content
stringlengths 7
1.05M
|
|---|
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2', # Add 'postgresql_psycopg2', 'mysql', 'sqlite3' or 'oracle'.
'NAME': 'voicex_dev', # Or path to database file if using sqlite3.
'USER': 'postgres', # Not used with sqlite3.
'PASSWORD': 'postgres', # Not used with sqlite3.
'HOST': 'localhost', # Set to empty string for localhost. Not used with sqlite3.
'PORT': '', # Set to empty string for default. Not used with sqlite3.
}
}
# A sample logging configuration. The only tangible logging
# performed by this configuration is to send an email to
# the site admins on every HTTP 500 error when DEBUG=False.
# See http://docs.djangoproject.com/en/dev/topics/logging for
# more details on how to customize your logging configuration.
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'standard': {
'format' : "[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s",
'datefmt' : "%d/%b/%Y %H:%M:%S"
},
},
'handlers': {
'null': {
'level':'DEBUG',
'class':'django.utils.log.NullHandler',
},
'logfile': {
'level':'DEBUG',
'class':'logging.handlers.RotatingFileHandler',
'filename':"/workspace/voicex/logs/voicex.log",
'maxBytes': 50000,
'backupCount': 2,
'formatter': 'standard',
},
'console':{
'level':'DEBUG',
'class':'logging.StreamHandler',
'formatter': 'standard'
},
},
'loggers': {
'django': {
'handlers':['console'],
'propagate': True,
'level':'WARN',
},
'django.request': {
'handlers': ['console'],
'level': 'ERROR',
'propagate': True,
},
'django.db.backends': {
'handlers': ['console'],
'level': 'DEBUG',
'propagate': False,
},
'voicex': {
'handlers': ['console', 'logfile'],
'level': 'DEBUG',
},
'transport': {
'handlers': ['console', 'logfile'],
'level': 'DEBUG',
},
}
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
if __name__ == '__main__':
a = tuple(map(float, input().split()))
C = float(input())
count = 0
for elem in a:
if elem > C:
count += 1
print("Количество чисел больших С:", count)
max_elem = 0
for i, item in enumerate(a):
elem_abs = abs(item)
if elem_abs > max_elem:
max_elem, max_elem_ind = elem_abs, i
if max_elem_ind == len(a) - 1:
print(0)
else:
res = 1
for elem in a[max_elem_ind + 1:len(a)]:
res *= elem
print("Произведение чисел после максимального по модулю числа", res)
a_pos = tuple()
a_neg = tuple()
for elem in a:
if elem >= 0:
a_pos += tuple([elem])
else:
a_neg += tuple([elem])
print("Кортеж сначала с отрицательными, а потом позитивными числами:", a_neg + a_pos)
|
#Why is the error and how to fix it?
#A: A TypeError menas you are using the wrong type to make an operation. Change print(a+b) to return a+b
def foo(a, b):
print(a + b)
x = foo(2, 3) * 10
|
n=int(input('enter a program : '))
for i in range(n):
for j in range(n-1,i,-1):
print(' ',end=' ')
for k in range(0,i):
print(chr(65+k),end=' ')
print() |
# Aula de Condicionais
# se carro.esquerda() if carro.esquerda():
# bloco_V_ bloco True
# senão else:
# bloco_F_ bloco False
n1 = float(input('Digite a primeira nota:'))
n2 = float(input('Digite a segunda nota:'))
m = (n1 + n2) / 2
print('A sua média foi {:.1f}'.format(m))
if m >= 6.0:
print('Sua média foi boa! PARABÉNS!')
else:
print('Sua média foi baixa! ESTUDE MAIS!')
|
notes = [4,5,3,3,1]
students=["Harry", "Ron","Hemrine", "Ginny", "Draco"]
for i in range(len(students)):
print(f'{students[i]} hat im Fach Zaubertranke die Note {notes[i]} erhalten.') |
def dfs(numbers, target, current_value, index):
if index == len(numbers):
if current_value == target:
return 1
return 0
number = numbers[index]
return dfs(numbers, target, current_value + number, index + 1) + \
dfs(numbers, target, current_value - number, index + 1)
def solution(numbers, target):
return dfs(numbers, target, 0, 0)
# 심플하게 dfs로 풀이했다.
# 배열을 탐색해가면서 더한 결과와 뺀것 결과를 탐색해나간다.
# 제한사항이 그렇게 크지 않기에 가능한 풀이인 것 같다.
|
file = open("/home/matheus/Documentos/Repositorios_Linux/Exercicios-Python/Curso Udemy 2022/Nova_organizacao/aula_89_criando-lendo-escrevendo-apagando.py/abc.txt","w+") # w = escrita; + permite leitura e escrita
file.write("Linha 01 \n")
file.write("Linha 02 \n")
file.write("Linha 03 \n")
file.write("Linha 04 \n")
file.write("Linha 05 \n")
file.seek(0,0) # O primeiro par me permite configurar a partir de qual caracter ele vai ler
print("Lendo Linhas")
print(file.read()) #preciso fazer o seek para resetar o cursor de leitura
print("-="*30)
print("Ler linha por linha")
file.seek(0,0)
print(file.readline(),end="")
print(file.readline(),end="")
print(file.readline(),end="")
print(file.readline(),end="")
print(next(file),end="") # percebi que funciona como um iterador
print("-="*30)
print("Salvando as linhas em um dicionáriio")
file.seek(0,0)
print(file.readlines())
file.seek(0,0)
for linha in file.readlines():
print(linha,end="")
file.seek(0,0)
for linha in file:
print(linha,end="")
file.close()
|
class Thesis():
""" Used to define all the characteristics of a thesis.
Args:
thesis_id (int): thesis identifier
title (str): thesis title
authors (list): list of the authors who writed the thesis
advisors (list): list of advisors who helped this theses
url (str): url to the thesis pdf
keywords (list): keywords that relates to the thesis theme.
university (str): university name
institution (str): institution name
course (str): authors course name
language (str): language that the thesis got published.
year (int): year that the thesis got published
"""
def __init__(self, thesis_id, title, authors, advisors, url, keywords, university, institution, course, language, year):
self.thesis_id = thesis_id
self.title = title
self.url = url
self.authors = authors
self.advisors = advisors
self.keywords = keywords
self.university = university
self.institution = institution
self.course = course
self.language = language
self.year = year
def __eq__(self, thesis):
if self.title == thesis.title:
return True
return False
def serialize(self):
"""Serializes the Thesis Object
Returns:
dictionary: serialized object
"""
return {"ThesisId": self.thesis_id,
"Title": self.title,
"Url": self.url,
"Authors": self.authors,
"Advisors": self.advisors,
"Keywords": self.keywords,
"University": self.university,
"Institution": self.institution,
"Course": self.course,
"Language": self.language,
"Year": self.year}
|
#
# Automatically generated
#
class RTOpcodes(object):
pass
RTOpcodes.JSR = 0x00
RTOpcodes.IMMSHORT = 0x00
RTOpcodes.IMMLONG = 0x10
RTOpcodes.VARSHORT = 0x20
RTOpcodes.VARLONG = 0x30
RTOpcodes.ABS = 0x40
RTOpcodes.LDR = 0x80
RTOpcodes.AND = 0x81
RTOpcodes.ORR = 0x82
RTOpcodes.XOR = 0x83
RTOpcodes.ADD = 0x84
RTOpcodes.SUB = 0x85
RTOpcodes.MLT = 0x86
RTOpcodes.DIV = 0x87
RTOpcodes.MOD = 0x88
RTOpcodes.DCV = 0x89
RTOpcodes.STR = 0x8a
RTOpcodes.BRA = 0x8b
RTOpcodes.BEQ = 0x8c
RTOpcodes.BNE = 0x8d
RTOpcodes.BMI = 0x8e
RTOpcodes.BPL = 0x8f
RTOpcodes.INC = 0xf0
RTOpcodes.DEC = 0xf1
RTOpcodes.SHL = 0xf2
RTOpcodes.SHR = 0xf3
RTOpcodes.CLR = 0xf4
RTOpcodes.RET = 0xf5
|
# The probability that a person with certain symptoms has hepatitis is 0.75.
# The blood test used to confirm this diagnosis gives positive results for 91% of people with the disease and 5% of those without the disease.
# What is the probability that an individual who has the symptoms and who reacts positively to the test actually has hepatitis?
pF = .75
pNF = 1 - pF
pE = .91
pNE = 1 - pE
pH = .05
bayes = ( pF * pE ) / ( pF * pE + pNF * pH )
round( bayes, 4 ) |
"""django-sendgrid"""
VERSION = (1, 0, 1)
__version__ = ".".join(map(str, VERSION[0:3])) + "".join(VERSION[3:])
__author__ = "Ryan Balfanz"
__contact__ = "[email protected]"
__homepage__ = "http://ryanbalfanz.github.com/django-sendgrid/"
__docformat__ = "restructuredtext"
# __license__ = "BSD (3 clause)"
|
"""
PASSENGERS
"""
numPassengers = 19150
passenger_arriving = (
(4, 5, 4, 9, 0, 2, 3, 0, 2, 1, 0, 1, 0, 4, 2, 4, 3, 4, 1, 2, 0, 0, 1, 1, 1, 0), # 0
(7, 6, 6, 3, 2, 3, 0, 1, 2, 1, 0, 1, 0, 2, 4, 2, 5, 5, 3, 2, 0, 3, 2, 1, 2, 0), # 1
(9, 5, 5, 2, 3, 1, 1, 2, 4, 3, 0, 0, 0, 6, 10, 2, 4, 5, 5, 2, 2, 2, 2, 1, 0, 0), # 2
(5, 2, 4, 2, 3, 4, 0, 3, 0, 3, 0, 0, 0, 8, 5, 1, 3, 7, 4, 5, 2, 6, 0, 1, 1, 0), # 3
(5, 5, 8, 9, 9, 3, 4, 1, 2, 1, 0, 3, 0, 6, 7, 1, 2, 6, 2, 0, 1, 1, 6, 0, 0, 0), # 4
(4, 10, 4, 2, 5, 4, 1, 4, 1, 2, 0, 0, 0, 8, 2, 3, 4, 6, 3, 1, 0, 5, 3, 1, 0, 0), # 5
(6, 3, 11, 3, 7, 4, 1, 1, 1, 0, 2, 1, 0, 8, 7, 4, 1, 3, 4, 3, 2, 5, 2, 1, 1, 0), # 6
(1, 6, 3, 6, 7, 4, 3, 3, 0, 1, 1, 5, 0, 8, 4, 5, 10, 7, 3, 1, 1, 3, 0, 1, 1, 0), # 7
(8, 7, 9, 11, 5, 2, 3, 3, 2, 1, 0, 0, 0, 10, 7, 7, 2, 6, 2, 1, 2, 4, 2, 0, 2, 0), # 8
(9, 6, 6, 8, 6, 2, 2, 4, 3, 3, 2, 1, 0, 5, 8, 7, 6, 8, 2, 5, 2, 6, 1, 4, 2, 0), # 9
(8, 7, 12, 6, 12, 5, 2, 1, 0, 1, 0, 0, 0, 10, 5, 3, 3, 13, 8, 6, 1, 5, 2, 2, 0, 0), # 10
(11, 7, 10, 4, 6, 1, 1, 4, 5, 2, 1, 2, 0, 9, 7, 5, 8, 5, 3, 4, 4, 3, 4, 0, 0, 0), # 11
(14, 8, 8, 7, 2, 1, 6, 2, 5, 3, 0, 0, 0, 7, 10, 8, 6, 10, 4, 3, 2, 2, 0, 1, 1, 0), # 12
(10, 10, 10, 7, 7, 3, 1, 5, 2, 5, 4, 0, 0, 8, 2, 9, 6, 8, 6, 6, 0, 3, 5, 2, 2, 0), # 13
(13, 9, 13, 6, 6, 4, 8, 4, 3, 0, 1, 0, 0, 9, 12, 7, 2, 8, 2, 4, 2, 4, 2, 2, 1, 0), # 14
(9, 14, 9, 12, 5, 2, 3, 7, 3, 1, 1, 2, 0, 9, 7, 5, 6, 11, 1, 3, 3, 3, 3, 0, 1, 0), # 15
(16, 12, 5, 8, 7, 2, 5, 5, 5, 2, 2, 1, 0, 7, 12, 4, 5, 8, 3, 5, 3, 3, 2, 5, 0, 0), # 16
