /= float(len(test_loader.dataset))\n print('\\nValidation: Accuracy: {0:.2f}% test_loss: {1:.6f}'.format(\n 100.0 * correct, test_loss))\n return test_loss, 100.0 * correct\n",
"<docstring token>\n<import token>\n<function token>\n<function token>\n"
] | false |
99,410 |
41de670ee0d6b761824c658aea3d176f19bfcd66
|
#!/usr/bin/env python
import sys
import re
# Calcula a media de tempo entre todas as simulacoes para entre:
# real, sys, user
soma_real = 0.0
soma_user = 0.0
soma_sys = 0.0
# Soma os tempos de execucao para cada execucao
with open(sys.argv[1]) as f:
for line in f:
if 'real' in line:
actual = line.split('m')[1]
soma_real = soma_real + float(actual.split('s')[0])
if 'user' in line:
actual = line.split('m')[1]
soma_user = soma_user + float(actual.split('s')[0])
if 'sys' in line:
actual = line.split('m')[1]
soma_sys = soma_sys + float(actual.split('s')[0])
# Calcula e imprime a media de tempos
print('real '+ "{0:.3f}".format(soma_real/int(sys.argv[2]))+'s')
print('user '+ "{0:.3f}".format(soma_user/int(sys.argv[2]))+'s')
print('sys '+ "{0:.3f}".format(soma_sys/int(sys.argv[2]))+'s')
|
[
"#!/usr/bin/env python\n\nimport sys\nimport re\n\n# Calcula a media de tempo entre todas as simulacoes para entre:\n# real, sys, user\n\nsoma_real = 0.0\nsoma_user = 0.0\nsoma_sys = 0.0\n\n# Soma os tempos de execucao para cada execucao\nwith open(sys.argv[1]) as f:\n for line in f:\n if 'real' in line:\n actual = line.split('m')[1]\n soma_real = soma_real + float(actual.split('s')[0])\n if 'user' in line:\n actual = line.split('m')[1]\n soma_user = soma_user + float(actual.split('s')[0])\n if 'sys' in line:\n actual = line.split('m')[1]\n soma_sys = soma_sys + float(actual.split('s')[0])\n\n# Calcula e imprime a media de tempos\nprint('real\t'+ \"{0:.3f}\".format(soma_real/int(sys.argv[2]))+'s')\nprint('user\t'+ \"{0:.3f}\".format(soma_user/int(sys.argv[2]))+'s')\nprint('sys\t\t'+ \"{0:.3f}\".format(soma_sys/int(sys.argv[2]))+'s')\n\n",
"import sys\nimport re\nsoma_real = 0.0\nsoma_user = 0.0\nsoma_sys = 0.0\nwith open(sys.argv[1]) as f:\n for line in f:\n if 'real' in line:\n actual = line.split('m')[1]\n soma_real = soma_real + float(actual.split('s')[0])\n if 'user' in line:\n actual = line.split('m')[1]\n soma_user = soma_user + float(actual.split('s')[0])\n if 'sys' in line:\n actual = line.split('m')[1]\n soma_sys = soma_sys + float(actual.split('s')[0])\nprint('real\\t' + '{0:.3f}'.format(soma_real / int(sys.argv[2])) + 's')\nprint('user\\t' + '{0:.3f}'.format(soma_user / int(sys.argv[2])) + 's')\nprint('sys\\t\\t' + '{0:.3f}'.format(soma_sys / int(sys.argv[2])) + 's')\n",
"<import token>\nsoma_real = 0.0\nsoma_user = 0.0\nsoma_sys = 0.0\nwith open(sys.argv[1]) as f:\n for line in f:\n if 'real' in line:\n actual = line.split('m')[1]\n soma_real = soma_real + float(actual.split('s')[0])\n if 'user' in line:\n actual = line.split('m')[1]\n soma_user = soma_user + float(actual.split('s')[0])\n if 'sys' in line:\n actual = line.split('m')[1]\n soma_sys = soma_sys + float(actual.split('s')[0])\nprint('real\\t' + '{0:.3f}'.format(soma_real / int(sys.argv[2])) + 's')\nprint('user\\t' + '{0:.3f}'.format(soma_user / int(sys.argv[2])) + 's')\nprint('sys\\t\\t' + '{0:.3f}'.format(soma_sys / int(sys.argv[2])) + 's')\n",
"<import token>\n<assignment token>\nwith open(sys.argv[1]) as f:\n for line in f:\n if 'real' in line:\n actual = line.split('m')[1]\n soma_real = soma_real + float(actual.split('s')[0])\n if 'user' in line:\n actual = line.split('m')[1]\n soma_user = soma_user + float(actual.split('s')[0])\n if 'sys' in line:\n actual = line.split('m')[1]\n soma_sys = soma_sys + float(actual.split('s')[0])\nprint('real\\t' + '{0:.3f}'.format(soma_real / int(sys.argv[2])) + 's')\nprint('user\\t' + '{0:.3f}'.format(soma_user / int(sys.argv[2])) + 's')\nprint('sys\\t\\t' + '{0:.3f}'.format(soma_sys / int(sys.argv[2])) + 's')\n",
"<import token>\n<assignment token>\n<code token>\n"
] | false |
99,411 |
9c93bd6dff47fbf21933b4e3648752f20e36258a
|
from django.conf.urls import url
from .views import *
app_name = 'dbms'
urlpatterns = [
url(r'^user-register$', user_register, name='user-register'),
url(r'^user-login$', user_login, name='user-login'),
url(r'^admin-register$', admin_register, name='admin-register'),
url(r'^admin-login$', admin_login, name='admin-login'),
url(r'^admin/websites', AdminWebsiteView.as_view(), name='admin-website'),
url(r'^keywords', KeywordsView.as_view(), name='keywords'),
url(r'^log', get_log, name='get-log'),
url(r'^get-result', get_result, name='get-result'),
url(r'^hello', hello, name='hello')
]
|
[
"from django.conf.urls import url\nfrom .views import *\n\napp_name = 'dbms'\n\nurlpatterns = [\n url(r'^user-register$', user_register, name='user-register'),\n url(r'^user-login$', user_login, name='user-login'),\n url(r'^admin-register$', admin_register, name='admin-register'),\n url(r'^admin-login$', admin_login, name='admin-login'),\n url(r'^admin/websites', AdminWebsiteView.as_view(), name='admin-website'),\n url(r'^keywords', KeywordsView.as_view(), name='keywords'),\n url(r'^log', get_log, name='get-log'),\n url(r'^get-result', get_result, name='get-result'),\n url(r'^hello', hello, name='hello')\n]\n",
"from django.conf.urls import url\nfrom .views import *\napp_name = 'dbms'\nurlpatterns = [url('^user-register$', user_register, name='user-register'),\n url('^user-login$', user_login, name='user-login'), url(\n '^admin-register$', admin_register, name='admin-register'), url(\n '^admin-login$', admin_login, name='admin-login'), url(\n '^admin/websites', AdminWebsiteView.as_view(), name='admin-website'),\n url('^keywords', KeywordsView.as_view(), name='keywords'), url('^log',\n get_log, name='get-log'), url('^get-result', get_result, name=\n 'get-result'), url('^hello', hello, name='hello')]\n",
"<import token>\napp_name = 'dbms'\nurlpatterns = [url('^user-register$', user_register, name='user-register'),\n url('^user-login$', user_login, name='user-login'), url(\n '^admin-register$', admin_register, name='admin-register'), url(\n '^admin-login$', admin_login, name='admin-login'), url(\n '^admin/websites', AdminWebsiteView.as_view(), name='admin-website'),\n url('^keywords', KeywordsView.as_view(), name='keywords'), url('^log',\n get_log, name='get-log'), url('^get-result', get_result, name=\n 'get-result'), url('^hello', hello, name='hello')]\n",
"<import token>\n<assignment token>\n"
] | false |
99,412 |
4f97db2c81729b3d4dbc610a6c9b30f003b98999
|
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 23 21:53:37 2021
@author: Cnoized
"""
#This is for problem 62
from time import time
from itertools import permutations
import numpy as np
P_List = []
start1 = time()
def CubicPermutations(Times):
Number = 0
N = 2
# Cubes = GenerateCubes(5)
while Number < Times:
N+=1
Number = 0
Cube = sorted([int(a) for a in str(N**3)])
P_List.append([Cube])
Check = P_List.count([Cube])
# if Check > 1:
# print(Check)
if Check > Number:
Number = Check
for Base in range(N):
Cube2 = sorted([int(a) for a in str(Base**3)])
if Cube2 == Cube:
print(Base,'is the smallest base for which its cube has 5 permutations which are also cubes.',Base**3,Cube2)
# print(P_List)
# print(Cubes)
# input()
# for a in P_List:
# if a in Cubes and a not in P_Int:
# Number += 1
# print(N,Number)
# P_Int.append(a)
# print(P_List)
# print(N)
return N
def GenerateCubes(Number):
Alpha=1
Cubes = []
Value = (10**len(str(Number)))
while Alpha < Value:
Cubes.append([a for a in str(Alpha**3)])
Alpha+=1
# print(len(Cubes))
# print(len(max(Cubes)))
return Cubes
LookingFor = 5
Answer = CubicPermutations(LookingFor)
print(Answer, 'at', LookingFor)
end1 = time()
print(end1-start1)
|
[
"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jul 23 21:53:37 2021\r\n\r\n@author: Cnoized\r\n\"\"\"\r\n#This is for problem 62\r\nfrom time import time\r\nfrom itertools import permutations\r\nimport numpy as np\r\nP_List = []\r\n\r\nstart1 = time()\r\n\r\n\r\n\r\ndef CubicPermutations(Times):\r\n Number = 0\r\n N = 2\r\n # Cubes = GenerateCubes(5)\r\n \r\n while Number < Times:\r\n N+=1\r\n\r\n Number = 0\r\n\r\n Cube = sorted([int(a) for a in str(N**3)])\r\n P_List.append([Cube])\r\n Check = P_List.count([Cube])\r\n # if Check > 1:\r\n # print(Check)\r\n if Check > Number:\r\n Number = Check\r\n for Base in range(N):\r\n Cube2 = sorted([int(a) for a in str(Base**3)])\r\n if Cube2 == Cube:\r\n print(Base,'is the smallest base for which its cube has 5 permutations which are also cubes.',Base**3,Cube2)\r\n # print(P_List)\r\n # print(Cubes)\r\n # input()\r\n \r\n\r\n # for a in P_List:\r\n\r\n # if a in Cubes and a not in P_Int:\r\n # Number += 1\r\n # print(N,Number)\r\n # P_Int.append(a)\r\n # print(P_List)\r\n # print(N)\r\n return N\r\n\r\n\r\ndef GenerateCubes(Number):\r\n Alpha=1\r\n Cubes = []\r\n Value = (10**len(str(Number)))\r\n while Alpha < Value:\r\n Cubes.append([a for a in str(Alpha**3)])\r\n Alpha+=1\r\n # print(len(Cubes))\r\n # print(len(max(Cubes)))\r\n return Cubes\r\n\r\nLookingFor = 5\r\nAnswer = CubicPermutations(LookingFor)\r\nprint(Answer, 'at', LookingFor)\r\n\r\nend1 = time()\r\n\r\nprint(end1-start1)",
"<docstring token>\nfrom time import time\nfrom itertools import permutations\nimport numpy as np\nP_List = []\nstart1 = time()\n\n\ndef CubicPermutations(Times):\n Number = 0\n N = 2\n while Number < Times:\n N += 1\n Number = 0\n Cube = sorted([int(a) for a in str(N ** 3)])\n P_List.append([Cube])\n Check = P_List.count([Cube])\n if Check > Number:\n Number = Check\n for Base in range(N):\n Cube2 = sorted([int(a) for a in str(Base ** 3)])\n if Cube2 == Cube:\n print(Base,\n 'is the smallest base for which its cube has 5 permutations which are also cubes.'\n , Base ** 3, Cube2)\n return N\n\n\ndef GenerateCubes(Number):\n Alpha = 1\n Cubes = []\n Value = 10 ** len(str(Number))\n while Alpha < Value:\n Cubes.append([a for a in str(Alpha ** 3)])\n Alpha += 1\n return Cubes\n\n\nLookingFor = 5\nAnswer = CubicPermutations(LookingFor)\nprint(Answer, 'at', LookingFor)\nend1 = time()\nprint(end1 - start1)\n",
"<docstring token>\n<import token>\nP_List = []\nstart1 = time()\n\n\ndef CubicPermutations(Times):\n Number = 0\n N = 2\n while Number < Times:\n N += 1\n Number = 0\n Cube = sorted([int(a) for a in str(N ** 3)])\n P_List.append([Cube])\n Check = P_List.count([Cube])\n if Check > Number:\n Number = Check\n for Base in range(N):\n Cube2 = sorted([int(a) for a in str(Base ** 3)])\n if Cube2 == Cube:\n print(Base,\n 'is the smallest base for which its cube has 5 permutations which are also cubes.'\n , Base ** 3, Cube2)\n return N\n\n\ndef GenerateCubes(Number):\n Alpha = 1\n Cubes = []\n Value = 10 ** len(str(Number))\n while Alpha < Value:\n Cubes.append([a for a in str(Alpha ** 3)])\n Alpha += 1\n return Cubes\n\n\nLookingFor = 5\nAnswer = CubicPermutations(LookingFor)\nprint(Answer, 'at', LookingFor)\nend1 = time()\nprint(end1 - start1)\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\ndef CubicPermutations(Times):\n Number = 0\n N = 2\n while Number < Times:\n N += 1\n Number = 0\n Cube = sorted([int(a) for a in str(N ** 3)])\n P_List.append([Cube])\n Check = P_List.count([Cube])\n if Check > Number:\n Number = Check\n for Base in range(N):\n Cube2 = sorted([int(a) for a in str(Base ** 3)])\n if Cube2 == Cube:\n print(Base,\n 'is the smallest base for which its cube has 5 permutations which are also cubes.'\n , Base ** 3, Cube2)\n return N\n\n\ndef GenerateCubes(Number):\n Alpha = 1\n Cubes = []\n Value = 10 ** len(str(Number))\n while Alpha < Value:\n Cubes.append([a for a in str(Alpha ** 3)])\n Alpha += 1\n return Cubes\n\n\n<assignment token>\nprint(Answer, 'at', LookingFor)\n<assignment token>\nprint(end1 - start1)\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\ndef CubicPermutations(Times):\n Number = 0\n N = 2\n while Number < Times:\n N += 1\n Number = 0\n Cube = sorted([int(a) for a in str(N ** 3)])\n P_List.append([Cube])\n Check = P_List.count([Cube])\n if Check > Number:\n Number = Check\n for Base in range(N):\n Cube2 = sorted([int(a) for a in str(Base ** 3)])\n if Cube2 == Cube:\n print(Base,\n 'is the smallest base for which its cube has 5 permutations which are also cubes.'\n , Base ** 3, Cube2)\n return N\n\n\ndef GenerateCubes(Number):\n Alpha = 1\n Cubes = []\n Value = 10 ** len(str(Number))\n while Alpha < Value:\n Cubes.append([a for a in str(Alpha ** 3)])\n Alpha += 1\n return Cubes\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<function token>\n\n\ndef GenerateCubes(Number):\n Alpha = 1\n Cubes = []\n Value = 10 ** len(str(Number))\n while Alpha < Value:\n Cubes.append([a for a in str(Alpha ** 3)])\n Alpha += 1\n return Cubes\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,413 |
9b6e4e00ed7af78f8eab4f1bbad44644f1654cdd
|
import pymorphy2
from django import template
register = template.Library()
@register.filter
def word_form(word, number):
morph = pymorphy2.MorphAnalyzer()
default_word = morph.parse(word)[0]
changed_word = default_word.make_agree_with_number(number).word
return changed_word
|
[
"import pymorphy2\nfrom django import template\n\nregister = template.Library()\n\n\[email protected]\ndef word_form(word, number):\n morph = pymorphy2.MorphAnalyzer()\n default_word = morph.parse(word)[0]\n changed_word = default_word.make_agree_with_number(number).word\n return changed_word\n",
"import pymorphy2\nfrom django import template\nregister = template.Library()\n\n\[email protected]\ndef word_form(word, number):\n morph = pymorphy2.MorphAnalyzer()\n default_word = morph.parse(word)[0]\n changed_word = default_word.make_agree_with_number(number).word\n return changed_word\n",
"<import token>\nregister = template.Library()\n\n\[email protected]\ndef word_form(word, number):\n morph = pymorphy2.MorphAnalyzer()\n default_word = morph.parse(word)[0]\n changed_word = default_word.make_agree_with_number(number).word\n return changed_word\n",
"<import token>\n<assignment token>\n\n\[email protected]\ndef word_form(word, number):\n morph = pymorphy2.MorphAnalyzer()\n default_word = morph.parse(word)[0]\n changed_word = default_word.make_agree_with_number(number).word\n return changed_word\n",
"<import token>\n<assignment token>\n<function token>\n"
] | false |
99,414 |
82d1f3125d271fdae99a93726c7cf886599f25a1
|
# ----------------------------------------------------------------------------------------
# Authors: Jonathan Jolivette | Matt Freeland | Enrique Morales
# Template Name: TA | Teacher's Assistant
# File: TA | APP.PY (MAIN FILE)
# App Version: 1.0
# ----------------------------------------------------------------------------------------
# from flask import render_template
from flask import Flask, g, request
from flask import render_template, flash, redirect, url_for
from flask_login import LoginManager, login_user, logout_user, login_required, current_user
from flask_bcrypt import check_password_hash
from flask_bootstrap import Bootstrap
from flask_fontawesome import FontAwesome
from config import Config
import secrets
import os
from PIL import Image
import moment
import models
from models import User
from models import Event
import forms
# set debug and port defaults
DEBUG = True
PORT = 8000
# can bootstrap wrap the app and in turn cover the entire app where all
# templates are under the influence of bootstrap with the need for
# any cdn or linking to downloaded files/folders????
# Bootstrap(app)
app = Flask(__name__)
app.config.from_object(Config)
# initializing the login manager module
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'login'
@login_manager.user_loader
def load_user(userid):
try:
return models.User.get(models.User.id == userid)
except models.DoesNotExist:
return None
# Connect to database before request
@app.before_request
def before_request():
"""Connect to database before each request """
g.db = models.DATABASE
g.db.connect()
g.user = current_user
@app.after_request
def after_request(response):
"""Close the database connection after each request."""
g.db.close()
return response
# ============ REGISTRATION PAGE ROUTE ============
@app.route('/register', methods=('GET', 'POST'))
def register():
form = forms.RegisterForm()
if form.validate_on_submit():
if "generalassemb.ly" in form.email.data:
flash("Registered as an instructor", 'success')
models.User.create_user(
username=form.username.data,
email=form.email.data,
role="Instructor",
password=form.password.data,
course=form.course.data
)
else:
flash("Registered as a student", 'success')
models.User.create_user(
username=form.username.data,
email=form.email.data,
role="Student",
password=form.password.data,
course=form.course.data
)
return redirect(url_for('index'))
return render_template('register.html', form=form)
# ============ LOGIN PAGE ROUTE ============
@app.route('/login', methods=('GET', 'POST'))
def login():
form = forms.LoginForm()
if form.validate_on_submit():
try:
user = models.User.get(models.User.email == form.email.data)
except models.DoesNotExist:
flash("your email or password doesn't match", "error")
else:
if check_password_hash(user.password, form.password.data):
login_user(user)
flash("You've been logged in", "success")
return redirect(url_for('dashboard'))
else:
flash("your email or password doesn't match", "error")
return render_template('login.html', form=form)
# ============ LOGOUT PAGE ROUTE ============
@app.route('/logout')
@login_required
def logout():
logout_user()
flash("You've been logged out", "success")
return redirect(url_for('index'))
# ============ EVENT PAGE ROUTE ============
@app.route('/event/', methods=('GET', 'POST'))
@app.route('/event', methods=('GET', 'POST'))
@login_required
def event():
events = Event.select().order_by(Event.date, Event.time)
return render_template('event.html', events=events)
# ============ EVENT CRUD ROUTES ============
# CREATE
@app.route('/event/create', methods=('GET', 'POST'))
@login_required
def create_event():
form = forms.CreateEventForm()
if g.user.role != "Instructor":
flash("You must be an instructor to create events")
return redirect(url_for('index'))
if form.validate_on_submit():
locator = Event.select().where(
(Event.instructor == current_user.id) &
(Event.date == form.date.data) &
(Event.time == form.time.data))
if locator.count() == 0:
flash("Created New Event","success")
models.Event.create_event(
instructor=g.user.id,
date=form.date.data,
time=form.time.data,
)
return redirect(url_for("dashboard"))
else:
flash("Event already exists","error")
return redirect(url_for("dashboard"))
return render_template('create_event.html', form=form)
# DELETE
@app.route('/event/delete/<id>', methods=['DELETE', 'GET'])
@login_required
def event_delete(id):
found_event = models.Event.get(models.Event.id == id)
if g.user.id == found_event.instructor_id:
if found_event.student != None:
unlock_student = User.update(event_assigned = False).where(User.id == found_event.student)
unlock_student.execute()
event_to_delete = Event.delete().where(Event.id == found_event.id)
event_to_delete.execute()
flash("Deleted event successfully","error")
else:
flash("You don't have permission to delete this event.","error")
return redirect(url_for('dashboard'))
# UPDATE
@app.route('/event/update/<id>', methods=('POST', 'GET'))
def event_update(id):
form = forms.EditEventForm()
found_event = Event.get(Event.id == id)
if g.user.id == found_event.instructor_id:
if form.validate_on_submit():
if found_event.date != form.date.data and found_event.time != form.time.data:
locator = Event.select().where(
(Event.instructor == current_user.id) &
(Event.date == form.date.data) &
(Event.time == form.time.data))
if locator.count() == 0:
update = Event.update(date=form.date.data, time=form.time.data).where(Event.id == id)
update.execute()
flash("Updated Event Successfully","success")
return redirect(url_for('event'))
else:
flash("Could not update, duplicate event exists","error")
return redirect(url_for('event'))
else:
flash("You do not have permission to edit this event", "error")
return redirect(url_for('dashboard'))
return render_template('edit_event.html', form=form, found_event=found_event)
# ADD STUDENT TO EVENT
@app.route('/event/add_student/<id>', methods=('POST', 'GET'))
def add_student_to_event(id):
found_event = Event.get(Event.id == id)
if found_event.student == None:
if current_user.event_assigned == False:
add_student = Event.update(student=current_user.id).where(Event.id == id)
add_student.execute()
lock_events = User.update(event_assigned=True).where(User.id == current_user.id)
lock_events.execute()
flash("Checked in for event", "success")
return redirect(url_for('dashboard'))
else:
flash("You can only be assigned one event at a time")
return redirect(url_for('dashboard'))
else:
flash("Even already has a student assigned", "error")
return redirect(url_for('dashboard'))
# REMOVE STUDENT FROM EVENT
@app.route('/event/remove_student/<id>', methods=('POST', 'GET'))
def remove_student_from_event(id):
found_event = Event.get(Event.id == id)
if found_event.student == current_user:
remove_student = Event.update(student_id=None).where(Event.id == id)
remove_student.execute()
unlock_events = User.update(event_assigned=False).where(User.id == current_user.id)
unlock_events.execute()
flash("Unscheduled successfully", "success")
else:
flash("Cannot unschedule other user events", "error")
return redirect(url_for('dashboard'))
# ============ HOME PAGE ROUTE ============
@app.route('/')
def index():
return render_template('hero.html')
# ============ STUDENT DASHBOARD ROUTE ============
@app.route('/student')
def student_dash():
return render_template('student-dashboard.html')
# ============ TEACHER DASHBOARD ROUTE ============
@app.route('/teacher')
def teacher_dash():
return render_template('teacher-dashboard.html')
# ============ Account update ROUTES ============
def save_picture(form_picture):
random_hex = secrets.token_hex(8)
# function returns filename without ext and ext itself,,,, underscores are a python way to throw away variables or "ignore"
_, f_ext = os.path.splitext(form_picture.filename)
# ignore photo name and concat hex with extension
picture_fn = random_hex + f_ext
# full path where image will be saved. full path of project directory
picture_path = os.path.join(app.root_path, 'static/profile_pics', picture_fn)
# sets image resize with pillow
output_size = (500, 500)
# open image we passed into the function
i = Image.open(form_picture)
i.thumbnail(output_size)
#saves at picture_path on file system
i.save(picture_path)
# return value to user
return picture_fn
@app.route("/account", methods=['GET','POST'])
@login_required
def account():
form = forms.UpdateAccountForm()
if form.validate_on_submit():
if form.picture.data:
# allows us to set users current image to profile picture
picture_file = save_picture(form.picture.data)
update_image = User.update(image_file=picture_file).where(User.id == current_user.id)
update_image.execute()
# current_user.username = form.username.data
# current_user.email = form.email.data
# g.db.commit()
flash('Your account has been updated!', 'success')
return redirect(url_for('account'))
elif request.method == 'GET':
form.username.data = current_user.username
form.email.data = current_user.email
image_location = User.get(User.id == current_user.id)
decoded_location = image_location.image_file.decode()
image_file = url_for('static', filename='profile_pics/' + decoded_location)
return render_template('account.html', title='Account', image_file=image_file, form=form)
@app.route("/dashboard", methods=['GET','POST'])
@login_required
def dashboard():
events = Event.select().order_by(Event.date, Event.time)
form = forms.UpdateAccountForm()
if form.validate_on_submit():
if form.picture.data:
picture_file = save_picture(form.picture.data)
update_image = User.update(image_file=picture_file).where(User.id == current_user.id)
update_image.execute()
# update_profile = User.update(username=form.username.data)
# update_profile.execute()
flash('Your account has been updated!', 'success')
return redirect(url_for('dashboard'))
elif request.method == 'GET':
form.username.data = current_user.username
image_location = User.get(User.id == current_user.id)
if image_location.image_file != "default.png":
decoded_location = image_location.image_file.decode()
image_file = url_for('static', filename='profile_pics/' + decoded_location)
else:
image_file = url_for('static', filename='profile_pics/default.png')
return render_template('dashboard.html', events=events, title='Account', image_file=image_file, form=form)
if __name__ == '__main__':
models.initialize()
try:
models.User.create_user(
username='jimbo',
email="[email protected]",
password='password',
course="General",
role="Instructor"
)
models.User.create_user(
username='joe student',
email="[email protected]",
password='password',
course="General",
role="Student"
)
models.User.create_user(
username='walrus',
email="[email protected]",
password='password',
course="General",
role="Instructor"
)
models.User.create_user(
username='rando calrissian',
email="[email protected]",
password='password',
course="General",
role="Student"
)
except ValueError:
pass
app.run(debug=DEBUG, port=PORT)
|
[
"# ----------------------------------------------------------------------------------------\n# Authors: Jonathan Jolivette | Matt Freeland | Enrique Morales\n# Template Name: TA | Teacher's Assistant\n# File: TA | APP.PY (MAIN FILE)\n# App Version: 1.0\n# ----------------------------------------------------------------------------------------\n\n# from flask import render_template\nfrom flask import Flask, g, request\nfrom flask import render_template, flash, redirect, url_for\nfrom flask_login import LoginManager, login_user, logout_user, login_required, current_user\nfrom flask_bcrypt import check_password_hash\nfrom flask_bootstrap import Bootstrap\nfrom flask_fontawesome import FontAwesome\nfrom config import Config\nimport secrets\nimport os\nfrom PIL import Image\n\nimport moment\n\nimport models\nfrom models import User\nfrom models import Event\nimport forms\n\n# set debug and port defaults\nDEBUG = True\nPORT = 8000\n\n# can bootstrap wrap the app and in turn cover the entire app where all\n# templates are under the influence of bootstrap with the need for\n# any cdn or linking to downloaded files/folders????\n# Bootstrap(app)\n\napp = Flask(__name__)\napp.config.from_object(Config)\n\n# initializing the login manager module\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n# Connect to database before request\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\[email protected]_request\ndef after_request(response):\n \"\"\"Close the database connection after each request.\"\"\"\n g.db.close()\n return response\n\n# ============ REGISTRATION PAGE ROUTE ============\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if \"generalassemb.ly\" in form.email.data:\n flash(\"Registered as an instructor\", 'success')\n models.User.create_user(\n username=form.username.data,\n email=form.email.data,\n role=\"Instructor\",\n password=form.password.data,\n course=form.course.data\n )\n else:\n flash(\"Registered as a student\", 'success')\n models.User.create_user(\n username=form.username.data,\n email=form.email.data,\n role=\"Student\",\n password=form.password.data,\n course=form.course.data\n )\n\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\n# ============ LOGIN PAGE ROUTE ============\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", \"error\")\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", \"success\")\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", \"error\")\n return render_template('login.html', form=form)\n\n\n# ============ LOGOUT PAGE ROUTE ============\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", \"success\")\n return redirect(url_for('index'))\n\n# ============ EVENT PAGE ROUTE ============\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\n# ============ EVENT CRUD ROUTES ============\n\n# CREATE\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != \"Instructor\":\n flash(\"You must be an instructor to create events\")\n return redirect(url_for('index'))\n\n if form.validate_on_submit():\n locator = Event.select().where(\n (Event.instructor == current_user.id) &\n (Event.date == form.date.data) &\n (Event.time == form.time.data))\n if locator.count() == 0:\n flash(\"Created New Event\",\"success\")\n models.Event.create_event(\n instructor=g.user.id,\n date=form.date.data,\n time=form.time.data,\n )\n return redirect(url_for(\"dashboard\"))\n else:\n flash(\"Event already exists\",\"error\")\n return redirect(url_for(\"dashboard\"))\n\n return render_template('create_event.html', form=form)\n\n# DELETE\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned = False).where(User.id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash(\"Deleted event successfully\",\"error\")\n else:\n flash(\"You don't have permission to delete this event.\",\"error\")\n return redirect(url_for('dashboard'))\n\n# UPDATE\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if found_event.date != form.date.data and found_event.time != form.time.data:\n locator = Event.select().where(\n (Event.instructor == current_user.id) &\n (Event.date == form.date.data) &\n (Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.time.data).where(Event.id == id)\n update.execute()\n flash(\"Updated Event Successfully\",\"success\")\n return redirect(url_for('event'))\n else:\n flash(\"Could not update, duplicate event exists\",\"error\")\n return redirect(url_for('event'))\n\n else:\n flash(\"You do not have permission to edit this event\", \"error\")\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=found_event)\n\n# ADD STUDENT TO EVENT\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event.id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id == current_user.id)\n lock_events.execute()\n flash(\"Checked in for event\", \"success\")\n return redirect(url_for('dashboard'))\n else:\n flash(\"You can only be assigned one event at a time\")\n return redirect(url_for('dashboard'))\n else:\n flash(\"Even already has a student assigned\", \"error\")\n return redirect(url_for('dashboard'))\n\n# REMOVE STUDENT FROM EVENT\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id == current_user.id)\n unlock_events.execute()\n flash(\"Unscheduled successfully\", \"success\")\n else:\n flash(\"Cannot unschedule other user events\", \"error\")\n return redirect(url_for('dashboard'))\n\n# ============ HOME PAGE ROUTE ============\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n# ============ STUDENT DASHBOARD ROUTE ============\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\n# ============ TEACHER DASHBOARD ROUTE ============\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n# ============ Account update ROUTES ============\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n # function returns filename without ext and ext itself,,,, underscores are a python way to throw away variables or \"ignore\"\n _, f_ext = os.path.splitext(form_picture.filename)\n # ignore photo name and concat hex with extension\n picture_fn = random_hex + f_ext\n # full path where image will be saved. full path of project directory\n picture_path = os.path.join(app.root_path, 'static/profile_pics', picture_fn)\n\n # sets image resize with pillow\n output_size = (500, 500)\n # open image we passed into the function\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n #saves at picture_path on file system\n i.save(picture_path)\n # return value to user\n return picture_fn\n\[email protected](\"/account\", methods=['GET','POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n # allows us to set users current image to profile picture\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.id == current_user.id)\n update_image.execute()\n # current_user.username = form.username.data\n # current_user.email = form.email.data\n # g.db.commit()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n \n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=image_file, form=form)\n\[email protected](\"/dashboard\", methods=['GET','POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.id == current_user.id)\n update_image.execute()\n # update_profile = User.update(username=form.username.data)\n # update_profile.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n \n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != \"default.png\":\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n \n\n return render_template('dashboard.html', events=events, title='Account', image_file=image_file, form=form)\n\nif __name__ == '__main__':\n models.initialize()\n try:\n models.User.create_user(\n username='jimbo',\n email=\"[email protected]\",\n password='password',\n course=\"General\",\n role=\"Instructor\"\n )\n models.User.create_user(\n username='joe student',\n email=\"[email protected]\",\n password='password',\n course=\"General\",\n role=\"Student\"\n )\n models.User.create_user(\n username='walrus',\n email=\"[email protected]\",\n password='password',\n course=\"General\",\n role=\"Instructor\"\n )\n models.User.create_user(\n username='rando calrissian',\n email=\"[email protected]\",\n password='password',\n course=\"General\",\n role=\"Student\"\n )\n except ValueError:\n pass\n\napp.run(debug=DEBUG, port=PORT)\n",
"from flask import Flask, g, request\nfrom flask import render_template, flash, redirect, url_for\nfrom flask_login import LoginManager, login_user, logout_user, login_required, current_user\nfrom flask_bcrypt import check_password_hash\nfrom flask_bootstrap import Bootstrap\nfrom flask_fontawesome import FontAwesome\nfrom config import Config\nimport secrets\nimport os\nfrom PIL import Image\nimport moment\nimport models\nfrom models import User\nfrom models import Event\nimport forms\nDEBUG = True\nPORT = 8000\napp = Flask(__name__)\napp.config.from_object(Config)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\[email protected]_request\ndef after_request(response):\n \"\"\"Close the database connection after each request.\"\"\"\n g.db.close()\n return response\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\nif __name__ == '__main__':\n models.initialize()\n try:\n models.User.create_user(username='jimbo', email='[email protected]',\n password='password', course='General', role='Instructor')\n models.User.create_user(username='joe student', email=\n '[email protected]', password='password', course='General', role=\n 'Student')\n models.User.create_user(username='walrus', email=\n '[email protected]', password='password', course='General',\n role='Instructor')\n models.User.create_user(username='rando calrissian', email=\n '[email protected]', password='password', course='General',\n role='Student')\n except ValueError:\n pass\napp.run(debug=DEBUG, port=PORT)\n",
"<import token>\nDEBUG = True\nPORT = 8000\napp = Flask(__name__)\napp.config.from_object(Config)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\[email protected]_request\ndef after_request(response):\n \"\"\"Close the database connection after each request.\"\"\"\n g.db.close()\n return response\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\nif __name__ == '__main__':\n models.initialize()\n try:\n models.User.create_user(username='jimbo', email='[email protected]',\n password='password', course='General', role='Instructor')\n models.User.create_user(username='joe student', email=\n '[email protected]', password='password', course='General', role=\n 'Student')\n models.User.create_user(username='walrus', email=\n '[email protected]', password='password', course='General',\n role='Instructor')\n models.User.create_user(username='rando calrissian', email=\n '[email protected]', password='password', course='General',\n role='Student')\n except ValueError:\n pass\napp.run(debug=DEBUG, port=PORT)\n",
"<import token>\n<assignment token>\napp.config.from_object(Config)\n<assignment token>\nlogin_manager.init_app(app)\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\[email protected]_request\ndef after_request(response):\n \"\"\"Close the database connection after each request.\"\"\"\n g.db.close()\n return response\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\nif __name__ == '__main__':\n models.initialize()\n try:\n models.User.create_user(username='jimbo', email='[email protected]',\n password='password', course='General', role='Instructor')\n models.User.create_user(username='joe student', email=\n '[email protected]', password='password', course='General', role=\n 'Student')\n models.User.create_user(username='walrus', email=\n '[email protected]', password='password', course='General',\n role='Instructor')\n models.User.create_user(username='rando calrissian', email=\n '[email protected]', password='password', course='General',\n role='Student')\n except ValueError:\n pass\napp.run(debug=DEBUG, port=PORT)\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\[email protected]_request\ndef after_request(response):\n \"\"\"Close the database connection after each request.\"\"\"\n g.db.close()\n return response\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\[email protected]('/event/', methods=('GET', 'POST'))\[email protected]('/event', methods=('GET', 'POST'))\n@login_required\ndef event():\n events = Event.select().order_by(Event.date, Event.time)\n return render_template('event.html', events=events)\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\[email protected]('/account', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n image_location = User.get(User.id == current_user.id)\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' + decoded_location)\n return render_template('account.html', title='Account', image_file=\n image_file, form=form)\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n\n\[email protected]('/dashboard', methods=['GET', 'POST'])\n@login_required\ndef dashboard():\n events = Event.select().order_by(Event.date, Event.time)\n form = forms.UpdateAccountForm()\n if form.validate_on_submit():\n if form.picture.data:\n picture_file = save_picture(form.picture.data)\n update_image = User.update(image_file=picture_file).where(User.\n id == current_user.id)\n update_image.execute()\n flash('Your account has been updated!', 'success')\n return redirect(url_for('dashboard'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n image_location = User.get(User.id == current_user.id)\n if image_location.image_file != 'default.png':\n decoded_location = image_location.image_file.decode()\n image_file = url_for('static', filename='profile_pics/' +\n decoded_location)\n else:\n image_file = url_for('static', filename='profile_pics/default.png')\n return render_template('dashboard.html', events=events, title='Account',\n image_file=image_file, form=form)\n\n\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\[email protected]_request\ndef before_request():\n \"\"\"Connect to database before each request \"\"\"\n g.db = models.DATABASE\n g.db.connect()\n g.user = current_user\n\n\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\[email protected]('/event/add_student/<id>', methods=('POST', 'GET'))\ndef add_student_to_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == None:\n if current_user.event_assigned == False:\n add_student = Event.update(student=current_user.id).where(Event\n .id == id)\n add_student.execute()\n lock_events = User.update(event_assigned=True).where(User.id ==\n current_user.id)\n lock_events.execute()\n flash('Checked in for event', 'success')\n return redirect(url_for('dashboard'))\n else:\n flash('You can only be assigned one event at a time')\n return redirect(url_for('dashboard'))\n else:\n flash('Even already has a student assigned', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\n<function token>\n\n\[email protected]('/event/remove_student/<id>', methods=('POST', 'GET'))\ndef remove_student_from_event(id):\n found_event = Event.get(Event.id == id)\n if found_event.student == current_user:\n remove_student = Event.update(student_id=None).where(Event.id == id)\n remove_student.execute()\n unlock_events = User.update(event_assigned=False).where(User.id ==\n current_user.id)\n unlock_events.execute()\n flash('Unscheduled successfully', 'success')\n else:\n flash('Cannot unschedule other user events', 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\[email protected]('/student')\ndef student_dash():\n return render_template('student-dashboard.html')\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n\n\[email protected]('/event/create', methods=('GET', 'POST'))\n@login_required\ndef create_event():\n form = forms.CreateEventForm()\n if g.user.role != 'Instructor':\n flash('You must be an instructor to create events')\n return redirect(url_for('index'))\n if form.validate_on_submit():\n locator = Event.select().where((Event.instructor == current_user.id\n ) & (Event.date == form.date.data) & (Event.time == form.time.data)\n )\n if locator.count() == 0:\n flash('Created New Event', 'success')\n models.Event.create_event(instructor=g.user.id, date=form.date.\n data, time=form.time.data)\n return redirect(url_for('dashboard'))\n else:\n flash('Event already exists', 'error')\n return redirect(url_for('dashboard'))\n return render_template('create_event.html', form=form)\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\[email protected]('/event/update/<id>', methods=('POST', 'GET'))\ndef event_update(id):\n form = forms.EditEventForm()\n found_event = Event.get(Event.id == id)\n if g.user.id == found_event.instructor_id:\n if form.validate_on_submit():\n if (found_event.date != form.date.data and found_event.time !=\n form.time.data):\n locator = Event.select().where((Event.instructor ==\n current_user.id) & (Event.date == form.date.data) & (\n Event.time == form.time.data))\n if locator.count() == 0:\n update = Event.update(date=form.date.data, time=form.\n time.data).where(Event.id == id)\n update.execute()\n flash('Updated Event Successfully', 'success')\n return redirect(url_for('event'))\n else:\n flash('Could not update, duplicate event exists', 'error')\n return redirect(url_for('event'))\n else:\n flash('You do not have permission to edit this event', 'error')\n return redirect(url_for('dashboard'))\n return render_template('edit_event.html', form=form, found_event=\n found_event)\n\n\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\[email protected]('/logout')\n@login_required\ndef logout():\n logout_user()\n flash(\"You've been logged out\", 'success')\n return redirect(url_for('index'))\n\n\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\ndef save_picture(form_picture):\n random_hex = secrets.token_hex(8)\n _, f_ext = os.path.splitext(form_picture.filename)\n picture_fn = random_hex + f_ext\n picture_path = os.path.join(app.root_path, 'static/profile_pics',\n picture_fn)\n output_size = 500, 500\n i = Image.open(form_picture)\n i.thumbnail(output_size)\n i.save(picture_path)\n return picture_fn\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\n@login_manager.user_loader\ndef load_user(userid):\n try:\n return models.User.get(models.User.id == userid)\n except models.DoesNotExist:\n return None\n\n\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/register', methods=('GET', 'POST'))\ndef register():\n form = forms.RegisterForm()\n if form.validate_on_submit():\n if 'generalassemb.ly' in form.email.data:\n flash('Registered as an instructor', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Instructor', password=form.password.data,\n course=form.course.data)\n else:\n flash('Registered as a student', 'success')\n models.User.create_user(username=form.username.data, email=form\n .email.data, role='Student', password=form.password.data,\n course=form.course.data)\n return redirect(url_for('index'))\n return render_template('register.html', form=form)\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/')\ndef index():\n return render_template('hero.html')\n\n\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/teacher')\ndef teacher_dash():\n return render_template('teacher-dashboard.html')\n\n\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/login', methods=('GET', 'POST'))\ndef login():\n form = forms.LoginForm()\n if form.validate_on_submit():\n try:\n user = models.User.get(models.User.email == form.email.data)\n except models.DoesNotExist:\n flash(\"your email or password doesn't match\", 'error')\n else:\n if check_password_hash(user.password, form.password.data):\n login_user(user)\n flash(\"You've been logged in\", 'success')\n return redirect(url_for('dashboard'))\n else:\n flash(\"your email or password doesn't match\", 'error')\n return render_template('login.html', form=form)\n\n\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/event/delete/<id>', methods=['DELETE', 'GET'])\n@login_required\ndef event_delete(id):\n found_event = models.Event.get(models.Event.id == id)\n if g.user.id == found_event.instructor_id:\n if found_event.student != None:\n unlock_student = User.update(event_assigned=False).where(User.\n id == found_event.student)\n unlock_student.execute()\n event_to_delete = Event.delete().where(Event.id == found_event.id)\n event_to_delete.execute()\n flash('Deleted event successfully', 'error')\n else:\n flash(\"You don't have permission to delete this event.\", 'error')\n return redirect(url_for('dashboard'))\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"
] | false |
99,415 |
5b8cc8ed84f98da8b7ec9ec1a0de9cced5f0f87d
|
#类调用自己的方法
#静态方法
class Room:
tag=1
# @classmethod#只能 访问类的属性
# def tell_info(cls,x):
# # print(cls)
# print("____>",cls.tag,x)#直接调用类
# Room.tell_info(10)
@staticmethod#和类和实例分别分隔开 只是名义上的归属类管理，不能使用类变量和实例变量，是类的工具包
def have_a_bath(a,b,c):
print('%s %s %s 正在洗澡'%(a,b,c))
Room.have_a_bath('pl','pll','plll')
|
[
"#类调用自己的方法\n#静态方法\nclass Room:\n tag=1\n# @classmethod#只能 访问类的属性\n# def tell_info(cls,x):\n# # print(cls)\n# print(\"____>\",cls.tag,x)#直接调用类\n# Room.tell_info(10)\n @staticmethod#和类和实例分别分隔开 只是名义上的归属类管理，不能使用类变量和实例变量，是类的工具包\n def have_a_bath(a,b,c):\n print('%s %s %s 正在洗澡'%(a,b,c))\nRoom.have_a_bath('pl','pll','plll')\n",
"class Room:\n tag = 1\n\n @staticmethod\n def have_a_bath(a, b, c):\n print('%s %s %s 正在洗澡' % (a, b, c))\n\n\nRoom.have_a_bath('pl', 'pll', 'plll')\n",
"class Room:\n tag = 1\n\n @staticmethod\n def have_a_bath(a, b, c):\n print('%s %s %s 正在洗澡' % (a, b, c))\n\n\n<code token>\n",
"class Room:\n <assignment token>\n\n @staticmethod\n def have_a_bath(a, b, c):\n print('%s %s %s 正在洗澡' % (a, b, c))\n\n\n<code token>\n",
"class Room:\n <assignment token>\n <function token>\n\n\n<code token>\n",
"<class token>\n<code token>\n"
] | false |
99,416 |
f24bb634c9b94148cae1f2fef97d2ec7a1c51b0c
|
EXPECTED = {'PKIX1Explicit88': {'extensibility-implied': False,
'imports': {},
'object-classes': {},
'object-sets': {},
'tags': 'EXPLICIT',
'types': {'AdministrationDomainName': {'members': [{'name': 'numeric',
'size': [(0,
'ub-domain-name-length')],
'type': 'NumericString'},
{'name': 'printable',
'size': [(0,
'ub-domain-name-length')],
'type': 'PrintableString'}],
'tag': {'class': 'APPLICATION',
'number': 2},
'type': 'CHOICE'},
'AlgorithmIdentifier': {'members': [{'name': 'algorithm',
'type': 'OBJECT '
'IDENTIFIER'},
{'choices': {},
'name': 'parameters',
'optional': True,
'type': 'ANY '
'DEFINED '
'BY',
'value': 'algorithm'}],
'type': 'SEQUENCE'},
'Attribute': {'members': [{'name': 'type',
'type': 'AttributeType'},
{'element': {'type': 'AttributeValue'},
'name': 'values',
'type': 'SET OF'}],
'type': 'SEQUENCE'},
'AttributeType': {'type': 'OBJECT IDENTIFIER'},
'AttributeTypeAndValue': {'members': [{'name': 'type',
'type': 'AttributeType'},
{'name': 'value',
'type': 'AttributeValue'}],
'type': 'SEQUENCE'},
'AttributeValue': {'choices': {},
'type': 'ANY DEFINED BY',
'value': 'type'},
'BuiltInDomainDefinedAttribute': {'members': [{'name': 'type',
'size': [(1,
'ub-domain-defined-attribute-type-length')],
'type': 'PrintableString'},
{'name': 'value',
'size': [(1,
'ub-domain-defined-attribute-value-length')],
'type': 'PrintableString'}],
'type': 'SEQUENCE'},
'BuiltInDomainDefinedAttributes': {'element': {'type': 'BuiltInDomainDefinedAttribute'},
'size': [(1,
'ub-domain-defined-attributes')],
'type': 'SEQUENCE '
'OF'},
'BuiltInStandardAttributes': {'members': [{'name': 'country-name',
'optional': True,
'type': 'CountryName'},
{'name': 'administration-domain-name',
'optional': True,
'type': 'AdministrationDomainName'},
{'name': 'network-address',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'NetworkAddress'},
{'name': 'terminal-identifier',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 1},
'type': 'TerminalIdentifier'},
{'name': 'private-domain-name',
'optional': True,
'tag': {'number': 2},
'type': 'PrivateDomainName'},
{'name': 'organization-name',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 3},
'type': 'OrganizationName'},
{'name': 'numeric-user-identifier',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 4},
'type': 'NumericUserIdentifier'},
{'name': 'personal-name',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 5},
'type': 'PersonalName'},
{'name': 'organizational-unit-names',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 6},
'type': 'OrganizationalUnitNames'}],
'type': 'SEQUENCE'},
'Certificate': {'members': [{'name': 'tbsCertificate',
'type': 'TBSCertificate'},
{'name': 'signatureAlgorithm',
'type': 'AlgorithmIdentifier'},
{'name': 'signature',
'type': 'BIT '
'STRING'}],
'type': 'SEQUENCE'},
'CertificateList': {'members': [{'name': 'tbsCertList',
'type': 'TBSCertList'},
{'name': 'signatureAlgorithm',
'type': 'AlgorithmIdentifier'},
{'name': 'signature',
'type': 'BIT '
'STRING'}],
'type': 'SEQUENCE'},
'CertificateSerialNumber': {'type': 'INTEGER'},
'CommonName': {'size': [(1,
'ub-common-name-length')],
'type': 'PrintableString'},
'CountryName': {'members': [{'name': 'x121-dcc-code',
'size': ['ub-country-name-numeric-length'],
'type': 'NumericString'},
{'name': 'iso-3166-alpha2-code',
'size': ['ub-country-name-alpha-length'],
'type': 'PrintableString'}],
'tag': {'class': 'APPLICATION',
'number': 1},
'type': 'CHOICE'},
'DirectoryString': {'members': [{'name': 'teletexString',
'size': [(1,
'MAX')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'MAX')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'MAX')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'MAX')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'MAX')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'DistinguishedName': {'type': 'RDNSequence'},
'DomainComponent': {'type': 'IA5String'},
'EmailAddress': {'size': [(1,
'ub-emailaddress-length')],
'type': 'IA5String'},
'ExtendedNetworkAddress': {'members': [{'members': [{'name': 'number',
'size': [(1,
'ub-e163-4-number-length')],
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'NumericString'},
{'name': 'sub-address',
'optional': True,
'size': [(1,
'ub-e163-4-sub-address-length')],
'tag': {'kind': 'IMPLICIT',
'number': 1},
'type': 'NumericString'}],
'name': 'e163-4-address',
'type': 'SEQUENCE'},
{'name': 'psap-address',
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'PresentationAddress'}],
'type': 'CHOICE'},
'Extension': {'members': [{'name': 'extnID',
'type': 'OBJECT '
'IDENTIFIER'},
{'default': False,
'name': 'critical',
'type': 'BOOLEAN'},
{'name': 'extnValue',
'type': 'OCTET '
'STRING'}],
'type': 'SEQUENCE'},
'ExtensionAttribute': {'members': [{'name': 'extension-attribute-type',
'restricted-to': [(0,
'ub-extension-attributes')],
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'INTEGER'},
{'choices': {},
'name': 'extension-attribute-value',
'tag': {'number': 1},
'type': 'ANY '
'DEFINED '
'BY',
'value': 'extension-attribute-type'}],
'type': 'SEQUENCE'},
'ExtensionAttributes': {'element': {'type': 'ExtensionAttribute'},
'size': [(1,
'ub-extension-attributes')],
'type': 'SET OF'},
'ExtensionORAddressComponents': {'type': 'PDSParameter'},
'ExtensionPhysicalDeliveryAddressComponents': {'type': 'PDSParameter'},
'Extensions': {'element': {'type': 'Extension'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'LocalPostalAttributes': {'type': 'PDSParameter'},
'Name': {'members': [{'name': 'rdnSequence',
'type': 'RDNSequence'}],
'type': 'CHOICE'},
'NetworkAddress': {'type': 'X121Address'},
'NumericUserIdentifier': {'size': [(1,
'ub-numeric-user-id-length')],
'type': 'NumericString'},
'ORAddress': {'members': [{'name': 'built-in-standard-attributes',
'type': 'BuiltInStandardAttributes'},
{'name': 'built-in-domain-defined-attributes',
'optional': True,
'type': 'BuiltInDomainDefinedAttributes'},
{'name': 'extension-attributes',
'optional': True,
'type': 'ExtensionAttributes'}],
'type': 'SEQUENCE'},
'OrganizationName': {'size': [(1,
'ub-organization-name-length')],
'type': 'PrintableString'},
'OrganizationalUnitName': {'size': [(1,
'ub-organizational-unit-name-length')],
'type': 'PrintableString'},
'OrganizationalUnitNames': {'element': {'type': 'OrganizationalUnitName'},
'size': [(1,
'ub-organizational-units')],
'type': 'SEQUENCE '
'OF'},
'PDSName': {'size': [(1, 'ub-pds-name-length')],
'type': 'PrintableString'},
'PDSParameter': {'members': [{'name': 'printable-string',
'optional': True,
'size': [(1,
'ub-pds-parameter-length')],
'type': 'PrintableString'},
{'name': 'teletex-string',
'optional': True,
'size': [(1,
'ub-pds-parameter-length')],
'type': 'TeletexString'}],
'type': 'SET'},
'PersonalName': {'members': [{'name': 'surname',
'size': [(1,
'ub-surname-length')],
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'PrintableString'},
{'name': 'given-name',
'optional': True,
'size': [(1,
'ub-given-name-length')],
'tag': {'kind': 'IMPLICIT',
'number': 1},
'type': 'PrintableString'},
{'name': 'initials',
'optional': True,
'size': [(1,
'ub-initials-length')],
'tag': {'kind': 'IMPLICIT',
'number': 2},
'type': 'PrintableString'},
{'name': 'generation-qualifier',
'optional': True,
'size': [(1,
'ub-generation-qualifier-length')],
'tag': {'kind': 'IMPLICIT',
'number': 3},
'type': 'PrintableString'}],
'type': 'SET'},
'PhysicalDeliveryCountryName': {'members': [{'name': 'x121-dcc-code',
'size': ['ub-country-name-numeric-length'],
'type': 'NumericString'},
{'name': 'iso-3166-alpha2-code',
'size': ['ub-country-name-alpha-length'],
'type': 'PrintableString'}],
'type': 'CHOICE'},
'PhysicalDeliveryOfficeName': {'type': 'PDSParameter'},
'PhysicalDeliveryOfficeNumber': {'type': 'PDSParameter'},
'PhysicalDeliveryOrganizationName': {'type': 'PDSParameter'},
'PhysicalDeliveryPersonalName': {'type': 'PDSParameter'},
'PostOfficeBoxAddress': {'type': 'PDSParameter'},
'PostalCode': {'members': [{'name': 'numeric-code',
'size': [(1,
'ub-postal-code-length')],
'type': 'NumericString'},
{'name': 'printable-code',
'size': [(1,
'ub-postal-code-length')],
'type': 'PrintableString'}],
'type': 'CHOICE'},
'PosteRestanteAddress': {'type': 'PDSParameter'},
'PresentationAddress': {'members': [{'name': 'pSelector',
'optional': True,
'tag': {'kind': 'EXPLICIT',
'number': 0},
'type': 'OCTET '
'STRING'},
{'name': 'sSelector',
'optional': True,
'tag': {'kind': 'EXPLICIT',
'number': 1},
'type': 'OCTET '
'STRING'},
{'name': 'tSelector',
'optional': True,
'tag': {'kind': 'EXPLICIT',
'number': 2},
'type': 'OCTET '
'STRING'},
{'element': {'type': 'OCTET '
'STRING'},
'name': 'nAddresses',
'size': [(1,
'MAX')],
'tag': {'kind': 'EXPLICIT',
'number': 3},
'type': 'SET '
'OF'}],
'type': 'SEQUENCE'},
'PrivateDomainName': {'members': [{'name': 'numeric',
'size': [(1,
'ub-domain-name-length')],
'type': 'NumericString'},
{'name': 'printable',
'size': [(1,
'ub-domain-name-length')],
'type': 'PrintableString'}],
'type': 'CHOICE'},
'RDNSequence': {'element': {'type': 'RelativeDistinguishedName'},
'type': 'SEQUENCE OF'},
'RelativeDistinguishedName': {'element': {'type': 'AttributeTypeAndValue'},
'size': [(1,
'MAX')],
'type': 'SET OF'},
'StreetAddress': {'type': 'PDSParameter'},
'SubjectPublicKeyInfo': {'members': [{'name': 'algorithm',
'type': 'AlgorithmIdentifier'},
{'name': 'subjectPublicKey',
'type': 'BIT '
'STRING'}],
'type': 'SEQUENCE'},
'TBSCertList': {'members': [{'name': 'version',
'optional': True,
'type': 'Version'},
{'name': 'signature',
'type': 'AlgorithmIdentifier'},
{'name': 'issuer',
'type': 'Name'},
{'name': 'thisUpdate',
'type': 'Time'},
{'name': 'nextUpdate',
'optional': True,
'type': 'Time'},
{'element': {'members': [{'name': 'userCertificate',
'type': 'CertificateSerialNumber'},
{'name': 'revocationDate',
'type': 'Time'},
{'name': 'crlEntryExtensions',
'optional': True,
'type': 'Extensions'}],
'type': 'SEQUENCE'},
'name': 'revokedCertificates',
'optional': True,
'type': 'SEQUENCE '
'OF'},
{'name': 'crlExtensions',
'optional': True,
'tag': {'number': 0},
'type': 'Extensions'}],
'type': 'SEQUENCE'},
'TBSCertificate': {'members': [{'default': 'v1',
'name': 'version',
'tag': {'number': 0},
'type': 'Version'},
{'name': 'serialNumber',
'type': 'CertificateSerialNumber'},
{'name': 'signature',
'type': 'AlgorithmIdentifier'},
{'name': 'issuer',
'type': 'Name'},
{'name': 'validity',
'type': 'Validity'},
{'name': 'subject',
'type': 'Name'},
{'name': 'subjectPublicKeyInfo',
'type': 'SubjectPublicKeyInfo'},
{'name': 'issuerUniqueID',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 1},
'type': 'UniqueIdentifier'},
{'name': 'subjectUniqueID',
'optional': True,
'tag': {'kind': 'IMPLICIT',
'number': 2},
'type': 'UniqueIdentifier'},
{'name': 'extensions',
'optional': True,
'tag': {'number': 3},
'type': 'Extensions'}],
'type': 'SEQUENCE'},
'TeletexCommonName': {'size': [(1,
'ub-common-name-length')],
'type': 'TeletexString'},
'TeletexDomainDefinedAttribute': {'members': [{'name': 'type',
'size': [(1,
'ub-domain-defined-attribute-type-length')],
'type': 'TeletexString'},
{'name': 'value',
'size': [(1,
'ub-domain-defined-attribute-value-length')],
'type': 'TeletexString'}],
'type': 'SEQUENCE'},
'TeletexDomainDefinedAttributes': {'element': {'type': 'TeletexDomainDefinedAttribute'},
'size': [(1,
'ub-domain-defined-attributes')],
'type': 'SEQUENCE '
'OF'},
'TeletexOrganizationName': {'size': [(1,
'ub-organization-name-length')],
'type': 'TeletexString'},
'TeletexOrganizationalUnitName': {'size': [(1,
'ub-organizational-unit-name-length')],
'type': 'TeletexString'},
'TeletexOrganizationalUnitNames': {'element': {'type': 'TeletexOrganizationalUnitName'},
'size': [(1,
'ub-organizational-units')],
'type': 'SEQUENCE '
'OF'},
'TeletexPersonalName': {'members': [{'name': 'surname',
'size': [(1,
'ub-surname-length')],
'tag': {'kind': 'IMPLICIT',
'number': 0},
'type': 'TeletexString'},
{'name': 'given-name',
'optional': True,
'size': [(1,
'ub-given-name-length')],
'tag': {'kind': 'IMPLICIT',
'number': 1},
'type': 'TeletexString'},
{'name': 'initials',
'optional': True,
'size': [(1,
'ub-initials-length')],
'tag': {'kind': 'IMPLICIT',
'number': 2},
'type': 'TeletexString'},
{'name': 'generation-qualifier',
'optional': True,
'size': [(1,
'ub-generation-qualifier-length')],
'tag': {'kind': 'IMPLICIT',
'number': 3},
'type': 'TeletexString'}],
'type': 'SET'},
'TerminalIdentifier': {'size': [(1,
'ub-terminal-id-length')],
'type': 'PrintableString'},
'TerminalType': {'named-numbers': {'g3-facsimile': 5,
'g4-facsimile': 6,
'ia5-terminal': 7,
'teletex': 4,
'telex': 3,
'videotex': 8},
'restricted-to': [(0,
'ub-integer-options')],
'type': 'INTEGER'},
'Time': {'members': [{'name': 'utcTime',
'type': 'UTCTime'},
{'name': 'generalTime',
'type': 'GeneralizedTime'}],
'type': 'CHOICE'},
'UnformattedPostalAddress': {'members': [{'element': {'size': [(1,
'ub-pds-parameter-length')],
'type': 'PrintableString'},
'name': 'printable-address',
'optional': True,
'size': [(1,
'ub-pds-physical-address-lines')],
'type': 'SEQUENCE '
'OF'},
{'name': 'teletex-string',
'optional': True,
'size': [(1,
'ub-unformatted-address-length')],
'type': 'TeletexString'}],
'type': 'SET'},
'UniqueIdentifier': {'type': 'BIT STRING'},
'UniquePostalName': {'type': 'PDSParameter'},
'Validity': {'members': [{'name': 'notBefore',
'type': 'Time'},
{'name': 'notAfter',
'type': 'Time'}],
'type': 'SEQUENCE'},
'Version': {'named-numbers': {'v1': 0,
'v2': 1,
'v3': 2},
'type': 'INTEGER'},
'X121Address': {'size': [(1,
'ub-x121-address-length')],
'type': 'NumericString'},
'X520CommonName': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-common-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-common-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-common-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-common-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-common-name')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520LocalityName': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-locality-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-locality-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-locality-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-locality-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-locality-name')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520OrganizationName': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-organization-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-organization-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-organization-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-organization-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-organization-name')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520OrganizationalUnitName': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-organizational-unit-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-organizational-unit-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-organizational-unit-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-organizational-unit-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-organizational-unit-name')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520Pseudonym': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-pseudonym')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-pseudonym')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-pseudonym')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-pseudonym')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-pseudonym')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520SerialNumber': {'size': [(1,
'ub-serial-number')],
'type': 'PrintableString'},
'X520StateOrProvinceName': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-state-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-state-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-state-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-state-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-state-name')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520Title': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-title')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-title')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-title')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-title')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-title')],
'type': 'BMPString'}],
'type': 'CHOICE'},
'X520countryName': {'size': [2],
'type': 'PrintableString'},
'X520dnQualifier': {'type': 'PrintableString'},
'X520name': {'members': [{'name': 'teletexString',
'size': [(1,
'ub-name')],
'type': 'TeletexString'},
{'name': 'printableString',
'size': [(1,
'ub-name')],
'type': 'PrintableString'},
{'name': 'universalString',
'size': [(1,
'ub-name')],
'type': 'UniversalString'},
{'name': 'utf8String',
'size': [(1,
'ub-name')],
'type': 'UTF8String'},
{'name': 'bmpString',
'size': [(1,
'ub-name')],
'type': 'BMPString'}],
'type': 'CHOICE'}},
'values': {'common-name': {'type': 'INTEGER', 'value': 1},
'extended-network-address': {'type': 'INTEGER',
'value': 22},
'extension-OR-address-components': {'type': 'INTEGER',
'value': 12},
'extension-physical-delivery-address-components': {'type': 'INTEGER',
'value': 15},
'id-ad': {'type': 'OBJECT IDENTIFIER',
'value': ['id-pkix', 48]},
'id-ad-caIssuers': {'type': 'OBJECT IDENTIFIER',
'value': ['id-ad', 2]},
'id-ad-caRepository': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ad', 5]},
'id-ad-ocsp': {'type': 'OBJECT IDENTIFIER',
'value': ['id-ad', 1]},
'id-ad-timeStamping': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ad', 3]},
'id-at': {'type': 'OBJECT IDENTIFIER',
'value': [('joint-iso-ccitt', 2),
('ds', 5),
4]},
'id-at-commonName': {'type': 'AttributeType',
'value': None},
'id-at-countryName': {'type': 'AttributeType',
'value': None},
'id-at-dnQualifier': {'type': 'AttributeType',
'value': None},
'id-at-generationQualifier': {'type': 'AttributeType',
'value': None},
'id-at-givenName': {'type': 'AttributeType',
'value': None},
'id-at-initials': {'type': 'AttributeType',
'value': None},
'id-at-localityName': {'type': 'AttributeType',
'value': None},
'id-at-name': {'type': 'AttributeType',
'value': None},
'id-at-organizationName': {'type': 'AttributeType',
'value': None},
'id-at-organizationalUnitName': {'type': 'AttributeType',
'value': None},
'id-at-pseudonym': {'type': 'AttributeType',
'value': None},
'id-at-serialNumber': {'type': 'AttributeType',
'value': None},
'id-at-stateOrProvinceName': {'type': 'AttributeType',
'value': None},
'id-at-surname': {'type': 'AttributeType',
'value': None},
'id-at-title': {'type': 'AttributeType',
'value': None},
'id-domainComponent': {'type': 'AttributeType',
'value': None},
'id-emailAddress': {'type': 'AttributeType',
'value': None},
'id-kp': {'type': 'OBJECT IDENTIFIER',
'value': ['id-pkix', 3]},
'id-pe': {'type': 'OBJECT IDENTIFIER',
'value': ['id-pkix', 1]},
'id-pkix': {'type': 'OBJECT IDENTIFIER',
'value': [('iso', 1),
('identified-organization',
3),
('dod', 6),
('internet', 1),
('security', 5),
('mechanisms', 5),
('pkix', 7)]},
'id-qt': {'type': 'OBJECT IDENTIFIER',
'value': ['id-pkix', 2]},
'id-qt-cps': {'type': 'OBJECT IDENTIFIER',
'value': ['id-qt', 1]},
'id-qt-unotice': {'type': 'OBJECT IDENTIFIER',
'value': ['id-qt', 2]},
'local-postal-attributes': {'type': 'INTEGER',
'value': 21},
'pds-name': {'type': 'INTEGER', 'value': 7},
'physical-delivery-country-name': {'type': 'INTEGER',
'value': 8},
'physical-delivery-office-name': {'type': 'INTEGER',
'value': 10},
'physical-delivery-office-number': {'type': 'INTEGER',
'value': 11},
'physical-delivery-organization-name': {'type': 'INTEGER',
'value': 14},
'physical-delivery-personal-name': {'type': 'INTEGER',
'value': 13},
'pkcs-9': {'type': 'OBJECT IDENTIFIER',
'value': [('iso', 1),
('member-body', 2),
('us', 840),
('rsadsi', 113549),
('pkcs', 1),
9]},
'post-office-box-address': {'type': 'INTEGER',
'value': 18},
'postal-code': {'type': 'INTEGER', 'value': 9},
'poste-restante-address': {'type': 'INTEGER',
'value': 19},
'street-address': {'type': 'INTEGER',
'value': 17},
'teletex-common-name': {'type': 'INTEGER',
'value': 2},
'teletex-domain-defined-attributes': {'type': 'INTEGER',
'value': 6},
'teletex-organization-name': {'type': 'INTEGER',
'value': 3},
'teletex-organizational-unit-names': {'type': 'INTEGER',
'value': 5},
'teletex-personal-name': {'type': 'INTEGER',
'value': 4},
'terminal-type': {'type': 'INTEGER',
'value': 23},
'ub-common-name': {'type': 'INTEGER',
'value': 64},
'ub-common-name-length': {'type': 'INTEGER',
'value': 64},
'ub-country-name-alpha-length': {'type': 'INTEGER',
'value': 2},
'ub-country-name-numeric-length': {'type': 'INTEGER',
'value': 3},
'ub-domain-defined-attribute-type-length': {'type': 'INTEGER',
'value': 8},
'ub-domain-defined-attribute-value-length': {'type': 'INTEGER',
'value': 128},
'ub-domain-defined-attributes': {'type': 'INTEGER',
'value': 4},
'ub-domain-name-length': {'type': 'INTEGER',
'value': 16},
'ub-e163-4-number-length': {'type': 'INTEGER',
'value': 15},
'ub-e163-4-sub-address-length': {'type': 'INTEGER',
'value': 40},
'ub-emailaddress-length': {'type': 'INTEGER',
'value': 255},
'ub-extension-attributes': {'type': 'INTEGER',
'value': 256},
'ub-generation-qualifier-length': {'type': 'INTEGER',
'value': 3},
'ub-given-name-length': {'type': 'INTEGER',
'value': 16},
'ub-initials-length': {'type': 'INTEGER',
'value': 5},
'ub-integer-options': {'type': 'INTEGER',
'value': 256},
'ub-locality-name': {'type': 'INTEGER',
'value': 128},
'ub-match': {'type': 'INTEGER', 'value': 128},
'ub-name': {'type': 'INTEGER', 'value': 32768},
'ub-numeric-user-id-length': {'type': 'INTEGER',
'value': 32},
'ub-organization-name': {'type': 'INTEGER',
'value': 64},
'ub-organization-name-length': {'type': 'INTEGER',
'value': 64},
'ub-organizational-unit-name': {'type': 'INTEGER',
'value': 64},
'ub-organizational-unit-name-length': {'type': 'INTEGER',
'value': 32},
'ub-organizational-units': {'type': 'INTEGER',
'value': 4},
'ub-pds-name-length': {'type': 'INTEGER',
'value': 16},
'ub-pds-parameter-length': {'type': 'INTEGER',
'value': 30},
'ub-pds-physical-address-lines': {'type': 'INTEGER',
'value': 6},
'ub-postal-code-length': {'type': 'INTEGER',
'value': 16},
'ub-pseudonym': {'type': 'INTEGER',
'value': 128},
'ub-serial-number': {'type': 'INTEGER',
'value': 64},
'ub-state-name': {'type': 'INTEGER',
'value': 128},
'ub-surname-length': {'type': 'INTEGER',
'value': 40},
'ub-terminal-id-length': {'type': 'INTEGER',
'value': 24},
'ub-title': {'type': 'INTEGER', 'value': 64},
'ub-unformatted-address-length': {'type': 'INTEGER',
'value': 180},
'ub-x121-address-length': {'type': 'INTEGER',
'value': 16},
'unformatted-postal-address': {'type': 'INTEGER',
'value': 16},
'unique-postal-name': {'type': 'INTEGER',
'value': 20}}},
'PKIX1Implicit88': {'extensibility-implied': False,
'imports': {'PKIX1Explicit88': ['Attribute',
'BMPString',
'CertificateSerialNumber',
'DirectoryString',
'Name',
'ORAddress',
'RelativeDistinguishedName',
'UTF8String',
'id-kp',
'id-pe',
'id-qt-cps',
'id-qt-unotice']},
'object-classes': {},
'object-sets': {},
'tags': 'IMPLICIT',
'types': {'AccessDescription': {'members': [{'name': 'accessMethod',
'type': 'OBJECT '
'IDENTIFIER'},
{'name': 'accessLocation',
'type': 'GeneralName'}],
'type': 'SEQUENCE'},
'AnotherName': {'members': [{'name': 'type-id',
'type': 'OBJECT '
'IDENTIFIER'},
{'choices': {},
'name': 'value',
'tag': {'kind': 'EXPLICIT',
'number': 0},
'type': 'ANY '
'DEFINED '
'BY',
'value': 'type-id'}],
'type': 'SEQUENCE'},
'AuthorityInfoAccessSyntax': {'element': {'type': 'AccessDescription'},
'size': [(1,
'MAX')],
'type': 'SEQUENCE '
'OF'},
'AuthorityKeyIdentifier': {'members': [{'name': 'keyIdentifier',
'optional': True,
'tag': {'number': 0},
'type': 'KeyIdentifier'},
{'name': 'authorityCertIssuer',
'optional': True,
'tag': {'number': 1},
'type': 'GeneralNames'},
{'name': 'authorityCertSerialNumber',
'optional': True,
'tag': {'number': 2},
'type': 'CertificateSerialNumber'}],
'type': 'SEQUENCE'},
'BaseCRLNumber': {'type': 'CRLNumber'},
'BaseDistance': {'restricted-to': [(0, 'MAX')],
'type': 'INTEGER'},
'BasicConstraints': {'members': [{'default': False,
'name': 'cA',
'type': 'BOOLEAN'},
{'name': 'pathLenConstraint',
'optional': True,
'restricted-to': [(0,
'MAX')],
'type': 'INTEGER'}],
'type': 'SEQUENCE'},
'CPSuri': {'type': 'IA5String'},
'CRLDistributionPoints': {'element': {'type': 'DistributionPoint'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'CRLNumber': {'restricted-to': [(0, 'MAX')],
'type': 'INTEGER'},
'CRLReason': {'type': 'ENUMERATED',
'values': [('unspecified', 0),
('keyCompromise', 1),
('cACompromise', 2),
('affiliationChanged',
3),
('superseded', 4),
('cessationOfOperation',
5),
('certificateHold', 6),
('removeFromCRL', 8),
('privilegeWithdrawn',
9),
('aACompromise', 10)]},
'CertPolicyId': {'type': 'OBJECT IDENTIFIER'},
'CertificateIssuer': {'type': 'GeneralNames'},
'CertificatePolicies': {'element': {'type': 'PolicyInformation'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'DisplayText': {'members': [{'name': 'ia5String',
'size': [(1, 200)],
'type': 'IA5String'},
{'name': 'visibleString',
'size': [(1, 200)],
'type': 'VisibleString'},
{'name': 'bmpString',
'size': [(1, 200)],
'type': 'BMPString'},
{'name': 'utf8String',
'size': [(1, 200)],
'type': 'UTF8String'}],
'type': 'CHOICE'},
'DistributionPoint': {'members': [{'name': 'distributionPoint',
'optional': True,
'tag': {'number': 0},
'type': 'DistributionPointName'},
{'name': 'reasons',
'optional': True,
'tag': {'number': 1},
'type': 'ReasonFlags'},
{'name': 'cRLIssuer',
'optional': True,
'tag': {'number': 2},
'type': 'GeneralNames'}],
'type': 'SEQUENCE'},
'DistributionPointName': {'members': [{'name': 'fullName',
'tag': {'number': 0},
'type': 'GeneralNames'},
{'name': 'nameRelativeToCRLIssuer',
'tag': {'number': 1},
'type': 'RelativeDistinguishedName'}],
'type': 'CHOICE'},
'EDIPartyName': {'members': [{'name': 'nameAssigner',
'optional': True,
'tag': {'number': 0},
'type': 'DirectoryString'},
{'name': 'partyName',
'tag': {'number': 1},
'type': 'DirectoryString'}],
'type': 'SEQUENCE'},
'ExtKeyUsageSyntax': {'element': {'type': 'KeyPurposeId'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'FreshestCRL': {'type': 'CRLDistributionPoints'},
'GeneralName': {'members': [{'name': 'otherName',
'tag': {'number': 0},
'type': 'AnotherName'},
{'name': 'rfc822Name',
'tag': {'number': 1},
'type': 'IA5String'},
{'name': 'dNSName',
'tag': {'number': 2},
'type': 'IA5String'},
{'name': 'x400Address',
'tag': {'number': 3},
'type': 'ORAddress'},
{'name': 'directoryName',
'tag': {'number': 4},
'type': 'Name'},
{'name': 'ediPartyName',
'tag': {'number': 5},
'type': 'EDIPartyName'},
{'name': 'uniformResourceIdentifier',
'tag': {'number': 6},
'type': 'IA5String'},
{'name': 'iPAddress',
'tag': {'number': 7},
'type': 'OCTET '
'STRING'},
{'name': 'registeredID',
'tag': {'number': 8},
'type': 'OBJECT '
'IDENTIFIER'}],
'type': 'CHOICE'},
'GeneralNames': {'element': {'type': 'GeneralName'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'GeneralSubtree': {'members': [{'name': 'base',
'type': 'GeneralName'},
{'default': 0,
'name': 'minimum',
'tag': {'number': 0},
'type': 'BaseDistance'},
{'name': 'maximum',
'optional': True,
'tag': {'number': 1},
'type': 'BaseDistance'}],
'type': 'SEQUENCE'},
'GeneralSubtrees': {'element': {'type': 'GeneralSubtree'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'HoldInstructionCode': {'type': 'OBJECT '
'IDENTIFIER'},
'InhibitAnyPolicy': {'type': 'SkipCerts'},
'InvalidityDate': {'type': 'GeneralizedTime'},
'IssuerAltName': {'type': 'GeneralNames'},
'IssuingDistributionPoint': {'members': [{'name': 'distributionPoint',
'optional': True,
'tag': {'number': 0},
'type': 'DistributionPointName'},
{'default': False,
'name': 'onlyContainsUserCerts',
'tag': {'number': 1},
'type': 'BOOLEAN'},
{'default': False,
'name': 'onlyContainsCACerts',
'tag': {'number': 2},
'type': 'BOOLEAN'},
{'name': 'onlySomeReasons',
'optional': True,
'tag': {'number': 3},
'type': 'ReasonFlags'},
{'default': False,
'name': 'indirectCRL',
'tag': {'number': 4},
'type': 'BOOLEAN'},
{'default': False,
'name': 'onlyContainsAttributeCerts',
'tag': {'number': 5},
'type': 'BOOLEAN'}],
'type': 'SEQUENCE'},
'KeyIdentifier': {'type': 'OCTET STRING'},
'KeyPurposeId': {'type': 'OBJECT IDENTIFIER'},
'KeyUsage': {'named-bits': [('digitalSignature',
'0'),
('nonRepudiation',
'1'),
('keyEncipherment',
'2'),
('dataEncipherment',
'3'),
('keyAgreement',
'4'),
('keyCertSign', '5'),
('cRLSign', '6'),
('encipherOnly',
'7'),
('decipherOnly',
'8')],
'type': 'BIT STRING'},
'NameConstraints': {'members': [{'name': 'permittedSubtrees',
'optional': True,
'tag': {'number': 0},
'type': 'GeneralSubtrees'},
{'name': 'excludedSubtrees',
'optional': True,
'tag': {'number': 1},
'type': 'GeneralSubtrees'}],
'type': 'SEQUENCE'},
'NoticeReference': {'members': [{'name': 'organization',
'type': 'DisplayText'},
{'element': {'type': 'INTEGER'},
'name': 'noticeNumbers',
'type': 'SEQUENCE '
'OF'}],
'type': 'SEQUENCE'},
'PolicyConstraints': {'members': [{'name': 'requireExplicitPolicy',
'optional': True,
'tag': {'number': 0},
'type': 'SkipCerts'},
{'name': 'inhibitPolicyMapping',
'optional': True,
'tag': {'number': 1},
'type': 'SkipCerts'}],
'type': 'SEQUENCE'},
'PolicyInformation': {'members': [{'name': 'policyIdentifier',
'type': 'CertPolicyId'},
{'element': {'type': 'PolicyQualifierInfo'},
'name': 'policyQualifiers',
'optional': True,
'size': [(1,
'MAX')],
'type': 'SEQUENCE '
'OF'}],
'type': 'SEQUENCE'},
'PolicyMappings': {'element': {'members': [{'name': 'issuerDomainPolicy',
'type': 'CertPolicyId'},
{'name': 'subjectDomainPolicy',
'type': 'CertPolicyId'}],
'type': 'SEQUENCE'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE OF'},
'PolicyQualifierId': {'type': 'OBJECT '
'IDENTIFIER'},
'PolicyQualifierInfo': {'members': [{'name': 'policyQualifierId',
'type': 'PolicyQualifierId'},
{'choices': {},
'name': 'qualifier',
'type': 'ANY '
'DEFINED '
'BY',
'value': 'policyQualifierId'}],
'type': 'SEQUENCE'},
'PrivateKeyUsagePeriod': {'members': [{'name': 'notBefore',
'optional': True,
'tag': {'number': 0},
'type': 'GeneralizedTime'},
{'name': 'notAfter',
'optional': True,
'tag': {'number': 1},
'type': 'GeneralizedTime'}],
'type': 'SEQUENCE'},
'ReasonFlags': {'named-bits': [('unused', '0'),
('keyCompromise',
'1'),
('cACompromise',
'2'),
('affiliationChanged',
'3'),
('superseded',
'4'),
('cessationOfOperation',
'5'),
('certificateHold',
'6'),
('privilegeWithdrawn',
'7'),
('aACompromise',
'8')],
'type': 'BIT STRING'},
'SkipCerts': {'restricted-to': [(0, 'MAX')],
'type': 'INTEGER'},
'SubjectAltName': {'type': 'GeneralNames'},
'SubjectDirectoryAttributes': {'element': {'type': 'Attribute'},
'size': [(1,
'MAX')],
'type': 'SEQUENCE '
'OF'},
'SubjectInfoAccessSyntax': {'element': {'type': 'AccessDescription'},
'size': [(1, 'MAX')],
'type': 'SEQUENCE '
'OF'},
'SubjectKeyIdentifier': {'type': 'KeyIdentifier'},
'UserNotice': {'members': [{'name': 'noticeRef',
'optional': True,
'type': 'NoticeReference'},
{'name': 'explicitText',
'optional': True,
'type': 'DisplayText'}],
'type': 'SEQUENCE'}},
'values': {'anyExtendedKeyUsage': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce-extKeyUsage',
0]},
'anyPolicy': {'type': 'OBJECT IDENTIFIER',
'value': ['id-ce-certificatePolicies',
0]},
'holdInstruction': {'type': 'OBJECT IDENTIFIER',
'value': [('joint-iso-itu-t',
2),
('member-body',
2),
('us', 840),
('x9cm', 10040),
2]},
'id-ce': {'type': 'OBJECT IDENTIFIER',
'value': [('joint-iso-ccitt', 2),
('ds', 5),
29]},
'id-ce-authorityKeyIdentifier': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
35]},
'id-ce-basicConstraints': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
19]},
'id-ce-cRLDistributionPoints': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
31]},
'id-ce-cRLNumber': {'type': 'OBJECT IDENTIFIER',
'value': ['id-ce', 20]},
'id-ce-cRLReasons': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce', 21]},
'id-ce-certificateIssuer': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
29]},
'id-ce-certificatePolicies': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
32]},
'id-ce-deltaCRLIndicator': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
27]},
'id-ce-extKeyUsage': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce', 37]},
'id-ce-freshestCRL': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce', 46]},
'id-ce-holdInstructionCode': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
23]},
'id-ce-inhibitAnyPolicy': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
54]},
'id-ce-invalidityDate': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
24]},
'id-ce-issuerAltName': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce', 18]},
'id-ce-issuingDistributionPoint': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
28]},
'id-ce-keyUsage': {'type': 'OBJECT IDENTIFIER',
'value': ['id-ce', 15]},
'id-ce-nameConstraints': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
30]},
'id-ce-policyConstraints': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
36]},
'id-ce-policyMappings': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
33]},
'id-ce-privateKeyUsagePeriod': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
16]},
'id-ce-subjectAltName': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
17]},
'id-ce-subjectDirectoryAttributes': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
9]},
'id-ce-subjectKeyIdentifier': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-ce',
14]},
'id-holdinstruction-callissuer': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['holdInstruction',
2]},
'id-holdinstruction-none': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['holdInstruction',
1]},
'id-holdinstruction-reject': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['holdInstruction',
3]},
'id-kp-OCSPSigning': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp', 9]},
'id-kp-clientAuth': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp', 2]},
'id-kp-codeSigning': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp', 3]},
'id-kp-emailProtection': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp',
4]},
'id-kp-serverAuth': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp', 1]},
'id-kp-timeStamping': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-kp', 8]},
'id-pe-authorityInfoAccess': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-pe',
1]},
'id-pe-subjectInfoAccess': {'type': 'OBJECT '
'IDENTIFIER',
'value': ['id-pe',
11]}}}}
|
[
"EXPECTED = {'PKIX1Explicit88': {'extensibility-implied': False,\n 'imports': {},\n 'object-classes': {},\n 'object-sets': {},\n 'tags': 'EXPLICIT',\n 'types': {'AdministrationDomainName': {'members': [{'name': 'numeric',\n 'size': [(0,\n 'ub-domain-name-length')],\n 'type': 'NumericString'},\n {'name': 'printable',\n 'size': [(0,\n 'ub-domain-name-length')],\n 'type': 'PrintableString'}],\n 'tag': {'class': 'APPLICATION',\n 'number': 2},\n 'type': 'CHOICE'},\n 'AlgorithmIdentifier': {'members': [{'name': 'algorithm',\n 'type': 'OBJECT '\n 'IDENTIFIER'},\n {'choices': {},\n 'name': 'parameters',\n 'optional': True,\n 'type': 'ANY '\n 'DEFINED '\n 'BY',\n 'value': 'algorithm'}],\n 'type': 'SEQUENCE'},\n 'Attribute': {'members': [{'name': 'type',\n 'type': 'AttributeType'},\n {'element': {'type': 'AttributeValue'},\n 'name': 'values',\n 'type': 'SET OF'}],\n 'type': 'SEQUENCE'},\n 'AttributeType': {'type': 'OBJECT IDENTIFIER'},\n 'AttributeTypeAndValue': {'members': [{'name': 'type',\n 'type': 'AttributeType'},\n {'name': 'value',\n 'type': 'AttributeValue'}],\n 'type': 'SEQUENCE'},\n 'AttributeValue': {'choices': {},\n 'type': 'ANY DEFINED BY',\n 'value': 'type'},\n 'BuiltInDomainDefinedAttribute': {'members': [{'name': 'type',\n 'size': [(1,\n 'ub-domain-defined-attribute-type-length')],\n 'type': 'PrintableString'},\n {'name': 'value',\n 'size': [(1,\n 'ub-domain-defined-attribute-value-length')],\n 'type': 'PrintableString'}],\n 'type': 'SEQUENCE'},\n 'BuiltInDomainDefinedAttributes': {'element': {'type': 'BuiltInDomainDefinedAttribute'},\n 'size': [(1,\n 'ub-domain-defined-attributes')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'BuiltInStandardAttributes': {'members': [{'name': 'country-name',\n 'optional': True,\n 'type': 'CountryName'},\n {'name': 'administration-domain-name',\n 'optional': True,\n 'type': 'AdministrationDomainName'},\n {'name': 'network-address',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'NetworkAddress'},\n {'name': 'terminal-identifier',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 1},\n 'type': 'TerminalIdentifier'},\n {'name': 'private-domain-name',\n 'optional': True,\n 'tag': {'number': 2},\n 'type': 'PrivateDomainName'},\n {'name': 'organization-name',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 3},\n 'type': 'OrganizationName'},\n {'name': 'numeric-user-identifier',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 4},\n 'type': 'NumericUserIdentifier'},\n {'name': 'personal-name',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 5},\n 'type': 'PersonalName'},\n {'name': 'organizational-unit-names',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 6},\n 'type': 'OrganizationalUnitNames'}],\n 'type': 'SEQUENCE'},\n 'Certificate': {'members': [{'name': 'tbsCertificate',\n 'type': 'TBSCertificate'},\n {'name': 'signatureAlgorithm',\n 'type': 'AlgorithmIdentifier'},\n {'name': 'signature',\n 'type': 'BIT '\n 'STRING'}],\n 'type': 'SEQUENCE'},\n 'CertificateList': {'members': [{'name': 'tbsCertList',\n 'type': 'TBSCertList'},\n {'name': 'signatureAlgorithm',\n 'type': 'AlgorithmIdentifier'},\n {'name': 'signature',\n 'type': 'BIT '\n 'STRING'}],\n 'type': 'SEQUENCE'},\n 'CertificateSerialNumber': {'type': 'INTEGER'},\n 'CommonName': {'size': [(1,\n 'ub-common-name-length')],\n 'type': 'PrintableString'},\n 'CountryName': {'members': [{'name': 'x121-dcc-code',\n 'size': ['ub-country-name-numeric-length'],\n 'type': 'NumericString'},\n {'name': 'iso-3166-alpha2-code',\n 'size': ['ub-country-name-alpha-length'],\n 'type': 'PrintableString'}],\n 'tag': {'class': 'APPLICATION',\n 'number': 1},\n 'type': 'CHOICE'},\n 'DirectoryString': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'MAX')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'MAX')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'MAX')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'MAX')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'MAX')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'DistinguishedName': {'type': 'RDNSequence'},\n 'DomainComponent': {'type': 'IA5String'},\n 'EmailAddress': {'size': [(1,\n 'ub-emailaddress-length')],\n 'type': 'IA5String'},\n 'ExtendedNetworkAddress': {'members': [{'members': [{'name': 'number',\n 'size': [(1,\n 'ub-e163-4-number-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'NumericString'},\n {'name': 'sub-address',\n 'optional': True,\n 'size': [(1,\n 'ub-e163-4-sub-address-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 1},\n 'type': 'NumericString'}],\n 'name': 'e163-4-address',\n 'type': 'SEQUENCE'},\n {'name': 'psap-address',\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'PresentationAddress'}],\n 'type': 'CHOICE'},\n 'Extension': {'members': [{'name': 'extnID',\n 'type': 'OBJECT '\n 'IDENTIFIER'},\n {'default': False,\n 'name': 'critical',\n 'type': 'BOOLEAN'},\n {'name': 'extnValue',\n 'type': 'OCTET '\n 'STRING'}],\n 'type': 'SEQUENCE'},\n 'ExtensionAttribute': {'members': [{'name': 'extension-attribute-type',\n 'restricted-to': [(0,\n 'ub-extension-attributes')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'INTEGER'},\n {'choices': {},\n 'name': 'extension-attribute-value',\n 'tag': {'number': 1},\n 'type': 'ANY '\n 'DEFINED '\n 'BY',\n 'value': 'extension-attribute-type'}],\n 'type': 'SEQUENCE'},\n 'ExtensionAttributes': {'element': {'type': 'ExtensionAttribute'},\n 'size': [(1,\n 'ub-extension-attributes')],\n 'type': 'SET OF'},\n 'ExtensionORAddressComponents': {'type': 'PDSParameter'},\n 'ExtensionPhysicalDeliveryAddressComponents': {'type': 'PDSParameter'},\n 'Extensions': {'element': {'type': 'Extension'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'LocalPostalAttributes': {'type': 'PDSParameter'},\n 'Name': {'members': [{'name': 'rdnSequence',\n 'type': 'RDNSequence'}],\n 'type': 'CHOICE'},\n 'NetworkAddress': {'type': 'X121Address'},\n 'NumericUserIdentifier': {'size': [(1,\n 'ub-numeric-user-id-length')],\n 'type': 'NumericString'},\n 'ORAddress': {'members': [{'name': 'built-in-standard-attributes',\n 'type': 'BuiltInStandardAttributes'},\n {'name': 'built-in-domain-defined-attributes',\n 'optional': True,\n 'type': 'BuiltInDomainDefinedAttributes'},\n {'name': 'extension-attributes',\n 'optional': True,\n 'type': 'ExtensionAttributes'}],\n 'type': 'SEQUENCE'},\n 'OrganizationName': {'size': [(1,\n 'ub-organization-name-length')],\n 'type': 'PrintableString'},\n 'OrganizationalUnitName': {'size': [(1,\n 'ub-organizational-unit-name-length')],\n 'type': 'PrintableString'},\n 'OrganizationalUnitNames': {'element': {'type': 'OrganizationalUnitName'},\n 'size': [(1,\n 'ub-organizational-units')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'PDSName': {'size': [(1, 'ub-pds-name-length')],\n 'type': 'PrintableString'},\n 'PDSParameter': {'members': [{'name': 'printable-string',\n 'optional': True,\n 'size': [(1,\n 'ub-pds-parameter-length')],\n 'type': 'PrintableString'},\n {'name': 'teletex-string',\n 'optional': True,\n 'size': [(1,\n 'ub-pds-parameter-length')],\n 'type': 'TeletexString'}],\n 'type': 'SET'},\n 'PersonalName': {'members': [{'name': 'surname',\n 'size': [(1,\n 'ub-surname-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'PrintableString'},\n {'name': 'given-name',\n 'optional': True,\n 'size': [(1,\n 'ub-given-name-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 1},\n 'type': 'PrintableString'},\n {'name': 'initials',\n 'optional': True,\n 'size': [(1,\n 'ub-initials-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 2},\n 'type': 'PrintableString'},\n {'name': 'generation-qualifier',\n 'optional': True,\n 'size': [(1,\n 'ub-generation-qualifier-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 3},\n 'type': 'PrintableString'}],\n 'type': 'SET'},\n 'PhysicalDeliveryCountryName': {'members': [{'name': 'x121-dcc-code',\n 'size': ['ub-country-name-numeric-length'],\n 'type': 'NumericString'},\n {'name': 'iso-3166-alpha2-code',\n 'size': ['ub-country-name-alpha-length'],\n 'type': 'PrintableString'}],\n 'type': 'CHOICE'},\n 'PhysicalDeliveryOfficeName': {'type': 'PDSParameter'},\n 'PhysicalDeliveryOfficeNumber': {'type': 'PDSParameter'},\n 'PhysicalDeliveryOrganizationName': {'type': 'PDSParameter'},\n 'PhysicalDeliveryPersonalName': {'type': 'PDSParameter'},\n 'PostOfficeBoxAddress': {'type': 'PDSParameter'},\n 'PostalCode': {'members': [{'name': 'numeric-code',\n 'size': [(1,\n 'ub-postal-code-length')],\n 'type': 'NumericString'},\n {'name': 'printable-code',\n 'size': [(1,\n 'ub-postal-code-length')],\n 'type': 'PrintableString'}],\n 'type': 'CHOICE'},\n 'PosteRestanteAddress': {'type': 'PDSParameter'},\n 'PresentationAddress': {'members': [{'name': 'pSelector',\n 'optional': True,\n 'tag': {'kind': 'EXPLICIT',\n 'number': 0},\n 'type': 'OCTET '\n 'STRING'},\n {'name': 'sSelector',\n 'optional': True,\n 'tag': {'kind': 'EXPLICIT',\n 'number': 1},\n 'type': 'OCTET '\n 'STRING'},\n {'name': 'tSelector',\n 'optional': True,\n 'tag': {'kind': 'EXPLICIT',\n 'number': 2},\n 'type': 'OCTET '\n 'STRING'},\n {'element': {'type': 'OCTET '\n 'STRING'},\n 'name': 'nAddresses',\n 'size': [(1,\n 'MAX')],\n 'tag': {'kind': 'EXPLICIT',\n 'number': 3},\n 'type': 'SET '\n 'OF'}],\n 'type': 'SEQUENCE'},\n 'PrivateDomainName': {'members': [{'name': 'numeric',\n 'size': [(1,\n 'ub-domain-name-length')],\n 'type': 'NumericString'},\n {'name': 'printable',\n 'size': [(1,\n 'ub-domain-name-length')],\n 'type': 'PrintableString'}],\n 'type': 'CHOICE'},\n 'RDNSequence': {'element': {'type': 'RelativeDistinguishedName'},\n 'type': 'SEQUENCE OF'},\n 'RelativeDistinguishedName': {'element': {'type': 'AttributeTypeAndValue'},\n 'size': [(1,\n 'MAX')],\n 'type': 'SET OF'},\n 'StreetAddress': {'type': 'PDSParameter'},\n 'SubjectPublicKeyInfo': {'members': [{'name': 'algorithm',\n 'type': 'AlgorithmIdentifier'},\n {'name': 'subjectPublicKey',\n 'type': 'BIT '\n 'STRING'}],\n 'type': 'SEQUENCE'},\n 'TBSCertList': {'members': [{'name': 'version',\n 'optional': True,\n 'type': 'Version'},\n {'name': 'signature',\n 'type': 'AlgorithmIdentifier'},\n {'name': 'issuer',\n 'type': 'Name'},\n {'name': 'thisUpdate',\n 'type': 'Time'},\n {'name': 'nextUpdate',\n 'optional': True,\n 'type': 'Time'},\n {'element': {'members': [{'name': 'userCertificate',\n 'type': 'CertificateSerialNumber'},\n {'name': 'revocationDate',\n 'type': 'Time'},\n {'name': 'crlEntryExtensions',\n 'optional': True,\n 'type': 'Extensions'}],\n 'type': 'SEQUENCE'},\n 'name': 'revokedCertificates',\n 'optional': True,\n 'type': 'SEQUENCE '\n 'OF'},\n {'name': 'crlExtensions',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'Extensions'}],\n 'type': 'SEQUENCE'},\n 'TBSCertificate': {'members': [{'default': 'v1',\n 'name': 'version',\n 'tag': {'number': 0},\n 'type': 'Version'},\n {'name': 'serialNumber',\n 'type': 'CertificateSerialNumber'},\n {'name': 'signature',\n 'type': 'AlgorithmIdentifier'},\n {'name': 'issuer',\n 'type': 'Name'},\n {'name': 'validity',\n 'type': 'Validity'},\n {'name': 'subject',\n 'type': 'Name'},\n {'name': 'subjectPublicKeyInfo',\n 'type': 'SubjectPublicKeyInfo'},\n {'name': 'issuerUniqueID',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 1},\n 'type': 'UniqueIdentifier'},\n {'name': 'subjectUniqueID',\n 'optional': True,\n 'tag': {'kind': 'IMPLICIT',\n 'number': 2},\n 'type': 'UniqueIdentifier'},\n {'name': 'extensions',\n 'optional': True,\n 'tag': {'number': 3},\n 'type': 'Extensions'}],\n 'type': 'SEQUENCE'},\n 'TeletexCommonName': {'size': [(1,\n 'ub-common-name-length')],\n 'type': 'TeletexString'},\n 'TeletexDomainDefinedAttribute': {'members': [{'name': 'type',\n 'size': [(1,\n 'ub-domain-defined-attribute-type-length')],\n 'type': 'TeletexString'},\n {'name': 'value',\n 'size': [(1,\n 'ub-domain-defined-attribute-value-length')],\n 'type': 'TeletexString'}],\n 'type': 'SEQUENCE'},\n 'TeletexDomainDefinedAttributes': {'element': {'type': 'TeletexDomainDefinedAttribute'},\n 'size': [(1,\n 'ub-domain-defined-attributes')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'TeletexOrganizationName': {'size': [(1,\n 'ub-organization-name-length')],\n 'type': 'TeletexString'},\n 'TeletexOrganizationalUnitName': {'size': [(1,\n 'ub-organizational-unit-name-length')],\n 'type': 'TeletexString'},\n 'TeletexOrganizationalUnitNames': {'element': {'type': 'TeletexOrganizationalUnitName'},\n 'size': [(1,\n 'ub-organizational-units')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'TeletexPersonalName': {'members': [{'name': 'surname',\n 'size': [(1,\n 'ub-surname-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 0},\n 'type': 'TeletexString'},\n {'name': 'given-name',\n 'optional': True,\n 'size': [(1,\n 'ub-given-name-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 1},\n 'type': 'TeletexString'},\n {'name': 'initials',\n 'optional': True,\n 'size': [(1,\n 'ub-initials-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 2},\n 'type': 'TeletexString'},\n {'name': 'generation-qualifier',\n 'optional': True,\n 'size': [(1,\n 'ub-generation-qualifier-length')],\n 'tag': {'kind': 'IMPLICIT',\n 'number': 3},\n 'type': 'TeletexString'}],\n 'type': 'SET'},\n 'TerminalIdentifier': {'size': [(1,\n 'ub-terminal-id-length')],\n 'type': 'PrintableString'},\n 'TerminalType': {'named-numbers': {'g3-facsimile': 5,\n 'g4-facsimile': 6,\n 'ia5-terminal': 7,\n 'teletex': 4,\n 'telex': 3,\n 'videotex': 8},\n 'restricted-to': [(0,\n 'ub-integer-options')],\n 'type': 'INTEGER'},\n 'Time': {'members': [{'name': 'utcTime',\n 'type': 'UTCTime'},\n {'name': 'generalTime',\n 'type': 'GeneralizedTime'}],\n 'type': 'CHOICE'},\n 'UnformattedPostalAddress': {'members': [{'element': {'size': [(1,\n 'ub-pds-parameter-length')],\n 'type': 'PrintableString'},\n 'name': 'printable-address',\n 'optional': True,\n 'size': [(1,\n 'ub-pds-physical-address-lines')],\n 'type': 'SEQUENCE '\n 'OF'},\n {'name': 'teletex-string',\n 'optional': True,\n 'size': [(1,\n 'ub-unformatted-address-length')],\n 'type': 'TeletexString'}],\n 'type': 'SET'},\n 'UniqueIdentifier': {'type': 'BIT STRING'},\n 'UniquePostalName': {'type': 'PDSParameter'},\n 'Validity': {'members': [{'name': 'notBefore',\n 'type': 'Time'},\n {'name': 'notAfter',\n 'type': 'Time'}],\n 'type': 'SEQUENCE'},\n 'Version': {'named-numbers': {'v1': 0,\n 'v2': 1,\n 'v3': 2},\n 'type': 'INTEGER'},\n 'X121Address': {'size': [(1,\n 'ub-x121-address-length')],\n 'type': 'NumericString'},\n 'X520CommonName': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-common-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-common-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-common-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-common-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-common-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520LocalityName': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-locality-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-locality-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-locality-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-locality-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-locality-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520OrganizationName': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-organization-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-organization-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-organization-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-organization-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-organization-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520OrganizationalUnitName': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-organizational-unit-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-organizational-unit-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-organizational-unit-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-organizational-unit-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-organizational-unit-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520Pseudonym': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-pseudonym')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-pseudonym')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-pseudonym')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-pseudonym')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-pseudonym')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520SerialNumber': {'size': [(1,\n 