(9, 19, 6, 11, 4, 4, 1, 8, 7, 2, 1, 0, 0, 9, 5, 7, 11, 8, 7, 6, 0, 6, 2, 2, 1, 0), # 17
(8, 6, 7, 13, 6, 2, 4, 3, 5, 2, 1, 0, 0, 13, 10, 3, 8, 5, 8, 3, 0, 7, 4, 0, 0, 0), # 18
(9, 7, 11, 6, 7, 6, 6, 2, 2, 2, 0, 0, 0, 13, 14, 7, 0, 4, 11, 3, 4, 5, 4, 0, 0, 0), # 19
(5, 9, 13, 6, 9, 2, 4, 5, 7, 4, 0, 0, 0, 16, 3, 10, 8, 6, 2, 3, 3, 2, 1, 2, 1, 0), # 20
(13, 10, 8, 13, 8, 3, 8, 6, 1, 2, 2, 0, 0, 11, 10, 4, 10, 9, 4, 6, 1, 3, 5, 1, 1, 0), # 21
(11, 11, 7, 5, 10, 4, 11, 4, 6, 0, 1, 0, 0, 11, 9, 11, 6, 8, 8, 2, 4, 7, 0, 1, 1, 0), # 22
(11, 5, 4, 12, 10, 4, 2, 1, 1, 1, 1, 0, 0, 12, 10, 6, 8, 16, 5, 1, 0, 3, 4, 3, 1, 0), # 23
(16, 11, 8, 9, 6, 2, 3, 4, 4, 1, 1, 0, 0, 8, 10, 3, 9, 9, 6, 1, 1, 2, 2, 2, 2, 0), # 24
(14, 10, 12, 11, 8, 3, 3, 3, 2, 0, 1, 1, 0, 8, 8, 10, 4, 9, 3, 7, 2, 2, 0, 1, 1, 0), # 25
(11, 10, 13, 14, 6, 6, 5, 5, 0, 4, 1, 0, 0, 11, 12, 7, 5, 8, 8, 4, 0, 6, 0, 1, 2, 0), # 26
(14, 8, 9, 9, 3, 4, 1, 7, 5, 1, 2, 1, 0, 10, 6, 7, 9, 3, 7, 1, 3, 5, 3, 3, 0, 0), # 27
(10, 9, 8, 10, 12, 5, 3, 9, 2, 2, 1, 0, 0, 7, 7, 7, 7, 8, 2, 7, 1, 3, 4, 1, 0, 0), # 28
(11, 5, 5, 9, 9, 1, 7, 5, 6, 1, 2, 1, 0, 11, 6, 13, 7, 5, 6, 4, 1, 5, 3, 1, 1, 0), # 29
(11, 15, 11, 8, 6, 7, 4, 4, 3, 3, 2, 2, 0, 8, 11, 5, 5, 8, 9, 5, 3, 7, 4, 1, 0, 0), # 30
(8, 11, 7, 6, 12, 2, 5, 5, 5, 1, 1, 0, 0, 10, 12, 9, 7, 6, 8, 6, 1, 9, 2, 0, 0, 0), # 31
(6, 12, 8, 15, 12, 4, 2, 4, 9, 0, 1, 1, 0, 7, 7, 8, 8, 9, 4, 4, 2, 1, 3, 2, 1, 0), # 32
(10, 13, 9, 13, 9, 2, 4, 3, 3, 1, 0, 0, 0, 4, 13, 7, 6, 3, 8, 6, 1, 2, 3, 1, 1, 0), # 33
(13, 6, 6, 12, 7, 2, 3, 2, 6, 3, 0, 3, 0, 14, 5, 7, 5, 10, 5, 3, 1, 6, 4, 0, 0, 0), # 34
(19, 18, 2, 9, 13, 8, 4, 5, 6, 1, 3, 1, 0, 10, 10, 5, 6, 10, 4, 1, 3, 4, 1, 0, 0, 0), # 35
(12, 10, 10, 7, 15, 2, 4, 4, 6, 3, 0, 0, 0, 6, 9, 2, 5, 6, 4, 2, 1, 4, 4, 1, 0, 0), # 36
(10, 18, 5, 12, 9, 10, 5, 2, 2, 0, 1, 1, 0, 7, 3, 9, 7, 12, 3, 3, 2, 3, 1, 2, 0, 0), # 37
(8, 11, 8, 18, 4, 7, 0, 4, 1, 2, 3, 1, 0, 10, 10, 6, 5, 8, 4, 8, 5, 7, 5, 1, 1, 0), # 38
(17, 8, 7, 5, 13, 4, 1, 2, 3, 1, 2, 0, 0, 12, 12, 6, 5, 8, 4, 6, 3, 8, 1, 2, 0, 0), # 39
(13, 7, 10, 14, 1, 0, 2, 5, 5, 3, 0, 1, 0, 14, 12, 6, 5, 9, 6, 8, 1, 2, 5, 1, 2, 0), # 40
(8, 7, 4, 13, 4, 2, 3, 4, 1, 1, 3, 2, 0, 6, 10, 3, 7, 5, 3, 7, 1, 7, 4, 1, 0, 0), # 41
(11, 13, 7, 10, 9, 6, 1, 4, 5, 1, 1, 0, 0, 12, 11, 3, 3, 5, 8, 5, 2, 1, 1, 1, 0, 0), # 42
(9, 4, 7, 5, 13, 3, 6, 7, 5, 3, 1, 0, 0, 9, 9, 8, 3, 8, 6, 5, 4, 4, 5, 1, 1, 0), # 43
(13, 9, 9, 6, 11, 4, 0, 7, 3, 5, 2, 0, 0, 10, 9, 6, 4, 10, 5, 5, 3, 7, 4, 1, 0, 0), # 44
(6, 7, 10, 13, 9, 4, 11, 5, 5, 1, 0, 1, 0, 10, 12, 7, 7, 4, 6, 2, 3, 4, 4, 0, 2, 0), # 45
(9, 12, 8, 13, 7, 1, 4, 3, 6, 4, 4, 0, 0, 9, 13, 4, 6, 13, 4, 3, 6, 3, 5, 3, 1, 0), # 46
(12, 14, 5, 9, 12, 2, 7, 6, 5, 1, 0, 0, 0, 10, 10, 3, 2, 6, 3, 7, 6, 5, 2, 0, 1, 0), # 47
(6, 7, 9, 20, 10, 3, 9, 1, 6, 1, 1, 2, 0, 6, 7, 8, 5, 9, 3, 6, 3, 6, 0, 2, 2, 0), # 48
(10, 10, 3, 7, 8, 5, 3, 1, 2, 6, 3, 2, 0, 14, 10, 4, 1, 6, 1, 1, 3, 4, 3, 1, 0, 0), # 49
(15, 10, 16, 10, 3, 6, 3, 6, 7, 1, 2, 0, 0, 16, 4, 11, 9, 13, 4, 1, 5, 2, 5, 2, 1, 0), # 50
(9, 7, 5, 11, 7, 1, 4, 4, 5, 2, 3, 0, 0, 6, 5, 7, 8, 11, 6, 6, 1, 6, 1, 3, 0, 0), # 51
(7, 11, 7, 10, 4, 3, 4, 1, 7, 0, 2, 1, 0, 6, 10, 7, 5, 12, 9, 2, 2, 4, 2, 1, 0, 0), # 52
(4, 12, 7, 6, 8, 4, 2, 7, 4, 1, 2, 3, 0, 7, 9, 7, 5, 4, 5, 2, 2, 1, 4, 1, 1, 0), # 53
(3, 6, 8, 8, 12, 3, 1, 6, 8, 1, 2, 0, 0, 4, 8, 5, 7, 6, 4, 3, 3, 5, 2, 2, 1, 0), # 54
(14, 4, 3, 10, 9, 3, 3, 1, 1, 3, 1, 0, 0, 8, 11, 3, 1, 5, 6, 4, 4, 3, 3, 0, 1, 0), # 55
(16, 6, 11, 6, 10, 3, 1, 7, 3, 1, 2, 0, 0, 13, 6, 6, 2, 11, 2, 5, 0, 5, 3, 1, 0, 0), # 56
(7, 12, 7, 6, 9, 4, 3, 4, 4, 5, 2, 1, 0, 7, 11, 12, 7, 6, 6, 3, 2, 3, 3, 3, 1, 0), # 57
(10, 12, 7, 9, 9, 2, 5, 7, 9, 2, 0, 1, 0, 14, 13, 6, 6, 7, 3, 6, 2, 2, 5, 2, 1, 0), # 58
(10, 8, 7, 9, 4, 4, 4, 0, 1, 3, 1, 0, 0, 7, 11, 15, 4, 7, 2, 2, 1, 2, 4, 0, 1, 0), # 59
(6, 13, 6, 3, 11, 6, 2, 2, 6, 1, 3, 2, 0, 12, 11, 9, 4, 10, 4, 6, 9, 4, 4, 4, 1, 0), # 60
(3, 17, 7, 4, 10, 5, 3, 3, 5, 0, 0, 4, 0, 11, 5, 11, 11, 6, 2, 3, 3, 0, 3, 1, 1, 0), # 61
(15, 9, 10, 11, 8, 3, 2, 4, 3, 1, 4, 0, 0, 7, 6, 6, 4, 8, 5, 4, 4, 2, 3, 1, 0, 0), # 62
(6, 12, 8, 9, 9, 4, 1, 1, 5, 1, 2, 0, 0, 9, 5, 7, 3, 7, 6, 8, 1, 3, 7, 2, 0, 0), # 63
(8, 6, 9, 8, 15, 6, 5, 2, 4, 2, 0, 0, 0, 9, 9, 7, 6, 7, 2, 1, 5, 8, 2, 4, 0, 0), # 64
(14, 10, 2, 9, 3, 2, 2, 5, 3, 2, 1, 0, 0, 7, 11, 3, 5, 20, 5, 1, 6, 0, 5, 2, 1, 0), # 65
(8, 9, 8, 9, 6, 3, 7, 3, 9, 5, 2, 3, 0, 10, 3, 9, 5, 5, 7, 6, 2, 7, 1, 3, 1, 0), # 66
(16, 10, 9, 8, 4, 3, 3, 2, 3, 2, 2, 1, 0, 5, 10, 10, 8, 5, 5, 7, 0, 5, 3, 0, 1, 0), # 67
(9, 3, 9, 9, 8, 3, 5, 1, 4, 1, 0, 1, 0, 9, 6, 3, 4, 8, 10, 4, 3, 7, 3, 2, 1, 0), # 68
(13, 5, 10, 3, 5, 3, 3, 3, 4, 0, 2, 0, 0, 10, 9, 7, 6, 6, 2, 3, 1, 6, 2, 1, 0, 0), # 69
(11, 9, 16, 10, 7, 6, 0, 5, 6, 2, 3, 0, 0, 13, 10, 7, 3, 12, 6, 3, 3, 3, 4, 0, 1, 0), # 70
(9, 8, 8, 5, 9, 3, 2, 2, 4, 3, 3, 0, 0, 9, 9, 8, 5, 3, 12, 2, 1, 7, 0, 1, 0, 0), # 71
(11, 7, 9, 10, 9, 2, 4, 4, 6, 3, 0, 1, 0, 6, 10, 7, 6, 7, 4, 7, 2, 4, 4, 2, 1, 0), # 72
(8, 6, 8, 15, 12, 0, 4, 3, 1, 2, 1, 1, 0, 11, 6, 5, 9, 6, 5, 2, 4, 4, 2, 2, 0, 0), # 73
(8, 7, 11, 9, 8, 4, 3, 5, 8, 0, 3, 1, 0, 11, 5, 8, 7, 8, 3, 4, 1, 0, 3, 1, 1, 0), # 74
(10, 10, 8, 7, 3, 2, 4, 7, 10, 2, 1, 0, 0, 13, 5, 6, 8, 10, 2, 4, 1, 4, 4, 4, 2, 0), # 75
(10, 12, 11, 7, 5, 6, 3, 2, 4, 1, 1, 1, 0, 9, 8, 6, 5, 13, 3, 6, 1, 5, 3, 0, 0, 0), # 76
(7, 8, 7, 9, 8, 8, 5, 4, 5, 2, 3, 1, 0, 7, 9, 5, 4, 8, 7, 4, 3, 0, 1, 2, 1, 0), # 77
(6, 8, 8, 9, 7, 6, 7, 5, 3, 4, 2, 1, 0, 8, 9, 7, 5, 7, 6, 5, 2, 7, 1, 1, 0, 0), # 78
(13, 14, 7, 8, 6, 3, 6, 2, 5, 1, 1, 1, 0, 12, 7, 6, 4, 8, 4, 4, 1, 7, 3, 0, 0, 0), # 79
(15, 10, 12, 8, 5, 3, 4, 4, 4, 0, 2, 2, 0, 6, 6, 9, 8, 9, 3, 1, 3, 7, 2, 1, 1, 0), # 80
(15, 8, 12, 11, 12, 1, 3, 5, 3, 1, 4, 0, 0, 18, 9, 4, 4, 4, 4, 3, 3, 3, 0, 1, 0, 0), # 81
(7, 5, 9, 9, 9, 6, 4, 3, 7, 3, 1, 1, 0, 9, 6, 10, 2, 11, 1, 8, 2, 5, 4, 3, 2, 0), # 82
(12, 10, 9, 7, 8, 5, 7, 5, 2, 3, 0, 1, 0, 12, 9, 10, 4, 8, 1, 5, 1, 6, 3, 2, 1, 0), # 83
(8, 10, 7, 11, 9, 3, 1, 3, 7, 1, 3, 1, 0, 9, 13, 5, 9, 6, 3, 6, 1, 2, 4, 0, 0, 0), # 84
(10, 8, 5, 4, 8, 3, 1, 0, 4, 2, 1, 0, 0, 7, 2, 7, 5, 13, 4, 4, 4, 1, 6, 1, 1, 0), # 85
(9, 6, 6, 11, 8, 2, 3, 5, 3, 1, 2, 2, 0, 12, 5, 7, 6, 5, 3, 2, 2, 5, 5, 1, 0, 0), # 86
(8, 9, 12, 7, 9, 6, 5, 3, 6, 1, 0, 1, 0, 12, 12, 5, 4, 8, 4, 3, 1, 3, 6, 0, 0, 0), # 87
(5, 7, 12, 8, 8, 3, 3, 1, 1, 3, 1, 1, 0, 8, 8, 6, 8, 8, 3, 3, 0, 2, 2, 2, 0, 0), # 88
(10, 10, 4, 3, 7, 2, 2, 0, 3, 1, 1, 1, 0, 16, 11, 7, 6, 5, 2, 2, 2, 1, 2, 0, 0, 0), # 89
(6, 13, 5, 13, 7, 4, 2, 4, 7, 3, 2, 0, 0, 8, 6, 5, 3, 9, 4, 5, 1, 3, 2, 2, 1, 0), # 90
(8, 11, 11, 7, 11, 4, 2, 3, 2, 2, 0, 0, 0, 10, 4, 8, 8, 7, 3, 4, 3, 2, 4, 2, 1, 0), # 91
(10, 8, 6, 10, 7, 5, 5, 6, 2, 1, 1, 1, 0, 5, 10, 10, 4, 11, 6, 5, 3, 2, 4, 3, 0, 0), # 92
(7, 7, 11, 5, 6, 1, 6, 2, 4, 1, 1, 2, 0, 12, 9, 8, 2, 9, 3, 5, 0, 5, 3, 1, 0, 0), # 93
(15, 6, 5, 5, 10, 4, 3, 1, 6, 0, 2, 1, 0, 9, 8, 6, 4, 7, 5, 5, 1, 6, 3, 2, 0, 0), # 94
(11, 4, 3, 9, 8, 3, 3, 4, 8, 1, 0, 1, 0, 10, 14, 1, 4, 9, 3, 1, 0, 2, 2, 1, 1, 0), # 95
(8, 10, 7, 6, 4, 1, 6, 1, 2, 0, 3, 1, 0, 19, 6, 4, 7, 7, 6, 0, 4, 4, 5, 3, 1, 0), # 96
(9, 9, 8, 12, 10, 3, 1, 4, 3, 1, 1, 0, 0, 12, 10, 8, 5, 8, 4, 3, 1, 2, 2, 3, 1, 0), # 97
(9, 10, 6, 9, 10, 2, 5, 3, 3, 3, 2, 2, 0, 14, 9, 5, 7, 8, 4, 2, 2, 5, 1, 0, 0, 0), # 98
(11, 7, 8, 8, 3, 7, 5, 3, 3, 0, 0, 0, 0, 11, 6, 5, 7, 11, 5, 5, 3, 5, 1, 1, 2, 0), # 99
(6, 2, 9, 6, 10, 3, 3, 3, 5, 0, 1, 2, 0, 12, 8, 9, 4, 10, 3, 3, 2, 6, 3, 3, 0, 0), # 100
(11, 11, 10, 7, 2, 6, 3, 4, 6, 5, 0, 0, 0, 7, 8, 5, 6, 13, 7, 3, 2, 3, 1, 1, 0, 0), # 101
(7, 7, 2, 7, 4, 3, 1, 3, 4, 2, 0, 0, 0, 16, 4, 3, 3, 5, 4, 3, 0, 3, 2, 1, 0, 0), # 102
(10, 8, 12, 3, 6, 3, 7, 5, 4, 1, 1, 2, 0, 7, 5, 5, 5, 4, 5, 3, 1, 0, 3, 4, 0, 0), # 103
(15, 12, 7, 6, 5, 3, 3, 4, 3, 1, 1, 0, 0, 5, 3, 11, 6, 8, 5, 2, 1, 4, 1, 1, 0, 0), # 104
(12, 10, 8, 8, 8, 3, 4, 5, 4, 2, 1, 0, 0, 14, 8, 4, 3, 6, 5, 3, 1, 3, 2, 2, 0, 0), # 105
(6, 4, 9, 13, 4, 2, 1, 1, 7, 3, 0, 0, 0, 14, 7, 5, 3, 6, 4, 5, 2, 8, 0, 2, 0, 0), # 106
(8, 3, 9, 8, 6, 0, 5, 5, 2, 2, 1, 1, 0, 7, 6, 7, 6, 6, 3, 4, 2, 4, 2, 2, 2, 0), # 107
(12, 9, 11, 9, 10, 2, 3, 4, 1, 1, 0, 0, 0, 7, 9, 7, 3, 4, 2, 2, 1, 4, 1, 1, 0, 0), # 108
(13, 9, 8, 10, 6, 6, 4, 2, 3, 1, 2, 1, 0, 15, 10, 6, 3, 7, 6, 5, 4, 3, 5, 2, 0, 0), # 109
(10, 11, 8, 15, 7, 1, 3, 3, 7, 2, 1, 1, 0, 9, 10, 9, 5, 9, 2, 6, 1, 3, 2, 0, 0, 0), # 110
(9, 2, 6, 7, 7, 7, 3, 4, 4, 0, 0, 0, 0, 3, 9, 9, 3, 2, 4, 2, 5, 5, 1, 3, 0, 0), # 111
(8, 4, 14, 13, 6, 1, 3, 3, 3, 2, 2, 1, 0, 8, 6, 7, 5, 7, 2, 2, 1, 2, 6, 3, 0, 0), # 112
(5, 12, 5, 5, 6, 1, 1, 3, 1, 1, 1, 0, 0, 12, 9, 10, 2, 4, 3, 5, 5, 2, 6, 1, 0, 0), # 113
(9, 9, 7, 12, 7, 2, 5, 2, 6, 0, 0, 0, 0, 12, 7, 5, 6, 10, 3, 3, 1, 5, 0, 0, 1, 0), # 114
(11, 8, 5, 9, 8, 2, 1, 1, 8, 1, 1, 1, 0, 9, 8, 8, 6, 7, 2, 4, 5, 0, 2, 3, 0, 0), # 115
(12, 7, 6, 7, 7, 2, 6, 2, 2, 2, 1, 0, 0, 16, 3, 4, 3, 7, 7, 8, 2, 7, 2, 0, 0, 0), # 116
(5, 11, 14, 7, 4, 3, 1, 2, 2, 2, 2, 0, 0, 7, 7, 8, 4, 5, 0, 2, 2, 6, 0, 3, 0, 0), # 117
(8, 10, 5, 5, 10, 2, 2, 3, 1, 1, 1, 0, 0, 4, 9, 4, 5, 8, 6, 3, 3, 5, 3, 1, 0, 0), # 118
(11, 8, 8, 9, 9, 5, 1, 2, 1, 2, 3, 1, 0, 9, 8, 8, 3, 12, 3, 3, 3, 3, 1, 2, 0, 0), # 119
(3, 5, 3, 9, 3, 2, 3, 2, 8, 3, 2, 0, 0, 7, 7, 6, 2, 8, 3, 3, 2, 1, 1, 1, 0, 0), # 120
(8, 8, 9, 11, 14, 2, 4, 0, 6, 2, 1, 1, 0, 14, 8, 7, 4, 7, 2, 4, 2, 5, 2, 0, 1, 0), # 121
(10, 5, 6, 13, 8, 3, 5, 4, 5, 2, 4, 2, 0, 12, 5, 6, 5, 7, 4, 4, 2, 4, 1, 0, 0, 0), # 122
(9, 10, 5, 8, 11, 6, 4, 1, 2, 0, 1, 0, 0, 9, 9, 8, 9, 6, 2, 2, 5, 2, 0, 0, 1, 0), # 123
(4, 6, 6, 7, 9, 3, 0, 4, 8, 2, 3, 2, 0, 7, 6, 1, 5, 5, 3, 1, 2, 1, 4, 3, 0, 0), # 124
(4, 8, 5, 6, 9, 2, 3, 2, 2, 1, 3, 0, 0, 7, 6, 3, 5, 7, 5, 5, 1, 1, 3, 0, 1, 0), # 125
(8, 4, 4, 9, 11, 6, 0, 3, 4, 0, 0, 0, 0, 10, 7, 3, 3, 4, 5, 5, 3, 4, 1, 1, 2, 0), # 126
(8, 7, 8, 5, 7, 2, 2, 4, 2, 1, 0, 0, 0, 17, 8, 2, 6, 7, 6, 3, 3, 3, 3, 2, 2, 0), # 127
(11, 4, 6, 10, 6, 6, 3, 4, 2, 0, 0, 3, 0, 6, 4, 8, 1, 5, 2, 2, 1, 4, 2, 1, 0, 0), # 128
(9, 10, 11, 6, 6, 6, 1, 2, 5, 0, 2, 3, 0, 13, 6, 7, 4, 3, 2, 2, 4, 3, 4, 1, 0, 0), # 129
(10, 9, 7, 8, 10, 5, 4, 1, 6, 0, 3, 0, 0, 13, 5, 8, 4, 2, 1, 2, 1, 3, 2, 0, 1, 0), # 130
(10, 6, 4, 9, 5, 3, 3, 2, 4, 3, 0, 1, 0, 10, 10, 2, 5, 6, 4, 1, 1, 5, 1, 1, 2, 0), # 131
(7, 5, 6, 10, 6, 2, 7, 5, 5, 2, 1, 3, 0, 4, 7, 8, 5, 9, 4, 4, 1, 5, 3, 0, 2, 0), # 132
(7, 1, 2, 9, 9, 5, 1, 0, 2, 1, 1, 0, 0, 13, 2, 4, 3, 5, 2, 4, 4, 6, 1, 3, 1, 0), # 133
(3, 10, 5, 6, 8, 1, 2, 0, 2, 1, 1, 0, 0, 8, 9, 3, 3, 3, 3, 2, 4, 1, 2, 1, 0, 0), # 134
(5, 5, 4, 8, 4, 1, 3, 1, 5, 1, 1, 0, 0, 6, 11, 7, 1, 7, 4, 2, 1, 5, 1, 1, 0, 0), # 135
(6, 9, 6, 10, 8, 2, 2, 2, 1, 2, 1, 0, 0, 9, 5, 11, 3, 5, 1, 3, 4, 2, 4, 0, 0, 0), # 136
(15, 7, 8, 9, 5, 1, 0, 1, 5, 1, 0, 1, 0, 7, 7, 8, 1, 3, 4, 4, 4, 3, 1, 3, 1, 0), # 137
(14, 2, 6, 8, 6, 3, 2, 1, 3, 1, 0, 1, 0, 8, 8, 9, 4, 3, 6, 3, 0, 4, 3, 2, 0, 0), # 138
(14, 6, 9, 7, 21, 1, 2, 2, 1, 1, 0, 1, 0, 5, 6, 5, 1, 8, 0, 6, 2, 2, 2, 0, 0, 0), # 139
(14, 9, 6, 6, 11, 5, 3, 4, 5, 3, 1, 2, 0, 4, 9, 7, 5, 7, 2, 1, 1, 6, 3, 2, 0, 0), # 140
(7, 3, 8, 5, 3, 3, 3, 1, 6, 0, 1, 0, 0, 10, 8, 7, 3, 7, 7, 5, 0, 4, 1, 1, 1, 0), # 141
(8, 5, 4, 12, 3, 0, 2, 2, 2, 1, 0, 0, 0, 11, 9, 9, 2, 6, 2, 2, 2, 2, 2, 1, 1, 0), # 142
(12, 5, 6, 10, 3, 1, 3, 2, 1, 3, 0, 0, 0, 8, 11, 8, 5, 7, 6, 2, 1, 2, 2, 2, 0, 0), # 143
(12, 6, 10, 7, 9, 4, 2, 3, 2, 2, 1, 2, 0, 7, 8, 4, 6, 5, 5, 2, 1, 4, 0, 4, 0, 0), # 144
(11, 8, 6, 13, 6, 4, 1, 2, 3, 1, 0, 0, 0, 16, 7, 5, 2, 6, 6, 3, 4, 6, 1, 0, 0, 0), # 145
(11, 4, 7, 9, 6, 3, 5, 5, 3, 1, 0, 1, 0, 3, 8, 7, 6, 7, 9, 5, 0, 3, 3, 1, 0, 0), # 146
(8, 6, 6, 3, 7, 4, 3, 1, 6, 2, 1, 1, 0, 7, 9, 2, 4, 13, 1, 1, 2, 3, 2, 1, 1, 0), # 147
(8, 4, 5, 7, 11, 2, 0, 0, 4, 0, 2, 2, 0, 5, 8, 6, 4, 7, 7, 0, 1, 3, 2, 1, 0, 0), # 148
(9, 5, 5, 12, 5, 4, 2, 2, 6, 0, 2, 0, 0, 9, 4, 5, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0), # 149
(13, 3, 5, 4, 5, 0, 4, 6, 0, 0, 0, 1, 0, 10, 5, 7, 4, 4, 3, 4, 1, 2, 4, 2, 1, 0), # 150
(13, 3, 5, 7, 6, 10, 2, 1, 5, 0, 0, 0, 0, 9, 10, 6, 5, 5, 2, 4, 0, 3, 1, 0, 1, 0), # 151
(6, 7, 3, 8, 7, 3, 0, 1, 7, 2, 1, 0, 0, 5, 9, 7, 6, 6, 2, 1, 2, 2, 3, 0, 0, 0), # 152
(6, 6, 7, 11, 3, 5, 3, 2, 2, 1, 1, 0, 0, 8, 6, 8, 4, 6, 3, 0, 3, 2, 1, 3, 1, 0), # 153
(8, 10, 4, 13, 7, 4, 3, 3, 2, 1, 0, 0, 0, 13, 6, 5, 3, 5, 3, 1, 2, 2, 6, 1, 0, 0), # 154
(6, 4, 8, 1, 2, 1, 1, 1, 3, 0, 0, 0, 0, 11, 10, 7, 7, 9, 4, 3, 1, 3, 1, 0, 0, 0), # 155
(8, 6, 4, 6, 4, 2, 1, 5, 1, 1, 0, 0, 0, 5, 5, 2, 6, 10, 7, 5, 3, 2, 2, 1, 2, 0), # 156
(6, 5, 6, 9, 9, 2, 1, 1, 1, 1, 2, 1, 0, 12, 11, 2, 0, 7, 4, 2, 5, 3, 3, 1, 0, 0), # 157
(2, 2, 6, 5, 9, 3, 2, 5, 4, 1, 3, 0, 0, 9, 5, 8, 2, 13, 6, 3, 2, 0, 2, 1, 0, 0), # 158
(3, 6, 6, 5, 6, 3, 0, 4, 5, 0, 0, 1, 0, 12, 6, 3, 3, 5, 5, 4, 3, 1, 2, 0, 0, 0), # 159
(9, 4, 5, 6, 16, 5, 1, 4, 3, 1, 1, 0, 0, 6, 12, 4, 4, 11, 5, 7, 3, 2, 1, 1, 1, 0), # 160
(3, 5, 6, 9, 10, 7, 4, 2, 4, 1, 0, 0, 0, 7, 5, 3, 2, 8, 3, 3, 4, 4, 3, 0, 0, 0), # 161
(6, 7, 4, 4, 6, 2, 0, 4, 3, 1, 0, 1, 0, 6, 6, 3, 3, 8, 1, 2, 2, 2, 1, 3, 0, 0), # 162
(5, 4, 9, 6, 9, 5, 2, 4, 3, 0, 2, 1, 0, 12, 4, 2, 3, 4, 3, 0, 1, 3, 1, 2, 1, 0), # 163
(8, 4, 7, 3, 7, 1, 0, 0, 3, 2, 0, 3, 0, 6, 7, 5, 2, 6, 3, 2, 4, 1, 0, 4, 0, 0), # 164
(4, 5, 9, 4, 6, 1, 1, 3, 3, 2, 2, 0, 0, 3, 4, 3, 5, 7, 1, 2, 5, 2, 8, 2, 1, 0), # 165
(4, 3, 3, 6, 2, 1, 1, 0, 6, 1, 0, 0, 0, 8, 6, 2, 5, 6, 1, 1, 1, 1, 2, 0, 0, 0), # 166
(11, 7, 7, 4, 9, 1, 1, 1, 2, 1, 1, 0, 0, 5, 8, 2, 0, 11, 2, 1, 3, 4, 4, 0, 3, 0), # 167
(7, 6, 2, 4, 1, 2, 1, 2, 4, 1, 1, 0, 0, 8, 1, 4, 5, 3, 2, 3, 2, 4, 2, 1, 0, 0), # 168
(8, 5, 2, 4, 6, 1, 0, 0, 1, 0, 0, 1, 0, 10, 4, 5, 3, 8, 4, 3, 1, 2, 2, 2, 0, 0), # 169
(5, 5, 5, 3, 7, 1, 4, 0, 0, 5, 1, 0, 0, 7, 6, 3, 2, 5, 3, 2, 1, 1, 2, 0, 0, 0), # 170
(6, 3, 3, 6, 5, 1, 1, 0, 4, 2, 2, 0, 0, 5, 1, 6, 3, 2, 1, 4, 1, 1, 0, 3, 0, 0), # 171
(9, 1, 2, 8, 0, 3, 0, 2, 4, 0, 2, 0, 0, 5, 4, 4, 4, 6, 0, 2, 1, 4, 2, 1, 0, 0), # 172
(4, 4, 7, 2, 1, 2, 3, 2, 0, 0, 3, 0, 0, 7, 4, 4, 3, 6, 7, 1, 0, 1, 2, 3, 0, 0), # 173
(9, 3, 5, 3, 5, 1, 1, 1, 2, 3, 3, 0, 0, 7, 3, 2, 3, 2, 1, 2, 1, 2, 1, 1, 0, 0), # 174
(2, 3, 11, 4, 1, 1, 1, 0, 2, 0, 2, 0, 0, 6, 4, 2, 0, 5, 4, 1, 1, 3, 1, 2, 0, 0), # 175
(3, 3, 5, 3, 5, 2, 0, 1, 0, 0, 1, 1, 0, 9, 2, 2, 1, 1, 5, 1, 1, 2, 0, 0, 1, 0), # 176
(2, 2, 4, 3, 3, 2, 2, 0, 1, 1, 0, 1, 0, 5, 3, 2, 2, 5, 1, 1, 1, 1, 3, 0, 1, 0), # 177
(2, 1, 5, 0, 2, 1, 2, 1, 0, 1, 0, 1, 0, 8, 5, 4, 2, 5, 2, 0, 0, 2, 2, 0, 0, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0
(5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1
(5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2
(6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3
(6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4
(6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5
(6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6
(7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7
(7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8
(7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9
(8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10
(8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11
(8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12
(8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13
(9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14
(9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15
(9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16
(9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17
(9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18
(9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19
(10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20
(10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21
(10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22
(10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23
(10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24
(10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25
(10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26
(10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27
(10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28
(10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29
(10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30
(10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31
(10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32
(10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 33
(10.23473881023881, 10.869745541838133, 10.338354595336076, 12.341393261316872, 11.180330495471466, 6.25, 8.15782090696361, 9.018154320987653, 12.089570061728397, 7.778149702789209, 8.354252961715924, 9.701139460448102, 10.125), # 34
(10.242847531796807, 10.847822359396433, 10.332133058984912, 12.335615997942385, 11.18439547615087, 6.25, 8.142328330509159, 8.979938271604938, 12.083526234567902, 7.761974165523548, 8.350643783514153, 9.692844078646548, 10.125), # 35
(10.250723266745005, 10.824499999999999, 10.3255, 12.3294375, 11.188340212172836, 6.25, 8.12585294117647, 8.9395, 12.077074999999999, 7.7448, 8.346772727272727, 9.684000000000001, 10.125), # 36
(10.258365219256524, 10.799844307270233, 10.318471879286694, 12.322870113168724, 11.192164519986921, 6.25, 8.108442185104494, 8.896987654320988, 12.070231172839506, 7.726680978509374, 8.34264577877541, 9.674629172382259, 10.125), # 37
(10.265772593504476, 10.773921124828533, 10.311065157750342, 12.315926183127573, 11.19586821604269, 6.25, 8.09014350843218, 8.85254938271605, 12.063009567901235, 7.707670873342479, 8.33826892380596, 9.664753543667125, 10.125), # 38
(10.272944593661986, 10.746796296296296, 10.303296296296297, 12.308618055555556, 11.199451116789703, 6.25, 8.071004357298476, 8.806333333333333, 12.055425000000001, 7.687823456790124, 8.333648148148148, 9.654395061728394, 10.125), # 39
(10.279880423902163, 10.718535665294924, 10.295181755829903, 12.300958076131687, 11.202913038677519, 6.25, 8.05107217784233, 8.758487654320989, 12.047492283950618, 7.667192501143119, 8.328789437585733, 9.643575674439873, 10.125), # 40
(10.286579288398128, 10.689205075445816, 10.286737997256516, 12.29295859053498, 11.206253798155702, 6.25, 8.030394416202695, 8.709160493827161, 12.0392262345679, 7.645831778692272, 8.323698777902482, 9.632317329675354, 10.125), # 41
(10.293040391323, 10.658870370370371, 10.277981481481483, 12.284631944444445, 11.209473211673808, 6.25, 8.009018518518518, 8.6585, 12.030641666666668, 7.623795061728395, 8.318382154882155, 9.620641975308642, 10.125), # 42