'ub-serial-number')],\n 'type': 'PrintableString'},\n 'X520StateOrProvinceName': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-state-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-state-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-state-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-state-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-state-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520Title': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-title')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-title')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-title')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-title')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-title')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'},\n 'X520countryName': {'size': [2],\n 'type': 'PrintableString'},\n 'X520dnQualifier': {'type': 'PrintableString'},\n 'X520name': {'members': [{'name': 'teletexString',\n 'size': [(1,\n 'ub-name')],\n 'type': 'TeletexString'},\n {'name': 'printableString',\n 'size': [(1,\n 'ub-name')],\n 'type': 'PrintableString'},\n {'name': 'universalString',\n 'size': [(1,\n 'ub-name')],\n 'type': 'UniversalString'},\n {'name': 'utf8String',\n 'size': [(1,\n 'ub-name')],\n 'type': 'UTF8String'},\n {'name': 'bmpString',\n 'size': [(1,\n 'ub-name')],\n 'type': 'BMPString'}],\n 'type': 'CHOICE'}},\n 'values': {'common-name': {'type': 'INTEGER', 'value': 1},\n 'extended-network-address': {'type': 'INTEGER',\n 'value': 22},\n 'extension-OR-address-components': {'type': 'INTEGER',\n 'value': 12},\n 'extension-physical-delivery-address-components': {'type': 'INTEGER',\n 'value': 15},\n 'id-ad': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-pkix', 48]},\n 'id-ad-caIssuers': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ad', 2]},\n 'id-ad-caRepository': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ad', 5]},\n 'id-ad-ocsp': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ad', 1]},\n 'id-ad-timeStamping': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ad', 3]},\n 'id-at': {'type': 'OBJECT IDENTIFIER',\n 'value': [('joint-iso-ccitt', 2),\n ('ds', 5),\n 4]},\n 'id-at-commonName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-countryName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-dnQualifier': {'type': 'AttributeType',\n 'value': None},\n 'id-at-generationQualifier': {'type': 'AttributeType',\n 'value': None},\n 'id-at-givenName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-initials': {'type': 'AttributeType',\n 'value': None},\n 'id-at-localityName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-name': {'type': 'AttributeType',\n 'value': None},\n 'id-at-organizationName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-organizationalUnitName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-pseudonym': {'type': 'AttributeType',\n 'value': None},\n 'id-at-serialNumber': {'type': 'AttributeType',\n 'value': None},\n 'id-at-stateOrProvinceName': {'type': 'AttributeType',\n 'value': None},\n 'id-at-surname': {'type': 'AttributeType',\n 'value': None},\n 'id-at-title': {'type': 'AttributeType',\n 'value': None},\n 'id-domainComponent': {'type': 'AttributeType',\n 'value': None},\n 'id-emailAddress': {'type': 'AttributeType',\n 'value': None},\n 'id-kp': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-pkix', 3]},\n 'id-pe': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-pkix', 1]},\n 'id-pkix': {'type': 'OBJECT IDENTIFIER',\n 'value': [('iso', 1),\n ('identified-organization',\n 3),\n ('dod', 6),\n ('internet', 1),\n ('security', 5),\n ('mechanisms', 5),\n ('pkix', 7)]},\n 'id-qt': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-pkix', 2]},\n 'id-qt-cps': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-qt', 1]},\n 'id-qt-unotice': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-qt', 2]},\n 'local-postal-attributes': {'type': 'INTEGER',\n 'value': 21},\n 'pds-name': {'type': 'INTEGER', 'value': 7},\n 'physical-delivery-country-name': {'type': 'INTEGER',\n 'value': 8},\n 'physical-delivery-office-name': {'type': 'INTEGER',\n 'value': 10},\n 'physical-delivery-office-number': {'type': 'INTEGER',\n 'value': 11},\n 'physical-delivery-organization-name': {'type': 'INTEGER',\n 'value': 14},\n 'physical-delivery-personal-name': {'type': 'INTEGER',\n 'value': 13},\n 'pkcs-9': {'type': 'OBJECT IDENTIFIER',\n 'value': [('iso', 1),\n ('member-body', 2),\n ('us', 840),\n ('rsadsi', 113549),\n ('pkcs', 1),\n 9]},\n 'post-office-box-address': {'type': 'INTEGER',\n 'value': 18},\n 'postal-code': {'type': 'INTEGER', 'value': 9},\n 'poste-restante-address': {'type': 'INTEGER',\n 'value': 19},\n 'street-address': {'type': 'INTEGER',\n 'value': 17},\n 'teletex-common-name': {'type': 'INTEGER',\n 'value': 2},\n 'teletex-domain-defined-attributes': {'type': 'INTEGER',\n 'value': 6},\n 'teletex-organization-name': {'type': 'INTEGER',\n 'value': 3},\n 'teletex-organizational-unit-names': {'type': 'INTEGER',\n 'value': 5},\n 'teletex-personal-name': {'type': 'INTEGER',\n 'value': 4},\n 'terminal-type': {'type': 'INTEGER',\n 'value': 23},\n 'ub-common-name': {'type': 'INTEGER',\n 'value': 64},\n 'ub-common-name-length': {'type': 'INTEGER',\n 'value': 64},\n 'ub-country-name-alpha-length': {'type': 'INTEGER',\n 'value': 2},\n 'ub-country-name-numeric-length': {'type': 'INTEGER',\n 'value': 3},\n 'ub-domain-defined-attribute-type-length': {'type': 'INTEGER',\n 'value': 8},\n 'ub-domain-defined-attribute-value-length': {'type': 'INTEGER',\n 'value': 128},\n 'ub-domain-defined-attributes': {'type': 'INTEGER',\n 'value': 4},\n 'ub-domain-name-length': {'type': 'INTEGER',\n 'value': 16},\n 'ub-e163-4-number-length': {'type': 'INTEGER',\n 'value': 15},\n 'ub-e163-4-sub-address-length': {'type': 'INTEGER',\n 'value': 40},\n 'ub-emailaddress-length': {'type': 'INTEGER',\n 'value': 255},\n 'ub-extension-attributes': {'type': 'INTEGER',\n 'value': 256},\n 'ub-generation-qualifier-length': {'type': 'INTEGER',\n 'value': 3},\n 'ub-given-name-length': {'type': 'INTEGER',\n 'value': 16},\n 'ub-initials-length': {'type': 'INTEGER',\n 'value': 5},\n 'ub-integer-options': {'type': 'INTEGER',\n 'value': 256},\n 'ub-locality-name': {'type': 'INTEGER',\n 'value': 128},\n 'ub-match': {'type': 'INTEGER', 'value': 128},\n 'ub-name': {'type': 'INTEGER', 'value': 32768},\n 'ub-numeric-user-id-length': {'type': 'INTEGER',\n 'value': 32},\n 'ub-organization-name': {'type': 'INTEGER',\n 'value': 64},\n 'ub-organization-name-length': {'type': 'INTEGER',\n 'value': 64},\n 'ub-organizational-unit-name': {'type': 'INTEGER',\n 'value': 64},\n 'ub-organizational-unit-name-length': {'type': 'INTEGER',\n 'value': 32},\n 'ub-organizational-units': {'type': 'INTEGER',\n 'value': 4},\n 'ub-pds-name-length': {'type': 'INTEGER',\n 'value': 16},\n 'ub-pds-parameter-length': {'type': 'INTEGER',\n 'value': 30},\n 'ub-pds-physical-address-lines': {'type': 'INTEGER',\n 'value': 6},\n 'ub-postal-code-length': {'type': 'INTEGER',\n 'value': 16},\n 'ub-pseudonym': {'type': 'INTEGER',\n 'value': 128},\n 'ub-serial-number': {'type': 'INTEGER',\n 'value': 64},\n 'ub-state-name': {'type': 'INTEGER',\n 'value': 128},\n 'ub-surname-length': {'type': 'INTEGER',\n 'value': 40},\n 'ub-terminal-id-length': {'type': 'INTEGER',\n 'value': 24},\n 'ub-title': {'type': 'INTEGER', 'value': 64},\n 'ub-unformatted-address-length': {'type': 'INTEGER',\n 'value': 180},\n 'ub-x121-address-length': {'type': 'INTEGER',\n 'value': 16},\n 'unformatted-postal-address': {'type': 'INTEGER',\n 'value': 16},\n 'unique-postal-name': {'type': 'INTEGER',\n 'value': 20}}},\n 'PKIX1Implicit88': {'extensibility-implied': False,\n 'imports': {'PKIX1Explicit88': ['Attribute',\n 'BMPString',\n 'CertificateSerialNumber',\n 'DirectoryString',\n 'Name',\n 'ORAddress',\n 'RelativeDistinguishedName',\n 'UTF8String',\n 'id-kp',\n 'id-pe',\n 'id-qt-cps',\n 'id-qt-unotice']},\n 'object-classes': {},\n 'object-sets': {},\n 'tags': 'IMPLICIT',\n 'types': {'AccessDescription': {'members': [{'name': 'accessMethod',\n 'type': 'OBJECT '\n 'IDENTIFIER'},\n {'name': 'accessLocation',\n 'type': 'GeneralName'}],\n 'type': 'SEQUENCE'},\n 'AnotherName': {'members': [{'name': 'type-id',\n 'type': 'OBJECT '\n 'IDENTIFIER'},\n {'choices': {},\n 'name': 'value',\n 'tag': {'kind': 'EXPLICIT',\n 'number': 0},\n 'type': 'ANY '\n 'DEFINED '\n 'BY',\n 'value': 'type-id'}],\n 'type': 'SEQUENCE'},\n 'AuthorityInfoAccessSyntax': {'element': {'type': 'AccessDescription'},\n 'size': [(1,\n 'MAX')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'AuthorityKeyIdentifier': {'members': [{'name': 'keyIdentifier',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'KeyIdentifier'},\n {'name': 'authorityCertIssuer',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'GeneralNames'},\n {'name': 'authorityCertSerialNumber',\n 'optional': True,\n 'tag': {'number': 2},\n 'type': 'CertificateSerialNumber'}],\n 'type': 'SEQUENCE'},\n 'BaseCRLNumber': {'type': 'CRLNumber'},\n 'BaseDistance': {'restricted-to': [(0, 'MAX')],\n 'type': 'INTEGER'},\n 'BasicConstraints': {'members': [{'default': False,\n 'name': 'cA',\n 'type': 'BOOLEAN'},\n {'name': 'pathLenConstraint',\n 'optional': True,\n 'restricted-to': [(0,\n 'MAX')],\n 'type': 'INTEGER'}],\n 'type': 'SEQUENCE'},\n 'CPSuri': {'type': 'IA5String'},\n 'CRLDistributionPoints': {'element': {'type': 'DistributionPoint'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'CRLNumber': {'restricted-to': [(0, 'MAX')],\n 'type': 'INTEGER'},\n 'CRLReason': {'type': 'ENUMERATED',\n 'values': [('unspecified', 0),\n ('keyCompromise', 1),\n ('cACompromise', 2),\n ('affiliationChanged',\n 3),\n ('superseded', 4),\n ('cessationOfOperation',\n 5),\n ('certificateHold', 6),\n ('removeFromCRL', 8),\n ('privilegeWithdrawn',\n 9),\n ('aACompromise', 10)]},\n 'CertPolicyId': {'type': 'OBJECT IDENTIFIER'},\n 'CertificateIssuer': {'type': 'GeneralNames'},\n 'CertificatePolicies': {'element': {'type': 'PolicyInformation'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'DisplayText': {'members': [{'name': 'ia5String',\n 'size': [(1, 200)],\n 'type': 'IA5String'},\n {'name': 'visibleString',\n 'size': [(1, 200)],\n 'type': 'VisibleString'},\n {'name': 'bmpString',\n 'size': [(1, 200)],\n 'type': 'BMPString'},\n {'name': 'utf8String',\n 'size': [(1, 200)],\n 'type': 'UTF8String'}],\n 'type': 'CHOICE'},\n 'DistributionPoint': {'members': [{'name': 'distributionPoint',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'DistributionPointName'},\n {'name': 'reasons',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'ReasonFlags'},\n {'name': 'cRLIssuer',\n 'optional': True,\n 'tag': {'number': 2},\n 'type': 'GeneralNames'}],\n 'type': 'SEQUENCE'},\n 'DistributionPointName': {'members': [{'name': 'fullName',\n 'tag': {'number': 0},\n 'type': 'GeneralNames'},\n {'name': 'nameRelativeToCRLIssuer',\n 'tag': {'number': 1},\n 'type': 'RelativeDistinguishedName'}],\n 'type': 'CHOICE'},\n 'EDIPartyName': {'members': [{'name': 'nameAssigner',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'DirectoryString'},\n {'name': 'partyName',\n 'tag': {'number': 1},\n 'type': 'DirectoryString'}],\n 'type': 'SEQUENCE'},\n 'ExtKeyUsageSyntax': {'element': {'type': 'KeyPurposeId'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'FreshestCRL': {'type': 'CRLDistributionPoints'},\n 'GeneralName': {'members': [{'name': 'otherName',\n 'tag': {'number': 0},\n 'type': 'AnotherName'},\n {'name': 'rfc822Name',\n 'tag': {'number': 1},\n 'type': 'IA5String'},\n {'name': 'dNSName',\n 'tag': {'number': 2},\n 'type': 'IA5String'},\n {'name': 'x400Address',\n 'tag': {'number': 3},\n 'type': 'ORAddress'},\n {'name': 'directoryName',\n 'tag': {'number': 4},\n 'type': 'Name'},\n {'name': 'ediPartyName',\n 'tag': {'number': 5},\n 'type': 'EDIPartyName'},\n {'name': 'uniformResourceIdentifier',\n 'tag': {'number': 6},\n 'type': 'IA5String'},\n {'name': 'iPAddress',\n 'tag': {'number': 7},\n 'type': 'OCTET '\n 'STRING'},\n {'name': 'registeredID',\n 'tag': {'number': 8},\n 'type': 'OBJECT '\n 'IDENTIFIER'}],\n 'type': 'CHOICE'},\n 'GeneralNames': {'element': {'type': 'GeneralName'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'GeneralSubtree': {'members': [{'name': 'base',\n 'type': 'GeneralName'},\n {'default': 0,\n 'name': 'minimum',\n 'tag': {'number': 0},\n 'type': 'BaseDistance'},\n {'name': 'maximum',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'BaseDistance'}],\n 'type': 'SEQUENCE'},\n 'GeneralSubtrees': {'element': {'type': 'GeneralSubtree'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'HoldInstructionCode': {'type': 'OBJECT '\n 'IDENTIFIER'},\n 'InhibitAnyPolicy': {'type': 'SkipCerts'},\n 'InvalidityDate': {'type': 'GeneralizedTime'},\n 'IssuerAltName': {'type': 'GeneralNames'},\n 'IssuingDistributionPoint': {'members': [{'name': 'distributionPoint',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'DistributionPointName'},\n {'default': False,\n 'name': 'onlyContainsUserCerts',\n 'tag': {'number': 1},\n 'type': 'BOOLEAN'},\n {'default': False,\n 'name': 'onlyContainsCACerts',\n 'tag': {'number': 2},\n 'type': 'BOOLEAN'},\n {'name': 'onlySomeReasons',\n 'optional': True,\n 'tag': {'number': 3},\n 'type': 'ReasonFlags'},\n {'default': False,\n 'name': 'indirectCRL',\n 'tag': {'number': 4},\n 'type': 'BOOLEAN'},\n {'default': False,\n 'name': 'onlyContainsAttributeCerts',\n 'tag': {'number': 5},\n 'type': 'BOOLEAN'}],\n 'type': 'SEQUENCE'},\n 'KeyIdentifier': {'type': 'OCTET STRING'},\n 'KeyPurposeId': {'type': 'OBJECT IDENTIFIER'},\n 'KeyUsage': {'named-bits': [('digitalSignature',\n '0'),\n ('nonRepudiation',\n '1'),\n ('keyEncipherment',\n '2'),\n ('dataEncipherment',\n '3'),\n ('keyAgreement',\n '4'),\n ('keyCertSign', '5'),\n ('cRLSign', '6'),\n ('encipherOnly',\n '7'),\n ('decipherOnly',\n '8')],\n 'type': 'BIT STRING'},\n 'NameConstraints': {'members': [{'name': 'permittedSubtrees',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'GeneralSubtrees'},\n {'name': 'excludedSubtrees',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'GeneralSubtrees'}],\n 'type': 'SEQUENCE'},\n 'NoticeReference': {'members': [{'name': 'organization',\n 'type': 'DisplayText'},\n {'element': {'type': 'INTEGER'},\n 'name': 'noticeNumbers',\n 'type': 'SEQUENCE '\n 'OF'}],\n 'type': 'SEQUENCE'},\n 'PolicyConstraints': {'members': [{'name': 'requireExplicitPolicy',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'SkipCerts'},\n {'name': 'inhibitPolicyMapping',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'SkipCerts'}],\n 'type': 'SEQUENCE'},\n 'PolicyInformation': {'members': [{'name': 'policyIdentifier',\n 'type': 'CertPolicyId'},\n {'element': {'type': 'PolicyQualifierInfo'},\n 'name': 'policyQualifiers',\n 'optional': True,\n 'size': [(1,\n 'MAX')],\n 'type': 'SEQUENCE '\n 'OF'}],\n 'type': 'SEQUENCE'},\n 'PolicyMappings': {'element': {'members': [{'name': 'issuerDomainPolicy',\n 'type': 'CertPolicyId'},\n {'name': 'subjectDomainPolicy',\n 'type': 'CertPolicyId'}],\n 'type': 'SEQUENCE'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'},\n 'PolicyQualifierId': {'type': 'OBJECT '\n 'IDENTIFIER'},\n 'PolicyQualifierInfo': {'members': [{'name': 'policyQualifierId',\n 'type': 'PolicyQualifierId'},\n {'choices': {},\n 'name': 'qualifier',\n 'type': 'ANY '\n 'DEFINED '\n 'BY',\n 'value': 'policyQualifierId'}],\n 'type': 'SEQUENCE'},\n 'PrivateKeyUsagePeriod': {'members': [{'name': 'notBefore',\n 'optional': True,\n 'tag': {'number': 0},\n 'type': 'GeneralizedTime'},\n {'name': 'notAfter',\n 'optional': True,\n 'tag': {'number': 1},\n 'type': 'GeneralizedTime'}],\n 'type': 'SEQUENCE'},\n 'ReasonFlags': {'named-bits': [('unused', '0'),\n ('keyCompromise',\n '1'),\n ('cACompromise',\n '2'),\n ('affiliationChanged',\n '3'),\n ('superseded',\n '4'),\n ('cessationOfOperation',\n '5'),\n ('certificateHold',\n '6'),\n ('privilegeWithdrawn',\n '7'),\n ('aACompromise',\n '8')],\n 'type': 'BIT STRING'},\n 'SkipCerts': {'restricted-to': [(0, 'MAX')],\n 'type': 'INTEGER'},\n 'SubjectAltName': {'type': 'GeneralNames'},\n 'SubjectDirectoryAttributes': {'element': {'type': 'Attribute'},\n 'size': [(1,\n 'MAX')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'SubjectInfoAccessSyntax': {'element': {'type': 'AccessDescription'},\n 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE '\n 'OF'},\n 'SubjectKeyIdentifier': {'type': 'KeyIdentifier'},\n 'UserNotice': {'members': [{'name': 'noticeRef',\n 'optional': True,\n 'type': 'NoticeReference'},\n {'name': 'explicitText',\n 'optional': True,\n 'type': 'DisplayText'}],\n 'type': 'SEQUENCE'}},\n 'values': {'anyExtendedKeyUsage': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce-extKeyUsage',\n 0]},\n 'anyPolicy': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce-certificatePolicies',\n 0]},\n 'holdInstruction': {'type': 'OBJECT IDENTIFIER',\n 'value': [('joint-iso-itu-t',\n 2),\n ('member-body',\n 2),\n ('us', 840),\n ('x9cm', 10040),\n 2]},\n 'id-ce': {'type': 'OBJECT IDENTIFIER',\n 'value': [('joint-iso-ccitt', 2),\n ('ds', 5),\n 29]},\n 'id-ce-authorityKeyIdentifier': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 35]},\n 'id-ce-basicConstraints': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 19]},\n 'id-ce-cRLDistributionPoints': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 31]},\n 'id-ce-cRLNumber': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce', 20]},\n 'id-ce-cRLReasons': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce', 21]},\n 'id-ce-certificateIssuer': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 29]},\n 'id-ce-certificatePolicies': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 32]},\n 'id-ce-deltaCRLIndicator': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 27]},\n 'id-ce-extKeyUsage': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce', 37]},\n 'id-ce-freshestCRL': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce', 46]},\n 'id-ce-holdInstructionCode': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 23]},\n 'id-ce-inhibitAnyPolicy': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 54]},\n 'id-ce-invalidityDate': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 24]},\n 'id-ce-issuerAltName': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce', 18]},\n 'id-ce-issuingDistributionPoint': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 28]},\n 'id-ce-keyUsage': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce', 15]},\n 'id-ce-nameConstraints': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 30]},\n 'id-ce-policyConstraints': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 36]},\n 'id-ce-policyMappings': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 33]},\n 'id-ce-privateKeyUsagePeriod': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 16]},\n 'id-ce-subjectAltName': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 17]},\n 'id-ce-subjectDirectoryAttributes': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 9]},\n 'id-ce-subjectKeyIdentifier': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-ce',\n 14]},\n 'id-holdinstruction-callissuer': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['holdInstruction',\n 2]},\n 'id-holdinstruction-none': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['holdInstruction',\n 1]},\n 'id-holdinstruction-reject': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['holdInstruction',\n 3]},\n 'id-kp-OCSPSigning': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp', 9]},\n 'id-kp-clientAuth': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp', 2]},\n 'id-kp-codeSigning': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp', 3]},\n 'id-kp-emailProtection': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp',\n 4]},\n 'id-kp-serverAuth': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp', 1]},\n 'id-kp-timeStamping': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-kp', 8]},\n 'id-pe-authorityInfoAccess': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-pe',\n 1]},\n 'id-pe-subjectInfoAccess': {'type': 'OBJECT '\n 'IDENTIFIER',\n 'value': ['id-pe',\n 11]}}}}",
"EXPECTED = {'PKIX1Explicit88': {'extensibility-implied': False, 'imports':\n {}, 'object-classes': {}, 'object-sets': {}, 'tags': 'EXPLICIT',\n 'types': {'AdministrationDomainName': {'members': [{'name': 'numeric',\n 'size': [(0, 'ub-domain-name-length')], 'type': 'NumericString'}, {\n 'name': 'printable', 'size': [(0, 'ub-domain-name-length')], 'type':\n 'PrintableString'}], 'tag': {'class': 'APPLICATION', 'number': 2},\n 'type': 'CHOICE'}, 'AlgorithmIdentifier': {'members': [{'name':\n 'algorithm', 'type': 'OBJECT IDENTIFIER'}, {'choices': {}, 'name':\n 'parameters', 'optional': True, 'type': 'ANY DEFINED BY', 'value':\n 'algorithm'}], 'type': 'SEQUENCE'}, 'Attribute': {'members': [{'name':\n 'type', 'type': 'AttributeType'}, {'element': {'type': 'AttributeValue'\n }, 'name': 'values', 'type': 'SET OF'}], 'type': 'SEQUENCE'},\n 'AttributeType': {'type': 'OBJECT IDENTIFIER'}, 'AttributeTypeAndValue':\n {'members': [{'name': 'type', 'type': 'AttributeType'}, {'name':\n 'value', 'type': 'AttributeValue'}], 'type': 'SEQUENCE'},\n 'AttributeValue': {'choices': {}, 'type': 'ANY DEFINED BY', 'value':\n 'type'}, 'BuiltInDomainDefinedAttribute': {'members': [{'name': 'type',\n 'size': [(1, 'ub-domain-defined-attribute-type-length')], 'type':\n 'PrintableString'}, {'name': 'value', 'size': [(1,\n 'ub-domain-defined-attribute-value-length')], 'type': 'PrintableString'\n }], 'type': 'SEQUENCE'}, 'BuiltInDomainDefinedAttributes': {'element':\n {'type': 'BuiltInDomainDefinedAttribute'}, 'size': [(1,\n 'ub-domain-defined-attributes')], 'type': 'SEQUENCE OF'},\n 'BuiltInStandardAttributes': {'members': [{'name': 'country-name',\n 'optional': True, 'type': 'CountryName'}, {'name':\n 'administration-domain-name', 'optional': True, 'type':\n 'AdministrationDomainName'}, {'name': 'network-address', 'optional': \n True, 'tag': {'kind': 'IMPLICIT', 'number': 0}, 'type':\n 'NetworkAddress'}, {'name': 'terminal-identifier', 'optional': True,\n 'tag': {'kind': 'IMPLICIT', 'number': 1}, 'type': 'TerminalIdentifier'},\n {'name': 'private-domain-name', 'optional': True, 'tag': {'number': 2},\n 'type': 'PrivateDomainName'}, {'name': 'organization-name', 'optional':\n True, 'tag': {'kind': 'IMPLICIT', 'number': 3}, 'type':\n 'OrganizationName'}, {'name': 'numeric-user-identifier', 'optional': \n True, 'tag': {'kind': 'IMPLICIT', 'number': 4}, 'type':\n 'NumericUserIdentifier'}, {'name': 'personal-name', 'optional': True,\n 'tag': {'kind': 'IMPLICIT', 'number': 5}, 'type': 'PersonalName'}, {\n 'name': 'organizational-unit-names', 'optional': True, 'tag': {'kind':\n 'IMPLICIT', 'number': 6}, 'type': 'OrganizationalUnitNames'}], 'type':\n 'SEQUENCE'}, 'Certificate': {'members': [{'name': 'tbsCertificate',\n 'type': 'TBSCertificate'}, {'name': 'signatureAlgorithm', 'type':\n 'AlgorithmIdentifier'}, {'name': 'signature', 'type': 'BIT STRING'}],\n 'type': 'SEQUENCE'}, 'CertificateList': {'members': [{'name':\n 'tbsCertList', 'type': 'TBSCertList'}, {'name': 'signatureAlgorithm',\n 'type': 'AlgorithmIdentifier'}, {'name': 'signature', 'type':\n 'BIT STRING'}], 'type': 'SEQUENCE'}, 'CertificateSerialNumber': {'type':\n 'INTEGER'}, 'CommonName': {'size': [(1, 'ub-common-name-length')],\n 'type': 'PrintableString'}, 'CountryName': {'members': [{'name':\n 'x121-dcc-code', 'size': ['ub-country-name-numeric-length'], 'type':\n 'NumericString'}, {'name': 'iso-3166-alpha2-code', 'size': [\n 'ub-country-name-alpha-length'], 'type': 'PrintableString'}], 'tag': {\n 'class': 'APPLICATION', 'number': 1}, 'type': 'CHOICE'},\n 'DirectoryString': {'members': [{'name': 'teletexString', 'size': [(1,\n 'MAX')], 'type': 'TeletexString'}, {'name': 'printableString', 'size':\n [(1, 'MAX')], 'type': 'PrintableString'}, {'name': 'universalString',\n 'size': [(1, 'MAX')], 'type': 'UniversalString'}, {'name': 'utf8String',\n 'size': [(1, 'MAX')], 'type': 'UTF8String'}, {'name': 'bmpString',\n 'size': [(1, 'MAX')], 'type': 'BMPString'}], 'type': 'CHOICE'},\n 'DistinguishedName': {'type': 'RDNSequence'}, 'DomainComponent': {\n 'type': 'IA5String'}, 'EmailAddress': {'size': [(1,\n 'ub-emailaddress-length')], 'type': 'IA5String'},\n 'ExtendedNetworkAddress': {'members': [{'members': [{'name': 'number',\n 'size': [(1, 'ub-e163-4-number-length')], 'tag': {'kind': 'IMPLICIT',\n 'number': 0}, 'type': 'NumericString'}, {'name': 'sub-address',\n 'optional': True, 'size': [(1, 'ub-e163-4-sub-address-length')], 'tag':\n {'kind': 'IMPLICIT', 'number': 1}, 'type': 'NumericString'}], 'name':\n 'e163-4-address', 'type': 'SEQUENCE'}, {'name': 'psap-address', 'tag':\n {'kind': 'IMPLICIT', 'number': 0}, 'type': 'PresentationAddress'}],\n 'type': 'CHOICE'}, 'Extension': {'members': [{'name': 'extnID', 'type':\n 'OBJECT IDENTIFIER'}, {'default': False, 'name': 'critical', 'type':\n 'BOOLEAN'}, {'name': 'extnValue', 'type': 'OCTET STRING'}], 'type':\n 'SEQUENCE'}, 'ExtensionAttribute': {'members': [{'name':\n 'extension-attribute-type', 'restricted-to': [(0,\n 'ub-extension-attributes')], 'tag': {'kind': 'IMPLICIT', 'number': 0},\n 'type': 'INTEGER'}, {'choices': {}, 'name': 'extension-attribute-value',\n 'tag': {'number': 1}, 'type': 'ANY DEFINED BY', 'value':\n 'extension-attribute-type'}], 'type': 'SEQUENCE'},\n 'ExtensionAttributes': {'element': {'type': 'ExtensionAttribute'},\n 'size': [(1, 'ub-extension-attributes')], 'type': 'SET OF'},\n 'ExtensionORAddressComponents': {'type': 'PDSParameter'},\n 'ExtensionPhysicalDeliveryAddressComponents': {'type': 'PDSParameter'},\n 'Extensions': {'element': {'type': 'Extension'}, 'size': [(1, 'MAX')],\n 'type': 'SEQUENCE OF'}, 'LocalPostalAttributes': {'type':\n 'PDSParameter'}, 'Name': {'members': [{'name': 'rdnSequence', 'type':\n 'RDNSequence'}], 'type': 'CHOICE'}, 'NetworkAddress': {'type':\n 'X121Address'}, 'NumericUserIdentifier': {'size': [(1,\n 'ub-numeric-user-id-length')], 'type': 'NumericString'}, 'ORAddress': {\n 'members': [{'name': 'built-in-standard-attributes', 'type':\n 'BuiltInStandardAttributes'}, {'name':\n 'built-in-domain-defined-attributes', 'optional': True, 'type':\n 'BuiltInDomainDefinedAttributes'}, {'name': 'extension-attributes',\n 'optional': True, 'type': 'ExtensionAttributes'}], 'type': 'SEQUENCE'},\n 'OrganizationName': {'size': [(1, 'ub-organization-name-length')],\n 'type': 'PrintableString'}, 'OrganizationalUnitName': {'size': [(1,\n 'ub-organizational-unit-name-length')], 'type': 'PrintableString'},\n 'OrganizationalUnitNames': {'element': {'type':\n 'OrganizationalUnitName'}, 'size': [(1, 'ub-organizational-units')],\n 'type': 'SEQUENCE OF'}, 'PDSName': {'size': [(1, 'ub-pds-name-length')],\n 'type': 'PrintableString'}, 'PDSParameter': {'members': [{'name':\n 'printable-string', 'optional': True, 'size': [(1,\n 'ub-pds-parameter-length')], 'type': 'PrintableString'}, {'name':\n 'teletex-string', 'optional': True, 'size': [(1,\n 'ub-pds-parameter-length')], 'type': 'TeletexString'}], 'type': 'SET'},\n 'PersonalName': {'members': [{'name': 'surname', 'size': [(1,\n 'ub-surname-length')], 'tag': {'kind': 'IMPLICIT', 'number': 0}, 'type':\n 'PrintableString'}, {'name': 'given-name', 'optional': True, 'size': [(\n 1, 'ub-given-name-length')], 'tag': {'kind': 'IMPLICIT', 'number': 1},\n 'type': 'PrintableString'}, {'name': 'initials', 'optional': True,\n 'size': [(1, 'ub-initials-length')], 'tag': {'kind': 'IMPLICIT',\n 'number': 2}, 'type': 'PrintableString'}, {'name':\n 'generation-qualifier', 'optional': True, 'size': [(1,\n 'ub-generation-qualifier-length')], 'tag': {'kind': 'IMPLICIT',\n 'number': 3}, 'type': 'PrintableString'}], 'type': 'SET'},\n 'PhysicalDeliveryCountryName': {'members': [{'name': 'x121-dcc-code',\n 'size': ['ub-country-name-numeric-length'], 'type': 'NumericString'}, {\n 'name': 'iso-3166-alpha2-code', 'size': ['ub-country-name-alpha-length'\n ], 'type': 'PrintableString'}], 'type': 'CHOICE'},\n 'PhysicalDeliveryOfficeName': {'type': 'PDSParameter'},\n 'PhysicalDeliveryOfficeNumber': {'type': 'PDSParameter'},\n 'PhysicalDeliveryOrganizationName': {'type': 'PDSParameter'},\n 'PhysicalDeliveryPersonalName': {'type': 'PDSParameter'},\n 'PostOfficeBoxAddress': {'type': 'PDSParameter'}, 'PostalCode': {\n 'members': [{'name': 'numeric-code', 'size': [(1,\n 'ub-postal-code-length')], 'type': 'NumericString'}, {'name':\n 'printable-code', 'size': [(1, 'ub-postal-code-length')], 'type':\n 'PrintableString'}], 'type': 'CHOICE'}, 'PosteRestanteAddress': {'type':\n 'PDSParameter'}, 'PresentationAddress': {'members': [{'name':\n 'pSelector', 'optional': True, 'tag': {'kind': 'EXPLICIT', 'number': 0},\n 'type': 'OCTET STRING'}, {'name': 'sSelector', 'optional': True, 'tag':\n {'kind': 'EXPLICIT', 'number': 1}, 'type': 'OCTET STRING'}, {'name':\n 'tSelector', 'optional': True, 'tag': {'kind': 'EXPLICIT', 'number': 2},\n 'type': 'OCTET STRING'}, {'element': {'type': 'OCTET STRING'}, 'name':\n 'nAddresses', 'size': [(1, 'MAX')], 'tag': {'kind': 'EXPLICIT',\n 'number': 3}, 'type': 'SET OF'}], 'type': 'SEQUENCE'},\n 'PrivateDomainName': {'members': [{'name': 'numeric', 'size': [(1,\n 'ub-domain-name-length')], 'type': 'NumericString'}, {'name':\n 'printable', 'size': [(1, 'ub-domain-name-length')], 'type':\n 'PrintableString'}], 'type': 'CHOICE'}, 'RDNSequence': {'element': {\n 'type': 'RelativeDistinguishedName'}, 'type': 'SEQUENCE OF'},\n 'RelativeDistinguishedName': {'element': {'type':\n 'AttributeTypeAndValue'}, 'size': [(1, 'MAX')], 'type': 'SET OF'},\n 'StreetAddress': {'type': 'PDSParameter'}, 'SubjectPublicKeyInfo': {\n 'members': [{'name': 'algorithm', 'type': 'AlgorithmIdentifier'}, {\n 'name': 'subjectPublicKey', 'type': 'BIT STRING'}], 'type': 'SEQUENCE'},\n 'TBSCertList': {'members': [{'name': 'version', 'optional': True,\n 'type': 'Version'}, {'name': 'signature', 'type': 'AlgorithmIdentifier'\n }, {'name': 'issuer', 'type': 'Name'}, {'name': 'thisUpdate', 'type':\n 'Time'}, {'name': 'nextUpdate', 'optional': True, 'type': 'Time'}, {\n 'element': {'members': [{'name': 'userCertificate', 'type':\n 'CertificateSerialNumber'}, {'name': 'revocationDate', 'type': 'Time'},\n {'name': 'crlEntryExtensions', 'optional': True, 'type': 'Extensions'}],\n 'type': 'SEQUENCE'}, 'name': 'revokedCertificates', 'optional': True,\n 'type': 'SEQUENCE OF'}, {'name': 'crlExtensions', 'optional': True,\n 'tag': {'number': 0}, 'type': 'Extensions'}], 'type': 'SEQUENCE'},\n 'TBSCertificate': {'members': [{'default': 'v1', 'name': 'version',\n 'tag': {'number': 0}, 'type': 'Version'}, {'name': 'serialNumber',\n 'type': 'CertificateSerialNumber'}, {'name': 'signature', 'type':\n 'AlgorithmIdentifier'}, {'name': 'issuer', 'type': 'Name'}, {'name':\n 'validity', 'type': 'Validity'}, {'name': 'subject', 'type': 'Name'}, {\n 'name': 'subjectPublicKeyInfo', 'type': 'SubjectPublicKeyInfo'}, {\n 'name': 'issuerUniqueID', 'optional': True, 'tag': {'kind': 'IMPLICIT',\n 'number': 1}, 'type': 'UniqueIdentifier'}, {'name': 'subjectUniqueID',\n 'optional': True, 'tag': {'kind': 'IMPLICIT', 'number': 2}, 'type':\n 'UniqueIdentifier'}, {'name': 'extensions', 'optional': True, 'tag': {\n 'number': 3}, 'type': 'Extensions'}], 'type': 'SEQUENCE'},\n 'TeletexCommonName': {'size': [(1, 'ub-common-name-length')], 'type':\n 'TeletexString'}, 'TeletexDomainDefinedAttribute': {'members': [{'name':\n 'type', 'size': [(1, 'ub-domain-defined-attribute-type-length')],\n 'type': 'TeletexString'}, {'name': 'value', 'size': [(1,\n 'ub-domain-defined-attribute-value-length')], 'type': 'TeletexString'}],\n 'type': 'SEQUENCE'}, 'TeletexDomainDefinedAttributes': {'element': {\n 'type': 'TeletexDomainDefinedAttribute'}, 'size': [(1,\n 'ub-domain-defined-attributes')], 'type': 'SEQUENCE OF'},\n 'TeletexOrganizationName': {'size': [(1, 'ub-organization-name-length')\n ], 'type': 'TeletexString'}, 'TeletexOrganizationalUnitName': {'size':\n [(1, 'ub-organizational-unit-name-length')], 'type': 'TeletexString'},\n 'TeletexOrganizationalUnitNames': {'element': {'type':\n 'TeletexOrganizationalUnitName'}, 'size': [(1,\n 'ub-organizational-units')], 'type': 'SEQUENCE OF'},\n 'TeletexPersonalName': {'members': [{'name': 'surname', 'size': [(1,\n 'ub-surname-length')], 'tag': {'kind': 'IMPLICIT', 'number': 0}, 'type':\n 'TeletexString'}, {'name': 'given-name', 'optional': True, 'size': [(1,\n 'ub-given-name-length')], 'tag': {'kind': 'IMPLICIT', 'number': 1},\n 'type': 'TeletexString'}, {'name': 'initials', 'optional': True, 'size':\n [(1, 'ub-initials-length')], 'tag': {'kind': 'IMPLICIT', 'number': 2},\n 'type': 'TeletexString'}, {'name': 'generation-qualifier', 'optional': \n True, 'size': [(1, 'ub-generation-qualifier-length')], 'tag': {'kind':\n 'IMPLICIT', 'number': 3}, 'type': 'TeletexString'}], 'type': 'SET'},\n 'TerminalIdentifier': {'size': [(1, 'ub-terminal-id-length')], 'type':\n 'PrintableString'}, 'TerminalType': {'named-numbers': {'g3-facsimile': \n 5, 'g4-facsimile': 6, 'ia5-terminal': 7, 'teletex': 4, 'telex': 3,\n 'videotex': 8}, 'restricted-to': [(0, 'ub-integer-options')], 'type':\n 'INTEGER'}, 'Time': {'members': [{'name': 'utcTime', 'type': 'UTCTime'},\n {'name': 'generalTime', 'type': 'GeneralizedTime'}], 'type': 'CHOICE'},\n 'UnformattedPostalAddress': {'members': [{'element': {'size': [(1,\n 'ub-pds-parameter-length')], 'type': 'PrintableString'}, 'name':\n 'printable-address', 'optional': True, 'size': [(1,\n 'ub-pds-physical-address-lines')], 'type': 'SEQUENCE OF'}, {'name':\n 'teletex-string', 'optional': True, 'size': [(1,\n 'ub-unformatted-address-length')], 'type': 'TeletexString'}], 'type':\n 'SET'}, 'UniqueIdentifier': {'type': 'BIT STRING'}, 'UniquePostalName':\n {'type': 'PDSParameter'}, 'Validity': {'members': [{'name': 'notBefore',\n 'type': 'Time'}, {'name': 'notAfter', 'type': 'Time'}], 'type':\n 'SEQUENCE'}, 'Version': {'named-numbers': {'v1': 0, 'v2': 1, 'v3': 2},\n 'type': 'INTEGER'}, 'X121Address': {'size': [(1,\n 'ub-x121-address-length')], 'type': 'NumericString'}, 'X520CommonName':\n {'members': [{'name': 'teletexString', 'size': [(1, 'ub-common-name')],\n 'type': 'TeletexString'}, {'name': 'printableString', 'size': [(1,\n 'ub-common-name')], 'type': 'PrintableString'}, {'name':\n 'universalString', 'size': [(1, 'ub-common-name')], 'type':\n 'UniversalString'}, {'name': 'utf8String', 'size': [(1,\n 'ub-common-name')], 'type': 'UTF8String'}, {'name': 'bmpString', 'size':\n [(1, 'ub-common-name')], 'type': 'BMPString'}], 'type': 'CHOICE'},\n 'X520LocalityName': {'members': [{'name': 'teletexString', 'size': [(1,\n 'ub-locality-name')], 'type': 'TeletexString'}, {'name':\n 'printableString', 'size': [(1, 'ub-locality-name')], 'type':\n 'PrintableString'}, {'name': 'universalString', 'size': [(1,\n 'ub-locality-name')], 'type': 'UniversalString'}, {'name': 'utf8String',\n 'size': [(1, 'ub-locality-name')], 'type': 'UTF8String'}, {'name':\n 'bmpString', 'size': [(1, 'ub-locality-name')], 'type': 'BMPString'}],\n 'type': 'CHOICE'}, 'X520OrganizationName': {'members': [{'name':\n 'teletexString', 'size': [(1, 'ub-organization-name')], 'type':\n 'TeletexString'}, {'name': 'printableString', 'size': [(1,\n 'ub-organization-name')], 'type': 'PrintableString'}, {'name':\n 'universalString', 'size': [(1, 'ub-organization-name')], 'type':\n 'UniversalString'}, {'name': 'utf8String', 'size': [(1,\n 'ub-organization-name')], 'type': 'UTF8String'}, {'name': 'bmpString',\n 'size': [(1, 'ub-organization-name')], 'type': 'BMPString'}], 'type':\n 'CHOICE'}, 'X520OrganizationalUnitName': {'members': [{'name':\n 'teletexString', 'size': [(1, 'ub-organizational-unit-name')], 'type':\n 'TeletexString'}, {'name': 'printableString', 'size': [(1,\n 'ub-organizational-unit-name')], 'type': 'PrintableString'}, {'name':\n 'universalString', 'size': [(1, 'ub-organizational-unit-name')], 'type':\n 'UniversalString'}, {'name': 'utf8String', 'size': [(1,\n 'ub-organizational-unit-name')], 'type': 'UTF8String'}, {'name':\n 'bmpString', 'size': [(1, 'ub-organizational-unit-name')], 'type':\n 'BMPString'}], 'type': 'CHOICE'}, 'X520Pseudonym': {'members': [{'name':\n 'teletexString', 'size': [(1, 'ub-pseudonym')], 'type': 'TeletexString'\n }, {'name': 'printableString', 'size': [(1, 'ub-pseudonym')], 'type':\n 'PrintableString'}, {'name': 'universalString', 'size': [(1,\n 'ub-pseudonym')], 'type': 'UniversalString'}, {'name': 'utf8String',\n 'size': [(1, 'ub-pseudonym')], 'type': 'UTF8String'}, {'name':\n 'bmpString', 'size': [(1, 'ub-pseudonym')], 'type': 'BMPString'}],\n 'type': 'CHOICE'}, 'X520SerialNumber': {'size': [(1, 'ub-serial-number'\n )], 'type': 'PrintableString'}, 'X520StateOrProvinceName': {'members':\n [{'name': 'teletexString', 'size': [(1, 'ub-state-name')], 'type':\n 'TeletexString'}, {'name': 'printableString', 'size': [(1,\n 'ub-state-name')], 'type': 'PrintableString'}, {'name':\n 'universalString', 'size': [(1, 'ub-state-name')], 'type':\n 'UniversalString'}, {'name': 'utf8String', 'size': [(1, 'ub-state-name'\n )], 'type': 'UTF8String'}, {'name': 'bmpString', 'size': [(1,\n 'ub-state-name')], 'type': 'BMPString'}], 'type': 'CHOICE'},\n 'X520Title': {'members': [{'name': 'teletexString', 'size': [(1,\n 'ub-title')], 'type': 'TeletexString'}, {'name': 'printableString',\n 'size': [(1, 'ub-title')], 'type': 'PrintableString'}, {'name':\n 'universalString', 'size': [(1, 'ub-title')], 'type': 'UniversalString'\n }, {'name': 'utf8String', 'size': [(1, 'ub-title')], 'type':\n 'UTF8String'}, {'name': 'bmpString', 'size': [(1, 'ub-title')], 'type':\n 'BMPString'}], 'type': 'CHOICE'}, 'X520countryName': {'size': [2],\n 'type': 'PrintableString'}, 'X520dnQualifier': {'type':\n 'PrintableString'}, 'X520name': {'members': [{'name': 'teletexString',\n 'size': [(1, 'ub-name')], 'type': 'TeletexString'}, {'name':\n 'printableString', 'size': [(1, 'ub-name')], 'type': 'PrintableString'},\n {'name': 'universalString', 'size': [(1, 'ub-name')], 'type':\n 'UniversalString'}, {'name': 'utf8String', 'size': [(1, 'ub-name')],\n 'type': 'UTF8String'}, {'name': 'bmpString', 'size': [(1, 'ub-name')],\n 'type': 'BMPString'}], 'type': 'CHOICE'}}, 'values': {'common-name': {\n 'type': 'INTEGER', 'value': 1}, 'extended-network-address': {'type':\n 'INTEGER', 'value': 22}, 'extension-OR-address-components': {'type':\n 'INTEGER', 'value': 12},\n 'extension-physical-delivery-address-components': {'type': 'INTEGER',\n 'value': 15}, 'id-ad': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-pkix', 48]}, 'id-ad-caIssuers': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ad', 2]}, 'id-ad-caRepository': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ad', 5]}, 'id-ad-ocsp': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ad', 1]}, 'id-ad-timeStamping': {\n 'type': 'OBJECT IDENTIFIER', 'value': ['id-ad', 3]}, 'id-at': {'type':\n 'OBJECT IDENTIFIER', 'value': [('joint-iso-ccitt', 2), ('ds', 5), 4]},\n 'id-at-commonName': {'type': 'AttributeType', 'value': None},\n 'id-at-countryName': {'type': 'AttributeType', 'value': None},\n 'id-at-dnQualifier': {'type': 'AttributeType', 'value': None},\n 'id-at-generationQualifier': {'type': 'AttributeType', 'value': None},\n 'id-at-givenName': {'type': 'AttributeType', 'value': None},\n 'id-at-initials': {'type': 'AttributeType', 'value': None},\n 'id-at-localityName': {'type': 'AttributeType', 'value': None},\n 'id-at-name': {'type': 'AttributeType', 'value': None},\n 'id-at-organizationName': {'type': 'AttributeType', 'value': None},\n 'id-at-organizationalUnitName': {'type': 'AttributeType', 'value': None\n }, 'id-at-pseudonym': {'type': 'AttributeType', 'value': None},\n 'id-at-serialNumber': {'type': 'AttributeType', 'value': None},\n 'id-at-stateOrProvinceName': {'type': 'AttributeType', 'value': None},\n 'id-at-surname': {'type': 'AttributeType', 'value': None},\n 'id-at-title': {'type': 'AttributeType', 'value': None},\n 'id-domainComponent': {'type': 'AttributeType', 'value': None},\n 'id-emailAddress': {'type': 'AttributeType', 'value': None}, 'id-kp': {\n 'type': 'OBJECT IDENTIFIER', 'value': ['id-pkix', 3]}, 'id-pe': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-pkix', 1]}, 'id-pkix': {'type':\n 'OBJECT IDENTIFIER', 'value': [('iso', 1), ('identified-organization', \n 3), ('dod', 6), ('internet', 1), ('security', 5), ('mechanisms', 5), (\n 'pkix', 7)]}, 'id-qt': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-pkix', 2]}, 'id-qt-cps': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-qt', 1]}, 'id-qt-unotice': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-qt', 2]}, 'local-postal-attributes': {'type': 'INTEGER', 'value': \n 21}, 'pds-name': {'type': 'INTEGER', 'value': 7},\n 'physical-delivery-country-name': {'type': 'INTEGER', 'value': 8},\n 'physical-delivery-office-name': {'type': 'INTEGER', 'value': 10},\n 'physical-delivery-office-number': {'type': 'INTEGER', 'value': 11},\n 'physical-delivery-organization-name': {'type': 'INTEGER', 'value': 14},\n 'physical-delivery-personal-name': {'type': 'INTEGER', 'value': 13},\n 'pkcs-9': {'type': 'OBJECT IDENTIFIER', 'value': [('iso', 1), (\n 'member-body', 2), ('us', 840), ('rsadsi', 113549), ('pkcs', 1), 9]},\n 'post-office-box-address': {'type': 'INTEGER', 'value': 18},\n 'postal-code': {'type': 'INTEGER', 'value': 9},\n 'poste-restante-address': {'type': 'INTEGER', 'value': 19},\n 'street-address': {'type': 'INTEGER', 'value': 17},\n 'teletex-common-name': {'type': 'INTEGER', 'value': 2},\n 'teletex-domain-defined-attributes': {'type': 'INTEGER', 'value': 6},\n 'teletex-organization-name': {'type': 'INTEGER', 'value': 3},\n 'teletex-organizational-unit-names': {'type': 'INTEGER', 'value': 5},\n 'teletex-personal-name': {'type': 'INTEGER', 'value': 4},\n 'terminal-type': {'type': 'INTEGER', 'value': 23}, 'ub-common-name': {\n 'type': 'INTEGER', 'value': 64}, 'ub-common-name-length': {'type':\n 'INTEGER', 'value': 64}, 'ub-country-name-alpha-length': {'type':\n 'INTEGER', 'value': 2}, 'ub-country-name-numeric-length': {'type':\n 'INTEGER', 'value': 3}, 'ub-domain-defined-attribute-type-length': {\n 'type': 'INTEGER', 'value': 8},\n 'ub-domain-defined-attribute-value-length': {'type': 'INTEGER', 'value':\n 128}, 'ub-domain-defined-attributes': {'type': 'INTEGER', 'value': 4},\n 'ub-domain-name-length': {'type': 'INTEGER', 'value': 16},\n 'ub-e163-4-number-length': {'type': 'INTEGER', 'value': 15},\n 'ub-e163-4-sub-address-length': {'type': 'INTEGER', 'value': 40},\n 'ub-emailaddress-length': {'type': 'INTEGER', 'value': 255},\n 'ub-extension-attributes': {'type': 'INTEGER', 'value': 256},\n 'ub-generation-qualifier-length': {'type': 'INTEGER', 'value': 3},\n 'ub-given-name-length': {'type': 'INTEGER', 'value': 16},\n 'ub-initials-length': {'type': 'INTEGER', 'value': 5},\n 'ub-integer-options': {'type': 'INTEGER', 'value': 256},\n 'ub-locality-name': {'type': 'INTEGER', 'value': 128}, 'ub-match': {\n 'type': 'INTEGER', 'value': 128}, 'ub-name': {'type': 'INTEGER',\n 'value': 32768}, 'ub-numeric-user-id-length': {'type': 'INTEGER',\n 'value': 32}, 'ub-organization-name': {'type': 'INTEGER', 'value': 64},\n 'ub-organization-name-length': {'type': 'INTEGER', 'value': 64},\n 'ub-organizational-unit-name': {'type': 'INTEGER', 'value': 64},\n 'ub-organizational-unit-name-length': {'type': 'INTEGER', 'value': 32},\n 'ub-organizational-units': {'type': 'INTEGER', 'value': 4},\n 'ub-pds-name-length': {'type': 'INTEGER', 'value': 16},\n 'ub-pds-parameter-length': {'type': 'INTEGER', 'value': 30},\n 'ub-pds-physical-address-lines': {'type': 'INTEGER', 'value': 6},\n 'ub-postal-code-length': {'type': 'INTEGER', 'value': 16},\n 'ub-pseudonym': {'type': 'INTEGER', 'value': 128}, 'ub-serial-number':\n {'type': 'INTEGER', 'value': 64}, 'ub-state-name': {'type': 'INTEGER',\n 'value': 128}, 'ub-surname-length': {'type': 'INTEGER', 'value': 40},\n 'ub-terminal-id-length': {'type': 'INTEGER', 'value': 24}, 'ub-title':\n {'type': 'INTEGER', 'value': 64}, 'ub-unformatted-address-length': {\n 'type': 'INTEGER', 'value': 180}, 'ub-x121-address-length': {'type':\n 'INTEGER', 'value': 16}, 'unformatted-postal-address': {'type':\n 'INTEGER', 'value': 16}, 'unique-postal-name': {'type': 'INTEGER',\n 'value': 20}}}, 'PKIX1Implicit88': {'extensibility-implied': False,\n 'imports': {'PKIX1Explicit88': ['Attribute', 'BMPString',\n 'CertificateSerialNumber', 'DirectoryString', 'Name', 'ORAddress',\n 'RelativeDistinguishedName', 'UTF8String', 'id-kp', 'id-pe',\n 'id-qt-cps', 'id-qt-unotice']}, 'object-classes': {}, 'object-sets': {},\n 'tags': 'IMPLICIT', 'types': {'AccessDescription': {'members': [{'name':\n 'accessMethod', 'type': 'OBJECT IDENTIFIER'}, {'name': 'accessLocation',\n 'type': 'GeneralName'}], 'type': 'SEQUENCE'}, 'AnotherName': {'members':\n [{'name': 'type-id', 'type': 'OBJECT IDENTIFIER'}, {'choices': {},\n 'name': 'value', 'tag': {'kind': 'EXPLICIT', 'number': 0}, 'type':\n 'ANY DEFINED BY', 'value': 'type-id'}], 'type': 'SEQUENCE'},\n 'AuthorityInfoAccessSyntax': {'element': {'type': 'AccessDescription'},\n 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'}, 'AuthorityKeyIdentifier':\n {'members': [{'name': 'keyIdentifier', 'optional': True, 'tag': {\n 'number': 0}, 'type': 'KeyIdentifier'}, {'name': 'authorityCertIssuer',\n 'optional': True, 'tag': {'number': 1}, 'type': 'GeneralNames'}, {\n 'name': 'authorityCertSerialNumber', 'optional': True, 'tag': {'number':\n 2}, 'type': 'CertificateSerialNumber'}], 'type': 'SEQUENCE'},\n 'BaseCRLNumber': {'type': 'CRLNumber'}, 'BaseDistance': {\n 'restricted-to': [(0, 'MAX')], 'type': 'INTEGER'}, 'BasicConstraints':\n {'members': [{'default': False, 'name': 'cA', 'type': 'BOOLEAN'}, {\n 'name': 'pathLenConstraint', 'optional': True, 'restricted-to': [(0,\n 'MAX')], 'type': 'INTEGER'}], 'type': 'SEQUENCE'}, 'CPSuri': {'type':\n 'IA5String'}, 'CRLDistributionPoints': {'element': {'type':\n 'DistributionPoint'}, 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'},\n 'CRLNumber': {'restricted-to': [(0, 'MAX')], 'type': 'INTEGER'},\n 'CRLReason': {'type': 'ENUMERATED', 'values': [('unspecified', 0), (\n 'keyCompromise', 1), ('cACompromise', 2), ('affiliationChanged', 3), (\n 'superseded', 4), ('cessationOfOperation', 5), ('certificateHold', 6),\n ('removeFromCRL', 8), ('privilegeWithdrawn', 9), ('aACompromise', 10)]},\n 'CertPolicyId': {'type': 'OBJECT IDENTIFIER'}, 'CertificateIssuer': {\n 'type': 'GeneralNames'}, 'CertificatePolicies': {'element': {'type':\n 'PolicyInformation'}, 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'},\n 'DisplayText': {'members': [{'name': 'ia5String', 'size': [(1, 200)],\n 'type': 'IA5String'}, {'name': 'visibleString', 'size': [(1, 200)],\n 'type': 'VisibleString'}, {'name': 'bmpString', 'size': [(1, 200)],\n 'type': 'BMPString'}, {'name': 'utf8String', 'size': [(1, 200)], 'type':\n 'UTF8String'}], 'type': 'CHOICE'}, 'DistributionPoint': {'members': [{\n 'name': 'distributionPoint', 'optional': True, 'tag': {'number': 0},\n 'type': 'DistributionPointName'}, {'name': 'reasons', 'optional': True,\n 'tag': {'number': 1}, 'type': 'ReasonFlags'}, {'name': 'cRLIssuer',\n 'optional': True, 'tag': {'number': 2}, 'type': 'GeneralNames'}],\n 'type': 'SEQUENCE'}, 'DistributionPointName': {'members': [{'name':\n 'fullName', 'tag': {'number': 0}, 'type': 'GeneralNames'}, {'name':\n 'nameRelativeToCRLIssuer', 'tag': {'number': 1}, 'type':\n 'RelativeDistinguishedName'}], 'type': 'CHOICE'}, 'EDIPartyName': {\n 'members': [{'name': 'nameAssigner', 'optional': True, 'tag': {'number':\n 0}, 'type': 'DirectoryString'}, {'name': 'partyName', 'tag': {'number':\n 1}, 'type': 'DirectoryString'}], 'type': 'SEQUENCE'},\n 'ExtKeyUsageSyntax': {'element': {'type': 'KeyPurposeId'}, 'size': [(1,\n 'MAX')], 'type': 'SEQUENCE OF'}, 'FreshestCRL': {'type':\n 'CRLDistributionPoints'}, 'GeneralName': {'members': [{'name':\n 'otherName', 'tag': {'number': 0}, 'type': 'AnotherName'}, {'name':\n 'rfc822Name', 'tag': {'number': 1}, 'type': 'IA5String'}, {'name':\n 'dNSName', 'tag': {'number': 2}, 'type': 'IA5String'}, {'name':\n 'x400Address', 'tag': {'number': 3}, 'type': 'ORAddress'}, {'name':\n 'directoryName', 'tag': {'number': 4}, 'type': 'Name'}, {'name':\n 'ediPartyName', 'tag': {'number': 5}, 'type': 'EDIPartyName'}, {'name':\n 'uniformResourceIdentifier', 'tag': {'number': 6}, 'type': 'IA5String'},\n {'name': 'iPAddress', 'tag': {'number': 7}, 'type': 'OCTET STRING'}, {\n 'name': 'registeredID', 'tag': {'number': 8}, 'type':\n 'OBJECT IDENTIFIER'}], 'type': 'CHOICE'}, 'GeneralNames': {'element': {\n 'type': 'GeneralName'}, 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'},\n 'GeneralSubtree': {'members': [{'name': 'base', 'type': 'GeneralName'},\n {'default': 0, 'name': 'minimum', 'tag': {'number': 0}, 'type':\n 'BaseDistance'}, {'name': 'maximum', 'optional': True, 'tag': {'number':\n 1}, 'type': 'BaseDistance'}], 'type': 'SEQUENCE'}, 'GeneralSubtrees': {\n 'element': {'type': 'GeneralSubtree'}, 'size': [(1, 'MAX')], 'type':\n 'SEQUENCE OF'}, 'HoldInstructionCode': {'type': 'OBJECT IDENTIFIER'},\n 'InhibitAnyPolicy': {'type': 'SkipCerts'}, 'InvalidityDate': {'type':\n 'GeneralizedTime'}, 'IssuerAltName': {'type': 'GeneralNames'},\n 'IssuingDistributionPoint': {'members': [{'name': 'distributionPoint',\n 'optional': True, 'tag': {'number': 0}, 'type': 'DistributionPointName'\n }, {'default': False, 'name': 'onlyContainsUserCerts', 'tag': {'number':\n 1}, 'type': 'BOOLEAN'}, {'default': False, 'name':\n 'onlyContainsCACerts', 'tag': {'number': 2}, 'type': 'BOOLEAN'}, {\n 'name': 'onlySomeReasons', 'optional': True, 'tag': {'number': 3},\n 'type': 'ReasonFlags'}, {'default': False, 'name': 'indirectCRL', 'tag':\n {'number': 4}, 'type': 'BOOLEAN'}, {'default': False, 'name':\n 'onlyContainsAttributeCerts', 'tag': {'number': 5}, 'type': 'BOOLEAN'}],\n 'type': 'SEQUENCE'}, 'KeyIdentifier': {'type': 'OCTET STRING'},\n 'KeyPurposeId': {'type': 'OBJECT IDENTIFIER'}, 'KeyUsage': {\n 'named-bits': [('digitalSignature', '0'), ('nonRepudiation', '1'), (\n 'keyEncipherment', '2'), ('dataEncipherment', '3'), ('keyAgreement',\n '4'), ('keyCertSign', '5'), ('cRLSign', '6'), ('encipherOnly', '7'), (\n 'decipherOnly', '8')], 'type': 'BIT STRING'}, 'NameConstraints': {\n 'members': [{'name': 'permittedSubtrees', 'optional': True, 'tag': {\n 'number': 0}, 'type': 'GeneralSubtrees'}, {'name': 'excludedSubtrees',\n 'optional': True, 'tag': {'number': 1}, 'type': 'GeneralSubtrees'}],\n 'type': 'SEQUENCE'}, 'NoticeReference': {'members': [{'name':\n 'organization', 'type': 'DisplayText'}, {'element': {'type': 'INTEGER'},\n 'name': 'noticeNumbers', 'type': 'SEQUENCE OF'}], 'type': 'SEQUENCE'},\n 'PolicyConstraints': {'members': [{'name': 'requireExplicitPolicy',\n 'optional': True, 'tag': {'number': 0}, 'type': 'SkipCerts'}, {'name':\n 'inhibitPolicyMapping', 'optional': True, 'tag': {'number': 1}, 'type':\n 'SkipCerts'}], 'type': 'SEQUENCE'}, 'PolicyInformation': {'members': [{\n 'name': 'policyIdentifier', 'type': 'CertPolicyId'}, {'element': {\n 'type': 'PolicyQualifierInfo'}, 'name': 'policyQualifiers', 'optional':\n True, 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'}], 'type': 'SEQUENCE'\n }, 'PolicyMappings': {'element': {'members': [{'name':\n 'issuerDomainPolicy', 'type': 'CertPolicyId'}, {'name':\n 'subjectDomainPolicy', 'type': 'CertPolicyId'}], 'type': 'SEQUENCE'},\n 'size': [(1, 'MAX')], 'type': 'SEQUENCE OF'}, 'PolicyQualifierId': {\n 'type': 'OBJECT IDENTIFIER'}, 'PolicyQualifierInfo': {'members': [{\n 'name': 'policyQualifierId', 'type': 'PolicyQualifierId'}, {'choices':\n {}, 'name': 'qualifier', 'type': 'ANY DEFINED BY', 'value':\n 'policyQualifierId'}], 'type': 'SEQUENCE'}, 'PrivateKeyUsagePeriod': {\n 'members': [{'name': 'notBefore', 'optional': True, 'tag': {'number': 0\n }, 'type': 'GeneralizedTime'}, {'name': 'notAfter', 'optional': True,\n 'tag': {'number': 1}, 'type': 'GeneralizedTime'}], 'type': 'SEQUENCE'},\n 'ReasonFlags': {'named-bits': [('unused', '0'), ('keyCompromise', '1'),\n ('cACompromise', '2'), ('affiliationChanged', '3'), ('superseded', '4'),\n ('cessationOfOperation', '5'), ('certificateHold', '6'), (\n 'privilegeWithdrawn', '7'), ('aACompromise', '8')], 'type':\n 'BIT STRING'}, 'SkipCerts': {'restricted-to': [(0, 'MAX')], 'type':\n 'INTEGER'}, 'SubjectAltName': {'type': 'GeneralNames'},\n 'SubjectDirectoryAttributes': {'element': {'type': 'Attribute'}, 'size':\n [(1, 'MAX')], 'type': 'SEQUENCE OF'}, 'SubjectInfoAccessSyntax': {\n 'element': {'type': 'AccessDescription'}, 'size': [(1, 'MAX')], 'type':\n 'SEQUENCE OF'}, 'SubjectKeyIdentifier': {'type': 'KeyIdentifier'},\n 'UserNotice': {'members': [{'name': 'noticeRef', 'optional': True,\n 'type': 'NoticeReference'}, {'name': 'explicitText', 'optional': True,\n 'type': 'DisplayText'}], 'type': 'SEQUENCE'}}, 'values': {\n 'anyExtendedKeyUsage': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-ce-extKeyUsage', 0]}, 'anyPolicy': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce-certificatePolicies', 0]}, 'holdInstruction': {'type':\n 'OBJECT IDENTIFIER', 'value': [('joint-iso-itu-t', 2), ('member-body', \n 2), ('us', 840), ('x9cm', 10040), 2]}, 'id-ce': {'type':\n 'OBJECT IDENTIFIER', 'value': [('joint-iso-ccitt', 2), ('ds', 5), 29]},\n 'id-ce-authorityKeyIdentifier': {'type': 'OBJECT IDENTIFIER', 'value':\n ['id-ce', 35]}, 'id-ce-basicConstraints': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce', 19]}, 'id-ce-cRLDistributionPoints': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 31]}, 'id-ce-cRLNumber': {\n 'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 20]},\n 'id-ce-cRLReasons': {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 21\n ]}, 'id-ce-certificateIssuer': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-ce', 29]}, 'id-ce-certificatePolicies': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 32]}, 'id-ce-deltaCRLIndicator':\n {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 27]},\n 'id-ce-extKeyUsage': {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', \n 37]}, 'id-ce-freshestCRL': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-ce', 46]}, 'id-ce-holdInstructionCode': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 23]}, 'id-ce-inhibitAnyPolicy':\n {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 54]},\n 'id-ce-invalidityDate': {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce',\n 24]}, 'id-ce-issuerAltName': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-ce', 18]}, 'id-ce-issuingDistributionPoint': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 28]}, 'id-ce-keyUsage': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 15]}, 'id-ce-nameConstraints':\n {'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 30]},\n 'id-ce-policyConstraints': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-ce', 36]}, 'id-ce-policyMappings': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce', 33]}, 'id-ce-privateKeyUsagePeriod': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 16]}, 'id-ce-subjectAltName': {\n 'type': 'OBJECT IDENTIFIER', 'value': ['id-ce', 17]},\n 'id-ce-subjectDirectoryAttributes': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-ce', 9]}, 'id-ce-subjectKeyIdentifier': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-ce', 14]},\n 'id-holdinstruction-callissuer': {'type': 'OBJECT IDENTIFIER', 'value':\n ['holdInstruction', 2]}, 'id-holdinstruction-none': {'type':\n 'OBJECT IDENTIFIER', 'value': ['holdInstruction', 1]},\n 'id-holdinstruction-reject': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'holdInstruction', 3]}, 'id-kp-OCSPSigning': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-kp', 9]}, 'id-kp-clientAuth': {\n 'type': 'OBJECT IDENTIFIER', 'value': ['id-kp', 2]},\n 'id-kp-codeSigning': {'type': 'OBJECT IDENTIFIER', 'value': ['id-kp', 3\n ]}, 'id-kp-emailProtection': {'type': 'OBJECT IDENTIFIER', 'value': [\n 'id-kp', 4]}, 'id-kp-serverAuth': {'type': 'OBJECT IDENTIFIER', 'value':\n ['id-kp', 1]}, 'id-kp-timeStamping': {'type': 'OBJECT IDENTIFIER',\n 'value': ['id-kp', 8]}, 'id-pe-authorityInfoAccess': {'type':\n 'OBJECT IDENTIFIER', 'value': ['id-pe', 1]}, 'id-pe-subjectInfoAccess':\n {'type': 'OBJECT IDENTIFIER', 'value': ['id-pe', 11]}}}}\n",
"<assignment token>\n"
] | false |
99,417 |
cd5991cdd0aedbf240459d66dc5243154a30f857
|
import random as rnd
import time
import inputs.inputs as inputs
import representation.encoding as encoding
import representation.decoding as decoding
import functions.best_solution_update as best_solution_update
import algorithms.classical_fo_maths as classical_fo_maths
import algorithms.fo_maths as fo_maths
import algorithms.pso_maths as pso_maths
import functions.mutation as mutation
def FO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):
## get the initial solution
SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)
## encoding the initial solution
encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]
## decoding the initial solution
for i in range(nParticle):
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## update the global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
change = 0
## start the algorithm
start_time = time.time()
for iteration in range(nIteration):
for i in range(nParticle):
## choose type of solution update and update the solutions
if gbest == 99999:
Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]
Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]
elif Cost[i] < gbest:
Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i], alfa, minCoordination, maxCoordination)
elif Cost[i] == gbest:
aux_mutation += 1
aux_break += 1
Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i], alfa, minCoordination, maxCoordination)
else:
aux_mutation += 1
aux_break += 1
Solution[i] = classical_fo_maths.FO(i, nParticle, nVessel, Solution, beta0, alfa, gamma,
minCoordination, maxCoordination)
## encode solution
encode[i] = encoding.Ordering(nVessel, Solution[i])
## decode solution
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p,
structure, NAB)
## control of best solution changing
if Cost[i] < gbest:
change = 1
else:
change = 0
if change == 1:
aux_break = 0
## updateglobal best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
## decision of mutation
if aux_mutation / nParticle >= round(nIteration * 0.10):
aux_mutation = 0
Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel,
minCoordination, maxCoordination)
## stopping criteria 2
if aux_break / nParticle >= round(nIteration * 0.33):
break
end_time = time.time()
TIMES.append(end_time - start_time)
if len(SOLVS) == 0 or gbest < min(SOLVS):
ggbest = gbest
gG = GloSOL
SOLVS.append(gbest)
print("Solution : ", gbest)
print("Time : ", (end_time - start_time), " sec.")
return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG
#### PARTICLE SWARM OPTIMIZATION ###
#### --------------------------- ###
def PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):
## get the initial solution
SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)
## encoding the initial solution
encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]
## decoding the initial solution
for i in range(nParticle):
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update the global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
change = 0
## start the algorithm
start_time = time.time()
for iteration in range(nIteration):
for i in range(nParticle):
## choose type of solution update and update the solutions
if gbest == 99999:
Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]
Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]
else:
aux_mutation += 1
aux_break += 1
Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)
## encode solution
encode[i] = encoding.Ordering(nVessel, Solution[i])
## decode solution
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## control of best solution changing
if Cost[i] < gbest:
change = 1
else:
change = 0
if change == 1:
aux_break = 0
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
## decision of mutation
if aux_mutation / nParticle >= round(nIteration * 0.10):
aux_mutation = 0
Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)
## stopping criteria 2
if aux_break / nParticle >= round(nIteration * 0.33):
break
end_time = time.time()
TIMES.append(end_time - start_time)
if len(SOLVS) == 0 or gbest < min(SOLVS):
ggbest = gbest
gG = GloSOL
SOLVS.append(gbest)
print("Solution : ", gbest)
print("Time : ", (end_time - start_time), " sec.")
return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG
#### PARTICLE SWARM OPTIMIZATION ###
#### --------------------------- ###
def HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):
## get the initial solution
SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)
## encoding the initial solution
encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]
## decoding the initial solution
for i in range(nParticle):
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update the global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
change = 0
change2 = 0
change3 = 0
## start the algorithm
start_time = time.time()
for iteration in range(nIteration):
if change2 == 1:
change = 1
change2 = 0
change3 = 0
else:
change = 0
for i in range(nParticle):
## choose type of solution update and update the solutions
if gbest == 99999:
Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]
Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]
else:
if change == 0:
aux_mutation += 1
aux_break += 1
Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)
else:
aux_break = 0
Solution[i], Velocity[i] = fo_maths.FO(nVessel,G,Solution[i],Velocity[i],beta0,alfa,gamma,minCoordination,maxCoordination,minVel,maxVel)
## encode solution
encode[i] = encoding.Ordering(nVessel, Solution[i])
## decode solution
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## control of best solution changing
if Cost[i] < gbest:
change2 = 1
change3 = 1
else:
change3 = 0
if change3 == 1:
aux_break = 0
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
## decision of mutation
if aux_mutation / nParticle >= round(nIteration * 0.10):
aux_mutation = 0
Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)
## stopping criteria 2
if aux_break / nParticle >= round(nIteration * 0.33):
break
end_time = time.time()
TIMES.append(end_time - start_time)
if len(SOLVS) == 0 or gbest < min(SOLVS):
ggbest = gbest
gG = GloSOL
SOLVS.append(gbest)
print("Solution : ", gbest)
print("Time : ", (end_time - start_time), " sec.")
return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG
#### PARTICLE SWARM OPTIMIZATION ###
#### --------------------------- ###
def HFPSO2(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):
## get the initial solution
SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)
## encoding the initial solution
encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]
## decoding the initial solution
for i in range(nParticle):
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update the global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
change = 0
change2 = 0
change3 = 0
## start the algorithm
start_time = time.time()
for iteration in range(nIteration):
if change2 == 1:
change = 1
change2 = 0
change3 = 0
else:
change = 0
for i in range(nParticle):
## choose type of solution update and update the solutions
if gbest == 99999:
Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]
Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]
else:
if change == 1:
aux_break = 0
Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)
else:
aux_mutation += 1
aux_break += 1
Solution[i], Velocity[i] = fo_maths.FO(nVessel,G,Solution[i],Velocity[i],beta0,alfa,gamma,minCoordination,maxCoordination,minVel,maxVel)
## encode solution
encode[i] = encoding.Ordering(nVessel, Solution[i])
## decode solution
SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)
## control of best solution changing
if Cost[i] < gbest:
change2 = 1
change3 = 1
else:
change3 = 0
if change3 == 1:
aux_break = 0
## update personal best solution
P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)
## update global best solution
G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)
## decision of mutation
if aux_mutation / nParticle >= round(nIteration * 0.10):
aux_mutation = 0
Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)
## stopping criteria 2
if aux_break / nParticle >= round(nIteration * 0.33):
break
end_time = time.time()
TIMES.append(end_time - start_time)
if len(SOLVS) == 0 or gbest < min(SOLVS):
ggbest = gbest
gG = GloSOL
SOLVS.append(gbest)
print("Solution : ", gbest)
print("Time : ", (end_time - start_time), " sec.")
return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG
|
[
"import random as rnd\r\nimport time\r\n\r\nimport inputs.inputs as inputs\r\nimport representation.encoding as encoding\r\nimport representation.decoding as decoding\r\nimport functions.best_solution_update as best_solution_update\r\nimport algorithms.classical_fo_maths as classical_fo_maths\r\nimport algorithms.fo_maths as fo_maths\r\nimport algorithms.pso_maths as pso_maths\r\nimport functions.mutation as mutation\r\n\r\ndef FO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\r\n\r\n ## get the initial solution\r\n SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)\r\n ## encoding the initial solution\r\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]\r\n ## decoding the initial solution\r\n for i in range(nParticle):\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n ## update the global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n change = 0\r\n\r\n ## start the algorithm\r\n start_time = time.time()\r\n\r\n for iteration in range(nIteration):\r\n\r\n for i in range(nParticle):\r\n\r\n ## choose type of solution update and update the solutions\r\n if gbest == 99999:\r\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]\r\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]\r\n elif Cost[i] < gbest:\r\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i], alfa, minCoordination, maxCoordination)\r\n elif Cost[i] == gbest:\r\n aux_mutation += 1\r\n aux_break += 1\r\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i], alfa, minCoordination, maxCoordination)\r\n else:\r\n aux_mutation += 1\r\n aux_break += 1\r\n Solution[i] = classical_fo_maths.FO(i, nParticle, nVessel, Solution, beta0, alfa, gamma,\r\n minCoordination, maxCoordination)\r\n\r\n ## encode solution\r\n encode[i] = encoding.Ordering(nVessel, Solution[i])\r\n\r\n ## decode solution\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p,\r\n structure, NAB)\r\n\r\n ## control of best solution changing\r\n if Cost[i] < gbest:\r\n change = 1\r\n else:\r\n change = 0\r\n if change == 1:\r\n aux_break = 0\r\n\r\n ## updateglobal best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n\r\n ## decision of mutation\r\n if aux_mutation / nParticle >= round(nIteration * 0.10):\r\n aux_mutation = 0\r\n Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel,\r\n minCoordination, maxCoordination)\r\n\r\n ## stopping criteria 2\r\n if aux_break / nParticle >= round(nIteration * 0.33):\r\n break\r\n\r\n end_time = time.time()\r\n\r\n TIMES.append(end_time - start_time)\r\n if len(SOLVS) == 0 or gbest < min(SOLVS):\r\n ggbest = gbest\r\n gG = GloSOL\r\n SOLVS.append(gbest)\r\n print(\"Solution : \", gbest)\r\n print(\"Time : \", (end_time - start_time), \" sec.\")\r\n\r\n\r\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\r\n\r\n\r\n\r\n\r\n\r\n\r\n#### PARTICLE SWARM OPTIMIZATION ###\r\n#### --------------------------- ###\r\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\r\n\r\n ## get the initial solution\r\n SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)\r\n ## encoding the initial solution\r\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]\r\n ## decoding the initial solution\r\n for i in range(nParticle):\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n ## update the global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n change = 0\r\n\r\n ## start the algorithm\r\n start_time = time.time()\r\n\r\n for iteration in range(nIteration):\r\n\r\n for i in range(nParticle):\r\n\r\n ## choose type of solution update and update the solutions\r\n if gbest == 99999:\r\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]\r\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]\r\n else:\r\n aux_mutation += 1\r\n aux_break += 1\r\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)\r\n\r\n ## encode solution\r\n encode[i] = encoding.Ordering(nVessel, Solution[i])\r\n\r\n ## decode solution\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n\r\n ## control of best solution changing\r\n if Cost[i] < gbest:\r\n change = 1\r\n else:\r\n change = 0\r\n if change == 1:\r\n aux_break = 0\r\n\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n\r\n ## update global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n\r\n ## decision of mutation\r\n if aux_mutation / nParticle >= round(nIteration * 0.10):\r\n aux_mutation = 0\r\n Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)\r\n\r\n ## stopping criteria 2\r\n if aux_break / nParticle >= round(nIteration * 0.33):\r\n break\r\n\r\n end_time = time.time()\r\n\r\n TIMES.append(end_time - start_time)\r\n if len(SOLVS) == 0 or gbest < min(SOLVS):\r\n ggbest = gbest\r\n gG = GloSOL\r\n SOLVS.append(gbest)\r\n print(\"Solution : \", gbest)\r\n print(\"Time : \", (end_time - start_time), \" sec.\")\r\n\r\n\r\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\r\n\r\n\r\n\r\n\r\n\r\n\r\n#### PARTICLE SWARM OPTIMIZATION ###\r\n#### --------------------------- ###\r\ndef HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\r\n\r\n ## get the initial solution\r\n SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)\r\n ## encoding the initial solution\r\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]\r\n ## decoding the initial solution\r\n for i in range(nParticle):\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n ## update the global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n change = 0\r\n change2 = 0\r\n change3 = 0\r\n\r\n ## start the algorithm\r\n start_time = time.time()\r\n\r\n for iteration in range(nIteration):\r\n\r\n if change2 == 1:\r\n change = 1\r\n change2 = 0\r\n change3 = 0\r\n else:\r\n change = 0\r\n\r\n for i in range(nParticle):\r\n\r\n ## choose type of solution update and update the solutions\r\n if gbest == 99999:\r\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]\r\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]\r\n else:\r\n if change == 0:\r\n aux_mutation += 1\r\n aux_break += 1\r\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)\r\n else:\r\n aux_break = 0\r\n Solution[i], Velocity[i] = fo_maths.FO(nVessel,G,Solution[i],Velocity[i],beta0,alfa,gamma,minCoordination,maxCoordination,minVel,maxVel)\r\n\r\n\r\n ## encode solution\r\n encode[i] = encoding.Ordering(nVessel, Solution[i])\r\n\r\n ## decode solution\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n\r\n ## control of best solution changing\r\n if Cost[i] < gbest:\r\n change2 = 1\r\n change3 = 1\r\n else:\r\n change3 = 0\r\n if change3 == 1:\r\n aux_break = 0\r\n\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n\r\n ## update global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n\r\n ## decision of mutation\r\n if aux_mutation / nParticle >= round(nIteration * 0.10):\r\n aux_mutation = 0\r\n Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)\r\n\r\n ## stopping criteria 2\r\n if aux_break / nParticle >= round(nIteration * 0.33):\r\n break\r\n\r\n end_time = time.time()\r\n\r\n TIMES.append(end_time - start_time)\r\n if len(SOLVS) == 0 or gbest < min(SOLVS):\r\n ggbest = gbest\r\n gG = GloSOL\r\n SOLVS.append(gbest)\r\n print(\"Solution : \", gbest)\r\n print(\"Time : \", (end_time - start_time), \" sec.\")\r\n\r\n\r\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\r\n\r\n\r\n\r\n\r\n\r\n\r\n#### PARTICLE SWARM OPTIMIZATION ###\r\n#### --------------------------- ###\r\ndef HFPSO2(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim, length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax, minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\r\n\r\n ## get the initial solution\r\n SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity, aux_mutation, aux_break = inputs.Initial_solution(nParticle, nVessel, minVel, maxVel, minCoordination, maxCoordination)\r\n ## encoding the initial solution\r\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)]\r\n ## decoding the initial solution\r\n for i in range(nParticle):\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n ## update the global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n change = 0\r\n change2 = 0\r\n change3 = 0\r\n\r\n ## start the algorithm\r\n start_time = time.time()\r\n\r\n for iteration in range(nIteration):\r\n\r\n if change2 == 1:\r\n change = 1\r\n change2 = 0\r\n change3 = 0\r\n else:\r\n change = 0\r\n\r\n for i in range(nParticle):\r\n\r\n ## choose type of solution update and update the solutions\r\n if gbest == 99999:\r\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination) for _ in range(nVessel)]\r\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(nVessel)]\r\n else:\r\n if change == 1:\r\n aux_break = 0\r\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel, nIteration, Solution[i], P[i], G, Velocity[i], c1, c2, wmin, wmax, iteration, minVel, maxVel, minCoordination, maxCoordination)\r\n else:\r\n aux_mutation += 1\r\n aux_break += 1\r\n Solution[i], Velocity[i] = fo_maths.FO(nVessel,G,Solution[i],Velocity[i],beta0,alfa,gamma,minCoordination,maxCoordination,minVel,maxVel)\r\n\r\n\r\n ## encode solution\r\n encode[i] = encoding.Ordering(nVessel, Solution[i])\r\n\r\n ## decode solution\r\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\r\n\r\n ## control of best solution changing\r\n if Cost[i] < gbest:\r\n change2 = 1\r\n change3 = 1\r\n else:\r\n change3 = 0\r\n if change3 == 1:\r\n aux_break = 0\r\n\r\n ## update personal best solution\r\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest, Cost, P)\r\n\r\n ## update global best solution\r\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle, Solution, gbest, Cost, G, SOLS, GloSOL)\r\n\r\n ## decision of mutation\r\n if aux_mutation / nParticle >= round(nIteration * 0.10):\r\n aux_mutation = 0\r\n Solution, Velocity = mutation.Mutation(nParticle, nVessel, Solution, Velocity, minVel, maxVel, minCoordination, maxCoordination)\r\n\r\n ## stopping criteria 2\r\n if aux_break / nParticle >= round(nIteration * 0.33):\r\n break\r\n\r\n end_time = time.time()\r\n\r\n TIMES.append(end_time - start_time)\r\n if len(SOLVS) == 0 or gbest < min(SOLVS):\r\n ggbest = gbest\r\n gG = GloSOL\r\n SOLVS.append(gbest)\r\n print(\"Solution : \", gbest)\r\n print(\"Time : \", (end_time - start_time), \" sec.\")\r\n\r\n\r\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG",
"import random as rnd\nimport time\nimport inputs.inputs as inputs\nimport representation.encoding as encoding\nimport representation.decoding as decoding\nimport functions.best_solution_update as best_solution_update\nimport algorithms.classical_fo_maths as classical_fo_maths\nimport algorithms.fo_maths as fo_maths\nimport algorithms.pso_maths as pso_maths\nimport functions.mutation as mutation\n\n\ndef FO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif Cost[i] < gbest:\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n elif Cost[i] == gbest:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO(i, nParticle, nVessel,\n Solution, beta0, alfa, gamma, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 0:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_break = 0\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO2(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 1:\n aux_break = 0\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n",
"<import token>\n\n\ndef FO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif Cost[i] < gbest:\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n elif Cost[i] == gbest:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO(i, nParticle, nVessel,\n Solution, beta0, alfa, gamma, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 0:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_break = 0\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO2(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 1:\n aux_break = 0\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n",
"<import token>\n\n\ndef FO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif Cost[i] < gbest:\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n elif Cost[i] == gbest:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO2(nVessel, Solution[i],\n alfa, minCoordination, maxCoordination)\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i] = classical_fo_maths.FO(i, nParticle, nVessel,\n Solution, beta0, alfa, gamma, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 0:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_break = 0\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\n<function token>\n",
"<import token>\n<function token>\n\n\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\ndef HFPSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, alfa, gamma, beta0, c1, c2, wmin, wmax,\n minCoordination, maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG\n ):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n change2 = 0\n change3 = 0\n start_time = time.time()\n for iteration in range(nIteration):\n if change2 == 1:\n change = 1\n change2 = 0\n change3 = 0\n else:\n change = 0\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n elif change == 0:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n else:\n aux_break = 0\n Solution[i], Velocity[i] = fo_maths.FO(nVessel, G, Solution\n [i], Velocity[i], beta0, alfa, gamma, minCoordination,\n maxCoordination, minVel, maxVel)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change2 = 1\n change3 = 1\n else:\n change3 = 0\n if change3 == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\n<function token>\n",
"<import token>\n<function token>\n\n\ndef PSO(structure, nIteration, nParticle, nVessel, nBerth, p, pro_tim,\n length, t1, t2, s, e, NAB, c1, c2, wmin, wmax, minCoordination,\n maxCoordination, minVel, maxVel, TIMES, SOLVS, ggbest, gG):\n (SOLS, nf, Cost, GloSOL, P, pbest, G, gbest, Solution, Velocity,\n aux_mutation, aux_break) = (inputs.Initial_solution(nParticle,\n nVessel, minVel, maxVel, minCoordination, maxCoordination))\n encode = [encoding.Ordering(nVessel, Solution[i]) for i in range(nParticle)\n ]\n for i in range(nParticle):\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth, encode[\n i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution, pbest,\n Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n change = 0\n start_time = time.time()\n for iteration in range(nIteration):\n for i in range(nParticle):\n if gbest == 99999:\n Solution[i] = [rnd.uniform(minCoordination, maxCoordination\n ) for _ in range(nVessel)]\n Velocity[i] = [rnd.uniform(minVel, maxVel) for _ in range(\n nVessel)]\n else:\n aux_mutation += 1\n aux_break += 1\n Solution[i], Velocity[i] = pso_maths.PSO(nVessel,\n nIteration, Solution[i], P[i], G, Velocity[i], c1, c2,\n wmin, wmax, iteration, minVel, maxVel, minCoordination,\n maxCoordination)\n encode[i] = encoding.Ordering(nVessel, Solution[i])\n SOLS[i], Cost[i] = decoding.Represantation(nVessel, nBerth,\n encode[i], pro_tim, length, t1, t2, s, e, p, structure, NAB)\n if Cost[i] < gbest:\n change = 1\n else:\n change = 0\n if change == 1:\n aux_break = 0\n P, pbest = best_solution_update.Update_pbest(nParticle, Solution,\n pbest, Cost, P)\n G, gbest, GloSOL = best_solution_update.Update_gbest(nParticle,\n Solution, gbest, Cost, G, SOLS, GloSOL)\n if aux_mutation / nParticle >= round(nIteration * 0.1):\n aux_mutation = 0\n Solution, Velocity = mutation.Mutation(nParticle, nVessel,\n Solution, Velocity, minVel, maxVel, minCoordination,\n maxCoordination)\n if aux_break / nParticle >= round(nIteration * 0.33):\n break\n end_time = time.time()\n TIMES.append(end_time - start_time)\n if len(SOLVS) == 0 or gbest < min(SOLVS):\n ggbest = gbest\n gG = GloSOL\n SOLVS.append(gbest)\n print('Solution : ', gbest)\n print('Time : ', end_time - start_time, ' sec.')\n return G, gbest, GloSOL, TIMES, SOLVS, ggbest, gG\n\n\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,418 |
52fe10656c908224d97a6dc394d50682c30e7bf4
|
import pytest
from streamsets.testframework.decorators import stub
@stub
@pytest.mark.parametrize('stage_attributes', [{'enable_udp_multithreading': True}])
def test_accept_threads(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
def test_broker_uri(sdc_builder, sdc_executor):
pass
@stub
def test_charset(sdc_builder, sdc_executor):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'data_format': 'COLLECTD'},
{'data_format': 'NETFLOW'},
{'data_format': 'SYSLOG'}])
def test_data_format(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'enable_udp_multithreading': False}, {'enable_udp_multithreading': True}])
def test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
def test_kafka_configuration(sdc_builder, sdc_executor):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'message_key_format': 'AVRO'}, {'message_key_format': 'STRING'}])
def test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'key_serializer': 'CONFLUENT', 'message_key_format': 'AVRO'},
{'key_serializer': 'STRING', 'message_key_format': 'AVRO'}])
def test_key_serializer(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'message_key_format': 'AVRO'}, {'message_key_format': 'STRING'}])
def test_message_key_format(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'on_missing_field': 'ERROR'}, {'on_missing_field': 'IGNORE'}])
def test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'on_record_error': 'DISCARD'},
{'on_record_error': 'STOP_PIPELINE'},
{'on_record_error': 'TO_ERROR'}])
def test_on_record_error(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
def test_port(sdc_builder, sdc_executor):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'pretty_format': False}, {'pretty_format': True}])
def test_pretty_format(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'quote_mode': 'ALL'}, {'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])
def test_quote_mode(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{}, {}])
def test_topic(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'validate_schema': False}, {'validate_schema': True}])
def test_validate_schema(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'value_serializer': 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])
def test_value_serializer(sdc_builder, sdc_executor, stage_attributes):
pass
@stub
def test_write_concurrency(sdc_builder, sdc_executor):
pass
@stub
@pytest.mark.parametrize('stage_attributes', [{'validate_schema': True}])
def test_xml_schema(sdc_builder, sdc_executor, stage_attributes):
pass
|
[
"import pytest\n\nfrom streamsets.testframework.decorators import stub\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading': True}])\ndef test_accept_threads(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n@stub\ndef test_charset(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'},\n {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading': False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'}, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer': 'CONFLUENT', 'message_key_format': 'AVRO'},\n {'key_serializer': 'STRING', 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'}, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'}, {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'},\n {'on_record_error': 'STOP_PIPELINE'},\n {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'pretty_format': False}, {'pretty_format': True}])\ndef test_pretty_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer': 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n",
"import pytest\nfrom streamsets.testframework.decorators import stub\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n True}])\ndef test_accept_threads(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n@stub\ndef test_charset(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'pretty_format': False}, {\n 'pretty_format': True}])\ndef test_pretty_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {\n 'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n True}])\ndef test_accept_threads(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n@stub\ndef test_charset(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'pretty_format': False}, {\n 'pretty_format': True}])\ndef test_pretty_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {\n 'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n@stub\ndef test_charset(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'pretty_format': False}, {\n 'pretty_format': True}])\ndef test_pretty_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {\n 'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'pretty_format': False}, {\n 'pretty_format': True}])\ndef test_pretty_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {\n 'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'quote_mode': 'ALL'}, {\n 'quote_mode': 'MINIMAL'}, {'quote_mode': 'NONE'}])\ndef test_quote_mode(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n\n\n@stub\ndef test_broker_uri(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'key_serializer':\n 'CONFLUENT', 'message_key_format': 'AVRO'}, {'key_serializer': 'STRING',\n 'message_key_format': 'AVRO'}])\ndef test_key_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': True}])\ndef test_xml_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n",
"<import token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'data_format': 'COLLECTD'},\n {'data_format': 'NETFLOW'}, {'data_format': 'SYSLOG'}])\ndef test_data_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'enable_udp_multithreading':\n False}, {'enable_udp_multithreading': True}])\ndef test_enable_udp_multithreading(sdc_builder, sdc_executor, stage_attributes\n ):\n pass\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\ndef test_kafka_configuration(sdc_builder, sdc_executor):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_record_error': 'DISCARD'\n }, {'on_record_error': 'STOP_PIPELINE'}, {'on_record_error': 'TO_ERROR'}])\ndef test_on_record_error(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\ndef test_port(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{}, {}])\ndef test_topic(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'value_serializer':\n 'CONFLUENT'}, {'value_serializer': 'DEFAULT'}])\ndef test_value_serializer(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\ndef test_write_concurrency(sdc_builder, sdc_executor):\n pass\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_message_key_format(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'message_key_format': 'AVRO'\n }, {'message_key_format': 'STRING'}])\ndef test_kafka_message_key(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'on_missing_field': 'ERROR'},\n {'on_missing_field': 'IGNORE'}])\ndef test_on_missing_field(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\n@stub\[email protected]('stage_attributes', [{'validate_schema': False}, {\n 'validate_schema': True}])\ndef test_validate_schema(sdc_builder, sdc_executor, stage_attributes):\n pass\n\n\n<function token>\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,419 |
0cc1e84428b9f056e3d8b2869869037e27a89eed
|
from PIL import Image
class FilterEditor:
filter_dict = {
'black_and_white': 'make_black_and_white',
'sepia': 'make_sepia',
'bright': 'make_bright'
}
@staticmethod
def add_filter(image, user_filter):
# TODO Проверка на тот случай, если передавать словарь с настройками для цвета, но это в будущем
return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(image)
@staticmethod
def make_black_and_white(image):
result = Image.new('RGB', image.size)
for x in range(image.size[0]):
for y in range(image.size[1]):
r, g, b = image.getpixel((x, y))
gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)
result.putpixel((x, y), (gray, gray, gray))
return result
@staticmethod
def make_sepia(image):
result = Image.new('RGB', image.size)
for x in range(image.size[0]):
for y in range(image.size[1]):
r, g, b = image.getpixel((x, y))
red = int(r * 0.393 + g * 0.769 + b * 0.189)
green = int(r * 0.349 + g * 0.686 + b * 0.168)
blue = int(r * 0.272 + g * 0.534 + b * 0.131)
result.putpixel((x, y), (red, green, blue))
return result
@staticmethod
def make_bright(image):
brightness = 2
result = Image.new('RGB', image.size)
for x in range(image.size[0]):
for y in range(image.size[1]):
r, g, b = image.getpixel((x, y))
red = int(r * brightness)
red = min(255, max(0, red))
green = int(g * brightness)
green = min(255, max(0, green))
blue = int(b * brightness)
blue = min(255, max(0, blue))
result.putpixel((x, y), (red, green, blue))
return result
|
[
"from PIL import Image\n\nclass FilterEditor:\n filter_dict = {\n 'black_and_white': 'make_black_and_white',\n 'sepia': 'make_sepia',\n 'bright': 'make_bright'\n }\n\n @staticmethod\n def add_filter(image, user_filter):\n # TODO Проверка на тот случай, если передавать словарь с настройками для цвета, но это в будущем\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n\n @staticmethod\n def make_sepia(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * 0.393 + g * 0.769 + b * 0.189)\n green = int(r * 0.349 + g * 0.686 + b * 0.168)\n blue = int(r * 0.272 + g * 0.534 + b * 0.131)\n result.putpixel((x, y), (red, green, blue))\n return result\n\n @staticmethod\n def make_bright(image):\n brightness = 2\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n\n red = int(r * brightness)\n red = min(255, max(0, red))\n\n green = int(g * brightness)\n green = min(255, max(0, green))\n\n blue = int(b * brightness)\n blue = min(255, max(0, blue))\n\n result.putpixel((x, y), (red, green, blue))\n return result\n",
"from PIL import Image\n\n\nclass FilterEditor:\n filter_dict = {'black_and_white': 'make_black_and_white', 'sepia':\n 'make_sepia', 'bright': 'make_bright'}\n\n @staticmethod\n def add_filter(image, user_filter):\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(\n image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n\n @staticmethod\n def make_sepia(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * 0.393 + g * 0.769 + b * 0.189)\n green = int(r * 0.349 + g * 0.686 + b * 0.168)\n blue = int(r * 0.272 + g * 0.534 + b * 0.131)\n result.putpixel((x, y), (red, green, blue))\n return result\n\n @staticmethod\n def make_bright(image):\n brightness = 2\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * brightness)\n red = min(255, max(0, red))\n green = int(g * brightness)\n green = min(255, max(0, green))\n blue = int(b * brightness)\n blue = min(255, max(0, blue))\n result.putpixel((x, y), (red, green, blue))\n return result\n",
"<import token>\n\n\nclass FilterEditor:\n filter_dict = {'black_and_white': 'make_black_and_white', 'sepia':\n 'make_sepia', 'bright': 'make_bright'}\n\n @staticmethod\n def add_filter(image, user_filter):\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(\n image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n\n @staticmethod\n def make_sepia(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * 0.393 + g * 0.769 + b * 0.189)\n green = int(r * 0.349 + g * 0.686 + b * 0.168)\n blue = int(r * 0.272 + g * 0.534 + b * 0.131)\n result.putpixel((x, y), (red, green, blue))\n return result\n\n @staticmethod\n def make_bright(image):\n brightness = 2\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * brightness)\n red = min(255, max(0, red))\n green = int(g * brightness)\n green = min(255, max(0, green))\n blue = int(b * brightness)\n blue = min(255, max(0, blue))\n result.putpixel((x, y), (red, green, blue))\n return result\n",
"<import token>\n\n\nclass FilterEditor:\n <assignment token>\n\n @staticmethod\n def add_filter(image, user_filter):\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(\n image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n\n @staticmethod\n def make_sepia(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * 0.393 + g * 0.769 + b * 0.189)\n green = int(r * 0.349 + g * 0.686 + b * 0.168)\n blue = int(r * 0.272 + g * 0.534 + b * 0.131)\n result.putpixel((x, y), (red, green, blue))\n return result\n\n @staticmethod\n def make_bright(image):\n brightness = 2\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * brightness)\n red = min(255, max(0, red))\n green = int(g * brightness)\n green = min(255, max(0, green))\n blue = int(b * brightness)\n blue = min(255, max(0, blue))\n result.putpixel((x, y), (red, green, blue))\n return result\n",
"<import token>\n\n\nclass FilterEditor:\n <assignment token>\n\n @staticmethod\n def add_filter(image, user_filter):\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(\n image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n <function token>\n\n @staticmethod\n def make_bright(image):\n brightness = 2\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n red = int(r * brightness)\n red = min(255, max(0, red))\n green = int(g * brightness)\n green = min(255, max(0, green))\n blue = int(b * brightness)\n blue = min(255, max(0, blue))\n result.putpixel((x, y), (red, green, blue))\n return result\n",
"<import token>\n\n\nclass FilterEditor:\n <assignment token>\n\n @staticmethod\n def add_filter(image, user_filter):\n return getattr(FilterEditor, FilterEditor.filter_dict[user_filter])(\n image)\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n <function token>\n <function token>\n",
"<import token>\n\n\nclass FilterEditor:\n <assignment token>\n <function token>\n\n @staticmethod\n def make_black_and_white(image):\n result = Image.new('RGB', image.size)\n for x in range(image.size[0]):\n for y in range(image.size[1]):\n r, g, b = image.getpixel((x, y))\n gray = int(r * 0.2126 + g * 0.7152 + b * 0.0722)\n result.putpixel((x, y), (gray, gray, gray))\n return result\n <function token>\n <function token>\n",
"<import token>\n\n\nclass FilterEditor:\n <assignment token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,420 |
ee2f616b2d9541fc3dc7401ab9121d94d17aedb4
|
"""Semantic shift benchmark."""
import dataclasses
import os
import pickle
import numpy as np
import requests
import torchvision.transforms as tv_transforms
import shifthappens.config
import shifthappens.data.base as sh_data
import shifthappens.data.torch as sh_data_torch
from shifthappens import benchmark as sh_benchmark
from shifthappens.models import base as sh_models
from shifthappens.models.base import PredictionTargets
from shifthappens.tasks.base import abstract_variable
from shifthappens.tasks.base import Task
from shifthappens.tasks.base import variable
from shifthappens.tasks.metrics import Metric
from shifthappens.tasks.mixins import OODScoreTaskMixin
from shifthappens.tasks.ssb.imagenet_ssb import _get_imagenet_ssb_subset
from shifthappens.tasks.ssb.imagenet_ssb import assert_data_downloaded
from shifthappens.tasks.task_result import TaskResult
from shifthappens.tasks.utils import auroc_ood
from shifthappens.tasks.utils import fpr_at_tpr
@dataclasses.dataclass
class _SSB(Task, OODScoreTaskMixin):
"""
Prepares the ImageNet evaluation from the Semantic Shift Benchmark for open-set recognition (OSR)
Downloads SSB OSR splits to Task.data_root
Assumes ImageNet-21KP validation splits are downloaded to shifthappens.config.imagenet21k_preprocessed_validation_path
To download the ImageNet21k-P data:
Follow instructions at https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md
Ensure data is from the Winter21 ImageNet release!