(10.299262936849892, 10.627597393689987, 10.268928669410151, 12.275990483539095, 11.212571095681403, 6.25, 7.98699193092875, 8.606654320987655, 12.021753395061728, 7.601136122542296, 8.312845554308517, 9.608571559213535, 10.125), # 43
(10.305246129151927, 10.595451989026063, 10.259596021947875, 12.267046553497943, 11.215547266628045, 6.25, 7.964362099572339, 8.553771604938273, 12.0125762345679, 7.577908733424783, 8.307094961965332, 9.596128029263832, 10.125), # 44
(10.310989172402216, 10.5625, 10.25, 12.2578125, 11.218401540963296, 6.25, 7.9411764705882355, 8.5, 12.003124999999999, 7.554166666666667, 8.301136363636363, 9.583333333333332, 10.125), # 45
(10.31649127077388, 10.528807270233196, 10.240157064471878, 12.24830066872428, 11.221133735136716, 6.25, 7.917482490115388, 8.445487654320988, 11.993414506172838, 7.529963694558756, 8.294975745105374, 9.57020941929584, 10.125), # 46
(10.321751628440035, 10.49443964334705, 10.230083676268862, 12.238523405349794, 11.223743665597867, 6.25, 7.893327604292747, 8.390382716049382, 11.983459567901235, 7.505353589391861, 8.288619092156129, 9.55677823502515, 10.125), # 47
(10.326769449573796, 10.459462962962963, 10.219796296296296, 12.228493055555557, 11.22623114879631, 6.25, 7.868759259259259, 8.334833333333334, 11.973275000000001, 7.4803901234567896, 8.28207239057239, 9.543061728395061, 10.125), # 48
(10.331543938348286, 10.42394307270233, 10.209311385459534, 12.218221965020577, 11.228596001181607, 6.25, 7.8438249011538765, 8.278987654320987, 11.96287561728395, 7.455127069044353, 8.275341626137923, 9.529081847279379, 10.125), # 49
(10.336074298936616, 10.387945816186559, 10.198645404663925, 12.207722479423868, 11.230838039203315, 6.25, 7.81857197611555, 8.222993827160494, 11.9522762345679, 7.429618198445358, 8.268432784636488, 9.514860539551899, 10.125), # 50
(10.34035973551191, 10.351537037037037, 10.187814814814814, 12.197006944444444, 11.232957079310998, 6.25, 7.793047930283224, 8.167, 11.941491666666668, 7.403917283950617, 8.261351851851853, 9.50041975308642, 10.125), # 51
(10.344399452247279, 10.314782578875173, 10.176836076817558, 12.186087705761317, 11.234952937954214, 6.25, 7.767300209795852, 8.111154320987653, 11.930536728395062, 7.3780780978509375, 8.254104813567777, 9.485781435756746, 10.125), # 52
(10.348192653315843, 10.27774828532236, 10.165725651577505, 12.174977109053497, 11.23682543158253, 6.25, 7.741376260792383, 8.055604938271605, 11.919426234567903, 7.3521544124371285, 8.246697655568026, 9.470967535436671, 10.125), # 53
(10.351738542890716, 10.2405, 10.154499999999999, 12.1636875, 11.238574376645502, 6.25, 7.715323529411765, 8.000499999999999, 11.908175, 7.3262, 8.239136363636362, 9.456, 10.125), # 54
(10.355036325145022, 10.203103566529492, 10.143175582990398, 12.152231224279834, 11.24019958959269, 6.25, 7.689189461792948, 7.945987654320987, 11.896797839506172, 7.300268632830361, 8.231426923556553, 9.44090077732053, 10.125), # 55
(10.358085204251871, 10.165624828532236, 10.131768861454047, 12.140620627572016, 11.241700886873659, 6.25, 7.663021504074881, 7.892216049382716, 11.885309567901235, 7.274414083219022, 8.223575321112358, 9.425691815272062, 10.125), # 56
(10.360884384384383, 10.12812962962963, 10.120296296296297, 12.128868055555555, 11.243078084937967, 6.25, 7.636867102396514, 7.839333333333334, 11.873725, 7.24869012345679, 8.215587542087542, 9.410395061728394, 10.125), # 57
(10.36343306971568, 10.090683813443073, 10.108774348422497, 12.116985853909464, 11.244331000235174, 6.25, 7.610773702896797, 7.787487654320987, 11.862058950617284, 7.223150525834477, 8.20746957226587, 9.395032464563329, 10.125), # 58
(10.36573046441887, 10.053353223593964, 10.097219478737998, 12.104986368312757, 11.245459449214845, 6.25, 7.584788751714678, 7.736827160493827, 11.850326234567902, 7.197849062642891, 8.1992273974311, 9.379625971650663, 10.125), # 59
(10.367775772667077, 10.016203703703704, 10.085648148148147, 12.092881944444445, 11.246463248326537, 6.25, 7.558959694989106, 7.6875, 11.838541666666668, 7.172839506172839, 8.190867003367003, 9.364197530864198, 10.125), # 60
(10.369568198633415, 9.97930109739369, 10.0740768175583, 12.080684927983539, 11.247342214019811, 6.25, 7.533333978859033, 7.639654320987654, 11.826720061728395, 7.148175628715135, 8.182394375857339, 9.348769090077733, 10.125), # 61
(10.371106946491004, 9.942711248285322, 10.062521947873801, 12.068407664609055, 11.248096162744234, 6.25, 7.507959049463406, 7.5934382716049384, 11.814876234567901, 7.123911202560586, 8.17381550068587, 9.333362597165067, 10.125), # 62
(10.37239122041296, 9.9065, 10.051, 12.056062500000001, 11.248724910949356, 6.25, 7.482882352941176, 7.549, 11.803025, 7.100099999999999, 8.165136363636364, 9.318, 10.125), # 63
(10.373420224572397, 9.870733196159122, 10.039527434842249, 12.043661779835391, 11.249228275084748, 6.25, 7.458151335431292, 7.506487654320988, 11.791181172839506, 7.076795793324188, 8.156362950492579, 9.302703246456334, 10.125), # 64
(10.374193163142438, 9.835476680384087, 10.0281207133059, 12.031217849794238, 11.249606071599967, 6.25, 7.433813443072703, 7.466049382716049, 11.779359567901235, 7.054052354823959, 8.147501247038285, 9.287494284407863, 10.125), # 65
(10.374709240296196, 9.800796296296298, 10.016796296296297, 12.018743055555555, 11.249858116944573, 6.25, 7.409916122004357, 7.427833333333334, 11.767575, 7.031923456790123, 8.138557239057238, 9.272395061728396, 10.125), # 66
(10.374967660206792, 9.766757887517146, 10.005570644718793, 12.006249742798353, 11.24998422756813, 6.25, 7.386506818365206, 7.391987654320989, 11.755842283950617, 7.010462871513489, 8.12953691233321, 9.257427526291723, 10.125), # 67
(10.374791614480825, 9.733248639320323, 9.994405949931412, 11.993641740472357, 11.249877955297345, 6.2498840115836, 7.363515194829646, 7.358343850022862, 11.744087848651121, 6.989620441647166, 8.120285988540376, 9.242530021899743, 10.124875150034294), # 68
(10.373141706924315, 9.699245519713262, 9.982988425925925, 11.980283514492752, 11.248910675381262, 6.248967078189301, 7.340268181346613, 7.325098765432099, 11.731797839506173, 6.968806390704429, 8.10986283891547, 9.227218973359324, 10.12388599537037), # 69
(10.369885787558895, 9.664592459843355, 9.971268432784635, 11.966087124261943, 11.246999314128942, 6.247161255906112, 7.31666013456137, 7.291952446273434, 11.718902892089622, 6.947919524462734, 8.09814888652608, 9.211422761292809, 10.121932334533609), # 70
(10.365069660642929, 9.62931016859153, 9.959250085733881, 11.951073503757382, 11.244168078754136, 6.244495808565767, 7.292701659538988, 7.258915866483768, 11.705422210791038, 6.926960359342639, 8.085187370783862, 9.195152937212715, 10.119039887688615), # 71
(10.358739130434783, 9.593419354838709, 9.946937499999999, 11.935263586956522, 11.240441176470588, 6.2410000000000005, 7.268403361344538, 7.226, 11.691375, 6.905929411764705, 8.07102153110048, 9.17842105263158, 10.115234375), # 72
(10.35094000119282, 9.556940727465816, 9.934334790809327, 11.918678307836823, 11.23584281449205, 6.236703094040542, 7.243775845043092, 7.193215820759031, 11.676780464106082, 6.884827198149493, 8.055694606887588, 9.161238659061919, 10.110541516632374), # 73
(10.341718077175404, 9.519894995353777, 9.921446073388202, 11.901338600375738, 11.230397200032275, 6.231634354519128, 7.218829715699722, 7.160574302697759, 11.661657807498857, 6.863654234917561, 8.039249837556856, 9.143617308016267, 10.104987032750344), # 74
(10.331119162640901, 9.482302867383511, 9.908275462962962, 11.883265398550725, 11.224128540305012, 6.22582304526749, 7.1935755783795, 7.128086419753086, 11.6460262345679, 6.84241103848947, 8.021730462519935, 9.125568551007147, 10.098596643518519), # 75
(10.319189061847677, 9.44418505243595, 9.894827074759945, 11.864479636339238, 11.217061042524005, 6.219298430117361, 7.168024038147495, 7.095763145861912, 11.629904949702789, 6.821098125285779, 8.003179721188491, 9.107103939547082, 10.091396069101508), # 76
(10.305973579054093, 9.40556225939201, 9.881105024005485, 11.845002247718732, 11.209218913903008, 6.212089772900472, 7.142185700068779, 7.063615454961135, 11.613313157293096, 6.7997160117270505, 7.983640852974187, 9.088235025148606, 10.083411029663925), # 77
(10.291518518518519, 9.366455197132618, 9.867113425925925, 11.824854166666666, 11.200626361655774, 6.204226337448559, 7.116071169208425, 7.031654320987655, 11.596270061728394, 6.7782652142338415, 7.9631570972886765, 9.068973359324238, 10.074667245370371), # 78
(10.275869684499314, 9.326884574538697, 9.8528563957476, 11.804056327160493, 11.191307592996047, 6.195737387593354, 7.089691050631501, 6.9998907178783725, 11.578794867398262, 6.756746249226714, 7.941771693543622, 9.049330493586504, 10.065190436385459), # 79
(10.259072881254847, 9.286871100491172, 9.838338048696844, 11.782629663177671, 11.181286815137579, 6.18665218716659, 7.063055949403081, 6.968335619570188, 11.560906778692273, 6.7351596331262265, 7.919527881150688, 9.029317979447935, 10.0550063228738), # 80
(10.241173913043479, 9.246435483870968, 9.8235625, 11.760595108695654, 11.170588235294117, 6.177, 7.036176470588235, 6.937, 11.542625, 6.713505882352941, 7.8964688995215315, 9.008947368421053, 10.044140624999999), # 81
(10.222218584123576, 9.205598433559008, 9.808533864883403, 11.737973597691894, 11.159236060679415, 6.166810089925317, 7.009063219252036, 6.90589483310471, 11.52396873571102, 6.691785513327416, 7.872637988067813, 8.988230212018387, 10.03261906292867), # 82
(10.202252698753504, 9.164380658436214, 9.793256258573388, 11.714786064143853, 11.147254498507221, 6.156111720774272, 6.981726800459553, 6.875031092821216, 11.504957190214906, 6.669999042470211, 7.848078386201194, 8.967178061752461, 10.020467356824417), # 83
(10.181322061191626, 9.122802867383513, 9.777733796296296, 11.691053442028986, 11.134667755991286, 6.144934156378601, 6.954177819275858, 6.844419753086419, 11.485609567901234, 6.648146986201889, 7.822833333333333, 8.945802469135803, 10.007711226851852), # 84
(10.159472475696308, 9.080885769281826, 9.761970593278463, 11.666796665324746, 11.121500040345357, 6.133306660570035, 6.926426880766024, 6.814071787837221, 11.465945073159578, 6.626229860943005, 7.796946068875894, 8.924114985680937, 9.994376393175584), # 85
(10.136749746525913, 9.03865007301208, 9.745970764746229, 11.64203666800859, 11.107775558783183, 6.121258497180309, 6.89848458999512, 6.783998171010516, 11.445982910379517, 6.604248183114124, 7.770459832240534, 8.902127162900394, 9.98048857596022), # 86
(10.113199677938807, 8.996116487455197, 9.729738425925925, 11.61679438405797, 11.09351851851852, 6.108818930041152, 6.870361552028219, 6.75420987654321, 11.425742283950619, 6.582202469135802, 7.743417862838915, 8.879850552306692, 9.96607349537037), # 87
(10.088868074193357, 8.9533057214921, 9.713277692043896, 11.59109074745035, 11.07875312676511, 6.096017222984301, 6.842068371930391, 6.724717878372199, 11.40524239826246, 6.560093235428601, 7.715863400082698, 8.857296705412365, 9.951156871570646), # 88
(10.063800739547922, 8.910238484003717, 9.696592678326475, 11.564946692163177, 11.063503590736707, 6.082882639841488, 6.813615654766708, 6.695533150434385, 11.384502457704619, 6.537920998413083, 7.687839683383544, 8.834477173729935, 9.935764424725651), # 89
(10.03804347826087, 8.866935483870968, 9.6796875, 11.538383152173914, 11.04779411764706, 6.069444444444445, 6.785014005602241, 6.666666666666666, 11.363541666666668, 6.515686274509804, 7.65938995215311, 8.81140350877193, 9.919921875), # 90
(10.011642094590563, 8.823417429974777, 9.662566272290809, 11.511421061460013, 11.031648914709915, 6.055731900624904, 6.756274029502062, 6.638129401005944, 11.342379229538182, 6.4933895801393255, 7.63055744580306, 8.788087262050874, 9.903654942558298), # 91
(9.984642392795372, 8.779705031196071, 9.64523311042524, 11.484081353998926, 11.015092189139029, 6.041774272214601, 6.727406331531242, 6.609932327389118, 11.321034350708734, 6.471031431722209, 7.601385403745053, 8.764539985079297, 9.886989347565157), # 92
(9.957090177133654, 8.735818996415771, 9.62769212962963, 11.456384963768118, 10.998148148148148, 6.027600823045267, 6.69842151675485, 6.582086419753087, 11.299526234567901, 6.448612345679011, 7.57191706539075, 8.74077322936972, 9.869950810185184), # 93
(9.92903125186378, 8.691780034514801, 9.609947445130317, 11.428352824745035, 10.98084099895102, 6.0132408169486355, 6.669330190237961, 6.554602652034752, 11.277874085505259, 6.426132838430297, 7.54219567015181, 8.716798546434674, 9.85256505058299), # 94
(9.90051142124411, 8.647608854374088, 9.592003172153635, 11.400005870907139, 10.963194948761398, 5.9987235177564395, 6.640142957045644, 6.527491998171011, 11.25609710791038, 6.403593426396621, 7.512264457439896, 8.69262748778668, 9.834857788923182), # 95
(9.871576489533012, 8.603326164874554, 9.573863425925927, 11.371365036231884, 10.945234204793028, 5.984078189300411, 6.610870422242971, 6.500765432098766, 11.234214506172838, 6.3809946259985475, 7.482166666666667, 8.668271604938273, 9.816854745370371), # 96
(9.842272260988848, 8.558952674897121, 9.555532321673525, 11.342451254696725, 10.926982974259664, 5.969334095412284, 6.581523190895013, 6.474433927754916, 11.212245484682214, 6.358336953656634, 7.451945537243782, 8.64374244940197, 9.798581640089164), # 97
(9.812644539869984, 8.514509093322713, 9.53701397462277, 11.31328546027912, 10.908465464375052, 5.954520499923793, 6.552111868066842, 6.44850845907636, 11.190209247828074, 6.335620925791441, 7.421644308582906, 8.619051572690298, 9.78006419324417), # 98
(9.782739130434782, 8.470016129032258, 9.5183125, 11.283888586956522, 10.889705882352942, 5.939666666666667, 6.52264705882353, 6.423, 11.168125, 6.312847058823529, 7.391306220095694, 8.59421052631579, 9.761328125), # 99
(9.752601836941611, 8.425494490906676, 9.49943201303155, 11.254281568706388, 10.870728435407084, 5.924801859472641, 6.493139368230145, 6.3979195244627345, 11.146011945587563, 6.290015869173458, 7.36097451119381, 8.569230861790967, 9.742399155521262), # 100
(9.722278463648834, 8.380964887826895, 9.480376628943759, 11.224485339506174, 10.85155733075123, 5.909955342173449, 6.463599401351762, 6.3732780064014625, 11.123889288980338, 6.267127873261788, 7.330692421288912, 8.544124130628353, 9.723303004972564), # 101
(9.691814814814816, 8.336448028673836, 9.461150462962962, 11.194520833333334, 10.832216775599129, 5.895156378600824, 6.43403776325345, 6.349086419753086, 11.1017762345679, 6.244183587509078, 7.300503189792663, 8.518901884340481, 9.704065393518519), # 102
(9.661256694697919, 8.291964622328422, 9.4417576303155, 11.164408984165325, 10.812730977164529, 5.880434232586496, 6.40446505900028, 6.325355738454504, 11.079691986739826, 6.221183528335889, 7.270450056116723, 8.493575674439873, 9.68471204132373), # 103
(9.63064990755651, 8.247535377671579, 9.422202246227709, 11.134170725979603, 10.79312414266118, 5.865818167962201, 6.374891893657326, 6.302096936442616, 11.057655749885688, 6.19812821216278, 7.24057625967275, 8.468157052439054, 9.665268668552812), # 104
(9.600040257648953, 8.203181003584229, 9.402488425925926, 11.103826992753623, 10.773420479302832, 5.851337448559671, 6.345328872289658, 6.279320987654321, 11.035686728395062, 6.175018155410313, 7.210925039872408, 8.442657569850553, 9.64576099537037), # 105
(9.569473549233614, 8.158922208947299, 9.382620284636488, 11.073398718464842, 10.753644194303236, 5.837021338210638, 6.315786599962345, 6.25703886602652, 11.01380412665752, 6.151853874499045, 7.181539636127355, 8.417088778186894, 9.626214741941014), # 106
(9.538995586568856, 8.11477970264171, 9.362601937585735, 11.042906837090714, 10.733819494876139, 5.822899100746838, 6.286275681740461, 6.235261545496114, 10.992027149062643, 6.128635885849539, 7.152463287849252, 8.391462228960604, 9.606655628429355), # 107
(9.508652173913044, 8.070774193548388, 9.3424375, 11.012372282608696, 10.713970588235293, 5.809, 6.256806722689075, 6.214, 10.970375, 6.105364705882353, 7.1237392344497605, 8.365789473684211, 9.587109375), # 108
(9.478489115524543, 8.026926390548255, 9.322131087105625, 10.98181598899624, 10.69412168159445, 5.795353299801859, 6.227390327873262, 6.193265203475081, 10.948866883859168, 6.082040851018047, 7.09541071534054, 8.340082063870238, 9.567601701817559), # 109
(9.448552215661715, 7.983257002522237, 9.301686814128946, 10.951258890230811, 10.674296982167354, 5.7819882639841484, 6.198037102358089, 6.173068129858253, 10.92752200502972, 6.058664837677183, 7.06752096993325, 8.314351551031214, 9.54815832904664), # 110
(9.41888727858293, 7.9397867383512555, 9.281108796296298, 10.920721920289855, 10.654520697167756, 5.768934156378601, 6.168757651208631, 6.153419753086419, 10.906359567901236, 6.035237182280319, 7.040113237639553, 8.288609486679663, 9.528804976851852), # 111
(9.38954010854655, 7.896536306916234, 9.26040114883402, 10.890226013150832, 10.634817033809409, 5.756220240816949, 6.139562579489958, 6.134331047096479, 10.885398776863282, 6.011758401248016, 7.013230757871109, 8.26286742232811, 9.509567365397805), # 112
(9.360504223703044, 7.853598618785952, 9.239617828252069, 10.85983388249204, 10.615175680173705, 5.7438697692145135, 6.1105259636567695, 6.115852568780606, 10.86471281125862, 5.988304736612729, 6.9869239061528665, 8.237192936504428, 9.490443900843221), # 113
(9.331480897900065, 7.811397183525536, 9.219045675021619, 10.829789421277336, 10.595393354566326, 5.731854608529901, 6.082018208410579, 6.09821125950512, 10.84461903571306, 5.965315167912783, 6.961244337113197, 8.211912172112974, 9.471275414160035), # 114
(9.302384903003995, 7.769947198683046, 9.198696932707318, 10.800084505181779, 10.5754076778886, 5.7201435124987645, 6.054059650191562, 6.081402654278709, 10.82512497866879, 5.942825327988077, 6.936154511427094, 8.187037582558851, 9.452006631660376), # 115
(9.273179873237634, 7.729188281291702, 9.178532189983873, 10.770666150266404, 10.555188526383779, 5.708708877287098, 6.026604817527893, 6.065380312898993, 10.80618133922783, 5.920793358449547, 6.911605931271481, 8.162523197487346, 9.43260725975589), # 116
(9.243829442823772, 7.689060048384721, 9.158512035525986, 10.741481372592244, 10.53470577629511, 5.6975230990608905, 5.9996082389477525, 6.050097795163585, 10.787738816492203, 5.899177400908129, 6.887550098823283, 8.13832304654375, 9.413047004858225), # 117