"""
OSR_URL = "https://github.com/sgvaze/osr_closed_set_all_you_need/raw/main/data/open_set_splits/imagenet_osr_splits_winter21.pkl"
subset_type: str = abstract_variable()
max_batch_size: int = 256
def setup(self):
"""Asserts data is downloaded and sets up open-set dataset"""
osr_split_path = os.path.join(
self.data_root, "imagenet_osr_splits_winter21.pkl"
)
if not os.path.exists(osr_split_path):
os.makedirs(self.data_root, exist_ok=True)
osr_split = requests.get(self.OSR_URL)
open(osr_split_path, "wb").write(osr_split.content)
else:
with open(osr_split_path, "rb") as f:
osr_split = pickle.load(f)
# Ensure data is downloaded
assert_data_downloaded(
osr_split, shifthappens.config.imagenet21k_preprocessed_validation_path
)
test_transform = tv_transforms.Compose(
[
tv_transforms.ToTensor(),
tv_transforms.Lambda(lambda x: x.permute(1, 2, 0)),
]
)
dataset_out = _get_imagenet_ssb_subset(
imagenet21k_root=shifthappens.config.imagenet21k_preprocessed_validation_path,
osr_split=osr_split,
test_transform=test_transform,
subset_type=self.subset_type,
)
self.dataset_out = sh_data_torch.IndexedTorchDataset(
sh_data_torch.ImagesOnlyTorchDataset(dataset_out)
)
def _prepare_dataloader(self):
dataloader_out = sh_data.DataLoader(
self.dataset_out, max_batch_size=self.max_batch_size
)
return dataloader_out
def _evaluate(self, model: sh_models.Model) -> TaskResult:
dataloader = self._prepare_dataloader()
ood_scores_out_list = []
for predictions_out in model.predict(
dataloader, PredictionTargets(ood_scores=True)
):
assert (
predictions_out.ood_scores is not None
), "OOD scores for SSB task is None"
ood_scores_out_list.append(predictions_out.ood_scores)
ood_scores_out = np.hstack(ood_scores_out_list)
auroc = auroc_ood(
np.array(model.imagenet_validation_result.ood_scores), ood_scores_out
)
fpr_at_95 = fpr_at_tpr(
np.array(model.imagenet_validation_result.ood_scores), ood_scores_out, 0.95
)
return TaskResult(
auroc=auroc,
fpr_at_95=fpr_at_95,
summary_metrics={
Metric.OODDetection: ("auroc", "fpr_at_95"),
},
)
@sh_benchmark.register_task(
name="SSB_easy", relative_data_folder="ssb", standalone=True
)
@dataclasses.dataclass
class SSBEasy(_SSB):
"""SSB Easy subset"""
subset_type: str = variable("easy")
@sh_benchmark.register_task(
name="SSB_hard", relative_data_folder="ssb", standalone=True
)
@dataclasses.dataclass
class SSBHard(_SSB):
"""SSB Hard subset"""
subset_type: str = variable("hard")
|
[
"\"\"\"Semantic shift benchmark.\"\"\"\n\nimport dataclasses\nimport os\nimport pickle\n\nimport numpy as np\nimport requests\nimport torchvision.transforms as tv_transforms\n\nimport shifthappens.config\nimport shifthappens.data.base as sh_data\nimport shifthappens.data.torch as sh_data_torch\nfrom shifthappens import benchmark as sh_benchmark\nfrom shifthappens.models import base as sh_models\nfrom shifthappens.models.base import PredictionTargets\nfrom shifthappens.tasks.base import abstract_variable\nfrom shifthappens.tasks.base import Task\nfrom shifthappens.tasks.base import variable\nfrom shifthappens.tasks.metrics import Metric\nfrom shifthappens.tasks.mixins import OODScoreTaskMixin\nfrom shifthappens.tasks.ssb.imagenet_ssb import _get_imagenet_ssb_subset\nfrom shifthappens.tasks.ssb.imagenet_ssb import assert_data_downloaded\nfrom shifthappens.tasks.task_result import TaskResult\nfrom shifthappens.tasks.utils import auroc_ood\nfrom shifthappens.tasks.utils import fpr_at_tpr\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n \"\"\"\n Prepares the ImageNet evaluation from the Semantic Shift Benchmark for open-set recognition (OSR)\n\n Downloads SSB OSR splits to Task.data_root\n Assumes ImageNet-21KP validation splits are downloaded to shifthappens.config.imagenet21k_preprocessed_validation_path\n To download the ImageNet21k-P data:\n Follow instructions at https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md\n Ensure data is from the Winter21 ImageNet release!\n \"\"\"\n\n OSR_URL = \"https://github.com/sgvaze/osr_closed_set_all_you_need/raw/main/data/open_set_splits/imagenet_osr_splits_winter21.pkl\"\n\n subset_type: str = abstract_variable()\n\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(\n self.data_root, \"imagenet_osr_splits_winter21.pkl\"\n )\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, \"wb\").write(osr_split.content)\n else:\n with open(osr_split_path, \"rb\") as f:\n osr_split = pickle.load(f)\n # Ensure data is downloaded\n assert_data_downloaded(\n osr_split, shifthappens.config.imagenet21k_preprocessed_validation_path\n )\n test_transform = tv_transforms.Compose(\n [\n tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0)),\n ]\n )\n\n dataset_out = _get_imagenet_ssb_subset(\n imagenet21k_root=shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split,\n test_transform=test_transform,\n subset_type=self.subset_type,\n )\n\n self.dataset_out = sh_data_torch.IndexedTorchDataset(\n sh_data_torch.ImagesOnlyTorchDataset(dataset_out)\n )\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(\n self.dataset_out, max_batch_size=self.max_batch_size\n )\n return dataloader_out\n\n def _evaluate(self, model: sh_models.Model) -> TaskResult:\n dataloader = self._prepare_dataloader()\n ood_scores_out_list = []\n for predictions_out in model.predict(\n dataloader, PredictionTargets(ood_scores=True)\n ):\n assert (\n predictions_out.ood_scores is not None\n ), \"OOD scores for SSB task is None\"\n ood_scores_out_list.append(predictions_out.ood_scores)\n ood_scores_out = np.hstack(ood_scores_out_list)\n\n auroc = auroc_ood(\n np.array(model.imagenet_validation_result.ood_scores), ood_scores_out\n )\n fpr_at_95 = fpr_at_tpr(\n np.array(model.imagenet_validation_result.ood_scores), ood_scores_out, 0.95\n )\n return TaskResult(\n auroc=auroc,\n fpr_at_95=fpr_at_95,\n summary_metrics={\n Metric.OODDetection: (\"auroc\", \"fpr_at_95\"),\n },\n )\n\n\n@sh_benchmark.register_task(\n name=\"SSB_easy\", relative_data_folder=\"ssb\", standalone=True\n)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n\n subset_type: str = variable(\"easy\")\n\n\n@sh_benchmark.register_task(\n name=\"SSB_hard\", relative_data_folder=\"ssb\", standalone=True\n)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n\n subset_type: str = variable(\"hard\")\n",
"<docstring token>\nimport dataclasses\nimport os\nimport pickle\nimport numpy as np\nimport requests\nimport torchvision.transforms as tv_transforms\nimport shifthappens.config\nimport shifthappens.data.base as sh_data\nimport shifthappens.data.torch as sh_data_torch\nfrom shifthappens import benchmark as sh_benchmark\nfrom shifthappens.models import base as sh_models\nfrom shifthappens.models.base import PredictionTargets\nfrom shifthappens.tasks.base import abstract_variable\nfrom shifthappens.tasks.base import Task\nfrom shifthappens.tasks.base import variable\nfrom shifthappens.tasks.metrics import Metric\nfrom shifthappens.tasks.mixins import OODScoreTaskMixin\nfrom shifthappens.tasks.ssb.imagenet_ssb import _get_imagenet_ssb_subset\nfrom shifthappens.tasks.ssb.imagenet_ssb import assert_data_downloaded\nfrom shifthappens.tasks.task_result import TaskResult\nfrom shifthappens.tasks.utils import auroc_ood\nfrom shifthappens.tasks.utils import fpr_at_tpr\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n \"\"\"\n Prepares the ImageNet evaluation from the Semantic Shift Benchmark for open-set recognition (OSR)\n\n Downloads SSB OSR splits to Task.data_root\n Assumes ImageNet-21KP validation splits are downloaded to shifthappens.config.imagenet21k_preprocessed_validation_path\n To download the ImageNet21k-P data:\n Follow instructions at https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md\n Ensure data is from the Winter21 ImageNet release!\n \"\"\"\n OSR_URL = (\n 'https://github.com/sgvaze/osr_closed_set_all_you_need/raw/main/data/open_set_splits/imagenet_osr_splits_winter21.pkl'\n )\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(self.dataset_out,\n max_batch_size=self.max_batch_size)\n return dataloader_out\n\n def _evaluate(self, model: sh_models.Model) ->TaskResult:\n dataloader = self._prepare_dataloader()\n ood_scores_out_list = []\n for predictions_out in model.predict(dataloader, PredictionTargets(\n ood_scores=True)):\n assert predictions_out.ood_scores is not None, 'OOD scores for SSB task is None'\n ood_scores_out_list.append(predictions_out.ood_scores)\n ood_scores_out = np.hstack(ood_scores_out_list)\n auroc = auroc_ood(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out)\n fpr_at_95 = fpr_at_tpr(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out, 0.95)\n return TaskResult(auroc=auroc, fpr_at_95=fpr_at_95, summary_metrics\n ={Metric.OODDetection: ('auroc', 'fpr_at_95')})\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n \"\"\"\n Prepares the ImageNet evaluation from the Semantic Shift Benchmark for open-set recognition (OSR)\n\n Downloads SSB OSR splits to Task.data_root\n Assumes ImageNet-21KP validation splits are downloaded to shifthappens.config.imagenet21k_preprocessed_validation_path\n To download the ImageNet21k-P data:\n Follow instructions at https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md\n Ensure data is from the Winter21 ImageNet release!\n \"\"\"\n OSR_URL = (\n 'https://github.com/sgvaze/osr_closed_set_all_you_need/raw/main/data/open_set_splits/imagenet_osr_splits_winter21.pkl'\n )\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(self.dataset_out,\n max_batch_size=self.max_batch_size)\n return dataloader_out\n\n def _evaluate(self, model: sh_models.Model) ->TaskResult:\n dataloader = self._prepare_dataloader()\n ood_scores_out_list = []\n for predictions_out in model.predict(dataloader, PredictionTargets(\n ood_scores=True)):\n assert predictions_out.ood_scores is not None, 'OOD scores for SSB task is None'\n ood_scores_out_list.append(predictions_out.ood_scores)\n ood_scores_out = np.hstack(ood_scores_out_list)\n auroc = auroc_ood(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out)\n fpr_at_95 = fpr_at_tpr(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out, 0.95)\n return TaskResult(auroc=auroc, fpr_at_95=fpr_at_95, summary_metrics\n ={Metric.OODDetection: ('auroc', 'fpr_at_95')})\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n <docstring token>\n OSR_URL = (\n 'https://github.com/sgvaze/osr_closed_set_all_you_need/raw/main/data/open_set_splits/imagenet_osr_splits_winter21.pkl'\n )\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(self.dataset_out,\n max_batch_size=self.max_batch_size)\n return dataloader_out\n\n def _evaluate(self, model: sh_models.Model) ->TaskResult:\n dataloader = self._prepare_dataloader()\n ood_scores_out_list = []\n for predictions_out in model.predict(dataloader, PredictionTargets(\n ood_scores=True)):\n assert predictions_out.ood_scores is not None, 'OOD scores for SSB task is None'\n ood_scores_out_list.append(predictions_out.ood_scores)\n ood_scores_out = np.hstack(ood_scores_out_list)\n auroc = auroc_ood(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out)\n fpr_at_95 = fpr_at_tpr(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out, 0.95)\n return TaskResult(auroc=auroc, fpr_at_95=fpr_at_95, summary_metrics\n ={Metric.OODDetection: ('auroc', 'fpr_at_95')})\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n <docstring token>\n <assignment token>\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(self.dataset_out,\n max_batch_size=self.max_batch_size)\n return dataloader_out\n\n def _evaluate(self, model: sh_models.Model) ->TaskResult:\n dataloader = self._prepare_dataloader()\n ood_scores_out_list = []\n for predictions_out in model.predict(dataloader, PredictionTargets(\n ood_scores=True)):\n assert predictions_out.ood_scores is not None, 'OOD scores for SSB task is None'\n ood_scores_out_list.append(predictions_out.ood_scores)\n ood_scores_out = np.hstack(ood_scores_out_list)\n auroc = auroc_ood(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out)\n fpr_at_95 = fpr_at_tpr(np.array(model.imagenet_validation_result.\n ood_scores), ood_scores_out, 0.95)\n return TaskResult(auroc=auroc, fpr_at_95=fpr_at_95, summary_metrics\n ={Metric.OODDetection: ('auroc', 'fpr_at_95')})\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n <docstring token>\n <assignment token>\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n\n def _prepare_dataloader(self):\n dataloader_out = sh_data.DataLoader(self.dataset_out,\n max_batch_size=self.max_batch_size)\n return dataloader_out\n <function token>\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n <docstring token>\n <assignment token>\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n\n def setup(self):\n \"\"\"Asserts data is downloaded and sets up open-set dataset\"\"\"\n osr_split_path = os.path.join(self.data_root,\n 'imagenet_osr_splits_winter21.pkl')\n if not os.path.exists(osr_split_path):\n os.makedirs(self.data_root, exist_ok=True)\n osr_split = requests.get(self.OSR_URL)\n open(osr_split_path, 'wb').write(osr_split.content)\n else:\n with open(osr_split_path, 'rb') as f:\n osr_split = pickle.load(f)\n assert_data_downloaded(osr_split, shifthappens.config.\n imagenet21k_preprocessed_validation_path)\n test_transform = tv_transforms.Compose([tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0))])\n dataset_out = _get_imagenet_ssb_subset(imagenet21k_root=\n shifthappens.config.imagenet21k_preprocessed_validation_path,\n osr_split=osr_split, test_transform=test_transform, subset_type\n =self.subset_type)\n self.dataset_out = sh_data_torch.IndexedTorchDataset(sh_data_torch.\n ImagesOnlyTorchDataset(dataset_out))\n <function token>\n <function token>\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass _SSB(Task, OODScoreTaskMixin):\n <docstring token>\n <assignment token>\n subset_type: str = abstract_variable()\n max_batch_size: int = 256\n <function token>\n <function token>\n <function token>\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n<class token>\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n \"\"\"SSB Easy subset\"\"\"\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n<class token>\n\n\n@sh_benchmark.register_task(name='SSB_easy', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBEasy(_SSB):\n <docstring token>\n subset_type: str = variable('easy')\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n<class token>\n<class token>\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n \"\"\"SSB Hard subset\"\"\"\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n<class token>\n<class token>\n\n\n@sh_benchmark.register_task(name='SSB_hard', relative_data_folder='ssb',\n standalone=True)\[email protected]\nclass SSBHard(_SSB):\n <docstring token>\n subset_type: str = variable('hard')\n",
"<docstring token>\n<import token>\n<class token>\n<class token>\n<class token>\n"
] | false |
99,421 |
8fa57a2826c2d01ecb91a9357572210404691eda
|
import urllib.parse
import urllib.request
import urllib.error
import concurrent.futures
from logger import get_logger
from crawler.cfg import MAX_HTTP_WORKERS
logger = get_logger(__name__)
class UrllibHandler:
"""
Http handler that uses urllib and allow proxies
"""
def __init__(self, url, proxy=None, **kwargs):
"""
Initialize the http handler to perform requests in a URL.
A proxy with the format "ip:port" can be provided.
All the extra arguments (kwargs) will be parsed and sent as
GET parameters in the request.
:param url: URL to perform the request
:type url: string
:param proxy: string with format "ip:port"
:type query: string
"""
self.proxy = proxy
self.query_params = urllib.parse.urlencode(kwargs)
self.url = url if not self.query_params else f"{url}?{self.query_params}"
logger.info("UrllibHandler initialized: url=%s, proxy=%s", self.url, self.proxy)
def get(self):
"""
Get a url and return a redeable object with the raw html retrived
"""
request = urllib.request.Request(self.url)
if self.proxy:
request.set_proxy(self.proxy, 'http')
logger.info("Attempt to do GET request: url=%s, proxy=%s",
self.url, self.proxy)
response = urllib.request.urlopen(request)
logger.info("GET request was successful: url=%s, proxy=%s",
self.url, self.proxy)
return response
def get_urls_async(urls, proxy=None, max_workers=MAX_HTTP_WORKERS):
"""
Perform async requests on each url in urls and return the result.
The max number of concurrent requests is controled by `max_workers`
If a proxy is provided, it will be used to make all the requests.
Return a dictionary with `url` as key and the resultant requests as value
(or None if an exception is rised during request)
"""
result = {}
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = {}
for url in urls:
handler = UrllibHandler(url, proxy)
future = executor.submit(handler.get)
futures[future] = url
for future in concurrent.futures.as_completed(futures):
url = futures[future]
try:
response = future.result()
except (urllib.error.URLError, urllib.error.HTTPError) as ex:
logger.error("Unexpected error during request: url=%s, proxy=%s, " + \
"error=%s", url, proxy, ex)
response = None
result[url] = response
return result
|
[
"import urllib.parse\nimport urllib.request\nimport urllib.error\nimport concurrent.futures\nfrom logger import get_logger\nfrom crawler.cfg import MAX_HTTP_WORKERS\n\nlogger = get_logger(__name__)\n\n\nclass UrllibHandler:\n \"\"\"\n Http handler that uses urllib and allow proxies\n \"\"\"\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = url if not self.query_params else f\"{url}?{self.query_params}\"\n logger.info(\"UrllibHandler initialized: url=%s, proxy=%s\", self.url, self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info(\"Attempt to do GET request: url=%s, proxy=%s\",\n self.url, self.proxy)\n response = urllib.request.urlopen(request)\n logger.info(\"GET request was successful: url=%s, proxy=%s\",\n self.url, self.proxy)\n return response\n\n\ndef get_urls_async(urls, proxy=None, max_workers=MAX_HTTP_WORKERS):\n \"\"\"\n Perform async requests on each url in urls and return the result.\n The max number of concurrent requests is controled by `max_workers`\n If a proxy is provided, it will be used to make all the requests.\n\n Return a dictionary with `url` as key and the resultant requests as value\n (or None if an exception is rised during request)\n \"\"\"\n result = {}\n with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:\n futures = {}\n for url in urls:\n handler = UrllibHandler(url, proxy)\n future = executor.submit(handler.get)\n futures[future] = url\n\n for future in concurrent.futures.as_completed(futures):\n url = futures[future]\n try:\n response = future.result()\n except (urllib.error.URLError, urllib.error.HTTPError) as ex:\n logger.error(\"Unexpected error during request: url=%s, proxy=%s, \" + \\\n \"error=%s\", url, proxy, ex)\n response = None\n\n result[url] = response\n return result\n",
"import urllib.parse\nimport urllib.request\nimport urllib.error\nimport concurrent.futures\nfrom logger import get_logger\nfrom crawler.cfg import MAX_HTTP_WORKERS\nlogger = get_logger(__name__)\n\n\nclass UrllibHandler:\n \"\"\"\n Http handler that uses urllib and allow proxies\n \"\"\"\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info('Attempt to do GET request: url=%s, proxy=%s', self.url,\n self.proxy)\n response = urllib.request.urlopen(request)\n logger.info('GET request was successful: url=%s, proxy=%s', self.\n url, self.proxy)\n return response\n\n\ndef get_urls_async(urls, proxy=None, max_workers=MAX_HTTP_WORKERS):\n \"\"\"\n Perform async requests on each url in urls and return the result.\n The max number of concurrent requests is controled by `max_workers`\n If a proxy is provided, it will be used to make all the requests.\n\n Return a dictionary with `url` as key and the resultant requests as value\n (or None if an exception is rised during request)\n \"\"\"\n result = {}\n with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers\n ) as executor:\n futures = {}\n for url in urls:\n handler = UrllibHandler(url, proxy)\n future = executor.submit(handler.get)\n futures[future] = url\n for future in concurrent.futures.as_completed(futures):\n url = futures[future]\n try:\n response = future.result()\n except (urllib.error.URLError, urllib.error.HTTPError) as ex:\n logger.error(\n 'Unexpected error during request: url=%s, proxy=%s, ' +\n 'error=%s', url, proxy, ex)\n response = None\n result[url] = response\n return result\n",
"<import token>\nlogger = get_logger(__name__)\n\n\nclass UrllibHandler:\n \"\"\"\n Http handler that uses urllib and allow proxies\n \"\"\"\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info('Attempt to do GET request: url=%s, proxy=%s', self.url,\n self.proxy)\n response = urllib.request.urlopen(request)\n logger.info('GET request was successful: url=%s, proxy=%s', self.\n url, self.proxy)\n return response\n\n\ndef get_urls_async(urls, proxy=None, max_workers=MAX_HTTP_WORKERS):\n \"\"\"\n Perform async requests on each url in urls and return the result.\n The max number of concurrent requests is controled by `max_workers`\n If a proxy is provided, it will be used to make all the requests.\n\n Return a dictionary with `url` as key and the resultant requests as value\n (or None if an exception is rised during request)\n \"\"\"\n result = {}\n with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers\n ) as executor:\n futures = {}\n for url in urls:\n handler = UrllibHandler(url, proxy)\n future = executor.submit(handler.get)\n futures[future] = url\n for future in concurrent.futures.as_completed(futures):\n url = futures[future]\n try:\n response = future.result()\n except (urllib.error.URLError, urllib.error.HTTPError) as ex:\n logger.error(\n 'Unexpected error during request: url=%s, proxy=%s, ' +\n 'error=%s', url, proxy, ex)\n response = None\n result[url] = response\n return result\n",
"<import token>\n<assignment token>\n\n\nclass UrllibHandler:\n \"\"\"\n Http handler that uses urllib and allow proxies\n \"\"\"\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info('Attempt to do GET request: url=%s, proxy=%s', self.url,\n self.proxy)\n response = urllib.request.urlopen(request)\n logger.info('GET request was successful: url=%s, proxy=%s', self.\n url, self.proxy)\n return response\n\n\ndef get_urls_async(urls, proxy=None, max_workers=MAX_HTTP_WORKERS):\n \"\"\"\n Perform async requests on each url in urls and return the result.\n The max number of concurrent requests is controled by `max_workers`\n If a proxy is provided, it will be used to make all the requests.\n\n Return a dictionary with `url` as key and the resultant requests as value\n (or None if an exception is rised during request)\n \"\"\"\n result = {}\n with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers\n ) as executor:\n futures = {}\n for url in urls:\n handler = UrllibHandler(url, proxy)\n future = executor.submit(handler.get)\n futures[future] = url\n for future in concurrent.futures.as_completed(futures):\n url = futures[future]\n try:\n response = future.result()\n except (urllib.error.URLError, urllib.error.HTTPError) as ex:\n logger.error(\n 'Unexpected error during request: url=%s, proxy=%s, ' +\n 'error=%s', url, proxy, ex)\n response = None\n result[url] = response\n return result\n",
"<import token>\n<assignment token>\n\n\nclass UrllibHandler:\n \"\"\"\n Http handler that uses urllib and allow proxies\n \"\"\"\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info('Attempt to do GET request: url=%s, proxy=%s', self.url,\n self.proxy)\n response = urllib.request.urlopen(request)\n logger.info('GET request was successful: url=%s, proxy=%s', self.\n url, self.proxy)\n return response\n\n\n<function token>\n",
"<import token>\n<assignment token>\n\n\nclass UrllibHandler:\n <docstring token>\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n\n def get(self):\n \"\"\"\n Get a url and return a redeable object with the raw html retrived\n \"\"\"\n request = urllib.request.Request(self.url)\n if self.proxy:\n request.set_proxy(self.proxy, 'http')\n logger.info('Attempt to do GET request: url=%s, proxy=%s', self.url,\n self.proxy)\n response = urllib.request.urlopen(request)\n logger.info('GET request was successful: url=%s, proxy=%s', self.\n url, self.proxy)\n return response\n\n\n<function token>\n",
"<import token>\n<assignment token>\n\n\nclass UrllibHandler:\n <docstring token>\n\n def __init__(self, url, proxy=None, **kwargs):\n \"\"\"\n Initialize the http handler to perform requests in a URL.\n A proxy with the format \"ip:port\" can be provided.\n All the extra arguments (kwargs) will be parsed and sent as\n GET parameters in the request.\n :param url: URL to perform the request\n :type url: string\n :param proxy: string with format \"ip:port\"\n :type query: string\n \"\"\"\n self.proxy = proxy\n self.query_params = urllib.parse.urlencode(kwargs)\n self.url = (url if not self.query_params else\n f'{url}?{self.query_params}')\n logger.info('UrllibHandler initialized: url=%s, proxy=%s', self.url,\n self.proxy)\n <function token>\n\n\n<function token>\n",
"<import token>\n<assignment token>\n\n\nclass UrllibHandler:\n <docstring token>\n <function token>\n <function token>\n\n\n<function token>\n",
"<import token>\n<assignment token>\n<class token>\n<function token>\n"
] | false |
99,422 |
0947162cdb7feed2868df723b999a411626ff826
|
"""Class and Instance Variables.
@see: https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables
Generally speaking, instance variables are for data unique to each instance and class variables are
for attributes and methods shared by all instances of the class.
"""
def test_class_and_instance_variables():
"""Class and Instance Variables."""
# pylint: disable=too-few-public-methods
class Dog:
"""Dog class example"""
kind = "canine" # Class variable shared by all instances.
def __init__(self, name):
self.name = name # Instance variable unique to each instance.
fido = Dog("Fido")
buddy = Dog("Buddy")
# Shared by all dogs.
assert fido.kind == "canine"
assert buddy.kind == "canine"
# Unique to fido.
assert fido.name == "Fido"
# Unique to buddy.
assert buddy.name == "Buddy"
# Shared data can have possibly surprising effects with involving mutable objects such as lists
# and dictionaries. For example, the tricks list in the following code should not be used as a
# class variable because just a single list would be shared by all Dog instances.
# pylint: disable=too-few-public-methods
class DogWithSharedTricks:
"""Dog class example with wrong shared variable usage"""
tricks = [] # Mistaken use of a class variable (see below) for mutable objects.
def __init__(self, name):
self.name = name # Instance variable unique to each instance.
def add_trick(self, trick):
"""Add trick to the dog
This function illustrate mistaken use of mutable class variable tricks (see below).
"""
self.tricks.append(trick)
fido = DogWithSharedTricks("Fido")
buddy = DogWithSharedTricks("Buddy")
fido.add_trick("roll over")
buddy.add_trick("play dead")
assert fido.tricks == ["roll over", "play dead"] # unexpectedly shared by all dogs
assert buddy.tricks == ["roll over", "play dead"] # unexpectedly shared by all dogs
# Correct design of the class should use an instance variable instead:
# pylint: disable=too-few-public-methods
class DogWithTricks:
"""Dog class example"""
def __init__(self, name):
self.name = name # Instance variable unique to each instance.
self.tricks = [] # creates a new empty list for each dog
def add_trick(self, trick):
"""Add trick to the dog
This function illustrate mistaken use of mutable class variable tricks (see below).
"""
self.tricks.append(trick)
fido = DogWithTricks("Fido")
buddy = DogWithTricks("Buddy")
fido.add_trick("roll over")
buddy.add_trick("play dead")
assert fido.tricks == ["roll over"]
assert buddy.tricks == ["play dead"]
|
[
"\"\"\"Class and Instance Variables.\n\n@see: https://docs.python.org/3/tutorial/classes.html#class-and-instance-variables\n\nGenerally speaking, instance variables are for data unique to each instance and class variables are\nfor attributes and methods shared by all instances of the class.\n\"\"\"\n\n\ndef test_class_and_instance_variables():\n \"\"\"Class and Instance Variables.\"\"\"\n\n # pylint: disable=too-few-public-methods\n class Dog:\n \"\"\"Dog class example\"\"\"\n\n kind = \"canine\" # Class variable shared by all instances.\n\n def __init__(self, name):\n self.name = name # Instance variable unique to each instance.\n\n fido = Dog(\"Fido\")\n buddy = Dog(\"Buddy\")\n\n # Shared by all dogs.\n assert fido.kind == \"canine\"\n assert buddy.kind == \"canine\"\n\n # Unique to fido.\n assert fido.name == \"Fido\"\n\n # Unique to buddy.\n assert buddy.name == \"Buddy\"\n\n # Shared data can have possibly surprising effects with involving mutable objects such as lists\n # and dictionaries. For example, the tricks list in the following code should not be used as a\n # class variable because just a single list would be shared by all Dog instances.\n\n # pylint: disable=too-few-public-methods\n class DogWithSharedTricks:\n \"\"\"Dog class example with wrong shared variable usage\"\"\"\n\n tricks = [] # Mistaken use of a class variable (see below) for mutable objects.\n\n def __init__(self, name):\n self.name = name # Instance variable unique to each instance.\n\n def add_trick(self, trick):\n \"\"\"Add trick to the dog\n\n This function illustrate mistaken use of mutable class variable tricks (see below).\n \"\"\"\n self.tricks.append(trick)\n\n fido = DogWithSharedTricks(\"Fido\")\n buddy = DogWithSharedTricks(\"Buddy\")\n\n fido.add_trick(\"roll over\")\n buddy.add_trick(\"play dead\")\n\n assert fido.tricks == [\"roll over\", \"play dead\"] # unexpectedly shared by all dogs\n assert buddy.tricks == [\"roll over\", \"play dead\"] # unexpectedly shared by all dogs\n\n # Correct design of the class should use an instance variable instead:\n\n # pylint: disable=too-few-public-methods\n class DogWithTricks:\n \"\"\"Dog class example\"\"\"\n\n def __init__(self, name):\n self.name = name # Instance variable unique to each instance.\n self.tricks = [] # creates a new empty list for each dog\n\n def add_trick(self, trick):\n \"\"\"Add trick to the dog\n\n This function illustrate mistaken use of mutable class variable tricks (see below).\n \"\"\"\n self.tricks.append(trick)\n\n fido = DogWithTricks(\"Fido\")\n buddy = DogWithTricks(\"Buddy\")\n\n fido.add_trick(\"roll over\")\n buddy.add_trick(\"play dead\")\n\n assert fido.tricks == [\"roll over\"]\n assert buddy.tricks == [\"play dead\"]\n",
"<docstring token>\n\n\ndef test_class_and_instance_variables():\n \"\"\"Class and Instance Variables.\"\"\"\n\n\n class Dog:\n \"\"\"Dog class example\"\"\"\n kind = 'canine'\n\n def __init__(self, name):\n self.name = name\n fido = Dog('Fido')\n buddy = Dog('Buddy')\n assert fido.kind == 'canine'\n assert buddy.kind == 'canine'\n assert fido.name == 'Fido'\n assert buddy.name == 'Buddy'\n\n\n class DogWithSharedTricks:\n \"\"\"Dog class example with wrong shared variable usage\"\"\"\n tricks = []\n\n def __init__(self, name):\n self.name = name\n\n def add_trick(self, trick):\n \"\"\"Add trick to the dog\n\n This function illustrate mistaken use of mutable class variable tricks (see below).\n \"\"\"\n self.tricks.append(trick)\n fido = DogWithSharedTricks('Fido')\n buddy = DogWithSharedTricks('Buddy')\n fido.add_trick('roll over')\n buddy.add_trick('play dead')\n assert fido.tricks == ['roll over', 'play dead']\n assert buddy.tricks == ['roll over', 'play dead']\n\n\n class DogWithTricks:\n \"\"\"Dog class example\"\"\"\n\n def __init__(self, name):\n self.name = name\n self.tricks = []\n\n def add_trick(self, trick):\n \"\"\"Add trick to the dog\n\n This function illustrate mistaken use of mutable class variable tricks (see below).\n \"\"\"\n self.tricks.append(trick)\n fido = DogWithTricks('Fido')\n buddy = DogWithTricks('Buddy')\n fido.add_trick('roll over')\n buddy.add_trick('play dead')\n assert fido.tricks == ['roll over']\n assert buddy.tricks == ['play dead']\n",
"<docstring token>\n<function token>\n"
] | false |
99,423 |
bc9e8ba60a8da444dc9fe43a595719fd477dd147
|
import numpy as np
import sqlite3 as sq
import sys
import lib_PB1SQLDB as libsq
class read_DB():
def __init__(self):
self.filename = 'tmp.db'
def read_BeamParams(self):
conn = sq.connect(self.filename)
c = conn.cursor()
c.execute('select * from BeamParams')
boloid=[]; boloname=[]; xpos=[]; ypos=[]; polang=[]; poleff=[]
sigma_x=[]; sigma_y=[]; amp=[]; beam_tilt=[]
for ar in c:
boloid.append(int(ar[0]))
boloname.append(str(ar[1]))
xpos.append(float(ar[2]))
ypos.append(float(ar[3]))
polang.append(float(ar[4]))
poleff.append(float(ar[5]))
sigma_x.append(float(ar[6]))
sigma_y.append(float(ar[7]))
amp.append(float(ar[8]))
beam_tilt.append(float(ar[9]))
c.close()
self.BeamParams = {'boloid':boloid,'boloname':boloname,'xpos':xpos,'ypos':ypos,
'polang':polang,'poleff':poleff,'sigma_x':sigma_x,'sigma_y':sigma_y,
'amp':amp,'beam_tilt':beam_tilt}
return self.BeamParams
read_db = libsq.read_DB()
read_db.filename = '/scratch/scratchdirs/tmatsumu/sim/PB1_NTP/DB/beamprm_20120530_031419_hwp112.5.db'
beam1 = read_db.read_BeamParams_selective([1,1,0,0,0,0,0,0,0,0])
read_db = libsq.read_DB()
read_db.filename = '/scratch/scratchdirs/tmatsumu/sim/PB1_NTP/DB/pb1_fpdb_ver0.db'
beam2 = read_db.read_BeamParams_selective([1,1,0,0,0,0,0,0,0,0])
num = len(beam2['boloid'])
for i in range(num):
ind = np.where(beam2['boloid'][i] == np.array(beam1['boloid']))
# print ind[0]
if ( beam1['boloname'][ind[0]] != beam2['boloname'][i] ):
print beam2['boloid'][i], beam1['boloname'][ind[0]], beam2['boloname'][i]
|
[
"import numpy as np\nimport sqlite3 as sq\nimport sys\nimport lib_PB1SQLDB as libsq\n\nclass read_DB():\n def __init__(self):\n self.filename = 'tmp.db'\n \n def read_BeamParams(self):\n conn = sq.connect(self.filename)\n c = conn.cursor()\n c.execute('select * from BeamParams')\n boloid=[]; boloname=[]; xpos=[]; ypos=[]; polang=[]; poleff=[]\n sigma_x=[]; sigma_y=[]; amp=[]; beam_tilt=[]\n for ar in c:\n boloid.append(int(ar[0]))\n boloname.append(str(ar[1]))\n xpos.append(float(ar[2]))\n ypos.append(float(ar[3]))\n polang.append(float(ar[4]))\n poleff.append(float(ar[5]))\n sigma_x.append(float(ar[6]))\n sigma_y.append(float(ar[7]))\n amp.append(float(ar[8]))\n beam_tilt.append(float(ar[9]))\n c.close()\n self.BeamParams = {'boloid':boloid,'boloname':boloname,'xpos':xpos,'ypos':ypos,\n 'polang':polang,'poleff':poleff,'sigma_x':sigma_x,'sigma_y':sigma_y,\n 'amp':amp,'beam_tilt':beam_tilt}\n return self.BeamParams\n\n\n\nread_db = libsq.read_DB()\nread_db.filename = '/scratch/scratchdirs/tmatsumu/sim/PB1_NTP/DB/beamprm_20120530_031419_hwp112.5.db'\nbeam1 = read_db.read_BeamParams_selective([1,1,0,0,0,0,0,0,0,0])\n\nread_db = libsq.read_DB()\nread_db.filename = '/scratch/scratchdirs/tmatsumu/sim/PB1_NTP/DB/pb1_fpdb_ver0.db'\nbeam2 = read_db.read_BeamParams_selective([1,1,0,0,0,0,0,0,0,0])\n\nnum = len(beam2['boloid'])\n\nfor i in range(num):\n ind = np.where(beam2['boloid'][i] == np.array(beam1['boloid']))\n# print ind[0]\n if ( beam1['boloname'][ind[0]] != beam2['boloname'][i] ):\n print beam2['boloid'][i], beam1['boloname'][ind[0]], beam2['boloname'][i]\n"
] | true |
99,424 |
f389a14f07407555d9dd995a6a5d9b7770989797
|
# -*- coding: utf-8 -*-
import numpy as np
from matplotlib import pyplot as plt
import proplot as pplt
from bayesian_inference import (
get_posterior, get_stats, plot_linear_dependency, plot_parameters,
)
from inputs import Inputs_
from catalog import catalog, Figure
del catalog[-1]
class Dependencies_(Inputs_):
def generate(self):
super().generate()
rms = self["Amplitude"]
distance = self["Distance"]
velocity = self["Vitesse"]
poids = self["Poids"]
STEPS = 24 # NOTE Reduce step size to make computations faster.
distance_dep = np.linspace(0.0, -.25, STEPS)
velocity_dep = np.linspace(0.0, 4.5, STEPS)
poids_dep = np.linspace(.0, 6E-6, STEPS)
rms_0 = np.linspace(-40, 120, STEPS)
rms_noise = np.logspace(1.3, 1.8, STEPS)
vars = [distance_dep, velocity_dep, poids_dep, rms_0, rms_noise]
posterior = get_posterior(
vars, [distance, velocity, poids, np.ones_like(rms)], rms,
)
print(posterior.sum())
_, _, vars_max, probs_mar, _, prob_null = get_stats(
posterior, vars, null_dims=[1, 2],
)
print("Against H0:", 1/prob_null)
print("Most probable model:", vars_max)
_, _, _, _, _, prob_velocity = get_stats(
posterior, vars, null_dims=[1],
)
print("Against H0 for velocity:", 1/prob_velocity)
_, _, _, _, _, prob_weight = get_stats(
posterior, vars, null_dims=[2],
)
print("Against H0 for weight:", 1/prob_weight)
self["vars"] = vars
self["posterior"] = posterior
self["vars_max"] = vars_max
self["probs_mar"] = probs_mar
class Dependencies(Figure):
Metadata = Dependencies_
def plot(self, data):
QTY_VARS = 5
_, axs = pplt.subplots(
[
[1, *[2]*QTY_VARS],
[3, *range(4, 4+QTY_VARS)],
],
ref=1,
wratios=(1, *[1/QTY_VARS]*QTY_VARS),
wspace=(None, *[.05]*(QTY_VARS-1)),
figsize=[7.66, 7.66],
sharey=False,
sharex=False,
)
rms = data["Amplitude"]
distance = data["Distance"]
velocity = data["Vitesse"]
weight = data["Poids"]
a1, a2, a3, b, std = data["vars_max"]
xs = [data["Distance"], data["Vitesse"], data["Poids"]]
ys = [
rms-a2*velocity-a3*weight,
rms-a1*distance-a3*weight,
rms-a1*distance-a2*velocity,
]
as_ = [a1, a2, a3]
xlabels = [
"Distance to railroad $d$ (m)",
"Train velocity $v$ (km/h)",
"Train weight $w$ (kg)",
]
ylabels = [
"Contribution of distance to RMS amplitude \n"
r"$y-\beta_v v-\beta_w w$ ($\frac{\mathrm{mm}}{\mathrm{s}}$)",
"Contribution of velocity to RMS amplitude \n"
r"$y-\beta_d d-\beta_w w$ ($\frac{\mathrm{mm}}{\mathrm{s}}$)",
"Contribution of weight to RMS amplitude \n"
r"$y-\beta_d d-\beta_v v$ ($\frac{\mathrm{mm}}{\mathrm{s}}$)",
]
for ax, x, y, a, xlabel, ylabel, in zip(
axs[:3], xs, ys, as_, xlabels, ylabels,
):
plt.sca(ax)
plot_linear_dependency(
x,
y,
a=a,
b=b,
std=std,
xlabel=xlabel,
ylabel=ylabel,
)
vars = data["vars"]
var_names = [
r"$\beta_d$", r"$\beta_v$", r"$\beta_w$", r"$y_0$",
r"$\sigma_\epsilon$",
]
probs_mar = data["probs_mar"]
plot_parameters(
vars,
var_names,
probs_mar,
axes=axs[3:],
units=[
r"\frac{\mathrm{mm}}{\mathrm{s} \cdot \mathrm{m}}",
r"\frac{\mathrm{mm} \cdot \mathrm{h}}"
r"{\mathrm{s} \cdot \mathrm{km}}",
r"\frac{\mathrm{mm}}{\mathrm{s} \cdot \mathrm{kg}}",
r"\frac{\mathrm{mm}}{\mathrm{s}}",
r"\frac{\mathrm{mm}}{\mathrm{s}}",
],
)
axs[3].format(
ylabel=(
"Normalized marginal probability "
"$\\frac{p(\\theta)}{p_{max}(\\theta)}$"
),
)
axs[3:].format(ylim=[0, 1], xmargin=.1)
for ax in axs[3:]:
ax.xaxis.label.set_fontsize(8)
axs[-5].format(xticks=[0, -.15])
axs[-4].format(xticks=[0, 3])
axs[-3].format(xticks=[0, 4E-6], xformatter='sci')
axs[-2].format(xticks=[0, 80])
axs[-1].format(xscale='log', xticks=[3E1, 5E1])
ticks = axs[2].get_xticks()
axs[2].set_xticks(ticks[1::2])
axs[2].format(xformatter='sci')
axs[:4].format(abc=True)
catalog.register(Dependencies)
|
[
"# -*- coding: utf-8 -*-\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport proplot as pplt\n\nfrom bayesian_inference import (\n get_posterior, get_stats, plot_linear_dependency, plot_parameters,\n)\nfrom inputs import Inputs_\nfrom catalog import catalog, Figure\n\ndel catalog[-1]\n\n\nclass Dependencies_(Inputs_):\n def generate(self):\n super().generate()\n\n rms = self[\"Amplitude\"]\n distance = self[\"Distance\"]\n velocity = self[\"Vitesse\"]\n poids = self[\"Poids\"]\n\n STEPS = 24 # NOTE Reduce step size to make computations faster.\n\n distance_dep = np.linspace(0.0, -.25, STEPS)\n velocity_dep = np.linspace(0.0, 4.5, STEPS)\n poids_dep = np.linspace(.0, 6E-6, STEPS)\n rms_0 = np.linspace(-40, 120, STEPS)\n rms_noise = np.logspace(1.3, 1.8, STEPS)\n vars = [distance_dep, velocity_dep, poids_dep, rms_0, rms_noise]\n\n posterior = get_posterior(\n vars, [distance, velocity, poids, np.ones_like(rms)], rms,\n )\n print(posterior.sum())\n _, _, vars_max, probs_mar, _, prob_null = get_stats(\n posterior, vars, null_dims=[1, 2],\n )\n print(\"Against H0:\", 1/prob_null)\n print(\"Most probable model:\", vars_max)\n _, _, _, _, _, prob_velocity = get_stats(\n posterior, vars, null_dims=[1],\n )\n print(\"Against H0 for velocity:\", 1/prob_velocity)\n _, _, _, _, _, prob_weight = get_stats(\n posterior, vars, null_dims=[2],\n )\n print(\"Against H0 for weight:\", 1/prob_weight)\n\n self[\"vars\"] = vars\n self[\"posterior\"] = posterior\n self[\"vars_max\"] = vars_max\n self[\"probs_mar\"] = probs_mar\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots(\n [\n [1, *[2]*QTY_VARS],\n [3, *range(4, 4+QTY_VARS)],\n ],\n ref=1,\n wratios=(1, *[1/QTY_VARS]*QTY_VARS),\n wspace=(None, *[.05]*(QTY_VARS-1)),\n figsize=[7.66, 7.66],\n sharey=False,\n sharex=False,\n )\n\n rms = data[\"Amplitude\"]\n distance = data[\"Distance\"]\n velocity = data[\"Vitesse\"]\n weight = data[\"Poids\"]\n a1, a2, a3, b, std = data[\"vars_max\"]\n\n xs = [data[\"Distance\"], data[\"Vitesse\"], data[\"Poids\"]]\n ys = [\n rms-a2*velocity-a3*weight,\n rms-a1*distance-a3*weight,\n rms-a1*distance-a2*velocity,\n ]\n as_ = [a1, a2, a3]\n xlabels = [\n \"Distance to railroad $d$ (m)\",\n \"Train velocity $v$ (km/h)\",\n \"Train weight $w$ (kg)\",\n ]\n ylabels = [\n \"Contribution of distance to RMS amplitude \\n\"\n r\"$y-\\beta_v v-\\beta_w w$ ($\\frac{\\mathrm{mm}}{\\mathrm{s}}$)\",\n \"Contribution of velocity to RMS amplitude \\n\"\n r\"$y-\\beta_d d-\\beta_w w$ ($\\frac{\\mathrm{mm}}{\\mathrm{s}}$)\",\n \"Contribution of weight to RMS amplitude \\n\"\n r\"$y-\\beta_d d-\\beta_v v$ ($\\frac{\\mathrm{mm}}{\\mathrm{s}}$)\",\n ]\n\n for ax, x, y, a, xlabel, ylabel, in zip(\n axs[:3], xs, ys, as_, xlabels, ylabels,\n ):\n plt.sca(ax)\n plot_linear_dependency(\n x,\n y,\n a=a,\n b=b,\n std=std,\n xlabel=xlabel,\n ylabel=ylabel,\n )\n\n vars = data[\"vars\"]\n var_names = [\n r\"$\\beta_d$\", r\"$\\beta_v$\", r\"$\\beta_w$\", r\"$y_0$\",\n r\"$\\sigma_\\epsilon$\",\n ]\n probs_mar = data[\"probs_mar\"]\n plot_parameters(\n vars,\n var_names,\n probs_mar,\n axes=axs[3:],\n units=[\n r\"\\frac{\\mathrm{mm}}{\\mathrm{s} \\cdot \\mathrm{m}}\",\n r\"\\frac{\\mathrm{mm} \\cdot \\mathrm{h}}\"\n r\"{\\mathrm{s} \\cdot \\mathrm{km}}\",\n r\"\\frac{\\mathrm{mm}}{\\mathrm{s} \\cdot \\mathrm{kg}}\",\n r\"\\frac{\\mathrm{mm}}{\\mathrm{s}}\",\n r\"\\frac{\\mathrm{mm}}{\\mathrm{s}}\",\n ],\n )\n axs[3].format(\n ylabel=(\n \"Normalized marginal probability \"\n \"$\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$\"\n ),\n )\n axs[3:].format(ylim=[0, 1], xmargin=.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n\n axs[-5].format(xticks=[0, -.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4E-6], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[3E1, 5E1])\n\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n\n axs[:4].format(abc=True)\n\n\ncatalog.register(Dependencies)\n",
"import numpy as np\nfrom matplotlib import pyplot as plt\nimport proplot as pplt\nfrom bayesian_inference import get_posterior, get_stats, plot_linear_dependency, plot_parameters\nfrom inputs import Inputs_\nfrom catalog import catalog, Figure\ndel catalog[-1]\n\n\nclass Dependencies_(Inputs_):\n\n def generate(self):\n super().generate()\n rms = self['Amplitude']\n distance = self['Distance']\n velocity = self['Vitesse']\n poids = self['Poids']\n STEPS = 24\n distance_dep = np.linspace(0.0, -0.25, STEPS)\n velocity_dep = np.linspace(0.0, 4.5, STEPS)\n poids_dep = np.linspace(0.0, 6e-06, STEPS)\n rms_0 = np.linspace(-40, 120, STEPS)\n rms_noise = np.logspace(1.3, 1.8, STEPS)\n vars = [distance_dep, velocity_dep, poids_dep, rms_0, rms_noise]\n posterior = get_posterior(vars, [distance, velocity, poids, np.\n ones_like(rms)], rms)\n print(posterior.sum())\n _, _, vars_max, probs_mar, _, prob_null = get_stats(posterior, vars,\n null_dims=[1, 2])\n print('Against H0:', 1 / prob_null)\n print('Most probable model:', vars_max)\n _, _, _, _, _, prob_velocity = get_stats(posterior, vars, null_dims=[1]\n )\n print('Against H0 for velocity:', 1 / prob_velocity)\n _, _, _, _, _, prob_weight = get_stats(posterior, vars, null_dims=[2])\n print('Against H0 for weight:', 1 / prob_weight)\n self['vars'] = vars\n self['posterior'] = posterior\n self['vars_max'] = vars_max\n self['probs_mar'] = probs_mar\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\ncatalog.register(Dependencies)\n",
"<import token>\ndel catalog[-1]\n\n\nclass Dependencies_(Inputs_):\n\n def generate(self):\n super().generate()\n rms = self['Amplitude']\n distance = self['Distance']\n velocity = self['Vitesse']\n poids = self['Poids']\n STEPS = 24\n distance_dep = np.linspace(0.0, -0.25, STEPS)\n velocity_dep = np.linspace(0.0, 4.5, STEPS)\n poids_dep = np.linspace(0.0, 6e-06, STEPS)\n rms_0 = np.linspace(-40, 120, STEPS)\n rms_noise = np.logspace(1.3, 1.8, STEPS)\n vars = [distance_dep, velocity_dep, poids_dep, rms_0, rms_noise]\n posterior = get_posterior(vars, [distance, velocity, poids, np.\n ones_like(rms)], rms)\n print(posterior.sum())\n _, _, vars_max, probs_mar, _, prob_null = get_stats(posterior, vars,\n null_dims=[1, 2])\n print('Against H0:', 1 / prob_null)\n print('Most probable model:', vars_max)\n _, _, _, _, _, prob_velocity = get_stats(posterior, vars, null_dims=[1]\n )\n print('Against H0 for velocity:', 1 / prob_velocity)\n _, _, _, _, _, prob_weight = get_stats(posterior, vars, null_dims=[2])\n print('Against H0 for weight:', 1 / prob_weight)\n self['vars'] = vars\n self['posterior'] = posterior\n self['vars_max'] = vars_max\n self['probs_mar'] = probs_mar\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\ncatalog.register(Dependencies)\n",
"<import token>\n<code token>\n\n\nclass Dependencies_(Inputs_):\n\n def generate(self):\n super().generate()\n rms = self['Amplitude']\n distance = self['Distance']\n velocity = self['Vitesse']\n poids = self['Poids']\n STEPS = 24\n distance_dep = np.linspace(0.0, -0.25, STEPS)\n velocity_dep = np.linspace(0.0, 4.5, STEPS)\n poids_dep = np.linspace(0.0, 6e-06, STEPS)\n rms_0 = np.linspace(-40, 120, STEPS)\n rms_noise = np.logspace(1.3, 1.8, STEPS)\n vars = [distance_dep, velocity_dep, poids_dep, rms_0, rms_noise]\n posterior = get_posterior(vars, [distance, velocity, poids, np.\n ones_like(rms)], rms)\n print(posterior.sum())\n _, _, vars_max, probs_mar, _, prob_null = get_stats(posterior, vars,\n null_dims=[1, 2])\n print('Against H0:', 1 / prob_null)\n print('Most probable model:', vars_max)\n _, _, _, _, _, prob_velocity = get_stats(posterior, vars, null_dims=[1]\n )\n print('Against H0 for velocity:', 1 / prob_velocity)\n _, _, _, _, _, prob_weight = get_stats(posterior, vars, null_dims=[2])\n print('Against H0 for weight:', 1 / prob_weight)\n self['vars'] = vars\n self['posterior'] = posterior\n self['vars_max'] = vars_max\n self['probs_mar'] = probs_mar\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\n<code token>\n",
"<import token>\n<code token>\n\n\nclass Dependencies_(Inputs_):\n <function token>\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\n<code token>\n",
"<import token>\n<code token>\n<class token>\n\n\nclass Dependencies(Figure):\n Metadata = Dependencies_\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\n<code token>\n",
"<import token>\n<code token>\n<class token>\n\n\nclass Dependencies(Figure):\n <assignment token>\n\n def plot(self, data):\n QTY_VARS = 5\n _, axs = pplt.subplots([[1, *([2] * QTY_VARS)], [3, *range(4, 4 +\n QTY_VARS)]], ref=1, wratios=(1, *([1 / QTY_VARS] * QTY_VARS)),\n wspace=(None, *([0.05] * (QTY_VARS - 1))), figsize=[7.66, 7.66],\n sharey=False, sharex=False)\n rms = data['Amplitude']\n distance = data['Distance']\n velocity = data['Vitesse']\n weight = data['Poids']\n a1, a2, a3, b, std = data['vars_max']\n xs = [data['Distance'], data['Vitesse'], data['Poids']]\n ys = [rms - a2 * velocity - a3 * weight, rms - a1 * distance - a3 *\n weight, rms - a1 * distance - a2 * velocity]\n as_ = [a1, a2, a3]\n xlabels = ['Distance to railroad $d$ (m)',\n 'Train velocity $v$ (km/h)', 'Train weight $w$ (kg)']\n ylabels = [\n \"\"\"Contribution of distance to RMS amplitude \n$y-\\\\beta_v v-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of velocity to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_w w$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ,\n \"\"\"Contribution of weight to RMS amplitude \n$y-\\\\beta_d d-\\\\beta_v v$ ($\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}$)\"\"\"\n ]\n for ax, x, y, a, xlabel, ylabel in zip(axs[:3], xs, ys, as_,\n xlabels, ylabels):\n plt.sca(ax)\n plot_linear_dependency(x, y, a=a, b=b, std=std, xlabel=xlabel,\n ylabel=ylabel)\n vars = data['vars']\n var_names = ['$\\\\beta_d$', '$\\\\beta_v$', '$\\\\beta_w$', '$y_0$',\n '$\\\\sigma_\\\\epsilon$']\n probs_mar = data['probs_mar']\n plot_parameters(vars, var_names, probs_mar, axes=axs[3:], units=[\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{m}}',\n '\\\\frac{\\\\mathrm{mm} \\\\cdot \\\\mathrm{h}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{km}}'\n , '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s} \\\\cdot \\\\mathrm{kg}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}',\n '\\\\frac{\\\\mathrm{mm}}{\\\\mathrm{s}}'])\n axs[3].format(ylabel=\n 'Normalized marginal probability $\\\\frac{p(\\\\theta)}{p_{max}(\\\\theta)}$'\n )\n axs[3:].format(ylim=[0, 1], xmargin=0.1)\n for ax in axs[3:]:\n ax.xaxis.label.set_fontsize(8)\n axs[-5].format(xticks=[0, -0.15])\n axs[-4].format(xticks=[0, 3])\n axs[-3].format(xticks=[0, 4e-06], xformatter='sci')\n axs[-2].format(xticks=[0, 80])\n axs[-1].format(xscale='log', xticks=[30.0, 50.0])\n ticks = axs[2].get_xticks()\n axs[2].set_xticks(ticks[1::2])\n axs[2].format(xformatter='sci')\n axs[:4].format(abc=True)\n\n\n<code token>\n",
"<import token>\n<code token>\n<class token>\n\n\nclass Dependencies(Figure):\n <assignment token>\n <function token>\n\n\n<code token>\n",
"<import token>\n<code token>\n<class token>\n<class token>\n<code token>\n"
] | false |
99,425 |
69dffc2ba8b2b81b27bf130ca79e8022ef2d248d
|
#!/usr/bin/env python
from threading import Thread
import rospy
import math
from robotis_mini_control.robotis_mini import RobotisMiniControlInterface
from geometry_msgs.msg import Twist
import time
from std_msgs.msg import Empty, Float64, Float64MultiArray
import argparse
import std_srvs.srv
import sys
class SinusoidFunction:
"""
SinusoidFunction for single joints CPG style
Provides a parameterized sine wave function as y=amplitude_offset+amplitude*(phase_offset+angular_frequency*x)
"""
def __init__(self):
self.amplitude_offset=0
self.amplitude=1
self.phase_offset=0
self.angular_frequency=1
def get(self, x):
""" x between 0 and 1"""
f = math.sin(self.phase_offset + self.angular_frequency*x)
return self.amplitude_offset + self.amplitude*f
def clone(self):
z=SinusoidFunction()
z.amplitude_offset=self.amplitude_offset
z.amplitude=self.amplitude
z.phase_offset=self.phase_offset
z.angular_frequency=self.angular_frequency
return z
def mirror(self):
z=self.clone()
z.amplitude_offset *= -1
z.amplitude *= -1
return z
def mirror_keep_amplitude_offset(self):
z=self.clone()
z.amplitude *= -1
return z
def mirror_freq(self):
z=self.clone()
z.phase_offset *= -1
z.angular_frequency *= -1
return z
def __str__(self):
return "y=%.2f+%.2f*sin(%.2f+%.2f*x)"%(self.amplitude_offset, self.amplitude, self.phase_offset, self.angular_frequency)
class WholeBodyWalkerFunction:
"""
Multi-joint walk function for RobotisMini
Creates SinusoidFunction for each joint with different parameters
"""
def __init__(self, walking_params):
self.parameters={}
print walking_params
for pn, pp in walking_params.iteritems():
self.parameters[pn + '_amplitude']=pp[0]
self.parameters[pn + '_amplitude_offset']=pp[1]
self.parameters[pn + '_phase_offset']=pp[2]
self.parameters["step_frequency"]=math.pi
self.parameters["vx_amplitude"]=0.5
self.parameters["vy_amplitude"]=0.5
self.parameters["vt_amplitude"]=0.4
self.generate()
def generate(self):
"""
Build CPG functions for walk-on-spot (no translation or rotation, only legs up/down)
"""
self.pfn={} # phase joint functions
self.afn={} # anti phase joint functions
## Foot and hip -> Lateral motion
foot_func=SinusoidFunction()
foot_func.angular_frequency= self.parameters["step_frequency"]
foot_func.amplitude= self.parameters["foot_amplitude"]
foot_func.amplitude_offset= self.parameters["foot_amplitude_offset"]
foot_func.phase_offset= self.parameters["foot_phase_offset"]
self.pfn["l_foot_joint"]=foot_func
foot_func_af=foot_func.mirror()
self.afn["l_foot_joint"]=foot_func_af
hip_func=SinusoidFunction()
hip_func.angular_frequency= self.parameters["step_frequency"]
hip_func.amplitude= self.parameters["hip_amplitude"]
hip_func.amplitude_offset= self.parameters["hip_amplitude_offset"]
hip_func.phase_offset= self.parameters["hip_phase_offset"]
self.pfn["l_hip_joint"]=hip_func
hip_func_af=hip_func.mirror()
self.afn["l_hip_joint"]=hip_func_af
## Thigh, ankle and knee -> Frontal motion
thigh_func=SinusoidFunction()
thigh_func.angular_frequency= self.parameters["step_frequency"]
thigh_func.amplitude= self.parameters["thigh_amplitude"]
thigh_func.amplitude_offset= self.parameters["thigh_amplitude_offset"]
thigh_func.phase_offset= self.parameters["thigh_phase_offset"]
self.pfn["l_thigh_joint"]=thigh_func
thigh_func_af=thigh_func.mirror_keep_amplitude_offset()
self.afn["l_thigh_joint"]=thigh_func_af
ankle_func=SinusoidFunction()
ankle_func.angular_frequency= self.parameters["step_frequency"]
ankle_func.amplitude= self.parameters["ankle_amplitude"]
ankle_func.amplitude_offset= self.parameters["ankle_amplitude_offset"]
ankle_func.phase_offset= self.parameters["ankle_phase_offset"]
self.pfn["l_ankle_joint"]=ankle_func
ankle_func_af=ankle_func.mirror_keep_amplitude_offset()
self.afn["l_ankle_joint"]=ankle_func_af
knee_func=SinusoidFunction()
knee_func.angular_frequency= self.parameters["step_frequency"]
knee_func.amplitude= self.parameters["knee_amplitude"]
knee_func.amplitude_offset= self.parameters["knee_amplitude_offset"]
knee_func.phase_offset= self.parameters["knee_phase_offset"]
self.pfn["l_knee_joint"]=knee_func
knee_func_af=knee_func.mirror_keep_amplitude_offset()
self.afn["l_knee_joint"]=knee_func_af
#f3=SinusoidFunction()
#f3.angular_frequency=self.parameters["step_frequency"]
#f3.amplitude=self.parameters["step_amplitude"]
#f3.amplitude_offset=self.parameters["step_amplitude_offset"]
#self.pfn["l_thigh_joint"]= f3
#f33=f3.clone()
#f33.amplitude_offset = self.parameters["ankle_amplitude_offset"]
#f33.amplitude = self.parameters["ankle_amplitude"]
#self.pfn["l_ankle_joint"]=f33
#f4=f3.mirror()
##f4.amplitude_offset -= 0.4
#self.pfn["l_knee_joint"]=f4
#f5=f3.mirror_keep_amplitude_offset()
#self.afn["l_thigh_joint"]=f5
#f6=f33.mirror_keep_amplitude_offset()
#self.afn["l_ankle_joint"]=f6
#f7=f5.mirror()
##f7.amplitude_offset -= 0.4
#self.afn["l_knee_joint"]=f7
self.generate_right()
self.show()
def generate_right(self):
"""
Mirror CPG functions from left to right and antiphase right
"""
l=[ v[2:] for v in self.pfn.keys()]
for j in l:
self.pfn["r_"+j]=self.afn["l_"+j].mirror_keep_amplitude_offset()
self.pfn["r_"+j].phase_offset += math.pi
self.afn["r_"+j]=self.pfn["l_"+j].mirror_keep_amplitude_offset()
self.afn["r_"+j].phase_offset += math.pi
def get(self, phase, x, velocity):
""" Obtain the joint angles for a given phase, position in cycle (x 0,1)) and velocity parameters """
angles={}
for j in self.pfn.keys():
if phase:
v=self.pfn[j].get(x)
angles[j]=v
else:
angles[j]=self.afn[j].get(x)
self.apply_velocity(angles,velocity,phase,x)
return angles
def show(self):
"""
Display the CPG functions used
"""
for j in self.pfn.keys():
print j,"p",self.pfn[j],"a",self.afn[j]
print self.pfn["l_knee_joint"].amplitude_offset
def apply_velocity(self, angles, velocity, phase, x):
""" Modify on the walk-on-spot joint angles to apply the velocity vector"""
# VX
v=velocity[0]*self.parameters["vx_amplitude"]
d=(x*2-1)*v
if phase:
angles["l_thigh_joint"]+=d
angles["l_ankle_joint"]+=d
angles["r_thigh_joint"]+=d
angles["r_ankle_joint"]+=d
else:
angles["l_thigh_joint"]-=d
angles["l_ankle_joint"]-=d
angles["r_thigh_joint"]-=d
angles["r_ankle_joint"]-=d
# VY
v=velocity[1]*self.parameters["vy_amplitude"]
d=(x)*v
d2=(1-x)*v
if v>=0:
if phase:
angles["l_hip_joint"]-=d
angles["l_foot_joint"]-=d
angles["r_hip_joint"]+=d
angles["r_foot_joint"]+=d
else:
angles["l_hip_joint"]-=d2
angles["l_foot_joint"]-=d2
angles["r_hip_joint"]+=d2
angles["r_foot_joint"]+=d2
else:
if phase:
angles["l_hip_joint"]+=d2
angles["l_foot_joint"]+=d2
angles["r_hip_joint"]-=d2
angles["r_foot_joint"]-=d2
else:
angles["l_hip_joint"]+=d
angles["l_foot_joint"]+=d
angles["r_hip_joint"]-=d
angles["r_foot_joint"]-=d
## VT
#v=velocity[2]*self.parameters["vt_amplitude"]
#d=(x)*v
#d2=(1-x)*v
#if v>=0:
#if phase:
#angles["j_pelvis_l"]=-d
#angles["j_pelvis_r"]=d
#else:
#angles["j_pelvis_l"]=-d2
#angles["j_pelvis_r"]=d2
#else:
#if phase:
#angles["j_pelvis_l"]=d2
#angles["j_pelvis_r"]=-d2
#else:
#angles["j_pelvis_l"]=d
#angles["j_pelvis_r"]=-d
class Walker:
"""
Class for making RobotisMini walk
"""
def __init__(self, robotis_mini_ci, real_robot):
self.robotis_mini_ci=robotis_mini_ci
self.real_robot = real_robot
self.displacing=False #When the robot is walking AND displacing in the plane (velocity is non-zero)
self.walking=False #When the robot is walking: moving up and down the legs
self.velocity=[0,0,0]
#Default walking params
self.walking_params = {}
self.walking_params['foot'] = [0.4,0,0]
self.walking_params['ankle'] = [-0.01,-0.20,0]
self.walking_params['knee'] = [0.4,0.7,0]
self.walking_params['thigh'] = [-0.4,-0.7,0]
self.walking_params['hip'] = [0.4,0,0]
self.wb_walkerfunc=WholeBodyWalkerFunction(self.walking_params)
self.initial_wq = self.wb_walkerfunc.get(True, 0, [0,0,0]) #First joint configuration to start the walking motion
print "__init__:initial_wq"
j_names=self.initial_wq.keys()
for jn in j_names:
print jn + str(":") + str(self.initial_wq[jn])
self._th_walk=None #Walking thread
self._cycle_period = 10 #seconds
self._sub_cmd_vel=rospy.Subscriber(robotis_mini_ci.ns+"cmd_vel", Twist,self._cb_cmd_vel, queue_size=1)
self._sub_cmd_stop=rospy.Subscriber(robotis_mini_ci.ns+"stop_srv", Empty,self._cb_cmd_stop, queue_size=1)
self._sub_cmd_restart=rospy.Subscriber(robotis_mini_ci.ns+"restart_srv", Empty,self._cb_cmd_restart, queue_size=1)
self._sub_cmd_restart=rospy.Subscriber(robotis_mini_ci.ns+"walking_params", Float64MultiArray,self._cb_new_walking_params, queue_size=1)
if not self.real_robot:
self.pubs = {}
# Wait until the joints have been populated
while self.robotis_mini_ci.q_names is None:
time.sleep(1)
for jn in self.robotis_mini_ci.q_names:
self.pubs[jn] = rospy.Publisher('/robotis_mini/' + jn + '_position_controller/command', Float64, queue_size=1)
rospy.loginfo("Waiting for gazebo services")
rospy.wait_for_service('/gazebo/pause_physics')
self.pause_simulation_srv = rospy.ServiceProxy('/gazebo/pause_physics', std_srvs.srv.Empty)
rospy.wait_for_service('/gazebo/reset_world')
self.reset_world_srv = rospy.ServiceProxy('/gazebo/reset_world', std_srvs.srv.Empty)
rospy.wait_for_service('/gazebo/unpause_physics')
self.unpause_simulation_srv = rospy.ServiceProxy('/gazebo/unpause_physics', std_srvs.srv.Empty)
def _cb_new_walking_params(self,msg):
"""
Processes a new set of parameters
"""
print "Walker new set of parameters received"
self._cycle_period = msg.data[0]
self.walking_params['foot'] = [msg.data[1],msg.data[2],msg.data[3]]
self.walking_params['ankle'] = [msg.data[4],msg.data[5],msg.data[6]]
self.walking_params['knee'] = [msg.data[7],msg.data[8],msg.data[9]]
self.walking_params['thigh'] = [msg.data[10],msg.data[11],msg.data[12]]
self.walking_params['hip'] = [msg.data[13],msg.data[14],msg.data[15]]
self.wb_walkerfunc=WholeBodyWalkerFunction(self.walking_params)
self.initial_wq = self.wb_walkerfunc.get(True, 0, [0,0,0]) #First joint configuration to start the walking motion
print "initial_wq"
j_names=self.initial_wq.keys()
for jn in j_names:
print jn + str(":") + str(self.initial_wq[jn])
def _cb_cmd_restart(self,msg):
"""
Processes cmd_restart and to start a new trial
"""
print "Walker restart command received"
#Stop the running thread
while self.displacing or self.walking or self._th_walk:
rospy.loginfo('Stopping walking thread')
self.stop()
#If the robot is simuated -> send to initial configuration
if not self.real_robot:
rospy.loginfo("Sending robot to zero configuration")
for jn in self.robotis_mini_ci.q_names:
self.pubs[jn].publish(0.0)
time.sleep(1)
#If the robot is simulated -> reset simulation
try:
self.pause_simulation_srv()
rospy.loginfo( "Paused gazebo")
time.sleep(1)
self.reset_world_srv()
rospy.loginfo( "Reseting gazebo")
time.sleep(1)
self.unpause_simulation_srv()
rospy.loginfo( "Unpaused gazebo")
time.sleep(1)
except rospy.ServiceException, e:
print "Service call failed: %s"%e
def _cb_cmd_vel(self,msg):
"""
Processes cmd_vel and update walker speed
"""
print "Walker velocity command received: ",msg
vx=msg.linear.x
vy=msg.linear.y
vt=msg.angular.z
self.start()
self.set_desired_velocity(vx,vy,vt)
def _cb_cmd_stop(self,msg):
"""
Processes cmd_stop
"""
print "Walker stop command received: "
self.stop()
def goto_initial_wq(self):
"""
If not there yet, go to initial walking configuration
"""
rospy.loginfo("Going to initial walking configuration")
while self.get_qdist_to_initial_wq()>0.1:
rospy.loginfo("Commanding to go to initial walking configuration")
print "Initial configuration"
print self.initial_wq
self.robotis_mini_ci.set_qd_interpolated(self.initial_wq, 2)
rospy.sleep(2)
rospy.loginfo("Initial walking configuration reached")
print "Distance",self.get_qdist_to_initial_wq()
def start(self):
if not self.displacing:
self.displacing=True
self.goto_initial_wq()
self._th_walk=Thread(target=self._do_walk)
self._th_walk.start()
self.walking=True
def stop(self):
if self.displacing:
self.walking=False
rospy.loginfo("Waiting for stopped")
while not rospy.is_shutdown() and self._th_walk is not None:
rospy.sleep(0.1)
rospy.loginfo("Stopped")
self.displacing=False
def set_desired_velocity(self,x,y,t):
self.desired_velocity=[x,y,t]
def _do_walk(self):
"""
Main walking loop, smoothly update velocity vectors and apply corresponding joint configurations
"""
rospy.loginfo("Started walking thread")
wb_walkerfunc=self.wb_walkerfunc
# Global walk loop
n=50
print "Thread rate", 1.0/(self._cycle_period/(2.0*n))
r=rospy.Rate(1.0/(self._cycle_period/(2.0*n)))
p=True
i=0
self.current_velocity=[0,0,0]
while not rospy.is_shutdown() and (self.walking or i<n or self.is_displacing()):
if not self.walking:
self.desired_velocity=[0,0,0]
#if not self.is_displacing() and i==0: # Do not move if nothing to do and already at 0
# self.update_current_velocity(self.desired_velocity, n)
# r.sleep()
# continue
x=float(i)/n
qd_curr=wb_walkerfunc.get(p, x, self.current_velocity)
self.update_current_velocity(self.desired_velocity, n)
self.robotis_mini_ci.set_qd(qd_curr)
i+=1
if i>n:
i=0
p=not p
r.sleep()
rospy.loginfo("Finished walking thread")
self._th_walk=None
def is_displacing(self):
"""
Checks if the current velocity is not zero and returns True in that case
"""
e=0.02
for v in self.current_velocity:
if abs(v)>e: return True
return False
def update_current_velocity(self, target_velocity, n):
"""
A pseudo-interpolation to a target velocity
"""
a=3/float(n)
b=1-a
self.current_velocity=[a*tv+b*cv for (tv,cv) in zip(target_velocity, self.current_velocity)]
def get_qdist_to_initial_wq(self):
"""
Computes the absolute distance between the current robot joint state and the initial walking configuration
"""
current_q=self.robotis_mini_ci.get_q()
return get_distance(self.initial_wq, current_q)
def get_distance(qa_dict, qb_dict):
"""
Computes sum of absolute distances between two sets of joints represented as dictionaries of (jointName, jointConfiguration)
"""
d=0
j_names=qa_dict.keys()
if len(j_names)==0:
rospy.loginfo("Length is 0")
return 0
for jn in j_names:
d+=abs(qb_dict[jn]-qa_dict[jn])
d/=len(j_names)
return d
if __name__=="__main__":
rospy.init_node("walker")
parser = argparse.ArgumentParser(description='Walker trajectory generator')
parser.add_argument('--real',action='store_true', help='define when using the real robot')
options, args = parser.parse_known_args()
if options.real:
rospy.loginfo("Real Robot!")
else:
rospy.loginfo("Simulated Robot!")
rospy.loginfo("Instantiating RobotisMini RobotisMiniControlInterface")
robotis_mini_ci=RobotisMiniControlInterface(real_robot=options.real)
rospy.loginfo("Instantiating RobotisMini Walker")
walker=Walker(robotis_mini_ci, options.real)
rospy.loginfo("RobotisMini Walker Ready")
while not rospy.is_shutdown():
time.sleep(1)
|
[
"#!/usr/bin/env python\r\n\r\nfrom threading import Thread\r\nimport rospy\r\nimport math\r\nfrom robotis_mini_control.robotis_mini import RobotisMiniControlInterface\r\nfrom geometry_msgs.msg import Twist\r\nimport time\r\nfrom std_msgs.msg import Empty, Float64, Float64MultiArray\r\nimport argparse\r\nimport std_srvs.srv\r\nimport sys\r\n\r\n\r\nclass SinusoidFunction:\r\n \"\"\"\r\n SinusoidFunction for single joints CPG style\r\n Provides a parameterized sine wave function as y=amplitude_offset+amplitude*(phase_offset+angular_frequency*x)\r\n \"\"\"\r\n def __init__(self):\r\n self.amplitude_offset=0\r\n self.amplitude=1\r\n self.phase_offset=0\r\n self.angular_frequency=1\r\n \r\n def get(self, x):\r\n \"\"\" x between 0 and 1\"\"\"\r\n f = math.sin(self.phase_offset + self.angular_frequency*x)\r\n return self.amplitude_offset + self.amplitude*f \r\n \r\n def clone(self):\r\n z=SinusoidFunction()\r\n z.amplitude_offset=self.amplitude_offset\r\n z.amplitude=self.amplitude\r\n z.phase_offset=self.phase_offset\r\n z.angular_frequency=self.angular_frequency\r\n return z\r\n \r\n def mirror(self):\r\n z=self.clone()\r\n z.amplitude_offset *= -1\r\n z.amplitude *= -1\r\n return z\r\n \r\n def mirror_keep_amplitude_offset(self):\r\n z=self.clone()\r\n z.amplitude *= -1\r\n return z\r\n \r\n def mirror_freq(self):\r\n z=self.clone()\r\n z.phase_offset *= -1\r\n z.angular_frequency *= -1\r\n return z\r\n \r\n def __str__(self):\r\n return \"y=%.2f+%.2f*sin(%.2f+%.2f*x)\"%(self.amplitude_offset, self.amplitude, self.phase_offset, self.angular_frequency)\r\n \r\nclass WholeBodyWalkerFunction:\r\n \"\"\"\r\n Multi-joint walk function for RobotisMini \r\n Creates SinusoidFunction for each joint with different parameters\r\n \"\"\"\r\n def __init__(self, walking_params):\r\n self.parameters={}\r\n \r\n print walking_params\r\n for pn, pp in walking_params.iteritems():\r\n self.parameters[pn + '_amplitude']=pp[0]\r\n self.parameters[pn + '_amplitude_offset']=pp[1]\r\n self.parameters[pn + '_phase_offset']=pp[2]\r\n\r\n self.parameters[\"step_frequency\"]=math.pi\r\n \r\n self.parameters[\"vx_amplitude\"]=0.5\r\n self.parameters[\"vy_amplitude\"]=0.5\r\n self.parameters[\"vt_amplitude\"]=0.4\r\n \r\n self.generate()\r\n \r\n def generate(self):\r\n \"\"\"\r\n Build CPG functions for walk-on-spot (no translation or rotation, only legs up/down)\r\n \"\"\" \r\n \r\n self.pfn={} # phase joint functions \r\n self.afn={} # anti phase joint functions\r\n\r\n ## Foot and hip -> Lateral motion\r\n foot_func=SinusoidFunction()\r\n foot_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n foot_func.amplitude= self.parameters[\"foot_amplitude\"]\r\n foot_func.amplitude_offset= self.parameters[\"foot_amplitude_offset\"]\r\n foot_func.phase_offset= self.parameters[\"foot_phase_offset\"]\r\n self.pfn[\"l_foot_joint\"]=foot_func \r\n foot_func_af=foot_func.mirror()\r\n self.afn[\"l_foot_joint\"]=foot_func_af\r\n \r\n hip_func=SinusoidFunction()\r\n hip_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n hip_func.amplitude= self.parameters[\"hip_amplitude\"]\r\n hip_func.amplitude_offset= self.parameters[\"hip_amplitude_offset\"]\r\n hip_func.phase_offset= self.parameters[\"hip_phase_offset\"]\r\n self.pfn[\"l_hip_joint\"]=hip_func\r\n hip_func_af=hip_func.mirror()\r\n self.afn[\"l_hip_joint\"]=hip_func_af\r\n \r\n ## Thigh, ankle and knee -> Frontal motion\r\n thigh_func=SinusoidFunction()\r\n thigh_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n thigh_func.amplitude= self.parameters[\"thigh_amplitude\"]\r\n thigh_func.amplitude_offset= self.parameters[\"thigh_amplitude_offset\"]\r\n thigh_func.phase_offset= self.parameters[\"thigh_phase_offset\"]\r\n self.pfn[\"l_thigh_joint\"]=thigh_func\r\n thigh_func_af=thigh_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_thigh_joint\"]=thigh_func_af\r\n \r\n ankle_func=SinusoidFunction()\r\n ankle_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n ankle_func.amplitude= self.parameters[\"ankle_amplitude\"]\r\n ankle_func.amplitude_offset= self.parameters[\"ankle_amplitude_offset\"]\r\n ankle_func.phase_offset= self.parameters[\"ankle_phase_offset\"]\r\n self.pfn[\"l_ankle_joint\"]=ankle_func\r\n ankle_func_af=ankle_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_ankle_joint\"]=ankle_func_af\r\n \r\n knee_func=SinusoidFunction()\r\n knee_func.angular_frequency= self.parameters[\"step_frequency\"]\r\n knee_func.amplitude= self.parameters[\"knee_amplitude\"]\r\n knee_func.amplitude_offset= self.parameters[\"knee_amplitude_offset\"]\r\n knee_func.phase_offset= self.parameters[\"knee_phase_offset\"]\r\n self.pfn[\"l_knee_joint\"]=knee_func\r\n knee_func_af=knee_func.mirror_keep_amplitude_offset()\r\n self.afn[\"l_knee_joint\"]=knee_func_af\r\n \r\n #f3=SinusoidFunction()\r\n #f3.angular_frequency=self.parameters[\"step_frequency\"]\r\n #f3.amplitude=self.parameters[\"step_amplitude\"]\r\n #f3.amplitude_offset=self.parameters[\"step_amplitude_offset\"]\r\n #self.pfn[\"l_thigh_joint\"]= f3\r\n #f33=f3.clone()\r\n #f33.amplitude_offset = self.parameters[\"ankle_amplitude_offset\"]\r\n #f33.amplitude = self.parameters[\"ankle_amplitude\"]\r\n #self.pfn[\"l_ankle_joint\"]=f33\r\n #f4=f3.mirror()\r\n ##f4.amplitude_offset -= 0.4\r\n #self.pfn[\"l_knee_joint\"]=f4\r\n \r\n #f5=f3.mirror_keep_amplitude_offset()\r\n #self.afn[\"l_thigh_joint\"]=f5\r\n \r\n #f6=f33.mirror_keep_amplitude_offset()\r\n #self.afn[\"l_ankle_joint\"]=f6\r\n #f7=f5.mirror()\r\n ##f7.amplitude_offset -= 0.4\r\n #self.afn[\"l_knee_joint\"]=f7\r\n \r\n self.generate_right()\r\n \r\n self.show()\r\n \r\n def generate_right(self):\r\n \"\"\"\r\n Mirror CPG functions from left to right and antiphase right\r\n \"\"\"\r\n l=[ v[2:] for v in self.pfn.keys()]\r\n for j in l:\r\n self.pfn[\"r_\"+j]=self.afn[\"l_\"+j].mirror_keep_amplitude_offset()\r\n self.pfn[\"r_\"+j].phase_offset += math.pi\r\n self.afn[\"r_\"+j]=self.pfn[\"l_\"+j].mirror_keep_amplitude_offset()\r\n self.afn[\"r_\"+j].phase_offset += math.pi\r\n \r\n def get(self, phase, x, velocity):\r\n \"\"\" Obtain the joint angles for a given phase, position in cycle (x 0,1)) and velocity parameters \"\"\"\r\n angles={}\r\n for j in self.pfn.keys():\r\n if phase:\r\n v=self.pfn[j].get(x)\r\n angles[j]=v\r\n else:\r\n angles[j]=self.afn[j].get(x)\r\n self.apply_velocity(angles,velocity,phase,x)\r\n return angles\r\n \r\n def show(self):\r\n \"\"\"\r\n Display the CPG functions used\r\n \"\"\"\r\n for j in self.pfn.keys():\r\n print j,\"p\",self.pfn[j],\"a\",self.afn[j] \r\n print self.pfn[\"l_knee_joint\"].amplitude_offset\r\n\r\n def apply_velocity(self, angles, velocity, phase, x):\r\n \"\"\" Modify on the walk-on-spot joint angles to apply the velocity vector\"\"\"\r\n \r\n # VX\r\n v=velocity[0]*self.parameters[\"vx_amplitude\"]\r\n d=(x*2-1)*v\r\n if phase:\r\n angles[\"l_thigh_joint\"]+=d\r\n angles[\"l_ankle_joint\"]+=d\r\n angles[\"r_thigh_joint\"]+=d\r\n angles[\"r_ankle_joint\"]+=d\r\n else:\r\n angles[\"l_thigh_joint\"]-=d\r\n angles[\"l_ankle_joint\"]-=d\r\n angles[\"r_thigh_joint\"]-=d\r\n angles[\"r_ankle_joint\"]-=d\r\n\r\n # VY\r\n v=velocity[1]*self.parameters[\"vy_amplitude\"]\r\n d=(x)*v\r\n d2=(1-x)*v\r\n if v>=0:\r\n if phase:\r\n angles[\"l_hip_joint\"]-=d\r\n angles[\"l_foot_joint\"]-=d\r\n angles[\"r_hip_joint\"]+=d\r\n angles[\"r_foot_joint\"]+=d\r\n else:\r\n angles[\"l_hip_joint\"]-=d2\r\n angles[\"l_foot_joint\"]-=d2\r\n angles[\"r_hip_joint\"]+=d2\r\n angles[\"r_foot_joint\"]+=d2\r\n else:\r\n if phase:\r\n angles[\"l_hip_joint\"]+=d2\r\n angles[\"l_foot_joint\"]+=d2\r\n angles[\"r_hip_joint\"]-=d2\r\n angles[\"r_foot_joint\"]-=d2\r\n else:\r\n angles[\"l_hip_joint\"]+=d\r\n angles[\"l_foot_joint\"]+=d\r\n angles[\"r_hip_joint\"]-=d\r\n angles[\"r_foot_joint\"]-=d\r\n \r\n ## VT\r\n #v=velocity[2]*self.parameters[\"vt_amplitude\"]\r\n #d=(x)*v\r\n #d2=(1-x)*v\r\n #if v>=0:\r\n #if phase:\r\n #angles[\"j_pelvis_l\"]=-d\r\n #angles[\"j_pelvis_r\"]=d\r\n #else:\r\n #angles[\"j_pelvis_l\"]=-d2\r\n #angles[\"j_pelvis_r\"]=d2\r\n #else:\r\n #if phase:\r\n #angles[\"j_pelvis_l\"]=d2\r\n #angles[\"j_pelvis_r\"]=-d2\r\n #else:\r\n #angles[\"j_pelvis_l\"]=d\r\n #angles[\"j_pelvis_r\"]=-d\r\n\r\nclass Walker:\r\n \"\"\"\r\n Class for making RobotisMini walk\r\n \"\"\"\r\n def __init__(self, robotis_mini_ci, real_robot):\r\n \r\n self.robotis_mini_ci=robotis_mini_ci\r\n \r\n self.real_robot = real_robot\r\n \r\n self.displacing=False #When the robot is walking AND displacing in the plane (velocity is non-zero)\r\n self.walking=False #When the robot is walking: moving up and down the legs\r\n self.velocity=[0,0,0]\r\n \r\n #Default walking params\r\n self.walking_params = {}\r\n self.walking_params['foot'] = [0.4,0,0]\r\n self.walking_params['ankle'] = [-0.01,-0.20,0]\r\n self.walking_params['knee'] = [0.4,0.7,0]\r\n self.walking_params['thigh'] = [-0.4,-0.7,0]\r\n self.walking_params['hip'] = [0.4,0,0]\r\n \r\n self.wb_walkerfunc=WholeBodyWalkerFunction(self.walking_params)\r\n\r\n self.initial_wq = self.wb_walkerfunc.get(True, 0, [0,0,0]) #First joint configuration to start the walking motion\r\n\r\n print \"__init__:initial_wq\"\r\n j_names=self.initial_wq.keys()\r\n for jn in j_names:\r\n print jn + str(\":\") + str(self.initial_wq[jn])\r\n \r\n self._th_walk=None #Walking thread\r\n \r\n self._cycle_period = 10 #seconds\r\n\r\n self._sub_cmd_vel=rospy.Subscriber(robotis_mini_ci.ns+\"cmd_vel\", Twist,self._cb_cmd_vel, queue_size=1)\r\n self._sub_cmd_stop=rospy.Subscriber(robotis_mini_ci.ns+\"stop_srv\", Empty,self._cb_cmd_stop, queue_size=1)\r\n self._sub_cmd_restart=rospy.Subscriber(robotis_mini_ci.ns+\"restart_srv\", Empty,self._cb_cmd_restart, queue_size=1)\r\n self._sub_cmd_restart=rospy.Subscriber(robotis_mini_ci.ns+\"walking_params\", Float64MultiArray,self._cb_new_walking_params, queue_size=1)\r\n \r\n if not self.real_robot:\r\n self.pubs = {}\r\n # Wait until the joints have been populated\r\n while self.robotis_mini_ci.q_names is None:\r\n time.sleep(1)\r\n for jn in self.robotis_mini_ci.q_names:\r\n self.pubs[jn] = rospy.Publisher('/robotis_mini/' + jn + '_position_controller/command', Float64, queue_size=1)\r\n rospy.loginfo(\"Waiting for gazebo services\")\r\n rospy.wait_for_service('/gazebo/pause_physics')\r\n self.pause_simulation_srv = rospy.ServiceProxy('/gazebo/pause_physics', std_srvs.srv.Empty)\r\n rospy.wait_for_service('/gazebo/reset_world')\r\n self.reset_world_srv = rospy.ServiceProxy('/gazebo/reset_world', std_srvs.srv.Empty)\r\n rospy.wait_for_service('/gazebo/unpause_physics')\r\n self.unpause_simulation_srv = rospy.ServiceProxy('/gazebo/unpause_physics', std_srvs.srv.Empty)\r\n\r\n def _cb_new_walking_params(self,msg):\r\n \"\"\"\r\n Processes a new set of parameters\r\n \"\"\"\r\n print \"Walker new set of parameters received\"\r\n self._cycle_period = msg.data[0]\r\n self.walking_params['foot'] = [msg.data[1],msg.data[2],msg.data[3]]\r\n self.walking_params['ankle'] = [msg.data[4],msg.data[5],msg.data[6]]\r\n self.walking_params['knee'] = [msg.data[7],msg.data[8],msg.data[9]]\r\n self.walking_params['thigh'] = [msg.data[10],msg.data[11],msg.data[12]]\r\n self.walking_params['hip'] = [msg.data[13],msg.data[14],msg.data[15]]\r\n \r\n self.wb_walkerfunc=WholeBodyWalkerFunction(self.walking_params)\r\n\r\n self.initial_wq = self.wb_walkerfunc.get(True, 0, [0,0,0]) #First joint configuration to start the walking motion\r\n\r\n print \"initial_wq\"\r\n j_names=self.initial_wq.keys()\r\n for jn in j_names:\r\n print jn + str(\":\") + str(self.initial_wq[jn])\r\n \r\n def _cb_cmd_restart(self,msg):\r\n \"\"\"\r\n Processes cmd_restart and to start a new trial\r\n \"\"\"\r\n print \"Walker restart command received\"\r\n \r\n #Stop the running thread\r\n while self.displacing or self.walking or self._th_walk:\r\n rospy.loginfo('Stopping walking thread')\r\n self.stop()\r\n \r\n #If the robot is simuated -> send to initial configuration\r\n if not self.real_robot:\r\n rospy.loginfo(\"Sending robot to zero configuration\")\r\n for jn in self.robotis_mini_ci.q_names:\r\n self.pubs[jn].publish(0.0)\r\n \r\n time.sleep(1)\r\n \r\n #If the robot is simulated -> reset simulation\r\n try:\r\n self.pause_simulation_srv()\r\n rospy.loginfo( \"Paused gazebo\")\r\n time.sleep(1)\r\n self.reset_world_srv()\r\n rospy.loginfo( \"Reseting gazebo\")\r\n time.sleep(1)\r\n self.unpause_simulation_srv()\r\n rospy.loginfo( \"Unpaused gazebo\")\r\n time.sleep(1)\r\n except rospy.ServiceException, e:\r\n print \"Service call failed: %s\"%e\r\n \r\n def _cb_cmd_vel(self,msg):\r\n \"\"\"\r\n Processes cmd_vel and update walker speed\r\n \"\"\"\r\n print \"Walker velocity command received: \",msg\r\n vx=msg.linear.x\r\n vy=msg.linear.y\r\n vt=msg.angular.z\r\n self.start()\r\n self.set_desired_velocity(vx,vy,vt)\r\n \r\n def _cb_cmd_stop(self,msg):\r\n \"\"\"\r\n Processes cmd_stop\r\n \"\"\"\r\n print \"Walker stop command received: \"\r\n self.stop()\r\n \r\n def goto_initial_wq(self):\r\n \"\"\"\r\n If not there yet, go to initial walking configuration\r\n \"\"\"\r\n rospy.loginfo(\"Going to initial walking configuration\")\r\n while self.get_qdist_to_initial_wq()>0.1:\r\n rospy.loginfo(\"Commanding to go to initial walking configuration\")\r\n print \"Initial configuration\"\r\n print self.initial_wq\r\n self.robotis_mini_ci.set_qd_interpolated(self.initial_wq, 2)\r\n rospy.sleep(2) \r\n rospy.loginfo(\"Initial walking configuration reached\")\r\n print \"Distance\",self.get_qdist_to_initial_wq() \r\n \r\n def start(self):\r\n if not self.displacing:\r\n self.displacing=True \r\n self.goto_initial_wq()\r\n self._th_walk=Thread(target=self._do_walk)\r\n self._th_walk.start()\r\n self.walking=True\r\n \r\n def stop(self):\r\n if self.displacing:\r\n self.walking=False\r\n rospy.loginfo(\"Waiting for stopped\")\r\n while not rospy.is_shutdown() and self._th_walk is not None:\r\n rospy.sleep(0.1) \r\n rospy.loginfo(\"Stopped\")\r\n self.displacing=False\r\n \r\n def set_desired_velocity(self,x,y,t):\r\n self.desired_velocity=[x,y,t]\r\n\r\n def _do_walk(self):\r\n \"\"\"\r\n Main walking loop, smoothly update velocity vectors and apply corresponding joint configurations\r\n \"\"\"\r\n rospy.loginfo(\"Started walking thread\")\r\n wb_walkerfunc=self.wb_walkerfunc\r\n \r\n # Global walk loop\r\n n=50\r\n print \"Thread rate\", 1.0/(self._cycle_period/(2.0*n))\r\n r=rospy.Rate(1.0/(self._cycle_period/(2.0*n)))\r\n p=True\r\n i=0\r\n self.current_velocity=[0,0,0]\r\n while not rospy.is_shutdown() and (self.walking or i<n or self.is_displacing()):\r\n if not self.walking:\r\n self.desired_velocity=[0,0,0]\r\n #if not self.is_displacing() and i==0: # Do not move if nothing to do and already at 0\r\n # self.update_current_velocity(self.desired_velocity, n)\r\n # r.sleep()\r\n # continue\r\n x=float(i)/n \r\n qd_curr=wb_walkerfunc.get(p, x, self.current_velocity)\r\n self.update_current_velocity(self.desired_velocity, n)\r\n self.robotis_mini_ci.set_qd(qd_curr)\r\n i+=1\r\n if i>n:\r\n i=0\r\n p=not p\r\n r.sleep()\r\n rospy.loginfo(\"Finished walking thread\")\r\n \r\n self._th_walk=None\r\n\r\n def is_displacing(self):\r\n \"\"\"\r\n Checks if the current velocity is not zero and returns True in that case\r\n \"\"\"\r\n e=0.02\r\n for v in self.current_velocity:\r\n if abs(v)>e: return True\r\n return False\r\n \r\n def update_current_velocity(self, target_velocity, n):\r\n \"\"\"\r\n A pseudo-interpolation to a target velocity\r\n \"\"\"\r\n a=3/float(n)\r\n b=1-a\r\n self.current_velocity=[a*tv+b*cv for (tv,cv) in zip(target_velocity, self.current_velocity)]\r\n \r\n def get_qdist_to_initial_wq(self):\r\n \"\"\"\r\n Computes the absolute distance between the current robot joint state and the initial walking configuration\r\n \"\"\"\r\n current_q=self.robotis_mini_ci.get_q()\r\n return get_distance(self.initial_wq, current_q)\r\n\r\ndef get_distance(qa_dict, qb_dict):\r\n \"\"\"\r\n Computes sum of absolute distances between two sets of joints represented as dictionaries of (jointName, jointConfiguration)\r\n \"\"\"\r\n d=0\r\n j_names=qa_dict.keys()\r\n if len(j_names)==0:\r\n rospy.loginfo(\"Length is 0\")\r\n return 0\r\n for jn in j_names:\r\n d+=abs(qb_dict[jn]-qa_dict[jn])\r\n d/=len(j_names)\r\n return d\r\n\r\nif __name__==\"__main__\":\r\n rospy.init_node(\"walker\")\r\n \r\n parser = argparse.ArgumentParser(description='Walker trajectory generator')\r\n parser.add_argument('--real',action='store_true', help='define when using the real robot')\r\n \r\n options, args = parser.parse_known_args()\r\n \r\n if options.real:\r\n rospy.loginfo(\"Real Robot!\")\r\n else:\r\n rospy.loginfo(\"Simulated Robot!\")\r\n \r\n rospy.loginfo(\"Instantiating RobotisMini RobotisMiniControlInterface\")\r\n robotis_mini_ci=RobotisMiniControlInterface(real_robot=options.real)\r\n rospy.loginfo(\"Instantiating RobotisMini Walker\")\r\n walker=Walker(robotis_mini_ci, options.real)\r\n \r\n rospy.loginfo(\"RobotisMini Walker Ready\")\r\n while not rospy.is_shutdown():\r\n time.sleep(1)\r\n"
] | true |
99,426 |
c4b02ea5abe6d31d23bce24384e080ca57a9d55e
|
from django.urls import path
from . import views
urlpatterns = [
path('',views.home,name='home'),
path('home',views.home,name='home'),
path('book',views.book,name='book'),
path('comment', views.comment, name='comment'),
path('sign',views.sign,name='sign'),
path('sub',views.sub,name='sub'),
path('login',views.login,name='login'),
path('new',views.new,name='new'),
path('old',views.old,name='old'),
path('oldsubmit',views.oldsubmit,name='oldsubmit'),
path('old_bookings',views.old_bookings,name='old_bookings'),
path('submit',views.submit,name='submit'),
path('doc',views.doc,name='doc'),
path('doclogin',views.doclogin,name='doclogin'),
path('logout',views.logout,name='logout'),
path('profile',views.profile,name='profile'),
path('appoinment',views.appoinment,name='appoinment'),
path('edit',views.edit,name='edit'),
path('save',views.save,name='save'),
path('today',views.today_booking,name='today'),
path('all',views.all,name='all'),
path('date',views.date,name='date')
#
]
|
[
"from django.urls import path\nfrom . import views\nurlpatterns = [\n\n path('',views.home,name='home'),\n path('home',views.home,name='home'),\n path('book',views.book,name='book'),\n path('comment', views.comment, name='comment'),\n path('sign',views.sign,name='sign'),\n path('sub',views.sub,name='sub'),\n path('login',views.login,name='login'),\n path('new',views.new,name='new'),\n path('old',views.old,name='old'),\n path('oldsubmit',views.oldsubmit,name='oldsubmit'),\n path('old_bookings',views.old_bookings,name='old_bookings'),\n path('submit',views.submit,name='submit'),\n path('doc',views.doc,name='doc'),\n path('doclogin',views.doclogin,name='doclogin'),\n path('logout',views.logout,name='logout'),\n path('profile',views.profile,name='profile'),\n path('appoinment',views.appoinment,name='appoinment'),\n path('edit',views.edit,name='edit'),\n path('save',views.save,name='save'),\n path('today',views.today_booking,name='today'),\n path('all',views.all,name='all'),\n path('date',views.date,name='date')\n\n #\n]\n",
"from django.urls import path\nfrom . import views\nurlpatterns = [path('', views.home, name='home'), path('home', views.home,\n name='home'), path('book', views.book, name='book'), path('comment',\n views.comment, name='comment'), path('sign', views.sign, name='sign'),\n path('sub', views.sub, name='sub'), path('login', views.login, name=\n 'login'), path('new', views.new, name='new'), path('old', views.old,\n name='old'), path('oldsubmit', views.oldsubmit, name='oldsubmit'), path\n ('old_bookings', views.old_bookings, name='old_bookings'), path(\n 'submit', views.submit, name='submit'), path('doc', views.doc, name=\n 'doc'), path('doclogin', views.doclogin, name='doclogin'), path(\n 'logout', views.logout, name='logout'), path('profile', views.profile,\n name='profile'), path('appoinment', views.appoinment, name='appoinment'\n ), path('edit', views.edit, name='edit'), path('save', views.save, name\n ='save'), path('today', views.today_booking, name='today'), path('all',\n views.all, name='all'), path('date', views.date, name='date')]\n",
"<import token>\nurlpatterns = [path('', views.home, name='home'), path('home', views.home,\n name='home'), path('book', views.book, name='book'), path('comment',\n views.comment, name='comment'), path('sign', views.sign, name='sign'),\n path('sub', views.sub, name='sub'), path('login', views.login, name=\n 'login'), path('new', views.new, name='new'), path('old', views.old,\n name='old'), path('oldsubmit', views.oldsubmit, name='oldsubmit'), path\n ('old_bookings', views.old_bookings, name='old_bookings'), path(\n 'submit', views.submit, name='submit'), path('doc', views.doc, name=\n 'doc'), path('doclogin', views.doclogin, name='doclogin'), path(\n 'logout', views.logout, name='logout'), path('profile', views.profile,\n name='profile'), path('appoinment', views.appoinment, name='appoinment'\n ), path('edit', views.edit, name='edit'), path('save', views.save, name\n ='save'), path('today', views.today_booking, name='today'), path('all',\n views.all, name='all'), path('date', views.date, name='date')]\n",
"<import token>\n<assignment token>\n"
] | false |
99,427 |
fac0b38882351719c929c32d0eb0f0d4cc8182fb
|
import os
import logging
from cloudpks import RestClient
logging.basicConfig(level=os.getenv('vke_log_level'),
format='%(asctime)s %(name)s %(levelname)s %(message)s'
)
logger = logging.getLogger(__name__)
logging.getLogger('requests').setLevel(logging.CRITICAL)
logging.getLogger('urllib3').setLevel(logging.CRITICAL)
class User(object):
"""
This is inspired by work from Grant
The user and organisation management runs through the centralised
Cloud Services Portal and as such, we use a different baseurl for
this module when compared with the other modules.