(9.214297245985211, 7.649502116995324, 9.138597058008367, 10.712477188220333, 10.513929303865842, 5.686558573986138, 5.973024442979315, 6.0355086608700965, 10.769748109563935, 5.877935596974759, 6.863938516259424, 8.11439115937335, 9.393295573379024), # 118
(9.184546916944742, 7.610454104156729, 9.118747846105723, 10.683600613211706, 10.492828985339221, 5.675787698228833, 5.946807958150756, 6.021566469816145, 10.752159917545043, 5.857026088260372, 6.840722685756828, 8.090681565621434, 9.373322671729932), # 119
(9.154542089925162, 7.571855626902158, 9.098924988492762, 10.654798663627394, 10.471374696958497, 5.665182867954965, 5.920913312990253, 6.008224781799343, 10.734924939537558, 5.836407016375905, 6.817854109492416, 8.067148294933297, 9.353098006322597), # 120
(9.124246399149268, 7.533646302264829, 9.079089073844187, 10.626018355528434, 10.449536314966918, 5.6547164793305305, 5.89529503602598, 5.995437156617307, 10.717993874643499, 5.816036522932296, 6.795284289643116, 8.043745376954222, 9.33259128356866), # 121
(9.093623478839854, 7.495765747277961, 9.059200690834711, 10.597206704975855, 10.427283715607734, 5.644360928521519, 5.869907655786117, 5.983157154067649, 10.70131742196489, 5.795872749540477, 6.772964728385851, 8.0204268413295, 9.31177220987977), # 122
(9.062636963219719, 7.458153578974774, 9.039220428139036, 10.568310728030694, 10.40458677512419, 5.634088611693925, 5.844705700798839, 5.971338333947983, 10.684846280603754, 5.775873837811387, 6.750846927897544, 7.997146717704421, 9.290610491667572), # 123
(9.031250486511654, 7.420749414388487, 9.01910887443187, 10.539277440753986, 10.381415369759537, 5.623871925013739, 5.819643699592319, 5.959934256055926, 10.668531149662115, 5.755997929355961, 6.728882390355119, 7.973859035724275, 9.269075835343711), # 124
(8.999427682938459, 7.38349287055232, 8.998826618387923, 10.51005385920676, 10.357739375757022, 5.613683264646956, 5.794676180694739, 5.948898480189091, 10.652322728241993, 5.736203165785134, 6.707022617935501, 7.950517825034348, 9.247137947319828), # 125
(8.967132186722928, 7.346323564499494, 8.978334248681898, 10.480586999450054, 10.333528669359893, 5.603495026759568, 5.76975767263427, 5.938184566145092, 10.636171715445418, 5.7164476887098425, 6.685219112815613, 7.927077115279934, 9.224766534007578), # 126
(8.93432763208786, 7.309181113263224, 8.957592353988504, 10.450823877544899, 10.308753126811398, 5.593279607517565, 5.744842703939094, 5.927746073721545, 10.620028810374407, 5.696689639741024, 6.6634233771723785, 7.903490936106316, 9.201931301818599), # 127
(8.900977653256046, 7.272005133876735, 8.93656152298245, 10.420711509552332, 10.28338262435479, 5.583009403086944, 5.719885803137382, 5.917536562716062, 10.603844712130984, 5.6768871604896125, 6.641586913182724, 7.879713317158788, 9.178601957164537), # 128
(8.867045884450281, 7.234735243373241, 8.91520234433844, 10.390196911533382, 10.257387038233311, 5.572656809633695, 5.694841498757313, 5.90750959292626, 10.587570119817174, 5.656998392566545, 6.619661223023571, 7.855698288082636, 9.154748206457038), # 129
(8.832495959893366, 7.197311058785966, 8.893475406731179, 10.359227099549086, 10.230736244690213, 5.562194223323808, 5.669664319327063, 5.89761872414975, 10.571155732535, 5.636981477582757, 6.5975978088718445, 7.831399878523152, 9.130339756107748), # 130
(8.797291513808094, 7.159672197148127, 8.87134129883538, 10.327749089660475, 10.203400119968745, 5.55159404032328, 5.644308793374809, 5.88781751618415, 10.554552249386486, 5.616794557149185, 6.575348172904468, 7.806772118125624, 9.105346312528312), # 131
(8.76139618041726, 7.121758275492944, 8.848760609325746, 10.295709897928587, 10.175348540312154, 5.540828656798102, 5.618729449428725, 5.878059528827073, 10.537710369473654, 5.596395772876765, 6.552863817298364, 7.781769036535342, 9.079737582130376), # 132
(8.724773593943663, 7.083508910853635, 8.825693926876983, 10.263056540414452, 10.146551381963686, 5.529870468914266, 5.592880816016989, 5.868298321876132, 10.520580791898526, 5.575743266376432, 6.53009624423046, 7.756344663397592, 9.053483271325586), # 133
(8.687387388610095, 7.044863720263423, 8.802101840163804, 10.229736033179103, 10.116978521166592, 5.518691872837765, 5.566717421667779, 5.858487455128944, 10.503114215763128, 5.5547951792591235, 6.506996955877678, 7.730453028357666, 9.026553086525583), # 134
(8.649201198639354, 7.005762320755524, 8.777944937860909, 10.195695392283579, 10.08659983416412, 5.507265264734592, 5.540193794909268, 5.84858048838312, 10.48526134016948, 5.533509653135776, 6.483517454416942, 7.704048161060852, 8.99891673414202), # 135
(8.610178658254235, 6.966144329363159, 8.753183808643008, 10.160881633788906, 10.055385197199517, 5.495563040770739, 5.513264464269635, 5.838530981436277, 10.466972864219606, 5.511844829617322, 6.459609242025177, 7.677084091152441, 8.970543920586536), # 136
(8.570283401677534, 6.925949363119547, 8.72777904118481, 10.125241773756125, 10.023304486516034, 5.483557597112198, 5.485883958277055, 5.828292494086029, 10.448199487015533, 5.4897588503147015, 6.435223820879306, 7.649514848277719, 8.941404352270776), # 137
(8.529479063132047, 6.885117039057908, 8.701691224161017, 10.088722828246263, 9.990327578356919, 5.471221329924964, 5.458006805459704, 5.81781858612999, 10.428891907659281, 5.4672098568388465, 6.410312693156252, 7.621294462081978, 8.91146773560639), # 138
(8.487729276840568, 6.843586974211461, 8.67488094624634, 10.051271813320358, 9.956424348965415, 5.458526635375026, 5.429587534345759, 5.807062817365774, 10.409000825252871, 5.444155990800697, 6.38482736103294, 7.592376962210506, 8.880703777005019), # 139
(8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140
(8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141
(8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142
(8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143
(8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144
(8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145
(8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146
(8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147
(8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148
(8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149
(7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150
(7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151
(7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152
(7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153
(7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154
(7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155
(7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156
(7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157
(7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158
(7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159
(7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160
(7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161
(7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162
(6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163
(6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164
(6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165
(6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166
(6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167
(6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168
(5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169
(5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170
(5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171
(5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172
(5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173
(4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174
(4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175
(4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176
(4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177
(3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(4, 5, 4, 9, 0, 2, 3, 0, 2, 1, 0, 1, 0, 4, 2, 4, 3, 4, 1, 2, 0, 0, 1, 1, 1, 0), # 0
(11, 11, 10, 12, 2, 5, 3, 1, 4, 2, 0, 2, 0, 6, 6, 6, 8, 9, 4, 4, 0, 3, 3, 2, 3, 0), # 1
(20, 16, 15, 14, 5, 6, 4, 3, 8, 5, 0, 2, 0, 12, 16, 8, 12, 14, 9, 6, 2, 5, 5, 3, 3, 0), # 2
(25, 18, 19, 16, 8, 10, 4, 6, 8, 8, 0, 2, 0, 20, 21, 9, 15, 21, 13, 11, 4, 11, 5, 4, 4, 0), # 3
(30, 23, 27, 25, 17, 13, 8, 7, 10, 9, 0, 5, 0, 26, 28, 10, 17, 27, 15, 11, 5, 12, 11, 4, 4, 0), # 4
(34, 33, 31, 27, 22, 17, 9, 11, 11, 11, 0, 5, 0, 34, 30, 13, 21, 33, 18, 12, 5, 17, 14, 5, 4, 0), # 5
(40, 36, 42, 30, 29, 21, 10, 12, 12, 11, 2, 6, 0, 42, 37, 17, 22, 36, 22, 15, 7, 22, 16, 6, 5, 0), # 6
(41, 42, 45, 36, 36, 25, 13, 15, 12, 12, 3, 11, 0, 50, 41, 22, 32, 43, 25, 16, 8, 25, 16, 7, 6, 0), # 7
(49, 49, 54, 47, 41, 27, 16, 18, 14, 13, 3, 11, 0, 60, 48, 29, 34, 49, 27, 17, 10, 29, 18, 7, 8, 0), # 8
(58, 55, 60, 55, 47, 29, 18, 22, 17, 16, 5, 12, 0, 65, 56, 36, 40, 57, 29, 22, 12, 35, 19, 11, 10, 0), # 9
(66, 62, 72, 61, 59, 34, 20, 23, 17, 17, 5, 12, 0, 75, 61, 39, 43, 70, 37, 28, 13, 40, 21, 13, 10, 0), # 10
(77, 69, 82, 65, 65, 35, 21, 27, 22, 19, 6, 14, 0, 84, 68, 44, 51, 75, 40, 32, 17, 43, 25, 13, 10, 0), # 11
(91, 77, 90, 72, 67, 36, 27, 29, 27, 22, 6, 14, 0, 91, 78, 52, 57, 85, 44, 35, 19, 45, 25, 14, 11, 0), # 12
(101, 87, 100, 79, 74, 39, 28, 34, 29, 27, 10, 14, 0, 99, 80, 61, 63, 93, 50, 41, 19, 48, 30, 16, 13, 0), # 13
(114, 96, 113, 85, 80, 43, 36, 38, 32, 27, 11, 14, 0, 108, 92, 68, 65, 101, 52, 45, 21, 52, 32, 18, 14, 0), # 14
(123, 110, 122, 97, 85, 45, 39, 45, 35, 28, 12, 16, 0, 117, 99, 73, 71, 112, 53, 48, 24, 55, 35, 18, 15, 0), # 15
(139, 122, 127, 105, 92, 47, 44, 50, 40, 30, 14, 17, 0, 124, 111, 77, 76, 120, 56, 53, 27, 58, 37, 23, 15, 0), # 16
(148, 141, 133, 116, 96, 51, 45, 58, 47, 32, 15, 17, 0, 133, 116, 84, 87, 128, 63, 59, 27, 64, 39, 25, 16, 0), # 17
(156, 147, 140, 129, 102, 53, 49, 61, 52, 34, 16, 17, 0, 146, 126, 87, 95, 133, 71, 62, 27, 71, 43, 25, 16, 0), # 18
(165, 154, 151, 135, 109, 59, 55, 63, 54, 36, 16, 17, 0, 159, 140, 94, 95, 137, 82, 65, 31, 76, 47, 25, 16, 0), # 19
(170, 163, 164, 141, 118, 61, 59, 68, 61, 40, 16, 17, 0, 175, 143, 104, 103, 143, 84, 68, 34, 78, 48, 27, 17, 0), # 20
(183, 173, 172, 154, 126, 64, 67, 74, 62, 42, 18, 17, 0, 186, 153, 108, 113, 152, 88, 74, 35, 81, 53, 28, 18, 0), # 21
(194, 184, 179, 159, 136, 68, 78, 78, 68, 42, 19, 17, 0, 197, 162, 119, 119, 160, 96, 76, 39, 88, 53, 29, 19, 0), # 22
(205, 189, 183, 171, 146, 72, 80, 79, 69, 43, 20, 17, 0, 209, 172, 125, 127, 176, 101, 77, 39, 91, 57, 32, 20, 0), # 23
(221, 200, 191, 180, 152, 74, 83, 83, 73, 44, 21, 17, 0, 217, 182, 128, 136, 185, 107, 78, 40, 93, 59, 34, 22, 0), # 24
(235, 210, 203, 191, 160, 77, 86, 86, 75, 44, 22, 18, 0, 225, 190, 138, 140, 194, 110, 85, 42, 95, 59, 35, 23, 0), # 25
(246, 220, 216, 205, 166, 83, 91, 91, 75, 48, 23, 18, 0, 236, 202, 145, 145, 202, 118, 89, 42, 101, 59, 36, 25, 0), # 26
(260, 228, 225, 214, 169, 87, 92, 98, 80, 49, 25, 19, 0, 246, 208, 152, 154, 205, 125, 90, 45, 106, 62, 39, 25, 0), # 27
(270, 237, 233, 224, 181, 92, 95, 107, 82, 51, 26, 19, 0, 253, 215, 159, 161, 213, 127, 97, 46, 109, 66, 40, 25, 0), # 28
(281, 242, 238, 233, 190, 93, 102, 112, 88, 52, 28, 20, 0, 264, 221, 172, 168, 218, 133, 101, 47, 114, 69, 41, 26, 0), # 29
(292, 257, 249, 241, 196, 100, 106, 116, 91, 55, 30, 22, 0, 272, 232, 177, 173, 226, 142, 106, 50, 121, 73, 42, 26, 0), # 30
(300, 268, 256, 247, 208, 102, 111, 121, 96, 56, 31, 22, 0, 282, 244, 186, 180, 232, 150, 112, 51, 130, 75, 42, 26, 0), # 31
(306, 280, 264, 262, 220, 106, 113, 125, 105, 56, 32, 23, 0, 289, 251, 194, 188, 241, 154, 116, 53, 131, 78, 44, 27, 0), # 32
(316, 293, 273, 275, 229, 108, 117, 128, 108, 57, 32, 23, 0, 293, 264, 201, 194, 244, 162, 122, 54, 133, 81, 45, 28, 0), # 33
(329, 299, 279, 287, 236, 110, 120, 130, 114, 60, 32, 26, 0, 307, 269, 208, 199, 254, 167, 125, 55, 139, 85, 45, 28, 0), # 34
(348, 317, 281, 296, 249, 118, 124, 135, 120, 61, 35, 27, 0, 317, 279, 213, 205, 264, 171, 126, 58, 143, 86, 45, 28, 0), # 35
(360, 327, 291, 303, 264, 120, 128, 139, 126, 64, 35, 27, 0, 323, 288, 215, 210, 270, 175, 128, 59, 147, 90, 46, 28, 0), # 36
(370, 345, 296, 315, 273, 130, 133, 141, 128, 64, 36, 28, 0, 330, 291, 224, 217, 282, 178, 131, 61, 150, 91, 48, 28, 0), # 37
(378, 356, 304, 333, 277, 137, 133, 145, 129, 66, 39, 29, 0, 340, 301, 230, 222, 290, 182, 139, 66, 157, 96, 49, 29, 0), # 38
(395, 364, 311, 338, 290, 141, 134, 147, 132, 67, 41, 29, 0, 352, 313, 236, 227, 298, 186, 145, 69, 165, 97, 51, 29, 0), # 39
(408, 371, 321, 352, 291, 141, 136, 152, 137, 70, 41, 30, 0, 366, 325, 242, 232, 307, 192, 153, 70, 167, 102, 52, 31, 0), # 40
(416, 378, 325, 365, 295, 143, 139, 156, 138, 71, 44, 32, 0, 372, 335, 245, 239, 312, 195, 160, 71, 174, 106, 53, 31, 0), # 41
(427, 391, 332, 375, 304, 149, 140, 160, 143, 72, 45, 32, 0, 384, 346, 248, 242, 317, 203, 165, 73, 175, 107, 54, 31, 0), # 42
(436, 395, 339, 380, 317, 152, 146, 167, 148, 75, 46, 32, 0, 393, 355, 256, 245, 325, 209, 170, 77, 179, 112, 55, 32, 0), # 43
(449, 404, 348, 386, 328, 156, 146, 174, 151, 80, 48, 32, 0, 403, 364, 262, 249, 335, 214, 175, 80, 186, 116, 56, 32, 0), # 44
(455, 411, 358, 399, 337, 160, 157, 179, 156, 81, 48, 33, 0, 413, 376, 269, 256, 339, 220, 177, 83, 190, 120, 56, 34, 0), # 45
(464, 423, 366, 412, 344, 161, 161, 182, 162, 85, 52, 33, 0, 422, 389, 273, 262, 352, 224, 180, 89, 193, 125, 59, 35, 0), # 46
(476, 437, 371, 421, 356, 163, 168, 188, 167, 86, 52, 33, 0, 432, 399, 276, 264, 358, 227, 187, 95, 198, 127, 59, 36, 0), # 47
(482, 444, 380, 441, 366, 166, 177, 189, 173, 87, 53, 35, 0, 438, 406, 284, 269, 367, 230, 193, 98, 204, 127, 61, 38, 0), # 48
(492, 454, 383, 448, 374, 171, 180, 190, 175, 93, 56, 37, 0, 452, 416, 288, 270, 373, 231, 194, 101, 208, 130, 62, 38, 0), # 49
(507, 464, 399, 458, 377, 177, 183, 196, 182, 94, 58, 37, 0, 468, 420, 299, 279, 386, 235, 195, 106, 210, 135, 64, 39, 0), # 50
(516, 471, 404, 469, 384, 178, 187, 200, 187, 96, 61, 37, 0, 474, 425, 306, 287, 397, 241, 201, 107, 216, 136, 67, 39, 0), # 51
(523, 482, 411, 479, 388, 181, 191, 201, 194, 96, 63, 38, 0, 480, 435, 313, 292, 409, 250, 203, 109, 220, 138, 68, 39, 0), # 52
(527, 494, 418, 485, 396, 185, 193, 208, 198, 97, 65, 41, 0, 487, 444, 320, 297, 413, 255, 205, 111, 221, 142, 69, 40, 0), # 53
(530, 500, 426, 493, 408, 188, 194, 214, 206, 98, 67, 41, 0, 491, 452, 325, 304, 419, 259, 208, 114, 226, 144, 71, 41, 0), # 54
(544, 504, 429, 503, 417, 191, 197, 215, 207, 101, 68, 41, 0, 499, 463, 328, 305, 424, 265, 212, 118, 229, 147, 71, 42, 0), # 55
(560, 510, 440, 509, 427, 194, 198, 222, 210, 102, 70, 41, 0, 512, 469, 334, 307, 435, 267, 217, 118, 234, 150, 72, 42, 0), # 56
(567, 522, 447, 515, 436, 198, 201, 226, 214, 107, 72, 42, 0, 519, 480, 346, 314, 441, 273, 220, 120, 237, 153, 75, 43, 0), # 57
(577, 534, 454, 524, 445, 200, 206, 233, 223, 109, 72, 43, 0, 533, 493, 352, 320, 448, 276, 226, 122, 239, 158, 77, 44, 0), # 58
(587, 542, 461, 533, 449, 204, 210, 233, 224, 112, 73, 43, 0, 540, 504, 367, 324, 455, 278, 228, 123, 241, 162, 77, 45, 0), # 59
(593, 555, 467, 536, 460, 210, 212, 235, 230, 113, 76, 45, 0, 552, 515, 376, 328, 465, 282, 234, 132, 245, 166, 81, 46, 0), # 60
(596, 572, 474, 540, 470, 215, 215, 238, 235, 113, 76, 49, 0, 563, 520, 387, 339, 471, 284, 237, 135, 245, 169, 82, 47, 0), # 61
(611, 581, 484, 551, 478, 218, 217, 242, 238, 114, 80, 49, 0, 570, 526, 393, 343, 479, 289, 241, 139, 247, 172, 83, 47, 0), # 62
(617, 593, 492, 560, 487, 222, 218, 243, 243, 115, 82, 49, 0, 579, 531, 400, 346, 486, 295, 249, 140, 250, 179, 85, 47, 0), # 63
(625, 599, 501, 568, 502, 228, 223, 245, 247, 117, 82, 49, 0, 588, 540, 407, 352, 493, 297, 250, 145, 258, 181, 89, 47, 0), # 64
(639, 609, 503, 577, 505, 230, 225, 250, 250, 119, 83, 49, 0, 595, 551, 410, 357, 513, 302, 251, 151, 258, 186, 91, 48, 0), # 65
(647, 618, 511, 586, 511, 233, 232, 253, 259, 124, 85, 52, 0, 605, 554, 419, 362, 518, 309, 257, 153, 265, 187, 94, 49, 0), # 66
(663, 628, 520, 594, 515, 236, 235, 255, 262, 126, 87, 53, 0, 610, 564, 429, 370, 523, 314, 264, 153, 270, 190, 94, 50, 0), # 67
(672, 631, 529, 603, 523, 239, 240, 256, 266, 127, 87, 54, 0, 619, 570, 432, 374, 531, 324, 268, 156, 277, 193, 96, 51, 0), # 68
(685, 636, 539, 606, 528, 242, 243, 259, 270, 127, 89, 54, 0, 629, 579, 439, 380, 537, 326, 271, 157, 283, 195, 97, 51, 0), # 69
(696, 645, 555, 616, 535, 248, 243, 264, 276, 129, 92, 54, 0, 642, 589, 446, 383, 549, 332, 274, 160, 286, 199, 97, 52, 0), # 70
(705, 653, 563, 621, 544, 251, 245, 266, 280, 132, 95, 54, 0, 651, 598, 454, 388, 552, 344, 276, 161, 293, 199, 98, 52, 0), # 71
(716, 660, 572, 631, 553, 253, 249, 270, 286, 135, 95, 55, 0, 657, 608, 461, 394, 559, 348, 283, 163, 297, 203, 100, 53, 0), # 72
(724, 666, 580, 646, 565, 253, 253, 273, 287, 137, 96, 56, 0, 668, 614, 466, 403, 565, 353, 285, 167, 301, 205, 102, 53, 0), # 73
(732, 673, 591, 655, 573, 257, 256, 278, 295, 137, 99, 57, 0, 679, 619, 474, 410, 573, 356, 289, 168, 301, 208, 103, 54, 0), # 74
(742, 683, 599, 662, 576, 259, 260, 285, 305, 139, 100, 57, 0, 692, 624, 480, 418, 583, 358, 293, 169, 305, 212, 107, 56, 0), # 75
(752, 695, 610, 669, 581, 265, 263, 287, 309, 140, 101, 58, 0, 701, 632, 486, 423, 596, 361, 299, 170, 310, 215, 107, 56, 0), # 76
(759, 703, 617, 678, 589, 273, 268, 291, 314, 142, 104, 59, 0, 708, 641, 491, 427, 604, 368, 303, 173, 310, 216, 109, 57, 0), # 77
(765, 711, 625, 687, 596, 279, 275, 296, 317, 146, 106, 60, 0, 716, 650, 498, 432, 611, 374, 308, 175, 317, 217, 110, 57, 0), # 78
(778, 725, 632, 695, 602, 282, 281, 298, 322, 147, 107, 61, 0, 728, 657, 504, 436, 619, 378, 312, 176, 324, 220, 110, 57, 0), # 79
(793, 735, 644, 703, 607, 285, 285, 302, 326, 147, 109, 63, 0, 734, 663, 513, 444, 628, 381, 313, 179, 331, 222, 111, 58, 0), # 80
(808, 743, 656, 714, 619, 286, 288, 307, 329, 148, 113, 63, 0, 752, 672, 517, 448, 632, 385, 316, 182, 334, 222, 112, 58, 0), # 81
(815, 748, 665, 723, 628, 292, 292, 310, 336, 151, 114, 64, 0, 761, 678, 527, 450, 643, 386, 324, 184, 339, 226, 115, 60, 0), # 82
(827, 758, 674, 730, 636, 297, 299, 315, 338, 154, 114, 65, 0, 773, 687, 537, 454, 651, 387, 329, 185, 345, 229, 117, 61, 0), # 83
(835, 768, 681, 741, 645, 300, 300, 318, 345, 155, 117, 66, 0, 782, 700, 542, 463, 657, 390, 335, 186, 347, 233, 117, 61, 0), # 84