"""
def __init__(self, server, api_key, auth_token):
self._server = server
self._api_key = api_key
self.header = {
'Content-Type': "application/json",
'csp-auth-token': auth_token
}
def remove(self, session, id, username):
payload = {
"emails": username
}
logger.debug(f'Payload: {payload}')
response = session.do_patch(self._server, self.header, f'{id}/users/', payload, 'DISCOVERY')
return response
def invite(self, session,
id,
usernames,
org_role='org_member',
vke=False
):
payload = {
'usernames': usernames,
'orgRoleName': org_role,
'serviceRolesDtos': []
}
if vke:
payload['serviceRolesDtos'].append({
'serviceDefinitionLink': ('/csp/gateway/slc/api/definitions'
'/external'
'/o3ecbsAvjpw6lmL3aliJX29zVhE_'
),
'serviceRoleNames':
[
'vke:service-user'
]
})
logger.debug(f'Payload: {payload}')
response = session.do_post(self._server, self.header, f'{id}/invitations', payload, 'DISCOVERY')
return response
def list(self, session, id):
return session.do_get(self._server, self.header, f'{id}/users/', 'DISCOVERY')
|
[
"import os\nimport logging\nfrom cloudpks import RestClient\n\nlogging.basicConfig(level=os.getenv('vke_log_level'),\n format='%(asctime)s %(name)s %(levelname)s %(message)s'\n )\nlogger = logging.getLogger(__name__)\nlogging.getLogger('requests').setLevel(logging.CRITICAL)\nlogging.getLogger('urllib3').setLevel(logging.CRITICAL)\n\nclass User(object):\n \"\"\"\n This is inspired by work from Grant \n The user and organisation management runs through the centralised\n Cloud Services Portal and as such, we use a different baseurl for\n this module when compared with the other modules.\n \"\"\"\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {\n 'Content-Type': \"application/json\",\n 'csp-auth-token': auth_token\n }\n\n def remove(self, session, id, username):\n payload = {\n \"emails\": username\n }\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header, f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session,\n id,\n usernames,\n org_role='org_member',\n vke=False\n ):\n payload = {\n 'usernames': usernames,\n 'orgRoleName': org_role,\n 'serviceRolesDtos': []\n }\n if vke:\n payload['serviceRolesDtos'].append({\n 'serviceDefinitionLink': ('/csp/gateway/slc/api/definitions'\n '/external'\n '/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n ),\n 'serviceRoleNames':\n [\n 'vke:service-user'\n ]\n })\n\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header, f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/', 'DISCOVERY')\n",
"import os\nimport logging\nfrom cloudpks import RestClient\nlogging.basicConfig(level=os.getenv('vke_log_level'), format=\n '%(asctime)s %(name)s %(levelname)s %(message)s')\nlogger = logging.getLogger(__name__)\nlogging.getLogger('requests').setLevel(logging.CRITICAL)\nlogging.getLogger('urllib3').setLevel(logging.CRITICAL)\n\n\nclass User(object):\n \"\"\"\n This is inspired by work from Grant \n The user and organisation management runs through the centralised\n Cloud Services Portal and as such, we use a different baseurl for\n this module when compared with the other modules.\n \"\"\"\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session, id, usernames, org_role='org_member', vke=False):\n payload = {'usernames': usernames, 'orgRoleName': org_role,\n 'serviceRolesDtos': []}\n if vke:\n payload['serviceRolesDtos'].append({'serviceDefinitionLink':\n '/csp/gateway/slc/api/definitions/external/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n , 'serviceRoleNames': ['vke:service-user']})\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header,\n f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\nlogging.basicConfig(level=os.getenv('vke_log_level'), format=\n '%(asctime)s %(name)s %(levelname)s %(message)s')\nlogger = logging.getLogger(__name__)\nlogging.getLogger('requests').setLevel(logging.CRITICAL)\nlogging.getLogger('urllib3').setLevel(logging.CRITICAL)\n\n\nclass User(object):\n \"\"\"\n This is inspired by work from Grant \n The user and organisation management runs through the centralised\n Cloud Services Portal and as such, we use a different baseurl for\n this module when compared with the other modules.\n \"\"\"\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session, id, usernames, org_role='org_member', vke=False):\n payload = {'usernames': usernames, 'orgRoleName': org_role,\n 'serviceRolesDtos': []}\n if vke:\n payload['serviceRolesDtos'].append({'serviceDefinitionLink':\n '/csp/gateway/slc/api/definitions/external/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n , 'serviceRoleNames': ['vke:service-user']})\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header,\n f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\nlogging.basicConfig(level=os.getenv('vke_log_level'), format=\n '%(asctime)s %(name)s %(levelname)s %(message)s')\n<assignment token>\nlogging.getLogger('requests').setLevel(logging.CRITICAL)\nlogging.getLogger('urllib3').setLevel(logging.CRITICAL)\n\n\nclass User(object):\n \"\"\"\n This is inspired by work from Grant \n The user and organisation management runs through the centralised\n Cloud Services Portal and as such, we use a different baseurl for\n this module when compared with the other modules.\n \"\"\"\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session, id, usernames, org_role='org_member', vke=False):\n payload = {'usernames': usernames, 'orgRoleName': org_role,\n 'serviceRolesDtos': []}\n if vke:\n payload['serviceRolesDtos'].append({'serviceDefinitionLink':\n '/csp/gateway/slc/api/definitions/external/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n , 'serviceRoleNames': ['vke:service-user']})\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header,\n f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n \"\"\"\n This is inspired by work from Grant \n The user and organisation management runs through the centralised\n Cloud Services Portal and as such, we use a different baseurl for\n this module when compared with the other modules.\n \"\"\"\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session, id, usernames, org_role='org_member', vke=False):\n payload = {'usernames': usernames, 'orgRoleName': org_role,\n 'serviceRolesDtos': []}\n if vke:\n payload['serviceRolesDtos'].append({'serviceDefinitionLink':\n '/csp/gateway/slc/api/definitions/external/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n , 'serviceRoleNames': ['vke:service-user']})\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header,\n f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n <docstring token>\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n\n def invite(self, session, id, usernames, org_role='org_member', vke=False):\n payload = {'usernames': usernames, 'orgRoleName': org_role,\n 'serviceRolesDtos': []}\n if vke:\n payload['serviceRolesDtos'].append({'serviceDefinitionLink':\n '/csp/gateway/slc/api/definitions/external/o3ecbsAvjpw6lmL3aliJX29zVhE_'\n , 'serviceRoleNames': ['vke:service-user']})\n logger.debug(f'Payload: {payload}')\n response = session.do_post(self._server, self.header,\n f'{id}/invitations', payload, 'DISCOVERY')\n return response\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n <docstring token>\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n <function token>\n\n def list(self, session, id):\n return session.do_get(self._server, self.header, f'{id}/users/',\n 'DISCOVERY')\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n <docstring token>\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n\n def remove(self, session, id, username):\n payload = {'emails': username}\n logger.debug(f'Payload: {payload}')\n response = session.do_patch(self._server, self.header,\n f'{id}/users/', payload, 'DISCOVERY')\n return response\n <function token>\n <function token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n <docstring token>\n\n def __init__(self, server, api_key, auth_token):\n self._server = server\n self._api_key = api_key\n self.header = {'Content-Type': 'application/json', 'csp-auth-token':\n auth_token}\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n\n\nclass User(object):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<class token>\n"
] | false |
99,428 |
3c3065547bbc9774a13103c4a7e8ed7b65874be4
|
"""mcpython - a minecraft clone written in python licenced under MIT-licence
authors: uuk, xkcdjerry
original game by forgleman licenced under MIT-licence
minecraft by Mojang
blocks based on 1.14.4.jar of minecraft, downloaded on 20th of July, 2019"""
import globals as G
import chat.command.Command
from chat.command.Command import ParseBridge, ParseType, ParseMode, SubCommand
import util.math
@G.registry
class CommandGenerate(chat.command.Command.Command):
"""
class for /generate command
"""
@staticmethod
def insert_parse_bridge(parsebridge: ParseBridge):
parsebridge.main_entry = "generate"
parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.OPTIONAL).add_subcommand(
SubCommand(ParseType.INT).add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.OPTIONAL).add_subcommand(
SubCommand(ParseType.INT)))))
@staticmethod
def parse(values: list, modes: list, info):
dim = G.world.get_active_dimension()
if len(values) > 0: # have we definite an chunk?
chunkf = tuple(values[:2])
chunkt = tuple(values[2:]) if len(values) > 2 else chunkf
else:
chunkf = chunkt = util.math.sectorize(G.window.position)
fx, fz = chunkf
tx, tz = chunkt
if fx > tx: fx, tx = tx, fx
if fz > tz: fz, tz = tz, fz
for x in range(fx, tx):
for z in range(fz, tz):
G.worldgenerationhandler.generate_chunk(dim.get_chunk(x, z, generate=False))
G.world.process_entire_queue()
@staticmethod
def get_help() -> list:
return ["/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the "
"specified area, else the specified"]
|
[
"\"\"\"mcpython - a minecraft clone written in python licenced under MIT-licence\nauthors: uuk, xkcdjerry\n\noriginal game by forgleman licenced under MIT-licence\nminecraft by Mojang\n\nblocks based on 1.14.4.jar of minecraft, downloaded on 20th of July, 2019\"\"\"\nimport globals as G\nimport chat.command.Command\nfrom chat.command.Command import ParseBridge, ParseType, ParseMode, SubCommand\nimport util.math\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n \"\"\"\n class for /generate command\n \"\"\"\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = \"generate\"\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.OPTIONAL).add_subcommand(\n SubCommand(ParseType.INT).add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.OPTIONAL).add_subcommand(\n SubCommand(ParseType.INT)))))\n\n @staticmethod\n def parse(values: list, modes: list, info):\n dim = G.world.get_active_dimension()\n if len(values) > 0: # have we definite an chunk?\n chunkf = tuple(values[:2])\n chunkt = tuple(values[2:]) if len(values) > 2 else chunkf\n else:\n chunkf = chunkt = util.math.sectorize(G.window.position)\n fx, fz = chunkf\n tx, tz = chunkt\n if fx > tx: fx, tx = tx, fx\n if fz > tz: fz, tz = tz, fz\n for x in range(fx, tx):\n for z in range(fz, tz):\n G.worldgenerationhandler.generate_chunk(dim.get_chunk(x, z, generate=False))\n G.world.process_entire_queue()\n\n @staticmethod\n def get_help() -> list:\n return [\"/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the \"\n \"specified area, else the specified\"]\n\n",
"<docstring token>\nimport globals as G\nimport chat.command.Command\nfrom chat.command.Command import ParseBridge, ParseType, ParseMode, SubCommand\nimport util.math\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n \"\"\"\n class for /generate command\n \"\"\"\n\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = 'generate'\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode\n .OPTIONAL).add_subcommand(SubCommand(ParseType.INT).\n add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.\n OPTIONAL).add_subcommand(SubCommand(ParseType.INT)))))\n\n @staticmethod\n def parse(values: list, modes: list, info):\n dim = G.world.get_active_dimension()\n if len(values) > 0:\n chunkf = tuple(values[:2])\n chunkt = tuple(values[2:]) if len(values) > 2 else chunkf\n else:\n chunkf = chunkt = util.math.sectorize(G.window.position)\n fx, fz = chunkf\n tx, tz = chunkt\n if fx > tx:\n fx, tx = tx, fx\n if fz > tz:\n fz, tz = tz, fz\n for x in range(fx, tx):\n for z in range(fz, tz):\n G.worldgenerationhandler.generate_chunk(dim.get_chunk(x, z,\n generate=False))\n G.world.process_entire_queue()\n\n @staticmethod\n def get_help() ->list:\n return [\n '/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the specified area, else the specified'\n ]\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n \"\"\"\n class for /generate command\n \"\"\"\n\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = 'generate'\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode\n .OPTIONAL).add_subcommand(SubCommand(ParseType.INT).\n add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.\n OPTIONAL).add_subcommand(SubCommand(ParseType.INT)))))\n\n @staticmethod\n def parse(values: list, modes: list, info):\n dim = G.world.get_active_dimension()\n if len(values) > 0:\n chunkf = tuple(values[:2])\n chunkt = tuple(values[2:]) if len(values) > 2 else chunkf\n else:\n chunkf = chunkt = util.math.sectorize(G.window.position)\n fx, fz = chunkf\n tx, tz = chunkt\n if fx > tx:\n fx, tx = tx, fx\n if fz > tz:\n fz, tz = tz, fz\n for x in range(fx, tx):\n for z in range(fz, tz):\n G.worldgenerationhandler.generate_chunk(dim.get_chunk(x, z,\n generate=False))\n G.world.process_entire_queue()\n\n @staticmethod\n def get_help() ->list:\n return [\n '/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the specified area, else the specified'\n ]\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n <docstring token>\n\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = 'generate'\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode\n .OPTIONAL).add_subcommand(SubCommand(ParseType.INT).\n add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.\n OPTIONAL).add_subcommand(SubCommand(ParseType.INT)))))\n\n @staticmethod\n def parse(values: list, modes: list, info):\n dim = G.world.get_active_dimension()\n if len(values) > 0:\n chunkf = tuple(values[:2])\n chunkt = tuple(values[2:]) if len(values) > 2 else chunkf\n else:\n chunkf = chunkt = util.math.sectorize(G.window.position)\n fx, fz = chunkf\n tx, tz = chunkt\n if fx > tx:\n fx, tx = tx, fx\n if fz > tz:\n fz, tz = tz, fz\n for x in range(fx, tx):\n for z in range(fz, tz):\n G.worldgenerationhandler.generate_chunk(dim.get_chunk(x, z,\n generate=False))\n G.world.process_entire_queue()\n\n @staticmethod\n def get_help() ->list:\n return [\n '/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the specified area, else the specified'\n ]\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n <docstring token>\n\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = 'generate'\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode\n .OPTIONAL).add_subcommand(SubCommand(ParseType.INT).\n add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.\n OPTIONAL).add_subcommand(SubCommand(ParseType.INT)))))\n <function token>\n\n @staticmethod\n def get_help() ->list:\n return [\n '/generate [<x> <z> [<tox> <toz>]]: generates the chunk you are in if no one is specified or the specified area, else the specified'\n ]\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n <docstring token>\n\n @staticmethod\n def insert_parse_bridge(parsebridge: ParseBridge):\n parsebridge.main_entry = 'generate'\n parsebridge.add_subcommand(SubCommand(ParseType.INT, mode=ParseMode\n .OPTIONAL).add_subcommand(SubCommand(ParseType.INT).\n add_subcommand(SubCommand(ParseType.INT, mode=ParseMode.\n OPTIONAL).add_subcommand(SubCommand(ParseType.INT)))))\n <function token>\n <function token>\n",
"<docstring token>\n<import token>\n\n\[email protected]\nclass CommandGenerate(chat.command.Command.Command):\n <docstring token>\n <function token>\n <function token>\n <function token>\n",
"<docstring token>\n<import token>\n<class token>\n"
] | false |
99,429 |
9933ca702421da2f0d6c3b9775bcc494b3452edb
|
import sys
import urllib.request, urllib.error, urllib.parse
import http.cookiejar
class HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):
"""Track HTTP Requests and responses with this custom handlers. Be sure to
add it your build_opener call, or use: handler_order = 900 """
def __init__(self, httpout = sys.stdout):
self.httpout = httpout
def http_request(self, request):
if __debug__:
host, full_url = request.get_host(), request.get_full_url()
url_path = full_url[full_url.find(host) + len(host):]
self.httpout.write("%s\n" % request.get_full_url())
self.httpout.write("\n")
self.httpout.write("%s %s\n" % (request.get_method(), url_path))
for header in request.header_items():
self.httpout.write("%s: %s\n" % header[:])
self.httpout.write("\n")
return request
def http_response(self, request, response):
if __debug__:
code, msg, hdrs = response.code, response.msg, response.info()
self.httpout.write("HTTP/1.x %s %s\n" % (code, msg))
self.httpout.write(str(hdrs))
return response
https_request = http_request
https_response = http_response
# Example
cjar = http.cookiejar.LWPCookieJar()
opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cjar),HTTPMyDebugProcessor(),)
#opener = urllib2.build_opener(HTTPMyDebugProcessor(),)
urllib.request.install_opener(opener)
##response = urllib2.urlopen("http://www.google.com")
#response = urllib2.urlopen("https://www.idcourts.us/repository/start.do")
#response = urllib2.urlopen("https://www.idcourts.us/repository/searchParty.do")
req = urllib.request.Request('http://www.microsoft.com/windows/windows-7/default.aspx')
#req = urllib2.Request('https://www.idcourts.us/repository/start.do')
res = opener.open(req)
print(cjar)
for c in cjar:
cookie_str = "%s=%s" % (c.name, c.value)
print(cookie_str)
req = urllib.request.Request('http://www.microsoft.com/windows/windows-xp/default.aspx')
#req.add_header("Cookie",cookie_str)
opener.open(req)
print(cjar)
|
[
"import sys\nimport urllib.request, urllib.error, urllib.parse\nimport http.cookiejar\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n \"\"\"Track HTTP Requests and responses with this custom handlers. Be sure to\n add it your build_opener call, or use: handler_order = 900 \"\"\"\n def __init__(self, httpout = sys.stdout):\n self.httpout = httpout\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write(\"%s\\n\" % request.get_full_url())\n self.httpout.write(\"\\n\")\n self.httpout.write(\"%s %s\\n\" % (request.get_method(), url_path))\n\n for header in request.header_items():\n self.httpout.write(\"%s: %s\\n\" % header[:])\n\n self.httpout.write(\"\\n\")\n\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write(\"HTTP/1.x %s %s\\n\" % (code, msg))\n self.httpout.write(str(hdrs))\n\n return response\n\n https_request = http_request\n https_response = http_response\n\n# Example\ncjar = http.cookiejar.LWPCookieJar()\nopener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cjar),HTTPMyDebugProcessor(),)\n#opener = urllib2.build_opener(HTTPMyDebugProcessor(),)\nurllib.request.install_opener(opener)\n##response = urllib2.urlopen(\"http://www.google.com\")\n#response = urllib2.urlopen(\"https://www.idcourts.us/repository/start.do\")\n#response = urllib2.urlopen(\"https://www.idcourts.us/repository/searchParty.do\")\nreq = urllib.request.Request('http://www.microsoft.com/windows/windows-7/default.aspx')\n#req = urllib2.Request('https://www.idcourts.us/repository/start.do')\nres = opener.open(req)\n\nprint(cjar)\nfor c in cjar:\n cookie_str = \"%s=%s\" % (c.name, c.value)\nprint(cookie_str)\n\nreq = urllib.request.Request('http://www.microsoft.com/windows/windows-xp/default.aspx')\n#req.add_header(\"Cookie\",cookie_str)\nopener.open(req)\nprint(cjar)\n",
"import sys\nimport urllib.request, urllib.error, urllib.parse\nimport http.cookiejar\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n \"\"\"Track HTTP Requests and responses with this custom handlers. Be sure to\n add it your build_opener call, or use: handler_order = 900 \"\"\"\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n https_request = http_request\n https_response = http_response\n\n\ncjar = http.cookiejar.LWPCookieJar()\nopener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(\n cjar), HTTPMyDebugProcessor())\nurllib.request.install_opener(opener)\nreq = urllib.request.Request(\n 'http://www.microsoft.com/windows/windows-7/default.aspx')\nres = opener.open(req)\nprint(cjar)\nfor c in cjar:\n cookie_str = '%s=%s' % (c.name, c.value)\nprint(cookie_str)\nreq = urllib.request.Request(\n 'http://www.microsoft.com/windows/windows-xp/default.aspx')\nopener.open(req)\nprint(cjar)\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n \"\"\"Track HTTP Requests and responses with this custom handlers. Be sure to\n add it your build_opener call, or use: handler_order = 900 \"\"\"\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n https_request = http_request\n https_response = http_response\n\n\ncjar = http.cookiejar.LWPCookieJar()\nopener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(\n cjar), HTTPMyDebugProcessor())\nurllib.request.install_opener(opener)\nreq = urllib.request.Request(\n 'http://www.microsoft.com/windows/windows-7/default.aspx')\nres = opener.open(req)\nprint(cjar)\nfor c in cjar:\n cookie_str = '%s=%s' % (c.name, c.value)\nprint(cookie_str)\nreq = urllib.request.Request(\n 'http://www.microsoft.com/windows/windows-xp/default.aspx')\nopener.open(req)\nprint(cjar)\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n \"\"\"Track HTTP Requests and responses with this custom handlers. Be sure to\n add it your build_opener call, or use: handler_order = 900 \"\"\"\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n https_request = http_request\n https_response = http_response\n\n\n<assignment token>\nurllib.request.install_opener(opener)\n<assignment token>\nprint(cjar)\nfor c in cjar:\n cookie_str = '%s=%s' % (c.name, c.value)\nprint(cookie_str)\n<assignment token>\nopener.open(req)\nprint(cjar)\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n \"\"\"Track HTTP Requests and responses with this custom handlers. Be sure to\n add it your build_opener call, or use: handler_order = 900 \"\"\"\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n https_request = http_request\n https_response = http_response\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n <docstring token>\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n https_request = http_request\n https_response = http_response\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n <docstring token>\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n\n def http_response(self, request, response):\n if __debug__:\n code, msg, hdrs = response.code, response.msg, response.info()\n self.httpout.write('HTTP/1.x %s %s\\n' % (code, msg))\n self.httpout.write(str(hdrs))\n return response\n <assignment token>\n <assignment token>\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n <docstring token>\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n\n def http_request(self, request):\n if __debug__:\n host, full_url = request.get_host(), request.get_full_url()\n url_path = full_url[full_url.find(host) + len(host):]\n self.httpout.write('%s\\n' % request.get_full_url())\n self.httpout.write('\\n')\n self.httpout.write('%s %s\\n' % (request.get_method(), url_path))\n for header in request.header_items():\n self.httpout.write('%s: %s\\n' % header[:])\n self.httpout.write('\\n')\n return request\n <function token>\n <assignment token>\n <assignment token>\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n <docstring token>\n\n def __init__(self, httpout=sys.stdout):\n self.httpout = httpout\n <function token>\n <function token>\n <assignment token>\n <assignment token>\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass HTTPMyDebugProcessor(urllib2.AbstractHTTPHandler):\n <docstring token>\n <function token>\n <function token>\n <function token>\n <assignment token>\n <assignment token>\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,430 |
89e187ab58e51020f3bbd157e808dba47151fa90
|
from copy import copy, deepcopy
import math
"""
Ce module pourvoie des fonctions utilitaires a appliquer
sur des formules en CNF(Conjonctive normal form)
Qui sont des conjonctions de disjonctions
Clause disjonctive de la forme:
"(x or y or not x or ...)"
Clause Conjonctive de la forme:
"(x and y and not x and ...)"
(avec ou sans les parentheses)
'cnf_from_string', elle tranforme une str
sous forme CNF en list de list de variables.
Les variables doivent etres des entiers.
Les litteraux de ces variables sont positifs,
ou negatifs pour representer des litteraux faux
ou vrais.
Exemple:
str_cnf = "(1 or 2) and ( -3 or 4)"
result = cnf_from_str(str_cnf)
print(result) # [[1, 2], [-3, 4]]
"""
def is_cnf(func):
def inner(*args, **kwargs):
error_msg_list = "'args[0]' should be 'list' of 'list'"
error_msg_elmt = "Elements of a 'clause' should only be 'integer'"
if type(args[0]) is not list:
raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')
for supposed_clause in args[0]:
if type(supposed_clause) is not list:
raise TypeError(f'{error_msg_list}, "{supposed_clause}"')
for el in supposed_clause:
if type(el) is not int:
raise TypeError(f'{error_msg_elmt}, "{el}"')
return func(*args, **kwargs)
return inner
def litteral(var, false=False):
if false:
return -var
return var
def cnf_from_str(prop):
"""
Utilitaire pour obtenir les clauses d'une formule sous forme
CNF ou Formule Normale Conjonctive donc de la forme:
"(1 or 2 or ...) and (3 or -2 or ...) ... "
Retourne un resultat sous la forme:
0: [1, 2]
1: [3, -2]
.
.
.
n: ...
Return: [[var1, var2], [var3, -var4], ...]
"""
splitted = prop.split('and')
clauses = []
for clause in splitted:
clause = clause.replace('(', '').replace(')', '')
str_clause = clause.split('or')
int_litterals = [int(el) for el in str_clause]
clauses.append(int_litterals)
return clauses
@is_cnf
def cnf_variables(cnf):
"""
'entry' doit etre issue de cnf_clauses()
return: 'set' de variable
"""
variabs = set()
for clause in cnf:
for var in clause:
var = abs(var)
if var not in variabs:
variabs.add(var)
return variabs
|
[
"from copy import copy, deepcopy\n\nimport math\n\"\"\"\nCe module pourvoie des fonctions utilitaires a appliquer\nsur des formules en CNF(Conjonctive normal form)\n\nQui sont des conjonctions de disjonctions \n\nClause disjonctive de la forme:\n\"(x or y or not x or ...)\"\n\nClause Conjonctive de la forme:\n\"(x and y and not x and ...)\"\n\n\n\n(avec ou sans les parentheses)\n\n'cnf_from_string', elle tranforme une str\nsous forme CNF en list de list de variables.\n\nLes variables doivent etres des entiers.\n\nLes litteraux de ces variables sont positifs, \nou negatifs pour representer des litteraux faux \nou vrais.\n\nExemple:\nstr_cnf = \"(1 or 2) and ( -3 or 4)\"\nresult = cnf_from_str(str_cnf)\nprint(result) # [[1, 2], [-3, 4]]\n\n\"\"\"\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n\n return func(*args, **kwargs)\n\n return inner\n\n\ndef litteral(var, false=False):\n if false:\n return -var\n\n return var\n\n\ndef cnf_from_str(prop):\n \"\"\"\n Utilitaire pour obtenir les clauses d'une formule sous forme \n CNF ou Formule Normale Conjonctive donc de la forme:\n\n \"(1 or 2 or ...) and (3 or -2 or ...) ... \"\n\n Retourne un resultat sous la forme:\n 0: [1, 2]\n 1: [3, -2]\n .\n .\n .\n n: ...\n\n Return: [[var1, var2], [var3, -var4], ...]\n\n \"\"\"\n splitted = prop.split('and')\n clauses = []\n\n for clause in splitted:\n clause = clause.replace('(', '').replace(')', '')\n str_clause = clause.split('or')\n\n int_litterals = [int(el) for el in str_clause]\n\n clauses.append(int_litterals)\n \n return clauses\n\n\n@is_cnf\ndef cnf_variables(cnf):\n \"\"\"\n 'entry' doit etre issue de cnf_clauses()\n\n return: 'set' de variable\n \"\"\"\n variabs = set()\n\n for clause in cnf:\n for var in clause:\n var = abs(var)\n\n if var not in variabs:\n variabs.add(var)\n\n return variabs\n\n\n",
"from copy import copy, deepcopy\nimport math\n<docstring token>\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n return func(*args, **kwargs)\n return inner\n\n\ndef litteral(var, false=False):\n if false:\n return -var\n return var\n\n\ndef cnf_from_str(prop):\n \"\"\"\n Utilitaire pour obtenir les clauses d'une formule sous forme \n CNF ou Formule Normale Conjonctive donc de la forme:\n\n \"(1 or 2 or ...) and (3 or -2 or ...) ... \"\n\n Retourne un resultat sous la forme:\n 0: [1, 2]\n 1: [3, -2]\n .\n .\n .\n n: ...\n\n Return: [[var1, var2], [var3, -var4], ...]\n\n \"\"\"\n splitted = prop.split('and')\n clauses = []\n for clause in splitted:\n clause = clause.replace('(', '').replace(')', '')\n str_clause = clause.split('or')\n int_litterals = [int(el) for el in str_clause]\n clauses.append(int_litterals)\n return clauses\n\n\n@is_cnf\ndef cnf_variables(cnf):\n \"\"\"\n 'entry' doit etre issue de cnf_clauses()\n\n return: 'set' de variable\n \"\"\"\n variabs = set()\n for clause in cnf:\n for var in clause:\n var = abs(var)\n if var not in variabs:\n variabs.add(var)\n return variabs\n",
"<import token>\n<docstring token>\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n return func(*args, **kwargs)\n return inner\n\n\ndef litteral(var, false=False):\n if false:\n return -var\n return var\n\n\ndef cnf_from_str(prop):\n \"\"\"\n Utilitaire pour obtenir les clauses d'une formule sous forme \n CNF ou Formule Normale Conjonctive donc de la forme:\n\n \"(1 or 2 or ...) and (3 or -2 or ...) ... \"\n\n Retourne un resultat sous la forme:\n 0: [1, 2]\n 1: [3, -2]\n .\n .\n .\n n: ...\n\n Return: [[var1, var2], [var3, -var4], ...]\n\n \"\"\"\n splitted = prop.split('and')\n clauses = []\n for clause in splitted:\n clause = clause.replace('(', '').replace(')', '')\n str_clause = clause.split('or')\n int_litterals = [int(el) for el in str_clause]\n clauses.append(int_litterals)\n return clauses\n\n\n@is_cnf\ndef cnf_variables(cnf):\n \"\"\"\n 'entry' doit etre issue de cnf_clauses()\n\n return: 'set' de variable\n \"\"\"\n variabs = set()\n for clause in cnf:\n for var in clause:\n var = abs(var)\n if var not in variabs:\n variabs.add(var)\n return variabs\n",
"<import token>\n<docstring token>\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n return func(*args, **kwargs)\n return inner\n\n\n<function token>\n\n\ndef cnf_from_str(prop):\n \"\"\"\n Utilitaire pour obtenir les clauses d'une formule sous forme \n CNF ou Formule Normale Conjonctive donc de la forme:\n\n \"(1 or 2 or ...) and (3 or -2 or ...) ... \"\n\n Retourne un resultat sous la forme:\n 0: [1, 2]\n 1: [3, -2]\n .\n .\n .\n n: ...\n\n Return: [[var1, var2], [var3, -var4], ...]\n\n \"\"\"\n splitted = prop.split('and')\n clauses = []\n for clause in splitted:\n clause = clause.replace('(', '').replace(')', '')\n str_clause = clause.split('or')\n int_litterals = [int(el) for el in str_clause]\n clauses.append(int_litterals)\n return clauses\n\n\n@is_cnf\ndef cnf_variables(cnf):\n \"\"\"\n 'entry' doit etre issue de cnf_clauses()\n\n return: 'set' de variable\n \"\"\"\n variabs = set()\n for clause in cnf:\n for var in clause:\n var = abs(var)\n if var not in variabs:\n variabs.add(var)\n return variabs\n",
"<import token>\n<docstring token>\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n return func(*args, **kwargs)\n return inner\n\n\n<function token>\n\n\ndef cnf_from_str(prop):\n \"\"\"\n Utilitaire pour obtenir les clauses d'une formule sous forme \n CNF ou Formule Normale Conjonctive donc de la forme:\n\n \"(1 or 2 or ...) and (3 or -2 or ...) ... \"\n\n Retourne un resultat sous la forme:\n 0: [1, 2]\n 1: [3, -2]\n .\n .\n .\n n: ...\n\n Return: [[var1, var2], [var3, -var4], ...]\n\n \"\"\"\n splitted = prop.split('and')\n clauses = []\n for clause in splitted:\n clause = clause.replace('(', '').replace(')', '')\n str_clause = clause.split('or')\n int_litterals = [int(el) for el in str_clause]\n clauses.append(int_litterals)\n return clauses\n\n\n<function token>\n",
"<import token>\n<docstring token>\n\n\ndef is_cnf(func):\n\n def inner(*args, **kwargs):\n error_msg_list = \"'args[0]' should be 'list' of 'list'\"\n error_msg_elmt = \"Elements of a 'clause' should only be 'integer'\"\n if type(args[0]) is not list:\n raise TypeError(f'{error_msg_list}, args[0]:{args[0]}')\n for supposed_clause in args[0]:\n if type(supposed_clause) is not list:\n raise TypeError(f'{error_msg_list}, \"{supposed_clause}\"')\n for el in supposed_clause:\n if type(el) is not int:\n raise TypeError(f'{error_msg_elmt}, \"{el}\"')\n return func(*args, **kwargs)\n return inner\n\n\n<function token>\n<function token>\n<function token>\n",
"<import token>\n<docstring token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,431 |
7b3f1432f7ea778e3d5c4a6ebd68a161c171d406
|
from collections import defaultdict
class Subject:
"""
Represents a Row in a matrix of training data.
It contains several features (represents the columns of the data)
and a class label.
"""
def __init__(self, features, class_label=None):
self.class_label = class_label
self.class_features = features
def print(self):
print("Subject [ class: " + str(self.class_label) + " ]")
def group_has_same_label(subjects):
"""
Returns true if all subjects contains the same class label.
:param subjects:
:return: boolean
"""
first_label = subjects[0].class_label
for subject in subjects:
if subject.class_label != first_label:
# if the subjects class label is not the same as the first label
# then this group does not all have the same label
return False
# They all have the same class label
return True
def most_common_class_label(subjects):
"""
Picks the class label which is most common amongst the given set of subjects.
:param subjects:
:return: class label (Any type)
"""
result_set = defaultdict(int)
for subject in subjects:
result_set[subject.class_label[0]] += 1
return max(result_set, key=result_set.get)
|
[
"from collections import defaultdict\n\nclass Subject:\n \"\"\"\n Represents a Row in a matrix of training data.\n It contains several features (represents the columns of the data)\n and a class label.\n \"\"\"\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print(\"Subject [ class: \" + str(self.class_label) + \" ]\")\n\n\ndef group_has_same_label(subjects):\n \"\"\"\n Returns true if all subjects contains the same class label.\n :param subjects:\n :return: boolean\n \"\"\"\n first_label = subjects[0].class_label\n for subject in subjects:\n if subject.class_label != first_label:\n # if the subjects class label is not the same as the first label\n # then this group does not all have the same label\n return False\n\n # They all have the same class label\n return True\n\n\ndef most_common_class_label(subjects):\n \"\"\"\n Picks the class label which is most common amongst the given set of subjects.\n :param subjects:\n :return: class label (Any type)\n \"\"\"\n result_set = defaultdict(int)\n for subject in subjects:\n result_set[subject.class_label[0]] += 1\n\n return max(result_set, key=result_set.get)\n",
"from collections import defaultdict\n\n\nclass Subject:\n \"\"\"\n Represents a Row in a matrix of training data.\n It contains several features (represents the columns of the data)\n and a class label.\n \"\"\"\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print('Subject [ class: ' + str(self.class_label) + ' ]')\n\n\ndef group_has_same_label(subjects):\n \"\"\"\n Returns true if all subjects contains the same class label.\n :param subjects:\n :return: boolean\n \"\"\"\n first_label = subjects[0].class_label\n for subject in subjects:\n if subject.class_label != first_label:\n return False\n return True\n\n\ndef most_common_class_label(subjects):\n \"\"\"\n Picks the class label which is most common amongst the given set of subjects.\n :param subjects:\n :return: class label (Any type)\n \"\"\"\n result_set = defaultdict(int)\n for subject in subjects:\n result_set[subject.class_label[0]] += 1\n return max(result_set, key=result_set.get)\n",
"<import token>\n\n\nclass Subject:\n \"\"\"\n Represents a Row in a matrix of training data.\n It contains several features (represents the columns of the data)\n and a class label.\n \"\"\"\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print('Subject [ class: ' + str(self.class_label) + ' ]')\n\n\ndef group_has_same_label(subjects):\n \"\"\"\n Returns true if all subjects contains the same class label.\n :param subjects:\n :return: boolean\n \"\"\"\n first_label = subjects[0].class_label\n for subject in subjects:\n if subject.class_label != first_label:\n return False\n return True\n\n\ndef most_common_class_label(subjects):\n \"\"\"\n Picks the class label which is most common amongst the given set of subjects.\n :param subjects:\n :return: class label (Any type)\n \"\"\"\n result_set = defaultdict(int)\n for subject in subjects:\n result_set[subject.class_label[0]] += 1\n return max(result_set, key=result_set.get)\n",
"<import token>\n\n\nclass Subject:\n \"\"\"\n Represents a Row in a matrix of training data.\n It contains several features (represents the columns of the data)\n and a class label.\n \"\"\"\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print('Subject [ class: ' + str(self.class_label) + ' ]')\n\n\n<function token>\n\n\ndef most_common_class_label(subjects):\n \"\"\"\n Picks the class label which is most common amongst the given set of subjects.\n :param subjects:\n :return: class label (Any type)\n \"\"\"\n result_set = defaultdict(int)\n for subject in subjects:\n result_set[subject.class_label[0]] += 1\n return max(result_set, key=result_set.get)\n",
"<import token>\n\n\nclass Subject:\n \"\"\"\n Represents a Row in a matrix of training data.\n It contains several features (represents the columns of the data)\n and a class label.\n \"\"\"\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print('Subject [ class: ' + str(self.class_label) + ' ]')\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass Subject:\n <docstring token>\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n\n def print(self):\n print('Subject [ class: ' + str(self.class_label) + ' ]')\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass Subject:\n <docstring token>\n\n def __init__(self, features, class_label=None):\n self.class_label = class_label\n self.class_features = features\n <function token>\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass Subject:\n <docstring token>\n <function token>\n <function token>\n\n\n<function token>\n<function token>\n",
"<import token>\n<class token>\n<function token>\n<function token>\n"
] | false |
99,432 |
23fa3fb8197959f16d03e14e20544103bf2cff50
|
import subprocess
import time
from random import random, randint, randrange
import uuid
from bertopic import BERTopic
import numpy as np
from BuisnessLayer.AnalysisManager.DataObjects import AnalyzedTweet, Claim
import pandas as pd
import nltk
# nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import text2emotion as te
from BuisnessLayer.AnalysisManager.DataObjects import *
def get_emotion_by_id(id):
if id == 1:
return 'Anger'
elif id == 2:
return 'Disgust'
elif id == 3:
return 'Sad'
elif id == 4:
return 'Happy'
elif id == 5:
return 'Surprise'
else:
return 'Fear'
author_columns = ['name', 'domain', 'author_guid', 'author_screen_name',
'author_full_name', 'author_osn_id', 'description', 'created_at',
'statuses_count', 'followers_count', 'favourites_count',
'friends_count', 'listed_count', 'language', 'profile_background_color',
'profile_background_tile', 'profile_banner_url', 'profile_image_url',
'profile_link_color', 'profile_sidebar_fill_color',
'profile_text_color', 'default_profile', 'contributors_enabled',
'default_profile_image', 'geo_enabled', 'protected', 'location',
'notifications', 'time_zone', 'url', 'utc_offset', 'verified',
'is_suspended_or_not_exists', 'default_post_format', 'likes_count',
'allow_questions', 'allow_anonymous_questions', 'image_size',
'media_path', 'author_type', 'bad_actors_collector_insertion_date',
'xml_importer_insertion_date', 'vico_dump_insertion_date',
'missing_data_complementor_insertion_date',
'bad_actors_markup_insertion_date',
'mark_missing_bad_actor_retweeters_insertion_date', 'author_sub_type',
'timeline_overlap_insertion_date',
'original_tweet_importer_insertion_date']
post_columns = ['post_id', 'author', 'guid', 'title', 'url', 'date', 'content',
'description', 'is_detailed', 'is_LB', 'is_valid', 'domain',
'author_guid', 'media_path', 'post_osn_guid', 'post_type',
'post_format', 'reblog_key', 'tags', 'is_created_via_bookmarklet',
'is_created_via_mobile', 'source_url', 'source_title', 'is_liked',
'post_state', 'post_osn_id', 'retweet_count', 'favorite_count',
'created_at', 'xml_importer_insertion_date',
'timeline_importer_insertion_date',
'original_tweet_importer_insertion_date']
claims_columns = ['claim_id', 'title', 'description', 'url', 'verdict_date', 'keywords',
'domain', 'verdict', 'category', 'sub_category']
connection_columns = ['claim_id', 'post_id']
# subprocess.call(['python','run_dataset_builder.py','configuration/config_demo.ini'],cwd= r'D:\aviad fake v3\fake-news-framework_Py3',shell=True)
# ours, should write also stub
class ClassifierAdapter:
def __init__(self):
self.sid = SentimentIntensityAnalyzer()
self.i=0
def get_sentiment(self,text) -> int:
snt = self.sid.polarity_scores(text)
return round(snt['pos']*3-snt['neg']*3)
def get_emotion(self,text):
emo = te.get_emotion(text)
return max(emo, key=emo.get) # The output we received,
def _trends_to_csv(self, trends_dict, path="C:/fake-news-framework_Py3/data/input/tryout/"):
topics = []
tweets = []
authors = []
topic_tweet_connection = []
for trend in trends_dict.keys():
for topic in trends_dict[trend].claims:
topics.append({'claim_id':topic.id,'title': topic.name}) # check what is the input
for tweet in topic.tweets:
topic_tweet_connection.append({'claim_id': topic.id, 'post_id': tweet.id})
tweets.append({'post_id':tweet.id,'author':tweet.author_name,'content':tweet.content,'retweet_count':tweet.retweet_count, 'favorite_count':tweet.favorite_count})
authors.append({'name':tweet.author_name})
pd.DataFrame(topics, columns=claims_columns).to_csv(path + "claims.csv",index=False)
pd.DataFrame(tweets, columns=post_columns).to_csv(path + "posts.csv",index=False)
pd.DataFrame(authors, columns=author_columns).to_csv(path + "authors.csv",index=False)
pd.DataFrame(topic_tweet_connection, columns=connection_columns).to_csv(path + "claim_tweet_connection.csv",index=False)
self.i+=1
def _classify_topic(self):
subprocess.call(['python','run_dataset_builder.py','configuration/config_demo.ini'],cwd= r'C:/fake-news-framework_Py3',shell=True)
results = pd.read_csv("C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv")[['author_guid','pred']]
return results
def analyze_trends(self, trends_dict, callback): # trends_dict is type of dict {<trend name> : <Trend>}
processed_data = {}
if len(trends_dict)==0:
return
self._trends_to_csv(trends_dict)
results = self._classify_topic()
print("got classifier results\nparsing the results and running sentiment and emotion")
for trend in trends_dict.keys():
print("start trend {}".format(trend))
if trend not in processed_data:
processed_data[trend] = list()
for topic in trends_dict[trend].claims:
tweets = list()
for tweet in topic.tweets:
rand = randrange(100)
if rand < 50:
prediction = "fake"
else:
prediction = "true"
# sentiment = randint(-3, 3)
sentiment = self.get_sentiment(tweet.content)
# rand = randrange(6)
emotion = self.get_emotion(tweet.content)
analyzed_tweet = AnalyzedTweet(tweet.id, tweet.author_name, tweet.content,tweet.location,tweet.date,
tweet.trend_id,tweet.favorite_count,tweet.retweet_count, emotion, sentiment,
prediction)
tweets.append(analyzed_tweet)
print(f"add tweet {tweet} to the topic {topic}")
print(f"save the topic {topic}, with the list of tweets: {tweets}")
processed_data[trend].append(Claim(topic.name, tweets,topic.id))
time.sleep(1)
results['pred'] = results['pred'].apply(lambda x:"True" if x else "Fake")
return callback(processed_data, trends_dict,results)
def analyze_snopes(self, data, callback): # data is type of dict {<claim name> : list <tweets>}
# print(data)
# processed_data = {}
# for key in data.keys():
# if key not in processed_data:
# processed_data[key]={}
# for tweet in data[key].keys():
# processed_data[key][tweet]={}
# rand = randrange(100)
# if rand < 50:
# processed_data[key][tweet]['prediction'] = "wow it's fake"
# else:
# processed_data[key][tweet]['prediction'] = "100% true"
# sentiment = randint(-3, 3)
# processed_data[key][tweet]['sentiment'] = sentiment
# rand = randrange(6)
# processed_data[key][tweet]['emotional'] = get_emotion_by_id(rand)
processed_data = {}
for claim in data.keys():
# if claim not in processed_data:
# processed_data[claim]= list()
tweets = list()
for tweet in data[claim]:
rand = randrange(100)
if rand < 50:
prediction = "fake"
else:
prediction = "true"
sentiment = randint(-3, 3)
rand = randrange(6)
emotion = get_emotion_by_id(rand)
analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'], tweet['content'], emotion, sentiment,
prediction)
tweets.append(analyzed_tweet)
if claim in processed_data.keys():
processed_data[claim].append(Claim(claim, tweets))
else:
processed_data[claim] = Claim(claim, tweets)
time.sleep(1)
return callback(processed_data)
def get_claims_from_trend(self, trends_tweets):
claims = {'claim1': {}, 'claim2': {}}
for status in trends_tweets:
rand = randrange(10)
# print(status.id)
# print(status.text)
# print(status.author.name)
if rand < 5:
claims["claim1"][status.id]= {'id': status.id, 'author': status.author_name, 'content': status.content}
else:
# print(status)
claims["claim2"][status.id]= {'id': status.id, 'author': status.author_name, 'content': status.content}
return claims
def _get_claim_from_trend(self, trends_tweets):
print("topic model")
df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])
df = df[['id', 'content','author_name']]
if len(df) < 15:
print("less then 15 tweets, creating 1 topic")
from collections import Counter
claim_text = ' '.join([txt[0] for txt in
Counter(" ".join(df['content'].str.replace("RT", '').values).split(' ')).most_common(
10)])
return [Claim(claim_text,trends_tweets,0)]
print("build bertopic")
bt = BERTopic()
print("fit bertopic")
topics = bt.fit_transform(df['content'].str.replace("RT", '').values)
print("done fitting")
df['topic_id'] = topics[0]
topic_info = bt.get_topics()
topics_text = {}
for key in topic_info.keys():
lst = topic_info[key]
topics_text[key] = ' '.join([x[0] for x in lst])
# df['topic_text'] = df['topic_id'].apply(lambda x:topics_text[x])
claims = []
print("attaching tweet object for topics")
for t in topic_info.keys():
fitered = df[df['topic_id'] == t]
tweets = list(filter(lambda t:t.id in fitered['id'].values,trends_tweets))
claims.append(Claim(topics_text[t], tweets,0))
return claims
|
[
"import subprocess\nimport time\nfrom random import random, randint, randrange\nimport uuid\nfrom bertopic import BERTopic\nimport numpy as np\nfrom BuisnessLayer.AnalysisManager.DataObjects import AnalyzedTweet, Claim\nimport pandas as pd\nimport nltk\n# nltk.download('vader_lexicon')\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport text2emotion as te\nfrom BuisnessLayer.AnalysisManager.DataObjects import *\n\ndef get_emotion_by_id(id):\n if id == 1:\n return 'Anger'\n elif id == 2:\n return 'Disgust'\n elif id == 3:\n return 'Sad'\n elif id == 4:\n return 'Happy'\n elif id == 5:\n return 'Surprise'\n else:\n return 'Fear'\n\n\nauthor_columns = ['name', 'domain', 'author_guid', 'author_screen_name',\n 'author_full_name', 'author_osn_id', 'description', 'created_at',\n 'statuses_count', 'followers_count', 'favourites_count',\n 'friends_count', 'listed_count', 'language', 'profile_background_color',\n 'profile_background_tile', 'profile_banner_url', 'profile_image_url',\n 'profile_link_color', 'profile_sidebar_fill_color',\n 'profile_text_color', 'default_profile', 'contributors_enabled',\n 'default_profile_image', 'geo_enabled', 'protected', 'location',\n 'notifications', 'time_zone', 'url', 'utc_offset', 'verified',\n 'is_suspended_or_not_exists', 'default_post_format', 'likes_count',\n 'allow_questions', 'allow_anonymous_questions', 'image_size',\n 'media_path', 'author_type', 'bad_actors_collector_insertion_date',\n 'xml_importer_insertion_date', 'vico_dump_insertion_date',\n 'missing_data_complementor_insertion_date',\n 'bad_actors_markup_insertion_date',\n 'mark_missing_bad_actor_retweeters_insertion_date', 'author_sub_type',\n 'timeline_overlap_insertion_date',\n 'original_tweet_importer_insertion_date']\n\npost_columns = ['post_id', 'author', 'guid', 'title', 'url', 'date', 'content',\n 'description', 'is_detailed', 'is_LB', 'is_valid', 'domain',\n 'author_guid', 'media_path', 'post_osn_guid', 'post_type',\n 'post_format', 'reblog_key', 'tags', 'is_created_via_bookmarklet',\n 'is_created_via_mobile', 'source_url', 'source_title', 'is_liked',\n 'post_state', 'post_osn_id', 'retweet_count', 'favorite_count',\n 'created_at', 'xml_importer_insertion_date',\n 'timeline_importer_insertion_date',\n 'original_tweet_importer_insertion_date']\n\nclaims_columns = ['claim_id', 'title', 'description', 'url', 'verdict_date', 'keywords',\n 'domain', 'verdict', 'category', 'sub_category']\n\nconnection_columns = ['claim_id', 'post_id']\n\n# subprocess.call(['python','run_dataset_builder.py','configuration/config_demo.ini'],cwd= r'D:\\aviad fake v3\\fake-news-framework_Py3',shell=True)\n# ours, should write also stub\nclass ClassifierAdapter:\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i=0\n def get_sentiment(self,text) -> int:\n snt = self.sid.polarity_scores(text)\n return round(snt['pos']*3-snt['neg']*3)\n\n def get_emotion(self,text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get) # The output we received,\n\n def _trends_to_csv(self, trends_dict, path=\"C:/fake-news-framework_Py3/data/input/tryout/\"):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id':topic.id,'title': topic.name}) # check what is the input\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id, 'post_id': tweet.id})\n tweets.append({'post_id':tweet.id,'author':tweet.author_name,'content':tweet.content,'retweet_count':tweet.retweet_count, 'favorite_count':tweet.favorite_count})\n authors.append({'name':tweet.author_name})\n\n pd.DataFrame(topics, columns=claims_columns).to_csv(path + \"claims.csv\",index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path + \"posts.csv\",index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path + \"authors.csv\",index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns).to_csv(path + \"claim_tweet_connection.csv\",index=False)\n self.i+=1\n\n def _classify_topic(self):\n subprocess.call(['python','run_dataset_builder.py','configuration/config_demo.ini'],cwd= r'C:/fake-news-framework_Py3',shell=True)\n results = pd.read_csv(\"C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv\")[['author_guid','pred']]\n return results\n\n\n def analyze_trends(self, trends_dict, callback): # trends_dict is type of dict {<trend name> : <Trend>}\n processed_data = {}\n if len(trends_dict)==0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\"got classifier results\\nparsing the results and running sentiment and emotion\")\n for trend in trends_dict.keys():\n print(\"start trend {}\".format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = \"fake\"\n else:\n prediction = \"true\"\n # sentiment = randint(-3, 3)\n sentiment = self.get_sentiment(tweet.content)\n # rand = randrange(6)\n emotion = self.get_emotion(tweet.content)\n\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.author_name, tweet.content,tweet.location,tweet.date,\n tweet.trend_id,tweet.favorite_count,tweet.retweet_count, emotion, sentiment,\n prediction)\n tweets.append(analyzed_tweet)\n print(f\"add tweet {tweet} to the topic {topic}\")\n print(f\"save the topic {topic}, with the list of tweets: {tweets}\")\n processed_data[trend].append(Claim(topic.name, tweets,topic.id))\n\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x:\"True\" if x else \"Fake\")\n return callback(processed_data, trends_dict,results)\n\n def analyze_snopes(self, data, callback): # data is type of dict {<claim name> : list <tweets>}\n # print(data)\n # processed_data = {}\n # for key in data.keys():\n # if key not in processed_data:\n # processed_data[key]={}\n # for tweet in data[key].keys():\n # processed_data[key][tweet]={}\n # rand = randrange(100)\n # if rand < 50:\n # processed_data[key][tweet]['prediction'] = \"wow it's fake\"\n # else:\n # processed_data[key][tweet]['prediction'] = \"100% true\"\n # sentiment = randint(-3, 3)\n # processed_data[key][tweet]['sentiment'] = sentiment\n # rand = randrange(6)\n # processed_data[key][tweet]['emotional'] = get_emotion_by_id(rand)\n\n processed_data = {}\n for claim in data.keys():\n # if claim not in processed_data:\n # processed_data[claim]= list()\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = \"fake\"\n else:\n prediction = \"true\"\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'], tweet['content'], emotion, sentiment,\n prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n # print(status.id)\n # print(status.text)\n # print(status.author.name)\n if rand < 5:\n claims[\"claim1\"][status.id]= {'id': status.id, 'author': status.author_name, 'content': status.content}\n else:\n # print(status)\n claims[\"claim2\"][status.id]= {'id': status.id, 'author': status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print(\"topic model\")\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content','author_name']]\n if len(df) < 15:\n print(\"less then 15 tweets, creating 1 topic\")\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in\n Counter(\" \".join(df['content'].str.replace(\"RT\", '').values).split(' ')).most_common(\n 10)])\n return [Claim(claim_text,trends_tweets,0)]\n print(\"build bertopic\")\n bt = BERTopic()\n print(\"fit bertopic\")\n topics = bt.fit_transform(df['content'].str.replace(\"RT\", '').values)\n print(\"done fitting\")\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n\n topics_text[key] = ' '.join([x[0] for x in lst])\n\n # df['topic_text'] = df['topic_id'].apply(lambda x:topics_text[x])\n claims = []\n print(\"attaching tweet object for topics\")\n for t in topic_info.keys():\n\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t:t.id in fitered['id'].values,trends_tweets))\n claims.append(Claim(topics_text[t], tweets,0))\n return claims\n",
"import subprocess\nimport time\nfrom random import random, randint, randrange\nimport uuid\nfrom bertopic import BERTopic\nimport numpy as np\nfrom BuisnessLayer.AnalysisManager.DataObjects import AnalyzedTweet, Claim\nimport pandas as pd\nimport nltk\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport text2emotion as te\nfrom BuisnessLayer.AnalysisManager.DataObjects import *\n\n\ndef get_emotion_by_id(id):\n if id == 1:\n return 'Anger'\n elif id == 2:\n return 'Disgust'\n elif id == 3:\n return 'Sad'\n elif id == 4:\n return 'Happy'\n elif id == 5:\n return 'Surprise'\n else:\n return 'Fear'\n\n\nauthor_columns = ['name', 'domain', 'author_guid', 'author_screen_name',\n 'author_full_name', 'author_osn_id', 'description', 'created_at',\n 'statuses_count', 'followers_count', 'favourites_count',\n 'friends_count', 'listed_count', 'language', 'profile_background_color',\n 'profile_background_tile', 'profile_banner_url', 'profile_image_url',\n 'profile_link_color', 'profile_sidebar_fill_color',\n 'profile_text_color', 'default_profile', 'contributors_enabled',\n 'default_profile_image', 'geo_enabled', 'protected', 'location',\n 'notifications', 'time_zone', 'url', 'utc_offset', 'verified',\n 'is_suspended_or_not_exists', 'default_post_format', 'likes_count',\n 'allow_questions', 'allow_anonymous_questions', 'image_size',\n 'media_path', 'author_type', 'bad_actors_collector_insertion_date',\n 'xml_importer_insertion_date', 'vico_dump_insertion_date',\n 'missing_data_complementor_insertion_date',\n 'bad_actors_markup_insertion_date',\n 'mark_missing_bad_actor_retweeters_insertion_date', 'author_sub_type',\n 'timeline_overlap_insertion_date', 'original_tweet_importer_insertion_date'\n ]\npost_columns = ['post_id', 'author', 'guid', 'title', 'url', 'date',\n 'content', 'description', 'is_detailed', 'is_LB', 'is_valid', 'domain',\n 'author_guid', 'media_path', 'post_osn_guid', 'post_type',\n 'post_format', 'reblog_key', 'tags', 'is_created_via_bookmarklet',\n 'is_created_via_mobile', 'source_url', 'source_title', 'is_liked',\n 'post_state', 'post_osn_id', 'retweet_count', 'favorite_count',\n 'created_at', 'xml_importer_insertion_date',\n 'timeline_importer_insertion_date',\n 'original_tweet_importer_insertion_date']\nclaims_columns = ['claim_id', 'title', 'description', 'url', 'verdict_date',\n 'keywords', 'domain', 'verdict', 'category', 'sub_category']\nconnection_columns = ['claim_id', 'post_id']\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n\n def get_sentiment(self, text) ->int:\n snt = self.sid.polarity_scores(text)\n return round(snt['pos'] * 3 - snt['neg'] * 3)\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n\n def _trends_to_csv(self, trends_dict, path=\n 'C:/fake-news-framework_Py3/data/input/tryout/'):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id': topic.id, 'title': topic.name})\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id,\n 'post_id': tweet.id})\n tweets.append({'post_id': tweet.id, 'author': tweet.\n author_name, 'content': tweet.content,\n 'retweet_count': tweet.retweet_count,\n 'favorite_count': tweet.favorite_count})\n authors.append({'name': tweet.author_name})\n pd.DataFrame(topics, columns=claims_columns).to_csv(path +\n 'claims.csv', index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path +\n 'posts.csv', index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path +\n 'authors.csv', index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns\n ).to_csv(path + 'claim_tweet_connection.csv', index=False)\n self.i += 1\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n\n\ndef get_emotion_by_id(id):\n if id == 1:\n return 'Anger'\n elif id == 2:\n return 'Disgust'\n elif id == 3:\n return 'Sad'\n elif id == 4:\n return 'Happy'\n elif id == 5:\n return 'Surprise'\n else:\n return 'Fear'\n\n\nauthor_columns = ['name', 'domain', 'author_guid', 'author_screen_name',\n 'author_full_name', 'author_osn_id', 'description', 'created_at',\n 'statuses_count', 'followers_count', 'favourites_count',\n 'friends_count', 'listed_count', 'language', 'profile_background_color',\n 'profile_background_tile', 'profile_banner_url', 'profile_image_url',\n 'profile_link_color', 'profile_sidebar_fill_color',\n 'profile_text_color', 'default_profile', 'contributors_enabled',\n 'default_profile_image', 'geo_enabled', 'protected', 'location',\n 'notifications', 'time_zone', 'url', 'utc_offset', 'verified',\n 'is_suspended_or_not_exists', 'default_post_format', 'likes_count',\n 'allow_questions', 'allow_anonymous_questions', 'image_size',\n 'media_path', 'author_type', 'bad_actors_collector_insertion_date',\n 'xml_importer_insertion_date', 'vico_dump_insertion_date',\n 'missing_data_complementor_insertion_date',\n 'bad_actors_markup_insertion_date',\n 'mark_missing_bad_actor_retweeters_insertion_date', 'author_sub_type',\n 'timeline_overlap_insertion_date', 'original_tweet_importer_insertion_date'\n ]\npost_columns = ['post_id', 'author', 'guid', 'title', 'url', 'date',\n 'content', 'description', 'is_detailed', 'is_LB', 'is_valid', 'domain',\n 'author_guid', 'media_path', 'post_osn_guid', 'post_type',\n 'post_format', 'reblog_key', 'tags', 'is_created_via_bookmarklet',\n 'is_created_via_mobile', 'source_url', 'source_title', 'is_liked',\n 'post_state', 'post_osn_id', 'retweet_count', 'favorite_count',\n 'created_at', 'xml_importer_insertion_date',\n 'timeline_importer_insertion_date',\n 'original_tweet_importer_insertion_date']\nclaims_columns = ['claim_id', 'title', 'description', 'url', 'verdict_date',\n 'keywords', 'domain', 'verdict', 'category', 'sub_category']\nconnection_columns = ['claim_id', 'post_id']\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n\n def get_sentiment(self, text) ->int:\n snt = self.sid.polarity_scores(text)\n return round(snt['pos'] * 3 - snt['neg'] * 3)\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n\n def _trends_to_csv(self, trends_dict, path=\n 'C:/fake-news-framework_Py3/data/input/tryout/'):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id': topic.id, 'title': topic.name})\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id,\n 'post_id': tweet.id})\n tweets.append({'post_id': tweet.id, 'author': tweet.\n author_name, 'content': tweet.content,\n 'retweet_count': tweet.retweet_count,\n 'favorite_count': tweet.favorite_count})\n authors.append({'name': tweet.author_name})\n pd.DataFrame(topics, columns=claims_columns).to_csv(path +\n 'claims.csv', index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path +\n 'posts.csv', index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path +\n 'authors.csv', index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns\n ).to_csv(path + 'claim_tweet_connection.csv', index=False)\n self.i += 1\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n\n\ndef get_emotion_by_id(id):\n if id == 1:\n return 'Anger'\n elif id == 2:\n return 'Disgust'\n elif id == 3:\n return 'Sad'\n elif id == 4:\n return 'Happy'\n elif id == 5:\n return 'Surprise'\n else:\n return 'Fear'\n\n\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n\n def get_sentiment(self, text) ->int:\n snt = self.sid.polarity_scores(text)\n return round(snt['pos'] * 3 - snt['neg'] * 3)\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n\n def _trends_to_csv(self, trends_dict, path=\n 'C:/fake-news-framework_Py3/data/input/tryout/'):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id': topic.id, 'title': topic.name})\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id,\n 'post_id': tweet.id})\n tweets.append({'post_id': tweet.id, 'author': tweet.\n author_name, 'content': tweet.content,\n 'retweet_count': tweet.retweet_count,\n 'favorite_count': tweet.favorite_count})\n authors.append({'name': tweet.author_name})\n pd.DataFrame(topics, columns=claims_columns).to_csv(path +\n 'claims.csv', index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path +\n 'posts.csv', index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path +\n 'authors.csv', index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns\n ).to_csv(path + 'claim_tweet_connection.csv', index=False)\n self.i += 1\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n\n def get_sentiment(self, text) ->int:\n snt = self.sid.polarity_scores(text)\n return round(snt['pos'] * 3 - snt['neg'] * 3)\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n\n def _trends_to_csv(self, trends_dict, path=\n 'C:/fake-news-framework_Py3/data/input/tryout/'):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id': topic.id, 'title': topic.name})\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id,\n 'post_id': tweet.id})\n tweets.append({'post_id': tweet.id, 'author': tweet.\n author_name, 'content': tweet.content,\n 'retweet_count': tweet.retweet_count,\n 'favorite_count': tweet.favorite_count})\n authors.append({'name': tweet.author_name})\n pd.DataFrame(topics, columns=claims_columns).to_csv(path +\n 'claims.csv', index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path +\n 'posts.csv', index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path +\n 'authors.csv', index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns\n ).to_csv(path + 'claim_tweet_connection.csv', index=False)\n self.i += 1\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n\n def _trends_to_csv(self, trends_dict, path=\n 'C:/fake-news-framework_Py3/data/input/tryout/'):\n topics = []\n tweets = []\n authors = []\n topic_tweet_connection = []\n for trend in trends_dict.keys():\n for topic in trends_dict[trend].claims:\n topics.append({'claim_id': topic.id, 'title': topic.name})\n for tweet in topic.tweets:\n topic_tweet_connection.append({'claim_id': topic.id,\n 'post_id': tweet.id})\n tweets.append({'post_id': tweet.id, 'author': tweet.\n author_name, 'content': tweet.content,\n 'retweet_count': tweet.retweet_count,\n 'favorite_count': tweet.favorite_count})\n authors.append({'name': tweet.author_name})\n pd.DataFrame(topics, columns=claims_columns).to_csv(path +\n 'claims.csv', index=False)\n pd.DataFrame(tweets, columns=post_columns).to_csv(path +\n 'posts.csv', index=False)\n pd.DataFrame(authors, columns=author_columns).to_csv(path +\n 'authors.csv', index=False)\n pd.DataFrame(topic_tweet_connection, columns=connection_columns\n ).to_csv(path + 'claim_tweet_connection.csv', index=False)\n self.i += 1\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n <function token>\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n\n def analyze_snopes(self, data, callback):\n processed_data = {}\n for claim in data.keys():\n tweets = list()\n for tweet in data[claim]:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = randint(-3, 3)\n rand = randrange(6)\n emotion = get_emotion_by_id(rand)\n analyzed_tweet = AnalyzedTweet(tweet['id'], tweet['author'],\n tweet['content'], emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n if claim in processed_data.keys():\n processed_data[claim].append(Claim(claim, tweets))\n else:\n processed_data[claim] = Claim(claim, tweets)\n time.sleep(1)\n return callback(processed_data)\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n <function token>\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n <function token>\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n\n def _get_claim_from_trend(self, trends_tweets):\n print('topic model')\n df = pd.DataFrame([tweet.__dict__ for tweet in trends_tweets])\n df = df[['id', 'content', 'author_name']]\n if len(df) < 15:\n print('less then 15 tweets, creating 1 topic')\n from collections import Counter\n claim_text = ' '.join([txt[0] for txt in Counter(' '.join(df[\n 'content'].str.replace('RT', '').values).split(' ')).\n most_common(10)])\n return [Claim(claim_text, trends_tweets, 0)]\n print('build bertopic')\n bt = BERTopic()\n print('fit bertopic')\n topics = bt.fit_transform(df['content'].str.replace('RT', '').values)\n print('done fitting')\n df['topic_id'] = topics[0]\n topic_info = bt.get_topics()\n topics_text = {}\n for key in topic_info.keys():\n lst = topic_info[key]\n topics_text[key] = ' '.join([x[0] for x in lst])\n claims = []\n print('attaching tweet object for topics')\n for t in topic_info.keys():\n fitered = df[df['topic_id'] == t]\n tweets = list(filter(lambda t: t.id in fitered['id'].values,\n trends_tweets))\n claims.append(Claim(topics_text[t], tweets, 0))\n return claims\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n\n def get_emotion(self, text):\n emo = te.get_emotion(text)\n return max(emo, key=emo.get)\n <function token>\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n <function token>\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n <function token>\n <function token>\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n\n def analyze_trends(self, trends_dict, callback):\n processed_data = {}\n if len(trends_dict) == 0:\n return\n self._trends_to_csv(trends_dict)\n results = self._classify_topic()\n print(\n 'got classifier results\\nparsing the results and running sentiment and emotion'\n )\n for trend in trends_dict.keys():\n print('start trend {}'.format(trend))\n if trend not in processed_data:\n processed_data[trend] = list()\n for topic in trends_dict[trend].claims:\n tweets = list()\n for tweet in topic.tweets:\n rand = randrange(100)\n if rand < 50:\n prediction = 'fake'\n else:\n prediction = 'true'\n sentiment = self.get_sentiment(tweet.content)\n emotion = self.get_emotion(tweet.content)\n analyzed_tweet = AnalyzedTweet(tweet.id, tweet.\n author_name, tweet.content, tweet.location, tweet.\n date, tweet.trend_id, tweet.favorite_count, tweet.\n retweet_count, emotion, sentiment, prediction)\n tweets.append(analyzed_tweet)\n print(f'add tweet {tweet} to the topic {topic}')\n print(\n f'save the topic {topic}, with the list of tweets: {tweets}'\n )\n processed_data[trend].append(Claim(topic.name, tweets,\n topic.id))\n time.sleep(1)\n results['pred'] = results['pred'].apply(lambda x: 'True' if x else\n 'Fake')\n return callback(processed_data, trends_dict, results)\n <function token>\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n <function token>\n <function token>\n\n def _classify_topic(self):\n subprocess.call(['python', 'run_dataset_builder.py',\n 'configuration/config_demo.ini'], cwd=\n 'C:/fake-news-framework_Py3', shell=True)\n results = pd.read_csv(\n 'C:/fake-news-framework_Py3/data/output/D/labeled_predictions.csv'\n )[['author_guid', 'pred']]\n return results\n <function token>\n <function token>\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_claims_from_trend(self, trends_tweets):\n claims = {'claim1': {}, 'claim2': {}}\n for status in trends_tweets:\n rand = randrange(10)\n if rand < 5:\n claims['claim1'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n else:\n claims['claim2'][status.id] = {'id': status.id, 'author':\n status.author_name, 'content': status.content}\n return claims\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n\n def __init__(self):\n self.sid = SentimentIntensityAnalyzer()\n self.i = 0\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n\n\nclass ClassifierAdapter:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<function token>\n<assignment token>\n<class token>\n"
] | false |
99,433 |
201dd6d498e77b6b604f512aca1af0c55688f5ae
|
n1=float(input('digite uma nota :'))
n2=float(input('digite outra nota : '))
m = (n1+n2) /2
print('digitando notas entre {:.1f} e {:.1f} sua média é \n{:.1f}'.format(n1,n2,m))
|
[
"n1=float(input('digite uma nota :'))\nn2=float(input('digite outra nota : '))\nm = (n1+n2) /2\n\nprint('digitando notas entre {:.1f} e {:.1f} sua média é \\n{:.1f}'.format(n1,n2,m))\n",
"n1 = float(input('digite uma nota :'))\nn2 = float(input('digite outra nota : '))\nm = (n1 + n2) / 2\nprint(\"\"\"digitando notas entre {:.1f} e {:.1f} sua média é \n{:.1f}\"\"\".\n format(n1, n2, m))\n",
"<assignment token>\nprint(\"\"\"digitando notas entre {:.1f} e {:.1f} sua média é \n{:.1f}\"\"\".\n format(n1, n2, m))\n",
"<assignment token>\n<code token>\n"
] | false |
99,434 |
8c111c1967b0d38e31252b3193cb487e4fa3ae95
|
from django.db import models
# Create your models here.
class Programming_Authors(models.Model):
programming_languages = models.CharField(max_length=20)
authors = models.CharField(max_length=100)
date_of_birth = models.DateField()
def __str__(self):
return self.authors
class ProgrammingFramework(models.Model):
framework_name = models.CharField(max_length=40)
framework_type = models.CharField(max_length=40)
programming_authors = models.ForeignKey(Programming_Authors,on_delete=models.CASCADE)
def __str__(self):
return self.framework_name
|
[
"from django.db import models\n\n# Create your models here.\nclass Programming_Authors(models.Model):\n programming_languages = models.CharField(max_length=20)\n authors = models.CharField(max_length=100)\n date_of_birth = models.DateField() \n def __str__(self):\n return self.authors\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors,on_delete=models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"from django.db import models\n\n\nclass Programming_Authors(models.Model):\n programming_languages = models.CharField(max_length=20)\n authors = models.CharField(max_length=100)\n date_of_birth = models.DateField()\n\n def __str__(self):\n return self.authors\n\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors, on_delete=\n models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n\n\nclass Programming_Authors(models.Model):\n programming_languages = models.CharField(max_length=20)\n authors = models.CharField(max_length=100)\n date_of_birth = models.DateField()\n\n def __str__(self):\n return self.authors\n\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors, on_delete=\n models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n\n\nclass Programming_Authors(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.authors\n\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors, on_delete=\n models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n\n\nclass Programming_Authors(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors, on_delete=\n models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n<class token>\n\n\nclass ProgrammingFramework(models.Model):\n framework_name = models.CharField(max_length=40)\n framework_type = models.CharField(max_length=40)\n programming_authors = models.ForeignKey(Programming_Authors, on_delete=\n models.CASCADE)\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n<class token>\n\n\nclass ProgrammingFramework(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __str__(self):\n return self.framework_name\n",
"<import token>\n<class token>\n\n\nclass ProgrammingFramework(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n",
"<import token>\n<class token>\n<class token>\n"
] | false |
99,435 |
88e5a31ae5cd1d4224ebe650171669263b108c16
|
from IPython.core.debugger import set_trace
from importlib import reload
import rosbag
import matplotlib.pyplot as plt
import numpy as np
from collections import namedtuple
from sim_parser import Parser
from statistics import mode
def main():
filename = '_2019-09-06-10-46-56.bag'
path = '../../../data/holodeck_sim/'
vals = plot(filename, path)
return vals
def plot(filename, pathname):
parse = Parser()
bag = rosbag.Bag(pathname + filename)
variables = parse.get_variables(bag, filename)
sec = variables.sec
nsec = variables.nsec
X = variables.X
Y = variables.Y
Z = variables.Z
time = [0]*len(sec)
for i in range(0,len(sec)):
time[i] = sec[i] + nsec[i]*1e-9
sec_c = [0]*(2*len(variables.sec_c))
Xc = [0]*(2*len(variables.sec_c))
Yc = [0]*(2*len(variables.sec_c))
Zc = [0]*(2*len(variables.sec_c))
for i in range(0,len(variables.sec_c)):
sec_c[2*i] = variables.sec_c[i]
if i < len(variables.sec_c)-1:
sec_c[2*i+1] = variables.sec_c[i+1]
else:
sec_c[2*i+1] = time[len(time)-1]
Xc[2*i] = variables.Xc[i]
Xc[2*i+1] = variables.Xc[i]
Yc[2*i] = variables.Yc[i]
Yc[2*i+1] = variables.Yc[i]
Zc[2*i] = variables.Fc[i]
Zc[2*i+1] = variables.Fc[i]
fig = plt.figure(1)
plt.plot(sec_c, Xc, color = "blue", linewidth = 1, label = "Xc")
plt.plot(time, X, color = "red", linewidth = 1, label = "X")
# plt.xlim([-5, 165])
# plt.ylim([-.5, 14.5])
plt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)
plt.xlabel('Time (s)')
plt.ylabel('X Position')
fig.suptitle('X')
# fig.savefig(path + filename + x')
plt.show()
fig = plt.figure(2)
plt.plot(sec_c, Yc, color = "blue", linewidth = 1, label = "Yc")
plt.plot(time, Y, color = "red", linewidth = 1, label = "Y")
# plt.xlim([-5, 165])
# plt.ylim([-7.5, 7.5])
plt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)
plt.xlabel('Time (s)')
plt.ylabel('Y Position')
fig.suptitle('Y')
# fig.savefig(path + filename + 'y')
fig = plt.figure(3)
plt.plot(sec_c, Zc, color = "blue", linewidth = 1, label = "Zc")
plt.plot(time, Z, color = "red", linewidth = 1, label = "Z")
# plt.xlim([-5, 165])
# plt.ylim([-50, 5])
plt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)
plt.xlabel('Time (s)')
plt.ylabel('Z Position')
fig.suptitle('Z')
# fig.savefig(path + filename + 'z')
MyStruct = namedtuple("mystruct", ("sec_c", "Xc", "Yc", "Zc", "Fc", "sec", "nsec", "time", "X", "Y", "Z"))
vals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.Zc, variables.Fc, sec, nsec, time, X, Y, Z)
return vals
if __name__ == '__main__':
vals = main()
|
[
"from IPython.core.debugger import set_trace\nfrom importlib import reload\nimport rosbag\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom collections import namedtuple\nfrom sim_parser import Parser\nfrom statistics import mode\n\ndef main():\n\tfilename = '_2019-09-06-10-46-56.bag'\n\tpath = '../../../data/holodeck_sim/'\n\tvals = plot(filename, path)\n\n\treturn vals\n\n\ndef plot(filename, pathname):\n\tparse = Parser()\n\tbag = rosbag.Bag(pathname + filename)\n\tvariables = parse.get_variables(bag, filename)\n\n\tsec = variables.sec\n\tnsec = variables.nsec\n\tX = variables.X\n\tY = variables.Y\n\tZ = variables.Z\n\ttime = [0]*len(sec)\n\tfor i in range(0,len(sec)):\n\t\ttime[i] = sec[i] + nsec[i]*1e-9\n\n\tsec_c = [0]*(2*len(variables.sec_c))\n\tXc = [0]*(2*len(variables.sec_c))\n\tYc = [0]*(2*len(variables.sec_c))\n\tZc = [0]*(2*len(variables.sec_c))\n\tfor i in range(0,len(variables.sec_c)):\n\t\tsec_c[2*i] = variables.sec_c[i]\n\t\tif i < len(variables.sec_c)-1:\n\t\t\tsec_c[2*i+1] = variables.sec_c[i+1]\n\t\telse:\n\t\t\tsec_c[2*i+1] = time[len(time)-1]\n\n\t\tXc[2*i] = variables.Xc[i]\n\t\tXc[2*i+1] = variables.Xc[i]\t\t\n\t\tYc[2*i] = variables.Yc[i]\n\t\tYc[2*i+1] = variables.Yc[i]\t\t\n\t\tZc[2*i] = variables.Fc[i]\n\t\tZc[2*i+1] = variables.Fc[i]\t\t\n\n\tfig = plt.figure(1)\n\tplt.plot(sec_c, Xc, color = \"blue\", linewidth = 1, label = \"Xc\")\n\tplt.plot(time, X, color = \"red\", linewidth = 1, label = \"X\")\n\t# plt.xlim([-5, 165])\n\t# plt.ylim([-.5, 14.5])\n\tplt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)\n\tplt.xlabel('Time (s)')\n\tplt.ylabel('X Position')\n\tfig.suptitle('X')\n\t# fig.savefig(path + filename + x')\n\tplt.show()\n\n\tfig = plt.figure(2)\n\tplt.plot(sec_c, Yc, color = \"blue\", linewidth = 1, label = \"Yc\")\n\tplt.plot(time, Y, color = \"red\", linewidth = 1, label = \"Y\")\n\t# plt.xlim([-5, 165])\n\t# plt.ylim([-7.5, 7.5])\n\tplt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)\n\tplt.xlabel('Time (s)')\n\tplt.ylabel('Y Position')\n\tfig.suptitle('Y')\n\t# fig.savefig(path + filename + 'y')\n\n\tfig = plt.figure(3)\n\tplt.plot(sec_c, Zc, color = \"blue\", linewidth = 1, label = \"Zc\")\n\tplt.plot(time, Z, color = \"red\", linewidth = 1, label = \"Z\")\n\t# plt.xlim([-5, 165])\n\t# plt.ylim([-50, 5])\n\tplt.legend(bbox_to_anchor=(1, .4), prop={'size': 8}, frameon=True)\n\tplt.xlabel('Time (s)')\n\tplt.ylabel('Z Position')\n\tfig.suptitle('Z')\n\t# fig.savefig(path + filename + 'z')\n\n\tMyStruct = namedtuple(\"mystruct\", (\"sec_c\", \"Xc\", \"Yc\", \"Zc\", \"Fc\", \"sec\", \"nsec\", \"time\", \"X\", \"Y\", \"Z\"))\n\tvals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.Zc, variables.Fc, sec, nsec, time, X, Y, Z)\n\n\treturn vals\t\n\nif __name__ == '__main__':\n vals = main()",
"from IPython.core.debugger import set_trace\nfrom importlib import reload\nimport rosbag\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom collections import namedtuple\nfrom sim_parser import Parser\nfrom statistics import mode\n\n\ndef main():\n filename = '_2019-09-06-10-46-56.bag'\n path = '../../../data/holodeck_sim/'\n vals = plot(filename, path)\n return vals\n\n\ndef plot(filename, pathname):\n parse = Parser()\n bag = rosbag.Bag(pathname + filename)\n variables = parse.get_variables(bag, filename)\n sec = variables.sec\n nsec = variables.nsec\n X = variables.X\n Y = variables.Y\n Z = variables.Z\n time = [0] * len(sec)\n for i in range(0, len(sec)):\n time[i] = sec[i] + nsec[i] * 1e-09\n sec_c = [0] * (2 * len(variables.sec_c))\n Xc = [0] * (2 * len(variables.sec_c))\n Yc = [0] * (2 * len(variables.sec_c))\n Zc = [0] * (2 * len(variables.sec_c))\n for i in range(0, len(variables.sec_c)):\n sec_c[2 * i] = variables.sec_c[i]\n if i < len(variables.sec_c) - 1:\n sec_c[2 * i + 1] = variables.sec_c[i + 1]\n else:\n sec_c[2 * i + 1] = time[len(time) - 1]\n Xc[2 * i] = variables.Xc[i]\n Xc[2 * i + 1] = variables.Xc[i]\n Yc[2 * i] = variables.Yc[i]\n Yc[2 * i + 1] = variables.Yc[i]\n Zc[2 * i] = variables.Fc[i]\n Zc[2 * i + 1] = variables.Fc[i]\n fig = plt.figure(1)\n plt.plot(sec_c, Xc, color='blue', linewidth=1, label='Xc')\n plt.plot(time, X, color='red', linewidth=1, label='X')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('X Position')\n fig.suptitle('X')\n plt.show()\n fig = plt.figure(2)\n plt.plot(sec_c, Yc, color='blue', linewidth=1, label='Yc')\n plt.plot(time, Y, color='red', linewidth=1, label='Y')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Y Position')\n fig.suptitle('Y')\n fig = plt.figure(3)\n plt.plot(sec_c, Zc, color='blue', linewidth=1, label='Zc')\n plt.plot(time, Z, color='red', linewidth=1, label='Z')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Z Position')\n fig.suptitle('Z')\n MyStruct = namedtuple('mystruct', ('sec_c', 'Xc', 'Yc', 'Zc', 'Fc',\n 'sec', 'nsec', 'time', 'X', 'Y', 'Z'))\n vals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.\n Zc, variables.Fc, sec, nsec, time, X, Y, Z)\n return vals\n\n\nif __name__ == '__main__':\n vals = main()\n",
"<import token>\n\n\ndef main():\n filename = '_2019-09-06-10-46-56.bag'\n path = '../../../data/holodeck_sim/'\n vals = plot(filename, path)\n return vals\n\n\ndef plot(filename, pathname):\n parse = Parser()\n bag = rosbag.Bag(pathname + filename)\n variables = parse.get_variables(bag, filename)\n sec = variables.sec\n nsec = variables.nsec\n X = variables.X\n Y = variables.Y\n Z = variables.Z\n time = [0] * len(sec)\n for i in range(0, len(sec)):\n time[i] = sec[i] + nsec[i] * 1e-09\n sec_c = [0] * (2 * len(variables.sec_c))\n Xc = [0] * (2 * len(variables.sec_c))\n Yc = [0] * (2 * len(variables.sec_c))\n Zc = [0] * (2 * len(variables.sec_c))\n for i in range(0, len(variables.sec_c)):\n sec_c[2 * i] = variables.sec_c[i]\n if i < len(variables.sec_c) - 1:\n sec_c[2 * i + 1] = variables.sec_c[i + 1]\n else:\n sec_c[2 * i + 1] = time[len(time) - 1]\n Xc[2 * i] = variables.Xc[i]\n Xc[2 * i + 1] = variables.Xc[i]\n Yc[2 * i] = variables.Yc[i]\n Yc[2 * i + 1] = variables.Yc[i]\n Zc[2 * i] = variables.Fc[i]\n Zc[2 * i + 1] = variables.Fc[i]\n fig = plt.figure(1)\n plt.plot(sec_c, Xc, color='blue', linewidth=1, label='Xc')\n plt.plot(time, X, color='red', linewidth=1, label='X')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('X Position')\n fig.suptitle('X')\n plt.show()\n fig = plt.figure(2)\n plt.plot(sec_c, Yc, color='blue', linewidth=1, label='Yc')\n plt.plot(time, Y, color='red', linewidth=1, label='Y')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Y Position')\n fig.suptitle('Y')\n fig = plt.figure(3)\n plt.plot(sec_c, Zc, color='blue', linewidth=1, label='Zc')\n plt.plot(time, Z, color='red', linewidth=1, label='Z')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Z Position')\n fig.suptitle('Z')\n MyStruct = namedtuple('mystruct', ('sec_c', 'Xc', 'Yc', 'Zc', 'Fc',\n 'sec', 'nsec', 'time', 'X', 'Y', 'Z'))\n vals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.\n Zc, variables.Fc, sec, nsec, time, X, Y, Z)\n return vals\n\n\nif __name__ == '__main__':\n vals = main()\n",
"<import token>\n\n\ndef main():\n filename = '_2019-09-06-10-46-56.bag'\n path = '../../../data/holodeck_sim/'\n vals = plot(filename, path)\n return vals\n\n\ndef plot(filename, pathname):\n parse = Parser()\n bag = rosbag.Bag(pathname + filename)\n variables = parse.get_variables(bag, filename)\n sec = variables.sec\n nsec = variables.nsec\n X = variables.X\n Y = variables.Y\n Z = variables.Z\n time = [0] * len(sec)\n for i in range(0, len(sec)):\n time[i] = sec[i] + nsec[i] * 1e-09\n sec_c = [0] * (2 * len(variables.sec_c))\n Xc = [0] * (2 * len(variables.sec_c))\n Yc = [0] * (2 * len(variables.sec_c))\n Zc = [0] * (2 * len(variables.sec_c))\n for i in range(0, len(variables.sec_c)):\n sec_c[2 * i] = variables.sec_c[i]\n if i < len(variables.sec_c) - 1:\n sec_c[2 * i + 1] = variables.sec_c[i + 1]\n else:\n sec_c[2 * i + 1] = time[len(time) - 1]\n Xc[2 * i] = variables.Xc[i]\n Xc[2 * i + 1] = variables.Xc[i]\n Yc[2 * i] = variables.Yc[i]\n Yc[2 * i + 1] = variables.Yc[i]\n Zc[2 * i] = variables.Fc[i]\n Zc[2 * i + 1] = variables.Fc[i]\n fig = plt.figure(1)\n plt.plot(sec_c, Xc, color='blue', linewidth=1, label='Xc')\n plt.plot(time, X, color='red', linewidth=1, label='X')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('X Position')\n fig.suptitle('X')\n plt.show()\n fig = plt.figure(2)\n plt.plot(sec_c, Yc, color='blue', linewidth=1, label='Yc')\n plt.plot(time, Y, color='red', linewidth=1, label='Y')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Y Position')\n fig.suptitle('Y')\n fig = plt.figure(3)\n plt.plot(sec_c, Zc, color='blue', linewidth=1, label='Zc')\n plt.plot(time, Z, color='red', linewidth=1, label='Z')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Z Position')\n fig.suptitle('Z')\n MyStruct = namedtuple('mystruct', ('sec_c', 'Xc', 'Yc', 'Zc', 'Fc',\n 'sec', 'nsec', 'time', 'X', 'Y', 'Z'))\n vals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.\n Zc, variables.Fc, sec, nsec, time, X, Y, Z)\n return vals\n\n\n<code token>\n",
"<import token>\n<function token>\n\n\ndef plot(filename, pathname):\n parse = Parser()\n bag = rosbag.Bag(pathname + filename)\n variables = parse.get_variables(bag, filename)\n sec = variables.sec\n nsec = variables.nsec\n X = variables.X\n Y = variables.Y\n Z = variables.Z\n time = [0] * len(sec)\n for i in range(0, len(sec)):\n time[i] = sec[i] + nsec[i] * 1e-09\n sec_c = [0] * (2 * len(variables.sec_c))\n Xc = [0] * (2 * len(variables.sec_c))\n Yc = [0] * (2 * len(variables.sec_c))\n Zc = [0] * (2 * len(variables.sec_c))\n for i in range(0, len(variables.sec_c)):\n sec_c[2 * i] = variables.sec_c[i]\n if i < len(variables.sec_c) - 1:\n sec_c[2 * i + 1] = variables.sec_c[i + 1]\n else:\n sec_c[2 * i + 1] = time[len(time) - 1]\n Xc[2 * i] = variables.Xc[i]\n Xc[2 * i + 1] = variables.Xc[i]\n Yc[2 * i] = variables.Yc[i]\n Yc[2 * i + 1] = variables.Yc[i]\n Zc[2 * i] = variables.Fc[i]\n Zc[2 * i + 1] = variables.Fc[i]\n fig = plt.figure(1)\n plt.plot(sec_c, Xc, color='blue', linewidth=1, label='Xc')\n plt.plot(time, X, color='red', linewidth=1, label='X')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('X Position')\n fig.suptitle('X')\n plt.show()\n fig = plt.figure(2)\n plt.plot(sec_c, Yc, color='blue', linewidth=1, label='Yc')\n plt.plot(time, Y, color='red', linewidth=1, label='Y')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Y Position')\n fig.suptitle('Y')\n fig = plt.figure(3)\n plt.plot(sec_c, Zc, color='blue', linewidth=1, label='Zc')\n plt.plot(time, Z, color='red', linewidth=1, label='Z')\n plt.legend(bbox_to_anchor=(1, 0.4), prop={'size': 8}, frameon=True)\n plt.xlabel('Time (s)')\n plt.ylabel('Z Position')\n fig.suptitle('Z')\n MyStruct = namedtuple('mystruct', ('sec_c', 'Xc', 'Yc', 'Zc', 'Fc',\n 'sec', 'nsec', 'time', 'X', 'Y', 'Z'))\n vals = MyStruct(variables.sec_c, variables.Xc, variables.Yc, variables.\n Zc, variables.Fc, sec, nsec, time, X, Y, Z)\n return vals\n\n\n<code token>\n",
"<import token>\n<function token>\n<function token>\n<code token>\n"
] | false |
99,436 |
b8791052ff396600f23a92df2e52d3b65634ea40
|
class Solution:
def subArrayRanges(self, nums: List[int]) -> int:
ret = 0
for i in range(len(nums)) :
mine = maxe = nums[i]
for j in range(i + 1, len(nums)) :
mine = min(mine, nums[j])
maxe = max(maxe, nums[j])
ret += maxe - mine
return ret
|
[
"class Solution:\n def subArrayRanges(self, nums: List[int]) -> int:\n ret = 0\n for i in range(len(nums)) :\n mine = maxe = nums[i]\n for j in range(i + 1, len(nums)) :\n mine = min(mine, nums[j])\n maxe = max(maxe, nums[j])\n ret += maxe - mine\n return ret\n",
"class Solution:\n\n def subArrayRanges(self, nums: List[int]) ->int:\n ret = 0\n for i in range(len(nums)):\n mine = maxe = nums[i]\n for j in range(i + 1, len(nums)):\n mine = min(mine, nums[j])\n maxe = max(maxe, nums[j])\n ret += maxe - mine\n return ret\n",
"class Solution:\n <function token>\n",
"<class token>\n"
] | false |
99,437 |
5b6d34f562efcd3126f01ef3dbf6e1bd6f8b9b69
|
from django.test import TestCase
from django.urls import reverse
from django.contrib.auth.models import User
from tests.models import TestGroup, TestUnit
from .models import Interview
# Create your tests here.
# Тестирование представлений
# - все представления требуют login_required (тестирование с анонимным пользователем и аутентифицированным)
# - представление interview/test/<test_id>
# - представление interview/<interview_id>/question
# - представление interview/<interview_id>/report
# - представление interview/test/<test_id>
# - если тест не задан в базе данных -> 404
# - если тест завершен -> redirect(report)
# - если тест новый -> redirect(question)
# - если тест не завершен -> redirect(question)
# - представление interview/<interview_id>/question
# - интервью отсутствует в базе данных -> 404
# - интервью есть в базе данных - получениие вопроса
# - интервью - после последнего вопроса (опрос завершен) перенаправление на report
# - представление interview/<interview_id>/report
# - если интервью отсутствует в базе данных -> 404
# - если интервью закончено - отображение результатов
# - если интервью не закончено -> 404
def create_test(test_owner):
tg = TestGroup.objects.create(
name = 'testing group',
description = 'group for testing interview',
)
tu = tg.testunit_set.create(
name = 'test unit',
description = 'test in group',
owner = test_owner,
)
q = tu.question_set.create(
text = 'test question text',
)
q.answer_set.create(
name = 'question answer 1',
right = True,
)
q.answer_set.create(
name = 'question answer 2',
right = False,
)
return tu
def create_user(**test_data):
user = User.objects.create_user(**test_data)
return user
class TestLoginRequried(TestCase):
def setUp(self):
self.user = create_user(
username='testuser',
password='password',
)
self.testunit = create_test(self.user)
def test_anonymous_interviews_open(self):
response = self.client.get(reverse('interview:open', args=(1,)))
self.assertEqual(response.status_code, 302)
self.assertIn('login', response.url)
def test_anonymous_interviews_question(self):
response = self.client.get(reverse('interview:question', args=(1,)))
self.assertEqual(response.status_code, 302)
self.assertIn('login', response.url)
def test_anonymous_interviews_report(self):
response = self.client.get(reverse('interview:report', args=(1,)))
self.assertEqual(response.status_code, 302)
self.assertIn('login', response.url)
class TestViewInterviewOpen(TestCase):
def setUp(self):
self.user = create_user(
username='testuser',
password='password',
)
self.testunit = create_test(self.user)
self.client.login(
username='testuser',
password='password',
)
def test_open_broken_test_interview(self):
response = self.client.get(reverse('interview:open', args=(2,)))
self.assertEqual(response.status_code, 404)
def test_open_complete_test_interview(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = True,
)
response = self.client.get(reverse('interview:open', args=(tu.id,)))
self.assertEqual(response.status_code, 302)
self.assertIn('report', response.url)
def test_open_new_test_interview(self):
tu = TestUnit.objects.get()
response = self.client.get(reverse('interview:open', args=(tu.id,)))
self.assertEqual(response.status_code, 302)
self.assertIn('question', response.url)
def test_open_early_runed_test_interview(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = False,
)
response = self.client.get(reverse('interview:open', args=(tu.id,)))
self.assertEqual(response.status_code, 302)
self.assertIn('question', response.url)
class TestViewReplyQuestion(TestCase):
def setUp(self):
self.user = create_user(
username='testuser',
password='password',
)
self.testunit = create_test(self.user)
self.client.login(
username='testuser',
password='password',
)
def test_open_broken_interview_question(self):
response = self.client.get(reverse('interview:question', args=(2,)))
self.assertEqual(response.status_code, 404)
def test_open_new_interview_question(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = False,
)
response = self.client.get(reverse('interview:question', args=(tu.id,)))
self.assertEqual(response.status_code, 200)
self.assertContains(
response,
interview.get_next_question().question.text
)
def test_open_report_after_reply_last_question(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = False,
)
question = interview.get_next_question().question
answer = question.answer_set.last()
response = self.client.post(
reverse('interview:question', args=(interview.id,)),
{str(answer.id): 'on'},
)
response = self.client.get(reverse('interview:question', args=(tu.id,)))
self.assertEqual(response.status_code, 302)
self.assertIn('report', response.url)
class TestViewInterviewReport(TestCase):
def setUp(self):
self.user = create_user(
username='testuser',
password='password',
)
self.testunit = create_test(self.user)
self.client.login(
username='testuser',
password='password',
)
def test_report_for_broken_test_interview(self):
response = self.client.get(reverse('interview:report', args=(2,)))
self.assertEqual(response.status_code, 404)
def test_report_for_complete_test_interview(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = False,
)
question = interview.get_next_question().question
answer = question.answer_set.first()
response = self.client.post(
reverse('interview:question', args=(interview.id,)),
{str(answer.id): 'on'},
follow=True,
)
response = self.client.get(reverse('interview:report', args=(interview.id,)))
self.assertEqual(response.status_code, 200)
def test_report_for_early_runed_test_interview(self):
tu = TestUnit.objects.get()
interview = Interview.objects.create(
user = self.user,
testunit = tu,
is_complete = False,
)
response = self.client.get(reverse('interview:report', args=(interview.id,)))
self.assertEqual(response.status_code, 404)
|
[
"from django.test import TestCase\nfrom django.urls import reverse\n\nfrom django.contrib.auth.models import User\nfrom tests.models import TestGroup, TestUnit\nfrom .models import Interview\n\n# Create your tests here.\n# Тестирование представлений\n# - все представления требуют login_required (тестирование с анонимным пользователем и аутентифицированным)\n# - представление interview/test/<test_id>\n# - представление interview/<interview_id>/question\n# - представление interview/<interview_id>/report\n# - представление interview/test/<test_id>\n# - если тест не задан в базе данных -> 404\n# - если тест завершен -> redirect(report)\n# - если тест новый -> redirect(question)\n# - если тест не завершен -> redirect(question)\n# - представление interview/<interview_id>/question\n# - интервью отсутствует в базе данных -> 404\n# - интервью есть в базе данных - получениие вопроса\n# - интервью - после последнего вопроса (опрос завершен) перенаправление на report\n# - представление interview/<interview_id>/report\n# - если интервью отсутствует в базе данных -> 404\n# - если интервью закончено - отображение результатов\n# - если интервью не закончено -> 404\n\n\ndef create_test(test_owner):\n tg = TestGroup.objects.create(\n name = 'testing group',\n description = 'group for testing interview',\n )\n tu = tg.testunit_set.create(\n name = 'test unit',\n description = 'test in group',\n owner = test_owner,\n )\n q = tu.question_set.create(\n text = 'test question text',\n )\n q.answer_set.create(\n name = 'question answer 1',\n right = True,\n )\n q.answer_set.create(\n name = 'question answer 2',\n right = False,\n )\n return tu\n\n\ndef create_user(**test_data):\n user = User.objects.create_user(**test_data)\n return user\n\n\nclass TestLoginRequried(TestCase):\n def setUp(self):\n self.user = create_user(\n username='testuser',\n password='password',\n )\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_question(self):\n response = self.client.get(reverse('interview:question', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n def setUp(self):\n self.user = create_user(\n username='testuser',\n password='password',\n )\n self.testunit = create_test(self.user)\n self.client.login(\n username='testuser',\n password='password',\n )\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = True,\n )\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = False,\n )\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n def setUp(self):\n self.user = create_user(\n username='testuser',\n password='password',\n )\n self.testunit = create_test(self.user)\n self.client.login(\n username='testuser',\n password='password',\n )\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = False,\n )\n response = self.client.get(reverse('interview:question', args=(tu.id,)))\n self.assertEqual(response.status_code, 200)\n self.assertContains(\n response,\n interview.get_next_question().question.text\n )\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = False,\n )\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(\n reverse('interview:question', args=(interview.id,)),\n {str(answer.id): 'on'},\n )\n response = self.client.get(reverse('interview:question', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\nclass TestViewInterviewReport(TestCase):\n def setUp(self):\n self.user = create_user(\n username='testuser',\n password='password',\n )\n self.testunit = create_test(self.user)\n self.client.login(\n username='testuser',\n password='password',\n )\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = False,\n )\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(\n reverse('interview:question', args=(interview.id,)),\n {str(answer.id): 'on'},\n follow=True,\n )\n response = self.client.get(reverse('interview:report', args=(interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(\n user = self.user,\n testunit = tu,\n is_complete = False,\n )\n response = self.client.get(reverse('interview:report', args=(interview.id,)))\n self.assertEqual(response.status_code, 404)",
"from django.test import TestCase\nfrom django.urls import reverse\nfrom django.contrib.auth.models import User\nfrom tests.models import TestGroup, TestUnit\nfrom .models import Interview\n\n\ndef create_test(test_owner):\n tg = TestGroup.objects.create(name='testing group', description=\n 'group for testing interview')\n tu = tg.testunit_set.create(name='test unit', description=\n 'test in group', owner=test_owner)\n q = tu.question_set.create(text='test question text')\n q.answer_set.create(name='question answer 1', right=True)\n q.answer_set.create(name='question answer 2', right=False)\n return tu\n\n\ndef create_user(**test_data):\n user = User.objects.create_user(**test_data)\n return user\n\n\nclass TestLoginRequried(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_question(self):\n response = self.client.get(reverse('interview:question', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n\n\ndef create_test(test_owner):\n tg = TestGroup.objects.create(name='testing group', description=\n 'group for testing interview')\n tu = tg.testunit_set.create(name='test unit', description=\n 'test in group', owner=test_owner)\n q = tu.question_set.create(text='test question text')\n q.answer_set.create(name='question answer 1', right=True)\n q.answer_set.create(name='question answer 2', right=False)\n return tu\n\n\ndef create_user(**test_data):\n user = User.objects.create_user(**test_data)\n return user\n\n\nclass TestLoginRequried(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_question(self):\n response = self.client.get(reverse('interview:question', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n\n\ndef create_test(test_owner):\n tg = TestGroup.objects.create(name='testing group', description=\n 'group for testing interview')\n tu = tg.testunit_set.create(name='test unit', description=\n 'test in group', owner=test_owner)\n q = tu.question_set.create(text='test question text')\n q.answer_set.create(name='question answer 1', right=True)\n q.answer_set.create(name='question answer 2', right=False)\n return tu\n\n\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_question(self):\n response = self.client.get(reverse('interview:question', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_question(self):\n response = self.client.get(reverse('interview:question', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n <function token>\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n <function token>\n\n def test_anonymous_interviews_open(self):\n response = self.client.get(reverse('interview:open', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n <function token>\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_anonymous_interviews_report(self):\n response = self.client.get(reverse('interview:report', args=(1,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('login', response.url)\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n\n\nclass TestLoginRequried(TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n <function token>\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n def test_open_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n <function token>\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n def test_open_new_test_interview(self):\n tu = TestUnit.objects.get()\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('question', response.url)\n <function token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n <function token>\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=True)\n response = self.client.get(reverse('interview:open', args=(tu.id,)))\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n <function token>\n <function token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n <function token>\n\n def test_open_broken_test_interview(self):\n response = self.client.get(reverse('interview:open', args=(2,)))\n self.assertEqual(response.status_code, 404)\n <function token>\n <function token>\n <function token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n\n\nclass TestViewInterviewOpen(TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_open_new_interview_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, interview.get_next_question().\n question.text)\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n <function token>\n\n def test_open_report_after_reply_last_question(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.last()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'})\n response = self.client.get(reverse('interview:question', args=(tu.id,))\n )\n self.assertEqual(response.status_code, 302)\n self.assertIn('report', response.url)\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_open_broken_interview_question(self):\n response = self.client.get(reverse('interview:question', args=(2,)))\n self.assertEqual(response.status_code, 404)\n <function token>\n <function token>\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestViewReplyQuestion(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n <function token>\n <function token>\n <function token>\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n\n\nclass TestViewReplyQuestion(TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass TestViewInterviewReport(TestCase):\n\n def setUp(self):\n self.user = create_user(username='testuser', password='password')\n self.testunit = create_test(self.user)\n self.client.login(username='testuser', password='password')\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass TestViewInterviewReport(TestCase):\n <function token>\n\n def test_report_for_broken_test_interview(self):\n response = self.client.get(reverse('interview:report', args=(2,)))\n self.assertEqual(response.status_code, 404)\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass TestViewInterviewReport(TestCase):\n <function token>\n <function token>\n\n def test_report_for_complete_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n question = interview.get_next_question().question\n answer = question.answer_set.first()\n response = self.client.post(reverse('interview:question', args=(\n interview.id,)), {str(answer.id): 'on'}, follow=True)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 200)\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass TestViewInterviewReport(TestCase):\n <function token>\n <function token>\n <function token>\n\n def test_report_for_early_runed_test_interview(self):\n tu = TestUnit.objects.get()\n interview = Interview.objects.create(user=self.user, testunit=tu,\n is_complete=False)\n response = self.client.get(reverse('interview:report', args=(\n interview.id,)))\n self.assertEqual(response.status_code, 404)\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n\n\nclass TestViewInterviewReport(TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<function token>\n<function token>\n<class token>\n<class token>\n<class token>\n<class token>\n"
] | false |
99,438 |
d08df0175a4bbe51b1a76b8232ce8c46ef045d7f
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import numpy as np
import itertools
sys.path.append("..")
import george
from george.kernels import TaskKernel
num_tasks = 10
kernel = TaskKernel(1,0,num_tasks)
print(kernel.vector)
kernel.vector=range(1, len(kernel.vector)+1)
print(kernel.vector)
K = np.zeros([num_tasks, num_tasks])
for (i,j) in itertools.product(range(num_tasks), repeat=2):
K[i,j] = (kernel.value(np.array([[i]]),np.array([[j]]))[0,0])
print(K)
print(np.linalg.cholesky(K))
|
[
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport numpy as np\nimport itertools\n\nsys.path.append(\"..\")\n\nimport george\nfrom george.kernels import TaskKernel\n\n\nnum_tasks = 10\n\n\nkernel = TaskKernel(1,0,num_tasks)\n\nprint(kernel.vector)\nkernel.vector=range(1, len(kernel.vector)+1)\nprint(kernel.vector)\n\n\nK = np.zeros([num_tasks, num_tasks])\n\nfor (i,j) in itertools.product(range(num_tasks), repeat=2):\n\tK[i,j] = (kernel.value(np.array([[i]]),np.array([[j]]))[0,0])\n\nprint(K)\n\n\nprint(np.linalg.cholesky(K))\n",
"import os\nimport sys\nimport numpy as np\nimport itertools\nsys.path.append('..')\nimport george\nfrom george.kernels import TaskKernel\nnum_tasks = 10\nkernel = TaskKernel(1, 0, num_tasks)\nprint(kernel.vector)\nkernel.vector = range(1, len(kernel.vector) + 1)\nprint(kernel.vector)\nK = np.zeros([num_tasks, num_tasks])\nfor i, j in itertools.product(range(num_tasks), repeat=2):\n K[i, j] = kernel.value(np.array([[i]]), np.array([[j]]))[0, 0]\nprint(K)\nprint(np.linalg.cholesky(K))\n",
"<import token>\nsys.path.append('..')\n<import token>\nnum_tasks = 10\nkernel = TaskKernel(1, 0, num_tasks)\nprint(kernel.vector)\nkernel.vector = range(1, len(kernel.vector) + 1)\nprint(kernel.vector)\nK = np.zeros([num_tasks, num_tasks])\nfor i, j in itertools.product(range(num_tasks), repeat=2):\n K[i, j] = kernel.value(np.array([[i]]), np.array([[j]]))[0, 0]\nprint(K)\nprint(np.linalg.cholesky(K))\n",
"<import token>\nsys.path.append('..')\n<import token>\n<assignment token>\nprint(kernel.vector)\n<assignment token>\nprint(kernel.vector)\n<assignment token>\nfor i, j in itertools.product(range(num_tasks), repeat=2):\n K[i, j] = kernel.value(np.array([[i]]), np.array([[j]]))[0, 0]\nprint(K)\nprint(np.linalg.cholesky(K))\n",
"<import token>\n<code token>\n<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,439 |
eda136454aba14062953bb8894923a6d340a8b2f
|
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model, discriminant_analysis
from sklearn.model_selection import train_test_split
def load_data():
iris = datasets.load_iris()
X_train = iris.data
y_train = iris.target
return train_test_split(X_train,y_train,test_size=0.25,random_state=0,stratify=y_train)
def test_linearDiscriminantAnalysis(*data):
X_train,X_test,y_train,y_test = data
lda = discriminant_analysis.LinearDiscriminantAnalysis()
lda.fit(X_train,y_train)
print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))
print('Score:%.2f' % lda.score(X_test, y_test))
def plot_LDA(converted_X,y):
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
colors = 'rgb'
markers = 'o*s'
for target,colors,markers in zip([0,1,2],colors,markers):
pos = (y == target).ravel()
X = converted_X[pos,:]
ax.scatter(X[:,0],X[:,1],X[:,2],color=colors,marker=markers,label='Label %d'%target)
ax.legend(loc = 'best')
fig.suptitle('Iris After LDA')
plt.show()
def test_linearDiscriminantAnalysis_solver(*data):
X_train,X_test,y_train,y_test = data
solvers = ['svd','lsqr','eigen']
for solver in solvers:
if(solver=='svd'):
lda = discriminant_analysis.LinearDiscriminantAnalysis(solver = solver)
else:
lda = discriminant_analysis.LinearDiscriminantAnalysis(solver = solver,shrinkage=None)
lda.fit(X_train,y_train)
print('Score at solve=%s:%.2f'%(solver,lda.score(X_test,y_test)))
def test_linearDiscriminantAnalysis_shrinkage(*data):
X_train,X_test,y_train,y_test = data
shrinkages = np.linspace(0.0,1.0,num=20)
scores = []
for shrinkage in shrinkages:
lda = discriminant_analysis.LinearDiscriminantAnalysis(solver='lsqr',shrinkage=shrinkage)
lda.fit(X_train,y_train)
scores.append(lda.score(X_test,y_test))
##plot
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(shrinkages,scores)
ax.set_xlabel(r'shrinkage')
ax.set_ylabel(r'score')
ax.set_ylim(0,1.05)
ax.set_title("LinearDiscriminanAnalysis")
plt.show()
if __name__ == "__main__":
X_train,X_test,y_train,y_test = load_data()
"""
X = np.vstack((X_train,X_test))
Y = np.vstack((y_train.reshape(y_train.size,1),y_test.reshape(y_test.size,1)))
lda = discriminant_analysis.LinearDiscriminantAnalysis()
lda.fit(X,Y)
converted_X = np.dot(X,np.transpose(lda.coef_)) + lda.intercept_
plot_LDA(converted_X,Y)
"""
#test_linearDiscriminantAnalysis_solver(X_train,X_test,y_train,y_test)
test_linearDiscriminantAnalysis_shrinkage(X_train,X_test,y_train,y_test)
|
[
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model, discriminant_analysis\nfrom sklearn.model_selection import train_test_split\n\ndef load_data():\n iris = datasets.load_iris()\n X_train = iris.data\n y_train = iris.target\n return train_test_split(X_train,y_train,test_size=0.25,random_state=0,stratify=y_train)\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train,X_test,y_train,y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train,y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\ndef plot_LDA(converted_X,y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target,colors,markers in zip([0,1,2],colors,markers):\n pos = (y == target).ravel()\n X = converted_X[pos,:]\n ax.scatter(X[:,0],X[:,1],X[:,2],color=colors,marker=markers,label='Label %d'%target)\n ax.legend(loc = 'best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\ndef test_linearDiscriminantAnalysis_solver(*data):\n X_train,X_test,y_train,y_test = data\n solvers = ['svd','lsqr','eigen']\n for solver in solvers:\n if(solver=='svd'):\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver = solver)\n else:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver = solver,shrinkage=None)\n lda.fit(X_train,y_train)\n print('Score at solve=%s:%.2f'%(solver,lda.score(X_test,y_test)))\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train,X_test,y_train,y_test = data\n shrinkages = np.linspace(0.0,1.0,num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver='lsqr',shrinkage=shrinkage)\n lda.fit(X_train,y_train)\n scores.append(lda.score(X_test,y_test))\n ##plot\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n ax.plot(shrinkages,scores)\n ax.set_xlabel(r'shrinkage')\n ax.set_ylabel(r'score')\n ax.set_ylim(0,1.05)\n ax.set_title(\"LinearDiscriminanAnalysis\")\n plt.show()\n\n\nif __name__ == \"__main__\":\n X_train,X_test,y_train,y_test = load_data()\n \"\"\"\n X = np.vstack((X_train,X_test))\n Y = np.vstack((y_train.reshape(y_train.size,1),y_test.reshape(y_test.size,1)))\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X,Y)\n converted_X = np.dot(X,np.transpose(lda.coef_)) + lda.intercept_\n plot_LDA(converted_X,Y)\n \"\"\"\n #test_linearDiscriminantAnalysis_solver(X_train,X_test,y_train,y_test)\n\n test_linearDiscriminantAnalysis_shrinkage(X_train,X_test,y_train,y_test)",
"import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model, discriminant_analysis\nfrom sklearn.model_selection import train_test_split\n\n\ndef load_data():\n iris = datasets.load_iris()\n X_train = iris.data\n y_train = iris.target\n return train_test_split(X_train, y_train, test_size=0.25, random_state=\n 0, stratify=y_train)\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\ndef test_linearDiscriminantAnalysis_solver(*data):\n X_train, X_test, y_train, y_test = data\n solvers = ['svd', 'lsqr', 'eigen']\n for solver in solvers:\n if solver == 'svd':\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver)\n else:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver, shrinkage=None)\n lda.fit(X_train, y_train)\n print('Score at solve=%s:%.2f' % (solver, lda.score(X_test, y_test)))\n\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train, X_test, y_train, y_test = data\n shrinkages = np.linspace(0.0, 1.0, num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n 'lsqr', shrinkage=shrinkage)\n lda.fit(X_train, y_train)\n scores.append(lda.score(X_test, y_test))\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(shrinkages, scores)\n ax.set_xlabel('shrinkage')\n ax.set_ylabel('score')\n ax.set_ylim(0, 1.05)\n ax.set_title('LinearDiscriminanAnalysis')\n plt.show()\n\n\nif __name__ == '__main__':\n X_train, X_test, y_train, y_test = load_data()\n \"\"\"\n X = np.vstack((X_train,X_test))\n Y = np.vstack((y_train.reshape(y_train.size,1),y_test.reshape(y_test.size,1)))\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X,Y)\n converted_X = np.dot(X,np.transpose(lda.coef_)) + lda.intercept_\n plot_LDA(converted_X,Y)\n \"\"\"\n test_linearDiscriminantAnalysis_shrinkage(X_train, X_test, y_train, y_test)\n",
"<import token>\n\n\ndef load_data():\n iris = datasets.load_iris()\n X_train = iris.data\n y_train = iris.target\n return train_test_split(X_train, y_train, test_size=0.25, random_state=\n 0, stratify=y_train)\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\ndef test_linearDiscriminantAnalysis_solver(*data):\n X_train, X_test, y_train, y_test = data\n solvers = ['svd', 'lsqr', 'eigen']\n for solver in solvers:\n if solver == 'svd':\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver)\n else:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver, shrinkage=None)\n lda.fit(X_train, y_train)\n print('Score at solve=%s:%.2f' % (solver, lda.score(X_test, y_test)))\n\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train, X_test, y_train, y_test = data\n shrinkages = np.linspace(0.0, 1.0, num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n 'lsqr', shrinkage=shrinkage)\n lda.fit(X_train, y_train)\n scores.append(lda.score(X_test, y_test))\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(shrinkages, scores)\n ax.set_xlabel('shrinkage')\n ax.set_ylabel('score')\n ax.set_ylim(0, 1.05)\n ax.set_title('LinearDiscriminanAnalysis')\n plt.show()\n\n\nif __name__ == '__main__':\n X_train, X_test, y_train, y_test = load_data()\n \"\"\"\n X = np.vstack((X_train,X_test))\n Y = np.vstack((y_train.reshape(y_train.size,1),y_test.reshape(y_test.size,1)))\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X,Y)\n converted_X = np.dot(X,np.transpose(lda.coef_)) + lda.intercept_\n plot_LDA(converted_X,Y)\n \"\"\"\n test_linearDiscriminantAnalysis_shrinkage(X_train, X_test, y_train, y_test)\n",
"<import token>\n\n\ndef load_data():\n iris = datasets.load_iris()\n X_train = iris.data\n y_train = iris.target\n return train_test_split(X_train, y_train, test_size=0.25, random_state=\n 0, stratify=y_train)\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\ndef test_linearDiscriminantAnalysis_solver(*data):\n X_train, X_test, y_train, y_test = data\n solvers = ['svd', 'lsqr', 'eigen']\n for solver in solvers:\n if solver == 'svd':\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver)\n else:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n solver, shrinkage=None)\n lda.fit(X_train, y_train)\n print('Score at solve=%s:%.2f' % (solver, lda.score(X_test, y_test)))\n\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train, X_test, y_train, y_test = data\n shrinkages = np.linspace(0.0, 1.0, num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n 'lsqr', shrinkage=shrinkage)\n lda.fit(X_train, y_train)\n scores.append(lda.score(X_test, y_test))\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(shrinkages, scores)\n ax.set_xlabel('shrinkage')\n ax.set_ylabel('score')\n ax.set_ylim(0, 1.05)\n ax.set_title('LinearDiscriminanAnalysis')\n plt.show()\n\n\n<code token>\n",
"<import token>\n\n\ndef load_data():\n iris = datasets.load_iris()\n X_train = iris.data\n y_train = iris.target\n return train_test_split(X_train, y_train, test_size=0.25, random_state=\n 0, stratify=y_train)\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\n<function token>\n\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train, X_test, y_train, y_test = data\n shrinkages = np.linspace(0.0, 1.0, num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n 'lsqr', shrinkage=shrinkage)\n lda.fit(X_train, y_train)\n scores.append(lda.score(X_test, y_test))\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(shrinkages, scores)\n ax.set_xlabel('shrinkage')\n ax.set_ylabel('score')\n ax.set_ylim(0, 1.05)\n ax.set_title('LinearDiscriminanAnalysis')\n plt.show()\n\n\n<code token>\n",
"<import token>\n<function token>\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\n<function token>\n\n\ndef test_linearDiscriminantAnalysis_shrinkage(*data):\n X_train, X_test, y_train, y_test = data\n shrinkages = np.linspace(0.0, 1.0, num=20)\n scores = []\n for shrinkage in shrinkages:\n lda = discriminant_analysis.LinearDiscriminantAnalysis(solver=\n 'lsqr', shrinkage=shrinkage)\n lda.fit(X_train, y_train)\n scores.append(lda.score(X_test, y_test))\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(shrinkages, scores)\n ax.set_xlabel('shrinkage')\n ax.set_ylabel('score')\n ax.set_ylim(0, 1.05)\n ax.set_title('LinearDiscriminanAnalysis')\n plt.show()\n\n\n<code token>\n",
"<import token>\n<function token>\n\n\ndef test_linearDiscriminantAnalysis(*data):\n X_train, X_test, y_train, y_test = data\n lda = discriminant_analysis.LinearDiscriminantAnalysis()\n lda.fit(X_train, y_train)\n print('Coefficients:%s,intercept %s' % (lda.coef_, lda.intercept_))\n print('Score:%.2f' % lda.score(X_test, y_test))\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<function token>\n<function token>\n\n\ndef plot_LDA(converted_X, y):\n from mpl_toolkits.mplot3d import Axes3D\n fig = plt.figure()\n ax = Axes3D(fig)\n colors = 'rgb'\n markers = 'o*s'\n for target, colors, markers in zip([0, 1, 2], colors, markers):\n pos = (y == target).ravel()\n X = converted_X[pos, :]\n ax.scatter(X[:, 0], X[:, 1], X[:, 2], color=colors, marker=markers,\n label='Label %d' % target)\n ax.legend(loc='best')\n fig.suptitle('Iris After LDA')\n plt.show()\n\n\n<function token>\n<function token>\n<code token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"
] | false |
99,440 |
8f09e98b7f8e0f20a6b4d0012688669d4a1932ce
|
import os
#this script download the AI2020 library from github
def download_AI_library():
os.system('rm -r AI2020/')
os.system('git clone https://github.com/UmbertoJr/AI2020.git &> /dev/null')
|
[
"import os\n\n#this script download the AI2020 library from github\ndef download_AI_library():\n\tos.system('rm -r AI2020/')\n\tos.system('git clone https://github.com/UmbertoJr/AI2020.git &> /dev/null')",
"import os\n\n\ndef download_AI_library():\n os.system('rm -r AI2020/')\n os.system('git clone https://github.com/UmbertoJr/AI2020.git &> /dev/null')\n",
"<import token>\n\n\ndef download_AI_library():\n os.system('rm -r AI2020/')\n os.system('git clone https://github.com/UmbertoJr/AI2020.git &> /dev/null')\n",
"<import token>\n<function token>\n"
] | false |
99,441 |
5614a3bde5a5e5860d04f3ac1124332dce8bae3f
|
# Importing this module will bind routes to the app.
# This could be futher split up into submodules if the number of endpoints grows too large for one file.
from . import app
from .models import User
from flask import abort, jsonify, request, session, render_template as render
from flask_user import current_user, login_required
from flask.ext.login import login_user
# home
@app.route('/')
def index():
return render('index.html')
# view all users
@app.route('/user/list')
@login_required
def user_list():
return render('users.html', users=User.query.all())
# view a user's status
# (should have some security on this)
@app.route('/user/<uid>')
@login_required
def user_view(uid):
return render('map.html',user=User.query.get(uid))
# view myself
@app.route('/user/me')
@login_required
def user_view_me():
return render('map.html',user=current_user)
# update my position
@app.route('/api/user/update', methods=['POST'])
@login_required
def user_update():
current_user.set_location(request.form['lng'],request.form['lat'])
return "Location updated."
# get my user info
@app.route('/api/user/info', methods=['GET'])
@login_required
def api_user():
user = current_user
return jsonify({
'id':user.id,
'name':user.name,
'updated_location':user.updated_location,
'lat':user.lat,
'lng':user.lng
})
@app.route('/api/login', methods=['POST'])
def api_login():
email = request.form['email']
password = request.form['password']
user, user_email = app.user_manager.find_user_by_email(email)
ok = False
if user and user.active:
if app.user_manager.verify_password(password, user) is True:
user.authenticated = True
login_user(user, remember=True)
ok = True
return jsonify({ 'success': ok })
|
[
"# Importing this module will bind routes to the app.\n# This could be futher split up into submodules if the number of endpoints grows too large for one file.\n\nfrom . import app\nfrom .models import User\nfrom flask import abort, jsonify, request, session, render_template as render\nfrom flask_user import current_user, login_required\n\nfrom flask.ext.login import login_user \n\n# home\[email protected]('/')\ndef index():\n return render('index.html')\n\n# view all users\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n# view a user's status\n# (should have some security on this)\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html',user=User.query.get(uid))\n\n# view myself\[email protected]('/user/me')\n@login_required\ndef user_view_me():\n return render('map.html',user=current_user)\n\n# update my position\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'],request.form['lat'])\n return \"Location updated.\"\n\n# get my user info\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({\n 'id':user.id,\n 'name':user.name,\n 'updated_location':user.updated_location,\n 'lat':user.lat,\n 'lng':user.lng\n })\n\[email protected]('/api/login', methods=['POST'])\ndef api_login():\n email = request.form['email']\n password = request.form['password']\n \n user, user_email = app.user_manager.find_user_by_email(email)\n\n ok = False\n\n if user and user.active:\n if app.user_manager.verify_password(password, user) is True:\n user.authenticated = True\n login_user(user, remember=True)\n ok = True\n\n return jsonify({ 'success': ok })\n",
"from . import app\nfrom .models import User\nfrom flask import abort, jsonify, request, session, render_template as render\nfrom flask_user import current_user, login_required\nfrom flask.ext.login import login_user\n\n\[email protected]('/')\ndef index():\n return render('index.html')\n\n\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\[email protected]('/user/me')\n@login_required\ndef user_view_me():\n return render('map.html', user=current_user)\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\[email protected]('/api/login', methods=['POST'])\ndef api_login():\n email = request.form['email']\n password = request.form['password']\n user, user_email = app.user_manager.find_user_by_email(email)\n ok = False\n if user and user.active:\n if app.user_manager.verify_password(password, user) is True:\n user.authenticated = True\n login_user(user, remember=True)\n ok = True\n return jsonify({'success': ok})\n",
"<import token>\n\n\[email protected]('/')\ndef index():\n return render('index.html')\n\n\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\[email protected]('/user/me')\n@login_required\ndef user_view_me():\n return render('map.html', user=current_user)\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\[email protected]('/api/login', methods=['POST'])\ndef api_login():\n email = request.form['email']\n password = request.form['password']\n user, user_email = app.user_manager.find_user_by_email(email)\n ok = False\n if user and user.active:\n if app.user_manager.verify_password(password, user) is True:\n user.authenticated = True\n login_user(user, remember=True)\n ok = True\n return jsonify({'success': ok})\n",
"<import token>\n\n\[email protected]('/')\ndef index():\n return render('index.html')\n\n\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\n<function token>\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\[email protected]('/api/login', methods=['POST'])\ndef api_login():\n email = request.form['email']\n password = request.form['password']\n user, user_email = app.user_manager.find_user_by_email(email)\n ok = False\n if user and user.active:\n if app.user_manager.verify_password(password, user) is True:\n user.authenticated = True\n login_user(user, remember=True)\n ok = True\n return jsonify({'success': ok})\n",
"<import token>\n<function token>\n\n\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\n<function token>\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\[email protected]('/api/login', methods=['POST'])\ndef api_login():\n email = request.form['email']\n password = request.form['password']\n user, user_email = app.user_manager.find_user_by_email(email)\n ok = False\n if user and user.active:\n if app.user_manager.verify_password(password, user) is True:\n user.authenticated = True\n login_user(user, remember=True)\n ok = True\n return jsonify({'success': ok})\n",
"<import token>\n<function token>\n\n\[email protected]('/user/list')\n@login_required\ndef user_list():\n return render('users.html', users=User.query.all())\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\n<function token>\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n\n\[email protected]('/user/<uid>')\n@login_required\ndef user_view(uid):\n return render('map.html', user=User.query.get(uid))\n\n\n<function token>\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/api/user/update', methods=['POST'])\n@login_required\ndef user_update():\n current_user.set_location(request.form['lng'], request.form['lat'])\n return 'Location updated.'\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n\n\[email protected]('/api/user/info', methods=['GET'])\n@login_required\ndef api_user():\n user = current_user\n return jsonify({'id': user.id, 'name': user.name, 'updated_location':\n user.updated_location, 'lat': user.lat, 'lng': user.lng})\n\n\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,442 |
cf20d21c1d95e4cdf7015f609d289cc7454333dc
|
# -*- coding: utf-8 -*-
import logging
from flask import Blueprint, abort, render_template, request
from flask.ext.login import login_required
from troika.card.models import Card
from troika.history.models import CardsHistory
logger = logging.getLogger(__name__)
blueprint = Blueprint("history", __name__, url_prefix='/history',
static_folder="../static")
@blueprint.route("/", methods=['GET'])
@login_required
def list():
try:
page = int(request.args.get('page', 1))
card_id = int(request.args.get('card_id', 0))
except ValueError:
abort(404)
card = None
query = CardsHistory.query.order_by('action_date desc')
if card_id:
card = Card.query.get(card_id)
if not card:
abort(404)
query = query.filter_by(card_id=card_id)
history = query.paginate(page, CardsHistory.PER_PAGE, False)
return render_template("history/list.html",
card=card,
history=history,
action_title=CardsHistory.ACTION_TITLE,
card_id=card_id)
@blueprint.route("/<int:history_id>", methods=['GET'])
@login_required
def show(history_id):
history = CardsHistory.query.get(history_id)
if not history:
abort(404)
return render_template("history/show.html",
history=history,
to_text=CardsHistory.to_text,
action_title=CardsHistory.ACTION_TITLE)
|
[
"# -*- coding: utf-8 -*-\nimport logging\n\nfrom flask import Blueprint, abort, render_template, request\nfrom flask.ext.login import login_required\n\nfrom troika.card.models import Card\nfrom troika.history.models import CardsHistory\n\nlogger = logging.getLogger(__name__)\n\nblueprint = Blueprint(\"history\", __name__, url_prefix='/history',\n static_folder=\"../static\")\n\n\[email protected](\"/\", methods=['GET'])\n@login_required\ndef list():\n\n try:\n page = int(request.args.get('page', 1))\n card_id = int(request.args.get('card_id', 0))\n except ValueError:\n abort(404)\n\n card = None\n query = CardsHistory.query.order_by('action_date desc')\n if card_id:\n card = Card.query.get(card_id)\n if not card:\n abort(404)\n\n query = query.filter_by(card_id=card_id)\n history = query.paginate(page, CardsHistory.PER_PAGE, False)\n return render_template(\"history/list.html\",\n card=card,\n history=history,\n action_title=CardsHistory.ACTION_TITLE,\n card_id=card_id)\n\n\[email protected](\"/<int:history_id>\", methods=['GET'])\n@login_required\ndef show(history_id):\n\n history = CardsHistory.query.get(history_id)\n if not history:\n abort(404)\n\n return render_template(\"history/show.html\",\n history=history,\n to_text=CardsHistory.to_text,\n action_title=CardsHistory.ACTION_TITLE)\n",
"import logging\nfrom flask import Blueprint, abort, render_template, request\nfrom flask.ext.login import login_required\nfrom troika.card.models import Card\nfrom troika.history.models import CardsHistory\nlogger = logging.getLogger(__name__)\nblueprint = Blueprint('history', __name__, url_prefix='/history',\n static_folder='../static')\n\n\[email protected]('/', methods=['GET'])\n@login_required\ndef list():\n try:\n page = int(request.args.get('page', 1))\n card_id = int(request.args.get('card_id', 0))\n except ValueError:\n abort(404)\n card = None\n query = CardsHistory.query.order_by('action_date desc')\n if card_id:\n card = Card.query.get(card_id)\n if not card:\n abort(404)\n query = query.filter_by(card_id=card_id)\n history = query.paginate(page, CardsHistory.PER_PAGE, False)\n return render_template('history/list.html', card=card, history=history,\n action_title=CardsHistory.ACTION_TITLE, card_id=card_id)\n\n\[email protected]('/<int:history_id>', methods=['GET'])\n@login_required\ndef show(history_id):\n history = CardsHistory.query.get(history_id)\n if not history:\n abort(404)\n return render_template('history/show.html', history=history, to_text=\n CardsHistory.to_text, action_title=CardsHistory.ACTION_TITLE)\n",
"<import token>\nlogger = logging.getLogger(__name__)\nblueprint = Blueprint('history', __name__, url_prefix='/history',\n static_folder='../static')\n\n\[email protected]('/', methods=['GET'])\n@login_required\ndef list():\n try:\n page = int(request.args.get('page', 1))\n card_id = int(request.args.get('card_id', 0))\n except ValueError:\n abort(404)\n card = None\n query = CardsHistory.query.order_by('action_date desc')\n if card_id:\n card = Card.query.get(card_id)\n if not card:\n abort(404)\n query = query.filter_by(card_id=card_id)\n history = query.paginate(page, CardsHistory.PER_PAGE, False)\n return render_template('history/list.html', card=card, history=history,\n action_title=CardsHistory.ACTION_TITLE, card_id=card_id)\n\n\[email protected]('/<int:history_id>', methods=['GET'])\n@login_required\ndef show(history_id):\n history = CardsHistory.query.get(history_id)\n if not history:\n abort(404)\n return render_template('history/show.html', history=history, to_text=\n CardsHistory.to_text, action_title=CardsHistory.ACTION_TITLE)\n",
"<import token>\n<assignment token>\n\n\[email protected]('/', methods=['GET'])\n@login_required\ndef list():\n try:\n page = int(request.args.get('page', 1))\n card_id = int(request.args.get('card_id', 0))\n except ValueError:\n abort(404)\n card = None\n query = CardsHistory.query.order_by('action_date desc')\n if card_id:\n card = Card.query.get(card_id)\n if not card:\n abort(404)\n query = query.filter_by(card_id=card_id)\n history = query.paginate(page, CardsHistory.PER_PAGE, False)\n return render_template('history/list.html', card=card, history=history,\n action_title=CardsHistory.ACTION_TITLE, card_id=card_id)\n\n\[email protected]('/<int:history_id>', methods=['GET'])\n@login_required\ndef show(history_id):\n history = CardsHistory.query.get(history_id)\n if not history:\n abort(404)\n return render_template('history/show.html', history=history, to_text=\n CardsHistory.to_text, action_title=CardsHistory.ACTION_TITLE)\n",
"<import token>\n<assignment token>\n<function token>\n\n\[email protected]('/<int:history_id>', methods=['GET'])\n@login_required\ndef show(history_id):\n history = CardsHistory.query.get(history_id)\n if not history:\n abort(404)\n return render_template('history/show.html', history=history, to_text=\n CardsHistory.to_text, action_title=CardsHistory.ACTION_TITLE)\n",
"<import token>\n<assignment token>\n<function token>\n<function token>\n"
] | false |
99,443 |
50a85d0d41a1f5c8e197659644b2f6a961035110
|
#coding=utf-8
'''
convert excel to python data
author: cowboyyang
date: 2016-12-18
'''
import xlrd
import os
import sys
from xml.dom import minidom
import optparse
import json
type_convert_map = {}
type_convert_map["int"] = long
type_convert_map["long"] = long
type_convert_map["string"] = str
type_convert_map["float"] = float
class Excel2PythonDataConverter:
def __init__(self, excelname, excelsheet, outdir, targetfilename, messagemeta, xmlfie):
self.excel = excelname
self.sheet = excelsheet
self.outdir = outdir
self.targetfile = targetfilename
self.metaname = messagemeta
self.xmlfile = xmlfie
self.metadict = {}
def build_xml_dict(self):
'''构造一个元数据字典'''
domtree = minidom.parse(self.xmlfile)
value = domtree.documentElement
for node in value.childNodes:
if node.nodeName == "struct":
structname = node.getAttribute("name")
self.metadict[structname] = {}
for child in node.childNodes:
if child.nodeName == "entry":
cname = child.getAttribute("cname")
self.metadict[structname][cname] = {}
self.metadict[structname][cname]["name"]=child.getAttribute("name")
self.metadict[structname][cname]["type"]=child.getAttribute("type")
self.metadict[structname][cname]["option"]=child.getAttribute("option")
def set_raw_filed(self, sheet, row, meta, key, itemmsg, leftkey="", rightkey=""):
'''设置好一个属性'''
keytype = meta[key].get("type")
keyname = meta[key].get("name")
pType = type_convert_map.get(keytype)
properkey= leftkey + key + rightkey
bFound = False
bSetValue = False
bStr = False
for col in xrange(0, sheet.ncols):
cname=sheet.cell_value(0, col)
if cname == properkey:
bFound = True
value = sheet.cell_value(row, col)
vlen = 0
bStr = False
if keytype == "string":
#value = value.encode('utf-8')
vlen = len(value)
bStr = True
else:
if str(value) == "":
vlen = 0
else:
vlen = len(str(pType(value)))
# 无数据，不写入字典
if vlen > 0 and isinstance(itemmsg, dict):
if bStr:
itemmsg[keyname] = value
else:
itemmsg[keyname] = pType(value)
bSetValue = True
elif vlen > 0 and isinstance(itemmsg, list):
itemmsg.append(pType(value))
bSetValue = True
break
if bFound is False:
# 说明没有找到对应key字段
return -1
elif bSetValue is False:
# 说明对应key数据为空
return 1
else:
# 说明写入了对应key的数据
return 0
def gen_one_row_data(self, sheet, row):
'''解析sheet中的第row行，生成python字典数据'''
metadata = self.metadict.get(self.metaname)
onerowdata = {}
for key in metadata:
keytype = metadata[key].get("type")
keyname = metadata[key].get("name")
option = metadata[key].get("option")
if option == "repeated":
# 说明是数组类型
#print "found repeated %s " % key
array = []
structmeta = self.metadict.get(keytype)
if type_convert_map.get(keytype):
seq = 1
while True:
ret = self.set_raw_filed(sheet, row, metadata, key, array, rightkey=str(seq))
if ret < 0:
break
seq += 1
else:
# 复合结构的数组类型
seq = 1
while True:
structitem = {}
for structkey in structmeta:
ret = self.set_raw_filed(sheet, row, structmeta, structkey, structitem, leftkey=key+str(seq))
if ret < 0 :
break
if structitem:
array.append(structitem)
else:
# 一个值都没有设置上，终止
break
seq += 1
if array:
onerowdata[keyname] = array
else:
# 非数组类型
# 原始类型
if type_convert_map.get(keytype):
self.set_raw_filed(sheet, row, metadata, key, onerowdata)
else:
# 结构体类型
structmeta = self.metadict.get(keytype)
structitem = {}
for structkey in structmeta:
self.set_raw_filed(sheet, row, structmeta, structkey, structitem, leftkey=key)
if structitem:
onerowdata[keyname] = structitem
return onerowdata
def convert_excel_to_python(self):
'''将excel转换成python格式'''
# 首先构建一个元数据字典
self.build_xml_dict()
# 读取excel
workbook = xlrd.open_workbook(self.excel)
sheet = workbook.sheet_by_name(self.sheet)
# 生成python字典
row_array_msg = []
for row in xrange(1, sheet.nrows):
onerow = self.gen_one_row_data(sheet, row)
if onerow:
row_array_msg.append(onerow)
self.write_to_file(row_array_msg)
def write_to_file(self, msg):
content = json.dumps(msg, indent=2, ensure_ascii=False).encode('utf-8')
visualfile = self.targetfile.split('.')[0] + ".py"
realfilename = os.path.join(self.outdir, visualfile)
if os.path.exists(realfilename):
# 如果有旧文件，先删除
os.remove(realfilename)
handle = open(realfilename, 'w')
# 写入编码格式
handle.writelines("#coding=utf-8\n")
dictname = "configdata_" + self.metaname + " = \\" + "\n"
handle.writelines(dictname)
handle.writelines(content)
handle.flush()
handle.close()
if __name__ == "__main__":
# cmdline config info
parser = optparse.OptionParser()
parser.add_option("--xmlfile", dest="xmlfile", help="process target xml files")
parser.add_option("--outdir", dest="outdir", help="target file store dir")
parser.add_option("--excelfile", dest="excelfile", help="excel file name")
parser.add_option("--sheetname", dest="sheetname", help="excel sheet name")
parser.add_option("--messagemeta", dest="messagemeta", help="message meta data")
parser.add_option("--dataname", dest="dataname", help="convert protobuf data name")
(options, args) = parser.parse_args()
procxmlfilelist = []
if options.xmlfile is None:
print "no input xml file"
parser.print_help()
exit(1)
else:
procxmlfilelist = options.xmlfile.split(" ")
if options.outdir is None:
print "need store target dir"
parser.print_help()
exit(1)
outdir = os.path.abspath(options.outdir)
excelfile = str(options.excelfile).strip()
excelsheetname = str(options.sheetname).strip().decode("utf-8")
targetfilename = str(options.dataname).strip().decode("utf-8")
messagemeta = str(options.messagemeta).strip()
msgxmlfile = procxmlfilelist[0]
excelconvert = Excel2PythonDataConverter(excelfile,
excelsheetname,
outdir,
targetfilename,
messagemeta,
msgxmlfile)
excelconvert.convert_excel_to_python()
|
[
"#coding=utf-8\n\n'''\nconvert excel to python data\nauthor: cowboyyang\ndate: 2016-12-18\n'''\n\nimport xlrd\nimport os\nimport sys\nfrom xml.dom import minidom\nimport optparse\nimport json\n\ntype_convert_map = {}\ntype_convert_map[\"int\"] = long\ntype_convert_map[\"long\"] = long\ntype_convert_map[\"string\"] = str\ntype_convert_map[\"float\"] = float\n\nclass Excel2PythonDataConverter:\n def __init__(self, excelname, excelsheet, outdir, targetfilename, messagemeta, xmlfie):\n self.excel = excelname\n self.sheet = excelsheet\n self.outdir = outdir\n self.targetfile = targetfilename\n self.metaname = messagemeta\n self.xmlfile = xmlfie\n self.metadict = {}\n\n def build_xml_dict(self):\n '''构造一个元数据字典'''\n domtree = minidom.parse(self.xmlfile)\n value = domtree.documentElement\n for node in value.childNodes:\n if node.nodeName == \"struct\":\n structname = node.getAttribute(\"name\")\n self.metadict[structname] = {}\n for child in node.childNodes:\n if child.nodeName == \"entry\":\n cname = child.getAttribute(\"cname\")\n self.metadict[structname][cname] = {}\n self.metadict[structname][cname][\"name\"]=child.getAttribute(\"name\")\n self.metadict[structname][cname][\"type\"]=child.getAttribute(\"type\")\n self.metadict[structname][cname][\"option\"]=child.getAttribute(\"option\")\n\n def set_raw_filed(self, sheet, row, meta, key, itemmsg, leftkey=\"\", rightkey=\"\"):\n '''设置好一个属性'''\n keytype = meta[key].get(\"type\")\n keyname = meta[key].get(\"name\")\n pType = type_convert_map.get(keytype)\n properkey= leftkey + key + rightkey\n bFound = False\n bSetValue = False\n bStr = False\n for col in xrange(0, sheet.ncols):\n cname=sheet.cell_value(0, col)\n if cname == properkey:\n bFound = True\n value = sheet.cell_value(row, col)\n vlen = 0\n bStr = False\n if keytype == \"string\":\n #value = value.encode('utf-8')\n vlen = len(value)\n bStr = True\n else:\n if str(value) == \"\":\n vlen = 0\n else:\n vlen = len(str(pType(value)))\n\n # 无数据，不写入字典\n if vlen > 0 and isinstance(itemmsg, dict):\n if bStr:\n itemmsg[keyname] = value\n else:\n itemmsg[keyname] = pType(value)\n bSetValue = True\n elif vlen > 0 and isinstance(itemmsg, list):\n itemmsg.append(pType(value))\n bSetValue = True\n break\n\n if bFound is False:\n # 说明没有找到对应key字段\n return -1\n elif bSetValue is False:\n # 说明对应key数据为空\n return 1\n else:\n # 说明写入了对应key的数据\n return 0\n\n def gen_one_row_data(self, sheet, row):\n '''解析sheet中的第row行，生成python字典数据'''\n metadata = self.metadict.get(self.metaname)\n onerowdata = {}\n for key in metadata:\n keytype = metadata[key].get(\"type\")\n keyname = metadata[key].get(\"name\")\n option = metadata[key].get(\"option\")\n\n if option == \"repeated\":\n # 说明是数组类型\n #print \"found repeated %s \" % key\n array = []\n structmeta = self.metadict.get(keytype)\n if type_convert_map.get(keytype):\n seq = 1\n while True:\n ret = self.set_raw_filed(sheet, row, metadata, key, array, rightkey=str(seq))\n if ret < 0:\n break\n seq += 1\n else:\n # 复合结构的数组类型\n seq = 1\n while True:\n structitem = {}\n for structkey in structmeta:\n ret = self.set_raw_filed(sheet, row, structmeta, structkey, structitem, leftkey=key+str(seq))\n if ret < 0 :\n break\n if structitem:\n array.append(structitem)\n else:\n # 一个值都没有设置上，终止\n break\n seq += 1\n if array:\n onerowdata[keyname] = array\n else:\n # 非数组类型\n # 原始类型\n if type_convert_map.get(keytype):\n self.set_raw_filed(sheet, row, metadata, key, onerowdata)\n else:\n # 结构体类型\n structmeta = self.metadict.get(keytype)\n structitem = {}\n for structkey in structmeta:\n self.set_raw_filed(sheet, row, structmeta, structkey, structitem, leftkey=key)\n if structitem:\n onerowdata[keyname] = structitem\n return onerowdata\n\n def convert_excel_to_python(self):\n '''将excel转换成python格式'''\n # 首先构建一个元数据字典\n self.build_xml_dict()\n # 读取excel\n workbook = xlrd.open_workbook(self.excel)\n sheet = workbook.sheet_by_name(self.sheet)\n\n # 生成python字典\n row_array_msg = []\n for row in xrange(1, sheet.nrows):\n onerow = self.gen_one_row_data(sheet, row)\n if onerow:\n row_array_msg.append(onerow)\n\n self.write_to_file(row_array_msg)\n\n def write_to_file(self, msg):\n content = json.dumps(msg, indent=2, ensure_ascii=False).encode('utf-8')\n visualfile = self.targetfile.split('.')[0] + \".py\"\n realfilename = os.path.join(self.outdir, visualfile)\n if os.path.exists(realfilename):\n # 如果有旧文件，先删除\n os.remove(realfilename)\n handle = open(realfilename, 'w')\n # 写入编码格式\n handle.writelines(\"#coding=utf-8\\n\")\n dictname = \"configdata_\" + self.metaname + \" = \\\\\" + \"\\n\"\n handle.writelines(dictname)\n handle.writelines(content)\n handle.flush()\n handle.close()\n\nif __name__ == \"__main__\":\n # cmdline config info\n parser = optparse.OptionParser()\n parser.add_option(\"--xmlfile\", dest=\"xmlfile\", help=\"process target xml files\")\n parser.add_option(\"--outdir\", dest=\"outdir\", help=\"target file store dir\")\n parser.add_option(\"--excelfile\", dest=\"excelfile\", help=\"excel file name\")\n parser.add_option(\"--sheetname\", dest=\"sheetname\", help=\"excel sheet name\")\n parser.add_option(\"--messagemeta\", dest=\"messagemeta\", help=\"message meta data\")\n parser.add_option(\"--dataname\", dest=\"dataname\", help=\"convert protobuf data name\")\n\n (options, args) = parser.parse_args()\n procxmlfilelist = []\n if options.xmlfile is None:\n print \"no input xml file\"\n parser.print_help()\n exit(1)\n else:\n procxmlfilelist = options.xmlfile.split(\" \")\n\n if options.outdir is None:\n print \"need store target dir\"\n parser.print_help()\n exit(1)\n\n outdir = os.path.abspath(options.outdir)\n excelfile = str(options.excelfile).strip()\n excelsheetname = str(options.sheetname).strip().decode(\"utf-8\")\n targetfilename = str(options.dataname).strip().decode(\"utf-8\")\n messagemeta = str(options.messagemeta).strip()\n msgxmlfile = procxmlfilelist[0]\n excelconvert = Excel2PythonDataConverter(excelfile,\n excelsheetname,\n outdir,\n targetfilename,\n messagemeta,\n msgxmlfile)\n excelconvert.convert_excel_to_python()"
] | true |
99,444 |
4cc8a92880490c0915cafe45dcc8f8582407cbe3
|
version https://git-lfs.github.com/spec/v1
oid sha256:6d7020584427040bc4b4d457dbb070e50ef5121fe0cb6e4a2a75939d22dceed8
size 1003
|
[
"version https://git-lfs.github.com/spec/v1\noid sha256:6d7020584427040bc4b4d457dbb070e50ef5121fe0cb6e4a2a75939d22dceed8\nsize 1003\n"
] | true |
99,445 |
57d433d91139ce656cb82e3281fa10e1ff02ba7d
|
class InterfaceActivation:
def __init__(self):
self.menu_active = False
self.game_active = False
def get_game_active(self):
return self.game_active
class GameStats():
def __init__(self, ai_game):
self.screen = ai_game.screen
self.setting = ai_game.setting
self.high_score = 0
self.score = 0
self.level = 1
self.ships_left = self.setting.ship_limit
def reset_stats(self):
self.ships_left = self.setting.ship_limit
self.score = 0
self.level = 1
|
[
"class InterfaceActivation:\n def __init__(self):\n self.menu_active = False\n self.game_active = False\n\n def get_game_active(self):\n return self.game_active\n\n\nclass GameStats():\n def __init__(self, ai_game):\n self.screen = ai_game.screen\n self.setting = ai_game.setting\n self.high_score = 0\n self.score = 0\n self.level = 1\n self.ships_left = self.setting.ship_limit\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"class InterfaceActivation:\n\n def __init__(self):\n self.menu_active = False\n self.game_active = False\n\n def get_game_active(self):\n return self.game_active\n\n\nclass GameStats:\n\n def __init__(self, ai_game):\n self.screen = ai_game.screen\n self.setting = ai_game.setting\n self.high_score = 0\n self.score = 0\n self.level = 1\n self.ships_left = self.setting.ship_limit\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"class InterfaceActivation:\n\n def __init__(self):\n self.menu_active = False\n self.game_active = False\n <function token>\n\n\nclass GameStats:\n\n def __init__(self, ai_game):\n self.screen = ai_game.screen\n self.setting = ai_game.setting\n self.high_score = 0\n self.score = 0\n self.level = 1\n self.ships_left = self.setting.ship_limit\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"class InterfaceActivation:\n <function token>\n <function token>\n\n\nclass GameStats:\n\n def __init__(self, ai_game):\n self.screen = ai_game.screen\n self.setting = ai_game.setting\n self.high_score = 0\n self.score = 0\n self.level = 1\n self.ships_left = self.setting.ship_limit\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"<class token>\n\n\nclass GameStats:\n\n def __init__(self, ai_game):\n self.screen = ai_game.screen\n self.setting = ai_game.setting\n self.high_score = 0\n self.score = 0\n self.level = 1\n self.ships_left = self.setting.ship_limit\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"<class token>\n\n\nclass GameStats:\n <function token>\n\n def reset_stats(self):\n self.ships_left = self.setting.ship_limit\n self.score = 0\n self.level = 1\n",
"<class token>\n\n\nclass GameStats:\n <function token>\n <function token>\n",
"<class token>\n<class token>\n"
] | false |
99,446 |
f1a6e0a540b9b7e003c96a767c83cc7dc72919bf
|
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 27 01:12:06 2018
@author: Lourenço Neto
"""
"""
O problema consiste em verificar quais das figuras podem ser desenhadas atendendo o requisito
de passar por cada aresta somente uma vez
Levando em conta que cada figura dessa pode ser tratada como um grafo não direcionado e cíclico,
O percurso, conforme indicação do exercício, se trata de um caminho Euleriano:
Caso o número de vértices do grafo que possuam grau ímpar seja 0 ou 2, é possível traçar um caminho Euleriano por ele
Sendo assim, para verificação que a figura é possível de se desenhar ou não,
Bastaria passar a matriz de adjacências do grafo e contar quantos vértices está ligado a uma lista ligada (Que informa com quais vértices ele está conectado)
com número ímpar de elementos. Se a quantidade de vértices que atingem esse requisito for 0 ou 2, a figura pode ser desenhada.
"""
|
[
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 27 01:12:06 2018\n\n@author: Lourenço Neto\n\"\"\"\n\n\"\"\"\nO problema consiste em verificar quais das figuras podem ser desenhadas atendendo o requisito\nde passar por cada aresta somente uma vez\n\nLevando em conta que cada figura dessa pode ser tratada como um grafo não direcionado e cíclico,\nO percurso, conforme indicação do exercício, se trata de um caminho Euleriano:\n Caso o número de vértices do grafo que possuam grau ímpar seja 0 ou 2, é possível traçar um caminho Euleriano por ele\nSendo assim, para verificação que a figura é possível de se desenhar ou não,\nBastaria passar a matriz de adjacências do grafo e contar quantos vértices está ligado a uma lista ligada (Que informa com quais vértices ele está conectado) \ncom número ímpar de elementos. Se a quantidade de vértices que atingem esse requisito for 0 ou 2, a figura pode ser desenhada.\n\"\"\"",
"<docstring token>\n"
] | false |
99,447 |
ff78282e7ec53e97ff631b49caefae5774290043
|
import time
import sys
sys.setrecursionlimit(20999)
start_time = time.time()
f = open('names_1.txt', 'r')
names_1 = f.read().split("\n") # List containing 10000 names
f.close()
f = open('names_2.txt', 'r')
names_2 = f.read().split("\n") # List containing 10000 names
f.close()
duplicates = [] # Return the list of duplicates in this data structure
# Replace the nested for loops below with your improvements
# THE RUNTIME FOR THIS STARTER CODE IS O(m*n) BECAUSE THE INPUT COMES FROM TWO DIFFERENT FILES THAT COULD BE DIFFERENT SIZES.
# for name_1 in names_1:
# for name_2 in names_2:
# if name_1 == name_2:
# duplicates.append(name_1)
class BinarySearchTree:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
def insert(self, value):
if value[0] < self.value[0]:
if self.left is None:
self.left = BinarySearchTree(value)
else:
self.left.insert(value)
# elif value[0] >= self.value[0]:
else:
if self.right is None:
self.right = BinarySearchTree(value)
else:
self.right.insert(value)
def contains(self, target):
if target == self.value:
return True
if target[0] < self.value[0]:
if self.left is None:
return False
else:
return self.left.contains(target)
else:
if self.right is None:
return False
else:
return self.right.contains(target)
# def contains(self, target):
# current = self
# while current is not None:
# # if target == current.value:
# # return True
# if target[0] < current.value[0]:
# current = current.left
# elif target[0] > current.value[0]:
# current = current.right
# # elif target == current.value:
# # return True
# else:
# print(current.value)
# return True
# return False
# name_1_tree = []
# i = 0
# for name_1 in names_1:
# if i == 0:
# name_1_tree = BinarySearchTree(name_1)
# i += 1
# else:
# name_1_tree.insert(name_1)
# i += 1
# for name_2 in names_2:
# if name_1_tree.contains(name_2):
# duplicates.append(name_2)
# First, we create a Binary Search Tree using Names as the root node.
name_1_tree = BinarySearchTree("Names")
# Second, we populate the tree by looping through the first file
for name_1 in names_1:
name_1_tree.insert(name_1)
# Then we loop through the second file and check to see which names from the second list are in the tree we just created, and for any that return true, we append that name to the duplicates list.
for name_2 in names_2:
if name_1_tree.contains(name_2):
duplicates.append(name_2)
end_time = time.time()
print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
print (f"runtime: {end_time - start_time} seconds")
# ---------- Stretch Goal -----------
# Python has built-in tools that allow for a very efficient approach to this problem
# What's the best time you can accomplish? Thare are no restrictions on techniques or data
# structures, but you may not import any additional libraries that you did not write yourself.
|
[
"import time\nimport sys\n\nsys.setrecursionlimit(20999)\n\nstart_time = time.time()\n\nf = open('names_1.txt', 'r')\nnames_1 = f.read().split(\"\\n\") # List containing 10000 names\nf.close()\n\nf = open('names_2.txt', 'r')\nnames_2 = f.read().split(\"\\n\") # List containing 10000 names\nf.close()\n\nduplicates = [] # Return the list of duplicates in this data structure\n\n# Replace the nested for loops below with your improvements\n# THE RUNTIME FOR THIS STARTER CODE IS O(m*n) BECAUSE THE INPUT COMES FROM TWO DIFFERENT FILES THAT COULD BE DIFFERENT SIZES.\n# for name_1 in names_1:\n# for name_2 in names_2:\n# if name_1 == name_2:\n# duplicates.append(name_1)\n\nclass BinarySearchTree:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n # elif value[0] >= self.value[0]:\n else:\n if self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n \n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n else:\n if self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n # def contains(self, target):\n # current = self\n # while current is not None:\n # # if target == current.value:\n # # return True\n # if target[0] < current.value[0]:\n # current = current.left\n # elif target[0] > current.value[0]:\n # current = current.right\n # # elif target == current.value:\n # # return True\n # else:\n # print(current.value)\n # return True\n # return False\n\n# name_1_tree = []\n# i = 0\n\n# for name_1 in names_1:\n# if i == 0:\n# name_1_tree = BinarySearchTree(name_1)\n# i += 1\n# else:\n# name_1_tree.insert(name_1)\n# i += 1\n# for name_2 in names_2:\n# if name_1_tree.contains(name_2):\n# duplicates.append(name_2)\n\n# First, we create a Binary Search Tree using Names as the root node.\nname_1_tree = BinarySearchTree(\"Names\")\n\n# Second, we populate the tree by looping through the first file\nfor name_1 in names_1:\n name_1_tree.insert(name_1)\n\n# Then we loop through the second file and check to see which names from the second list are in the tree we just created, and for any that return true, we append that name to the duplicates list.\nfor name_2 in names_2:\n if name_1_tree.contains(name_2):\n duplicates.append(name_2)\n\n\nend_time = time.time()\nprint (f\"{len(duplicates)} duplicates:\\n\\n{', '.join(duplicates)}\\n\\n\")\nprint (f\"runtime: {end_time - start_time} seconds\")\n\n# ---------- Stretch Goal -----------\n# Python has built-in tools that allow for a very efficient approach to this problem\n# What's the best time you can accomplish? Thare are no restrictions on techniques or data\n# structures, but you may not import any additional libraries that you did not write yourself.\n\n\n",
"import time\nimport sys\nsys.setrecursionlimit(20999)\nstart_time = time.time()\nf = open('names_1.txt', 'r')\nnames_1 = f.read().split('\\n')\nf.close()\nf = open('names_2.txt', 'r')\nnames_2 = f.read().split('\\n')\nf.close()\nduplicates = []\n\n\nclass BinarySearchTree:\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n elif self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\nname_1_tree = BinarySearchTree('Names')\nfor name_1 in names_1:\n name_1_tree.insert(name_1)\nfor name_2 in names_2:\n if name_1_tree.contains(name_2):\n duplicates.append(name_2)\nend_time = time.time()\nprint(f\"\"\"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n\"\"\")\nprint(f'runtime: {end_time - start_time} seconds')\n",
"<import token>\nsys.setrecursionlimit(20999)\nstart_time = time.time()\nf = open('names_1.txt', 'r')\nnames_1 = f.read().split('\\n')\nf.close()\nf = open('names_2.txt', 'r')\nnames_2 = f.read().split('\\n')\nf.close()\nduplicates = []\n\n\nclass BinarySearchTree:\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n elif self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\nname_1_tree = BinarySearchTree('Names')\nfor name_1 in names_1:\n name_1_tree.insert(name_1)\nfor name_2 in names_2:\n if name_1_tree.contains(name_2):\n duplicates.append(name_2)\nend_time = time.time()\nprint(f\"\"\"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n\"\"\")\nprint(f'runtime: {end_time - start_time} seconds')\n",
"<import token>\nsys.setrecursionlimit(20999)\n<assignment token>\nf.close()\n<assignment token>\nf.close()\n<assignment token>\n\n\nclass BinarySearchTree:\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n elif self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\n<assignment token>\nfor name_1 in names_1:\n name_1_tree.insert(name_1)\nfor name_2 in names_2:\n if name_1_tree.contains(name_2):\n duplicates.append(name_2)\n<assignment token>\nprint(f\"\"\"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n\"\"\")\nprint(f'runtime: {end_time - start_time} seconds')\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\nclass BinarySearchTree:\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n elif self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\nclass BinarySearchTree:\n <function token>\n\n def insert(self, value):\n if value[0] < self.value[0]:\n if self.left is None:\n self.left = BinarySearchTree(value)\n else:\n self.left.insert(value)\n elif self.right is None:\n self.right = BinarySearchTree(value)\n else:\n self.right.insert(value)\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\nclass BinarySearchTree:\n <function token>\n <function token>\n\n def contains(self, target):\n if target == self.value:\n return True\n if target[0] < self.value[0]:\n if self.left is None:\n return False\n else:\n return self.left.contains(target)\n elif self.right is None:\n return False\n else:\n return self.right.contains(target)\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n\n\nclass BinarySearchTree:\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n",
"<import token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<class token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,448 |
7907f4d2cf067225a4fff288673c33b7c3072d33
|
import traceback
import logging
import dataset
from technews_nlp_aggregator.common.util import extract_host, extract_normpath, extract_source_without_www, extract_start_url
from technews_nlp_aggregator.scraping.main.scrapy.spiders import all_start_urls
class ArticlesSpiderRepo:
def get_connection(self):
return self.dataset_connection
def __init__(self, db_connection):
self.db_connection = db_connection
self.dataset_connection = dataset.connect(self.db_connection, engine_kwargs={
'connect_args': {'charset': 'utf8'}
})
self.engine = self.dataset_connection.engine
def retrieve_urls_queued(self):
sql_user_similar = "SELECT UTA_SPIDER, UTA_URL FROM URLS_TO_ADD WHERE UTA_PROCESSED IS NULL"
similar_stories = []
con = self.get_connection()
query_result= con.query(sql_user_similar )
return query_result
def add_url_list(self, url_list):
sql_add_user = "INSERT INTO URLS_TO_ADD (UTA_SPIDER, UTA_URL) VALUES (:uta_spider, :uta_url) "
con = self.get_connection()
messages = []
for url in url_list:
url = extract_normpath(url)
start_url = extract_start_url(url)
logging.info("Starting url: {}".format(start_url))
if (start_url in all_start_urls):
host = extract_source_without_www(url).lower().capitalize()
if url and host:
try:
con.begin()
con.query(sql_add_user , {"uta_spider": host, "uta_url": url.strip()})
messages.append("Added {} : {}".format(host, url))
con.commit()
except:
con.rollback()
messages.append('Could not add {}: {}'.format(host, url))
traceback.print_stack()
else:
messages.append('Urls from {} cannot be parsed yet'.format(start_url))
return messages
def update_to_crawled(self, con=None):
sql_update = "UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()"
con = self.get_connection() if not con else con
try:
con.begin()
article_query = con.query(sql_update)
con.commit()
except:
con.rollback()
traceback.print_stack()
|
[
"import traceback\nimport logging\nimport dataset\nfrom technews_nlp_aggregator.common.util import extract_host, extract_normpath, extract_source_without_www, extract_start_url\nfrom technews_nlp_aggregator.scraping.main.scrapy.spiders import all_start_urls\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n\n def __init__(self, db_connection):\n self.db_connection = db_connection\n self.dataset_connection = dataset.connect(self.db_connection, engine_kwargs={\n 'connect_args': {'charset': 'utf8'}\n })\n self.engine = self.dataset_connection.engine\n\n def retrieve_urls_queued(self):\n sql_user_similar = \"SELECT UTA_SPIDER, UTA_URL FROM URLS_TO_ADD WHERE UTA_PROCESSED IS NULL\"\n similar_stories = []\n con = self.get_connection()\n query_result= con.query(sql_user_similar )\n\n return query_result\n\n def add_url_list(self, url_list):\n sql_add_user = \"INSERT INTO URLS_TO_ADD (UTA_SPIDER, UTA_URL) VALUES (:uta_spider, :uta_url) \"\n con = self.get_connection()\n messages = []\n for url in url_list:\n url = extract_normpath(url)\n start_url = extract_start_url(url)\n logging.info(\"Starting url: {}\".format(start_url))\n if (start_url in all_start_urls):\n host = extract_source_without_www(url).lower().capitalize()\n if url and host:\n try:\n con.begin()\n con.query(sql_add_user , {\"uta_spider\": host, \"uta_url\": url.strip()})\n messages.append(\"Added {} : {}\".format(host, url))\n con.commit()\n except:\n con.rollback()\n messages.append('Could not add {}: {}'.format(host, url))\n traceback.print_stack()\n else:\n messages.append('Urls from {} cannot be parsed yet'.format(start_url))\n return messages\n\n\n def update_to_crawled(self, con=None):\n sql_update = \"UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()\"\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()",
"import traceback\nimport logging\nimport dataset\nfrom technews_nlp_aggregator.common.util import extract_host, extract_normpath, extract_source_without_www, extract_start_url\nfrom technews_nlp_aggregator.scraping.main.scrapy.spiders import all_start_urls\n\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n\n def __init__(self, db_connection):\n self.db_connection = db_connection\n self.dataset_connection = dataset.connect(self.db_connection,\n engine_kwargs={'connect_args': {'charset': 'utf8'}})\n self.engine = self.dataset_connection.engine\n\n def retrieve_urls_queued(self):\n sql_user_similar = (\n 'SELECT UTA_SPIDER, UTA_URL FROM URLS_TO_ADD WHERE UTA_PROCESSED IS NULL'\n )\n similar_stories = []\n con = self.get_connection()\n query_result = con.query(sql_user_similar)\n return query_result\n\n def add_url_list(self, url_list):\n sql_add_user = (\n 'INSERT INTO URLS_TO_ADD (UTA_SPIDER, UTA_URL) VALUES (:uta_spider, :uta_url) '\n )\n con = self.get_connection()\n messages = []\n for url in url_list:\n url = extract_normpath(url)\n start_url = extract_start_url(url)\n logging.info('Starting url: {}'.format(start_url))\n if start_url in all_start_urls:\n host = extract_source_without_www(url).lower().capitalize()\n if url and host:\n try:\n con.begin()\n con.query(sql_add_user, {'uta_spider': host,\n 'uta_url': url.strip()})\n messages.append('Added {} : {}'.format(host, url))\n con.commit()\n except:\n con.rollback()\n messages.append('Could not add {}: {}'.format(host,\n url))\n traceback.print_stack()\n else:\n messages.append('Urls from {} cannot be parsed yet'.format(\n start_url))\n return messages\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n\n def __init__(self, db_connection):\n self.db_connection = db_connection\n self.dataset_connection = dataset.connect(self.db_connection,\n engine_kwargs={'connect_args': {'charset': 'utf8'}})\n self.engine = self.dataset_connection.engine\n\n def retrieve_urls_queued(self):\n sql_user_similar = (\n 'SELECT UTA_SPIDER, UTA_URL FROM URLS_TO_ADD WHERE UTA_PROCESSED IS NULL'\n )\n similar_stories = []\n con = self.get_connection()\n query_result = con.query(sql_user_similar)\n return query_result\n\n def add_url_list(self, url_list):\n sql_add_user = (\n 'INSERT INTO URLS_TO_ADD (UTA_SPIDER, UTA_URL) VALUES (:uta_spider, :uta_url) '\n )\n con = self.get_connection()\n messages = []\n for url in url_list:\n url = extract_normpath(url)\n start_url = extract_start_url(url)\n logging.info('Starting url: {}'.format(start_url))\n if start_url in all_start_urls:\n host = extract_source_without_www(url).lower().capitalize()\n if url and host:\n try:\n con.begin()\n con.query(sql_add_user, {'uta_spider': host,\n 'uta_url': url.strip()})\n messages.append('Added {} : {}'.format(host, url))\n con.commit()\n except:\n con.rollback()\n messages.append('Could not add {}: {}'.format(host,\n url))\n traceback.print_stack()\n else:\n messages.append('Urls from {} cannot be parsed yet'.format(\n start_url))\n return messages\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n\n def __init__(self, db_connection):\n self.db_connection = db_connection\n self.dataset_connection = dataset.connect(self.db_connection,\n engine_kwargs={'connect_args': {'charset': 'utf8'}})\n self.engine = self.dataset_connection.engine\n <function token>\n\n def add_url_list(self, url_list):\n sql_add_user = (\n 'INSERT INTO URLS_TO_ADD (UTA_SPIDER, UTA_URL) VALUES (:uta_spider, :uta_url) '\n )\n con = self.get_connection()\n messages = []\n for url in url_list:\n url = extract_normpath(url)\n start_url = extract_start_url(url)\n logging.info('Starting url: {}'.format(start_url))\n if start_url in all_start_urls:\n host = extract_source_without_www(url).lower().capitalize()\n if url and host:\n try:\n con.begin()\n con.query(sql_add_user, {'uta_spider': host,\n 'uta_url': url.strip()})\n messages.append('Added {} : {}'.format(host, url))\n con.commit()\n except:\n con.rollback()\n messages.append('Could not add {}: {}'.format(host,\n url))\n traceback.print_stack()\n else:\n messages.append('Urls from {} cannot be parsed yet'.format(\n start_url))\n return messages\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n\n def __init__(self, db_connection):\n self.db_connection = db_connection\n self.dataset_connection = dataset.connect(self.db_connection,\n engine_kwargs={'connect_args': {'charset': 'utf8'}})\n self.engine = self.dataset_connection.engine\n <function token>\n <function token>\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n\n def get_connection(self):\n return self.dataset_connection\n <function token>\n <function token>\n <function token>\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n <function token>\n <function token>\n <function token>\n <function token>\n\n def update_to_crawled(self, con=None):\n sql_update = 'UPDATE URLS_TO_ADD SET UTA_PROCESSED = SYSDATE()'\n con = self.get_connection() if not con else con\n try:\n con.begin()\n article_query = con.query(sql_update)\n con.commit()\n except:\n con.rollback()\n traceback.print_stack()\n",
"<import token>\n\n\nclass ArticlesSpiderRepo:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,449 |
2e5ea871e96d8cdcf969a4e76f3551092888b7af
|
import logging
def logging_config():
logging.getLogger("PIL").setLevel(logging.WARNING)
logging.getLogger("openapi_spec_validator").setLevel(logging.WARNING)
logging.getLogger("connexion").setLevel(logging.WARNING)
logging.getLogger("pika").setLevel(logging.WARNING)
|
[
"import logging\n\n\ndef logging_config():\n logging.getLogger(\"PIL\").setLevel(logging.WARNING)\n logging.getLogger(\"openapi_spec_validator\").setLevel(logging.WARNING)\n logging.getLogger(\"connexion\").setLevel(logging.WARNING)\n logging.getLogger(\"pika\").setLevel(logging.WARNING)\n",
"import logging\n\n\ndef logging_config():\n logging.getLogger('PIL').setLevel(logging.WARNING)\n logging.getLogger('openapi_spec_validator').setLevel(logging.WARNING)\n logging.getLogger('connexion').setLevel(logging.WARNING)\n logging.getLogger('pika').setLevel(logging.WARNING)\n",
"<import token>\n\n\ndef logging_config():\n logging.getLogger('PIL').setLevel(logging.WARNING)\n logging.getLogger('openapi_spec_validator').setLevel(logging.WARNING)\n logging.getLogger('connexion').setLevel(logging.WARNING)\n logging.getLogger('pika').setLevel(logging.WARNING)\n",
"<import token>\n<function token>\n"
] | false |
99,450 |
49f3e1ed0234c39fd38658c5753308b219ff3aa7
|
import beverages
import random
class CoffeeMachine:
def __init__(self):
self.serve_count = 0
class EmptyCup (beverages.HotBeverage):
def __init__(self, price = 0.90, name = "empty cup"):
beverages.HotBeverage.__init__(self, price, name)
def description(serlf):
return "An empty cup ?! Gimme my money back!"
class BrokenMachineException (Exception):
def __init__(self):
Exception.__init__(self, "This coffee machine has to be repaired.")
def repair(self):
self.serve_count = 0
def serve(self, obj: beverages.HotBeverage):
if (self.serve_count > 9):
raise self.BrokenMachineException()
if random.randint(0, 1):
self.serve_count = self.serve_count + 1
# print (self.serve_count)
return obj
else:
return self.EmptyCup()
if __name__ == '__main__':
machine = CoffeeMachine()
for i in range(0, 8):
try:
print(machine.serve(beverages.HotBeverage()))
print(machine.serve(beverages.Coffee()))
print(machine.serve(beverages.Tea()))
print(machine.serve(beverages.Chocolate()))
print(machine.serve(beverages.Cappuccino()))
except Exception as a:
print("***************** Warning ********************")
print(a)
print("==============================================\n")
machine.repair()
|
[
"import beverages\nimport random\n\nclass CoffeeMachine:\n def __init__(self):\n self.serve_count = 0\n\n class EmptyCup (beverages.HotBeverage):\n def __init__(self, price = 0.90, name = \"empty cup\"):\n beverages.HotBeverage.__init__(self, price, name)\n \n def description(serlf):\n return \"An empty cup ?! Gimme my money back!\"\n\n class BrokenMachineException (Exception):\n def __init__(self):\n Exception.__init__(self, \"This coffee machine has to be repaired.\")\n\n def repair(self):\n self.serve_count = 0\n\n def serve(self, obj: beverages.HotBeverage):\n if (self.serve_count > 9):\n raise self.BrokenMachineException()\n \n if random.randint(0, 1):\n self.serve_count = self.serve_count + 1\n # print (self.serve_count)\n return obj\n else:\n return self.EmptyCup()\n\nif __name__ == '__main__':\n\n machine = CoffeeMachine()\n for i in range(0, 8):\n try:\n print(machine.serve(beverages.HotBeverage()))\n print(machine.serve(beverages.Coffee()))\n print(machine.serve(beverages.Tea()))\n print(machine.serve(beverages.Chocolate()))\n print(machine.serve(beverages.Cappuccino()))\n except Exception as a:\n print(\"***************** Warning ********************\")\n print(a)\n print(\"==============================================\\n\")\n machine.repair()\n \n\n",
"import beverages\nimport random\n\n\nclass CoffeeMachine:\n\n def __init__(self):\n self.serve_count = 0\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n\n def repair(self):\n self.serve_count = 0\n\n def serve(self, obj: beverages.HotBeverage):\n if self.serve_count > 9:\n raise self.BrokenMachineException()\n if random.randint(0, 1):\n self.serve_count = self.serve_count + 1\n return obj\n else:\n return self.EmptyCup()\n\n\nif __name__ == '__main__':\n machine = CoffeeMachine()\n for i in range(0, 8):\n try:\n print(machine.serve(beverages.HotBeverage()))\n print(machine.serve(beverages.Coffee()))\n print(machine.serve(beverages.Tea()))\n print(machine.serve(beverages.Chocolate()))\n print(machine.serve(beverages.Cappuccino()))\n except Exception as a:\n print('***************** Warning ********************')\n print(a)\n print('==============================================\\n')\n machine.repair()\n",
"<import token>\n\n\nclass CoffeeMachine:\n\n def __init__(self):\n self.serve_count = 0\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n\n def repair(self):\n self.serve_count = 0\n\n def serve(self, obj: beverages.HotBeverage):\n if self.serve_count > 9:\n raise self.BrokenMachineException()\n if random.randint(0, 1):\n self.serve_count = self.serve_count + 1\n return obj\n else:\n return self.EmptyCup()\n\n\nif __name__ == '__main__':\n machine = CoffeeMachine()\n for i in range(0, 8):\n try:\n print(machine.serve(beverages.HotBeverage()))\n print(machine.serve(beverages.Coffee()))\n print(machine.serve(beverages.Tea()))\n print(machine.serve(beverages.Chocolate()))\n print(machine.serve(beverages.Cappuccino()))\n except Exception as a:\n print('***************** Warning ********************')\n print(a)\n print('==============================================\\n')\n machine.repair()\n",
"<import token>\n\n\nclass CoffeeMachine:\n\n def __init__(self):\n self.serve_count = 0\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n\n def repair(self):\n self.serve_count = 0\n\n def serve(self, obj: beverages.HotBeverage):\n if self.serve_count > 9:\n raise self.BrokenMachineException()\n if random.randint(0, 1):\n self.serve_count = self.serve_count + 1\n return obj\n else:\n return self.EmptyCup()\n\n\n<code token>\n",
"<import token>\n\n\nclass CoffeeMachine:\n\n def __init__(self):\n self.serve_count = 0\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n\n def repair(self):\n self.serve_count = 0\n <function token>\n\n\n<code token>\n",
"<import token>\n\n\nclass CoffeeMachine:\n\n def __init__(self):\n self.serve_count = 0\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n <function token>\n <function token>\n\n\n<code token>\n",
"<import token>\n\n\nclass CoffeeMachine:\n <function token>\n\n\n class EmptyCup(beverages.HotBeverage):\n\n def __init__(self, price=0.9, name='empty cup'):\n beverages.HotBeverage.__init__(self, price, name)\n\n def description(serlf):\n return 'An empty cup ?! Gimme my money back!'\n\n\n class BrokenMachineException(Exception):\n\n def __init__(self):\n Exception.__init__(self, 'This coffee machine has to be repaired.')\n <function token>\n <function token>\n\n\n<code token>\n",
"<import token>\n<class token>\n<code token>\n"
] | false |
99,451 |
ce04c698fdb95fdec1c792b19ee9aee70f914d1e
|
"""systemcall URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.conf.urls import include
from django.contrib import admin
from django.urls import path
from rest_framework import routers
from django.conf.urls import url
from rest_framework_swagger.views import get_swagger_view
from rest_framework.authtoken.views import obtain_auth_token
from apps.registercall.views import RegisterCallViewSet
from apps.phonebill.views import PhoneBillViewSet
from apps.phonebill.views import RegisterViewSet
schema_view = get_swagger_view(title='System Call')
router = routers.DefaultRouter()
router.register(r'registercall', RegisterCallViewSet, base_name='RegisterCall')
router.register(r'phonebill', PhoneBillViewSet, base_name='PhoneBill')
router.register(r'registers', RegisterViewSet, base_name='Registers')
urlpatterns = [
url(r'^$', schema_view),
path('', include(router.urls)),
path('admin/', admin.site.urls),
path('api-token-auth/', obtain_auth_token)
]
|
[
"\"\"\"systemcall URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import include\nfrom django.contrib import admin\nfrom django.urls import path\nfrom rest_framework import routers\nfrom django.conf.urls import url\nfrom rest_framework_swagger.views import get_swagger_view\nfrom rest_framework.authtoken.views import obtain_auth_token\n\nfrom apps.registercall.views import RegisterCallViewSet\nfrom apps.phonebill.views import PhoneBillViewSet\nfrom apps.phonebill.views import RegisterViewSet\n\nschema_view = get_swagger_view(title='System Call')\n\nrouter = routers.DefaultRouter()\nrouter.register(r'registercall', RegisterCallViewSet, base_name='RegisterCall')\nrouter.register(r'phonebill', PhoneBillViewSet, base_name='PhoneBill')\nrouter.register(r'registers', RegisterViewSet, base_name='Registers')\n\nurlpatterns = [\n url(r'^$', schema_view),\n path('', include(router.urls)),\n path('admin/', admin.site.urls),\n path('api-token-auth/', obtain_auth_token)\n]\n",
"<docstring token>\nfrom django.conf.urls import include\nfrom django.contrib import admin\nfrom django.urls import path\nfrom rest_framework import routers\nfrom django.conf.urls import url\nfrom rest_framework_swagger.views import get_swagger_view\nfrom rest_framework.authtoken.views import obtain_auth_token\nfrom apps.registercall.views import RegisterCallViewSet\nfrom apps.phonebill.views import PhoneBillViewSet\nfrom apps.phonebill.views import RegisterViewSet\nschema_view = get_swagger_view(title='System Call')\nrouter = routers.DefaultRouter()\nrouter.register('registercall', RegisterCallViewSet, base_name='RegisterCall')\nrouter.register('phonebill', PhoneBillViewSet, base_name='PhoneBill')\nrouter.register('registers', RegisterViewSet, base_name='Registers')\nurlpatterns = [url('^$', schema_view), path('', include(router.urls)), path\n ('admin/', admin.site.urls), path('api-token-auth/', obtain_auth_token)]\n",
"<docstring token>\n<import token>\nschema_view = get_swagger_view(title='System Call')\nrouter = routers.DefaultRouter()\nrouter.register('registercall', RegisterCallViewSet, base_name='RegisterCall')\nrouter.register('phonebill', PhoneBillViewSet, base_name='PhoneBill')\nrouter.register('registers', RegisterViewSet, base_name='Registers')\nurlpatterns = [url('^$', schema_view), path('', include(router.urls)), path\n ('admin/', admin.site.urls), path('api-token-auth/', obtain_auth_token)]\n",
"<docstring token>\n<import token>\n<assignment token>\nrouter.register('registercall', RegisterCallViewSet, base_name='RegisterCall')\nrouter.register('phonebill', PhoneBillViewSet, base_name='PhoneBill')\nrouter.register('registers', RegisterViewSet, base_name='Registers')\n<assignment token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<code token>\n<assignment token>\n"
] | false |
99,452 |
403a0f3d6b01b226d28d7bfe97d9c1bc3199f80e
|
""" tessbatman.py
This file contains helper functions for the tessbatman pipeline.
It is divided into Batman, TESS, and Convolve functions.
"""
from time import time
import glob
import os.path as p
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.signal as sig
import scipy.stats as stat
import astropy as ast
import astropy.table as tbl
import batman
# Batman Functions
def make_batman_config(tmin, tmax, tstep, wmin, wmax, wnum, wlog=True, suffix="", path="."):
"""
Write batman parameters to a JSON param file used to generate batmanCurves.
Parameters
----------
tmin (num): minimum time
tmax (num): maximum time
tnum (num): time step
wmin (num): minimum width
wmax (num): maximum width
wnum (num): number of widths to generate
wlog (bool): use logspace for widths if True, else use linspace
suffix (str): append suffix to config and curve file names
"""
params = {}
params["curves_fname"] = p.join(path, 'batmanCurves{}.csv'.format(suffix))
params["params_fname"] = p.join(path, 'batmanParams{}.csv'.format(suffix))
params["tmin"] = tmin
params["tmax"] = tmax
params["tstep"] = tstep
params["wmin"] = wmin
params["wmax"] = wmax
params["wnum"] = wnum
params["wlog"] = wlog
outfile = p.join(path, 'batmanConfig{}.param'.format(suffix))
with open(outfile, "w+") as f:
json.dump(params, f)
print("Batman config written to {}".format(outfile))
def make_lightcurve(t0, r, i, p, width, u_type, u_param, t):
"""
Generate a batman lightcurve with the given parameters.
Parameters
----------
t0 (num): time of inferior conjunction
r (num): planet radius (in stellar radii)
i (num): orbital inclination (in degrees)
p (num): orbital period
width (num): width parameter (defined as a**3/p**2)
u_type (str): limb darkening model
u_param (list): parameters for limb darkening
t: timesteps that you want the fluxes at
assume circular orbit
"""
# Init batman model
params = batman.TransitParams()
params.rp = r
params.inc = i
params.w = 0 # longitude of periastron (degenerate with width)
params.ecc = 0 # eccentricity (0 for circular orbits)
params.per = p # orbital period
params.t0 = t0
params.a = (width * p ** 2) ** (1 / 3) # semi-major axis (stellar radii)
params.limb_dark = u_type
params.u = u_param
model = batman.TransitModel(params, t)
# Generate curve
flux = model.light_curve(params) # compute light curve
return flux
def make_batman(paramfile, outdir, norm=False, write=True, verbose=True):
"""
Return astropy tables of batman params and generated curves based on the
parameters given in paramfile.
Parameters
----------
paramfile (str): path to JSON param file written by make_batman_config
outdir (str): path to write output curve and param files
norm (bool): normalize curves to unit integrated area
write (bool): write param and curve tables to files
verbose (bool): print logging and timing info
"""
# read batman param file
if verbose:
print("Reading param file", flush=True)
with open(paramfile, "r") as f:
d = json.load(f)
# init time array and parameter ranges
if verbose:
print("Setting param ranges", flush=True)
t = np.arange(d['tmin'], d['tmax'], d['tstep'])
if d['wlog']:
widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])
else:
widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])
nparams = len(widths)
radii = 0.1 * np.ones(nparams)
incs = 90 * np.ones(nparams)
u = ['0.1 0.3'] * nparams
ld = ['quadratic'] * nparams
per = 100*np.ones(nparams)
t0 = np.zeros(nparams)
e = np.zeros(nparams)
w = np.zeros(nparams)
# Old
# radii = []
# widths = []
# incs = []
# widths_arr = np.logspace(d['wmin'], d['wmax'], d['wnum'])
# radii_arr = np.logspace(d['rmin'], d['rmax'], d['rnum'])
# for r in radii_arr:
# for w in widths_arr:
# a = (w * (100)**2)**(1.0/3.0)
# lim = np.arccos((1 + r)/(a))/(2 * np.pi) * 360
# inc = np.linspace(90, lim, 11)[:-1] # last inc always fails so exclude
# for i in inc:
# incs.append(i)
# radii.append(r)
# widths.append(w)
# add params to batman param table
curveID = ['curve{}'.format(i) for i in range(nparams)]
cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]
colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w']
batmanParams = tbl.Table(cols, names=colnames)
# generate curves
if verbose:
print("Generating curves", flush=True)
start = time()
batmanDict = {'times': t}
err = 0 # keep track of errored curves
for i in range(len(batmanParams)):
p = batmanParams[i]
cID = p['curveID']
c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'], p['ld'],
[float(val) for val in p['u'].split()], t)
# normalize curve c
if norm:
cmax = np.max(c)
cmin = np.min(c)
c = (c-cmin)/(cmax-cmin) # scale to [0,1]
c = 1-c # flip
c = c / np.sum(c) # normalize area under curve to 1
c = 1-c # flip back
if np.isnan(c).any() or (sum(c==1) < 5):
print("Batman {} failed".format(cID), flush=True)
err += 1
continue
# Save curve to dict
batmanDict[cID] = c
# Progress report every 100
if verbose and (i % 100 == 0):
elapsed = time() - start
print("Generated {}/{} curves in {} s".format(i+1-err, nparams,
elapsed), flush=True)
# add curves to table
batmanCurves = tbl.Table(batmanDict)
if verbose:
elapsed = time() - start
print("Generated {}/{} curves in {} s".format(nparams-err, nparams,
elapsed), flush=True)
# Write batman params and curves tables to files
if write:
if verbose:
start = time()
print("Writing files", flush=True)
ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',
overwrite=True, comment='#', fast_writer=False)
if verbose:
print("Wrote params to {}".format(d['params_fname']))
ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',
overwrite=True, comment='#', fast_writer=False)
if verbose:
print("Wrote curves to {}".format(d['curves_fname']))
elapsed = time() - start
print("Wrote files in {} s".format(elapsed), flush=True)
return(batmanParams, batmanCurves)
def read_batman(batmancurves_file):
"""
Return times, cureve name, and batman curves from a batmanCurves file.
Parameters
----------
batmancurves_file (str): Path to a batmanCurves file
Return
------
times (numpy Array): The times array (x axis) of all batmanCurves
curve_names (numpy Array): The name of each batmanCurve
batmanCurves (astropy Table): The table of batmanCurves
"""
# Read in Batman Curves
print("Reading batmanCurves from {}...".format(batmancurves_file))
batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1, format='csv')
times = np.array(batmanCurves['times'])
curve_names = np.array(batmanCurves.colnames[1:])
return times, curve_names, batmanCurves
# TESS Functions
def read_tess(tess_dir, sector_name, start=0, end=None):
"""
Return list of tess .fits files in tess_dir from [start:end]. Default
to all fits files in directory if start and end are not specified.
Parameters
----------
tess_dir (str): path to tess data directory
sector_name (str): name of sector subdirectory (e.g. Sector1)
start (int): (Optional) Index of file in directory to start at
end (int): (Optional) Index of file to end at
Return
------
tess_names (list): List of file paths to tess .fits data
"""
print("Reading TESS from {}, s:{}, e:{}...".format(sector_name, start, end))
sector_path = p.join(tess_dir, sector_name)
sector_files = glob.glob(p.join(sector_path,"*.fits"))
tess_names = sector_files[start:end]
return tess_names
def open_tess_fits(tess_fpath, norm=False):
try:
with ast.io.fits.open(tess_fpath, mode="readonly") as hdulist:
hdr = hdulist[0].header
tess_time = hdulist[1].data['TIME']
tess_flux = hdulist[1].data['PDCSAP_FLUX']
# set NaNs to median
med = np.nanmedian(tess_flux)
tess_flux[np.isnan(tess_flux)] = med
if norm:
# tess_flux[tess_flux > np.median(tess_flux)] = np.median(tess_flux)
tmin = np.min(tess_flux)
tmax = np.max(tess_flux)
tess_flux = (tess_flux - tmin)/(tmax-tmin)
except Exception as e:
print("ERROR reading file: ", tess_fpath, " with error: ", e,flush=True)
return None, None
return tess_time, tess_flux
# Convolve Fucntions
def convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10, plot=False):
conv_start = time()
curves = []
times = np.zeros(num_keep)
convs = np.zeros(num_keep)
print("Starting convolutions...",flush=True)
for i, curvename in enumerate(curve_names):
# do convolution
batman_curve = batmanCurves[curvename]
conv = np.abs(sig.fftconvolve(1-tess_flux, (1-batman_curve), 'same'))
ind_max = np.argmax(conv)
conv_max = conv[ind_max]
# if num_keep, save only the top num_keep curves
if num_keep < len(curve_names):
if conv_max > convs[-1]:
# insert in reverse sorted order
ind = np.searchsorted(-convs, -conv_max)
curves = curves[:ind] + [curvename] + curves[ind:-1]
times = np.insert(times, ind, tess_time[ind_max])[:-1]
convs = np.insert(convs, ind, conv_max)[:-1]
else:
curves.append(curvename)
times[i] = tess_time[ind_max]
convs[i] = conv_max
if plot:
plt.plot(tess_time, conv, label=curvename)
conv_time = time() - conv_start
print("Convolved {} curves in {:.3} s".format(len(curve_names), conv_time),flush=True)
return curves, times, convs
def tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end, output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10, verbosity=0):
"""
Parameters
----------
tess_dir(str): directory to TESS data
batman_dir (str): directory to model data
batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)
sector (int): sector to pull data from
start (int): file to start at
end (int): file to end at
output_dir (str): directory to write candidates.csv
"""
tconv_start = time()
print("===START TCONVOLVE===",flush=True)
# Handle relative paths
tess_dir = p.abspath(tess_dir)
batman_dir = p.abspath(batman_dir)
output_dir = p.abspath(output_dir)
# Read in TESS Sector data
sector_name = "Sector{}".format(sector)
if sector == 0:
sector_name = "sample_"+sector_name
tess_names = read_tess(tess_dir, sector_name, start, end)
ntess = len(tess_names)
print("Found {} TESS files to process".format(ntess),flush=True)
if ntess < 1:
print("No tess curves found, quitting....")
return None
# Read in Batman Curves
batmanCurves_file = p.join(batman_dir,"batmanCurves{}.csv".format(batman_suffix))
times, curve_names, batmanCurves = read_batman(batmanCurves_file)
nbatman = len(curve_names)
print("Found {} Batman curves".format(nbatman),flush=True)
if ntess < 1:
print("No batman curves found, quitting....")
return None
# Read in Batman Params
params = pd.read_csv(p.join(batman_dir, "batmanParams{}.csv".format(batman_suffix)))
#Init dict for saving best batman curves
colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation', 'chisq']
d = {key : [] for key in colnames}
s = 0
nerr = 0 # count number of failed files
# Do convolution on all tess files
for tind, tess_fpath in enumerate(tess_names):
tess_start = time()
tess_fname = p.basename(tess_fpath)
print("Starting TESS file: {}".format(tess_fname),flush=True)
# Read tess lightcurve
tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)
if tess_time is None:
nerr += 1
continue # skip to next iter if read failed
# Do convolution and keep num_keep best curves
if num_keep < 1:
num_keep = len(curve_names)
curves, times, convs = convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep)
# Save this TESS curve's best batman curves to dict
d['sector'].extend([sector_name]*num_keep)
d['tessFile'].extend([tess_fname]*num_keep)
d['curveID'].extend(curves)
d['tcorr'].extend(times)
d['correlation'].extend(convs)
d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))
print(len(d['tcorr']), len(d['chisq']))
if write:
# Make table every writechunk tess curves
if (tind % writechunk == writechunk-1) or (tind == len(tess_names)-1):
e = start+tind
outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e)
outpath = p.join(output_dir, outname)
# Convert to astropy table and write to csv
candidates = tbl.Table(d,names=colnames)
ast.io.ascii.write(candidates, outpath, format='csv', overwrite=True, comment='#', fast_writer=False)
print("Wrote file {} at {} s".format(outname,time()-tess_start),flush=True)
# reset dicts
# d = {key : [] for key in ['sector','tessFile','curveID','tcorr','correlation']}
s=e+1
candidates = tbl.Table(d,names=colnames)
# make merged table
cdf = pd.DataFrame.from_dict(d)
cdf = cdf[colnames]
df = pd.merge(cdf, params, on="curveID", how="left")
df.to_csv(p.join(output_dir, "chisq{}.csv".format(batman_suffix)))
tconv_time = time() - tconv_start
print("Convolved {}/{} tess files with {} curves in {:.3} s".format(ntess-nerr, ntess, nbatman, tconv_time),flush=True)
print("===END TCONVOLVE===",flush=True)
return candidates
def get_chi_sq(tess_time, tess_flux, tcorr, params):
current_fname = ""
chi_squared = []
#find the lightcurve minima to calculate the exoplanet period
arr = tess_flux / np.nanmedian(tess_flux)
arr[np.isnan(arr)] = np.nanmedian(arr)
arr[arr==0] = np.nanmedian(arr)
mu, std = stat.norm.fit(1 / arr)
peaks, _ = sig.find_peaks(1 / arr, height = mu + 4 * std, distance = 1000)
p = np.diff(tess_time[peaks])
#define parameters
PER = np.mean(p)
u_type = 'quadratic'
u_param = [0.1, 0.3]
t = tess_time - tess_time[0]
#normalize flux
outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])
mu, sigma = stat.norm.fit(outcounts)
normalized_fluxes = tess_flux / mu
normalized_sigma = np.sqrt(tess_flux)/mu
for i, row in params.iterrows():
#get params for this row
T0 = tcorr[i]- tess_time[0]
RP = row["rp"]
INC = row["i"]
width = row["width"]
#calculate reduced chi-squared
chi_squared.append(np.nansum(((normalized_fluxes - make_lightcurve(T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / normalized_sigma ** 2) / 8))
return chi_squared
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("tess_dir", type=str)
parser.add_argument("batman_dir", type=str)
parser.add_argument("sector", type=int)
parser.add_argument("start", type=int)
parser.add_argument("end", type=int)
parser.add_argument("output_dir", type=str)
parser.add_argument("batman_suffix",type=str,default="")
parser.add_argument("-v", "--verbosity", default=False,
action="store_true", help="Print console output")
args = parser.parse_args()
tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.sector, args.start,
args.end, args.output_dir, num_keep=-1, norm_tess=True, verbosity=args.verbosity)
if __name__ == '__main__':
main()
|
[
"\"\"\" tessbatman.py\nThis file contains helper functions for the tessbatman pipeline.\n\nIt is divided into Batman, TESS, and Convolve functions.\n\"\"\"\nfrom time import time\nimport glob\nimport os.path as p\nimport json\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.signal as sig\nimport scipy.stats as stat\n\nimport astropy as ast\nimport astropy.table as tbl\nimport batman\n\n\n# Batman Functions\ndef make_batman_config(tmin, tmax, tstep, wmin, wmax, wnum, wlog=True, suffix=\"\", path=\".\"):\n \"\"\"\n Write batman parameters to a JSON param file used to generate batmanCurves.\n\n Parameters\n ----------\n tmin (num): minimum time\n tmax (num): maximum time\n tnum (num): time step\n wmin (num): minimum width\n wmax (num): maximum width\n wnum (num): number of widths to generate\n wlog (bool): use logspace for widths if True, else use linspace\n suffix (str): append suffix to config and curve file names\n \"\"\"\n params = {}\n params[\"curves_fname\"] = p.join(path, 'batmanCurves{}.csv'.format(suffix))\n params[\"params_fname\"] = p.join(path, 'batmanParams{}.csv'.format(suffix))\n params[\"tmin\"] = tmin\n params[\"tmax\"] = tmax\n params[\"tstep\"] = tstep\n params[\"wmin\"] = wmin\n params[\"wmax\"] = wmax\n params[\"wnum\"] = wnum\n params[\"wlog\"] = wlog\n\n outfile = p.join(path, 'batmanConfig{}.param'.format(suffix))\n with open(outfile, \"w+\") as f:\n json.dump(params, f)\n print(\"Batman config written to {}\".format(outfile))\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n # Init batman model\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0 # longitude of periastron (degenerate with width)\n params.ecc = 0 # eccentricity (0 for circular orbits)\n params.per = p # orbital period\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3) # semi-major axis (stellar radii)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n \n # Generate curve\n flux = model.light_curve(params) # compute light curve\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n # read batman param file\n if verbose:\n print(\"Reading param file\", flush=True)\n\n with open(paramfile, \"r\") as f:\n d = json.load(f)\n\n # init time array and parameter ranges\n if verbose:\n print(\"Setting param ranges\", flush=True)\n\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100*np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n\n # Old\n # radii = []\n # widths = []\n # incs = []\n # widths_arr = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n # radii_arr = np.logspace(d['rmin'], d['rmax'], d['rnum'])\n # for r in radii_arr:\n # for w in widths_arr:\n # a = (w * (100)**2)**(1.0/3.0)\n # lim = np.arccos((1 + r)/(a))/(2 * np.pi) * 360\n # inc = np.linspace(90, lim, 11)[:-1] # last inc always fails so exclude\n # for i in inc: \n # incs.append(i)\n # radii.append(r)\n # widths.append(w)\n \n # add params to batman param table\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w']\n batmanParams = tbl.Table(cols, names=colnames)\n\n # generate curves\n if verbose:\n print(\"Generating curves\", flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0 # keep track of errored curves\n for i in range(len(batmanParams)): \n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'], p['ld'], \n [float(val) for val in p['u'].split()], t)\n\n # normalize curve c\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c-cmin)/(cmax-cmin) # scale to [0,1]\n c = 1-c # flip\n c = c / np.sum(c) # normalize area under curve to 1\n c = 1-c # flip back\n if np.isnan(c).any() or (sum(c==1) < 5):\n print(\"Batman {} failed\".format(cID), flush=True)\n err += 1\n continue \n\n # Save curve to dict\n batmanDict[cID] = c\n\n # Progress report every 100\n if verbose and (i % 100 == 0):\n elapsed = time() - start\n print(\"Generated {}/{} curves in {} s\".format(i+1-err, nparams,\n elapsed), flush=True)\n \n # add curves to table\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print(\"Generated {}/{} curves in {} s\".format(nparams-err, nparams,\n elapsed), flush=True) \n \n # Write batman params and curves tables to files\n if write:\n if verbose:\n start = time()\n print(\"Writing files\", flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv', \n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print(\"Wrote params to {}\".format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv', \n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print(\"Wrote curves to {}\".format(d['curves_fname']))\n elapsed = time() - start\n print(\"Wrote files in {} s\".format(elapsed), flush=True)\n return(batmanParams, batmanCurves)\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n # Read in Batman Curves \n print(\"Reading batmanCurves from {}...\".format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1, format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\n# TESS Functions\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print(\"Reading TESS from {}, s:{}, e:{}...\".format(sector_name, start, end))\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path,\"*.fits\"))\n tess_names = sector_files[start:end]\n return tess_names\n\n \ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode=\"readonly\") as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n # set NaNs to median\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n \n if norm:\n# tess_flux[tess_flux > np.median(tess_flux)] = np.median(tess_flux)\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin)/(tmax-tmin)\n\n except Exception as e: \n print(\"ERROR reading file: \", tess_fpath, \" with error: \", e,flush=True)\n return None, None\n return tess_time, tess_flux\n \n \n# Convolve Fucntions\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10, plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print(\"Starting convolutions...\",flush=True)\n for i, curvename in enumerate(curve_names):\n # do convolution\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1-tess_flux, (1-batman_curve), 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n \n # if num_keep, save only the top num_keep curves\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n # insert in reverse sorted order\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n\n conv_time = time() - conv_start\n print(\"Convolved {} curves in {:.3} s\".format(len(curve_names), conv_time),flush=True)\n return curves, times, convs\n\n \ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end, output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10, verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\" \n tconv_start = time()\n print(\"===START TCONVOLVE===\",flush=True)\n \n # Handle relative paths\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n \n # Read in TESS Sector data\n sector_name = \"Sector{}\".format(sector)\n if sector == 0:\n sector_name = \"sample_\"+sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print(\"Found {} TESS files to process\".format(ntess),flush=True)\n if ntess < 1:\n print(\"No tess curves found, quitting....\")\n return None\n \n # Read in Batman Curves\n batmanCurves_file = p.join(batman_dir,\"batmanCurves{}.csv\".format(batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print(\"Found {} Batman curves\".format(nbatman),flush=True)\n if ntess < 1:\n print(\"No batman curves found, quitting....\")\n return None\n\n # Read in Batman Params\n params = pd.read_csv(p.join(batman_dir, \"batmanParams{}.csv\".format(batman_suffix)))\n\n\n\n #Init dict for saving best batman curves \n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation', 'chisq']\n d = {key : [] for key in colnames}\n s = 0\n nerr = 0 # count number of failed files\n \n # Do convolution on all tess files\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print(\"Starting TESS file: {}\".format(tess_fname),flush=True)\n \n # Read tess lightcurve\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue # skip to next iter if read failed\n \n # Do convolution and keep num_keep best curves\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep)\n \n # Save this TESS curve's best batman curves to dict\n d['sector'].extend([sector_name]*num_keep)\n d['tessFile'].extend([tess_fname]*num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n # Make table every writechunk tess curves\n if (tind % writechunk == writechunk-1) or (tind == len(tess_names)-1):\n e = start+tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e)\n outpath = p.join(output_dir, outname)\n # Convert to astropy table and write to csv\n candidates = tbl.Table(d,names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv', overwrite=True, comment='#', fast_writer=False)\n print(\"Wrote file {} at {} s\".format(outname,time()-tess_start),flush=True)\n # reset dicts\n# d = {key : [] for key in ['sector','tessFile','curveID','tcorr','correlation']}\n s=e+1\n candidates = tbl.Table(d,names=colnames)\n \n # make merged table\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on=\"curveID\", how=\"left\")\n df.to_csv(p.join(output_dir, \"chisq{}.csv\".format(batman_suffix)))\n \n tconv_time = time() - tconv_start\n print(\"Convolved {}/{} tess files with {} curves in {:.3} s\".format(ntess-nerr, ntess, nbatman, tconv_time),flush=True)\n print(\"===END TCONVOLVE===\",flush=True)\n return candidates\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = \"\"\n chi_squared = []\n #find the lightcurve minima to calculate the exoplanet period\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr==0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height = mu + 4 * std, distance = 1000)\n p = np.diff(tess_time[peaks])\n #define parameters\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n #normalize flux\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux)/mu\n \n for i, row in params.iterrows():\n #get params for this row\n T0 = tcorr[i]- tess_time[0]\n RP = row[\"rp\"]\n INC = row[\"i\"]\n width = row[\"width\"]\n\n #calculate reduced chi-squared\n chi_squared.append(np.nansum(((normalized_fluxes - make_lightcurve(T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / normalized_sigma ** 2) / 8))\n\n return chi_squared\n \ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"tess_dir\", type=str)\n parser.add_argument(\"batman_dir\", type=str)\n parser.add_argument(\"sector\", type=int)\n parser.add_argument(\"start\", type=int)\n parser.add_argument(\"end\", type=int)\n parser.add_argument(\"output_dir\", type=str) \n parser.add_argument(\"batman_suffix\",type=str,default=\"\")\n parser.add_argument(\"-v\", \"--verbosity\", default=False, \n action=\"store_true\", help=\"Print console output\")\n args = parser.parse_args()\n tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.sector, args.start, \n args.end, args.output_dir, num_keep=-1, norm_tess=True, verbosity=args.verbosity)\n \nif __name__ == '__main__':\n main()\n",
"<docstring token>\nfrom time import time\nimport glob\nimport os.path as p\nimport json\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport scipy.signal as sig\nimport scipy.stats as stat\nimport astropy as ast\nimport astropy.table as tbl\nimport batman\n\n\ndef make_batman_config(tmin, tmax, tstep, wmin, wmax, wnum, wlog=True,\n suffix='', path='.'):\n \"\"\"\n Write batman parameters to a JSON param file used to generate batmanCurves.\n\n Parameters\n ----------\n tmin (num): minimum time\n tmax (num): maximum time\n tnum (num): time step\n wmin (num): minimum width\n wmax (num): maximum width\n wnum (num): number of widths to generate\n wlog (bool): use logspace for widths if True, else use linspace\n suffix (str): append suffix to config and curve file names\n \"\"\"\n params = {}\n params['curves_fname'] = p.join(path, 'batmanCurves{}.csv'.format(suffix))\n params['params_fname'] = p.join(path, 'batmanParams{}.csv'.format(suffix))\n params['tmin'] = tmin\n params['tmax'] = tmax\n params['tstep'] = tstep\n params['wmin'] = wmin\n params['wmax'] = wmax\n params['wnum'] = wnum\n params['wlog'] = wlog\n outfile = p.join(path, 'batmanConfig{}.param'.format(suffix))\n with open(outfile, 'w+') as f:\n json.dump(params, f)\n print('Batman config written to {}'.format(outfile))\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print('Reading TESS from {}, s:{}, e:{}...'.format(sector_name, start, end)\n )\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path, '*.fits'))\n tess_names = sector_files[start:end]\n return tess_names\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('tess_dir', type=str)\n parser.add_argument('batman_dir', type=str)\n parser.add_argument('sector', type=int)\n parser.add_argument('start', type=int)\n parser.add_argument('end', type=int)\n parser.add_argument('output_dir', type=str)\n parser.add_argument('batman_suffix', type=str, default='')\n parser.add_argument('-v', '--verbosity', default=False, action=\n 'store_true', help='Print console output')\n args = parser.parse_args()\n tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.\n sector, args.start, args.end, args.output_dir, num_keep=-1,\n norm_tess=True, verbosity=args.verbosity)\n\n\nif __name__ == '__main__':\n main()\n",
"<docstring token>\n<import token>\n\n\ndef make_batman_config(tmin, tmax, tstep, wmin, wmax, wnum, wlog=True,\n suffix='', path='.'):\n \"\"\"\n Write batman parameters to a JSON param file used to generate batmanCurves.\n\n Parameters\n ----------\n tmin (num): minimum time\n tmax (num): maximum time\n tnum (num): time step\n wmin (num): minimum width\n wmax (num): maximum width\n wnum (num): number of widths to generate\n wlog (bool): use logspace for widths if True, else use linspace\n suffix (str): append suffix to config and curve file names\n \"\"\"\n params = {}\n params['curves_fname'] = p.join(path, 'batmanCurves{}.csv'.format(suffix))\n params['params_fname'] = p.join(path, 'batmanParams{}.csv'.format(suffix))\n params['tmin'] = tmin\n params['tmax'] = tmax\n params['tstep'] = tstep\n params['wmin'] = wmin\n params['wmax'] = wmax\n params['wnum'] = wnum\n params['wlog'] = wlog\n outfile = p.join(path, 'batmanConfig{}.param'.format(suffix))\n with open(outfile, 'w+') as f:\n json.dump(params, f)\n print('Batman config written to {}'.format(outfile))\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print('Reading TESS from {}, s:{}, e:{}...'.format(sector_name, start, end)\n )\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path, '*.fits'))\n tess_names = sector_files[start:end]\n return tess_names\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('tess_dir', type=str)\n parser.add_argument('batman_dir', type=str)\n parser.add_argument('sector', type=int)\n parser.add_argument('start', type=int)\n parser.add_argument('end', type=int)\n parser.add_argument('output_dir', type=str)\n parser.add_argument('batman_suffix', type=str, default='')\n parser.add_argument('-v', '--verbosity', default=False, action=\n 'store_true', help='Print console output')\n args = parser.parse_args()\n tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.\n sector, args.start, args.end, args.output_dir, num_keep=-1,\n norm_tess=True, verbosity=args.verbosity)\n\n\nif __name__ == '__main__':\n main()\n",
"<docstring token>\n<import token>\n\n\ndef make_batman_config(tmin, tmax, tstep, wmin, wmax, wnum, wlog=True,\n suffix='', path='.'):\n \"\"\"\n Write batman parameters to a JSON param file used to generate batmanCurves.\n\n Parameters\n ----------\n tmin (num): minimum time\n tmax (num): maximum time\n tnum (num): time step\n wmin (num): minimum width\n wmax (num): maximum width\n wnum (num): number of widths to generate\n wlog (bool): use logspace for widths if True, else use linspace\n suffix (str): append suffix to config and curve file names\n \"\"\"\n params = {}\n params['curves_fname'] = p.join(path, 'batmanCurves{}.csv'.format(suffix))\n params['params_fname'] = p.join(path, 'batmanParams{}.csv'.format(suffix))\n params['tmin'] = tmin\n params['tmax'] = tmax\n params['tstep'] = tstep\n params['wmin'] = wmin\n params['wmax'] = wmax\n params['wnum'] = wnum\n params['wlog'] = wlog\n outfile = p.join(path, 'batmanConfig{}.param'.format(suffix))\n with open(outfile, 'w+') as f:\n json.dump(params, f)\n print('Batman config written to {}'.format(outfile))\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print('Reading TESS from {}, s:{}, e:{}...'.format(sector_name, start, end)\n )\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path, '*.fits'))\n tess_names = sector_files[start:end]\n return tess_names\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('tess_dir', type=str)\n parser.add_argument('batman_dir', type=str)\n parser.add_argument('sector', type=int)\n parser.add_argument('start', type=int)\n parser.add_argument('end', type=int)\n parser.add_argument('output_dir', type=str)\n parser.add_argument('batman_suffix', type=str, default='')\n parser.add_argument('-v', '--verbosity', default=False, action=\n 'store_true', help='Print console output')\n args = parser.parse_args()\n tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.\n sector, args.start, args.end, args.output_dir, num_keep=-1,\n norm_tess=True, verbosity=args.verbosity)\n\n\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print('Reading TESS from {}, s:{}, e:{}...'.format(sector_name, start, end)\n )\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path, '*.fits'))\n tess_names = sector_files[start:end]\n return tess_names\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('tess_dir', type=str)\n parser.add_argument('batman_dir', type=str)\n parser.add_argument('sector', type=int)\n parser.add_argument('start', type=int)\n parser.add_argument('end', type=int)\n parser.add_argument('output_dir', type=str)\n parser.add_argument('batman_suffix', type=str, default='')\n parser.add_argument('-v', '--verbosity', default=False, action=\n 'store_true', help='Print console output')\n args = parser.parse_args()\n tbconvolve(args.tess_dir, args.batman_dir, args.batman_suffix, args.\n sector, args.start, args.end, args.output_dir, num_keep=-1,\n norm_tess=True, verbosity=args.verbosity)\n\n\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\ndef read_tess(tess_dir, sector_name, start=0, end=None):\n \"\"\"\n Return list of tess .fits files in tess_dir from [start:end]. Default\n to all fits files in directory if start and end are not specified.\n\n Parameters\n ----------\n tess_dir (str): path to tess data directory\n sector_name (str): name of sector subdirectory (e.g. Sector1)\n start (int): (Optional) Index of file in directory to start at\n end (int): (Optional) Index of file to end at\n \n Return\n ------\n tess_names (list): List of file paths to tess .fits data\n \"\"\"\n print('Reading TESS from {}, s:{}, e:{}...'.format(sector_name, start, end)\n )\n sector_path = p.join(tess_dir, sector_name)\n sector_files = glob.glob(p.join(sector_path, '*.fits'))\n tess_names = sector_files[start:end]\n return tess_names\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\n<function token>\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\ndef get_chi_sq(tess_time, tess_flux, tcorr, params):\n current_fname = ''\n chi_squared = []\n arr = tess_flux / np.nanmedian(tess_flux)\n arr[np.isnan(arr)] = np.nanmedian(arr)\n arr[arr == 0] = np.nanmedian(arr)\n mu, std = stat.norm.fit(1 / arr)\n peaks, _ = sig.find_peaks(1 / arr, height=mu + 4 * std, distance=1000)\n p = np.diff(tess_time[peaks])\n PER = np.mean(p)\n u_type = 'quadratic'\n u_param = [0.1, 0.3]\n t = tess_time - tess_time[0]\n outcounts = np.nan_to_num(tess_flux[tess_flux > np.nanmean(tess_flux)])\n mu, sigma = stat.norm.fit(outcounts)\n normalized_fluxes = tess_flux / mu\n normalized_sigma = np.sqrt(tess_flux) / mu\n for i, row in params.iterrows():\n T0 = tcorr[i] - tess_time[0]\n RP = row['rp']\n INC = row['i']\n width = row['width']\n chi_squared.append(np.nansum((normalized_fluxes - make_lightcurve(\n T0, RP, INC, PER, width, u_type, u_param, t)) ** 2 / \n normalized_sigma ** 2 / 8))\n return chi_squared\n\n\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\ndef read_batman(batmancurves_file):\n \"\"\"\n Return times, cureve name, and batman curves from a batmanCurves file.\n \n Parameters\n ----------\n batmancurves_file (str): Path to a batmanCurves file\n\n Return\n ------\n times (numpy Array): The times array (x axis) of all batmanCurves\n curve_names (numpy Array): The name of each batmanCurve\n batmanCurves (astropy Table): The table of batmanCurves\n \"\"\"\n print('Reading batmanCurves from {}...'.format(batmancurves_file))\n batmanCurves = ast.io.ascii.read(batmancurves_file, data_start=1,\n format='csv')\n times = np.array(batmanCurves['times'])\n curve_names = np.array(batmanCurves.colnames[1:])\n return times, curve_names, batmanCurves\n\n\n<function token>\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\n<function token>\n<function token>\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\ndef convolve(tess_time, tess_flux, batmanCurves, curve_names, num_keep=10,\n plot=False):\n conv_start = time()\n curves = []\n times = np.zeros(num_keep)\n convs = np.zeros(num_keep)\n print('Starting convolutions...', flush=True)\n for i, curvename in enumerate(curve_names):\n batman_curve = batmanCurves[curvename]\n conv = np.abs(sig.fftconvolve(1 - tess_flux, 1 - batman_curve, 'same'))\n ind_max = np.argmax(conv)\n conv_max = conv[ind_max]\n if num_keep < len(curve_names):\n if conv_max > convs[-1]:\n ind = np.searchsorted(-convs, -conv_max)\n curves = curves[:ind] + [curvename] + curves[ind:-1]\n times = np.insert(times, ind, tess_time[ind_max])[:-1]\n convs = np.insert(convs, ind, conv_max)[:-1]\n else:\n curves.append(curvename)\n times[i] = tess_time[ind_max]\n convs[i] = conv_max\n if plot:\n plt.plot(tess_time, conv, label=curvename)\n conv_time = time() - conv_start\n print('Convolved {} curves in {:.3} s'.format(len(curve_names),\n conv_time), flush=True)\n return curves, times, convs\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\n<function token>\n<function token>\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\n<function token>\n\n\ndef tbconvolve(tess_dir, batman_dir, batman_suffix, sector, start, end,\n output_dir, num_keep=10, norm_tess=False, write=True, writechunk=10,\n verbosity=0):\n \"\"\"\n \n Parameters\n ----------\n tess_dir(str): directory to TESS data\n batman_dir (str): directory to model data\n batman_suffix(str): suffix to append to barmanCurves file (e.g. _small)\n sector (int): sector to pull data from\n start (int): file to start at\n end (int): file to end at\n output_dir (str): directory to write candidates.csv\n \"\"\"\n tconv_start = time()\n print('===START TCONVOLVE===', flush=True)\n tess_dir = p.abspath(tess_dir)\n batman_dir = p.abspath(batman_dir)\n output_dir = p.abspath(output_dir)\n sector_name = 'Sector{}'.format(sector)\n if sector == 0:\n sector_name = 'sample_' + sector_name\n tess_names = read_tess(tess_dir, sector_name, start, end)\n ntess = len(tess_names)\n print('Found {} TESS files to process'.format(ntess), flush=True)\n if ntess < 1:\n print('No tess curves found, quitting....')\n return None\n batmanCurves_file = p.join(batman_dir, 'batmanCurves{}.csv'.format(\n batman_suffix))\n times, curve_names, batmanCurves = read_batman(batmanCurves_file)\n nbatman = len(curve_names)\n print('Found {} Batman curves'.format(nbatman), flush=True)\n if ntess < 1:\n print('No batman curves found, quitting....')\n return None\n params = pd.read_csv(p.join(batman_dir, 'batmanParams{}.csv'.format(\n batman_suffix)))\n colnames = ['sector', 'tessFile', 'curveID', 'tcorr', 'correlation',\n 'chisq']\n d = {key: [] for key in colnames}\n s = 0\n nerr = 0\n for tind, tess_fpath in enumerate(tess_names):\n tess_start = time()\n tess_fname = p.basename(tess_fpath)\n print('Starting TESS file: {}'.format(tess_fname), flush=True)\n tess_time, tess_flux = open_tess_fits(tess_fpath, norm_tess)\n if tess_time is None:\n nerr += 1\n continue\n if num_keep < 1:\n num_keep = len(curve_names)\n curves, times, convs = convolve(tess_time, tess_flux, batmanCurves,\n curve_names, num_keep)\n d['sector'].extend([sector_name] * num_keep)\n d['tessFile'].extend([tess_fname] * num_keep)\n d['curveID'].extend(curves)\n d['tcorr'].extend(times)\n d['correlation'].extend(convs)\n d['chisq'].extend(get_chi_sq(tess_time, tess_flux, times, params))\n print(len(d['tcorr']), len(d['chisq']))\n if write:\n if tind % writechunk == writechunk - 1 or tind == len(tess_names\n ) - 1:\n e = start + tind\n outname = 'candidates_sector{}_s{}_e{}.csv'.format(sector, s, e\n )\n outpath = p.join(output_dir, outname)\n candidates = tbl.Table(d, names=colnames)\n ast.io.ascii.write(candidates, outpath, format='csv',\n overwrite=True, comment='#', fast_writer=False)\n print('Wrote file {} at {} s'.format(outname, time() -\n tess_start), flush=True)\n s = e + 1\n candidates = tbl.Table(d, names=colnames)\n cdf = pd.DataFrame.from_dict(d)\n cdf = cdf[colnames]\n df = pd.merge(cdf, params, on='curveID', how='left')\n df.to_csv(p.join(output_dir, 'chisq{}.csv'.format(batman_suffix)))\n tconv_time = time() - tconv_start\n print('Convolved {}/{} tess files with {} curves in {:.3} s'.format(\n ntess - nerr, ntess, nbatman, tconv_time), flush=True)\n print('===END TCONVOLVE===', flush=True)\n return candidates\n\n\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\n<function token>\n<function token>\n\n\ndef open_tess_fits(tess_fpath, norm=False):\n try:\n with ast.io.fits.open(tess_fpath, mode='readonly') as hdulist:\n hdr = hdulist[0].header\n tess_time = hdulist[1].data['TIME']\n tess_flux = hdulist[1].data['PDCSAP_FLUX']\n med = np.nanmedian(tess_flux)\n tess_flux[np.isnan(tess_flux)] = med\n if norm:\n tmin = np.min(tess_flux)\n tmax = np.max(tess_flux)\n tess_flux = (tess_flux - tmin) / (tmax - tmin)\n except Exception as e:\n print('ERROR reading file: ', tess_fpath, ' with error: ', e, flush\n =True)\n return None, None\n return tess_time, tess_flux\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\ndef make_batman(paramfile, outdir, norm=False, write=True, verbose=True):\n \"\"\" \n Return astropy tables of batman params and generated curves based on the\n parameters given in paramfile. \n\n Parameters\n ----------\n paramfile (str): path to JSON param file written by make_batman_config\n outdir (str): path to write output curve and param files\n norm (bool): normalize curves to unit integrated area\n write (bool): write param and curve tables to files\n verbose (bool): print logging and timing info\n \"\"\"\n if verbose:\n print('Reading param file', flush=True)\n with open(paramfile, 'r') as f:\n d = json.load(f)\n if verbose:\n print('Setting param ranges', flush=True)\n t = np.arange(d['tmin'], d['tmax'], d['tstep'])\n if d['wlog']:\n widths = np.logspace(d['wmin'], d['wmax'], d['wnum'])\n else:\n widths = np.linspace(d['wmin'], d['wmax'], d['wnum'])\n nparams = len(widths)\n radii = 0.1 * np.ones(nparams)\n incs = 90 * np.ones(nparams)\n u = ['0.1 0.3'] * nparams\n ld = ['quadratic'] * nparams\n per = 100 * np.ones(nparams)\n t0 = np.zeros(nparams)\n e = np.zeros(nparams)\n w = np.zeros(nparams)\n curveID = ['curve{}'.format(i) for i in range(nparams)]\n cols = [curveID, radii, incs, widths, per, u, ld, t0, e, w]\n colnames = ['curveID', 'rp', 'i', 'width', 'per', 'u', 'ld', 't0', 'e', 'w'\n ]\n batmanParams = tbl.Table(cols, names=colnames)\n if verbose:\n print('Generating curves', flush=True)\n start = time()\n batmanDict = {'times': t}\n err = 0\n for i in range(len(batmanParams)):\n p = batmanParams[i]\n cID = p['curveID']\n c = make_lightcurve(p['t0'], p['rp'], p['i'], p['per'], p['width'],\n p['ld'], [float(val) for val in p['u'].split()], t)\n if norm:\n cmax = np.max(c)\n cmin = np.min(c)\n c = (c - cmin) / (cmax - cmin)\n c = 1 - c\n c = c / np.sum(c)\n c = 1 - c\n if np.isnan(c).any() or sum(c == 1) < 5:\n print('Batman {} failed'.format(cID), flush=True)\n err += 1\n continue\n batmanDict[cID] = c\n if verbose and i % 100 == 0:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(i + 1 - err,\n nparams, elapsed), flush=True)\n batmanCurves = tbl.Table(batmanDict)\n if verbose:\n elapsed = time() - start\n print('Generated {}/{} curves in {} s'.format(nparams - err,\n nparams, elapsed), flush=True)\n if write:\n if verbose:\n start = time()\n print('Writing files', flush=True)\n ast.io.ascii.write(batmanParams, d['params_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote params to {}'.format(d['params_fname']))\n ast.io.ascii.write(batmanCurves, d['curves_fname'], format='csv',\n overwrite=True, comment='#', fast_writer=False)\n if verbose:\n print('Wrote curves to {}'.format(d['curves_fname']))\n elapsed = time() - start\n print('Wrote files in {} s'.format(elapsed), flush=True)\n return batmanParams, batmanCurves\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n\n\ndef make_lightcurve(t0, r, i, p, width, u_type, u_param, t):\n \"\"\"\n Generate a batman lightcurve with the given parameters.\n \n Parameters\n ----------\n t0 (num): time of inferior conjunction\n r (num): planet radius (in stellar radii)\n i (num): orbital inclination (in degrees)\n p (num): orbital period\n width (num): width parameter (defined as a**3/p**2)\n u_type (str): limb darkening model\n u_param (list): parameters for limb darkening\n \n t: timesteps that you want the fluxes at\n \n assume circular orbit\n \"\"\"\n params = batman.TransitParams()\n params.rp = r\n params.inc = i\n params.w = 0\n params.ecc = 0\n params.per = p\n params.t0 = t0\n params.a = (width * p ** 2) ** (1 / 3)\n params.limb_dark = u_type\n params.u = u_param\n model = batman.TransitModel(params, t)\n flux = model.light_curve(params)\n return flux\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n",
"<docstring token>\n<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"
] | false |
99,453 |
45a27d5d0c146d37bd41e9da914f40f9362cd32e
|
"""
Utility functions for storing ML-derived task annotations related to buildings.
The functions here are meant to help organize data in databases stored outside
of HOT's Tasking Manager.
"""
from sqlalchemy import (Column, Integer, String, Float,
ForeignKey)
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base
from ml_tm_utils_pub.utils_geodata import (get_tile_pyramid)
#######################################
# Set the declarative base to prep creation of SQL classes
Base = declarative_base()
class Project(Base):
"""Project class meant to hold information on mapping projects in TM.
Attributes
----------
id: int
The object's UID for the relational DB
tm_index: int
ID of the project on Tasking Manager's servers
md5_hash: str
MD5 hash of the project geometry. Useful for checking if a split
occured
json_geometry: str
Stripped down version of the geojson project geometry.
"""
__tablename__ = 'ml_projects'
id = Column(Integer, primary_key=True)
tm_index = Column(Integer)
md5_hash = Column(String)
json_geometry = Column(String)
# Add a relationship with the tile prediction class
building_tiles = relationship(
"TilePredBA", back_populates="project")
def __repr__(self):
"""Define string representation."""
return "<Project(TM index={}, md5_hash={}, {} tiles>".format(
self.tm_index, self.md5_hash, len(self.building_tiles))
class TilePredBA(Base):
"""Tile prediction building area (storing both ML estimate and OSM)
Attributes
----------
id: int
The tile objects UID for the relational DB
project_id: int
Project ID keyed to the project table
tile_index: str
Tile index in string format specifying the x/y/z tile coords.
building_area_ml: float
Total building area for a tile as predicted by the ML algorithm
building_area_osm: float
Total building area for a tile mapped in OSM
"""
__tablename__ = 'tile_pred_buildings'
id = Column(Integer, primary_key=True)
project_id = Column(Integer, ForeignKey('ml_projects.id'))
tile_index = Column(String)
building_area_ml = Column(Float)
building_area_osm = Column(Float)
# Add a relationship with the project class
project = relationship('Project', back_populates='building_tiles')
def __repr__(self):
"""Define string representation."""
return ("<TilePredBA(Project={}, Tile Index={} "
"Building Area ML={}, Building Area OSM={}>").format(
self.project.tm_index, self.tile_index,
self.building_area_ml, self.building_area_osm)
def get_total_tiles_building_area(tile_ind_list, session):
"""Get total area of all tile indices specified in a list.
Parameters
-----------
tile_ind_list: list of str
List of tile indices to query
session: sqlalchemy.orm.session.Session
Handle to database
Returns
-------
total_area_ml: float
Sum of predicted building area for all tiles
total_area_osm: float
Sum of mapped building area in OSM for all tiles
"""
total_area_ml, total_area_osm = 0, 0
for row in session.query(TilePredBA).filter(
TilePredBA.tile_index.in_(tile_ind_list)):
total_area_ml += row.building_area_ml
total_area_osm += row.building_area_osm
return total_area_ml, total_area_osm
def augment_geojson_building_area(project, session):
"""Add building area information to each tile in a geojson dict.
Parameters
----------
project: dict
geojson to be augmented with new information
session: sqlalchemy.orm.session.Session
Handle to database
"""
# Loop through tasks in TM visualization
for ti, task in enumerate(project['tasks']['features']):
# Get total area
tile_dict = dict(x=task['properties']['taskX'],
y=task['properties']['taskY'],
z=task['properties']['taskZoom'])
child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)
area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)
# Add information to geojson
task['properties']['building_area_ml_pred'] = area_ml
task['properties']['building_area_osm'] = area_osm
project['tasks']['features'][ti] = task
# Return geojson
return project
def update_db_project(proj_id, geojson, geojson_hash, session):
"""Update a project geojson and hash
Parameters
----------
proj_id: int
TM Project ID corresponding to database entry for updating
geojson: str
Geojson string of project geometry
geojson_hash: str
MD5 hash of geojson object
session: sqlalchemy.orm.session.Session
Handle to database
"""
project = session.query(Project).filter(
Project.tm_index == proj_id).one()
project.json_geometry = geojson
project.md5_hash = geojson_hash
|
[
"\"\"\"\nUtility functions for storing ML-derived task annotations related to buildings.\n\nThe functions here are meant to help organize data in databases stored outside\nof HOT's Tasking Manager.\n\"\"\"\n\n\nfrom sqlalchemy import (Column, Integer, String, Float,\n ForeignKey)\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom ml_tm_utils_pub.utils_geodata import (get_tile_pyramid)\n\n#######################################\n# Set the declarative base to prep creation of SQL classes\nBase = declarative_base()\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n\n # Add a relationship with the tile prediction class\n building_tiles = relationship(\n \"TilePredBA\", back_populates=\"project\")\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return \"<Project(TM index={}, md5_hash={}, {} tiles>\".format(\n self.tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n\n # Add a relationship with the project class\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\"<TilePredBA(Project={}, Tile Index={} \"\n \"Building Area ML={}, Building Area OSM={}>\").format(\n self.project.tm_index, self.tile_index,\n self.building_area_ml, self.building_area_osm)\n\n\ndef get_total_tiles_building_area(tile_ind_list, session):\n \"\"\"Get total area of all tile indices specified in a list.\n\n Parameters\n -----------\n tile_ind_list: list of str\n List of tile indices to query\n session: sqlalchemy.orm.session.Session\n Handle to database\n\n Returns\n -------\n total_area_ml: float\n Sum of predicted building area for all tiles\n total_area_osm: float\n Sum of mapped building area in OSM for all tiles\n \"\"\"\n\n total_area_ml, total_area_osm = 0, 0\n for row in session.query(TilePredBA).filter(\n TilePredBA.tile_index.in_(tile_ind_list)):\n total_area_ml += row.building_area_ml\n total_area_osm += row.building_area_osm\n\n return total_area_ml, total_area_osm\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n\n # Loop through tasks in TM visualization\n for ti, task in enumerate(project['tasks']['features']):\n\n # Get total area\n tile_dict = dict(x=task['properties']['taskX'],\n y=task['properties']['taskY'],\n z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n\n # Add information to geojson\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n\n # Return geojson\n return project\n\n\ndef update_db_project(proj_id, geojson, geojson_hash, session):\n \"\"\"Update a project geojson and hash\n\n Parameters\n ----------\n proj_id: int\n TM Project ID corresponding to database entry for updating\n geojson: str\n Geojson string of project geometry\n geojson_hash: str\n MD5 hash of geojson object\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n\n project = session.query(Project).filter(\n Project.tm_index == proj_id).one()\n project.json_geometry = geojson\n project.md5_hash = geojson_hash\n",
"<docstring token>\nfrom sqlalchemy import Column, Integer, String, Float, ForeignKey\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom ml_tm_utils_pub.utils_geodata import get_tile_pyramid\nBase = declarative_base()\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\ndef get_total_tiles_building_area(tile_ind_list, session):\n \"\"\"Get total area of all tile indices specified in a list.\n\n Parameters\n -----------\n tile_ind_list: list of str\n List of tile indices to query\n session: sqlalchemy.orm.session.Session\n Handle to database\n\n Returns\n -------\n total_area_ml: float\n Sum of predicted building area for all tiles\n total_area_osm: float\n Sum of mapped building area in OSM for all tiles\n \"\"\"\n total_area_ml, total_area_osm = 0, 0\n for row in session.query(TilePredBA).filter(TilePredBA.tile_index.in_(\n tile_ind_list)):\n total_area_ml += row.building_area_ml\n total_area_osm += row.building_area_osm\n return total_area_ml, total_area_osm\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n for ti, task in enumerate(project['tasks']['features']):\n tile_dict = dict(x=task['properties']['taskX'], y=task['properties'\n ]['taskY'], z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n return project\n\n\ndef update_db_project(proj_id, geojson, geojson_hash, session):\n \"\"\"Update a project geojson and hash\n\n Parameters\n ----------\n proj_id: int\n TM Project ID corresponding to database entry for updating\n geojson: str\n Geojson string of project geometry\n geojson_hash: str\n MD5 hash of geojson object\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n project = session.query(Project).filter(Project.tm_index == proj_id).one()\n project.json_geometry = geojson\n project.md5_hash = geojson_hash\n",
"<docstring token>\n<import token>\nBase = declarative_base()\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\ndef get_total_tiles_building_area(tile_ind_list, session):\n \"\"\"Get total area of all tile indices specified in a list.\n\n Parameters\n -----------\n tile_ind_list: list of str\n List of tile indices to query\n session: sqlalchemy.orm.session.Session\n Handle to database\n\n Returns\n -------\n total_area_ml: float\n Sum of predicted building area for all tiles\n total_area_osm: float\n Sum of mapped building area in OSM for all tiles\n \"\"\"\n total_area_ml, total_area_osm = 0, 0\n for row in session.query(TilePredBA).filter(TilePredBA.tile_index.in_(\n tile_ind_list)):\n total_area_ml += row.building_area_ml\n total_area_osm += row.building_area_osm\n return total_area_ml, total_area_osm\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n for ti, task in enumerate(project['tasks']['features']):\n tile_dict = dict(x=task['properties']['taskX'], y=task['properties'\n ]['taskY'], z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n return project\n\n\ndef update_db_project(proj_id, geojson, geojson_hash, session):\n \"\"\"Update a project geojson and hash\n\n Parameters\n ----------\n proj_id: int\n TM Project ID corresponding to database entry for updating\n geojson: str\n Geojson string of project geometry\n geojson_hash: str\n MD5 hash of geojson object\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n project = session.query(Project).filter(Project.tm_index == proj_id).one()\n project.json_geometry = geojson\n project.md5_hash = geojson_hash\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\ndef get_total_tiles_building_area(tile_ind_list, session):\n \"\"\"Get total area of all tile indices specified in a list.\n\n Parameters\n -----------\n tile_ind_list: list of str\n List of tile indices to query\n session: sqlalchemy.orm.session.Session\n Handle to database\n\n Returns\n -------\n total_area_ml: float\n Sum of predicted building area for all tiles\n total_area_osm: float\n Sum of mapped building area in OSM for all tiles\n \"\"\"\n total_area_ml, total_area_osm = 0, 0\n for row in session.query(TilePredBA).filter(TilePredBA.tile_index.in_(\n tile_ind_list)):\n total_area_ml += row.building_area_ml\n total_area_osm += row.building_area_osm\n return total_area_ml, total_area_osm\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n for ti, task in enumerate(project['tasks']['features']):\n tile_dict = dict(x=task['properties']['taskX'], y=task['properties'\n ]['taskY'], z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n return project\n\n\ndef update_db_project(proj_id, geojson, geojson_hash, session):\n \"\"\"Update a project geojson and hash\n\n Parameters\n ----------\n proj_id: int\n TM Project ID corresponding to database entry for updating\n geojson: str\n Geojson string of project geometry\n geojson_hash: str\n MD5 hash of geojson object\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n project = session.query(Project).filter(Project.tm_index == proj_id).one()\n project.json_geometry = geojson\n project.md5_hash = geojson_hash\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\ndef get_total_tiles_building_area(tile_ind_list, session):\n \"\"\"Get total area of all tile indices specified in a list.\n\n Parameters\n -----------\n tile_ind_list: list of str\n List of tile indices to query\n session: sqlalchemy.orm.session.Session\n Handle to database\n\n Returns\n -------\n total_area_ml: float\n Sum of predicted building area for all tiles\n total_area_osm: float\n Sum of mapped building area in OSM for all tiles\n \"\"\"\n total_area_ml, total_area_osm = 0, 0\n for row in session.query(TilePredBA).filter(TilePredBA.tile_index.in_(\n tile_ind_list)):\n total_area_ml += row.building_area_ml\n total_area_osm += row.building_area_osm\n return total_area_ml, total_area_osm\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n for ti, task in enumerate(project['tasks']['features']):\n tile_dict = dict(x=task['properties']['taskX'], y=task['properties'\n ]['taskY'], z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n return project\n\n\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n\n\ndef augment_geojson_building_area(project, session):\n \"\"\"Add building area information to each tile in a geojson dict.\n\n Parameters\n ----------\n project: dict\n geojson to be augmented with new information\n session: sqlalchemy.orm.session.Session\n Handle to database\n \"\"\"\n for ti, task in enumerate(project['tasks']['features']):\n tile_dict = dict(x=task['properties']['taskX'], y=task['properties'\n ]['taskY'], z=task['properties']['taskZoom'])\n child_tiles = get_tile_pyramid(tile_dict, max_zoom=18)\n area_ml, area_osm = get_total_tiles_building_area(child_tiles, session)\n task['properties']['building_area_ml_pred'] = area_ml\n task['properties']['building_area_osm'] = area_osm\n project['tasks']['features'][ti] = task\n return project\n\n\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n \"\"\"Project class meant to hold information on mapping projects in TM.\n\n Attributes\n ----------\n id: int\n The object's UID for the relational DB\n tm_index: int\n ID of the project on Tasking Manager's servers\n md5_hash: str\n MD5 hash of the project geometry. Useful for checking if a split\n occured\n json_geometry: str\n Stripped down version of the geojson project geometry.\n \"\"\"\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n <docstring token>\n __tablename__ = 'ml_projects'\n id = Column(Integer, primary_key=True)\n tm_index = Column(Integer)\n md5_hash = Column(String)\n json_geometry = Column(String)\n building_tiles = relationship('TilePredBA', back_populates='project')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return '<Project(TM index={}, md5_hash={}, {} tiles>'.format(self.\n tm_index, self.md5_hash, len(self.building_tiles))\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n\n\nclass Project(Base):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<class token>\n\n\nclass TilePredBA(Base):\n \"\"\"Tile prediction building area (storing both ML estimate and OSM)\n\n Attributes\n ----------\n id: int\n The tile objects UID for the relational DB\n project_id: int\n Project ID keyed to the project table\n tile_index: str\n Tile index in string format specifying the x/y/z tile coords.\n building_area_ml: float\n Total building area for a tile as predicted by the ML algorithm\n building_area_osm: float\n Total building area for a tile mapped in OSM\n \"\"\"\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<class token>\n\n\nclass TilePredBA(Base):\n <docstring token>\n __tablename__ = 'tile_pred_buildings'\n id = Column(Integer, primary_key=True)\n project_id = Column(Integer, ForeignKey('ml_projects.id'))\n tile_index = Column(String)\n building_area_ml = Column(Float)\n building_area_osm = Column(Float)\n project = relationship('Project', back_populates='building_tiles')\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<class token>\n\n\nclass TilePredBA(Base):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n def __repr__(self):\n \"\"\"Define string representation.\"\"\"\n return (\n '<TilePredBA(Project={}, Tile Index={} Building Area ML={}, Building Area OSM={}>'\n .format(self.project.tm_index, self.tile_index, self.\n building_area_ml, self.building_area_osm))\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<class token>\n\n\nclass TilePredBA(Base):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <function token>\n\n\n<function token>\n<function token>\n<function token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<class token>\n<class token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,454 |
516aa7b8b1dca8d4075842b03e99d23d9d38a76b
|
class Qaslkdjfenurbasdfkjalsrke(object): pass
def g(a: Qaslkdjfenurbasdfkjalsrke, b: Qaslkdjfenurbasdfkjalsrke = 100) -> int: pass
class Goddamn(object):
__metaclass__= type
@staticmethod
def u (a , b ):
if a < b: return b- a
else:
return a -b
class Hey(Goddamn, Qaslkdjfenurbasdfkjalsrke): pass
print "Hello world"; print("Hello world")
|
[
"class Qaslkdjfenurbasdfkjalsrke(object): pass\ndef g(a: Qaslkdjfenurbasdfkjalsrke, b: Qaslkdjfenurbasdfkjalsrke = 100) -> int: pass\nclass Goddamn(object):\n __metaclass__= type\n @staticmethod\n def u (a , b ):\n if a < b: return b- a\n else:\n return a -b\n\n\n\n\nclass Hey(Goddamn, Qaslkdjfenurbasdfkjalsrke): pass\n\nprint \"Hello world\"; print(\"Hello world\")\n"
] | true |
99,455 |
5841061d81e2049ddbedbedb4d044647cb9ac7ff
|
##############################################################################
# Johnathan Clementi
# Advanced Python Programming for GIS - PSU GEOG 489
# Prof. James O’Brien, Grading Assistant Rossana Grzinic
# Final Project Deliverables
# Purpose: NJ Highlands Region annual preserved lands breakdown
##############################################################################
''' Import necessary libraries '''
import os, sys
import re
import arcpy
arcpy.env.overwriteOutput = True # For testing purposes, allows us to overwrite old outputs
import multiprocessing
from workers import worker
import time
startTime = time.time()
# Set workspace to in memory to increase efficiency
arcpy.env.workspace = r'in_memory'
''' Data Input/Output'''
# Municipalities of New Jersey:
# https://njogis-newjersey.opendata.arcgis.com/datasets/3d5d1db8a1b34b418c331f4ce1fd0fef_2
njMuni = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\HighlandsProtectedLands.gdb\NJ_Municipalities'
# Highlands Region
# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/highlands-boundary
highlandsBoundary = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\HighlandsProtectedLands.gdb\Highlands_Boundary'
# Municipalities of the Highlands Region (NJ_Municipalities clipped to Highlands_Boundary)
# Note: There are two 'Washington Townships' within the Highlands Region
highlandsMuni = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\HighlandsProtectedLands.gdb\highlandsMuni'
# Planning and Preservation Designations
# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/preservation-and-planning-area
planPresPoly = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\HighlandsProtectedLands.gdb\Preservation_and_Planning_Area'
# Preserved Lands within the Highlands Region
# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/preserved-lands
presLands = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\HighlandsProtectedLands.gdb\Preserved_Lands'
# Input feature classes - on disk
# clipper = highlandsMuni
# tobeclipped = [presLands, planPresPoly]
# Output directory
outFolder = r'C:\Users\Johnathan\Google Drive\Grad School\PSU_GIS_Cert\GEOG 489\FinalPrj\data\output'
# Check if output directory exists. Create a directory if one does not exist
if os.path.exists(outFolder):
if os.path.isdir(outFolder):
print('The proper output folder exists, moving on')
else:
os.mkdir(outFolder)
print('Created the output directory')
else:
os.mkdir(outFolder)
print('Created the output directory')
''' In Memory Data '''
# Make an in_memory feature layer for clip feature which is the Highlands Municipalities
clipper = "in_memory" + "\\" + "highlandsMuni"
arcpy.MakeFeatureLayer_management(highlandsMuni, clipper)
# Make an in_memory feature layer for Preserved lands
inMemPresLands = "in_memory" + "\\" + "Preserved_Lands"
arcpy.MakeFeatureLayer_management(presLands, inMemPresLands)
# Make an in_memory feature layer for Planning/Preservation Regions
inMemPlanPresPoly = "in_memory" + "\\" + "Preservation_and_Planning_Area"
arcpy.MakeFeatureLayer_management(planPresPoly, inMemPlanPresPoly)
# Add in memory preserved lands and planning/preservation regions to tobeclipped list
tobeclipped = [inMemPresLands, inMemPlanPresPoly]
''' Check for and use 64 bit processing '''
def get_install_path():
''' Return 64bit python install path from registry (if installed and registered),
otherwise fall back to current 32bit process install path.
'''
if sys.maxsize > 2**32: return sys.exec_prefix #We're running in a 64bit process
#We're 32 bit so see if there's a 64bit install
path = r'SOFTWARE\Python\PythonCore\2.7'
from _winreg import OpenKey, QueryValue
from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY
try:
with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY) as key:
return QueryValue(key, "InstallPath").strip(os.sep) #We have a 64bit install, so return that.
except: return sys.exec_prefix #No 64bit, so return 32bit path
''' Multiprocessing Handler Function '''
def mp_handler():
try:
print("Creating Polygon OID list...")
# These are the fields we want to grab from the clip feature layer
field = ['OID@', 'MUN_LABEL']
# Create a list of object IDs for clipper polygons
idList = []
# Initialize list of municipality names (municipalities are used as clip features)
clipperNameList = []
# Iterate through the rows of the municipality feature layer (clipper) and return the OID and name field data
with arcpy.da.SearchCursor(clipper, field) as cursor:
for row in cursor:
id = row[0] # Retrieve OID from first element in row
name = row[1] # Retrieve Municipality name from second element in row
name = name.replace(" ", "_") # Replace illegal characters so we can use this field as the name of the output file later on
name = name.replace("-", "_")
idList.append(id)
clipperNameList.append(name)
print("There are " + str(len(idList)) + " object IDs (polygons) to process.")
# Reset field variable to just that of the OIDFieldName of the municipality feature layer
clipperDescObj = arcpy.Describe(clipper)
field = clipperDescObj.OIDFieldName
# Initialize tuples (not list because tuples are immutable) of tasks that will be sent to workers
jobs = []
'''
Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer
Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)
'''
for i, item in enumerate (tobeclipped):
tobeclippeditem = tobeclipped[i] # Get just one clip input feature layer
j = 0 # Initialize index used for retrieving municipality name
for id in idList:
name = clipperNameList[j] # Get municipality name from current index
j += 1 # Advance municipality name index
jobs.append((clipper,tobeclippeditem,field,id,outFolder, name)) # Add tuples of the parameters that need to be given to the worker function to the jobs list
print("Job list has " + str(len(jobs)) + " elements.")
''' Multiprocessing Pool '''
# Create and run multiprocessing pool.
multiprocessing.set_executable(os.path.join(get_install_path(), 'pythonw.exe')) # make sure Python environment is used for running processes, even when this is run as a script tool
print("Sending to pool")
cpuNum = multiprocessing.cpu_count() # determine number of cores to use
print("There are: " + str(cpuNum) + " cpu cores on this machine")
with multiprocessing.Pool(processes=cpuNum) as pool: # Create the pool object
res = pool.starmap(worker, jobs) # run jobs in job list; res is a list with return values of the worker function
''' Error Reporting if successful try '''
failed = res.count(False) # count how many times False appears in the list with the return values
if failed > 0:
arcpy.AddError("{} workers failed!".format(failed))
print("{} workers failed!".format(failed))
# If the process was completed, print a message
arcpy.AddMessage("Finished multiprocessing!")
print("Finished multiprocessing!")
# Clean up in_memory
arcpy.Delete_management("in_memory")
# Print processing time
arcpy.AddMessage("Total time: %s seconds" % (time.time() - startTime))
# Error Reporting if unsuccessful try
except arcpy.ExecuteError:
# Geoprocessor threw an error
arcpy.AddError(arcpy.GetMessages(2))
print("Execute Error:", arcpy.ExecuteError)
except Exception as e:
# Capture all other errors
arcpy.AddError(str(e))
print("Exception:", e)
# Clean up in_memory
arcpy.Delete_management("in_memory")
# Print processing time
arcpy.AddMessage("Total time: %s seconds" % (time.time() - startTime))
''' Call multiprocessing handler function '''
if __name__ == '__main__':
mp_handler()
|
[
"##############################################################################\n# Johnathan Clementi\n# Advanced Python Programming for GIS - PSU GEOG 489\n# Prof. James O’Brien, Grading Assistant Rossana Grzinic\n# Final Project Deliverables\n# Purpose: NJ Highlands Region annual preserved lands breakdown\n##############################################################################\n\n''' Import necessary libraries '''\nimport os, sys\nimport re\nimport arcpy\narcpy.env.overwriteOutput = True # For testing purposes, allows us to overwrite old outputs\nimport multiprocessing\nfrom workers import worker\nimport time\nstartTime = time.time()\n\n# Set workspace to in memory to increase efficiency\narcpy.env.workspace = r'in_memory'\n\n\n''' Data Input/Output'''\n\n# Municipalities of New Jersey:\n# https://njogis-newjersey.opendata.arcgis.com/datasets/3d5d1db8a1b34b418c331f4ce1fd0fef_2\nnjMuni = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\HighlandsProtectedLands.gdb\\NJ_Municipalities'\n\n# Highlands Region\n# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/highlands-boundary\nhighlandsBoundary = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\HighlandsProtectedLands.gdb\\Highlands_Boundary'\n\n# Municipalities of the Highlands Region (NJ_Municipalities clipped to Highlands_Boundary)\n# Note: There are two 'Washington Townships' within the Highlands Region\nhighlandsMuni = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\HighlandsProtectedLands.gdb\\highlandsMuni'\n\n# Planning and Preservation Designations\n# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/preservation-and-planning-area\nplanPresPoly = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\HighlandsProtectedLands.gdb\\Preservation_and_Planning_Area'\n\n# Preserved Lands within the Highlands Region\n# http://highlands-data-njhighlands.opendata.arcgis.com/datasets/preserved-lands\npresLands = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\HighlandsProtectedLands.gdb\\Preserved_Lands'\n\n\n# Input feature classes - on disk\n# clipper = highlandsMuni \n# tobeclipped = [presLands, planPresPoly]\n\n# Output directory\noutFolder = r'C:\\Users\\Johnathan\\Google Drive\\Grad School\\PSU_GIS_Cert\\GEOG 489\\FinalPrj\\data\\output'\n\n# Check if output directory exists. Create a directory if one does not exist\nif os.path.exists(outFolder):\n if os.path.isdir(outFolder):\n print('The proper output folder exists, moving on')\n else:\n os.mkdir(outFolder)\n print('Created the output directory')\nelse: \n os.mkdir(outFolder)\n print('Created the output directory')\n\n\n\n''' In Memory Data '''\n\n# Make an in_memory feature layer for clip feature which is the Highlands Municipalities\nclipper = \"in_memory\" + \"\\\\\" + \"highlandsMuni\"\narcpy.MakeFeatureLayer_management(highlandsMuni, clipper)\n\n# Make an in_memory feature layer for Preserved lands\ninMemPresLands = \"in_memory\" + \"\\\\\" + \"Preserved_Lands\"\narcpy.MakeFeatureLayer_management(presLands, inMemPresLands)\n\n# Make an in_memory feature layer for Planning/Preservation Regions\ninMemPlanPresPoly = \"in_memory\" + \"\\\\\" + \"Preservation_and_Planning_Area\"\narcpy.MakeFeatureLayer_management(planPresPoly, inMemPlanPresPoly)\n\n# Add in memory preserved lands and planning/preservation regions to tobeclipped list\ntobeclipped = [inMemPresLands, inMemPlanPresPoly]\n\n\n''' Check for and use 64 bit processing '''\n\ndef get_install_path():\n ''' Return 64bit python install path from registry (if installed and registered),\n otherwise fall back to current 32bit process install path.\n '''\n if sys.maxsize > 2**32: return sys.exec_prefix #We're running in a 64bit process\n \n #We're 32 bit so see if there's a 64bit install\n path = r'SOFTWARE\\Python\\PythonCore\\2.7'\n \n from _winreg import OpenKey, QueryValue\n from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY\n \n try:\n with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY) as key:\n return QueryValue(key, \"InstallPath\").strip(os.sep) #We have a 64bit install, so return that.\n except: return sys.exec_prefix #No 64bit, so return 32bit path \n\n\n''' Multiprocessing Handler Function '''\n\ndef mp_handler():\n \n try:\n \n print(\"Creating Polygon OID list...\") \n \n # These are the fields we want to grab from the clip feature layer\n field = ['OID@', 'MUN_LABEL']\n \n # Create a list of object IDs for clipper polygons\n idList = []\n\n # Initialize list of municipality names (municipalities are used as clip features)\n clipperNameList = []\n\n # Iterate through the rows of the municipality feature layer (clipper) and return the OID and name field data\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0] # Retrieve OID from first element in row \n name = row[1] # Retrieve Municipality name from second element in row\n name = name.replace(\" \", \"_\") # Replace illegal characters so we can use this field as the name of the output file later on\n name = name.replace(\"-\", \"_\")\n idList.append(id)\n clipperNameList.append(name)\n \n print(\"There are \" + str(len(idList)) + \" object IDs (polygons) to process.\") \n\n\n # Reset field variable to just that of the OIDFieldName of the municipality feature layer\n clipperDescObj = arcpy.Describe(clipper) \n field = clipperDescObj.OIDFieldName\n\n\n # Initialize tuples (not list because tuples are immutable) of tasks that will be sent to workers \n jobs = []\n\n '''\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n '''\n for i, item in enumerate (tobeclipped):\n tobeclippeditem = tobeclipped[i] # Get just one clip input feature layer\n j = 0 # Initialize index used for retrieving municipality name \n for id in idList:\n name = clipperNameList[j] # Get municipality name from current index\n j += 1 # Advance municipality name index\n jobs.append((clipper,tobeclippeditem,field,id,outFolder, name)) # Add tuples of the parameters that need to be given to the worker function to the jobs list\n\n print(\"Job list has \" + str(len(jobs)) + \" elements.\") \n\n\n ''' Multiprocessing Pool '''\n\n # Create and run multiprocessing pool.\n multiprocessing.set_executable(os.path.join(get_install_path(), 'pythonw.exe')) # make sure Python environment is used for running processes, even when this is run as a script tool\n\n print(\"Sending to pool\") \n\n cpuNum = multiprocessing.cpu_count() # determine number of cores to use\n print(\"There are: \" + str(cpuNum) + \" cpu cores on this machine\") \n\n with multiprocessing.Pool(processes=cpuNum) as pool: # Create the pool object \n res = pool.starmap(worker, jobs) # run jobs in job list; res is a list with return values of the worker function\n\n\n ''' Error Reporting if successful try '''\n \n failed = res.count(False) # count how many times False appears in the list with the return values\n if failed > 0:\n arcpy.AddError(\"{} workers failed!\".format(failed)) \n print(\"{} workers failed!\".format(failed)) \n\n\n # If the process was completed, print a message \n arcpy.AddMessage(\"Finished multiprocessing!\") \n print(\"Finished multiprocessing!\")\n\n # Clean up in_memory\n arcpy.Delete_management(\"in_memory\") \n\n # Print processing time\n arcpy.AddMessage(\"Total time: %s seconds\" % (time.time() - startTime))\n \n\n \n # Error Reporting if unsuccessful try \n except arcpy.ExecuteError:\n # Geoprocessor threw an error \n arcpy.AddError(arcpy.GetMessages(2)) \n print(\"Execute Error:\", arcpy.ExecuteError) \n except Exception as e: \n # Capture all other errors \n arcpy.AddError(str(e)) \n print(\"Exception:\", e)\n\n\n # Clean up in_memory\n arcpy.Delete_management(\"in_memory\") \n\n # Print processing time\n arcpy.AddMessage(\"Total time: %s seconds\" % (time.time() - startTime))\n\n\n\n''' Call multiprocessing handler function ''' \nif __name__ == '__main__': \n mp_handler() \n",
"<docstring token>\nimport os, sys\nimport re\nimport arcpy\narcpy.env.overwriteOutput = True\nimport multiprocessing\nfrom workers import worker\nimport time\nstartTime = time.time()\narcpy.env.workspace = 'in_memory'\n<docstring token>\nnjMuni = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\NJ_Municipalities'\n )\nhighlandsBoundary = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Highlands_Boundary'\n )\nhighlandsMuni = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\highlandsMuni'\n )\nplanPresPoly = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Preservation_and_Planning_Area'\n )\npresLands = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Preserved_Lands'\n )\noutFolder = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\output'\n )\nif os.path.exists(outFolder):\n if os.path.isdir(outFolder):\n print('The proper output folder exists, moving on')\n else:\n os.mkdir(outFolder)\n print('Created the output directory')\nelse:\n os.mkdir(outFolder)\n print('Created the output directory')\n<docstring token>\nclipper = 'in_memory' + '\\\\' + 'highlandsMuni'\narcpy.MakeFeatureLayer_management(highlandsMuni, clipper)\ninMemPresLands = 'in_memory' + '\\\\' + 'Preserved_Lands'\narcpy.MakeFeatureLayer_management(presLands, inMemPresLands)\ninMemPlanPresPoly = 'in_memory' + '\\\\' + 'Preservation_and_Planning_Area'\narcpy.MakeFeatureLayer_management(planPresPoly, inMemPlanPresPoly)\ntobeclipped = [inMemPresLands, inMemPlanPresPoly]\n<docstring token>\n\n\ndef get_install_path():\n \"\"\" Return 64bit python install path from registry (if installed and registered),\n otherwise fall back to current 32bit process install path.\n \"\"\"\n if sys.maxsize > 2 ** 32:\n return sys.exec_prefix\n path = 'SOFTWARE\\\\Python\\\\PythonCore\\\\2.7'\n from _winreg import OpenKey, QueryValue\n from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY\n try:\n with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY\n ) as key:\n return QueryValue(key, 'InstallPath').strip(os.sep)\n except:\n return sys.exec_prefix\n\n\n<docstring token>\n\n\ndef mp_handler():\n try:\n print('Creating Polygon OID list...')\n field = ['OID@', 'MUN_LABEL']\n idList = []\n clipperNameList = []\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0]\n name = row[1]\n name = name.replace(' ', '_')\n name = name.replace('-', '_')\n idList.append(id)\n clipperNameList.append(name)\n print('There are ' + str(len(idList)) +\n ' object IDs (polygons) to process.')\n clipperDescObj = arcpy.Describe(clipper)\n field = clipperDescObj.OIDFieldName\n jobs = []\n \"\"\"\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n \"\"\"\n for i, item in enumerate(tobeclipped):\n tobeclippeditem = tobeclipped[i]\n j = 0\n for id in idList:\n name = clipperNameList[j]\n j += 1\n jobs.append((clipper, tobeclippeditem, field, id, outFolder,\n name))\n print('Job list has ' + str(len(jobs)) + ' elements.')\n \"\"\" Multiprocessing Pool \"\"\"\n multiprocessing.set_executable(os.path.join(get_install_path(),\n 'pythonw.exe'))\n print('Sending to pool')\n cpuNum = multiprocessing.cpu_count()\n print('There are: ' + str(cpuNum) + ' cpu cores on this machine')\n with multiprocessing.Pool(processes=cpuNum) as pool:\n res = pool.starmap(worker, jobs)\n \"\"\" Error Reporting if successful try \"\"\"\n failed = res.count(False)\n if failed > 0:\n arcpy.AddError('{} workers failed!'.format(failed))\n print('{} workers failed!'.format(failed))\n arcpy.AddMessage('Finished multiprocessing!')\n print('Finished multiprocessing!')\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n except arcpy.ExecuteError:\n arcpy.AddError(arcpy.GetMessages(2))\n print('Execute Error:', arcpy.ExecuteError)\n except Exception as e:\n arcpy.AddError(str(e))\n print('Exception:', e)\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n\n\n<docstring token>\nif __name__ == '__main__':\n mp_handler()\n",
"<docstring token>\n<import token>\narcpy.env.overwriteOutput = True\n<import token>\nstartTime = time.time()\narcpy.env.workspace = 'in_memory'\n<docstring token>\nnjMuni = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\NJ_Municipalities'\n )\nhighlandsBoundary = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Highlands_Boundary'\n )\nhighlandsMuni = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\highlandsMuni'\n )\nplanPresPoly = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Preservation_and_Planning_Area'\n )\npresLands = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\HighlandsProtectedLands.gdb\\\\Preserved_Lands'\n )\noutFolder = (\n 'C:\\\\Users\\\\Johnathan\\\\Google Drive\\\\Grad School\\\\PSU_GIS_Cert\\\\GEOG 489\\\\FinalPrj\\\\data\\\\output'\n )\nif os.path.exists(outFolder):\n if os.path.isdir(outFolder):\n print('The proper output folder exists, moving on')\n else:\n os.mkdir(outFolder)\n print('Created the output directory')\nelse:\n os.mkdir(outFolder)\n print('Created the output directory')\n<docstring token>\nclipper = 'in_memory' + '\\\\' + 'highlandsMuni'\narcpy.MakeFeatureLayer_management(highlandsMuni, clipper)\ninMemPresLands = 'in_memory' + '\\\\' + 'Preserved_Lands'\narcpy.MakeFeatureLayer_management(presLands, inMemPresLands)\ninMemPlanPresPoly = 'in_memory' + '\\\\' + 'Preservation_and_Planning_Area'\narcpy.MakeFeatureLayer_management(planPresPoly, inMemPlanPresPoly)\ntobeclipped = [inMemPresLands, inMemPlanPresPoly]\n<docstring token>\n\n\ndef get_install_path():\n \"\"\" Return 64bit python install path from registry (if installed and registered),\n otherwise fall back to current 32bit process install path.\n \"\"\"\n if sys.maxsize > 2 ** 32:\n return sys.exec_prefix\n path = 'SOFTWARE\\\\Python\\\\PythonCore\\\\2.7'\n from _winreg import OpenKey, QueryValue\n from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY\n try:\n with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY\n ) as key:\n return QueryValue(key, 'InstallPath').strip(os.sep)\n except:\n return sys.exec_prefix\n\n\n<docstring token>\n\n\ndef mp_handler():\n try:\n print('Creating Polygon OID list...')\n field = ['OID@', 'MUN_LABEL']\n idList = []\n clipperNameList = []\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0]\n name = row[1]\n name = name.replace(' ', '_')\n name = name.replace('-', '_')\n idList.append(id)\n clipperNameList.append(name)\n print('There are ' + str(len(idList)) +\n ' object IDs (polygons) to process.')\n clipperDescObj = arcpy.Describe(clipper)\n field = clipperDescObj.OIDFieldName\n jobs = []\n \"\"\"\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n \"\"\"\n for i, item in enumerate(tobeclipped):\n tobeclippeditem = tobeclipped[i]\n j = 0\n for id in idList:\n name = clipperNameList[j]\n j += 1\n jobs.append((clipper, tobeclippeditem, field, id, outFolder,\n name))\n print('Job list has ' + str(len(jobs)) + ' elements.')\n \"\"\" Multiprocessing Pool \"\"\"\n multiprocessing.set_executable(os.path.join(get_install_path(),\n 'pythonw.exe'))\n print('Sending to pool')\n cpuNum = multiprocessing.cpu_count()\n print('There are: ' + str(cpuNum) + ' cpu cores on this machine')\n with multiprocessing.Pool(processes=cpuNum) as pool:\n res = pool.starmap(worker, jobs)\n \"\"\" Error Reporting if successful try \"\"\"\n failed = res.count(False)\n if failed > 0:\n arcpy.AddError('{} workers failed!'.format(failed))\n print('{} workers failed!'.format(failed))\n arcpy.AddMessage('Finished multiprocessing!')\n print('Finished multiprocessing!')\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n except arcpy.ExecuteError:\n arcpy.AddError(arcpy.GetMessages(2))\n print('Execute Error:', arcpy.ExecuteError)\n except Exception as e:\n arcpy.AddError(str(e))\n print('Exception:', e)\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n\n\n<docstring token>\nif __name__ == '__main__':\n mp_handler()\n",
"<docstring token>\n<import token>\n<assignment token>\n<import token>\n<assignment token>\n<docstring token>\n<assignment token>\nif os.path.exists(outFolder):\n if os.path.isdir(outFolder):\n print('The proper output folder exists, moving on')\n else:\n os.mkdir(outFolder)\n print('Created the output directory')\nelse:\n os.mkdir(outFolder)\n print('Created the output directory')\n<docstring token>\n<assignment token>\narcpy.MakeFeatureLayer_management(highlandsMuni, clipper)\n<assignment token>\narcpy.MakeFeatureLayer_management(presLands, inMemPresLands)\n<assignment token>\narcpy.MakeFeatureLayer_management(planPresPoly, inMemPlanPresPoly)\n<assignment token>\n<docstring token>\n\n\ndef get_install_path():\n \"\"\" Return 64bit python install path from registry (if installed and registered),\n otherwise fall back to current 32bit process install path.\n \"\"\"\n if sys.maxsize > 2 ** 32:\n return sys.exec_prefix\n path = 'SOFTWARE\\\\Python\\\\PythonCore\\\\2.7'\n from _winreg import OpenKey, QueryValue\n from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY\n try:\n with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY\n ) as key:\n return QueryValue(key, 'InstallPath').strip(os.sep)\n except:\n return sys.exec_prefix\n\n\n<docstring token>\n\n\ndef mp_handler():\n try:\n print('Creating Polygon OID list...')\n field = ['OID@', 'MUN_LABEL']\n idList = []\n clipperNameList = []\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0]\n name = row[1]\n name = name.replace(' ', '_')\n name = name.replace('-', '_')\n idList.append(id)\n clipperNameList.append(name)\n print('There are ' + str(len(idList)) +\n ' object IDs (polygons) to process.')\n clipperDescObj = arcpy.Describe(clipper)\n field = clipperDescObj.OIDFieldName\n jobs = []\n \"\"\"\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n \"\"\"\n for i, item in enumerate(tobeclipped):\n tobeclippeditem = tobeclipped[i]\n j = 0\n for id in idList:\n name = clipperNameList[j]\n j += 1\n jobs.append((clipper, tobeclippeditem, field, id, outFolder,\n name))\n print('Job list has ' + str(len(jobs)) + ' elements.')\n \"\"\" Multiprocessing Pool \"\"\"\n multiprocessing.set_executable(os.path.join(get_install_path(),\n 'pythonw.exe'))\n print('Sending to pool')\n cpuNum = multiprocessing.cpu_count()\n print('There are: ' + str(cpuNum) + ' cpu cores on this machine')\n with multiprocessing.Pool(processes=cpuNum) as pool:\n res = pool.starmap(worker, jobs)\n \"\"\" Error Reporting if successful try \"\"\"\n failed = res.count(False)\n if failed > 0:\n arcpy.AddError('{} workers failed!'.format(failed))\n print('{} workers failed!'.format(failed))\n arcpy.AddMessage('Finished multiprocessing!')\n print('Finished multiprocessing!')\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n except arcpy.ExecuteError:\n arcpy.AddError(arcpy.GetMessages(2))\n print('Execute Error:', arcpy.ExecuteError)\n except Exception as e:\n arcpy.AddError(str(e))\n print('Exception:', e)\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n\n\n<docstring token>\nif __name__ == '__main__':\n mp_handler()\n",
"<docstring token>\n<import token>\n<assignment token>\n<import token>\n<assignment token>\n<docstring token>\n<assignment token>\n<code token>\n<docstring token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<docstring token>\n\n\ndef get_install_path():\n \"\"\" Return 64bit python install path from registry (if installed and registered),\n otherwise fall back to current 32bit process install path.\n \"\"\"\n if sys.maxsize > 2 ** 32:\n return sys.exec_prefix\n path = 'SOFTWARE\\\\Python\\\\PythonCore\\\\2.7'\n from _winreg import OpenKey, QueryValue\n from _winreg import HKEY_LOCAL_MACHINE, KEY_READ, KEY_WOW64_64KEY\n try:\n with OpenKey(HKEY_LOCAL_MACHINE, path, 0, KEY_READ | KEY_WOW64_64KEY\n ) as key:\n return QueryValue(key, 'InstallPath').strip(os.sep)\n except:\n return sys.exec_prefix\n\n\n<docstring token>\n\n\ndef mp_handler():\n try:\n print('Creating Polygon OID list...')\n field = ['OID@', 'MUN_LABEL']\n idList = []\n clipperNameList = []\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0]\n name = row[1]\n name = name.replace(' ', '_')\n name = name.replace('-', '_')\n idList.append(id)\n clipperNameList.append(name)\n print('There are ' + str(len(idList)) +\n ' object IDs (polygons) to process.')\n clipperDescObj = arcpy.Describe(clipper)\n field = clipperDescObj.OIDFieldName\n jobs = []\n \"\"\"\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n \"\"\"\n for i, item in enumerate(tobeclipped):\n tobeclippeditem = tobeclipped[i]\n j = 0\n for id in idList:\n name = clipperNameList[j]\n j += 1\n jobs.append((clipper, tobeclippeditem, field, id, outFolder,\n name))\n print('Job list has ' + str(len(jobs)) + ' elements.')\n \"\"\" Multiprocessing Pool \"\"\"\n multiprocessing.set_executable(os.path.join(get_install_path(),\n 'pythonw.exe'))\n print('Sending to pool')\n cpuNum = multiprocessing.cpu_count()\n print('There are: ' + str(cpuNum) + ' cpu cores on this machine')\n with multiprocessing.Pool(processes=cpuNum) as pool:\n res = pool.starmap(worker, jobs)\n \"\"\" Error Reporting if successful try \"\"\"\n failed = res.count(False)\n if failed > 0:\n arcpy.AddError('{} workers failed!'.format(failed))\n print('{} workers failed!'.format(failed))\n arcpy.AddMessage('Finished multiprocessing!')\n print('Finished multiprocessing!')\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n except arcpy.ExecuteError:\n arcpy.AddError(arcpy.GetMessages(2))\n print('Execute Error:', arcpy.ExecuteError)\n except Exception as e:\n arcpy.AddError(str(e))\n print('Exception:', e)\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n\n\n<docstring token>\n<code token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<import token>\n<assignment token>\n<docstring token>\n<assignment token>\n<code token>\n<docstring token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<docstring token>\n<function token>\n<docstring token>\n\n\ndef mp_handler():\n try:\n print('Creating Polygon OID list...')\n field = ['OID@', 'MUN_LABEL']\n idList = []\n clipperNameList = []\n with arcpy.da.SearchCursor(clipper, field) as cursor:\n for row in cursor:\n id = row[0]\n name = row[1]\n name = name.replace(' ', '_')\n name = name.replace('-', '_')\n idList.append(id)\n clipperNameList.append(name)\n print('There are ' + str(len(idList)) +\n ' object IDs (polygons) to process.')\n clipperDescObj = arcpy.Describe(clipper)\n field = clipperDescObj.OIDFieldName\n jobs = []\n \"\"\"\n Nested loop creates job list for each input feature layer of clip (preserved lands and planning/preservation regions) and each feature of clip feature layer\n Use enumerate to get index of tobeclipped list then assign value at that index to a variable holding one element (instead of a list)\n \"\"\"\n for i, item in enumerate(tobeclipped):\n tobeclippeditem = tobeclipped[i]\n j = 0\n for id in idList:\n name = clipperNameList[j]\n j += 1\n jobs.append((clipper, tobeclippeditem, field, id, outFolder,\n name))\n print('Job list has ' + str(len(jobs)) + ' elements.')\n \"\"\" Multiprocessing Pool \"\"\"\n multiprocessing.set_executable(os.path.join(get_install_path(),\n 'pythonw.exe'))\n print('Sending to pool')\n cpuNum = multiprocessing.cpu_count()\n print('There are: ' + str(cpuNum) + ' cpu cores on this machine')\n with multiprocessing.Pool(processes=cpuNum) as pool:\n res = pool.starmap(worker, jobs)\n \"\"\" Error Reporting if successful try \"\"\"\n failed = res.count(False)\n if failed > 0:\n arcpy.AddError('{} workers failed!'.format(failed))\n print('{} workers failed!'.format(failed))\n arcpy.AddMessage('Finished multiprocessing!')\n print('Finished multiprocessing!')\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n except arcpy.ExecuteError:\n arcpy.AddError(arcpy.GetMessages(2))\n print('Execute Error:', arcpy.ExecuteError)\n except Exception as e:\n arcpy.AddError(str(e))\n print('Exception:', e)\n arcpy.Delete_management('in_memory')\n arcpy.AddMessage('Total time: %s seconds' % (time.time() - startTime))\n\n\n<docstring token>\n<code token>\n",
"<docstring token>\n<import token>\n<assignment token>\n<import token>\n<assignment token>\n<docstring token>\n<assignment token>\n<code token>\n<docstring token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<docstring token>\n<function token>\n<docstring token>\n<function token>\n<docstring token>\n<code token>\n"
] | false |
99,456 |
9b551fbcd94e2ce2ebe7dd777d642477bdca94d1
|
from django.contrib.auth.models import User
from userbetting.models import Game, Team, Bet
from rest_framework import routers, serializers, viewsets
# Serializers define the API representation.
class BetSerializer(serializers.HyperlinkedModelSerializer):
user = serializers.StringRelatedField(many=False)
chosen_team = serializers.StringRelatedField(many=False)
class Meta:
model = Bet
fields = ('bet_id', 'user', 'chosen_team', 'amount')
class UserSerializer(serializers.HyperlinkedModelSerializer):
user_bets = BetSerializer(many=True, read_only=True)
class Meta:
model = User
fields = ('url', 'username', 'email', 'is_staff', 'user_bets')
class GameSerializer(serializers.HyperlinkedModelSerializer):
team_a = serializers.StringRelatedField(many=False)
team_b = serializers.StringRelatedField(many=False)
game_bets = BetSerializer(many=True, read_only=True)
class Meta:
model = Game
fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date', 'winning_team', 'game_bets')
# ViewSets define the view behavior.
class UserViewSet(viewsets.ModelViewSet):
queryset = User.objects.all()
serializer_class = UserSerializer
class GameViewSet(viewsets.ModelViewSet):
queryset = Game.objects.all()
serializer_class = GameSerializer
# Routers provide an easy way of automatically determining the URL conf.
router = routers.DefaultRouter()
router.register(r'users', UserViewSet)
router.register(r'games', GameViewSet)
|
[
"from django.contrib.auth.models import User\nfrom userbetting.models import Game, Team, Bet\nfrom rest_framework import routers, serializers, viewsets\n\n\n# Serializers define the API representation.\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n user = serializers.StringRelatedField(many=False)\n chosen_team = serializers.StringRelatedField(many=False)\n\n class Meta:\n model = Bet\n fields = ('bet_id', 'user', 'chosen_team', 'amount')\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n class Meta:\n model = User\n fields = ('url', 'username', 'email', 'is_staff', 'user_bets')\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date', 'winning_team', 'game_bets')\n\n# ViewSets define the view behavior.\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n# Routers provide an easy way of automatically determining the URL conf.\nrouter = routers.DefaultRouter()\nrouter.register(r'users', UserViewSet)\nrouter.register(r'games', GameViewSet)\n",
"from django.contrib.auth.models import User\nfrom userbetting.models import Game, Team, Bet\nfrom rest_framework import routers, serializers, viewsets\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n user = serializers.StringRelatedField(many=False)\n chosen_team = serializers.StringRelatedField(many=False)\n\n\n class Meta:\n model = Bet\n fields = 'bet_id', 'user', 'chosen_team', 'amount'\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\nrouter = routers.DefaultRouter()\nrouter.register('users', UserViewSet)\nrouter.register('games', GameViewSet)\n",
"<import token>\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n user = serializers.StringRelatedField(many=False)\n chosen_team = serializers.StringRelatedField(many=False)\n\n\n class Meta:\n model = Bet\n fields = 'bet_id', 'user', 'chosen_team', 'amount'\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\nrouter = routers.DefaultRouter()\nrouter.register('users', UserViewSet)\nrouter.register('games', GameViewSet)\n",
"<import token>\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n user = serializers.StringRelatedField(many=False)\n chosen_team = serializers.StringRelatedField(many=False)\n\n\n class Meta:\n model = Bet\n fields = 'bet_id', 'user', 'chosen_team', 'amount'\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\nrouter.register('users', UserViewSet)\nrouter.register('games', GameViewSet)\n",
"<import token>\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n user = serializers.StringRelatedField(many=False)\n chosen_team = serializers.StringRelatedField(many=False)\n\n\n class Meta:\n model = Bet\n fields = 'bet_id', 'user', 'chosen_team', 'amount'\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n\n\nclass BetSerializer(serializers.HyperlinkedModelSerializer):\n <assignment token>\n <assignment token>\n\n\n class Meta:\n model = Bet\n fields = 'bet_id', 'user', 'chosen_team', 'amount'\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n user_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n <assignment token>\n\n\n class Meta:\n model = User\n fields = 'url', 'username', 'email', 'is_staff', 'user_bets'\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n team_a = serializers.StringRelatedField(many=False)\n team_b = serializers.StringRelatedField(many=False)\n game_bets = BetSerializer(many=True, read_only=True)\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n\n\nclass GameSerializer(serializers.HyperlinkedModelSerializer):\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n model = Game\n fields = ('game_id', 'team_a', 'team_b', 'videogame', 'game_date',\n 'winning_team', 'game_bets')\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n queryset = Game.objects.all()\n serializer_class = GameSerializer\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n\n\nclass GameViewSet(viewsets.ModelViewSet):\n <assignment token>\n <assignment token>\n\n\n<assignment token>\n<code token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n<class token>\n<class token>\n<assignment token>\n<code token>\n"
] | false |
99,457 |
581cba92406db5624b8f86356f0aaade55c56180
|
token = '1159459407:AAH1Gj1BXjiy88GMxpMdyZHbV5UeO_tcirU'
help_text = 'Этот бот поможет тебе узнать текущую температру воздуха в твоем городе. Также он подберёт тебе одежду ' \
'под погоду. Просто введи название своего города '
|
[
"token = '1159459407:AAH1Gj1BXjiy88GMxpMdyZHbV5UeO_tcirU'\nhelp_text = 'Этот бот поможет тебе узнать текущую температру воздуха в твоем городе. Также он подберёт тебе одежду ' \\\n 'под погоду. Просто введи название своего города '\n",
"token = '1159459407:AAH1Gj1BXjiy88GMxpMdyZHbV5UeO_tcirU'\nhelp_text = (\n 'Этот бот поможет тебе узнать текущую температру воздуха в твоем городе. Также он подберёт тебе одежду под погоду. Просто введи название своего города '\n )\n",
"<assignment token>\n"
] | false |
99,458 |
026306035403ba02e7a5d266373f76cf21098bfc
|
#Python tiene funciones para crear, leer, actualizar y eliminar archivos
#Abrir un archivo
myFile= open('myfile.txt', 'w')
#Conseguir informacion
print('Nombre: ', myFile.name)
print('Cerrado: ', myFile.closed)
print('Modo de apertura: ', myFile.mode)
#Escribir en el archivo
myFile.write('Estoy aprendiendo Python,')
myFile.write(' es un lenguaje chido')
myFile.close()
#Adjuntar al archivo
myFile= open('myfile.txt', 'a')
myFile.write('\nDespues inicio Django')
myFile.close()
#Leer un archivo
myFile= open('myfile.txt', 'r+')
texto= myFile.read()
print(texto)
|
[
"#Python tiene funciones para crear, leer, actualizar y eliminar archivos\r\n\r\n#Abrir un archivo\r\nmyFile= open('myfile.txt', 'w')\r\n\r\n#Conseguir informacion\r\nprint('Nombre: ', myFile.name)\r\nprint('Cerrado: ', myFile.closed)\r\nprint('Modo de apertura: ', myFile.mode)\r\n\r\n#Escribir en el archivo\r\nmyFile.write('Estoy aprendiendo Python,')\r\nmyFile.write(' es un lenguaje chido')\r\nmyFile.close()\r\n\r\n#Adjuntar al archivo\r\nmyFile= open('myfile.txt', 'a')\r\nmyFile.write('\\nDespues inicio Django')\r\nmyFile.close()\r\n\r\n#Leer un archivo\r\nmyFile= open('myfile.txt', 'r+')\r\ntexto= myFile.read()\r\nprint(texto)\r\n",
"myFile = open('myfile.txt', 'w')\nprint('Nombre: ', myFile.name)\nprint('Cerrado: ', myFile.closed)\nprint('Modo de apertura: ', myFile.mode)\nmyFile.write('Estoy aprendiendo Python,')\nmyFile.write(' es un lenguaje chido')\nmyFile.close()\nmyFile = open('myfile.txt', 'a')\nmyFile.write(\"\"\"\nDespues inicio Django\"\"\")\nmyFile.close()\nmyFile = open('myfile.txt', 'r+')\ntexto = myFile.read()\nprint(texto)\n",
"<assignment token>\nprint('Nombre: ', myFile.name)\nprint('Cerrado: ', myFile.closed)\nprint('Modo de apertura: ', myFile.mode)\nmyFile.write('Estoy aprendiendo Python,')\nmyFile.write(' es un lenguaje chido')\nmyFile.close()\n<assignment token>\nmyFile.write(\"\"\"\nDespues inicio Django\"\"\")\nmyFile.close()\n<assignment token>\nprint(texto)\n",
"<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,459 |
5dd164ea920fbcb9be4b3846b7aaee2e245c6923
|
from setuptools import setup
pypi_classifiers = [
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
"Development Status :: 4 - Beta",
"Environment :: Console",
"Operating System :: OS Independent",
'Intended Audience :: Science/Research',
'Natural Language :: English',
'Topic :: Scientific/Engineering :: Bio-Informatics',
"Topic :: Software Development :: Libraries :: Python Modules",
'License :: OSI Approved :: MIT License',
]
install_requires = [
'biopython>=1.70',
]
desc = """For a set of reference transposons, collect instances from one or more genomes and write as multi-FASTA files."""
setup(name='reunite',
version='1.0.0',
description=desc,
url='https://github.com/Adamtaranto/TE-Reunite',
author='Adam Taranto',
author_email='[email protected]',
license='MIT',
packages=['reunite'],
classifiers=pypi_classifiers,
keywords=["Transposon","TE","repeat","transposon"],
install_requires=install_requires,
include_package_data=True,
zip_safe=False,
entry_points={
'console_scripts': [
'reunite=reunite.run_cmd:main',
],
},
)
|
[
"from setuptools import setup\n\npypi_classifiers = [\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Operating System :: OS Independent\",\n 'Intended Audience :: Science/Research',\n 'Natural Language :: English',\n 'Topic :: Scientific/Engineering :: Bio-Informatics',\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n 'License :: OSI Approved :: MIT License',\n]\n\ninstall_requires = [\n 'biopython>=1.70',\n]\n\ndesc = \"\"\"For a set of reference transposons, collect instances from one or more genomes and write as multi-FASTA files.\"\"\"\n\nsetup(name='reunite',\n version='1.0.0',\n description=desc,\n url='https://github.com/Adamtaranto/TE-Reunite',\n author='Adam Taranto',\n author_email='[email protected]',\n license='MIT',\n packages=['reunite'],\n classifiers=pypi_classifiers,\n keywords=[\"Transposon\",\"TE\",\"repeat\",\"transposon\"],\n install_requires=install_requires,\n include_package_data=True,\n zip_safe=False,\n entry_points={\n 'console_scripts': [\n 'reunite=reunite.run_cmd:main',\n ],\n },\n )\n",
"from setuptools import setup\npypi_classifiers = ['Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3', 'Development Status :: 4 - Beta',\n 'Environment :: Console', 'Operating System :: OS Independent',\n 'Intended Audience :: Science/Research', 'Natural Language :: English',\n 'Topic :: Scientific/Engineering :: Bio-Informatics',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'License :: OSI Approved :: MIT License']\ninstall_requires = ['biopython>=1.70']\ndesc = (\n 'For a set of reference transposons, collect instances from one or more genomes and write as multi-FASTA files.'\n )\nsetup(name='reunite', version='1.0.0', description=desc, url=\n 'https://github.com/Adamtaranto/TE-Reunite', author='Adam Taranto',\n author_email='[email protected]', license='MIT', packages=[\n 'reunite'], classifiers=pypi_classifiers, keywords=['Transposon', 'TE',\n 'repeat', 'transposon'], install_requires=install_requires,\n include_package_data=True, zip_safe=False, entry_points={\n 'console_scripts': ['reunite=reunite.run_cmd:main']})\n",
"<import token>\npypi_classifiers = ['Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3', 'Development Status :: 4 - Beta',\n 'Environment :: Console', 'Operating System :: OS Independent',\n 'Intended Audience :: Science/Research', 'Natural Language :: English',\n 'Topic :: Scientific/Engineering :: Bio-Informatics',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n 'License :: OSI Approved :: MIT License']\ninstall_requires = ['biopython>=1.70']\ndesc = (\n 'For a set of reference transposons, collect instances from one or more genomes and write as multi-FASTA files.'\n )\nsetup(name='reunite', version='1.0.0', description=desc, url=\n 'https://github.com/Adamtaranto/TE-Reunite', author='Adam Taranto',\n author_email='[email protected]', license='MIT', packages=[\n 'reunite'], classifiers=pypi_classifiers, keywords=['Transposon', 'TE',\n 'repeat', 'transposon'], install_requires=install_requires,\n include_package_data=True, zip_safe=False, entry_points={\n 'console_scripts': ['reunite=reunite.run_cmd:main']})\n",
"<import token>\n<assignment token>\nsetup(name='reunite', version='1.0.0', description=desc, url=\n 'https://github.com/Adamtaranto/TE-Reunite', author='Adam Taranto',\n author_email='[email protected]', license='MIT', packages=[\n 'reunite'], classifiers=pypi_classifiers, keywords=['Transposon', 'TE',\n 'repeat', 'transposon'], install_requires=install_requires,\n include_package_data=True, zip_safe=False, entry_points={\n 'console_scripts': ['reunite=reunite.run_cmd:main']})\n",
"<import token>\n<assignment token>\n<code token>\n"
] | false |
99,460 |
d6764c500c486f8c61d8b0983dead70e28b50c07
|
#!/usr/bin/env python
# -*- coding:utf8 -*-
# auther; 18793
# Date：2019/5/22 10:49
# filename: pymysql_有条件的查询.py
import pymysql
# 1.建立数据库连接
connection = pymysql.connect(host='localhost',
user='root',
password='admin#123',
database='hujianli2',
charset='utf8')
# 2.创建游标对象
try:
with connection.cursor() as cursor:
# 3.执行SQL操作
sql = 'select name,userid from user where userid > %(id)s'
cursor.execute(sql, {'id': 0})
# 4.提取结果集
result_set = cursor.fetchall()
for row in result_set:
print("id:{0} - name:{1}".format(row[1], row[0]))
# 5.with代码块结束，关闭游标
finally:
# 6.关闭数据连接
connection.close()
|
[
"#!/usr/bin/env python\n# -*- coding:utf8 -*-\n# auther; 18793\n# Date：2019/5/22 10:49\n# filename: pymysql_有条件的查询.py\nimport pymysql\n\n# 1.建立数据库连接\nconnection = pymysql.connect(host='localhost',\n user='root',\n password='admin#123',\n database='hujianli2',\n charset='utf8')\n\n# 2.创建游标对象\n\ntry:\n with connection.cursor() as cursor:\n # 3.执行SQL操作\n sql = 'select name,userid from user where userid > %(id)s'\n cursor.execute(sql, {'id': 0})\n\n # 4.提取结果集\n result_set = cursor.fetchall()\n\n for row in result_set:\n print(\"id:{0} - name:{1}\".format(row[1], row[0]))\n\n # 5.with代码块结束，关闭游标\nfinally:\n # 6.关闭数据连接\n connection.close()\n",
"import pymysql\nconnection = pymysql.connect(host='localhost', user='root', password=\n 'admin#123', database='hujianli2', charset='utf8')\ntry:\n with connection.cursor() as cursor:\n sql = 'select name,userid from user where userid > %(id)s'\n cursor.execute(sql, {'id': 0})\n result_set = cursor.fetchall()\n for row in result_set:\n print('id:{0} - name:{1}'.format(row[1], row[0]))\nfinally:\n connection.close()\n",
"<import token>\nconnection = pymysql.connect(host='localhost', user='root', password=\n 'admin#123', database='hujianli2', charset='utf8')\ntry:\n with connection.cursor() as cursor:\n sql = 'select name,userid from user where userid > %(id)s'\n cursor.execute(sql, {'id': 0})\n result_set = cursor.fetchall()\n for row in result_set:\n print('id:{0} - name:{1}'.format(row[1], row[0]))\nfinally:\n connection.close()\n",
"<import token>\n<assignment token>\ntry:\n with connection.cursor() as cursor:\n sql = 'select name,userid from user where userid > %(id)s'\n cursor.execute(sql, {'id': 0})\n result_set = cursor.fetchall()\n for row in result_set:\n print('id:{0} - name:{1}'.format(row[1], row[0]))\nfinally:\n connection.close()\n",
"<import token>\n<assignment token>\n<code token>\n"
] | false |
99,461 |
c07f97c83c4af66e752544be149e68121d8a88cf
|
from output.models.ms_data.errata10.err_e002_xsd.err_e002 import (
Root,
TestElement,
)
__all__ = [
"Root",
"TestElement",
]
|
[
"from output.models.ms_data.errata10.err_e002_xsd.err_e002 import (\n Root,\n TestElement,\n)\n\n__all__ = [\n \"Root\",\n \"TestElement\",\n]\n",
"from output.models.ms_data.errata10.err_e002_xsd.err_e002 import Root, TestElement\n__all__ = ['Root', 'TestElement']\n",
"<import token>\n__all__ = ['Root', 'TestElement']\n",
"<import token>\n<assignment token>\n"
] | false |
99,462 |
5be9cc511ed96d0d398c5abcd0b400612fa2d1ba
|
# Generated by Django 2.1.7 on 2019-03-27 00:25
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('donuts', '0005_auto_20190327_0023'),
]
operations = [
migrations.AlterModelOptions(
name='savory',
options={'verbose_name_plural': 'savories'},
),
]
|
[
"# Generated by Django 2.1.7 on 2019-03-27 00:25\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('donuts', '0005_auto_20190327_0023'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='savory',\n options={'verbose_name_plural': 'savories'},\n ),\n ]\n",
"from django.db import migrations\n\n\nclass Migration(migrations.Migration):\n dependencies = [('donuts', '0005_auto_20190327_0023')]\n operations = [migrations.AlterModelOptions(name='savory', options={\n 'verbose_name_plural': 'savories'})]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('donuts', '0005_auto_20190327_0023')]\n operations = [migrations.AlterModelOptions(name='savory', options={\n 'verbose_name_plural': 'savories'})]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,463 |
18543e8d3989bf115daa1d8fba8f26bf12a87c05
|
#!/usr/bin/env python
import socket
import ssl
import shutil
#envia um arquivo
def send_file(socket, filename):
with open('test.txt','rb') as inp:
out = socket.makefile('wb')
shutil.copyfileobj(inp, out)
TCP_IP = '127.0.0.1'
TCP_PORT = 7000
BUFFER_SIZE = 20 # Normally 1024, but we want fast response
#socket para o server
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
#ligacao com tp e porta
s.bind((TCP_IP, TCP_PORT))
#atende chamadas
s.listen(1)
#recebeu uma chamada
conn, addr = s.accept()
#wrap de ssl
connstream = ssl.wrap_socket(conn,
server_side=True,
certfile="selfsigned.cert",
keyfile="selfsigned.key")
print 'Connection address:', addr
#recebe dados do cliente
data = connstream.recv(BUFFER_SIZE)
if data == 'download':
print "Sending file..."
data = 'ok'
#confirma a requisicao do cliente
connstream.send(data)
send_file(connstream, "test.txt")
connstream.close()
|
[
"#!/usr/bin/env python\n\nimport socket\nimport ssl\nimport shutil\n\n#envia um arquivo\ndef send_file(socket, filename):\n with open('test.txt','rb') as inp:\n out = socket.makefile('wb')\n shutil.copyfileobj(inp, out)\n\n\nTCP_IP = '127.0.0.1'\nTCP_PORT = 7000\nBUFFER_SIZE = 20 # Normally 1024, but we want fast response\n\n#socket para o server\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n#ligacao com tp e porta\ns.bind((TCP_IP, TCP_PORT))\n#atende chamadas\ns.listen(1)\n\n#recebeu uma chamada\nconn, addr = s.accept()\n#wrap de ssl\nconnstream = ssl.wrap_socket(conn,\n server_side=True,\n certfile=\"selfsigned.cert\",\n keyfile=\"selfsigned.key\")\nprint 'Connection address:', addr\n\n#recebe dados do cliente\ndata = connstream.recv(BUFFER_SIZE)\n\nif data == 'download':\n print \"Sending file...\"\n data = 'ok'\n #confirma a requisicao do cliente\n connstream.send(data)\n send_file(connstream, \"test.txt\")\n\nconnstream.close()\n"
] | true |
99,464 |
23328911b618a2982fdf8aec67005459cb39a0c5
|
#!C:\Users\dell\AppData\Local\Programs\Python\Python35 python
# -*- coding: utf-8 -*-
import sys, os
class Fileoperation:
# def __init__(self):
# self.case_path = sys.path[0]
# self.case_path = os.getcwd()
def filewrite(self, fpath, filename, writeword):
filepath = os.path.join(fpath, filename)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(writeword)
f.close()
def fileread(self, fpath, filename):
filepath = os.path.join(fpath, filename)
with open(filepath, 'r', encoding='utf-8') as f:
try:
all_the_text = f.read()
finally:
f.close()
return (all_the_text)
# class Fileoperation:
# def __init__(self):
# self.case_path = sys.path[0]
# def filewrite(self,filename,writeword):
# filepath = os.path.join(self.case_path, filename)
# with open(filepath, 'w', encoding='utf-8') as f:
# f.write(writeword)
# f.close()
# def fileread(self,filename):
# filepath = os.path.join(self.case_path,filename)
# with open(filepath, 'r', encoding='utf-8') as f:
# try:
# all_the_text = f.read()
# finally:
# f.close()
# return(all_the_text)
|
[
"#!C:\\Users\\dell\\AppData\\Local\\Programs\\Python\\Python35 python\n# -*- coding: utf-8 -*-\nimport sys, os\n\nclass Fileoperation:\n # def __init__(self):\n # self.case_path = sys.path[0]\n # self.case_path = os.getcwd()\n\n def filewrite(self, fpath, filename, writeword):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'w', encoding='utf-8') as f:\n f.write(writeword)\n f.close()\n\n def fileread(self, fpath, filename):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'r', encoding='utf-8') as f:\n try:\n all_the_text = f.read()\n finally:\n f.close()\n return (all_the_text)\n\n# class Fileoperation:\n# def __init__(self):\n# self.case_path = sys.path[0]\n# def filewrite(self,filename,writeword):\n# filepath = os.path.join(self.case_path, filename)\n# with open(filepath, 'w', encoding='utf-8') as f:\n# f.write(writeword)\n# f.close()\n# def fileread(self,filename):\n# filepath = os.path.join(self.case_path,filename)\n# with open(filepath, 'r', encoding='utf-8') as f:\n# try:\n# all_the_text = f.read()\n# finally:\n# f.close()\n# return(all_the_text)\n",
"import sys, os\n\n\nclass Fileoperation:\n\n def filewrite(self, fpath, filename, writeword):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'w', encoding='utf-8') as f:\n f.write(writeword)\n f.close()\n\n def fileread(self, fpath, filename):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'r', encoding='utf-8') as f:\n try:\n all_the_text = f.read()\n finally:\n f.close()\n return all_the_text\n",
"<import token>\n\n\nclass Fileoperation:\n\n def filewrite(self, fpath, filename, writeword):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'w', encoding='utf-8') as f:\n f.write(writeword)\n f.close()\n\n def fileread(self, fpath, filename):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'r', encoding='utf-8') as f:\n try:\n all_the_text = f.read()\n finally:\n f.close()\n return all_the_text\n",
"<import token>\n\n\nclass Fileoperation:\n <function token>\n\n def fileread(self, fpath, filename):\n filepath = os.path.join(fpath, filename)\n with open(filepath, 'r', encoding='utf-8') as f:\n try:\n all_the_text = f.read()\n finally:\n f.close()\n return all_the_text\n",
"<import token>\n\n\nclass Fileoperation:\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,465 |
68e6435bc1b03f373871c41e20488533293e57d5
|
#-------------------------------------------------------
#Description : while loop
#syntax :
# while(condition):
# statements;
#About : Iterating through a given condition
#-------------------------------------------------------
print("-------------Iterating through given condition----------------");
i = 7
while i!=12:
print(i);
i += 1;
#-------------------------------------------------------
#Description : while loop
#syntax :
# while(condition):
# statements;
#About : Iterating through a list
#-------------------------------------------------------
print("-------------Iterating through a list----------------");
j=0;
teams = ['RCB','MI','KXIP','RR','CSK'];
while j!=len(teams):
print(teams[j]);
j += 1;
|
[
"#-------------------------------------------------------\n#Description : while loop\n#syntax : \n# while(condition):\n# statements;\n#About : Iterating through a given condition\n#-------------------------------------------------------\n\nprint(\"-------------Iterating through given condition----------------\");\ni = 7\nwhile i!=12:\n print(i);\n i += 1; \n\n#-------------------------------------------------------\n#Description : while loop\n#syntax : \n# while(condition):\n# statements;\n#About : Iterating through a list\n#-------------------------------------------------------\n\nprint(\"-------------Iterating through a list----------------\");\nj=0;\nteams = ['RCB','MI','KXIP','RR','CSK'];\nwhile j!=len(teams):\n print(teams[j]);\n j += 1;\n\n",
"print('-------------Iterating through given condition----------------')\ni = 7\nwhile i != 12:\n print(i)\n i += 1\nprint('-------------Iterating through a list----------------')\nj = 0\nteams = ['RCB', 'MI', 'KXIP', 'RR', 'CSK']\nwhile j != len(teams):\n print(teams[j])\n j += 1\n",
"print('-------------Iterating through given condition----------------')\n<assignment token>\nwhile i != 12:\n print(i)\n i += 1\nprint('-------------Iterating through a list----------------')\n<assignment token>\nwhile j != len(teams):\n print(teams[j])\n j += 1\n",
"<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,466 |
7b26eb93e8da779c61ae90a88b453e0ff250c639
|
from django.db import models
from fs_user.models import UserInfo
from fs_goods.models import GoodsInfo
class CartModel(models.Model):
"""
购物车商品记录表:
用户和购物车记录数据：一对多关系，同一个用户可以添加多个商品到记录表中；
商品和购物车记录数据：一对多关系，不同的用户可以添加相同的产品；
"""
user = models.ForeignKey(UserInfo, on_delete=models.CASCADE)
good = models.ForeignKey(GoodsInfo, on_delete=models.CASCADE)
count = models.IntegerField(default=1)
|
[
"from django.db import models\nfrom fs_user.models import UserInfo\nfrom fs_goods.models import GoodsInfo\n\n\nclass CartModel(models.Model):\n \"\"\"\n 购物车商品记录表:\n 用户和购物车记录数据：一对多关系，同一个用户可以添加多个商品到记录表中；\n 商品和购物车记录数据：一对多关系，不同的用户可以添加相同的产品；\n \"\"\"\n user = models.ForeignKey(UserInfo, on_delete=models.CASCADE)\n good = models.ForeignKey(GoodsInfo, on_delete=models.CASCADE)\n count = models.IntegerField(default=1)\n",
"<import token>\n\n\nclass CartModel(models.Model):\n \"\"\"\n 购物车商品记录表:\n 用户和购物车记录数据：一对多关系，同一个用户可以添加多个商品到记录表中；\n 商品和购物车记录数据：一对多关系，不同的用户可以添加相同的产品；\n \"\"\"\n user = models.ForeignKey(UserInfo, on_delete=models.CASCADE)\n good = models.ForeignKey(GoodsInfo, on_delete=models.CASCADE)\n count = models.IntegerField(default=1)\n",
"<import token>\n\n\nclass CartModel(models.Model):\n <docstring token>\n user = models.ForeignKey(UserInfo, on_delete=models.CASCADE)\n good = models.ForeignKey(GoodsInfo, on_delete=models.CASCADE)\n count = models.IntegerField(default=1)\n",
"<import token>\n\n\nclass CartModel(models.Model):\n <docstring token>\n <assignment token>\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,467 |
eb5f4c316ee444491f34525817ff88f047027322
|
from typing import Optional
import unittest
from ..layout import (
match_layouts_to_screens,
ScreenTileLayout, VirtualScreenArea,
)
class MatchLayoutsTest(unittest.TestCase):
def test_match_one_exact(self) -> None:
layout1 = mk_screen_tile_layout('a', 100, 200)
screen1 = mk_virtual_screen_area('1', 100, 200)
mpack, errs = match_layouts_to_screens(
[[layout1]],
[screen1]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout1, screen1,),)
)
def test_match_two_distant_layouts_one_screen(self) -> None:
layout1 = mk_screen_tile_layout('a', 100, 200)
layout2 = mk_screen_tile_layout('b', 200, 100)
screen1 = mk_virtual_screen_area('1', 100, 200)
mpack, errs = match_layouts_to_screens(
[[layout1], [layout2]],
[screen1]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout1, screen1,),)
)
def test_match_two_screens_index_match(self) -> None:
self.maxDiff = None
layout1 = mk_screen_tile_layout('a', 100, 200)
layout2 = mk_screen_tile_layout('b', 200, 100)
screen1 = mk_virtual_screen_area('1', 100, 200)
screen2 = mk_virtual_screen_area('2', 200, 100)
mpack, errs = match_layouts_to_screens(
[[layout1, layout2], [layout2, layout1]],
[screen1, screen2]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout1, screen1,), (layout2, screen2),)
)
mpack, errs = match_layouts_to_screens(
[[layout2, layout1], [layout1, layout2]],
[screen1, screen2]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout1, screen1,), (layout2, screen2),)
)
def test_std_config(self) -> None:
screen1 = mk_virtual_screen_area('primary', 1024, 768)
layout1 = mk_screen_tile_layout(None, 0, 0, False, True)
mpack, errs = match_layouts_to_screens(
[[layout1]],
[screen1]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout1, screen1,),)
)
def test_two_layouts_one_screen_vs_two(self) -> None:
screen = mk_virtual_screen_area('primary', 1024, 768)
layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)
layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)
layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)
mpack, errs = match_layouts_to_screens(
[[layout1_screen1, layout1_screen2], [layout2_screen1]],
[screen]
)
i, m = mpack
self.assertEqual(list(errs), [])
self.assertEqual(
m,
((layout2_screen1, screen,),)
)
def mk_screen_tile_layout(
name: Optional[str], w: int, h: int, direct: bool = True, primary: bool = True
) -> ScreenTileLayout:
return ScreenTileLayout(name, direct, primary, (w, h))
def mk_virtual_screen_area(
name: str, w: int, h: int, x: int = 0, y: int = 0, primary: bool = True
) -> VirtualScreenArea:
return VirtualScreenArea(name, (x, y, w, h,), primary)
|
[
"\nfrom typing import Optional\nimport unittest\nfrom ..layout import (\n match_layouts_to_screens,\n ScreenTileLayout, VirtualScreenArea,\n)\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n def test_match_one_exact(self) -> None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens(\n [[layout1]],\n [screen1]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout1, screen1,),)\n )\n\n def test_match_two_distant_layouts_one_screen(self) -> None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens(\n [[layout1], [layout2]],\n [screen1]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout1, screen1,),)\n )\n\n def test_match_two_screens_index_match(self) -> None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens(\n [[layout1, layout2], [layout2, layout1]],\n [screen1, screen2]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout1, screen1,), (layout2, screen2),)\n )\n mpack, errs = match_layouts_to_screens(\n [[layout2, layout1], [layout1, layout2]],\n [screen1, screen2]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout1, screen1,), (layout2, screen2),)\n )\n\n def test_std_config(self) -> None:\n screen1 = mk_virtual_screen_area('primary', 1024, 768)\n layout1 = mk_screen_tile_layout(None, 0, 0, False, True)\n mpack, errs = match_layouts_to_screens(\n [[layout1]],\n [screen1]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout1, screen1,),)\n )\n\n def test_two_layouts_one_screen_vs_two(self) -> None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens(\n [[layout1_screen1, layout1_screen2], [layout2_screen1]],\n [screen]\n )\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(\n m,\n ((layout2_screen1, screen,),)\n )\n\n\ndef mk_screen_tile_layout(\n name: Optional[str], w: int, h: int, direct: bool = True, primary: bool = True\n) -> ScreenTileLayout:\n return ScreenTileLayout(name, direct, primary, (w, h))\n\n\ndef mk_virtual_screen_area(\n name: str, w: int, h: int, x: int = 0, y: int = 0, primary: bool = True\n) -> VirtualScreenArea:\n return VirtualScreenArea(name, (x, y, w, h,), primary)\n",
"from typing import Optional\nimport unittest\nfrom ..layout import match_layouts_to_screens, ScreenTileLayout, VirtualScreenArea\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n\n def test_match_one_exact(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n\n def test_std_config(self) ->None:\n screen1 = mk_virtual_screen_area('primary', 1024, 768)\n layout1 = mk_screen_tile_layout(None, 0, 0, False, True)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\ndef mk_screen_tile_layout(name: Optional[str], w: int, h: int, direct: bool\n =True, primary: bool=True) ->ScreenTileLayout:\n return ScreenTileLayout(name, direct, primary, (w, h))\n\n\ndef mk_virtual_screen_area(name: str, w: int, h: int, x: int=0, y: int=0,\n primary: bool=True) ->VirtualScreenArea:\n return VirtualScreenArea(name, (x, y, w, h), primary)\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n\n def test_match_one_exact(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n\n def test_std_config(self) ->None:\n screen1 = mk_virtual_screen_area('primary', 1024, 768)\n layout1 = mk_screen_tile_layout(None, 0, 0, False, True)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\ndef mk_screen_tile_layout(name: Optional[str], w: int, h: int, direct: bool\n =True, primary: bool=True) ->ScreenTileLayout:\n return ScreenTileLayout(name, direct, primary, (w, h))\n\n\ndef mk_virtual_screen_area(name: str, w: int, h: int, x: int=0, y: int=0,\n primary: bool=True) ->VirtualScreenArea:\n return VirtualScreenArea(name, (x, y, w, h), primary)\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n\n def test_match_one_exact(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n\n def test_std_config(self) ->None:\n screen1 = mk_virtual_screen_area('primary', 1024, 768)\n layout1 = mk_screen_tile_layout(None, 0, 0, False, True)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\ndef mk_screen_tile_layout(name: Optional[str], w: int, h: int, direct: bool\n =True, primary: bool=True) ->ScreenTileLayout:\n return ScreenTileLayout(name, direct, primary, (w, h))\n\n\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n\n def test_match_one_exact(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n\n def test_std_config(self) ->None:\n screen1 = mk_virtual_screen_area('primary', 1024, 768)\n layout1 = mk_screen_tile_layout(None, 0, 0, False, True)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n\n def test_match_one_exact(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1]], [screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n <function token>\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n <function token>\n\n def test_match_two_distant_layouts_one_screen(self) ->None:\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n mpack, errs = match_layouts_to_screens([[layout1], [layout2]], [\n screen1])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1),))\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n <function token>\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n <function token>\n <function token>\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n <function token>\n\n def test_two_layouts_one_screen_vs_two(self) ->None:\n screen = mk_virtual_screen_area('primary', 1024, 768)\n layout1_screen1 = mk_screen_tile_layout(None, 2440, 1980, False, True)\n layout1_screen2 = mk_screen_tile_layout(None, 1080, 1920, False, False)\n layout2_screen1 = mk_screen_tile_layout(None, 1920, 1080, False, True)\n mpack, errs = match_layouts_to_screens([[layout1_screen1,\n layout1_screen2], [layout2_screen1]], [screen])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout2_screen1, screen),))\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n <function token>\n <function token>\n\n def test_match_two_screens_index_match(self) ->None:\n self.maxDiff = None\n layout1 = mk_screen_tile_layout('a', 100, 200)\n layout2 = mk_screen_tile_layout('b', 200, 100)\n screen1 = mk_virtual_screen_area('1', 100, 200)\n screen2 = mk_virtual_screen_area('2', 200, 100)\n mpack, errs = match_layouts_to_screens([[layout1, layout2], [\n layout2, layout1]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n mpack, errs = match_layouts_to_screens([[layout2, layout1], [\n layout1, layout2]], [screen1, screen2])\n i, m = mpack\n self.assertEqual(list(errs), [])\n self.assertEqual(m, ((layout1, screen1), (layout2, screen2)))\n <function token>\n <function token>\n\n\n<function token>\n<function token>\n",
"<import token>\n\n\nclass MatchLayoutsTest(unittest.TestCase):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<function token>\n<function token>\n",
"<import token>\n<class token>\n<function token>\n<function token>\n"
] | false |
99,468 |
3eb270da7a115046266d9565a5d980cf8ae62842
|
#NOME: LEONARDO ANDRADE
#DATA: 23/04/2019
#DESCRIÇAO:
print('Entre com o valor do salário bruto', end = ': ')
salBruto = float(input())
print('Entre com o desconto do Imposto de Renda (porcentagem)', end = ': ')
valor_IR = float(input())
print('Entre com o desconto do INSS', end = ': ')
valor__INSS = float(input())
print('Entre com o valor do Auxilio Moradia', end = ': ')
moradiaValue = float(input())
print ('Entre com o valor do Auxílio Alimentação', end = ': ')
alimentValue = float(input())
print()
print ('*********************************************************')
print ('Salario Bruto = ',salBruto)
print ('*********************************************************')
print('DESCONTOS')
if salBruto < 2500 :
desc_IR = 0
elif 6500 > salBruto >= 2500 :
desc_IR = (18 * salBruto) / 100
elif salBruto >= 6500 :
desc_IR = (27.5 * salBruto) / 100
print('Imposto de Renda = ',desc_IR)
desc_INSS = (11 * salBruto) / 100
if 600 >= desc_INSS :
print('Previdencia Social = ',desc_INSS)
else:
print('Previdencia Social = ', 600)
print()
print('Auxílios')
print('Moradia = ',moradiaValue)
print('Alimetação = ',alimentValue)
print('**********************************************************')
if 600 >= desc_INSS :
salLiquido = salBruto - desc_IR - desc_INSS + moradiaValue + alimentValue
else :
salLiquido = salBruto -desc_IR - 600 + moradiaValue + alimentValue
print('Salário Líquido = ', salLiquido)
|
[
"#NOME: LEONARDO ANDRADE\n#DATA: 23/04/2019\n#DESCRIÇAO: \n\nprint('Entre com o valor do salário bruto', end = ': ')\nsalBruto = float(input())\n\nprint('Entre com o desconto do Imposto de Renda (porcentagem)', end = ': ')\nvalor_IR = float(input())\n\nprint('Entre com o desconto do INSS', end = ': ')\nvalor__INSS = float(input())\n\nprint('Entre com o valor do Auxilio Moradia', end = ': ')\nmoradiaValue = float(input())\n\nprint ('Entre com o valor do Auxílio Alimentação', end = ': ')\nalimentValue = float(input())\n\nprint()\n\nprint ('*********************************************************')\n\nprint ('Salario Bruto = ',salBruto)\n\nprint ('*********************************************************')\n\nprint('DESCONTOS')\n\nif salBruto < 2500 :\n desc_IR = 0\n\nelif 6500 > salBruto >= 2500 :\n desc_IR = (18 * salBruto) / 100\n\nelif salBruto >= 6500 :\n desc_IR = (27.5 * salBruto) / 100\n\nprint('Imposto de Renda = ',desc_IR)\n\ndesc_INSS = (11 * salBruto) / 100\n\nif 600 >= desc_INSS :\n print('Previdencia Social = ',desc_INSS)\n\nelse:\n print('Previdencia Social = ', 600)\n\nprint()\n\nprint('Auxílios')\nprint('Moradia = ',moradiaValue)\nprint('Alimetação = ',alimentValue)\n\nprint('**********************************************************')\n\nif 600 >= desc_INSS : \n salLiquido = salBruto - desc_IR - desc_INSS + moradiaValue + alimentValue\n\nelse :\n salLiquido = salBruto -desc_IR - 600 + moradiaValue + alimentValue\n\nprint('Salário Líquido = ', salLiquido)\n",
"print('Entre com o valor do salário bruto', end=': ')\nsalBruto = float(input())\nprint('Entre com o desconto do Imposto de Renda (porcentagem)', end=': ')\nvalor_IR = float(input())\nprint('Entre com o desconto do INSS', end=': ')\nvalor__INSS = float(input())\nprint('Entre com o valor do Auxilio Moradia', end=': ')\nmoradiaValue = float(input())\nprint('Entre com o valor do Auxílio Alimentação', end=': ')\nalimentValue = float(input())\nprint()\nprint('*********************************************************')\nprint('Salario Bruto = ', salBruto)\nprint('*********************************************************')\nprint('DESCONTOS')\nif salBruto < 2500:\n desc_IR = 0\nelif 6500 > salBruto >= 2500:\n desc_IR = 18 * salBruto / 100\nelif salBruto >= 6500:\n desc_IR = 27.5 * salBruto / 100\nprint('Imposto de Renda = ', desc_IR)\ndesc_INSS = 11 * salBruto / 100\nif 600 >= desc_INSS:\n print('Previdencia Social = ', desc_INSS)\nelse:\n print('Previdencia Social = ', 600)\nprint()\nprint('Auxílios')\nprint('Moradia = ', moradiaValue)\nprint('Alimetação = ', alimentValue)\nprint('**********************************************************')\nif 600 >= desc_INSS:\n salLiquido = salBruto - desc_IR - desc_INSS + moradiaValue + alimentValue\nelse:\n salLiquido = salBruto - desc_IR - 600 + moradiaValue + alimentValue\nprint('Salário Líquido = ', salLiquido)\n",
"print('Entre com o valor do salário bruto', end=': ')\n<assignment token>\nprint('Entre com o desconto do Imposto de Renda (porcentagem)', end=': ')\n<assignment token>\nprint('Entre com o desconto do INSS', end=': ')\n<assignment token>\nprint('Entre com o valor do Auxilio Moradia', end=': ')\n<assignment token>\nprint('Entre com o valor do Auxílio Alimentação', end=': ')\n<assignment token>\nprint()\nprint('*********************************************************')\nprint('Salario Bruto = ', salBruto)\nprint('*********************************************************')\nprint('DESCONTOS')\nif salBruto < 2500:\n desc_IR = 0\nelif 6500 > salBruto >= 2500:\n desc_IR = 18 * salBruto / 100\nelif salBruto >= 6500:\n desc_IR = 27.5 * salBruto / 100\nprint('Imposto de Renda = ', desc_IR)\n<assignment token>\nif 600 >= desc_INSS:\n print('Previdencia Social = ', desc_INSS)\nelse:\n print('Previdencia Social = ', 600)\nprint()\nprint('Auxílios')\nprint('Moradia = ', moradiaValue)\nprint('Alimetação = ', alimentValue)\nprint('**********************************************************')\nif 600 >= desc_INSS:\n salLiquido = salBruto - desc_IR - desc_INSS + moradiaValue + alimentValue\nelse:\n salLiquido = salBruto - desc_IR - 600 + moradiaValue + alimentValue\nprint('Salário Líquido = ', salLiquido)\n",
"<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,469 |
f00c5fc115b93f55f3e91fac20c52f180a5e7644
|
#-*-coding:utf-8-*-
#socket编程示例
import time,datetime
from selenium import webdriver
from selenium import selenium
import os
import json
from email.mime.text import MIMEText
import smtplib
from test.test_imageop import AAAAA
# wd=webdriver.Ie()
# wd.get("http://mytestlink.vicp.net:8001/redirect1.html")
# wd.execute_script("alert('呵呵')".decode('gbk'))
# wd.execute_script("if('呵呵'==='呵呵'){alert(1)}".decode('gbk'))
# wd.quit()
# print "中文123".decode('gbk')
# wd.find_element_by_css_selector("#cm").click()
#wd.switch_to_alert().accept()
#wd.find_element_by_css_selector("a").click()
# import urllib
# str = '{json_name:PythonTab中文网,json_age:22}'
# #str = str.encode('utf-8')
# d = {'name':str,'age':'"18'}
# print len(d)
# q = urllib.urlencode(d)
# print q
#
# tmp='[{"field1":"[email protected]","field2":"","field3":"","text":"吴锦涛/wujt3","type":"","value":"31535033"},{"field1":"[email protected]","field2":"","field3":"","text":"wujt3344/wujt3344","type":"","value":"35897406"}]'
# tmp1=eval(tmp)
# print tmp1[0]['text']
# import cx_Oracle
# conn=cx_Oracle.connect("base/[email protected]:9403/nctstdb")
# cursor=conn.cursor()
# sql="select user_id,cust_id from so1.ins_user_771 a where a.bill_id = '13481118910' and a.state='1'"
# cursor.execute(sql)
# r=cursor.fetchall()
# print r
print '2091912938'[-1:]
if 'xxx' not in locals().keys():
print 'hehe'
print time.strftime("%Y%m")
import traceback
import sys
import logging
logging.basicConfig(level=10,format='%(asctime)s %(levelname)s %(message)s',datefmt='%Y-%m-%d %H:%M:%S')
try:
print 1+"1"
#print 1/0
except ZeroDivisionError,e:
logging.debug('hehe')
logging.exception(e)
logging.error('haha')
#print e
#traceback.print_exc(file=sys.stdout)
print 'start next'
except TypeError:
print 'type error'
print '==='
print map(lambda x: x[10:14], ['AT_SCRIPT_0003.py'])
for i in range(0,1):
print 'iiii'
import random
print time.strftime("%Y%m%d")
print str(random.randint(100, 200))
print '新增成功！集团号为：7717149428'[-10:]
a='''pass|
err|
pass|
'''
print a.count('\n')
|
[
"#-*-coding:utf-8-*-\r\n#socket编程示例\r\n\r\n \r\nimport time,datetime\r\nfrom selenium import webdriver\r\nfrom selenium import selenium\r\nimport os\r\nimport json\r\n\r\nfrom email.mime.text import MIMEText\r\nimport smtplib\r\nfrom test.test_imageop import AAAAA\r\n\r\n \r\n# wd=webdriver.Ie()\r\n# wd.get(\"http://mytestlink.vicp.net:8001/redirect1.html\")\r\n# wd.execute_script(\"alert('呵呵')\".decode('gbk'))\r\n# wd.execute_script(\"if('呵呵'==='呵呵'){alert(1)}\".decode('gbk'))\r\n# wd.quit()\r\n# print \"中文123\".decode('gbk')\r\n# wd.find_element_by_css_selector(\"#cm\").click()\r\n\r\n#wd.switch_to_alert().accept()\r\n#wd.find_element_by_css_selector(\"a\").click()\r\n\r\n\r\n\r\n# import urllib\r\n# str = '{json_name:PythonTab中文网,json_age:22}'\r\n# #str = str.encode('utf-8')\r\n# d = {'name':str,'age':'\"18'}\r\n# print len(d)\r\n# q = urllib.urlencode(d)\r\n# print q\r\n# \r\n# tmp='[{\"field1\":\"[email protected]\",\"field2\":\"\",\"field3\":\"\",\"text\":\"吴锦涛/wujt3\",\"type\":\"\",\"value\":\"31535033\"},{\"field1\":\"[email protected]\",\"field2\":\"\",\"field3\":\"\",\"text\":\"wujt3344/wujt3344\",\"type\":\"\",\"value\":\"35897406\"}]'\r\n# tmp1=eval(tmp)\r\n# print tmp1[0]['text']\r\n\r\n# import cx_Oracle\r\n# conn=cx_Oracle.connect(\"base/[email protected]:9403/nctstdb\")\r\n# cursor=conn.cursor()\r\n# sql=\"select user_id,cust_id from so1.ins_user_771 a where a.bill_id = '13481118910' and a.state='1'\"\r\n# cursor.execute(sql)\r\n# r=cursor.fetchall()\r\n# print r\r\nprint '2091912938'[-1:]\r\nif 'xxx' not in locals().keys():\r\n print 'hehe'\r\nprint time.strftime(\"%Y%m\")\r\nimport traceback\r\nimport sys\r\nimport logging\r\nlogging.basicConfig(level=10,format='%(asctime)s %(levelname)s %(message)s',datefmt='%Y-%m-%d %H:%M:%S')\r\ntry:\r\n print 1+\"1\"\r\n #print 1/0\r\nexcept ZeroDivisionError,e:\r\n logging.debug('hehe')\r\n logging.exception(e)\r\n logging.error('haha')\r\n #print e\r\n #traceback.print_exc(file=sys.stdout)\r\n print 'start next'\r\nexcept TypeError:\r\n print 'type error'\r\nprint '==='\r\n\r\nprint map(lambda x: x[10:14], ['AT_SCRIPT_0003.py'])\r\nfor i in range(0,1):\r\n print 'iiii'\r\n\r\nimport random\r\nprint time.strftime(\"%Y%m%d\")\r\nprint str(random.randint(100, 200))\r\n\r\nprint '新增成功！集团号为：7717149428'[-10:]\r\n\r\na='''pass|\r\nerr|\r\npass|\r\n'''\r\nprint a.count('\\n')\r\n\r\n"
] | true |
99,470 |
f4e5dcc404212a371b5e7ed5146e42782364b4df
|
from django.db import models
from django.core.urlresolvers import reverse
from redactor.fields import RedactorField
from django.utils.html import strip_tags
class Page(models.Model):
title = models.CharField(max_length=255, unique=True)
subtitle = models.CharField(max_length=255, null=True, blank=True)
slug = models.SlugField(max_length=255, unique=True)
order = models.IntegerField(unique=True)
content = RedactorField(verbose_name=u'Text')
published = models.BooleanField(default=True)
class Meta:
ordering = ['order']
def get_absolute_url(self):
return reverse('blog.views.page',args=[self.slug])
def __unicode__(self):
return u'%s' % self.title
class Tag(models.Model):
name = models.CharField(max_length=255, unique=True)
slug = models.SlugField(max_length=255, unique=True)
class Meta:
ordering = ['name']
def __str__(self):
return self.name
def get_absolute_url(self):
return reverse("blog.views.tag",args=[self.slug])
class Post(models.Model):
title = models.CharField(max_length=255)
slug = models.SlugField(unique=True,
max_length=255)
description = models.CharField(max_length=255, null=True, blank=True,help_text="Leave blank for auto-fill")
author = models.CharField(max_length=255,default="Honestly Curated")
content = RedactorField(verbose_name=u'Text')
published = models.BooleanField(default=True)
created = models.DateTimeField(auto_now_add=True)
tag = models.ManyToManyField(Tag, related_name="posts", related_query_name="post", blank=True)
class Meta:
ordering = ['-created']
def __unicode__(self):
return u'%s' % self.title
def get_absolute_url(self):
return reverse('blog.views.post',args=[self.slug])
def save(self, *args, **kwargs):
if self.content and (self.description is None or self.description == ""):
suffix = "..."
length = 100
content = strip_tags(self.content)
self.description = content if len(content) <= length else content[:length-len(suffix)].rsplit(' ', 1)[0] + suffix
super(Post, self).save(*args, **kwargs)
|
[
"from django.db import models\nfrom django.core.urlresolvers import reverse\nfrom redactor.fields import RedactorField\nfrom django.utils.html import strip_tags\n\nclass Page(models.Model):\n title = models.CharField(max_length=255, unique=True)\n subtitle = models.CharField(max_length=255, null=True, blank=True)\n slug = models.SlugField(max_length=255, unique=True)\n order = models.IntegerField(unique=True)\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n \n class Meta:\n ordering = ['order']\n \n def get_absolute_url(self):\n return reverse('blog.views.page',args=[self.slug])\n \n def __unicode__(self):\n return u'%s' % self.title\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n class Meta:\n ordering = ['name']\n \n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse(\"blog.views.tag\",args=[self.slug])\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True,\n max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,help_text=\"Leave blank for auto-fill\")\n author = models.CharField(max_length=255,default=\"Honestly Curated\")\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name=\"posts\", related_query_name=\"post\", blank=True)\n \n class Meta:\n ordering = ['-created']\n \n def __unicode__(self):\n return u'%s' % self.title\n \n def get_absolute_url(self):\n return reverse('blog.views.post',args=[self.slug])\n \n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == \"\"):\n suffix = \"...\"\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:length-len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"from django.db import models\nfrom django.core.urlresolvers import reverse\nfrom redactor.fields import RedactorField\nfrom django.utils.html import strip_tags\n\n\nclass Page(models.Model):\n title = models.CharField(max_length=255, unique=True)\n subtitle = models.CharField(max_length=255, null=True, blank=True)\n slug = models.SlugField(max_length=255, unique=True)\n order = models.IntegerField(unique=True)\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n\n\n class Meta:\n ordering = ['order']\n\n def get_absolute_url(self):\n return reverse('blog.views.page', args=[self.slug])\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n\n\nclass Page(models.Model):\n title = models.CharField(max_length=255, unique=True)\n subtitle = models.CharField(max_length=255, null=True, blank=True)\n slug = models.SlugField(max_length=255, unique=True)\n order = models.IntegerField(unique=True)\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n\n\n class Meta:\n ordering = ['order']\n\n def get_absolute_url(self):\n return reverse('blog.views.page', args=[self.slug])\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n\n\nclass Page(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['order']\n\n def get_absolute_url(self):\n return reverse('blog.views.page', args=[self.slug])\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n\n\nclass Page(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['order']\n <function token>\n\n def __unicode__(self):\n return u'%s' % self.title\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n\n\nclass Page(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['order']\n <function token>\n <function token>\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n\n\nclass Tag(models.Model):\n name = models.CharField(max_length=255, unique=True)\n slug = models.SlugField(max_length=255, unique=True)\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n\n\nclass Tag(models.Model):\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n\n def get_absolute_url(self):\n return reverse('blog.views.tag', args=[self.slug])\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n\n\nclass Tag(models.Model):\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name\n <function token>\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n\n\nclass Tag(models.Model):\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['name']\n <function token>\n <function token>\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n<class token>\n\n\nclass Post(models.Model):\n title = models.CharField(max_length=255)\n slug = models.SlugField(unique=True, max_length=255)\n description = models.CharField(max_length=255, null=True, blank=True,\n help_text='Leave blank for auto-fill')\n author = models.CharField(max_length=255, default='Honestly Curated')\n content = RedactorField(verbose_name=u'Text')\n published = models.BooleanField(default=True)\n created = models.DateTimeField(auto_now_add=True)\n tag = models.ManyToManyField(Tag, related_name='posts',\n related_query_name='post', blank=True)\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n<class token>\n\n\nclass Post(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n\n def save(self, *args, **kwargs):\n if self.content and (self.description is None or self.description == ''\n ):\n suffix = '...'\n length = 100\n content = strip_tags(self.content)\n self.description = content if len(content) <= length else content[:\n length - len(suffix)].rsplit(' ', 1)[0] + suffix\n super(Post, self).save(*args, **kwargs)\n",
"<import token>\n<class token>\n<class token>\n\n\nclass Post(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n\n def get_absolute_url(self):\n return reverse('blog.views.post', args=[self.slug])\n <function token>\n",
"<import token>\n<class token>\n<class token>\n\n\nclass Post(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['-created']\n\n def __unicode__(self):\n return u'%s' % self.title\n <function token>\n <function token>\n",
"<import token>\n<class token>\n<class token>\n\n\nclass Post(models.Model):\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n <assignment token>\n\n\n class Meta:\n ordering = ['-created']\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<class token>\n<class token>\n<class token>\n"
] | false |
99,471 |
3810d0ba664e77d03c2c8e5d2c289333b4060d24
|
import numpy as np
import torch
from scipy import stats as stats
from sklearn.metrics import f1_score
from torch import nn as nn
from my_functions import precision_k, print_num_on_tqdm, tqdm_with_num
def training(params, model, train_loader, optimizer):
device = params["device"]
batch_total = params["train_batch_total"]
loss_func = nn.BCELoss()
model.train()
losses = []
# Show loss with tqdm
with tqdm_with_num(train_loader, batch_total) as loader:
loader.set_description("Training ")
# Batch Loop
for idx, batch in enumerate(loader):
# ---------------------- Main Process -----------------------
data, target = (batch.text.to(device), batch.label.to(device))
optimizer.zero_grad()
outputs = model(data)
outputs = torch.sigmoid(outputs)
loss = loss_func(outputs, target)
loss.backward()
optimizer.step()
# -----------------------------------------------------------
# Print training progress
losses.append(loss.item())
if idx < batch_total - 1:
print_num_on_tqdm(loader, loss)
else:
loss_epoch = np.mean(losses)
print_num_on_tqdm(loader, loss_epoch, last=True)
def validating_testing(params, model, data_loader, is_valid=True):
device = params["device"]
measure = params["measure"]
doc_key = is_valid and "valid" or "test"
batch_total = params[doc_key + "_batch_total"]
model.eval()
eval_epoch = 0.0
target_all = np.empty((0, params["num_of_class"]), dtype=np.int8)
eval_all = np.empty((0, params["num_of_class"]), dtype=np.float32)
# Show p@k with tqdm
with tqdm_with_num(data_loader, batch_total) as loader:
# Set description to tqdm
is_valid and loader.set_description("Validating")
is_valid or loader.set_description("Testing ")
with torch.no_grad():
# Batch Loop
for idx, batch in enumerate(loader):
# ---------------------- Main Process -----------------------
data, target = (batch.text.to(device), batch.label.to("cpu"))
target = target.detach().numpy().copy()
outputs = model(data)
outputs = torch.sigmoid(outputs)
# -----------------------------------------------------------
# Print some progress
outputs = outputs.to("cpu").detach().numpy().copy()
if "f1" in measure:
outputs = outputs >= 0.5
target_all = np.concatenate([target_all, target])
eval_all = np.concatenate([eval_all, outputs])
if idx < batch_total - 1:
if "f1" in measure:
avg = measure[:-3]
eval_batch = f1_score(target, outputs, average=avg)
else:
k = int(measure[-1])
eval_batch = precision_k(target, outputs, k)
print_num_on_tqdm(loader, eval_batch, measure)
else:
if "f1" in measure:
avg = measure[:-3]
eval_epoch = f1_score(target_all, eval_all, average=avg)
else:
k = int(measure[-1])
eval_epoch = precision_k(target_all, eval_all, k)
print_num_on_tqdm(loader, eval_epoch, measure, True)
return eval_epoch
def custom_evaluation(params, model, data_loader, is_valid=True):
device = params["device"]
measure = params["measure"]
doc_key = is_valid and "valid" or "test"
batch_total = params[doc_key + "_batch_total"]
model.eval()
eval_epoch = 0.0
target_all = np.empty((0, params["num_of_class"]), dtype=np.int8)
eval_all = np.empty((0, params["num_of_class"]), dtype=np.float32)
sum_recall_1 = 0
sum_recall_2 = 0
sum_recall_3 = 0
sum_precision_1 = 0
sum_precision_2 = 0
sum_precision_3 = 0
num_test_data = 0
total_labels = 0
# Show p@k with tqdm
with tqdm_with_num(data_loader, batch_total) as loader:
# Set description to tqdm
is_valid and loader.set_description("Validating")
is_valid or loader.set_description("Testing ")
with torch.no_grad():
# Batch Loop
for idx, batch in enumerate(loader):
# ---------------------- Main Process -----------------------
data, target = (batch.text.to(device), batch.label.to("cpu"))
target = target.detach().numpy().copy()
outputs = model(data)
outputs = torch.sigmoid(outputs)
# -----------------------------------------------------------
# Print some progress
outputs = outputs.to("cpu").detach().numpy().copy()
num_test_data += len(outputs)
# loop through current batch
for i in range(len(outputs)):
actual_labels = np.nonzero(target[i])[0]
predicted_labels = np.argsort(outputs[i])[-3:][::-1]
correct_prediction = 0
if predicted_labels[0] in actual_labels:
correct_prediction += 1
sum_precision_1 += (correct_prediction / 1)
sum_recall_1 += (correct_prediction / len(actual_labels))
# K = 2
if predicted_labels[1] in actual_labels:
correct_prediction += 1
sum_precision_2 += (correct_prediction / 2)
sum_recall_2 += (correct_prediction / len(actual_labels))
# K = 3
if predicted_labels[2] in actual_labels:
correct_prediction += 1
sum_precision_3 += (correct_prediction / 3)
sum_recall_3 += (correct_prediction / len(actual_labels))
precision_1 = sum_precision_1 / num_test_data
precision_2 = sum_precision_2 / num_test_data
precision_3 = sum_precision_3 / num_test_data
recall_1 = sum_recall_1 / num_test_data
recall_2 = sum_recall_2 / num_test_data
recall_3 = sum_recall_3 / num_test_data
f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)
f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)
f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)
print("K = 1")
print("P@1 = " + precision_1.__str__())
print("R@1 = " + recall_1.__str__())
print("F@1 = " + f1_1.__str__())
print("K = 2")
print("P@2 = " + precision_2.__str__())
print("R@2 = " + recall_2.__str__())
print("F@2 = " + f1_2.__str__())
print("K = 3")
print("P@3 = " + precision_3.__str__())
print("R@3 = " + recall_3.__str__())
print("F@3 = " + f1_3.__str__())
return eval_epoch
|
[
"import numpy as np\r\nimport torch\r\nfrom scipy import stats as stats\r\nfrom sklearn.metrics import f1_score\r\nfrom torch import nn as nn\r\n\r\nfrom my_functions import precision_k, print_num_on_tqdm, tqdm_with_num\r\n\r\n\r\ndef training(params, model, train_loader, optimizer):\r\n device = params[\"device\"]\r\n batch_total = params[\"train_batch_total\"]\r\n loss_func = nn.BCELoss()\r\n\r\n model.train()\r\n losses = []\r\n\r\n # Show loss with tqdm\r\n with tqdm_with_num(train_loader, batch_total) as loader:\r\n loader.set_description(\"Training \")\r\n\r\n # Batch Loop\r\n for idx, batch in enumerate(loader):\r\n # ---------------------- Main Process -----------------------\r\n data, target = (batch.text.to(device), batch.label.to(device))\r\n\r\n optimizer.zero_grad()\r\n\r\n outputs = model(data)\r\n outputs = torch.sigmoid(outputs)\r\n loss = loss_func(outputs, target)\r\n\r\n loss.backward()\r\n optimizer.step()\r\n # -----------------------------------------------------------\r\n\r\n # Print training progress\r\n losses.append(loss.item())\r\n\r\n if idx < batch_total - 1:\r\n print_num_on_tqdm(loader, loss)\r\n else:\r\n loss_epoch = np.mean(losses)\r\n print_num_on_tqdm(loader, loss_epoch, last=True)\r\n\r\n\r\ndef validating_testing(params, model, data_loader, is_valid=True):\r\n device = params[\"device\"]\r\n measure = params[\"measure\"]\r\n doc_key = is_valid and \"valid\" or \"test\"\r\n batch_total = params[doc_key + \"_batch_total\"]\r\n\r\n model.eval()\r\n\r\n eval_epoch = 0.0\r\n target_all = np.empty((0, params[\"num_of_class\"]), dtype=np.int8)\r\n eval_all = np.empty((0, params[\"num_of_class\"]), dtype=np.float32)\r\n\r\n # Show p@k with tqdm\r\n with tqdm_with_num(data_loader, batch_total) as loader:\r\n # Set description to tqdm\r\n is_valid and loader.set_description(\"Validating\")\r\n is_valid or loader.set_description(\"Testing \")\r\n\r\n with torch.no_grad():\r\n # Batch Loop\r\n for idx, batch in enumerate(loader):\r\n # ---------------------- Main Process -----------------------\r\n data, target = (batch.text.to(device), batch.label.to(\"cpu\"))\r\n target = target.detach().numpy().copy()\r\n\r\n outputs = model(data)\r\n outputs = torch.sigmoid(outputs)\r\n # -----------------------------------------------------------\r\n\r\n # Print some progress\r\n outputs = outputs.to(\"cpu\").detach().numpy().copy()\r\n\r\n\r\n if \"f1\" in measure:\r\n outputs = outputs >= 0.5\r\n\r\n target_all = np.concatenate([target_all, target])\r\n eval_all = np.concatenate([eval_all, outputs])\r\n if idx < batch_total - 1:\r\n if \"f1\" in measure:\r\n avg = measure[:-3]\r\n eval_batch = f1_score(target, outputs, average=avg)\r\n else:\r\n k = int(measure[-1])\r\n eval_batch = precision_k(target, outputs, k)\r\n print_num_on_tqdm(loader, eval_batch, measure)\r\n else:\r\n if \"f1\" in measure:\r\n avg = measure[:-3]\r\n eval_epoch = f1_score(target_all, eval_all, average=avg)\r\n else:\r\n k = int(measure[-1])\r\n eval_epoch = precision_k(target_all, eval_all, k)\r\n print_num_on_tqdm(loader, eval_epoch, measure, True)\r\n\r\n return eval_epoch\r\n\r\n\r\n\r\ndef custom_evaluation(params, model, data_loader, is_valid=True):\r\n device = params[\"device\"]\r\n measure = params[\"measure\"]\r\n doc_key = is_valid and \"valid\" or \"test\"\r\n batch_total = params[doc_key + \"_batch_total\"]\r\n\r\n model.eval()\r\n\r\n eval_epoch = 0.0\r\n target_all = np.empty((0, params[\"num_of_class\"]), dtype=np.int8)\r\n eval_all = np.empty((0, params[\"num_of_class\"]), dtype=np.float32)\r\n\r\n sum_recall_1 = 0\r\n sum_recall_2 = 0\r\n sum_recall_3 = 0\r\n sum_precision_1 = 0\r\n sum_precision_2 = 0\r\n sum_precision_3 = 0\r\n num_test_data = 0\r\n total_labels = 0\r\n\r\n # Show p@k with tqdm\r\n with tqdm_with_num(data_loader, batch_total) as loader:\r\n # Set description to tqdm\r\n is_valid and loader.set_description(\"Validating\")\r\n is_valid or loader.set_description(\"Testing \")\r\n\r\n with torch.no_grad():\r\n # Batch Loop\r\n for idx, batch in enumerate(loader):\r\n # ---------------------- Main Process -----------------------\r\n data, target = (batch.text.to(device), batch.label.to(\"cpu\"))\r\n target = target.detach().numpy().copy()\r\n\r\n outputs = model(data)\r\n outputs = torch.sigmoid(outputs)\r\n # -----------------------------------------------------------\r\n\r\n # Print some progress\r\n outputs = outputs.to(\"cpu\").detach().numpy().copy()\r\n\r\n num_test_data += len(outputs)\r\n # loop through current batch\r\n for i in range(len(outputs)):\r\n actual_labels = np.nonzero(target[i])[0]\r\n predicted_labels = np.argsort(outputs[i])[-3:][::-1]\r\n\r\n correct_prediction = 0\r\n if predicted_labels[0] in actual_labels:\r\n correct_prediction += 1\r\n sum_precision_1 += (correct_prediction / 1)\r\n sum_recall_1 += (correct_prediction / len(actual_labels))\r\n\r\n # K = 2\r\n if predicted_labels[1] in actual_labels:\r\n correct_prediction += 1\r\n sum_precision_2 += (correct_prediction / 2)\r\n sum_recall_2 += (correct_prediction / len(actual_labels))\r\n\r\n # K = 3\r\n if predicted_labels[2] in actual_labels:\r\n correct_prediction += 1\r\n sum_precision_3 += (correct_prediction / 3)\r\n sum_recall_3 += (correct_prediction / len(actual_labels))\r\n\r\n precision_1 = sum_precision_1 / num_test_data\r\n precision_2 = sum_precision_2 / num_test_data\r\n precision_3 = sum_precision_3 / num_test_data\r\n recall_1 = sum_recall_1 / num_test_data\r\n recall_2 = sum_recall_2 / num_test_data\r\n recall_3 = sum_recall_3 / num_test_data\r\n f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)\r\n f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)\r\n f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)\r\n\r\n print(\"K = 1\")\r\n print(\"P@1 = \" + precision_1.__str__())\r\n print(\"R@1 = \" + recall_1.__str__())\r\n print(\"F@1 = \" + f1_1.__str__())\r\n\r\n print(\"K = 2\")\r\n print(\"P@2 = \" + precision_2.__str__())\r\n print(\"R@2 = \" + recall_2.__str__())\r\n print(\"F@2 = \" + f1_2.__str__())\r\n\r\n print(\"K = 3\")\r\n print(\"P@3 = \" + precision_3.__str__())\r\n print(\"R@3 = \" + recall_3.__str__())\r\n print(\"F@3 = \" + f1_3.__str__())\r\n\r\n return eval_epoch\r\n",
"import numpy as np\nimport torch\nfrom scipy import stats as stats\nfrom sklearn.metrics import f1_score\nfrom torch import nn as nn\nfrom my_functions import precision_k, print_num_on_tqdm, tqdm_with_num\n\n\ndef training(params, model, train_loader, optimizer):\n device = params['device']\n batch_total = params['train_batch_total']\n loss_func = nn.BCELoss()\n model.train()\n losses = []\n with tqdm_with_num(train_loader, batch_total) as loader:\n loader.set_description('Training ')\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to(device)\n optimizer.zero_grad()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n loss = loss_func(outputs, target)\n loss.backward()\n optimizer.step()\n losses.append(loss.item())\n if idx < batch_total - 1:\n print_num_on_tqdm(loader, loss)\n else:\n loss_epoch = np.mean(losses)\n print_num_on_tqdm(loader, loss_epoch, last=True)\n\n\ndef validating_testing(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n if 'f1' in measure:\n outputs = outputs >= 0.5\n target_all = np.concatenate([target_all, target])\n eval_all = np.concatenate([eval_all, outputs])\n if idx < batch_total - 1:\n if 'f1' in measure:\n avg = measure[:-3]\n eval_batch = f1_score(target, outputs, average=avg)\n else:\n k = int(measure[-1])\n eval_batch = precision_k(target, outputs, k)\n print_num_on_tqdm(loader, eval_batch, measure)\n else:\n if 'f1' in measure:\n avg = measure[:-3]\n eval_epoch = f1_score(target_all, eval_all, average=avg\n )\n else:\n k = int(measure[-1])\n eval_epoch = precision_k(target_all, eval_all, k)\n print_num_on_tqdm(loader, eval_epoch, measure, True)\n return eval_epoch\n\n\ndef custom_evaluation(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n sum_recall_1 = 0\n sum_recall_2 = 0\n sum_recall_3 = 0\n sum_precision_1 = 0\n sum_precision_2 = 0\n sum_precision_3 = 0\n num_test_data = 0\n total_labels = 0\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n num_test_data += len(outputs)\n for i in range(len(outputs)):\n actual_labels = np.nonzero(target[i])[0]\n predicted_labels = np.argsort(outputs[i])[-3:][::-1]\n correct_prediction = 0\n if predicted_labels[0] in actual_labels:\n correct_prediction += 1\n sum_precision_1 += correct_prediction / 1\n sum_recall_1 += correct_prediction / len(actual_labels)\n if predicted_labels[1] in actual_labels:\n correct_prediction += 1\n sum_precision_2 += correct_prediction / 2\n sum_recall_2 += correct_prediction / len(actual_labels)\n if predicted_labels[2] in actual_labels:\n correct_prediction += 1\n sum_precision_3 += correct_prediction / 3\n sum_recall_3 += correct_prediction / len(actual_labels)\n precision_1 = sum_precision_1 / num_test_data\n precision_2 = sum_precision_2 / num_test_data\n precision_3 = sum_precision_3 / num_test_data\n recall_1 = sum_recall_1 / num_test_data\n recall_2 = sum_recall_2 / num_test_data\n recall_3 = sum_recall_3 / num_test_data\n f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)\n f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)\n f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)\n print('K = 1')\n print('P@1 = ' + precision_1.__str__())\n print('R@1 = ' + recall_1.__str__())\n print('F@1 = ' + f1_1.__str__())\n print('K = 2')\n print('P@2 = ' + precision_2.__str__())\n print('R@2 = ' + recall_2.__str__())\n print('F@2 = ' + f1_2.__str__())\n print('K = 3')\n print('P@3 = ' + precision_3.__str__())\n print('R@3 = ' + recall_3.__str__())\n print('F@3 = ' + f1_3.__str__())\n return eval_epoch\n",
"<import token>\n\n\ndef training(params, model, train_loader, optimizer):\n device = params['device']\n batch_total = params['train_batch_total']\n loss_func = nn.BCELoss()\n model.train()\n losses = []\n with tqdm_with_num(train_loader, batch_total) as loader:\n loader.set_description('Training ')\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to(device)\n optimizer.zero_grad()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n loss = loss_func(outputs, target)\n loss.backward()\n optimizer.step()\n losses.append(loss.item())\n if idx < batch_total - 1:\n print_num_on_tqdm(loader, loss)\n else:\n loss_epoch = np.mean(losses)\n print_num_on_tqdm(loader, loss_epoch, last=True)\n\n\ndef validating_testing(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n if 'f1' in measure:\n outputs = outputs >= 0.5\n target_all = np.concatenate([target_all, target])\n eval_all = np.concatenate([eval_all, outputs])\n if idx < batch_total - 1:\n if 'f1' in measure:\n avg = measure[:-3]\n eval_batch = f1_score(target, outputs, average=avg)\n else:\n k = int(measure[-1])\n eval_batch = precision_k(target, outputs, k)\n print_num_on_tqdm(loader, eval_batch, measure)\n else:\n if 'f1' in measure:\n avg = measure[:-3]\n eval_epoch = f1_score(target_all, eval_all, average=avg\n )\n else:\n k = int(measure[-1])\n eval_epoch = precision_k(target_all, eval_all, k)\n print_num_on_tqdm(loader, eval_epoch, measure, True)\n return eval_epoch\n\n\ndef custom_evaluation(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n sum_recall_1 = 0\n sum_recall_2 = 0\n sum_recall_3 = 0\n sum_precision_1 = 0\n sum_precision_2 = 0\n sum_precision_3 = 0\n num_test_data = 0\n total_labels = 0\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n num_test_data += len(outputs)\n for i in range(len(outputs)):\n actual_labels = np.nonzero(target[i])[0]\n predicted_labels = np.argsort(outputs[i])[-3:][::-1]\n correct_prediction = 0\n if predicted_labels[0] in actual_labels:\n correct_prediction += 1\n sum_precision_1 += correct_prediction / 1\n sum_recall_1 += correct_prediction / len(actual_labels)\n if predicted_labels[1] in actual_labels:\n correct_prediction += 1\n sum_precision_2 += correct_prediction / 2\n sum_recall_2 += correct_prediction / len(actual_labels)\n if predicted_labels[2] in actual_labels:\n correct_prediction += 1\n sum_precision_3 += correct_prediction / 3\n sum_recall_3 += correct_prediction / len(actual_labels)\n precision_1 = sum_precision_1 / num_test_data\n precision_2 = sum_precision_2 / num_test_data\n precision_3 = sum_precision_3 / num_test_data\n recall_1 = sum_recall_1 / num_test_data\n recall_2 = sum_recall_2 / num_test_data\n recall_3 = sum_recall_3 / num_test_data\n f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)\n f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)\n f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)\n print('K = 1')\n print('P@1 = ' + precision_1.__str__())\n print('R@1 = ' + recall_1.__str__())\n print('F@1 = ' + f1_1.__str__())\n print('K = 2')\n print('P@2 = ' + precision_2.__str__())\n print('R@2 = ' + recall_2.__str__())\n print('F@2 = ' + f1_2.__str__())\n print('K = 3')\n print('P@3 = ' + precision_3.__str__())\n print('R@3 = ' + recall_3.__str__())\n print('F@3 = ' + f1_3.__str__())\n return eval_epoch\n",
"<import token>\n\n\ndef training(params, model, train_loader, optimizer):\n device = params['device']\n batch_total = params['train_batch_total']\n loss_func = nn.BCELoss()\n model.train()\n losses = []\n with tqdm_with_num(train_loader, batch_total) as loader:\n loader.set_description('Training ')\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to(device)\n optimizer.zero_grad()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n loss = loss_func(outputs, target)\n loss.backward()\n optimizer.step()\n losses.append(loss.item())\n if idx < batch_total - 1:\n print_num_on_tqdm(loader, loss)\n else:\n loss_epoch = np.mean(losses)\n print_num_on_tqdm(loader, loss_epoch, last=True)\n\n\n<function token>\n\n\ndef custom_evaluation(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n sum_recall_1 = 0\n sum_recall_2 = 0\n sum_recall_3 = 0\n sum_precision_1 = 0\n sum_precision_2 = 0\n sum_precision_3 = 0\n num_test_data = 0\n total_labels = 0\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n num_test_data += len(outputs)\n for i in range(len(outputs)):\n actual_labels = np.nonzero(target[i])[0]\n predicted_labels = np.argsort(outputs[i])[-3:][::-1]\n correct_prediction = 0\n if predicted_labels[0] in actual_labels:\n correct_prediction += 1\n sum_precision_1 += correct_prediction / 1\n sum_recall_1 += correct_prediction / len(actual_labels)\n if predicted_labels[1] in actual_labels:\n correct_prediction += 1\n sum_precision_2 += correct_prediction / 2\n sum_recall_2 += correct_prediction / len(actual_labels)\n if predicted_labels[2] in actual_labels:\n correct_prediction += 1\n sum_precision_3 += correct_prediction / 3\n sum_recall_3 += correct_prediction / len(actual_labels)\n precision_1 = sum_precision_1 / num_test_data\n precision_2 = sum_precision_2 / num_test_data\n precision_3 = sum_precision_3 / num_test_data\n recall_1 = sum_recall_1 / num_test_data\n recall_2 = sum_recall_2 / num_test_data\n recall_3 = sum_recall_3 / num_test_data\n f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)\n f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)\n f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)\n print('K = 1')\n print('P@1 = ' + precision_1.__str__())\n print('R@1 = ' + recall_1.__str__())\n print('F@1 = ' + f1_1.__str__())\n print('K = 2')\n print('P@2 = ' + precision_2.__str__())\n print('R@2 = ' + recall_2.__str__())\n print('F@2 = ' + f1_2.__str__())\n print('K = 3')\n print('P@3 = ' + precision_3.__str__())\n print('R@3 = ' + recall_3.__str__())\n print('F@3 = ' + f1_3.__str__())\n return eval_epoch\n",
"<import token>\n<function token>\n<function token>\n\n\ndef custom_evaluation(params, model, data_loader, is_valid=True):\n device = params['device']\n measure = params['measure']\n doc_key = is_valid and 'valid' or 'test'\n batch_total = params[doc_key + '_batch_total']\n model.eval()\n eval_epoch = 0.0\n target_all = np.empty((0, params['num_of_class']), dtype=np.int8)\n eval_all = np.empty((0, params['num_of_class']), dtype=np.float32)\n sum_recall_1 = 0\n sum_recall_2 = 0\n sum_recall_3 = 0\n sum_precision_1 = 0\n sum_precision_2 = 0\n sum_precision_3 = 0\n num_test_data = 0\n total_labels = 0\n with tqdm_with_num(data_loader, batch_total) as loader:\n is_valid and loader.set_description('Validating')\n is_valid or loader.set_description('Testing ')\n with torch.no_grad():\n for idx, batch in enumerate(loader):\n data, target = batch.text.to(device), batch.label.to('cpu')\n target = target.detach().numpy().copy()\n outputs = model(data)\n outputs = torch.sigmoid(outputs)\n outputs = outputs.to('cpu').detach().numpy().copy()\n num_test_data += len(outputs)\n for i in range(len(outputs)):\n actual_labels = np.nonzero(target[i])[0]\n predicted_labels = np.argsort(outputs[i])[-3:][::-1]\n correct_prediction = 0\n if predicted_labels[0] in actual_labels:\n correct_prediction += 1\n sum_precision_1 += correct_prediction / 1\n sum_recall_1 += correct_prediction / len(actual_labels)\n if predicted_labels[1] in actual_labels:\n correct_prediction += 1\n sum_precision_2 += correct_prediction / 2\n sum_recall_2 += correct_prediction / len(actual_labels)\n if predicted_labels[2] in actual_labels:\n correct_prediction += 1\n sum_precision_3 += correct_prediction / 3\n sum_recall_3 += correct_prediction / len(actual_labels)\n precision_1 = sum_precision_1 / num_test_data\n precision_2 = sum_precision_2 / num_test_data\n precision_3 = sum_precision_3 / num_test_data\n recall_1 = sum_recall_1 / num_test_data\n recall_2 = sum_recall_2 / num_test_data\n recall_3 = sum_recall_3 / num_test_data\n f1_1 = 2 * precision_1 * recall_1 / (precision_1 + recall_1)\n f1_2 = 2 * precision_2 * recall_2 / (precision_2 + recall_2)\n f1_3 = 2 * precision_3 * recall_3 / (precision_3 + recall_3)\n print('K = 1')\n print('P@1 = ' + precision_1.__str__())\n print('R@1 = ' + recall_1.__str__())\n print('F@1 = ' + f1_1.__str__())\n print('K = 2')\n print('P@2 = ' + precision_2.__str__())\n print('R@2 = ' + recall_2.__str__())\n print('F@2 = ' + f1_2.__str__())\n print('K = 3')\n print('P@3 = ' + precision_3.__str__())\n print('R@3 = ' + recall_3.__str__())\n print('F@3 = ' + f1_3.__str__())\n return eval_epoch\n",
"<import token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,472 |
94f92d55ad47a380d443d58f687fefe4a099025c
|
def diff21(n):
if n <= 21 :
return abs(n-21)
else:
return abs(n-21)*2
|
[
"def diff21(n):\n if n <= 21 : \n return abs(n-21) \n else:\n return abs(n-21)*2\n\n",
"def diff21(n):\n if n <= 21:\n return abs(n - 21)\n else:\n return abs(n - 21) * 2\n",
"<function token>\n"
] | false |
99,473 |
6947c1da87874e2123ed153a3d641dceeffc6a52
|
##import sys
##sys.path.append("C:\\Users\\Satyam\\Desktop\\test")
from testing import b
from testing import test
b()
test.c()
|
[
"##import sys\n##sys.path.append(\"C:\\\\Users\\\\Satyam\\\\Desktop\\\\test\")\nfrom testing import b\nfrom testing import test\nb()\ntest.c()\n\n",
"from testing import b\nfrom testing import test\nb()\ntest.c()\n",
"<import token>\nb()\ntest.c()\n",
"<import token>\n<code token>\n"
] | false |
99,474 |
d2ae5b4007217f1a653a875a14d497cdb910b684
|
#!/usr/bin/python
from mayavi import mlab
from scipy.special import gamma as Gamma
import numpy as np
import math
def dirichlet(a1, a2, a3):
x = [[t/10.0 for i in range(10)] for t in range(11)]
y = [[(1-xr[0])/10.0*i for i in range(1,11)] for xr in x]
x = np.array(x)
y = np.array(y)
k = Gamma(a1+a2+a3)/(Gamma(a1)*Gamma(a2)*Gamma(a3))
z = k*pow(x, a1-1)*pow(y, a2-1)*pow(1-x-y, a3-1)
for i in range(len(z)):
for j in range(len(z[i])):
if np.isinf(z[i][j]):
z[i][j] = np.nan
mlab.mesh(x, y, z)
mlab.show()
if __name__ == '__main__':
dirichlet(2, 2, 2)
|
[
"#!/usr/bin/python\n\nfrom mayavi import mlab\nfrom scipy.special import gamma as Gamma\nimport numpy as np\nimport math\n\ndef dirichlet(a1, a2, a3):\n\tx = [[t/10.0 for i in range(10)] for t in range(11)]\n\ty = [[(1-xr[0])/10.0*i for i in range(1,11)] for xr in x]\n\tx = np.array(x)\n\ty = np.array(y)\n\n\tk = Gamma(a1+a2+a3)/(Gamma(a1)*Gamma(a2)*Gamma(a3))\n\tz = k*pow(x, a1-1)*pow(y, a2-1)*pow(1-x-y, a3-1)\n\tfor i in range(len(z)):\n\t\tfor j in range(len(z[i])):\n\t\t\tif np.isinf(z[i][j]):\n\t\t\t\tz[i][j] = np.nan\n\n\n\tmlab.mesh(x, y, z)\n\tmlab.show()\n\nif __name__ == '__main__':\n\tdirichlet(2, 2, 2)\n",
"from mayavi import mlab\nfrom scipy.special import gamma as Gamma\nimport numpy as np\nimport math\n\n\ndef dirichlet(a1, a2, a3):\n x = [[(t / 10.0) for i in range(10)] for t in range(11)]\n y = [[((1 - xr[0]) / 10.0 * i) for i in range(1, 11)] for xr in x]\n x = np.array(x)\n y = np.array(y)\n k = Gamma(a1 + a2 + a3) / (Gamma(a1) * Gamma(a2) * Gamma(a3))\n z = k * pow(x, a1 - 1) * pow(y, a2 - 1) * pow(1 - x - y, a3 - 1)\n for i in range(len(z)):\n for j in range(len(z[i])):\n if np.isinf(z[i][j]):\n z[i][j] = np.nan\n mlab.mesh(x, y, z)\n mlab.show()\n\n\nif __name__ == '__main__':\n dirichlet(2, 2, 2)\n",
"<import token>\n\n\ndef dirichlet(a1, a2, a3):\n x = [[(t / 10.0) for i in range(10)] for t in range(11)]\n y = [[((1 - xr[0]) / 10.0 * i) for i in range(1, 11)] for xr in x]\n x = np.array(x)\n y = np.array(y)\n k = Gamma(a1 + a2 + a3) / (Gamma(a1) * Gamma(a2) * Gamma(a3))\n z = k * pow(x, a1 - 1) * pow(y, a2 - 1) * pow(1 - x - y, a3 - 1)\n for i in range(len(z)):\n for j in range(len(z[i])):\n if np.isinf(z[i][j]):\n z[i][j] = np.nan\n mlab.mesh(x, y, z)\n mlab.show()\n\n\nif __name__ == '__main__':\n dirichlet(2, 2, 2)\n",
"<import token>\n\n\ndef dirichlet(a1, a2, a3):\n x = [[(t / 10.0) for i in range(10)] for t in range(11)]\n y = [[((1 - xr[0]) / 10.0 * i) for i in range(1, 11)] for xr in x]\n x = np.array(x)\n y = np.array(y)\n k = Gamma(a1 + a2 + a3) / (Gamma(a1) * Gamma(a2) * Gamma(a3))\n z = k * pow(x, a1 - 1) * pow(y, a2 - 1) * pow(1 - x - y, a3 - 1)\n for i in range(len(z)):\n for j in range(len(z[i])):\n if np.isinf(z[i][j]):\n z[i][j] = np.nan\n mlab.mesh(x, y, z)\n mlab.show()\n\n\n<code token>\n",
"<import token>\n<function token>\n<code token>\n"
] | false |
99,475 |
196f46c79f09c5cfed7c1495f9353e34fead6e2b
|
import bs_main
def test_main():
map = bs_main.run()
assert len(map.keys()) == 20
|
[
"import bs_main\n\n\ndef test_main():\n map = bs_main.run()\n assert len(map.keys()) == 20\n",
"<import token>\n\n\ndef test_main():\n map = bs_main.run()\n assert len(map.keys()) == 20\n",
"<import token>\n<function token>\n"
] | false |
99,476 |
4e651ab3c46f0f2fe558834d6ebe7612ec256507
|
import csv
import os
def is_non_zero_file(fpath):
return True if os.path.isfile(fpath) and os.path.getsize(fpath) > 0 else False
number = 0
with open('tadawul_data.csv', 'rbU') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
next(spamreader)
for row in spamreader:
with open(row[0]+'.csv','a+') as datafile:
d = csv.writer(datafile)
if not is_non_zero_file(row[0]+'.csv'):
d.writerow(['Date','Open','High','Low','Close','Volume', 'Adj Close','Num_deals','Value','Change','Change_per'])
d = csv.writer(datafile)
d.writerow(row[1:])
print "####Done"
|
[
"\nimport csv\nimport os\n\ndef is_non_zero_file(fpath): \n return True if os.path.isfile(fpath) and os.path.getsize(fpath) > 0 else False\n\nnumber = 0\n\nwith open('tadawul_data.csv', 'rbU') as csvfile:\n\tspamreader = csv.reader(csvfile, delimiter=',')\n\tnext(spamreader)\n\n\tfor row in spamreader:\n\t\twith open(row[0]+'.csv','a+') as datafile:\n\t\t\td = csv.writer(datafile)\n\t\t\tif not is_non_zero_file(row[0]+'.csv'):\n\t\t\t\td.writerow(['Date','Open','High','Low','Close','Volume', 'Adj Close','Num_deals','Value','Change','Change_per'])\n\t\t\td = csv.writer(datafile)\n\t\t\td.writerow(row[1:])\n\n\n\nprint \"####Done\""
] | true |
99,477 |
d9230ea7d293a61146b47ad7b7cea23c3644b7d6
|
from django.contrib import admin
from .models import Pet, Feeding, Toy, Photo
# Register your models here.
admin.site.register(Pet)
admin.site.register(Feeding)
admin.site.register(Toy)
admin.site.register(Photo)
|
[
"from django.contrib import admin\nfrom .models import Pet, Feeding, Toy, Photo\n# Register your models here.\n\nadmin.site.register(Pet)\nadmin.site.register(Feeding)\nadmin.site.register(Toy)\nadmin.site.register(Photo)",
"from django.contrib import admin\nfrom .models import Pet, Feeding, Toy, Photo\nadmin.site.register(Pet)\nadmin.site.register(Feeding)\nadmin.site.register(Toy)\nadmin.site.register(Photo)\n",
"<import token>\nadmin.site.register(Pet)\nadmin.site.register(Feeding)\nadmin.site.register(Toy)\nadmin.site.register(Photo)\n",
"<import token>\n<code token>\n"
] | false |
99,478 |
0c3ea483d0f12cdd44962cfce25faa8893737163
|
"""
Write a Python program to append a list to the second list.
"""
list1=[1,2,3,4]
list2=[5,6,7,8]
list1+=list2
print(list1)
|
[
"\"\"\"\r\nWrite a Python program to append a list to the second list.\r\n\"\"\"\r\n\r\nlist1=[1,2,3,4]\r\nlist2=[5,6,7,8]\r\n\r\nlist1+=list2\r\nprint(list1)",
"<docstring token>\nlist1 = [1, 2, 3, 4]\nlist2 = [5, 6, 7, 8]\nlist1 += list2\nprint(list1)\n",
"<docstring token>\n<assignment token>\nlist1 += list2\nprint(list1)\n",
"<docstring token>\n<assignment token>\n<code token>\n"
] | false |
99,479 |
15e0b5c7d9636f53b85c4def9eb3acf8a7f230eb
|
my_name = 'Zed A. Shaw'
my_age = 35 # not a lie
my_height = 74 # inches, Study Drill 2: 0.0254 m to a inche
#height = 74 * 0.0254 # height of Zed in meters.
my_weight = 180 # lbs, Study Drill 2: 0.45 kg to a lb
#weight = 180 * 0.45 # weight of Zed in kilograms.
my_eyes = 'Blue'
my_teeth = 'White'
my_hair = 'Brown'
print("Let's talk about %s." % my_name)
print("He's %d inches tall." % my_height)
print("He's %d punds heavy." % my_weight)
print("Actually that's not too heavy.")
print("He's got %s eyes and %s hair." % (my_eyes, my_hair))
print("His teeth are usually %s depending on the coffee." % my_teeth)
# this line is tricky, try to get it exactly right
print("If I add %d, %d, and %d I get %d." % (
my_age, my_height, my_weight, my_age + my_height + my_weight))
|
[
"my_name = 'Zed A. Shaw'\nmy_age = 35 # not a lie\nmy_height = 74 # inches, Study Drill 2: 0.0254 m to a inche\n#height = 74 * 0.0254 # height of Zed in meters.\nmy_weight = 180 # lbs, Study Drill 2: 0.45 kg to a lb\n#weight = 180 * 0.45 # weight of Zed in kilograms.\nmy_eyes = 'Blue'\nmy_teeth = 'White'\nmy_hair = 'Brown'\n\nprint(\"Let's talk about %s.\" % my_name)\nprint(\"He's %d inches tall.\" % my_height)\nprint(\"He's %d punds heavy.\" % my_weight)\nprint(\"Actually that's not too heavy.\")\nprint(\"He's got %s eyes and %s hair.\" % (my_eyes, my_hair))\nprint(\"His teeth are usually %s depending on the coffee.\" % my_teeth)\n\n# this line is tricky, try to get it exactly right\nprint(\"If I add %d, %d, and %d I get %d.\" % (\n my_age, my_height, my_weight, my_age + my_height + my_weight))\n",
"my_name = 'Zed A. Shaw'\nmy_age = 35\nmy_height = 74\nmy_weight = 180\nmy_eyes = 'Blue'\nmy_teeth = 'White'\nmy_hair = 'Brown'\nprint(\"Let's talk about %s.\" % my_name)\nprint(\"He's %d inches tall.\" % my_height)\nprint(\"He's %d punds heavy.\" % my_weight)\nprint(\"Actually that's not too heavy.\")\nprint(\"He's got %s eyes and %s hair.\" % (my_eyes, my_hair))\nprint('His teeth are usually %s depending on the coffee.' % my_teeth)\nprint('If I add %d, %d, and %d I get %d.' % (my_age, my_height, my_weight, \n my_age + my_height + my_weight))\n",
"<assignment token>\nprint(\"Let's talk about %s.\" % my_name)\nprint(\"He's %d inches tall.\" % my_height)\nprint(\"He's %d punds heavy.\" % my_weight)\nprint(\"Actually that's not too heavy.\")\nprint(\"He's got %s eyes and %s hair.\" % (my_eyes, my_hair))\nprint('His teeth are usually %s depending on the coffee.' % my_teeth)\nprint('If I add %d, %d, and %d I get %d.' % (my_age, my_height, my_weight, \n my_age + my_height + my_weight))\n",
"<assignment token>\n<code token>\n"
] | false |
99,480 |
1d8c380d3d6bdaed4e956ff3127ddced5f23d1d3
|
from .od_route_extractor import OriginDestinationRouteExtractor # expose the feature extraction utility
|
[
"from .od_route_extractor import OriginDestinationRouteExtractor # expose the feature extraction utility\n",
"from .od_route_extractor import OriginDestinationRouteExtractor\n",
"<import token>\n"
] | false |
99,481 |
29261e47e1ee6c5901f6914380ebf11a97690e1d
|
# -*- coding: utf-8 -*-
from Tkinter import *
from tkFileDialog import *
from Tools import *
class Clusterization():
def __init__ (self):
self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey', 'cyan']
self.dots = list()
self.centers = list()
self.window = Tk()
self.window.title('Kohonen')
self.window.geometry('1000x900+100+100')
self.window.resizable(False, False)
self.drawing_area = Canvas(self.window, width=970, height=750, bd=2, cursor = 'dot', relief=RIDGE)
self.drawing_area.place(x = 15, y = 25, width = 970)
self.drawing_area.bind("<ButtonPress-1>", self.draw_dots)
self.drawing_area.bind("<ButtonPress-3>", self.draw_centers)
self.button_manhattan = Button(self.window, bd = 2, text = 'Manhattan', width = 30, height = 1, relief=RIDGE)
self.button_manhattan.place(x = 300, y = 800, width = 105)
self.button_manhattan.bind('<Button-1>', self.manhattan)
self.button_chebyshev = Button(self.window, bd = 2, text = 'Chebyshev', width = 30, height = 1, relief=RIDGE)
self.button_chebyshev.place(x = 410, y = 800, width = 105)
self.button_chebyshev.bind('<Button-1>', self.chebyshev)
self.button_upload_dots = Button(self.window, bd = 2, text = 'Upload dots', width = 30, height = 1, relief=RIDGE)
self.button_upload_dots.place(x = 520, y = 800, width = 105)
self.button_upload_dots.bind('<Button-1>', self.upload_dots)
self.button_upload_centers = Button(self.window, bd = 2, text = 'Upload centers', width = 30, height = 1, relief=RIDGE)
self.button_upload_centers.place(x = 630, y = 800, width = 105)
self.button_upload_centers.bind('<Button-1>', self.upload_centers)
self.button_clear_all = Button(self.window, bd = 2, text = 'C L E A R A L L', width = 30, height = 1, relief=RIDGE)
self.button_clear_all.place(x = 300, y = 850, width = 435)
self.button_clear_all.bind('<Button-1>', self.reset)
# self.button_write = Button(self.window, bd = 2, text = 'Write data to file', width = 30, height = 1, relief=RIDGE)
# self.button_write.place(x = 510, y = 850, width = 200)
#self.button_upload_centers.bind('<Button-1>', self.write_to_file)
def manhattan(self, event):
clusters = kohonen(self.dots, self.centers, 'Manhattan')
self.drawing_area.delete('all')
for i,center in enumerate(self.centers):
cl = list()
for dot,cluster in zip(self.dots,clusters):
if cluster == center:
cl.append(dot)
cl.insert(0, center)
x_center = cl[0][0]
y_center = cl[0][1]
self.drawing_area.create_oval(x_center, y_center, x_center + 7, y_center + 7, width=1, fill=self.colors[0])
for c in cl[1:]:
x = c[0]
y = c[1]
self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[i+1])
def chebyshev(self, event):
clusters = kohonen(self.dots, self.centers, 'Chebyshev')
self.drawing_area.delete('all')
for i,center in enumerate(self.centers):
cl = list()
for dot,cluster in zip(self.dots,clusters):
if cluster == center:
cl.append(dot)
cl.insert(0, center)
x_center = cl[0][0]
y_center = cl[0][1]
self.drawing_area.create_oval(x_center, y_center, x_center + 7, y_center + 7, width=1, fill=self.colors[0])
for c in cl[1:]:
x = c[0]
y = c[1]
self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[i+1])
def draw_dots(self, event):
event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7, width=1, fill=self.colors[0])
self.dots.append([event.x, event.y])
def draw_centers(self, event):
event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7, width=1, fill=self.colors[1])
self.centers.append([event.x, event.y])
def upload_dots(self, event):
Tk().withdraw()
filename = askopenfilename()
self.dots += list(read_file(filename))
for dot in self.dots:
x = dot[0]
y = dot[1]
self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[0])
def upload_centers(self, event):
Tk().withdraw()
filename = askopenfilename()
self.centers += list(read_file(filename))
for center in self.centers:
x = center[0]
y = center[1]
self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[1])
def reset (self, event):
self.drawing_area.delete('all')
self.dots = list()
self.centers = list()
def run (self):
self.window.mainloop()
|
[
"# -*- coding: utf-8 -*-\nfrom Tkinter import *\nfrom tkFileDialog import *\nfrom Tools import *\nclass Clusterization():\n def __init__ (self):\n\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey', 'cyan']\n\n self.dots = list()\n self.centers = list()\n\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2, cursor = 'dot', relief=RIDGE)\n self.drawing_area.place(x = 15, y = 25, width = 970)\n self.drawing_area.bind(\"<ButtonPress-1>\", self.draw_dots)\n self.drawing_area.bind(\"<ButtonPress-3>\", self.draw_centers)\n\n self.button_manhattan = Button(self.window, bd = 2, text = 'Manhattan', width = 30, height = 1, relief=RIDGE)\n self.button_manhattan.place(x = 300, y = 800, width = 105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n\n self.button_chebyshev = Button(self.window, bd = 2, text = 'Chebyshev', width = 30, height = 1, relief=RIDGE)\n self.button_chebyshev.place(x = 410, y = 800, width = 105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n\n self.button_upload_dots = Button(self.window, bd = 2, text = 'Upload dots', width = 30, height = 1, relief=RIDGE)\n self.button_upload_dots.place(x = 520, y = 800, width = 105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n\n self.button_upload_centers = Button(self.window, bd = 2, text = 'Upload centers', width = 30, height = 1, relief=RIDGE)\n self.button_upload_centers.place(x = 630, y = 800, width = 105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n\n self.button_clear_all = Button(self.window, bd = 2, text = 'C L E A R A L L', width = 30, height = 1, relief=RIDGE)\n self.button_clear_all.place(x = 300, y = 850, width = 435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n # self.button_write = Button(self.window, bd = 2, text = 'Write data to file', width = 30, height = 1, relief=RIDGE)\n # self.button_write.place(x = 510, y = 850, width = 200)\n #self.button_upload_centers.bind('<Button-1>', self.write_to_file)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i,center in enumerate(self.centers):\n cl = list()\n for dot,cluster in zip(self.dots,clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7, y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[i+1])\n\n def chebyshev(self, event):\n clusters = kohonen(self.dots, self.centers, 'Chebyshev')\n self.drawing_area.delete('all')\n for i,center in enumerate(self.centers):\n cl = list()\n for dot,cluster in zip(self.dots,clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7, y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[i+1])\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7, width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n\n def draw_centers(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7, width=1, fill=self.colors[1])\n self.centers.append([event.x, event.y])\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill=self.colors[1])\n\n def reset (self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n\n def run (self):\n self.window.mainloop()\n",
"from Tkinter import *\nfrom tkFileDialog import *\nfrom Tools import *\n\n\nclass Clusterization:\n\n def __init__(self):\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey',\n 'cyan']\n self.dots = list()\n self.centers = list()\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2,\n cursor='dot', relief=RIDGE)\n self.drawing_area.place(x=15, y=25, width=970)\n self.drawing_area.bind('<ButtonPress-1>', self.draw_dots)\n self.drawing_area.bind('<ButtonPress-3>', self.draw_centers)\n self.button_manhattan = Button(self.window, bd=2, text='Manhattan',\n width=30, height=1, relief=RIDGE)\n self.button_manhattan.place(x=300, y=800, width=105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n self.button_chebyshev = Button(self.window, bd=2, text='Chebyshev',\n width=30, height=1, relief=RIDGE)\n self.button_chebyshev.place(x=410, y=800, width=105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n self.button_upload_dots = Button(self.window, bd=2, text=\n 'Upload dots', width=30, height=1, relief=RIDGE)\n self.button_upload_dots.place(x=520, y=800, width=105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n self.button_upload_centers = Button(self.window, bd=2, text=\n 'Upload centers', width=30, height=1, relief=RIDGE)\n self.button_upload_centers.place(x=630, y=800, width=105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n self.button_clear_all = Button(self.window, bd=2, text=\n 'C L E A R A L L', width=30, height=1, relief=RIDGE)\n self.button_clear_all.place(x=300, y=850, width=435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n\n def chebyshev(self, event):\n clusters = kohonen(self.dots, self.centers, 'Chebyshev')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n\n def draw_centers(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[1])\n self.centers.append([event.x, event.y])\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n\n def run(self):\n self.window.mainloop()\n",
"<import token>\n\n\nclass Clusterization:\n\n def __init__(self):\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey',\n 'cyan']\n self.dots = list()\n self.centers = list()\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2,\n cursor='dot', relief=RIDGE)\n self.drawing_area.place(x=15, y=25, width=970)\n self.drawing_area.bind('<ButtonPress-1>', self.draw_dots)\n self.drawing_area.bind('<ButtonPress-3>', self.draw_centers)\n self.button_manhattan = Button(self.window, bd=2, text='Manhattan',\n width=30, height=1, relief=RIDGE)\n self.button_manhattan.place(x=300, y=800, width=105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n self.button_chebyshev = Button(self.window, bd=2, text='Chebyshev',\n width=30, height=1, relief=RIDGE)\n self.button_chebyshev.place(x=410, y=800, width=105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n self.button_upload_dots = Button(self.window, bd=2, text=\n 'Upload dots', width=30, height=1, relief=RIDGE)\n self.button_upload_dots.place(x=520, y=800, width=105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n self.button_upload_centers = Button(self.window, bd=2, text=\n 'Upload centers', width=30, height=1, relief=RIDGE)\n self.button_upload_centers.place(x=630, y=800, width=105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n self.button_clear_all = Button(self.window, bd=2, text=\n 'C L E A R A L L', width=30, height=1, relief=RIDGE)\n self.button_clear_all.place(x=300, y=850, width=435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n\n def chebyshev(self, event):\n clusters = kohonen(self.dots, self.centers, 'Chebyshev')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n\n def draw_centers(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[1])\n self.centers.append([event.x, event.y])\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n\n def run(self):\n self.window.mainloop()\n",
"<import token>\n\n\nclass Clusterization:\n\n def __init__(self):\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey',\n 'cyan']\n self.dots = list()\n self.centers = list()\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2,\n cursor='dot', relief=RIDGE)\n self.drawing_area.place(x=15, y=25, width=970)\n self.drawing_area.bind('<ButtonPress-1>', self.draw_dots)\n self.drawing_area.bind('<ButtonPress-3>', self.draw_centers)\n self.button_manhattan = Button(self.window, bd=2, text='Manhattan',\n width=30, height=1, relief=RIDGE)\n self.button_manhattan.place(x=300, y=800, width=105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n self.button_chebyshev = Button(self.window, bd=2, text='Chebyshev',\n width=30, height=1, relief=RIDGE)\n self.button_chebyshev.place(x=410, y=800, width=105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n self.button_upload_dots = Button(self.window, bd=2, text=\n 'Upload dots', width=30, height=1, relief=RIDGE)\n self.button_upload_dots.place(x=520, y=800, width=105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n self.button_upload_centers = Button(self.window, bd=2, text=\n 'Upload centers', width=30, height=1, relief=RIDGE)\n self.button_upload_centers.place(x=630, y=800, width=105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n self.button_clear_all = Button(self.window, bd=2, text=\n 'C L E A R A L L', width=30, height=1, relief=RIDGE)\n self.button_clear_all.place(x=300, y=850, width=435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n\n def draw_centers(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[1])\n self.centers.append([event.x, event.y])\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n\n def run(self):\n self.window.mainloop()\n",
"<import token>\n\n\nclass Clusterization:\n\n def __init__(self):\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey',\n 'cyan']\n self.dots = list()\n self.centers = list()\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2,\n cursor='dot', relief=RIDGE)\n self.drawing_area.place(x=15, y=25, width=970)\n self.drawing_area.bind('<ButtonPress-1>', self.draw_dots)\n self.drawing_area.bind('<ButtonPress-3>', self.draw_centers)\n self.button_manhattan = Button(self.window, bd=2, text='Manhattan',\n width=30, height=1, relief=RIDGE)\n self.button_manhattan.place(x=300, y=800, width=105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n self.button_chebyshev = Button(self.window, bd=2, text='Chebyshev',\n width=30, height=1, relief=RIDGE)\n self.button_chebyshev.place(x=410, y=800, width=105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n self.button_upload_dots = Button(self.window, bd=2, text=\n 'Upload dots', width=30, height=1, relief=RIDGE)\n self.button_upload_dots.place(x=520, y=800, width=105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n self.button_upload_centers = Button(self.window, bd=2, text=\n 'Upload centers', width=30, height=1, relief=RIDGE)\n self.button_upload_centers.place(x=630, y=800, width=105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n self.button_clear_all = Button(self.window, bd=2, text=\n 'C L E A R A L L', width=30, height=1, relief=RIDGE)\n self.button_clear_all.place(x=300, y=850, width=435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n\n def draw_centers(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[1])\n self.centers.append([event.x, event.y])\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n\n def __init__(self):\n self.colors = ['black', 'green', 'blue', 'red', 'magenta', 'grey',\n 'cyan']\n self.dots = list()\n self.centers = list()\n self.window = Tk()\n self.window.title('Kohonen')\n self.window.geometry('1000x900+100+100')\n self.window.resizable(False, False)\n self.drawing_area = Canvas(self.window, width=970, height=750, bd=2,\n cursor='dot', relief=RIDGE)\n self.drawing_area.place(x=15, y=25, width=970)\n self.drawing_area.bind('<ButtonPress-1>', self.draw_dots)\n self.drawing_area.bind('<ButtonPress-3>', self.draw_centers)\n self.button_manhattan = Button(self.window, bd=2, text='Manhattan',\n width=30, height=1, relief=RIDGE)\n self.button_manhattan.place(x=300, y=800, width=105)\n self.button_manhattan.bind('<Button-1>', self.manhattan)\n self.button_chebyshev = Button(self.window, bd=2, text='Chebyshev',\n width=30, height=1, relief=RIDGE)\n self.button_chebyshev.place(x=410, y=800, width=105)\n self.button_chebyshev.bind('<Button-1>', self.chebyshev)\n self.button_upload_dots = Button(self.window, bd=2, text=\n 'Upload dots', width=30, height=1, relief=RIDGE)\n self.button_upload_dots.place(x=520, y=800, width=105)\n self.button_upload_dots.bind('<Button-1>', self.upload_dots)\n self.button_upload_centers = Button(self.window, bd=2, text=\n 'Upload centers', width=30, height=1, relief=RIDGE)\n self.button_upload_centers.place(x=630, y=800, width=105)\n self.button_upload_centers.bind('<Button-1>', self.upload_centers)\n self.button_clear_all = Button(self.window, bd=2, text=\n 'C L E A R A L L', width=30, height=1, relief=RIDGE)\n self.button_clear_all.place(x=300, y=850, width=435)\n self.button_clear_all.bind('<Button-1>', self.reset)\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n <function token>\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n\n def manhattan(self, event):\n clusters = kohonen(self.dots, self.centers, 'Manhattan')\n self.drawing_area.delete('all')\n for i, center in enumerate(self.centers):\n cl = list()\n for dot, cluster in zip(self.dots, clusters):\n if cluster == center:\n cl.append(dot)\n cl.insert(0, center)\n x_center = cl[0][0]\n y_center = cl[0][1]\n self.drawing_area.create_oval(x_center, y_center, x_center + 7,\n y_center + 7, width=1, fill=self.colors[0])\n for c in cl[1:]:\n x = c[0]\n y = c[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1,\n fill=self.colors[i + 1])\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n <function token>\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n <function token>\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n <function token>\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n\n def reset(self, event):\n self.drawing_area.delete('all')\n self.dots = list()\n self.centers = list()\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n <function token>\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n <function token>\n\n def upload_dots(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.dots += list(read_file(filename))\n for dot in self.dots:\n x = dot[0]\n y = dot[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[0])\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n <function token>\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n <function token>\n <function token>\n\n def draw_dots(self, event):\n event.widget.create_oval(event.x, event.y, event.x + 7, event.y + 7,\n width=1, fill=self.colors[0])\n self.dots.append([event.x, event.y])\n <function token>\n <function token>\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n <function token>\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def upload_centers(self, event):\n Tk().withdraw()\n filename = askopenfilename()\n self.centers += list(read_file(filename))\n for center in self.centers:\n x = center[0]\n y = center[1]\n self.drawing_area.create_oval(x, y, x + 7, y + 7, width=1, fill\n =self.colors[1])\n <function token>\n <function token>\n",
"<import token>\n\n\nclass Clusterization:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<class token>\n"
] | false |
99,482 |
3ea7b7df0277669f301895732b3801b639e74829
|
#!/usr/bin/env python
# tf_reducer.py
# Syed Rafayal
# A reducer program for calculating TF by counting number for file_name, word
import sys
# declare and assign global variable
current_word = None
prev_filename = None
current_count = 0
word = None
N = 0
filename_count = {}
lines = []
# input comes from STDIN
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
# adding line in the list
lines.append(line)
# split the line into file_name, word, count by tab
file_name, word, count = line.split('\t')
# convert count (currently a string) to int
count = int(count)
# check previous file name with current file name
# if same then add count with N
# otherwise change previous file name and reset N value by 0
# and update dictionary 'filename_count' by previous filename
if prev_filename == file_name:
N = N + count
else:
if prev_filename != None:
filename_count[prev_filename] = N
N = 0
prev_filename = file_name
# update last value
filename_count[prev_filename] = N
# read all the line from list
for line in lines:
# split the line into file_name, word, count by tab
file_name, word, count = line.split('\t')
for name in filename_count:
if file_name == name:
# calculate Term Frequency
tf = float(count)/float(filename_count[name])
# write the results to STDOUT (standard output);
# what we output here will be the input for the
# next Mapper step, i.e. the input for idf_mapper.py
# tab-delimited;
print "%s\t%s\t%s" % (word, file_name, str(tf))
|
[
"#!/usr/bin/env python\n# tf_reducer.py\n# Syed Rafayal\n# A reducer program for calculating TF by counting number for file_name, word\nimport sys\n\n# declare and assign global variable\ncurrent_word = None\nprev_filename = None\ncurrent_count = 0\nword = None\nN = 0\nfilename_count = {}\nlines = []\n\n# input comes from STDIN\nfor line in sys.stdin:\n # remove leading and trailing whitespace\n line = line.strip()\n # adding line in the list\n lines.append(line)\n # split the line into file_name, word, count by tab\n file_name, word, count = line.split('\\t')\n # convert count (currently a string) to int\n count = int(count)\n # check previous file name with current file name\n # if same then add count with N\n # otherwise change previous file name and reset N value by 0\n # and update dictionary 'filename_count' by previous filename\n if prev_filename == file_name:\n N = N + count\n else:\n if prev_filename != None:\n filename_count[prev_filename] = N\n N = 0\n prev_filename = file_name\n# update last value\nfilename_count[prev_filename] = N\n\n# read all the line from list\nfor line in lines:\n # split the line into file_name, word, count by tab\n file_name, word, count = line.split('\\t')\n for name in filename_count:\n if file_name == name:\n # calculate Term Frequency\n tf = float(count)/float(filename_count[name])\n # write the results to STDOUT (standard output);\n # what we output here will be the input for the\n # next Mapper step, i.e. the input for idf_mapper.py\n # tab-delimited;\n print \"%s\\t%s\\t%s\" % (word, file_name, str(tf))\n"
] | true |
99,483 |
390dc5c2a0e51ae0345e83638b4c6a4d8cea7b6f
|
# Generated by Django 2.2.1 on 2019-05-08 21:54
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('github', '0007_auto_20190508_2149'),
]
operations = [
migrations.AddField(
model_name='repository',
name='forks',
field=models.IntegerField(default=1),
preserve_default=False,
),
migrations.AddField(
model_name='repository',
name='stars',
field=models.IntegerField(default=1),
preserve_default=False,
),
]
|
[
"# Generated by Django 2.2.1 on 2019-05-08 21:54\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('github', '0007_auto_20190508_2149'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='repository',\n name='forks',\n field=models.IntegerField(default=1),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='repository',\n name='stars',\n field=models.IntegerField(default=1),\n preserve_default=False,\n ),\n ]\n",
"from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('github', '0007_auto_20190508_2149')]\n operations = [migrations.AddField(model_name='repository', name='forks',\n field=models.IntegerField(default=1), preserve_default=False),\n migrations.AddField(model_name='repository', name='stars', field=\n models.IntegerField(default=1), preserve_default=False)]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('github', '0007_auto_20190508_2149')]\n operations = [migrations.AddField(model_name='repository', name='forks',\n field=models.IntegerField(default=1), preserve_default=False),\n migrations.AddField(model_name='repository', name='stars', field=\n models.IntegerField(default=1), preserve_default=False)]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,484 |
cb88f7ed0e44519032066a81ac3dad3bbfc9eaba
|
# coding=utf-8
# author huxh
# time 2020/4/7 10:29 AM
def isStraight(nums):
nums = [num for num in nums if num]
return len(set(nums)) == len(nums) and max(nums) - min(nums) < 5
|
[
"# coding=utf-8\n# author huxh\n# time 2020/4/7 10:29 AM\n\n\ndef isStraight(nums):\n nums = [num for num in nums if num]\n return len(set(nums)) == len(nums) and max(nums) - min(nums) < 5\n\n",
"def isStraight(nums):\n nums = [num for num in nums if num]\n return len(set(nums)) == len(nums) and max(nums) - min(nums) < 5\n",
"<function token>\n"
] | false |
99,485 |
355af566411cd3c017d14d3856561b62b0834833
|
'''
Given a non-negative integer numRows, generate the first numRows of Pascal's triangle.
In Pascal's triangle, each number is the sum of the two numbers directly above it.
Example:
Input: 5
Output:
[
[1],
[1,1],
[1,2,1],
[1,3,3,1],
[1,4,6,4,1]
]
'''
class Solution(object):
def generate(self, n):
"""
:type numRows: int
:rtype: List[List[int]]
"""
ans=[]
#temp1=[1]
#temp2=[1,1]
#ans.append(temp1)
# ans.append(temp2)
for i in range (0, n):
temp=[]
temp.append(1)
for j in range (1,i):
temp.append(ans[i-1][j-1]+ans[i-1][j])
#print("yui")
if i!=0:
temp.append(1)
ans.append(temp)
return ans
|
[
"'''\nGiven a non-negative integer numRows, generate the first numRows of Pascal's triangle.\n\n\nIn Pascal's triangle, each number is the sum of the two numbers directly above it.\n\nExample:\n\nInput: 5\nOutput:\n[\n [1],\n [1,1],\n [1,2,1],\n [1,3,3,1],\n [1,4,6,4,1]\n]\n'''\n\nclass Solution(object):\n def generate(self, n):\n \"\"\"\n :type numRows: int\n :rtype: List[List[int]]\n \"\"\"\n ans=[]\n \n \n #temp1=[1]\n #temp2=[1,1]\n #ans.append(temp1)\n # ans.append(temp2)\n \n for i in range (0, n):\n temp=[]\n temp.append(1)\n for j in range (1,i):\n temp.append(ans[i-1][j-1]+ans[i-1][j])\n #print(\"yui\")\n if i!=0:\n temp.append(1)\n ans.append(temp)\n \n return ans\n",
"<docstring token>\n\n\nclass Solution(object):\n\n def generate(self, n):\n \"\"\"\n :type numRows: int\n :rtype: List[List[int]]\n \"\"\"\n ans = []\n for i in range(0, n):\n temp = []\n temp.append(1)\n for j in range(1, i):\n temp.append(ans[i - 1][j - 1] + ans[i - 1][j])\n if i != 0:\n temp.append(1)\n ans.append(temp)\n return ans\n",
"<docstring token>\n\n\nclass Solution(object):\n <function token>\n",
"<docstring token>\n<class token>\n"
] | false |
99,486 |
3890af36e64abaa0742d829fd03efcb61ce18d6a
|
# Generated by Django 2.2.4 on 2019-09-11 03:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('clients', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='product',
name='priceUSD',
),
migrations.AddField(
model_name='client',
name='source',
field=models.CharField(default='OTHER', max_length=10, verbose_name='Source'),
),
migrations.AddField(
model_name='product',
name='currency',
field=models.CharField(choices=[('USD', 'USD'), ('RMB', 'RMB'), ('EUR', 'EUR')], default='USD', max_length=5),
),
migrations.AddField(
model_name='product',
name='price',
field=models.DecimalField(decimal_places=2, default=0, max_digits=6, verbose_name='Price'),
preserve_default=False,
),
migrations.AddField(
model_name='product',
name='priceRMB',
field=models.DecimalField(decimal_places=2, default=0, max_digits=6, verbose_name='Price(RMB)'),
preserve_default=False,
),
]
|
[
"# Generated by Django 2.2.4 on 2019-09-11 03:12\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('clients', '0001_initial'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='product',\n name='priceUSD',\n ),\n migrations.AddField(\n model_name='client',\n name='source',\n field=models.CharField(default='OTHER', max_length=10, verbose_name='Source'),\n ),\n migrations.AddField(\n model_name='product',\n name='currency',\n field=models.CharField(choices=[('USD', 'USD'), ('RMB', 'RMB'), ('EUR', 'EUR')], default='USD', max_length=5),\n ),\n migrations.AddField(\n model_name='product',\n name='price',\n field=models.DecimalField(decimal_places=2, default=0, max_digits=6, verbose_name='Price'),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='product',\n name='priceRMB',\n field=models.DecimalField(decimal_places=2, default=0, max_digits=6, verbose_name='Price(RMB)'),\n preserve_default=False,\n ),\n ]\n",
"from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('clients', '0001_initial')]\n operations = [migrations.RemoveField(model_name='product', name=\n 'priceUSD'), migrations.AddField(model_name='client', name='source',\n field=models.CharField(default='OTHER', max_length=10, verbose_name\n ='Source')), migrations.AddField(model_name='product', name=\n 'currency', field=models.CharField(choices=[('USD', 'USD'), ('RMB',\n 'RMB'), ('EUR', 'EUR')], default='USD', max_length=5)), migrations.\n AddField(model_name='product', name='price', field=models.\n DecimalField(decimal_places=2, default=0, max_digits=6,\n verbose_name='Price'), preserve_default=False), migrations.AddField\n (model_name='product', name='priceRMB', field=models.DecimalField(\n decimal_places=2, default=0, max_digits=6, verbose_name=\n 'Price(RMB)'), preserve_default=False)]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('clients', '0001_initial')]\n operations = [migrations.RemoveField(model_name='product', name=\n 'priceUSD'), migrations.AddField(model_name='client', name='source',\n field=models.CharField(default='OTHER', max_length=10, verbose_name\n ='Source')), migrations.AddField(model_name='product', name=\n 'currency', field=models.CharField(choices=[('USD', 'USD'), ('RMB',\n 'RMB'), ('EUR', 'EUR')], default='USD', max_length=5)), migrations.\n AddField(model_name='product', name='price', field=models.\n DecimalField(decimal_places=2, default=0, max_digits=6,\n verbose_name='Price'), preserve_default=False), migrations.AddField\n (model_name='product', name='priceRMB', field=models.DecimalField(\n decimal_places=2, default=0, max_digits=6, verbose_name=\n 'Price(RMB)'), preserve_default=False)]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,487 |
56e6360f47d12243d4a99863b58d798e1fc21e86
|
""" Binary Search Tree Node """
class BinarySearchTreeNode:
"""
Elemental Node in a Binary Search Tree
Attributes
----------
element: any type with comparable function
parent: BinarySearchTreeNode
left: BinarySearchTreeNode
right: BinarySearchTreeNode
Methods
-------
has_parent()
returns True if there is a node parent, False otherwise
has_left()
returns True if this node has a left node, False otherwise
has_right()
returns True if this node has a right node, False otherwise
"""
def __init__(self, element, parent=None, left=None, right=None):
"""
Node Constructor
Parameters
----------
element : any type with comparable function
Node element
parent=None : BinarySearchTreeNode
Node parent
left=None : BinarySearchTreeNode
Node in left
right=None : BinarySearchTreeNode
Node in right
"""
self.element = element
self.parent = parent
self.left = left
self.right = right
def __str__(self):
"""
Returns a String representation of this node.
"""
return 'BinarySearchTreeNode(' + str(self.element) + ')'
def __repr__(self):
"""
Returns a String representation of this node.
"""
return str(self)
def __eq__(self, node):
"""
Returns a Boolean depending if this and other node are equal.
"""
if node == None or self.element != node.element:
return False
return self.left == node.left and self.right == node.right
# All getters and setters
def get_element(self):
"""
Get the node element
Returns
-------
element
node element
"""
return self.element
def set_element(self, element):
"""
Set the node element
Parameters
----------
element : any type with comparable function
node element
"""
self.element = element
def get_parent(self):
"""
Get the node element
Returns
-------
parent : BinarySearchTreeNode
Node parent
"""
return self.parent
def set_parent(self, parent):
"""
Set the parent node
Parameters
----------
parent : BinarySearchTreeNode
Parent node
"""
self.parent = parent
def get_left(self):
"""
Get the node in left
Returns
-------
element : BinarySearchTreeNode
Node in left
"""
return self.left
def set_left(self, left):
"""
Set the node in right
Parameters
----------
left : BinarySearchTreeNode
Left node
"""
self.left = left
def get_right(self):
"""
Get the node in right
Returns
-------
right : BinarySearchTreeNode
Node in right
"""
return self.right
def set_right(self, right):
"""
Set the node in right
Parameters
----------
right : BinarySearchTreeNode
Right node
"""
self.right = right
def has_parent(self):
"""
Check if there is a parent
Returns
-------
Boolean
True if this node has a parent, False otherwise
"""
return self.parent != None
def has_left(self):
"""
Check if there is a left node
Returns
-------
Boolean
True if this node has a left node, False otherwise
"""
return self.left != None
def has_right(self):
"""
Check if there is a right node
Returns
-------
Boolean
True if this node has a right node, False otherwise
"""
return self.right != None
def is_left_child(self):
"""
Check if this node is a left child.
Returns
-------
Boolean
True if this node is a Left child, False otherwise
"""
if self.parent == None:
return False
return self.parent.left == self
def is_right_child(self):
"""
Check if this node is a right child.
Returns
-------
Boolean
True if this node is a right child, False otherwise
"""
if self.parent == None:
return False
return self.parent.right == self
|
[
"\"\"\" Binary Search Tree Node \"\"\"\n\nclass BinarySearchTreeNode:\n \"\"\"\n Elemental Node in a Binary Search Tree\n\n Attributes\n ----------\n element: any type with comparable function\n parent: BinarySearchTreeNode\n left: BinarySearchTreeNode\n right: BinarySearchTreeNode\n\n Methods\n -------\n has_parent()\n returns True if there is a node parent, False otherwise\n\n has_left()\n returns True if this node has a left node, False otherwise\n\n has_right()\n returns True if this node has a right node, False otherwise\n \"\"\"\n \n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n \n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n # All getters and setters\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n\n def get_parent(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n parent : BinarySearchTreeNode\n Node parent\n \"\"\"\n return self.parent\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n\n def set_right(self, right):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n right : BinarySearchTreeNode\n Right node\n \"\"\"\n self.right = right\n\n def has_parent(self):\n \"\"\"\n Check if there is a parent\n \n Returns\n -------\n Boolean\n True if this node has a parent, False otherwise\n \"\"\"\n return self.parent != None\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n \"\"\"\n Elemental Node in a Binary Search Tree\n\n Attributes\n ----------\n element: any type with comparable function\n parent: BinarySearchTreeNode\n left: BinarySearchTreeNode\n right: BinarySearchTreeNode\n\n Methods\n -------\n has_parent()\n returns True if there is a node parent, False otherwise\n\n has_left()\n returns True if this node has a left node, False otherwise\n\n has_right()\n returns True if this node has a right node, False otherwise\n \"\"\"\n\n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n\n def get_parent(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n parent : BinarySearchTreeNode\n Node parent\n \"\"\"\n return self.parent\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n\n def set_right(self, right):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n right : BinarySearchTreeNode\n Right node\n \"\"\"\n self.right = right\n\n def has_parent(self):\n \"\"\"\n Check if there is a parent\n \n Returns\n -------\n Boolean\n True if this node has a parent, False otherwise\n \"\"\"\n return self.parent != None\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n\n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n\n def get_parent(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n parent : BinarySearchTreeNode\n Node parent\n \"\"\"\n return self.parent\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n\n def set_right(self, right):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n right : BinarySearchTreeNode\n Right node\n \"\"\"\n self.right = right\n\n def has_parent(self):\n \"\"\"\n Check if there is a parent\n \n Returns\n -------\n Boolean\n True if this node has a parent, False otherwise\n \"\"\"\n return self.parent != None\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n\n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n\n def set_right(self, right):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n right : BinarySearchTreeNode\n Right node\n \"\"\"\n self.right = right\n\n def has_parent(self):\n \"\"\"\n Check if there is a parent\n \n Returns\n -------\n Boolean\n True if this node has a parent, False otherwise\n \"\"\"\n return self.parent != None\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n\n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n\n def has_parent(self):\n \"\"\"\n Check if there is a parent\n \n Returns\n -------\n Boolean\n True if this node has a parent, False otherwise\n \"\"\"\n return self.parent != None\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n\n def __init__(self, element, parent=None, left=None, right=None):\n \"\"\"\n Node Constructor\n \n Parameters\n ----------\n element : any type with comparable function\n Node element\n\n parent=None : BinarySearchTreeNode\n Node parent\n\n left=None : BinarySearchTreeNode\n Node in left\n\n right=None : BinarySearchTreeNode\n Node in right\n \"\"\"\n self.element = element\n self.parent = parent\n self.left = left\n self.right = right\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n\n def is_left_child(self):\n \"\"\"\n Check if this node is a left child.\n\n Returns\n -------\n Boolean\n True if this node is a Left child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.left == self\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n\n def set_left(self, left):\n \"\"\"\n Set the node in right\n \n Parameters\n ----------\n left : BinarySearchTreeNode\n Left node\n \"\"\"\n self.left = left\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n\n def set_parent(self, parent):\n \"\"\"\n Set the parent node\n \n Parameters\n ----------\n parent : BinarySearchTreeNode\n Parent node\n \"\"\"\n self.parent = parent\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n\n def has_left(self):\n \"\"\"\n Check if there is a left node\n \n Returns\n -------\n Boolean\n True if this node has a left node, False otherwise\n \"\"\"\n return self.left != None\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n\n def has_right(self):\n \"\"\"\n Check if there is a right node\n \n Returns\n -------\n Boolean\n True if this node has a right node, False otherwise\n \"\"\"\n return self.right != None\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n\n def __eq__(self, node):\n \"\"\"\n Returns a Boolean depending if this and other node are equal.\n \"\"\"\n if node == None or self.element != node.element:\n return False\n return self.left == node.left and self.right == node.right\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n\n def get_left(self):\n \"\"\"\n Get the node in left\n \n Returns\n -------\n element : BinarySearchTreeNode\n Node in left\n \"\"\"\n return self.left\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n\n def __repr__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return str(self)\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n\n def __str__(self):\n \"\"\"\n Returns a String representation of this node.\n \"\"\"\n return 'BinarySearchTreeNode(' + str(self.element) + ')'\n <function token>\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_right(self):\n \"\"\"\n Get the node in right\n \n Returns\n -------\n right : BinarySearchTreeNode\n Node in right\n \"\"\"\n return self.right\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def is_right_child(self):\n \"\"\"\n Check if this node is a right child.\n\n Returns\n -------\n Boolean\n True if this node is a right child, False otherwise\n \"\"\"\n if self.parent == None:\n return False\n return self.parent.right == self\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def get_element(self):\n \"\"\"\n Get the node element\n \n Returns\n -------\n element\n node element\n \"\"\"\n return self.element\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def set_element(self, element):\n \"\"\"\n Set the node element\n \n Parameters\n ----------\n element : any type with comparable function\n node element\n \"\"\"\n self.element = element\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<docstring token>\n\n\nclass BinarySearchTreeNode:\n <docstring token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<docstring token>\n<class token>\n"
] | false |
99,488 |
2f4766993847a2e4aac1a6f5c5aea786b690fd60
|
#Definition for singly-linked list.
class ListNode:
def __init__(self, x, y):
self.val = x
self.next = None
self.id = y
class Solution:
# @param {ListNode} head
# @return {ListNode}
def deleteDuplicates(self, head):
if not head:
return
node = head
prev = ListNode(0,-1)
prev.next = head
head = prev
while node.next:
flag = False
while node.next and node.val == node.next.val:
node.next = node.next.next
flag = True
node = node.next
if flag and prev.next:
prev.next = prev.next.next
else:
#node = node.next
prev = prev.next
if not node:
return head.next
return head.next
def print_nodes(l):
s = ''
while l:
s += '->' + str(l.val)
l = l.next
print s
l1 = ListNode(1,0)
l2 =ListNode(1,1)
l3 =ListNode(2,2)
l4 =ListNode(2,3)
l5 =ListNode(2,4)
l6 =ListNode(3,5)
l7 =ListNode(3,6)
l8 =ListNode(4,7)
l1.next = l2
#l2.next = l3
l3.next = l4
l4.next = l5
l5.next = l6
l6.next = l7
l7.next = l8
solution = Solution()
print_nodes(solution.deleteDuplicates(l1))
#print_nodes(l)
class Solution:
# @param {ListNode} head
# @return {ListNode}
def deleteDuplicates(self, head):
node = head
prev = ListNode(0)
prev.next = head
head = prev
while node:
#Check if current node is a duplicated node
flag = False
while node.next and node.val == node.next.val:
node.next = node.next.next
flag = True
#Node is now pointer of a list with each element appears only once
node = node.next
#Delete the current node if its value appeared more than once
if flag and prev.next:
prev.next = prev.next.next
#Put the node at the end of the result
else:
prev = prev.next
return head.next
|
[
"#Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x, y):\n self.val = x\n self.next = None\n self.id = y\n\n\nclass Solution:\n # @param {ListNode} head\n # @return {ListNode}\n def deleteDuplicates(self, head):\n if not head:\n return\n node = head\n\n prev = ListNode(0,-1)\n prev.next = head\n head = prev\n\n while node.next:\n flag = False\n while node.next and node.val == node.next.val:\n node.next = node.next.next\n flag = True\n node = node.next\n if flag and prev.next:\n prev.next = prev.next.next\n else:\n #node = node.next\n prev = prev.next\n if not node:\n return head.next\n return head.next\n\n\ndef print_nodes(l):\n s = ''\n while l:\n s += '->' + str(l.val)\n l = l.next\n print s\n\n\nl1 = ListNode(1,0)\nl2 =ListNode(1,1)\nl3 =ListNode(2,2)\nl4 =ListNode(2,3)\n\nl5 =ListNode(2,4)\nl6 =ListNode(3,5)\nl7 =ListNode(3,6)\nl8 =ListNode(4,7)\n\nl1.next = l2\n#l2.next = l3\nl3.next = l4\nl4.next = l5\nl5.next = l6\nl6.next = l7\nl7.next = l8\nsolution = Solution()\nprint_nodes(solution.deleteDuplicates(l1))\n\n#print_nodes(l)\n\nclass Solution:\n # @param {ListNode} head\n # @return {ListNode}\n def deleteDuplicates(self, head):\n node = head\n prev = ListNode(0)\n prev.next = head\n head = prev\n\n while node:\n #Check if current node is a duplicated node\n flag = False\n while node.next and node.val == node.next.val:\n node.next = node.next.next\n flag = True\n #Node is now pointer of a list with each element appears only once\n node = node.next\n #Delete the current node if its value appeared more than once\n if flag and prev.next:\n prev.next = prev.next.next\n #Put the node at the end of the result\n else:\n prev = prev.next\n\n return head.next"
] | true |
99,489 |
e031d4e5a98bde7da46592e766fcdffbb73cf349
|
# Generated by Django 2.2.2 on 2019-06-12 14:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('dbadmin', '0003_auto_20190611_1451'),
]
operations = [
migrations.CreateModel(
name='Outcomes',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
#('OutcomeID', models.IntegerField()),
('OutcomeDescription', models.CharField(max_length=1024)),
('CourseNumber', models.CharField(max_length=128)),
],
),
migrations.CreateModel(
name='Reviewer',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
#('ReviewerID', models.IntegerField()),
('ReviewerName', models.CharField(max_length=128)),
('ReviewerPhone', models.CharField(max_length=16)),
('ReviewerEmail', models.CharField(max_length=128)),
('ReviewerDepartment', models.CharField(max_length=8)),
],
),
]
|
[
"# Generated by Django 2.2.2 on 2019-06-12 14:59\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('dbadmin', '0003_auto_20190611_1451'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Outcomes',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n #('OutcomeID', models.IntegerField()),\n ('OutcomeDescription', models.CharField(max_length=1024)),\n ('CourseNumber', models.CharField(max_length=128)),\n ],\n ),\n migrations.CreateModel(\n name='Reviewer',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n #('ReviewerID', models.IntegerField()),\n ('ReviewerName', models.CharField(max_length=128)),\n ('ReviewerPhone', models.CharField(max_length=16)),\n ('ReviewerEmail', models.CharField(max_length=128)),\n ('ReviewerDepartment', models.CharField(max_length=8)),\n ],\n ),\n ]\n",
"from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('dbadmin', '0003_auto_20190611_1451')]\n operations = [migrations.CreateModel(name='Outcomes', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('OutcomeDescription', models.CharField\n (max_length=1024)), ('CourseNumber', models.CharField(max_length=\n 128))]), migrations.CreateModel(name='Reviewer', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('ReviewerName', models.CharField(\n max_length=128)), ('ReviewerPhone', models.CharField(max_length=16)\n ), ('ReviewerEmail', models.CharField(max_length=128)), (\n 'ReviewerDepartment', models.CharField(max_length=8))])]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('dbadmin', '0003_auto_20190611_1451')]\n operations = [migrations.CreateModel(name='Outcomes', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('OutcomeDescription', models.CharField\n (max_length=1024)), ('CourseNumber', models.CharField(max_length=\n 128))]), migrations.CreateModel(name='Reviewer', fields=[('id',\n models.AutoField(auto_created=True, primary_key=True, serialize=\n False, verbose_name='ID')), ('ReviewerName', models.CharField(\n max_length=128)), ('ReviewerPhone', models.CharField(max_length=16)\n ), ('ReviewerEmail', models.CharField(max_length=128)), (\n 'ReviewerDepartment', models.CharField(max_length=8))])]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
99,490 |
f86f6f9015b98d5b5fb058c57964a8af26931525
|
from fractions import gcd
def solve():
N, *A = map(int, open(0).read().split())
ans = 0
left = [0 for _ in range(N+1)]
right = [0 for _ in range(N+1)]
for i in range(N):
left[i+1] = gcd(left[i], A[i])
for i in range(N-1, -1, -1):
right[i] = gcd(right[i+1], A[i])
for i in range(N):
ans = max(ans, gcd(left[i], right[i+1]))
print(ans)
if __name__ == '__main__':
solve()
|
[
"from fractions import gcd\n\n\ndef solve():\n N, *A = map(int, open(0).read().split())\n \n ans = 0\n left = [0 for _ in range(N+1)]\n right = [0 for _ in range(N+1)]\n for i in range(N):\n left[i+1] = gcd(left[i], A[i])\n for i in range(N-1, -1, -1):\n right[i] = gcd(right[i+1], A[i])\n for i in range(N):\n ans = max(ans, gcd(left[i], right[i+1]))\n print(ans)\n\n\nif __name__ == '__main__':\n solve()\n",
"from fractions import gcd\n\n\ndef solve():\n N, *A = map(int, open(0).read().split())\n ans = 0\n left = [(0) for _ in range(N + 1)]\n right = [(0) for _ in range(N + 1)]\n for i in range(N):\n left[i + 1] = gcd(left[i], A[i])\n for i in range(N - 1, -1, -1):\n right[i] = gcd(right[i + 1], A[i])\n for i in range(N):\n ans = max(ans, gcd(left[i], right[i + 1]))\n print(ans)\n\n\nif __name__ == '__main__':\n solve()\n",
"<import token>\n\n\ndef solve():\n N, *A = map(int, open(0).read().split())\n ans = 0\n left = [(0) for _ in range(N + 1)]\n right = [(0) for _ in range(N + 1)]\n for i in range(N):\n left[i + 1] = gcd(left[i], A[i])\n for i in range(N - 1, -1, -1):\n right[i] = gcd(right[i + 1], A[i])\n for i in range(N):\n ans = max(ans, gcd(left[i], right[i + 1]))\n print(ans)\n\n\nif __name__ == '__main__':\n solve()\n",
"<import token>\n\n\ndef solve():\n N, *A = map(int, open(0).read().split())\n ans = 0\n left = [(0) for _ in range(N + 1)]\n right = [(0) for _ in range(N + 1)]\n for i in range(N):\n left[i + 1] = gcd(left[i], A[i])\n for i in range(N - 1, -1, -1):\n right[i] = gcd(right[i + 1], A[i])\n for i in range(N):\n ans = max(ans, gcd(left[i], right[i + 1]))\n print(ans)\n\n\n<code token>\n",
"<import token>\n<function token>\n<code token>\n"
] | false |
99,491 |
09f8a1cb74704d7367513ebe5147cd5370fce208
|
import asyncio
import logging
from functools import partial
from os import getenv
from aiofile import AIOFile, Writer
from chat_common import get_logged_message, chat_connector, get_argparser, _non_empty_printable, run_client, _port, setup_general_log
reader_log = logging.getLogger("Chat reader")
async def read_chat(filename, chat_connector):
assert bool(filename) and filename.isprintable(), AssertionError("Filename has to be non-empty printable.")
async with AIOFile(filename, mode="a", encoding="utf-8") as file:
writer = Writer(file)
try:
await chat_connector(writer)
await file.fsync()
except asyncio.CancelledError:
await file.fsync()
raise
async def read_write_lines(reader, _, writer):
data = await reader.readline()
while data:
await writer(get_logged_message(data.decode("utf-8", "ignore")))
data = await reader.readline()
def get_args():
parser = get_argparser()
parser.add_argument("-p", "--port", action="store", type=_port,
help="chat port, default is 5000",
default=int(getenv("CHAT_PORT", 5000)))
parser.add_argument("-H", "--history", action="store", type=_non_empty_printable,
help="messages history, default is ./messages.history",
default=getenv("CHAT_HISTORY", "./messages.history"))
return parser.parse_args()
if __name__ == '__main__':
options = get_args()
# logger settings
log_level = options.loglevel * 10
setup_general_log(options.log, log_level)
reader_log.setLevel(log_level)
connector = partial(chat_connector, options.host, options.port, options.delay, options.retries, read_write_lines)
chat_handler = partial(read_chat, options.history, connector)
reader_log.info(f"Chat reader is starting with options: {options}")
run_client(chat_handler)
|
[
"import asyncio\nimport logging\nfrom functools import partial\nfrom os import getenv\n\nfrom aiofile import AIOFile, Writer\n\nfrom chat_common import get_logged_message, chat_connector, get_argparser, _non_empty_printable, run_client, _port, setup_general_log\n\n\nreader_log = logging.getLogger(\"Chat reader\")\n\n\nasync def read_chat(filename, chat_connector):\n\n assert bool(filename) and filename.isprintable(), AssertionError(\"Filename has to be non-empty printable.\")\n\n async with AIOFile(filename, mode=\"a\", encoding=\"utf-8\") as file:\n writer = Writer(file)\n\n try:\n await chat_connector(writer)\n\n await file.fsync()\n\n except asyncio.CancelledError:\n await file.fsync()\n raise\n\n\nasync def read_write_lines(reader, _, writer):\n\n data = await reader.readline()\n\n while data:\n await writer(get_logged_message(data.decode(\"utf-8\", \"ignore\")))\n data = await reader.readline()\n\n\ndef get_args():\n\n parser = get_argparser()\n\n parser.add_argument(\"-p\", \"--port\", action=\"store\", type=_port,\n help=\"chat port, default is 5000\",\n default=int(getenv(\"CHAT_PORT\", 5000)))\n\n parser.add_argument(\"-H\", \"--history\", action=\"store\", type=_non_empty_printable,\n help=\"messages history, default is ./messages.history\",\n default=getenv(\"CHAT_HISTORY\", \"./messages.history\"))\n\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n options = get_args()\n\n # logger settings\n log_level = options.loglevel * 10\n setup_general_log(options.log, log_level)\n reader_log.setLevel(log_level)\n\n connector = partial(chat_connector, options.host, options.port, options.delay, options.retries, read_write_lines)\n chat_handler = partial(read_chat, options.history, connector)\n\n reader_log.info(f\"Chat reader is starting with options: {options}\")\n run_client(chat_handler)\n",
"import asyncio\nimport logging\nfrom functools import partial\nfrom os import getenv\nfrom aiofile import AIOFile, Writer\nfrom chat_common import get_logged_message, chat_connector, get_argparser, _non_empty_printable, run_client, _port, setup_general_log\nreader_log = logging.getLogger('Chat reader')\n\n\nasync def read_chat(filename, chat_connector):\n assert bool(filename) and filename.isprintable(), AssertionError(\n 'Filename has to be non-empty printable.')\n async with AIOFile(filename, mode='a', encoding='utf-8') as file:\n writer = Writer(file)\n try:\n await chat_connector(writer)\n await file.fsync()\n except asyncio.CancelledError:\n await file.fsync()\n raise\n\n\nasync def read_write_lines(reader, _, writer):\n data = await reader.readline()\n while data:\n await writer(get_logged_message(data.decode('utf-8', 'ignore')))\n data = await reader.readline()\n\n\ndef get_args():\n parser = get_argparser()\n parser.add_argument('-p', '--port', action='store', type=_port, help=\n 'chat port, default is 5000', default=int(getenv('CHAT_PORT', 5000)))\n parser.add_argument('-H', '--history', action='store', type=\n _non_empty_printable, help=\n 'messages history, default is ./messages.history', default=getenv(\n 'CHAT_HISTORY', './messages.history'))\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n options = get_args()\n log_level = options.loglevel * 10\n setup_general_log(options.log, log_level)\n reader_log.setLevel(log_level)\n connector = partial(chat_connector, options.host, options.port, options\n .delay, options.retries, read_write_lines)\n chat_handler = partial(read_chat, options.history, connector)\n reader_log.info(f'Chat reader is starting with options: {options}')\n run_client(chat_handler)\n",
"<import token>\nreader_log = logging.getLogger('Chat reader')\n\n\nasync def read_chat(filename, chat_connector):\n assert bool(filename) and filename.isprintable(), AssertionError(\n 'Filename has to be non-empty printable.')\n async with AIOFile(filename, mode='a', encoding='utf-8') as file:\n writer = Writer(file)\n try:\n await chat_connector(writer)\n await file.fsync()\n except asyncio.CancelledError:\n await file.fsync()\n raise\n\n\nasync def read_write_lines(reader, _, writer):\n data = await reader.readline()\n while data:\n await writer(get_logged_message(data.decode('utf-8', 'ignore')))\n data = await reader.readline()\n\n\ndef get_args():\n parser = get_argparser()\n parser.add_argument('-p', '--port', action='store', type=_port, help=\n 'chat port, default is 5000', default=int(getenv('CHAT_PORT', 5000)))\n parser.add_argument('-H', '--history', action='store', type=\n _non_empty_printable, help=\n 'messages history, default is ./messages.history', default=getenv(\n 'CHAT_HISTORY', './messages.history'))\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n options = get_args()\n log_level = options.loglevel * 10\n setup_general_log(options.log, log_level)\n reader_log.setLevel(log_level)\n connector = partial(chat_connector, options.host, options.port, options\n .delay, options.retries, read_write_lines)\n chat_handler = partial(read_chat, options.history, connector)\n reader_log.info(f'Chat reader is starting with options: {options}')\n run_client(chat_handler)\n",
"<import token>\n<assignment token>\n\n\nasync def read_chat(filename, chat_connector):\n assert bool(filename) and filename.isprintable(), AssertionError(\n 'Filename has to be non-empty printable.')\n async with AIOFile(filename, mode='a', encoding='utf-8') as file:\n writer = Writer(file)\n try:\n await chat_connector(writer)\n await file.fsync()\n except asyncio.CancelledError:\n await file.fsync()\n raise\n\n\nasync def read_write_lines(reader, _, writer):\n data = await reader.readline()\n while data:\n await writer(get_logged_message(data.decode('utf-8', 'ignore')))\n data = await reader.readline()\n\n\ndef get_args():\n parser = get_argparser()\n parser.add_argument('-p', '--port', action='store', type=_port, help=\n 'chat port, default is 5000', default=int(getenv('CHAT_PORT', 5000)))\n parser.add_argument('-H', '--history', action='store', type=\n _non_empty_printable, help=\n 'messages history, default is ./messages.history', default=getenv(\n 'CHAT_HISTORY', './messages.history'))\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n options = get_args()\n log_level = options.loglevel * 10\n setup_general_log(options.log, log_level)\n reader_log.setLevel(log_level)\n connector = partial(chat_connector, options.host, options.port, options\n .delay, options.retries, read_write_lines)\n chat_handler = partial(read_chat, options.history, connector)\n reader_log.info(f'Chat reader is starting with options: {options}')\n run_client(chat_handler)\n",
"<import token>\n<assignment token>\n<code token>\n\n\ndef get_args():\n parser = get_argparser()\n parser.add_argument('-p', '--port', action='store', type=_port, help=\n 'chat port, default is 5000', default=int(getenv('CHAT_PORT', 5000)))\n parser.add_argument('-H', '--history', action='store', type=\n _non_empty_printable, help=\n 'messages history, default is ./messages.history', default=getenv(\n 'CHAT_HISTORY', './messages.history'))\n return parser.parse_args()\n\n\n<code token>\n",
"<import token>\n<assignment token>\n<code token>\n<function token>\n<code token>\n"
] | false |
99,492 |
d8b12127cf7f0093bf3c4951c6131f6760cc27c0
|
from groupy import Client
import os
GROUP_ID = 48071223
client = Client.from_token(os.environ["GROUPME_ACCESS_TOKEN"])
group = client.groups.get(id=GROUP_ID)
group.leave()
while True:
group.post(text=input("> "))
|
[
"from groupy import Client\nimport os\n\nGROUP_ID = 48071223\n\n\nclient = Client.from_token(os.environ[\"GROUPME_ACCESS_TOKEN\"])\ngroup = client.groups.get(id=GROUP_ID)\ngroup.leave()\nwhile True:\n group.post(text=input(\"> \"))\n",
"from groupy import Client\nimport os\nGROUP_ID = 48071223\nclient = Client.from_token(os.environ['GROUPME_ACCESS_TOKEN'])\ngroup = client.groups.get(id=GROUP_ID)\ngroup.leave()\nwhile True:\n group.post(text=input('> '))\n",
"<import token>\nGROUP_ID = 48071223\nclient = Client.from_token(os.environ['GROUPME_ACCESS_TOKEN'])\ngroup = client.groups.get(id=GROUP_ID)\ngroup.leave()\nwhile True:\n group.post(text=input('> '))\n",
"<import token>\n<assignment token>\ngroup.leave()\nwhile True:\n group.post(text=input('> '))\n",
"<import token>\n<assignment token>\n<code token>\n"
] | false |
99,493 |
f1d04a86a825f717715b20e99d4614aa87eff647
|
#!/usr/bin/env python
# -*-coding:utf-8 -*-
'''
@author: daishilong
@contact: [email protected]
'''
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.externals import joblib
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared, Matern, ConstantKernel, RBF, RationalQuadratic
from sklearn.model_selection import train_test_split
from mpl_toolkits.mplot3d import Axes3D
from restore import Restore
from sklearn.preprocessing import StandardScaler
rng = np.random.RandomState(0)
# data1 = np.loadtxt("E:/WiFi/day1/3F/0 m per s.csv", dtype=float, delimiter=',')[:,1:]
#
# data3 = np.loadtxt("E:/WiFi/day3/1.5 m per s.txt", dtype=float, delimiter=',')[:,1:]
#
# data4 = np.loadtxt("E:/WiFi/day4/1.5 m per s.txt", dtype=float, delimiter=',')[:,1:]
#
# data5 = np.loadtxt("E:/WiFi/day5/1.5 m per s.txt", dtype=float, delimiter=',')[:,1:]
data1 = np.loadtxt("E:/WiFi/day2/1.5 m per s.txt", dtype=float, delimiter=',')[:,1:]
data2 = np.loadtxt("E:/project/PycharmProjects/wifiServer/3F/GP/0/meanRe.csv", dtype=float, delimiter=',')
dataAll = np.r_[data1, data2]
TrainChoice = range(0, len(dataAll), 1)
dataAll=dataAll[TrainChoice]
grid = np.loadtxt("candidate.csv", dtype=float, delimiter=',')
#grid = np.array(dataAll[:,:2])
gridMean = np.array(grid)
gridStd = np.array(grid)
default = -90
# dataAll[dataAll[:,:]==-100] = default
# testdataAll = np.loadtxt("E:/WiFi/实验室6楼/wifiData/行人验证/lastpdr.csv", dtype=float, delimiter=',')
# testdataAll = testdataAll[testdataAll[:,1]==2,2:]
testdataAll = np.loadtxt("E:/WiFi/day1/3F/2 m per s.csv", dtype=float, delimiter=',')[:,1:]
# testdataAll = np.loadtxt("E:/WiFi/实验室6楼/wifiData/实验/1.2m_human.csv", dtype=float, delimiter=',')[:,3:]
font1 = {'family' : 'Times New Roman',
'weight' : 'normal',
'size' : 20,}
modelPath = 'model/GP/'
inputNum = 2
interval = 19
scaler = StandardScaler().fit(dataAll[:,:inputNum])
ax = []
err = np.zeros([len(testdataAll),len(testdataAll[0])-2])
for Ap in range(0,interval):
testAP = inputNum + Ap
testAPBand = testAP + interval
testdata = testdataAll[testdataAll[:,testAP]!=-100,:]
dataRaw = dataAll[dataAll[:, testAP] != -100]
y,dy,reData = Restore(dataAll=dataAll,gap=interval,inputN=inputNum,num=Ap)
y[y[:]==-100]=default
X = dataAll[:,:inputNum]
kernel = 1.0* RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \
+WhiteKernel(noise_level=1)
gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
stime = time.time()
gpr.fit(scaler.transform(X), y)
print("Time for GPR fitting: %.3f" % (time.time() - stime))
X_predict =grid[:,:2]
# Predict using gaussian process regressor
stime = time.time()
y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)
gridMean = np.c_[gridMean,y_gpr]
gridStd = np.c_[gridStd, y_std]
print("Time for GPR prediction with standard-deviation: %.3f"
% (time.time() - stime))
print(gpr.kernel_)
#print(y_gpr-testdata[:,testAPBand])
# Plot results
ax.append(plt.figure().add_subplot(111, projection='3d'))
ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP], c='r')
dataUndetect = dataAll[dataAll[:, testAP] == -100]
# ax[Ap].scatter(reData[:, 0], reData[:, 1], reData[:, testAP], c='black')
ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP], c='b')
ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')
# if Ap ==9:
# np.savetxt("csv/measured.csv", np.array(np.c_[dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP]]), fmt='%f', delimiter=',', newline='\r\n')
# np.savetxt("csv/recovered.csv", np.array(np.c_[reData[:, 0], reData[:, 1], reData[:, testAP]]), fmt='%f',
# delimiter=',', newline='\r\n')
# np.savetxt("csv/undetected.csv", np.array(np.c_[dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',
# delimiter=',', newline='\r\n')
# np.savetxt("csv/fingerprint.csv", np.array(np.c_[X_predict[:, 0], X_predict[:, 1], y_gpr[:]]), fmt='%f',
# delimiter=',', newline='\r\n')
# ax[Ap].scatter(testdata[:, 0], testdata[:, 1], testdata[:,testAP], c='b')
ax[Ap].set_zlabel('RSSI (dBm)',font1) # 坐标轴
ax[Ap].set_ylabel('Y (m)',font1)
ax[Ap].set_xlabel('X (m)',font1)
ax[Ap].legend(['measured data','undetected data','fingerprint map'],prop=font1,loc = 'lower center', bbox_to_anchor=(0.6,0.95))
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
joblib.dump(gpr, modelPath + "ap" + str(Ap) + ".pkl")
for Ap in range(interval,2*interval):
testAP = inputNum + Ap
# ignore the default RSSI
data = dataAll[:, :]
data = data[:]
# trainingData, testingData = train_test_split(data, test_size=0.2)
# trainingData=trainingData[trainingData[:,0].argsort()]
# testingData = testingData[testingData[:, 0].argsort()]
testdata = testdataAll[testdataAll[:, testAP] != -100, :]
X = np.r_[data[:, 0:2]]
# X_show = np.r_[trainingData[:, 0].reshape(-1,1),testingData[:1,0].reshape(-1,1)]
# X_show = X_show[X_show[:, 0].argsort()]
# y = data[:,testAP]
dataRaw = dataAll[dataAll[:, testAP] != -100]
y, dy, reData = Restore(dataAll=dataAll, gap=-interval, inputN=inputNum, num=Ap)
y[y[:]==-100]=default
# dy = np.zeros(data[:, testAP].shape) + 4
kernel = 1.0* RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \
+WhiteKernel(noise_level=1)
gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
stime = time.time()
gpr.fit(scaler.transform(X), y)
print("Time for GPR fitting: %.3f" % (time.time() - stime))
X_predict = grid[:, :2]
# Predict using gaussian process regressor
stime = time.time()
y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)
gridMean = np.c_[gridMean, y_gpr]
gridStd = np.c_[gridStd, y_std]
print("Time for GPR prediction with standard-deviation: %.3f"
% (time.time() - stime))
ax.append(plt.figure().add_subplot(111, projection='3d'))
ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')
dataUndetect = dataAll[dataAll[:, testAP] == -100]
# ax[Ap].scatter(reData[:, 0], reData[:, 1], reData[:, testAP], c='black')
ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP], c='b')
ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')
# ax[Ap].scatter(testdata[:, 0], testdata[:, 1], testdata[:,testAP], c='b')
ax[Ap].set_zlabel('RSSI (dBm)', font1) # 坐标轴
ax[Ap].set_ylabel('Y (m)', font1)
ax[Ap].set_xlabel('X (m)', font1)
ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'], prop=font1,
loc='lower center',bbox_to_anchor=(0.6,0.95))
if Ap ==2*interval-1:
np.savetxt("csv/measured.csv", np.array(np.c_[dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP]]), fmt='%f', delimiter=',', newline='\r\n')
np.savetxt("csv/recovered.csv", np.array(np.c_[reData[:, 0], reData[:, 1], reData[:, testAP]]), fmt='%f',
delimiter=',', newline='\r\n')
np.savetxt("csv/undetected.csv", np.array(np.c_[dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',
delimiter=',', newline='\r\n')
np.savetxt("csv/fingerprint.csv", np.array(np.c_[X_predict[:, 0], X_predict[:, 1], y_gpr[:]]), fmt='%f',
delimiter=',', newline='\r\n')
# err[:len(y_gpr),Ap]=y_gpr-grid[:,testAP]
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
print(gpr.kernel_)
np.savetxt("6b/GP/sample/3/means.csv", np.array(gridMean), fmt='%f', delimiter=',', newline='\r\n')
np.savetxt("6b/GP/sample/3/stds.csv", np.array(gridStd), fmt='%f', delimiter=',', newline='\r\n')
# plt.xticks(())
# plt.yticks(())
plt.show()
|
[
"#!/usr/bin/env python\n# -*-coding:utf-8 -*- \n'''\n@author: daishilong\n@contact: [email protected]\n'''\n\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared, Matern, ConstantKernel, RBF, RationalQuadratic\nfrom sklearn.model_selection import train_test_split\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom restore import Restore\nfrom sklearn.preprocessing import StandardScaler\nrng = np.random.RandomState(0)\n# data1 = np.loadtxt(\"E:/WiFi/day1/3F/0 m per s.csv\", dtype=float, delimiter=',')[:,1:]\n#\n# data3 = np.loadtxt(\"E:/WiFi/day3/1.5 m per s.txt\", dtype=float, delimiter=',')[:,1:]\n#\n# data4 = np.loadtxt(\"E:/WiFi/day4/1.5 m per s.txt\", dtype=float, delimiter=',')[:,1:]\n#\n# data5 = np.loadtxt(\"E:/WiFi/day5/1.5 m per s.txt\", dtype=float, delimiter=',')[:,1:]\ndata1 = np.loadtxt(\"E:/WiFi/day2/1.5 m per s.txt\", dtype=float, delimiter=',')[:,1:]\ndata2 = np.loadtxt(\"E:/project/PycharmProjects/wifiServer/3F/GP/0/meanRe.csv\", dtype=float, delimiter=',')\ndataAll = np.r_[data1, data2]\nTrainChoice = range(0, len(dataAll), 1)\ndataAll=dataAll[TrainChoice]\ngrid = np.loadtxt(\"candidate.csv\", dtype=float, delimiter=',')\n#grid = np.array(dataAll[:,:2])\ngridMean = np.array(grid)\ngridStd = np.array(grid)\ndefault = -90\n# dataAll[dataAll[:,:]==-100] = default\n\n# testdataAll = np.loadtxt(\"E:/WiFi/实验室6楼/wifiData/行人验证/lastpdr.csv\", dtype=float, delimiter=',')\n# testdataAll = testdataAll[testdataAll[:,1]==2,2:]\ntestdataAll = np.loadtxt(\"E:/WiFi/day1/3F/2 m per s.csv\", dtype=float, delimiter=',')[:,1:]\n# testdataAll = np.loadtxt(\"E:/WiFi/实验室6楼/wifiData/实验/1.2m_human.csv\", dtype=float, delimiter=',')[:,3:]\nfont1 = {'family' : 'Times New Roman',\n'weight' : 'normal',\n'size' : 20,}\nmodelPath = 'model/GP/'\ninputNum = 2\ninterval = 19\nscaler = StandardScaler().fit(dataAll[:,:inputNum])\nax = []\nerr = np.zeros([len(testdataAll),len(testdataAll[0])-2])\nfor Ap in range(0,interval):\n testAP = inputNum + Ap\n testAPBand = testAP + interval\n testdata = testdataAll[testdataAll[:,testAP]!=-100,:]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y,dy,reData = Restore(dataAll=dataAll,gap=interval,inputN=inputNum,num=Ap)\n y[y[:]==-100]=default\n\n X = dataAll[:,:inputNum]\n\n kernel = 1.0* RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \\\n +WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print(\"Time for GPR fitting: %.3f\" % (time.time() - stime))\n\n X_predict =grid[:,:2]\n # Predict using gaussian process regressor\n\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean,y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print(\"Time for GPR prediction with standard-deviation: %.3f\"\n % (time.time() - stime))\n print(gpr.kernel_)\n #print(y_gpr-testdata[:,testAPBand])\n\n # Plot results\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n # ax[Ap].scatter(reData[:, 0], reData[:, 1], reData[:, testAP], c='black')\n\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP], c='b')\n\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n # if Ap ==9:\n # np.savetxt(\"csv/measured.csv\", np.array(np.c_[dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP]]), fmt='%f', delimiter=',', newline='\\r\\n')\n # np.savetxt(\"csv/recovered.csv\", np.array(np.c_[reData[:, 0], reData[:, 1], reData[:, testAP]]), fmt='%f',\n # delimiter=',', newline='\\r\\n')\n # np.savetxt(\"csv/undetected.csv\", np.array(np.c_[dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',\n # delimiter=',', newline='\\r\\n')\n # np.savetxt(\"csv/fingerprint.csv\", np.array(np.c_[X_predict[:, 0], X_predict[:, 1], y_gpr[:]]), fmt='%f',\n # delimiter=',', newline='\\r\\n')\n\n\n # ax[Ap].scatter(testdata[:, 0], testdata[:, 1], testdata[:,testAP], c='b')\n ax[Ap].set_zlabel('RSSI (dBm)',font1) # 坐标轴\n ax[Ap].set_ylabel('Y (m)',font1)\n ax[Ap].set_xlabel('X (m)',font1)\n ax[Ap].legend(['measured data','undetected data','fingerprint map'],prop=font1,loc = 'lower center', bbox_to_anchor=(0.6,0.95))\n\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n joblib.dump(gpr, modelPath + \"ap\" + str(Ap) + \".pkl\")\nfor Ap in range(interval,2*interval):\n testAP = inputNum + Ap\n\n # ignore the default RSSI\n data = dataAll[:, :]\n data = data[:]\n # trainingData, testingData = train_test_split(data, test_size=0.2)\n # trainingData=trainingData[trainingData[:,0].argsort()]\n # testingData = testingData[testingData[:, 0].argsort()]\n\n\n\n\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n\n X = np.r_[data[:, 0:2]]\n # X_show = np.r_[trainingData[:, 0].reshape(-1,1),testingData[:1,0].reshape(-1,1)]\n # X_show = X_show[X_show[:, 0].argsort()]\n # y = data[:,testAP]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=-interval, inputN=inputNum, num=Ap)\n y[y[:]==-100]=default\n # dy = np.zeros(data[:, testAP].shape) + 4\n kernel = 1.0* RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e3)) \\\n +WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print(\"Time for GPR fitting: %.3f\" % (time.time() - stime))\n\n\n X_predict = grid[:, :2]\n # Predict using gaussian process regressor\n\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print(\"Time for GPR prediction with standard-deviation: %.3f\"\n % (time.time() - stime))\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n # ax[Ap].scatter(reData[:, 0], reData[:, 1], reData[:, testAP], c='black')\n\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP], c='b')\n\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n # ax[Ap].scatter(testdata[:, 0], testdata[:, 1], testdata[:,testAP], c='b')\n ax[Ap].set_zlabel('RSSI (dBm)', font1) # 坐标轴\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'], prop=font1,\n loc='lower center',bbox_to_anchor=(0.6,0.95))\n if Ap ==2*interval-1:\n np.savetxt(\"csv/measured.csv\", np.array(np.c_[dataRaw[:, 0], dataRaw[:, 1], dataRaw[:,testAP]]), fmt='%f', delimiter=',', newline='\\r\\n')\n np.savetxt(\"csv/recovered.csv\", np.array(np.c_[reData[:, 0], reData[:, 1], reData[:, testAP]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n np.savetxt(\"csv/undetected.csv\", np.array(np.c_[dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n np.savetxt(\"csv/fingerprint.csv\", np.array(np.c_[X_predict[:, 0], X_predict[:, 1], y_gpr[:]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n # err[:len(y_gpr),Ap]=y_gpr-grid[:,testAP]\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n print(gpr.kernel_)\n\nnp.savetxt(\"6b/GP/sample/3/means.csv\", np.array(gridMean), fmt='%f', delimiter=',', newline='\\r\\n')\nnp.savetxt(\"6b/GP/sample/3/stds.csv\", np.array(gridStd), fmt='%f', delimiter=',', newline='\\r\\n')\n# plt.xticks(())\n# plt.yticks(())\nplt.show()\n\n\n",
"<docstring token>\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import WhiteKernel, ExpSineSquared, Matern, ConstantKernel, RBF, RationalQuadratic\nfrom sklearn.model_selection import train_test_split\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom restore import Restore\nfrom sklearn.preprocessing import StandardScaler\nrng = np.random.RandomState(0)\ndata1 = np.loadtxt('E:/WiFi/day2/1.5 m per s.txt', dtype=float, delimiter=','\n )[:, 1:]\ndata2 = np.loadtxt('E:/project/PycharmProjects/wifiServer/3F/GP/0/meanRe.csv',\n dtype=float, delimiter=',')\ndataAll = np.r_[data1, data2]\nTrainChoice = range(0, len(dataAll), 1)\ndataAll = dataAll[TrainChoice]\ngrid = np.loadtxt('candidate.csv', dtype=float, delimiter=',')\ngridMean = np.array(grid)\ngridStd = np.array(grid)\ndefault = -90\ntestdataAll = np.loadtxt('E:/WiFi/day1/3F/2 m per s.csv', dtype=float,\n delimiter=',')[:, 1:]\nfont1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 20}\nmodelPath = 'model/GP/'\ninputNum = 2\ninterval = 19\nscaler = StandardScaler().fit(dataAll[:, :inputNum])\nax = []\nerr = np.zeros([len(testdataAll), len(testdataAll[0]) - 2])\nfor Ap in range(0, interval):\n testAP = inputNum + Ap\n testAPBand = testAP + interval\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n X = dataAll[:, :inputNum]\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n print(gpr.kernel_)\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n joblib.dump(gpr, modelPath + 'ap' + str(Ap) + '.pkl')\nfor Ap in range(interval, 2 * interval):\n testAP = inputNum + Ap\n data = dataAll[:, :]\n data = data[:]\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n X = np.r_[data[:, 0:2]]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=-interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n if Ap == 2 * interval - 1:\n np.savetxt('csv/measured.csv', np.array(np.c_[dataRaw[:, 0],\n dataRaw[:, 1], dataRaw[:, testAP]]), fmt='%f', delimiter=',',\n newline='\\r\\n')\n np.savetxt('csv/recovered.csv', np.array(np.c_[reData[:, 0], reData\n [:, 1], reData[:, testAP]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n np.savetxt('csv/undetected.csv', np.array(np.c_[dataUndetect[:, 0],\n dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n np.savetxt('csv/fingerprint.csv', np.array(np.c_[X_predict[:, 0],\n X_predict[:, 1], y_gpr[:]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n print(gpr.kernel_)\nnp.savetxt('6b/GP/sample/3/means.csv', np.array(gridMean), fmt='%f',\n delimiter=',', newline='\\r\\n')\nnp.savetxt('6b/GP/sample/3/stds.csv', np.array(gridStd), fmt='%f',\n delimiter=',', newline='\\r\\n')\nplt.show()\n",
"<docstring token>\n<import token>\nrng = np.random.RandomState(0)\ndata1 = np.loadtxt('E:/WiFi/day2/1.5 m per s.txt', dtype=float, delimiter=','\n )[:, 1:]\ndata2 = np.loadtxt('E:/project/PycharmProjects/wifiServer/3F/GP/0/meanRe.csv',\n dtype=float, delimiter=',')\ndataAll = np.r_[data1, data2]\nTrainChoice = range(0, len(dataAll), 1)\ndataAll = dataAll[TrainChoice]\ngrid = np.loadtxt('candidate.csv', dtype=float, delimiter=',')\ngridMean = np.array(grid)\ngridStd = np.array(grid)\ndefault = -90\ntestdataAll = np.loadtxt('E:/WiFi/day1/3F/2 m per s.csv', dtype=float,\n delimiter=',')[:, 1:]\nfont1 = {'family': 'Times New Roman', 'weight': 'normal', 'size': 20}\nmodelPath = 'model/GP/'\ninputNum = 2\ninterval = 19\nscaler = StandardScaler().fit(dataAll[:, :inputNum])\nax = []\nerr = np.zeros([len(testdataAll), len(testdataAll[0]) - 2])\nfor Ap in range(0, interval):\n testAP = inputNum + Ap\n testAPBand = testAP + interval\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n X = dataAll[:, :inputNum]\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n print(gpr.kernel_)\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n joblib.dump(gpr, modelPath + 'ap' + str(Ap) + '.pkl')\nfor Ap in range(interval, 2 * interval):\n testAP = inputNum + Ap\n data = dataAll[:, :]\n data = data[:]\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n X = np.r_[data[:, 0:2]]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=-interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n if Ap == 2 * interval - 1:\n np.savetxt('csv/measured.csv', np.array(np.c_[dataRaw[:, 0],\n dataRaw[:, 1], dataRaw[:, testAP]]), fmt='%f', delimiter=',',\n newline='\\r\\n')\n np.savetxt('csv/recovered.csv', np.array(np.c_[reData[:, 0], reData\n [:, 1], reData[:, testAP]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n np.savetxt('csv/undetected.csv', np.array(np.c_[dataUndetect[:, 0],\n dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n np.savetxt('csv/fingerprint.csv', np.array(np.c_[X_predict[:, 0],\n X_predict[:, 1], y_gpr[:]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n print(gpr.kernel_)\nnp.savetxt('6b/GP/sample/3/means.csv', np.array(gridMean), fmt='%f',\n delimiter=',', newline='\\r\\n')\nnp.savetxt('6b/GP/sample/3/stds.csv', np.array(gridStd), fmt='%f',\n delimiter=',', newline='\\r\\n')\nplt.show()\n",
"<docstring token>\n<import token>\n<assignment token>\nfor Ap in range(0, interval):\n testAP = inputNum + Ap\n testAPBand = testAP + interval\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n X = dataAll[:, :inputNum]\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n print(gpr.kernel_)\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n joblib.dump(gpr, modelPath + 'ap' + str(Ap) + '.pkl')\nfor Ap in range(interval, 2 * interval):\n testAP = inputNum + Ap\n data = dataAll[:, :]\n data = data[:]\n testdata = testdataAll[testdataAll[:, testAP] != -100, :]\n X = np.r_[data[:, 0:2]]\n dataRaw = dataAll[dataAll[:, testAP] != -100]\n y, dy, reData = Restore(dataAll=dataAll, gap=-interval, inputN=inputNum,\n num=Ap)\n y[y[:] == -100] = default\n kernel = 1.0 * RBF(length_scale=1.0, length_scale_bounds=(0.01, 1000.0)\n ) + WhiteKernel(noise_level=1)\n gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)\n stime = time.time()\n gpr.fit(scaler.transform(X), y)\n print('Time for GPR fitting: %.3f' % (time.time() - stime))\n X_predict = grid[:, :2]\n stime = time.time()\n y_gpr, y_std = gpr.predict(scaler.transform(X_predict), return_std=True)\n gridMean = np.c_[gridMean, y_gpr]\n gridStd = np.c_[gridStd, y_std]\n print('Time for GPR prediction with standard-deviation: %.3f' % (time.\n time() - stime))\n ax.append(plt.figure().add_subplot(111, projection='3d'))\n ax[Ap].scatter(dataRaw[:, 0], dataRaw[:, 1], dataRaw[:, testAP], c='r')\n dataUndetect = dataAll[dataAll[:, testAP] == -100]\n ax[Ap].scatter(dataUndetect[:, 0], dataUndetect[:, 1], dataUndetect[:,\n testAP], c='b')\n ax[Ap].scatter(X_predict[:, 0], X_predict[:, 1], y_gpr[:], c='g')\n ax[Ap].set_zlabel('RSSI (dBm)', font1)\n ax[Ap].set_ylabel('Y (m)', font1)\n ax[Ap].set_xlabel('X (m)', font1)\n ax[Ap].legend(['measured data', 'undetected data', 'fingerprint map'],\n prop=font1, loc='lower center', bbox_to_anchor=(0.6, 0.95))\n if Ap == 2 * interval - 1:\n np.savetxt('csv/measured.csv', np.array(np.c_[dataRaw[:, 0],\n dataRaw[:, 1], dataRaw[:, testAP]]), fmt='%f', delimiter=',',\n newline='\\r\\n')\n np.savetxt('csv/recovered.csv', np.array(np.c_[reData[:, 0], reData\n [:, 1], reData[:, testAP]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n np.savetxt('csv/undetected.csv', np.array(np.c_[dataUndetect[:, 0],\n dataUndetect[:, 1], dataUndetect[:, testAP]]), fmt='%f',\n delimiter=',', newline='\\r\\n')\n np.savetxt('csv/fingerprint.csv', np.array(np.c_[X_predict[:, 0],\n X_predict[:, 1], y_gpr[:]]), fmt='%f', delimiter=',', newline=\n '\\r\\n')\n plt.xticks(fontsize=15)\n plt.yticks(fontsize=15)\n print(gpr.kernel_)\nnp.savetxt('6b/GP/sample/3/means.csv', np.array(gridMean), fmt='%f',\n delimiter=',', newline='\\r\\n')\nnp.savetxt('6b/GP/sample/3/stds.csv', np.array(gridStd), fmt='%f',\n delimiter=',', newline='\\r\\n')\nplt.show()\n",
"<docstring token>\n<import token>\n<assignment token>\n<code token>\n"
] | false |
99,494 |
6abdf9fde9b86cd9be2591c387837f41ac3aef7f
|
# -*- coding: utf-8 -*-
from tkinter import *
root = Tk()
root.title('RadioButton')
root.geometry('400x300+400+20')
# 创建Frame
fm = Frame(root, width=50, height=8)
fm.pack()
# 文本框在frame内创建
text = Text(fm, width=50, height=8)
text.pack(side=LEFT, fill=Y)
str = 'On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.'
# 插入字符串
text.insert(INSERT, str)
# 删除文本内容
# text.delete('1.0', 'end')
# 滚动条在frame内创建
scroll = Scrollbar(fm)
scroll.pack(side=RIGHT, fill=Y)
# 是文本和滚动条相互生效
scroll.config(command=text.yview)
text.config(yscrollcommand=scroll.set)
root.mainloop()
|
[
"# -*- coding: utf-8 -*-\nfrom tkinter import *\n\nroot = Tk()\n\nroot.title('RadioButton')\n\nroot.geometry('400x300+400+20')\n\n# 创建Frame\nfm = Frame(root, width=50, height=8)\nfm.pack()\n\n# 文本框在frame内创建\ntext = Text(fm, width=50, height=8)\ntext.pack(side=LEFT, fill=Y)\n\nstr = 'On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.'\n\n# 插入字符串\ntext.insert(INSERT, str)\n\n# 删除文本内容\n# text.delete('1.0', 'end')\n\n# 滚动条在frame内创建\nscroll = Scrollbar(fm)\nscroll.pack(side=RIGHT, fill=Y)\n\n# 是文本和滚动条相互生效\nscroll.config(command=text.yview)\ntext.config(yscrollcommand=scroll.set)\n\n\n\nroot.mainloop()",
"from tkinter import *\nroot = Tk()\nroot.title('RadioButton')\nroot.geometry('400x300+400+20')\nfm = Frame(root, width=50, height=8)\nfm.pack()\ntext = Text(fm, width=50, height=8)\ntext.pack(side=LEFT, fill=Y)\nstr = (\n 'On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.'\n )\ntext.insert(INSERT, str)\nscroll = Scrollbar(fm)\nscroll.pack(side=RIGHT, fill=Y)\nscroll.config(command=text.yview)\ntext.config(yscrollcommand=scroll.set)\nroot.mainloop()\n",
"<import token>\nroot = Tk()\nroot.title('RadioButton')\nroot.geometry('400x300+400+20')\nfm = Frame(root, width=50, height=8)\nfm.pack()\ntext = Text(fm, width=50, height=8)\ntext.pack(side=LEFT, fill=Y)\nstr = (\n 'On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.On October 18, TÜV Rheinland, a global leader for independent inspection services, unveiled the plaque for its Internet-of-Things Excellence Center in Longhua, Shenzhen to mark its official opening.'\n )\ntext.insert(INSERT, str)\nscroll = Scrollbar(fm)\nscroll.pack(side=RIGHT, fill=Y)\nscroll.config(command=text.yview)\ntext.config(yscrollcommand=scroll.set)\nroot.mainloop()\n",
"<import token>\n<assignment token>\nroot.title('RadioButton')\nroot.geometry('400x300+400+20')\n<assignment token>\nfm.pack()\n<assignment token>\ntext.pack(side=LEFT, fill=Y)\n<assignment token>\ntext.insert(INSERT, str)\n<assignment token>\nscroll.pack(side=RIGHT, fill=Y)\nscroll.config(command=text.yview)\ntext.config(yscrollcommand=scroll.set)\nroot.mainloop()\n",
"<import token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"
] | false |
99,495 |
bb5be6ae74ccc71edf6055883fc9ccdfa48ee6e4
|
import csv
import json
def pair_entity_ratio(found_pair_set_len, entity_count):
return found_pair_set_len / entity_count
def precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):
# if a neg_pair_set is provided,
# consider the "universe" to be only the what's inside pos_pair_set and neg_pair_set,
# because this means a previous blocking was applied
if neg_pair_set is not None:
found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)
true_positives = found_pair_set & pos_pair_set
false_positives = found_pair_set - pos_pair_set
if true_positives:
precision = len(true_positives) / (len(true_positives) + len(false_positives))
else:
precision = 0.0
recall = len(true_positives) / len(pos_pair_set)
return precision, recall
def f1_score(precision, recall):
if precision or recall:
return (2 * precision * recall) / (precision + recall)
else:
return 0.0
def evaluate_output_json(
unlabeled_csv_filepath, output_json_filepath, pos_pair_json_filepath, csv_encoding="utf-8"
):
with open(
unlabeled_csv_filepath, "r", newline="", encoding=csv_encoding
) as record_dict_csv_file:
record_count = sum(1 for __ in csv.DictReader(record_dict_csv_file))
with open(output_json_filepath, "r") as f:
found_pair_set = json.load(f)
found_pair_set = set(tuple(t) for t in found_pair_set)
with open(pos_pair_json_filepath, "r") as f:
pos_pair_set = json.load(f)
pos_pair_set = set(tuple(t) for t in pos_pair_set)
precision, recall = precision_and_recall(found_pair_set, pos_pair_set)
return (
precision,
recall,
f1_score(precision, recall),
pair_entity_ratio(len(found_pair_set), record_count),
)
|
[
"import csv\nimport json\n\n\ndef pair_entity_ratio(found_pair_set_len, entity_count):\n return found_pair_set_len / entity_count\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n # if a neg_pair_set is provided,\n # consider the \"universe\" to be only the what's inside pos_pair_set and neg_pair_set,\n # because this means a previous blocking was applied\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\ndef f1_score(precision, recall):\n if precision or recall:\n return (2 * precision * recall) / (precision + recall)\n else:\n return 0.0\n\n\ndef evaluate_output_json(\n unlabeled_csv_filepath, output_json_filepath, pos_pair_json_filepath, csv_encoding=\"utf-8\"\n):\n with open(\n unlabeled_csv_filepath, \"r\", newline=\"\", encoding=csv_encoding\n ) as record_dict_csv_file:\n record_count = sum(1 for __ in csv.DictReader(record_dict_csv_file))\n\n with open(output_json_filepath, \"r\") as f:\n found_pair_set = json.load(f)\n found_pair_set = set(tuple(t) for t in found_pair_set)\n\n with open(pos_pair_json_filepath, \"r\") as f:\n pos_pair_set = json.load(f)\n pos_pair_set = set(tuple(t) for t in pos_pair_set)\n\n precision, recall = precision_and_recall(found_pair_set, pos_pair_set)\n return (\n precision,\n recall,\n f1_score(precision, recall),\n pair_entity_ratio(len(found_pair_set), record_count),\n )\n",
"import csv\nimport json\n\n\ndef pair_entity_ratio(found_pair_set_len, entity_count):\n return found_pair_set_len / entity_count\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(\n false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\ndef f1_score(precision, recall):\n if precision or recall:\n return 2 * precision * recall / (precision + recall)\n else:\n return 0.0\n\n\ndef evaluate_output_json(unlabeled_csv_filepath, output_json_filepath,\n pos_pair_json_filepath, csv_encoding='utf-8'):\n with open(unlabeled_csv_filepath, 'r', newline='', encoding=csv_encoding\n ) as record_dict_csv_file:\n record_count = sum(1 for __ in csv.DictReader(record_dict_csv_file))\n with open(output_json_filepath, 'r') as f:\n found_pair_set = json.load(f)\n found_pair_set = set(tuple(t) for t in found_pair_set)\n with open(pos_pair_json_filepath, 'r') as f:\n pos_pair_set = json.load(f)\n pos_pair_set = set(tuple(t) for t in pos_pair_set)\n precision, recall = precision_and_recall(found_pair_set, pos_pair_set)\n return precision, recall, f1_score(precision, recall), pair_entity_ratio(\n len(found_pair_set), record_count)\n",
"<import token>\n\n\ndef pair_entity_ratio(found_pair_set_len, entity_count):\n return found_pair_set_len / entity_count\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(\n false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\ndef f1_score(precision, recall):\n if precision or recall:\n return 2 * precision * recall / (precision + recall)\n else:\n return 0.0\n\n\ndef evaluate_output_json(unlabeled_csv_filepath, output_json_filepath,\n pos_pair_json_filepath, csv_encoding='utf-8'):\n with open(unlabeled_csv_filepath, 'r', newline='', encoding=csv_encoding\n ) as record_dict_csv_file:\n record_count = sum(1 for __ in csv.DictReader(record_dict_csv_file))\n with open(output_json_filepath, 'r') as f:\n found_pair_set = json.load(f)\n found_pair_set = set(tuple(t) for t in found_pair_set)\n with open(pos_pair_json_filepath, 'r') as f:\n pos_pair_set = json.load(f)\n pos_pair_set = set(tuple(t) for t in pos_pair_set)\n precision, recall = precision_and_recall(found_pair_set, pos_pair_set)\n return precision, recall, f1_score(precision, recall), pair_entity_ratio(\n len(found_pair_set), record_count)\n",
"<import token>\n\n\ndef pair_entity_ratio(found_pair_set_len, entity_count):\n return found_pair_set_len / entity_count\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(\n false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\ndef f1_score(precision, recall):\n if precision or recall:\n return 2 * precision * recall / (precision + recall)\n else:\n return 0.0\n\n\n<function token>\n",
"<import token>\n\n\ndef pair_entity_ratio(found_pair_set_len, entity_count):\n return found_pair_set_len / entity_count\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(\n false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\n<function token>\n<function token>\n",
"<import token>\n<function token>\n\n\ndef precision_and_recall(found_pair_set, pos_pair_set, neg_pair_set=None):\n if neg_pair_set is not None:\n found_pair_set = found_pair_set & (pos_pair_set | neg_pair_set)\n true_positives = found_pair_set & pos_pair_set\n false_positives = found_pair_set - pos_pair_set\n if true_positives:\n precision = len(true_positives) / (len(true_positives) + len(\n false_positives))\n else:\n precision = 0.0\n recall = len(true_positives) / len(pos_pair_set)\n return precision, recall\n\n\n<function token>\n<function token>\n",
"<import token>\n<function token>\n<function token>\n<function token>\n<function token>\n"
] | false |
99,496 |
2d58ba49074e5af44fa7979ca2c0c8c98c38f9d2
|
import os
import sys
import battlefield
import bombfield
import ship
import player
nth = {
1: "primero",
2: "segundo",
3: "terceiro",
4: "quarto",
5: "quinto",
6: "sexto",
7: "setimo",
8: "oitavo"
}
rowlist = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14"]
class Game:
def clear(self):
os.system('cls' if os.name == 'nt' else 'clear')
def __init__(self):
self.p1 = ""
self.p2 = ""
self.p1Field = battlefield.Battlefield()
self.p2Field = battlefield.Battlefield()
self.p1BombField = bombfield.Bombfield()
self.p2BombField = bombfield.Bombfield()
self.ships = []
self.ships.append(ship.Ship(5))
self.ships.append(ship.Ship(4))
self.ships.append(ship.Ship(4))
self.ships.append(ship.Ship(2))
self.ships.append(ship.Ship(2))
self.ships.append(ship.Ship(2))
self.ships.append(ship.Ship(1))
self.ships.append(ship.Ship(1))
def columnExist(self, column):
if ("A" <= column <= "N"):
return True
else:
return False
def rowExist(self, row):
if (1 <= row <= 14):
return True
else:
return False
def printfield(self, f):
l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']
spacing = ' '.join(['{:<2}'] * len(l))
text = spacing.format(*l)
for v in range(1, len(l)):
text += "\n" + spacing.format(v, f['A'][v], f['B'][v], f['C'][v], f['D'][v], f['E'][v], f['F'][v],
f['G'][v], f['H'][v], f['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v],
f['N'][
v])
return text
def placeShips(self, player):
counter = 1
print(player.name + ", coloque seus navios na posição inicial,\n")
print("Depois diga a direção (right, left, up ou down)\n")
print(self.printfield(player.field.field))
for x in player.ships:
column = ""
row = ""
direction = ""
cellBusy = True
pff = player.field.field
while self.columnExist(column) == False or row not in rowlist or cellBusy == True:
userInput = input(
player.name + ", em que celula (A-N)(1-14) você quer colocar sua " + nth[counter] + " barca?\n")
if (len(userInput) >= 2):
column = userInput[0].upper()
row = userInput[1]
if len(userInput) >= 3:
row += userInput[2]
if (self.columnExist(column) and row in rowlist):
cellBusy = pff[column][int(row)]
row = int(row)
newrow = row
newcolumn = column
if len(x.parts)==1:
pff[newcolumn][newrow] = True
else:
while (
direction != "right" and direction != "left" and direction != "up" and direction != "down") or self.rowExist(
newrow) == False or self.columnExist(newcolumn) == False or cellBusy == True:
direction = input(player.name + ", qual direção (right, left, up or down) seu barco " + nth[
counter] + " está virado?\n")
cellBusy = False
partCounter = 0
for y in range(len(x.parts)):
newcolumn = column
newrow = row
if (direction == "down"):
newrow = row + partCounter
elif (direction == "up"):
newrow = row - partCounter
elif (direction == "left"):
newcolumn = chr(ord(column) - partCounter)
elif (direction == "right"):
newcolumn = chr(ord(column) + partCounter)
partCounter += 1
if self.columnExist(newcolumn) and self.rowExist(newrow):
if pff[newcolumn][newrow] == True:
cellBusy = pff[newcolumn][newrow]
elif pff[newcolumn][newrow] == False and partCounter == len(x.parts):
for p in range(0, partCounter):
if (ord(newcolumn) < ord(column)):
pff[chr(ord(column) - p)][newrow] = True
elif (ord(newcolumn) > ord(column)):
pff[chr(ord(column) + p)][newrow] = True
elif (newrow < row):
pff[newcolumn][newrow + p] = True
elif (newrow > row):
pff[newcolumn][newrow - p] = True
self.clear()
print(self.printfield(player.field.field))
counter += 1
def newPlayer(self, n, ships, field, bombfield):
newName = input("Player " + str(n) + ",qual teu nick?\n")
while newName == "":
newName = input("Digita ai mano\n")
self.clear()
p = player.Player(newName, ships[:], field, bombfield)
self.placeShips(p)
return p
def anythingLeft(self, d):
newList = []
def myprint(d):
for k, v in d.items():
if isinstance(v, dict):
myprint(v)
else:
newList.append(v)
myprint(d)
return True in newList
def selectCell(self, player):
column = ""
row = ""
while self.columnExist(column) == False or row not in rowlist:
userInput = input(player.name + ", onde (A-N)(1-14) tu quer mandar o pipoco?\n")
if (len(userInput) < 2):
column = ""
row = ""
else:
column = userInput[0].upper()
row = userInput[1]
if len(userInput) == 3:
row += userInput[2]
return [column, row]
def bomb(self, player, enemy, column, row):
eff = enemy.field.field
self.result = ''
row = int(row)
if (eff[column][row] == True):
self.result = 'X'
eff[column][row] = 'X'
player.bombfield.field[column][row] = 'X'
if self.anythingLeft(eff) == False:
self.result = player.name + " wins!"
else:
self.result = 'O'
eff[column][row] = '@'
if player.bombfield.field[column][row] != 'X':
player.bombfield.field[column][row] = 'O'
def start(self):
while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self.p2.field.field):
print('Teu campo:\n')
print(self.printfield(self.p1.field.field))
print('\nCampo delas:\n')
print(self.printfield(self.p1.bombfield.field))
cell = self.selectCell(self.p1)
self.bomb(self.p1, self.p2, cell[0], cell[1])
self.clear()
if self.result == 'X':
print('ACERTOU CARA!')
elif self.result == 'O':
print('ERROOOOOU!')
else:
print(self.result)
sys.exit() # Exit the application
print(self.printfield(self.p1.bombfield.field))
input('aperta enter men')
self.clear()
if self.anythingLeft(self.p1.field.field) and self.anythingLeft(self.p2.field.field):
print('Teu campo:\n')
print(self.printfield(self.p2.field.field))
print('\nCampo do babaca la:\n')
print(self.printfield(self.p2.bombfield.field))
cell = self.selectCell(self.p2)
self.bomb(self.p2, self.p1, cell[0], cell[1])
self.clear()
if self.result == 'X':
print('Acertou, mizera!')
elif self.result == 'O':
print('Errou de novo pora!')
else:
print(self.result)
sys.exit()
input('Aperta enter parça')
self.clear()
|
[
"import os\r\nimport sys\r\nimport battlefield\r\nimport bombfield\r\nimport ship\r\nimport player\r\nnth = {\r\n 1: \"primero\",\r\n 2: \"segundo\",\r\n 3: \"terceiro\",\r\n 4: \"quarto\",\r\n 5: \"quinto\",\r\n 6: \"sexto\",\r\n 7: \"setimo\",\r\n 8: \"oitavo\"\r\n}\r\n\r\nrowlist = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\"]\r\n\r\n\r\nclass Game:\r\n\r\n def clear(self):\r\n os.system('cls' if os.name == 'nt' else 'clear')\r\n\r\n def __init__(self):\r\n self.p1 = \"\"\r\n self.p2 = \"\"\r\n\r\n self.p1Field = battlefield.Battlefield()\r\n self.p2Field = battlefield.Battlefield()\r\n self.p1BombField = bombfield.Bombfield()\r\n self.p2BombField = bombfield.Bombfield()\r\n\r\n self.ships = []\r\n self.ships.append(ship.Ship(5))\r\n self.ships.append(ship.Ship(4))\r\n self.ships.append(ship.Ship(4))\r\n self.ships.append(ship.Ship(2))\r\n self.ships.append(ship.Ship(2))\r\n self.ships.append(ship.Ship(2))\r\n self.ships.append(ship.Ship(1))\r\n self.ships.append(ship.Ship(1))\r\n\r\n def columnExist(self, column):\r\n if (\"A\" <= column <= \"N\"):\r\n return True\r\n else:\r\n return False\r\n\r\n def rowExist(self, row):\r\n if (1 <= row <= 14):\r\n return True\r\n else:\r\n return False\r\n\r\n def printfield(self, f):\r\n\r\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N']\r\n spacing = ' '.join(['{:<2}'] * len(l))\r\n text = spacing.format(*l)\r\n for v in range(1, len(l)):\r\n text += \"\\n\" + spacing.format(v, f['A'][v], f['B'][v], f['C'][v], f['D'][v], f['E'][v], f['F'][v],\r\n f['G'][v], f['H'][v], f['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v],\r\n f['N'][\r\n v])\r\n\r\n return text\r\n\r\n def placeShips(self, player):\r\n counter = 1\r\n\r\n print(player.name + \", coloque seus navios na posição inicial,\\n\")\r\n print(\"Depois diga a direção (right, left, up ou down)\\n\")\r\n\r\n print(self.printfield(player.field.field))\r\n\r\n for x in player.ships:\r\n column = \"\"\r\n row = \"\"\r\n direction = \"\"\r\n cellBusy = True\r\n pff = player.field.field\r\n while self.columnExist(column) == False or row not in rowlist or cellBusy == True:\r\n userInput = input(\r\n player.name + \", em que celula (A-N)(1-14) você quer colocar sua \" + nth[counter] + \" barca?\\n\")\r\n if (len(userInput) >= 2):\r\n column = userInput[0].upper()\r\n row = userInput[1]\r\n if len(userInput) >= 3:\r\n row += userInput[2]\r\n if (self.columnExist(column) and row in rowlist):\r\n cellBusy = pff[column][int(row)]\r\n\r\n row = int(row)\r\n\r\n newrow = row\r\n newcolumn = column\r\n if len(x.parts)==1:\r\n pff[newcolumn][newrow] = True\r\n else:\r\n while (\r\n direction != \"right\" and direction != \"left\" and direction != \"up\" and direction != \"down\") or self.rowExist(\r\n newrow) == False or self.columnExist(newcolumn) == False or cellBusy == True:\r\n direction = input(player.name + \", qual direção (right, left, up or down) seu barco \" + nth[\r\n counter] + \" está virado?\\n\")\r\n cellBusy = False\r\n partCounter = 0\r\n\r\n for y in range(len(x.parts)):\r\n newcolumn = column\r\n newrow = row\r\n if (direction == \"down\"):\r\n newrow = row + partCounter\r\n\r\n elif (direction == \"up\"):\r\n newrow = row - partCounter\r\n\r\n elif (direction == \"left\"):\r\n newcolumn = chr(ord(column) - partCounter)\r\n\r\n elif (direction == \"right\"):\r\n newcolumn = chr(ord(column) + partCounter)\r\n\r\n partCounter += 1\r\n if self.columnExist(newcolumn) and self.rowExist(newrow):\r\n if pff[newcolumn][newrow] == True:\r\n cellBusy = pff[newcolumn][newrow]\r\n\r\n elif pff[newcolumn][newrow] == False and partCounter == len(x.parts):\r\n for p in range(0, partCounter):\r\n if (ord(newcolumn) < ord(column)):\r\n pff[chr(ord(column) - p)][newrow] = True\r\n elif (ord(newcolumn) > ord(column)):\r\n pff[chr(ord(column) + p)][newrow] = True\r\n elif (newrow < row):\r\n pff[newcolumn][newrow + p] = True\r\n elif (newrow > row):\r\n pff[newcolumn][newrow - p] = True\r\n\r\n self.clear()\r\n print(self.printfield(player.field.field))\r\n counter += 1\r\n\r\n def newPlayer(self, n, ships, field, bombfield):\r\n newName = input(\"Player \" + str(n) + \",qual teu nick?\\n\")\r\n while newName == \"\":\r\n newName = input(\"Digita ai mano\\n\")\r\n self.clear()\r\n p = player.Player(newName, ships[:], field, bombfield)\r\n\r\n self.placeShips(p)\r\n return p\r\n\r\n def anythingLeft(self, d):\r\n newList = []\r\n\r\n def myprint(d):\r\n for k, v in d.items():\r\n if isinstance(v, dict):\r\n myprint(v)\r\n else:\r\n newList.append(v)\r\n\r\n myprint(d)\r\n return True in newList\r\n\r\n def selectCell(self, player):\r\n column = \"\"\r\n row = \"\"\r\n while self.columnExist(column) == False or row not in rowlist:\r\n userInput = input(player.name + \", onde (A-N)(1-14) tu quer mandar o pipoco?\\n\")\r\n\r\n if (len(userInput) < 2):\r\n column = \"\"\r\n row = \"\"\r\n else:\r\n column = userInput[0].upper()\r\n row = userInput[1]\r\n if len(userInput) == 3:\r\n row += userInput[2]\r\n\r\n return [column, row]\r\n\r\n def bomb(self, player, enemy, column, row):\r\n eff = enemy.field.field\r\n self.result = ''\r\n\r\n row = int(row)\r\n if (eff[column][row] == True):\r\n self.result = 'X'\r\n eff[column][row] = 'X'\r\n player.bombfield.field[column][row] = 'X'\r\n\r\n if self.anythingLeft(eff) == False:\r\n self.result = player.name + \" wins!\"\r\n else:\r\n self.result = 'O'\r\n eff[column][row] = '@'\r\n if player.bombfield.field[column][row] != 'X':\r\n player.bombfield.field[column][row] = 'O'\r\n\r\n def start(self):\r\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self.p2.field.field):\r\n print('Teu campo:\\n')\r\n print(self.printfield(self.p1.field.field))\r\n print('\\nCampo delas:\\n')\r\n print(self.printfield(self.p1.bombfield.field))\r\n cell = self.selectCell(self.p1)\r\n self.bomb(self.p1, self.p2, cell[0], cell[1])\r\n self.clear()\r\n\r\n if self.result == 'X':\r\n print('ACERTOU CARA!')\r\n elif self.result == 'O':\r\n print('ERROOOOOU!')\r\n else:\r\n print(self.result)\r\n sys.exit() # Exit the application\r\n\r\n print(self.printfield(self.p1.bombfield.field))\r\n\r\n input('aperta enter men')\r\n self.clear()\r\n\r\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(self.p2.field.field):\r\n print('Teu campo:\\n')\r\n print(self.printfield(self.p2.field.field))\r\n print('\\nCampo do babaca la:\\n')\r\n print(self.printfield(self.p2.bombfield.field))\r\n cell = self.selectCell(self.p2)\r\n self.bomb(self.p2, self.p1, cell[0], cell[1])\r\n self.clear()\r\n\r\n if self.result == 'X':\r\n print('Acertou, mizera!')\r\n elif self.result == 'O':\r\n print('Errou de novo pora!')\r\n else:\r\n print(self.result)\r\n sys.exit()\r\n\r\n input('Aperta enter parça')\r\n self.clear()",
"import os\nimport sys\nimport battlefield\nimport bombfield\nimport ship\nimport player\nnth = {(1): 'primero', (2): 'segundo', (3): 'terceiro', (4): 'quarto', (5):\n 'quinto', (6): 'sexto', (7): 'setimo', (8): 'oitavo'}\nrowlist = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',\n '13', '14']\n\n\nclass Game:\n\n def clear(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n\n def placeShips(self, player):\n counter = 1\n print(player.name + ', coloque seus navios na posição inicial,\\n')\n print('Depois diga a direção (right, left, up ou down)\\n')\n print(self.printfield(player.field.field))\n for x in player.ships:\n column = ''\n row = ''\n direction = ''\n cellBusy = True\n pff = player.field.field\n while self.columnExist(column\n ) == False or row not in rowlist or cellBusy == True:\n userInput = input(player.name +\n ', em que celula (A-N)(1-14) você quer colocar sua ' +\n nth[counter] + ' barca?\\n')\n if len(userInput) >= 2:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) >= 3:\n row += userInput[2]\n if self.columnExist(column) and row in rowlist:\n cellBusy = pff[column][int(row)]\n row = int(row)\n newrow = row\n newcolumn = column\n if len(x.parts) == 1:\n pff[newcolumn][newrow] = True\n else:\n while (direction != 'right' and direction != 'left' and \n direction != 'up' and direction != 'down' or self.\n rowExist(newrow) == False or self.columnExist(newcolumn\n ) == False or cellBusy == True):\n direction = input(player.name +\n ', qual direção (right, left, up or down) seu barco ' +\n nth[counter] + ' está virado?\\n')\n cellBusy = False\n partCounter = 0\n for y in range(len(x.parts)):\n newcolumn = column\n newrow = row\n if direction == 'down':\n newrow = row + partCounter\n elif direction == 'up':\n newrow = row - partCounter\n elif direction == 'left':\n newcolumn = chr(ord(column) - partCounter)\n elif direction == 'right':\n newcolumn = chr(ord(column) + partCounter)\n partCounter += 1\n if self.columnExist(newcolumn) and self.rowExist(newrow\n ):\n if pff[newcolumn][newrow] == True:\n cellBusy = pff[newcolumn][newrow]\n elif pff[newcolumn][newrow\n ] == False and partCounter == len(x.parts):\n for p in range(0, partCounter):\n if ord(newcolumn) < ord(column):\n pff[chr(ord(column) - p)][newrow] = True\n elif ord(newcolumn) > ord(column):\n pff[chr(ord(column) + p)][newrow] = True\n elif newrow < row:\n pff[newcolumn][newrow + p] = True\n elif newrow > row:\n pff[newcolumn][newrow - p] = True\n self.clear()\n print(self.printfield(player.field.field))\n counter += 1\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n\n def selectCell(self, player):\n column = ''\n row = ''\n while self.columnExist(column) == False or row not in rowlist:\n userInput = input(player.name +\n ', onde (A-N)(1-14) tu quer mandar o pipoco?\\n')\n if len(userInput) < 2:\n column = ''\n row = ''\n else:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) == 3:\n row += userInput[2]\n return [column, row]\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\nnth = {(1): 'primero', (2): 'segundo', (3): 'terceiro', (4): 'quarto', (5):\n 'quinto', (6): 'sexto', (7): 'setimo', (8): 'oitavo'}\nrowlist = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',\n '13', '14']\n\n\nclass Game:\n\n def clear(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n\n def placeShips(self, player):\n counter = 1\n print(player.name + ', coloque seus navios na posição inicial,\\n')\n print('Depois diga a direção (right, left, up ou down)\\n')\n print(self.printfield(player.field.field))\n for x in player.ships:\n column = ''\n row = ''\n direction = ''\n cellBusy = True\n pff = player.field.field\n while self.columnExist(column\n ) == False or row not in rowlist or cellBusy == True:\n userInput = input(player.name +\n ', em que celula (A-N)(1-14) você quer colocar sua ' +\n nth[counter] + ' barca?\\n')\n if len(userInput) >= 2:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) >= 3:\n row += userInput[2]\n if self.columnExist(column) and row in rowlist:\n cellBusy = pff[column][int(row)]\n row = int(row)\n newrow = row\n newcolumn = column\n if len(x.parts) == 1:\n pff[newcolumn][newrow] = True\n else:\n while (direction != 'right' and direction != 'left' and \n direction != 'up' and direction != 'down' or self.\n rowExist(newrow) == False or self.columnExist(newcolumn\n ) == False or cellBusy == True):\n direction = input(player.name +\n ', qual direção (right, left, up or down) seu barco ' +\n nth[counter] + ' está virado?\\n')\n cellBusy = False\n partCounter = 0\n for y in range(len(x.parts)):\n newcolumn = column\n newrow = row\n if direction == 'down':\n newrow = row + partCounter\n elif direction == 'up':\n newrow = row - partCounter\n elif direction == 'left':\n newcolumn = chr(ord(column) - partCounter)\n elif direction == 'right':\n newcolumn = chr(ord(column) + partCounter)\n partCounter += 1\n if self.columnExist(newcolumn) and self.rowExist(newrow\n ):\n if pff[newcolumn][newrow] == True:\n cellBusy = pff[newcolumn][newrow]\n elif pff[newcolumn][newrow\n ] == False and partCounter == len(x.parts):\n for p in range(0, partCounter):\n if ord(newcolumn) < ord(column):\n pff[chr(ord(column) - p)][newrow] = True\n elif ord(newcolumn) > ord(column):\n pff[chr(ord(column) + p)][newrow] = True\n elif newrow < row:\n pff[newcolumn][newrow + p] = True\n elif newrow > row:\n pff[newcolumn][newrow - p] = True\n self.clear()\n print(self.printfield(player.field.field))\n counter += 1\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n\n def selectCell(self, player):\n column = ''\n row = ''\n while self.columnExist(column) == False or row not in rowlist:\n userInput = input(player.name +\n ', onde (A-N)(1-14) tu quer mandar o pipoco?\\n')\n if len(userInput) < 2:\n column = ''\n row = ''\n else:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) == 3:\n row += userInput[2]\n return [column, row]\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n\n def clear(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n\n def placeShips(self, player):\n counter = 1\n print(player.name + ', coloque seus navios na posição inicial,\\n')\n print('Depois diga a direção (right, left, up ou down)\\n')\n print(self.printfield(player.field.field))\n for x in player.ships:\n column = ''\n row = ''\n direction = ''\n cellBusy = True\n pff = player.field.field\n while self.columnExist(column\n ) == False or row not in rowlist or cellBusy == True:\n userInput = input(player.name +\n ', em que celula (A-N)(1-14) você quer colocar sua ' +\n nth[counter] + ' barca?\\n')\n if len(userInput) >= 2:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) >= 3:\n row += userInput[2]\n if self.columnExist(column) and row in rowlist:\n cellBusy = pff[column][int(row)]\n row = int(row)\n newrow = row\n newcolumn = column\n if len(x.parts) == 1:\n pff[newcolumn][newrow] = True\n else:\n while (direction != 'right' and direction != 'left' and \n direction != 'up' and direction != 'down' or self.\n rowExist(newrow) == False or self.columnExist(newcolumn\n ) == False or cellBusy == True):\n direction = input(player.name +\n ', qual direção (right, left, up or down) seu barco ' +\n nth[counter] + ' está virado?\\n')\n cellBusy = False\n partCounter = 0\n for y in range(len(x.parts)):\n newcolumn = column\n newrow = row\n if direction == 'down':\n newrow = row + partCounter\n elif direction == 'up':\n newrow = row - partCounter\n elif direction == 'left':\n newcolumn = chr(ord(column) - partCounter)\n elif direction == 'right':\n newcolumn = chr(ord(column) + partCounter)\n partCounter += 1\n if self.columnExist(newcolumn) and self.rowExist(newrow\n ):\n if pff[newcolumn][newrow] == True:\n cellBusy = pff[newcolumn][newrow]\n elif pff[newcolumn][newrow\n ] == False and partCounter == len(x.parts):\n for p in range(0, partCounter):\n if ord(newcolumn) < ord(column):\n pff[chr(ord(column) - p)][newrow] = True\n elif ord(newcolumn) > ord(column):\n pff[chr(ord(column) + p)][newrow] = True\n elif newrow < row:\n pff[newcolumn][newrow + p] = True\n elif newrow > row:\n pff[newcolumn][newrow - p] = True\n self.clear()\n print(self.printfield(player.field.field))\n counter += 1\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n\n def selectCell(self, player):\n column = ''\n row = ''\n while self.columnExist(column) == False or row not in rowlist:\n userInput = input(player.name +\n ', onde (A-N)(1-14) tu quer mandar o pipoco?\\n')\n if len(userInput) < 2:\n column = ''\n row = ''\n else:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) == 3:\n row += userInput[2]\n return [column, row]\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n\n def clear(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n\n def selectCell(self, player):\n column = ''\n row = ''\n while self.columnExist(column) == False or row not in rowlist:\n userInput = input(player.name +\n ', onde (A-N)(1-14) tu quer mandar o pipoco?\\n')\n if len(userInput) < 2:\n column = ''\n row = ''\n else:\n column = userInput[0].upper()\n row = userInput[1]\n if len(userInput) == 3:\n row += userInput[2]\n return [column, row]\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n\n def clear(self):\n os.system('cls' if os.name == 'nt' else 'clear')\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n\n def rowExist(self, row):\n if 1 <= row <= 14:\n return True\n else:\n return False\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n <function token>\n\n def printfield(self, f):\n l = [' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K',\n 'L', 'M', 'N']\n spacing = ' '.join(['{:<2}'] * len(l))\n text = spacing.format(*l)\n for v in range(1, len(l)):\n text += '\\n' + spacing.format(v, f['A'][v], f['B'][v], f['C'][v\n ], f['D'][v], f['E'][v], f['F'][v], f['G'][v], f['H'][v], f\n ['I'][v], f['J'][v], f['K'][v], f['L'][v], f['M'][v], f['N'][v]\n )\n return text\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n\n def __init__(self):\n self.p1 = ''\n self.p2 = ''\n self.p1Field = battlefield.Battlefield()\n self.p2Field = battlefield.Battlefield()\n self.p1BombField = bombfield.Bombfield()\n self.p2BombField = bombfield.Bombfield()\n self.ships = []\n self.ships.append(ship.Ship(5))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(4))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(2))\n self.ships.append(ship.Ship(1))\n self.ships.append(ship.Ship(1))\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n <function token>\n <function token>\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n <function token>\n <function token>\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n\n def bomb(self, player, enemy, column, row):\n eff = enemy.field.field\n self.result = ''\n row = int(row)\n if eff[column][row] == True:\n self.result = 'X'\n eff[column][row] = 'X'\n player.bombfield.field[column][row] = 'X'\n if self.anythingLeft(eff) == False:\n self.result = player.name + ' wins!'\n else:\n self.result = 'O'\n eff[column][row] = '@'\n if player.bombfield.field[column][row] != 'X':\n player.bombfield.field[column][row] = 'O'\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n <function token>\n <function token>\n <function token>\n\n def newPlayer(self, n, ships, field, bombfield):\n newName = input('Player ' + str(n) + ',qual teu nick?\\n')\n while newName == '':\n newName = input('Digita ai mano\\n')\n self.clear()\n p = player.Player(newName, ships[:], field, bombfield)\n self.placeShips(p)\n return p\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n <function token>\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n\n def columnExist(self, column):\n if 'A' <= column <= 'N':\n return True\n else:\n return False\n <function token>\n <function token>\n <function token>\n <function token>\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n <function token>\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n <function token>\n\n def start(self):\n while self.anythingLeft(self.p1.field.field) and self.anythingLeft(self\n .p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p1.field.field))\n print('\\nCampo delas:\\n')\n print(self.printfield(self.p1.bombfield.field))\n cell = self.selectCell(self.p1)\n self.bomb(self.p1, self.p2, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('ACERTOU CARA!')\n elif self.result == 'O':\n print('ERROOOOOU!')\n else:\n print(self.result)\n sys.exit()\n print(self.printfield(self.p1.bombfield.field))\n input('aperta enter men')\n self.clear()\n if self.anythingLeft(self.p1.field.field) and self.anythingLeft(\n self.p2.field.field):\n print('Teu campo:\\n')\n print(self.printfield(self.p2.field.field))\n print('\\nCampo do babaca la:\\n')\n print(self.printfield(self.p2.bombfield.field))\n cell = self.selectCell(self.p2)\n self.bomb(self.p2, self.p1, cell[0], cell[1])\n self.clear()\n if self.result == 'X':\n print('Acertou, mizera!')\n elif self.result == 'O':\n print('Errou de novo pora!')\n else:\n print(self.result)\n sys.exit()\n input('Aperta enter parça')\n self.clear()\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def anythingLeft(self, d):\n newList = []\n\n def myprint(d):\n for k, v in d.items():\n if isinstance(v, dict):\n myprint(v)\n else:\n newList.append(v)\n myprint(d)\n return True in newList\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<assignment token>\n\n\nclass Game:\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n",
"<import token>\n<assignment token>\n<class token>\n"
] | false |
99,497 |
702b72a34a79e84cd6065c29c659c86d41b44dd2
|
# -*- coding: UTF-8 -*-
'''
Authorized by vlon Jang
Created on May 15, 2016
Email:[email protected]
From Institute of Computing Technology
All Rights Reserved.
'''
featueName = 'keys'
from gen_X_features import fromDateTrain, toDateTrain, fromDateTest, toDateTest, fromDateSubmit, toDateSubmit
def gen(fromDate = None, toDate = None, table_name = None):
sqlTemplate = """
drop table if exists {table_name};
create table {table_name} as
SELECT user_id, song_id,
date_format(date_add(str_to_date(ds, '%Y%m%d'), interval 2 month), '%Y%m%d') as ds
from user_actions
where action_type = '1' and ds>='{fromDate}' and ds <= '{toDate}'
GROUP BY user_id, song_id, ds
order by ds desc;
create index IDX_{table_name} on {table_name}(user_id);
"""
return sqlTemplate.format(table_name=table_name, fromDate=fromDate, toDate=toDate)
def genAll():
'''
This function is used to generate keys which is used as the origin table of
left join on train, test, submit dataset.
'''
return (gen(fromDateTrain, toDateTrain, 'user_%s_train' %featueName),
gen(fromDateTest, toDateTest, 'user_%s_test' %featueName),
gen(fromDateSubmit, toDateSubmit, 'user_%s_submit' %featueName))
if __name__ == '__main__':
for sql in genAll():
print sql
|
[
"# -*- coding: UTF-8 -*- \n'''\nAuthorized by vlon Jang\nCreated on May 15, 2016\nEmail:[email protected]\nFrom Institute of Computing Technology\nAll Rights Reserved.\n'''\n\nfeatueName = 'keys'\nfrom gen_X_features import fromDateTrain, toDateTrain, fromDateTest, toDateTest, fromDateSubmit, toDateSubmit\n\ndef gen(fromDate = None, toDate = None, table_name = None):\n sqlTemplate = \"\"\"\n drop table if exists {table_name};\n create table {table_name} as\n SELECT user_id, song_id, \n date_format(date_add(str_to_date(ds, '%Y%m%d'), interval 2 month), '%Y%m%d') as ds \n from user_actions \n where action_type = '1' and ds>='{fromDate}' and ds <= '{toDate}'\n GROUP BY user_id, song_id, ds\n order by ds desc;\n create index IDX_{table_name} on {table_name}(user_id);\n \"\"\"\n return sqlTemplate.format(table_name=table_name, fromDate=fromDate, toDate=toDate)\n \n\ndef genAll():\n '''\n This function is used to generate keys which is used as the origin table of \n left join on train, test, submit dataset.\n '''\n return (gen(fromDateTrain, toDateTrain, 'user_%s_train' %featueName), \n gen(fromDateTest, toDateTest, 'user_%s_test' %featueName),\n gen(fromDateSubmit, toDateSubmit, 'user_%s_submit' %featueName))\n \nif __name__ == '__main__':\n for sql in genAll():\n print sql"
] | true |
99,498 |
9f64fe84e84155895d7258b9a691211a6137d289
|
#!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import os, sys, re, time, json, traceback
from datetime import datetime
from ftplib import FTP, error_temp
import cPickle
from StringIO import StringIO
import numpy as np
import redis, cv2
from skimage import feature
from email_send import send_email
MODELLIST = ['inside_empty', 'open_close']
MODELPATH = 'models'
REDIS_INPUT_LIST = 'garage_files2label'
REDIS_FAIL_LIST = 'garage_failed_files'
REDIS_OUTPUT_PREFIX = 'garage_label_'
NO_LABEL = '_'
NO_LABEL_QUEUE_PREFIX = 'no_label_'
def dbprint(text):
print >>sys.__stderr__, '[%s]:%s' % (datetime.fromtimestamp(time.time()).strftime('%d/%m/%Y %H:%M:%S.%f'), text)
def detect_label(model, image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = feature.hog(image,
orientations=9,
pixels_per_cell=[4, 4],
cells_per_block=[2, 2],
transform_sqrt=True)
hist[hist < 0] = 0
labels = model.predict_proba(hist)
labels = zip(model.classes_, labels[0])
labels.sort(key=lambda x: x[1], reverse=True)
rs = labels[0][0] if abs(labels[0][1] - labels[1][1]) > 0.4 else '_'
dbprint('%s -> %s' % (labels, rs))
return rs
def detect_image_label(model, ftp_h, fpath):
t = time.time()
reader = StringIO()
ftp_h.retrbinary("RETR %s" % fpath, reader.write)
imgdata = reader.getvalue()
img_array = np.asarray(bytearray(imgdata), dtype=np.uint8)
image = cv2.imdecode(img_array, cv2.CV_LOAD_IMAGE_UNCHANGED)
rs = (detect_label(model, image), imgdata)
tdiff = int(time.time() - t)
dbprint('image process time: %d:%02d' % (tdiff//60, tdiff%60))
return rs
#00D6FB009223(n800sau)_1_20160516142921_30928.jpg
failed_file = ''
ftp_h = None
models = None
r = re.compile('^[0-9A-F]+\(.*\)_\d_(\d+)_\d+\.jpg')
try:
redis = redis.Redis()
for i in range(2):
fpath = redis.lpop(REDIS_INPUT_LIST)
if fpath is None:
print 'End of files'
break
bname = os.path.basename(fpath)
dbprint('popped %s' % fpath)
m = r.match(bname)
if m:
dt = datetime.strptime(m.groups()[0], '%Y%m%d%H%M%S')
ts = time.mktime(dt.timetuple())
try:
if ftp_h is None:
ftp_h = FTP('192.168.1.1', timeout=30)
ftp_h.login('writer', 'pfgbcm')
if models is None:
t = time.time()
models = {}
for mname in MODELLIST:
models[mname] = cPickle.loads(open(os.path.join(MODELPATH, mname + '.svc')).read())
# cPickle.dump(models[mname], open(os.path.join(MODELPATH, mname + '.svc.new'), 'w'), protocol=cPickle.HIGHEST_PROTOCOL)
tdiff = int(time.time() - t)
dbprint('models load time: %d:%02d' % (tdiff//60, tdiff%60))
msglist = []
labellist = []
dbprint('model names: %s' % models.keys())
for mname,model in models.items():
output_name = REDIS_OUTPUT_PREFIX + mname
dbprint('Start %s' % bname)
label,imgdata = detect_image_label(model, ftp_h, fpath)
if label == NO_LABEL:
queue_pfx = NO_LABEL_QUEUE_PREFIX + mname
redis.rpush(queue_pfx, bname)
redis.ltrim(queue_pfx, max(0, redis.llen(queue_pfx) - 100), -1)
elif label in ('open', 'close'):
redis.set('gate', json.dumps({'label': label, 'ts': time.time()}))
if label != NO_LABEL:
last_rec = redis.lrange(output_name, -1, -1)
if last_rec:
last_rec = json.loads(last_rec[0])
if last_rec['ts'] < ts and last_rec['label'] != label:
msg = '%s changed at %s from %s to %s (diff=%d), %s' % (mname, dt.strftime('%d/%m %H:%M:%S'), last_rec['label'], label, ts - last_rec['ts'], bname)
dbprint('%s %s' % (bname, msg))
msglist.append(msg)
labellist.append((mname, label))
else:
msg = 'Initial at %s %s' % (dt.strftime('%d/%m %H:%M:%S'), label)
dbprint('%s %s' % (bname, msg))
msglist.append(msg)
labellist.append((mname, label))
dbprint(bname)
redis.rpush(output_name, json.dumps({'label': label, 'ts': ts, 'name': fpath}))
redis.ltrim(output_name, max(0, redis.llen(output_name) - 100), -1)
if msglist:
labellist = [label for mname,label in labellist if label != NO_LABEL]
if not labellist:
labellist = ['_']
send_email('[email protected]', '%s: %s' % (dt.strftime('%H:%M:%S %d/%m'), ','.join(labellist)), '\n'.join(msglist), [imgdata])
except:
# return fpath back to redis list
redis.rpush(REDIS_FAIL_LIST, fpath)
failed_file = fpath
raise
redis.rpush(REDIS_FAIL_LIST, fpath)
break
except Exception, e:
if not isinstance(e, error_temp):
send_email('[email protected]', '%s error occured: %s' % (failed_file, str(e)), 'Error details: %s' % traceback.format_exc())
traceback.print_exc(sys.stderr)
if not ftp_h is None:
ftp_h.quit()
print 'Finished at %s' % time.strftime('%d/%m %H:%M:%S')
|
[
"#!/usr/bin/env python\n\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nimport os, sys, re, time, json, traceback\nfrom datetime import datetime\nfrom ftplib import FTP, error_temp\nimport cPickle\nfrom StringIO import StringIO\nimport numpy as np\nimport redis, cv2\nfrom skimage import feature\n\nfrom email_send import send_email\n\nMODELLIST = ['inside_empty', 'open_close']\n\nMODELPATH = 'models'\nREDIS_INPUT_LIST = 'garage_files2label'\nREDIS_FAIL_LIST = 'garage_failed_files'\nREDIS_OUTPUT_PREFIX = 'garage_label_'\nNO_LABEL = '_'\nNO_LABEL_QUEUE_PREFIX = 'no_label_'\n\ndef dbprint(text):\n\tprint >>sys.__stderr__, '[%s]:%s' % (datetime.fromtimestamp(time.time()).strftime('%d/%m/%Y %H:%M:%S.%f'), text)\n\ndef detect_label(model, image):\n\timage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\thist = feature.hog(image,\n\t\torientations=9,\n\t\tpixels_per_cell=[4, 4],\n\t\tcells_per_block=[2, 2],\n\t\ttransform_sqrt=True)\n\thist[hist < 0] = 0\n\tlabels = model.predict_proba(hist)\n\tlabels = zip(model.classes_, labels[0])\n\tlabels.sort(key=lambda x: x[1], reverse=True)\n\trs = labels[0][0] if abs(labels[0][1] - labels[1][1]) > 0.4 else '_'\n\tdbprint('%s -> %s' % (labels, rs))\n\treturn rs\n\n\ndef detect_image_label(model, ftp_h, fpath):\n\tt = time.time()\n\treader = StringIO()\n\tftp_h.retrbinary(\"RETR %s\" % fpath, reader.write)\n\timgdata = reader.getvalue()\n\timg_array = np.asarray(bytearray(imgdata), dtype=np.uint8)\n\timage = cv2.imdecode(img_array, cv2.CV_LOAD_IMAGE_UNCHANGED)\n\trs = (detect_label(model, image), imgdata)\n\ttdiff = int(time.time() - t)\n\tdbprint('image process time: %d:%02d' % (tdiff//60, tdiff%60))\n\treturn rs\n\n#00D6FB009223(n800sau)_1_20160516142921_30928.jpg\nfailed_file = ''\nftp_h = None\nmodels = None\nr = re.compile('^[0-9A-F]+\\(.*\\)_\\d_(\\d+)_\\d+\\.jpg')\ntry:\n\tredis = redis.Redis()\n\tfor i in range(2):\n\t\tfpath = redis.lpop(REDIS_INPUT_LIST)\n\t\tif fpath is None:\n\t\t\tprint 'End of files'\n\t\t\tbreak\n\t\tbname = os.path.basename(fpath)\n\t\tdbprint('popped %s' % fpath)\n\t\tm = r.match(bname)\n\t\tif m:\n\t\t\tdt = datetime.strptime(m.groups()[0], '%Y%m%d%H%M%S')\n\t\t\tts = time.mktime(dt.timetuple())\n\t\t\ttry:\n\t\t\t\tif ftp_h is None:\n\t\t\t\t\tftp_h = FTP('192.168.1.1', timeout=30)\n\t\t\t\t\tftp_h.login('writer', 'pfgbcm')\n\t\t\t\tif models is None:\n\t\t\t\t\tt = time.time()\n\t\t\t\t\tmodels = {}\n\t\t\t\t\tfor mname in MODELLIST:\n\t\t\t\t\t\tmodels[mname] = cPickle.loads(open(os.path.join(MODELPATH, mname + '.svc')).read())\n#\t\t\t\t\t\tcPickle.dump(models[mname], open(os.path.join(MODELPATH, mname + '.svc.new'), 'w'), protocol=cPickle.HIGHEST_PROTOCOL)\n\t\t\t\t\ttdiff = int(time.time() - t)\n\t\t\t\t\tdbprint('models load time: %d:%02d' % (tdiff//60, tdiff%60))\n\t\t\t\tmsglist = []\n\t\t\t\tlabellist = []\n\t\t\t\tdbprint('model names: %s' % models.keys())\n\t\t\t\tfor mname,model in models.items():\n\t\t\t\t\toutput_name = REDIS_OUTPUT_PREFIX + mname\n\t\t\t\t\tdbprint('Start %s' % bname)\n\t\t\t\t\tlabel,imgdata = detect_image_label(model, ftp_h, fpath)\n\t\t\t\t\tif label == NO_LABEL:\n\t\t\t\t\t\tqueue_pfx = NO_LABEL_QUEUE_PREFIX + mname\n\t\t\t\t\t\tredis.rpush(queue_pfx, bname)\n\t\t\t\t\t\tredis.ltrim(queue_pfx, max(0, redis.llen(queue_pfx) - 100), -1)\n\t\t\t\t\telif label in ('open', 'close'):\n\t\t\t\t\t\tredis.set('gate', json.dumps({'label': label, 'ts': time.time()}))\n\t\t\t\t\tif label != NO_LABEL:\n\t\t\t\t\t\tlast_rec = redis.lrange(output_name, -1, -1)\n\t\t\t\t\t\tif last_rec:\n\t\t\t\t\t\t\tlast_rec = json.loads(last_rec[0])\n\t\t\t\t\t\t\tif last_rec['ts'] < ts and last_rec['label'] != label:\n\t\t\t\t\t\t\t\tmsg = '%s changed at %s from %s to %s (diff=%d), %s' % (mname, dt.strftime('%d/%m %H:%M:%S'), last_rec['label'], label, ts - last_rec['ts'], bname)\n\t\t\t\t\t\t\t\tdbprint('%s %s' % (bname, msg))\n\t\t\t\t\t\t\t\tmsglist.append(msg)\n\t\t\t\t\t\t\t\tlabellist.append((mname, label))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tmsg = 'Initial at %s %s' % (dt.strftime('%d/%m %H:%M:%S'), label)\n\t\t\t\t\t\t\tdbprint('%s %s' % (bname, msg))\n\t\t\t\t\t\t\tmsglist.append(msg)\n\t\t\t\t\t\t\tlabellist.append((mname, label))\n\t\t\t\t\t\tdbprint(bname)\n\t\t\t\t\t\tredis.rpush(output_name, json.dumps({'label': label, 'ts': ts, 'name': fpath}))\n\t\t\t\t\t\tredis.ltrim(output_name, max(0, redis.llen(output_name) - 100), -1)\n\t\t\t\tif msglist:\n\t\t\t\t\tlabellist = [label for mname,label in labellist if label != NO_LABEL]\n\t\t\t\t\tif not labellist:\n\t\t\t\t\t\tlabellist = ['_']\n\t\t\t\t\tsend_email('[email protected]', '%s: %s' % (dt.strftime('%H:%M:%S %d/%m'), ','.join(labellist)), '\\n'.join(msglist), [imgdata])\n\t\t\texcept:\n\t\t\t\t# return fpath back to redis list\n\t\t\t\tredis.rpush(REDIS_FAIL_LIST, fpath)\n\t\t\t\tfailed_file = fpath\n\t\t\t\traise\n\t\tredis.rpush(REDIS_FAIL_LIST, fpath)\n\t\tbreak\nexcept Exception, e:\n\tif not isinstance(e, error_temp):\n\t\tsend_email('[email protected]', '%s error occured: %s' % (failed_file, str(e)), 'Error details: %s' % traceback.format_exc())\n\ttraceback.print_exc(sys.stderr)\n\nif not ftp_h is None:\n\tftp_h.quit()\n\n\nprint 'Finished at %s' % time.strftime('%d/%m %H:%M:%S')\n"
] | true |
99,499 |
29eb2d76ead787c82e2de1c3f2ab127ea214d813
|
# Generated by Django 3.0.4 on 2020-03-21 15:37
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Car',
fields=[
('car_make', models.CharField(max_length=50)),
('car_model', models.CharField(max_length=50)),
('car_color', models.CharField(max_length=50)),
('id_car', models.CharField(max_length=50, primary_key=True, serialize=False)),
],
),
migrations.CreateModel(
name='Car_owner',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('owner_name', models.CharField(max_length=50)),
('owner_surname', models.CharField(max_length=50)),
('date_of_birth', models.DateField()),
],
),
migrations.CreateModel(
name='Owning',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('date_of_start_owning', models.DateField()),
('date_of_end_owning', models.DateField()),
('car', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car')),
('owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car_owner')),
],
),
migrations.CreateModel(
name='Driver_license',
fields=[
('id_license', models.CharField(max_length=50, primary_key=True, serialize=False)),
('date_of_issue', models.DateField()),
('owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car_owner')),
],
),
]
|
[
"# Generated by Django 3.0.4 on 2020-03-21 15:37\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Car',\n fields=[\n ('car_make', models.CharField(max_length=50)),\n ('car_model', models.CharField(max_length=50)),\n ('car_color', models.CharField(max_length=50)),\n ('id_car', models.CharField(max_length=50, primary_key=True, serialize=False)),\n ],\n ),\n migrations.CreateModel(\n name='Car_owner',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('owner_name', models.CharField(max_length=50)),\n ('owner_surname', models.CharField(max_length=50)),\n ('date_of_birth', models.DateField()),\n ],\n ),\n migrations.CreateModel(\n name='Owning',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('date_of_start_owning', models.DateField()),\n ('date_of_end_owning', models.DateField()),\n ('car', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car')),\n ('owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car_owner')),\n ],\n ),\n migrations.CreateModel(\n name='Driver_license',\n fields=[\n ('id_license', models.CharField(max_length=50, primary_key=True, serialize=False)),\n ('date_of_issue', models.DateField()),\n ('owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='project_first_app.Car_owner')),\n ],\n ),\n ]\n",
"from django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='Car', fields=[('car_make',\n models.CharField(max_length=50)), ('car_model', models.CharField(\n max_length=50)), ('car_color', models.CharField(max_length=50)), (\n 'id_car', models.CharField(max_length=50, primary_key=True,\n serialize=False))]), migrations.CreateModel(name='Car_owner',\n fields=[('id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('owner_name', models.\n CharField(max_length=50)), ('owner_surname', models.CharField(\n max_length=50)), ('date_of_birth', models.DateField())]),\n migrations.CreateModel(name='Owning', fields=[('id', models.\n AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('date_of_start_owning', models.DateField()),\n ('date_of_end_owning', models.DateField()), ('car', models.\n ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'project_first_app.Car')), ('owner', models.ForeignKey(on_delete=\n django.db.models.deletion.CASCADE, to='project_first_app.Car_owner'\n ))]), migrations.CreateModel(name='Driver_license', fields=[(\n 'id_license', models.CharField(max_length=50, primary_key=True,\n serialize=False)), ('date_of_issue', models.DateField()), ('owner',\n models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'project_first_app.Car_owner'))])]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n initial = True\n dependencies = []\n operations = [migrations.CreateModel(name='Car', fields=[('car_make',\n models.CharField(max_length=50)), ('car_model', models.CharField(\n max_length=50)), ('car_color', models.CharField(max_length=50)), (\n 'id_car', models.CharField(max_length=50, primary_key=True,\n serialize=False))]), migrations.CreateModel(name='Car_owner',\n fields=[('id', models.AutoField(auto_created=True, primary_key=True,\n serialize=False, verbose_name='ID')), ('owner_name', models.\n CharField(max_length=50)), ('owner_surname', models.CharField(\n max_length=50)), ('date_of_birth', models.DateField())]),\n migrations.CreateModel(name='Owning', fields=[('id', models.\n AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('date_of_start_owning', models.DateField()),\n ('date_of_end_owning', models.DateField()), ('car', models.\n ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'project_first_app.Car')), ('owner', models.ForeignKey(on_delete=\n django.db.models.deletion.CASCADE, to='project_first_app.Car_owner'\n ))]), migrations.CreateModel(name='Driver_license', fields=[(\n 'id_license', models.CharField(max_length=50, primary_key=True,\n serialize=False)), ('date_of_issue', models.DateField()), ('owner',\n models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=\n 'project_first_app.Car_owner'))])]\n",
"<import token>\n\n\nclass Migration(migrations.Migration):\n <assignment token>\n <assignment token>\n <assignment token>\n",
"<import token>\n<class token>\n"
] | false |
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