(845, 776, 686, 745, 653, 303, 301, 318, 349, 157, 118, 66, 0, 789, 702, 549, 468, 670, 394, 339, 190, 348, 239, 118, 62, 0), # 85
(854, 782, 692, 756, 661, 305, 304, 323, 352, 158, 120, 68, 0, 801, 707, 556, 474, 675, 397, 341, 192, 353, 244, 119, 62, 0), # 86
(862, 791, 704, 763, 670, 311, 309, 326, 358, 159, 120, 69, 0, 813, 719, 561, 478, 683, 401, 344, 193, 356, 250, 119, 62, 0), # 87
(867, 798, 716, 771, 678, 314, 312, 327, 359, 162, 121, 70, 0, 821, 727, 567, 486, 691, 404, 347, 193, 358, 252, 121, 62, 0), # 88
(877, 808, 720, 774, 685, 316, 314, 327, 362, 163, 122, 71, 0, 837, 738, 574, 492, 696, 406, 349, 195, 359, 254, 121, 62, 0), # 89
(883, 821, 725, 787, 692, 320, 316, 331, 369, 166, 124, 71, 0, 845, 744, 579, 495, 705, 410, 354, 196, 362, 256, 123, 63, 0), # 90
(891, 832, 736, 794, 703, 324, 318, 334, 371, 168, 124, 71, 0, 855, 748, 587, 503, 712, 413, 358, 199, 364, 260, 125, 64, 0), # 91
(901, 840, 742, 804, 710, 329, 323, 340, 373, 169, 125, 72, 0, 860, 758, 597, 507, 723, 419, 363, 202, 366, 264, 128, 64, 0), # 92
(908, 847, 753, 809, 716, 330, 329, 342, 377, 170, 126, 74, 0, 872, 767, 605, 509, 732, 422, 368, 202, 371, 267, 129, 64, 0), # 93
(923, 853, 758, 814, 726, 334, 332, 343, 383, 170, 128, 75, 0, 881, 775, 611, 513, 739, 427, 373, 203, 377, 270, 131, 64, 0), # 94
(934, 857, 761, 823, 734, 337, 335, 347, 391, 171, 128, 76, 0, 891, 789, 612, 517, 748, 430, 374, 203, 379, 272, 132, 65, 0), # 95
(942, 867, 768, 829, 738, 338, 341, 348, 393, 171, 131, 77, 0, 910, 795, 616, 524, 755, 436, 374, 207, 383, 277, 135, 66, 0), # 96
(951, 876, 776, 841, 748, 341, 342, 352, 396, 172, 132, 77, 0, 922, 805, 624, 529, 763, 440, 377, 208, 385, 279, 138, 67, 0), # 97
(960, 886, 782, 850, 758, 343, 347, 355, 399, 175, 134, 79, 0, 936, 814, 629, 536, 771, 444, 379, 210, 390, 280, 138, 67, 0), # 98
(971, 893, 790, 858, 761, 350, 352, 358, 402, 175, 134, 79, 0, 947, 820, 634, 543, 782, 449, 384, 213, 395, 281, 139, 69, 0), # 99
(977, 895, 799, 864, 771, 353, 355, 361, 407, 175, 135, 81, 0, 959, 828, 643, 547, 792, 452, 387, 215, 401, 284, 142, 69, 0), # 100
(988, 906, 809, 871, 773, 359, 358, 365, 413, 180, 135, 81, 0, 966, 836, 648, 553, 805, 459, 390, 217, 404, 285, 143, 69, 0), # 101
(995, 913, 811, 878, 777, 362, 359, 368, 417, 182, 135, 81, 0, 982, 840, 651, 556, 810, 463, 393, 217, 407, 287, 144, 69, 0), # 102
(1005, 921, 823, 881, 783, 365, 366, 373, 421, 183, 136, 83, 0, 989, 845, 656, 561, 814, 468, 396, 218, 407, 290, 148, 69, 0), # 103
(1020, 933, 830, 887, 788, 368, 369, 377, 424, 184, 137, 83, 0, 994, 848, 667, 567, 822, 473, 398, 219, 411, 291, 149, 69, 0), # 104
(1032, 943, 838, 895, 796, 371, 373, 382, 428, 186, 138, 83, 0, 1008, 856, 671, 570, 828, 478, 401, 220, 414, 293, 151, 69, 0), # 105
(1038, 947, 847, 908, 800, 373, 374, 383, 435, 189, 138, 83, 0, 1022, 863, 676, 573, 834, 482, 406, 222, 422, 293, 153, 69, 0), # 106
(1046, 950, 856, 916, 806, 373, 379, 388, 437, 191, 139, 84, 0, 1029, 869, 683, 579, 840, 485, 410, 224, 426, 295, 155, 71, 0), # 107
(1058, 959, 867, 925, 816, 375, 382, 392, 438, 192, 139, 84, 0, 1036, 878, 690, 582, 844, 487, 412, 225, 430, 296, 156, 71, 0), # 108
(1071, 968, 875, 935, 822, 381, 386, 394, 441, 193, 141, 85, 0, 1051, 888, 696, 585, 851, 493, 417, 229, 433, 301, 158, 71, 0), # 109
(1081, 979, 883, 950, 829, 382, 389, 397, 448, 195, 142, 86, 0, 1060, 898, 705, 590, 860, 495, 423, 230, 436, 303, 158, 71, 0), # 110
(1090, 981, 889, 957, 836, 389, 392, 401, 452, 195, 142, 86, 0, 1063, 907, 714, 593, 862, 499, 425, 235, 441, 304, 161, 71, 0), # 111
(1098, 985, 903, 970, 842, 390, 395, 404, 455, 197, 144, 87, 0, 1071, 913, 721, 598, 869, 501, 427, 236, 443, 310, 164, 71, 0), # 112
(1103, 997, 908, 975, 848, 391, 396, 407, 456, 198, 145, 87, 0, 1083, 922, 731, 600, 873, 504, 432, 241, 445, 316, 165, 71, 0), # 113
(1112, 1006, 915, 987, 855, 393, 401, 409, 462, 198, 145, 87, 0, 1095, 929, 736, 606, 883, 507, 435, 242, 450, 316, 165, 72, 0), # 114
(1123, 1014, 920, 996, 863, 395, 402, 410, 470, 199, 146, 88, 0, 1104, 937, 744, 612, 890, 509, 439, 247, 450, 318, 168, 72, 0), # 115
(1135, 1021, 926, 1003, 870, 397, 408, 412, 472, 201, 147, 88, 0, 1120, 940, 748, 615, 897, 516, 447, 249, 457, 320, 168, 72, 0), # 116
(1140, 1032, 940, 1010, 874, 400, 409, 414, 474, 203, 149, 88, 0, 1127, 947, 756, 619, 902, 516, 449, 251, 463, 320, 171, 72, 0), # 117
(1148, 1042, 945, 1015, 884, 402, 411, 417, 475, 204, 150, 88, 0, 1131, 956, 760, 624, 910, 522, 452, 254, 468, 323, 172, 72, 0), # 118
(1159, 1050, 953, 1024, 893, 407, 412, 419, 476, 206, 153, 89, 0, 1140, 964, 768, 627, 922, 525, 455, 257, 471, 324, 174, 72, 0), # 119
(1162, 1055, 956, 1033, 896, 409, 415, 421, 484, 209, 155, 89, 0, 1147, 971, 774, 629, 930, 528, 458, 259, 472, 325, 175, 72, 0), # 120
(1170, 1063, 965, 1044, 910, 411, 419, 421, 490, 211, 156, 90, 0, 1161, 979, 781, 633, 937, 530, 462, 261, 477, 327, 175, 73, 0), # 121
(1180, 1068, 971, 1057, 918, 414, 424, 425, 495, 213, 160, 92, 0, 1173, 984, 787, 638, 944, 534, 466, 263, 481, 328, 175, 73, 0), # 122
(1189, 1078, 976, 1065, 929, 420, 428, 426, 497, 213, 161, 92, 0, 1182, 993, 795, 647, 950, 536, 468, 268, 483, 328, 175, 74, 0), # 123
(1193, 1084, 982, 1072, 938, 423, 428, 430, 505, 215, 164, 94, 0, 1189, 999, 796, 652, 955, 539, 469, 270, 484, 332, 178, 74, 0), # 124
(1197, 1092, 987, 1078, 947, 425, 431, 432, 507, 216, 167, 94, 0, 1196, 1005, 799, 657, 962, 544, 474, 271, 485, 335, 178, 75, 0), # 125
(1205, 1096, 991, 1087, 958, 431, 431, 435, 511, 216, 167, 94, 0, 1206, 1012, 802, 660, 966, 549, 479, 274, 489, 336, 179, 77, 0), # 126
(1213, 1103, 999, 1092, 965, 433, 433, 439, 513, 217, 167, 94, 0, 1223, 1020, 804, 666, 973, 555, 482, 277, 492, 339, 181, 79, 0), # 127
(1224, 1107, 1005, 1102, 971, 439, 436, 443, 515, 217, 167, 97, 0, 1229, 1024, 812, 667, 978, 557, 484, 278, 496, 341, 182, 79, 0), # 128
(1233, 1117, 1016, 1108, 977, 445, 437, 445, 520, 217, 169, 100, 0, 1242, 1030, 819, 671, 981, 559, 486, 282, 499, 345, 183, 79, 0), # 129
(1243, 1126, 1023, 1116, 987, 450, 441, 446, 526, 217, 172, 100, 0, 1255, 1035, 827, 675, 983, 560, 488, 283, 502, 347, 183, 80, 0), # 130
(1253, 1132, 1027, 1125, 992, 453, 444, 448, 530, 220, 172, 101, 0, 1265, 1045, 829, 680, 989, 564, 489, 284, 507, 348, 184, 82, 0), # 131
(1260, 1137, 1033, 1135, 998, 455, 451, 453, 535, 222, 173, 104, 0, 1269, 1052, 837, 685, 998, 568, 493, 285, 512, 351, 184, 84, 0), # 132
(1267, 1138, 1035, 1144, 1007, 460, 452, 453, 537, 223, 174, 104, 0, 1282, 1054, 841, 688, 1003, 570, 497, 289, 518, 352, 187, 85, 0), # 133
(1270, 1148, 1040, 1150, 1015, 461, 454, 453, 539, 224, 175, 104, 0, 1290, 1063, 844, 691, 1006, 573, 499, 293, 519, 354, 188, 85, 0), # 134
(1275, 1153, 1044, 1158, 1019, 462, 457, 454, 544, 225, 176, 104, 0, 1296, 1074, 851, 692, 1013, 577, 501, 294, 524, 355, 189, 85, 0), # 135
(1281, 1162, 1050, 1168, 1027, 464, 459, 456, 545, 227, 177, 104, 0, 1305, 1079, 862, 695, 1018, 578, 504, 298, 526, 359, 189, 85, 0), # 136
(1296, 1169, 1058, 1177, 1032, 465, 459, 457, 550, 228, 177, 105, 0, 1312, 1086, 870, 696, 1021, 582, 508, 302, 529, 360, 192, 86, 0), # 137
(1310, 1171, 1064, 1185, 1038, 468, 461, 458, 553, 229, 177, 106, 0, 1320, 1094, 879, 700, 1024, 588, 511, 302, 533, 363, 194, 86, 0), # 138
(1324, 1177, 1073, 1192, 1059, 469, 463, 460, 554, 230, 177, 107, 0, 1325, 1100, 884, 701, 1032, 588, 517, 304, 535, 365, 194, 86, 0), # 139
(1338, 1186, 1079, 1198, 1070, 474, 466, 464, 559, 233, 178, 109, 0, 1329, 1109, 891, 706, 1039, 590, 518, 305, 541, 368, 196, 86, 0), # 140
(1345, 1189, 1087, 1203, 1073, 477, 469, 465, 565, 233, 179, 109, 0, 1339, 1117, 898, 709, 1046, 597, 523, 305, 545, 369, 197, 87, 0), # 141
(1353, 1194, 1091, 1215, 1076, 477, 471, 467, 567, 234, 179, 109, 0, 1350, 1126, 907, 711, 1052, 599, 525, 307, 547, 371, 198, 88, 0), # 142
(1365, 1199, 1097, 1225, 1079, 478, 474, 469, 568, 237, 179, 109, 0, 1358, 1137, 915, 716, 1059, 605, 527, 308, 549, 373, 200, 88, 0), # 143
(1377, 1205, 1107, 1232, 1088, 482, 476, 472, 570, 239, 180, 111, 0, 1365, 1145, 919, 722, 1064, 610, 529, 309, 553, 373, 204, 88, 0), # 144
(1388, 1213, 1113, 1245, 1094, 486, 477, 474, 573, 240, 180, 111, 0, 1381, 1152, 924, 724, 1070, 616, 532, 313, 559, 374, 204, 88, 0), # 145
(1399, 1217, 1120, 1254, 1100, 489, 482, 479, 576, 241, 180, 112, 0, 1384, 1160, 931, 730, 1077, 625, 537, 313, 562, 377, 205, 88, 0), # 146
(1407, 1223, 1126, 1257, 1107, 493, 485, 480, 582, 243, 181, 113, 0, 1391, 1169, 933, 734, 1090, 626, 538, 315, 565, 379, 206, 89, 0), # 147
(1415, 1227, 1131, 1264, 1118, 495, 485, 480, 586, 243, 183, 115, 0, 1396, 1177, 939, 738, 1097, 633, 538, 316, 568, 381, 207, 89, 0), # 148
(1424, 1232, 1136, 1276, 1123, 499, 487, 482, 592, 243, 185, 115, 0, 1405, 1181, 944, 741, 1100, 636, 541, 319, 571, 384, 207, 89, 0), # 149
(1437, 1235, 1141, 1280, 1128, 499, 491, 488, 592, 243, 185, 116, 0, 1415, 1186, 951, 745, 1104, 639, 545, 320, 573, 388, 209, 90, 0), # 150
(1450, 1238, 1146, 1287, 1134, 509, 493, 489, 597, 243, 185, 116, 0, 1424, 1196, 957, 750, 1109, 641, 549, 320, 576, 389, 209, 91, 0), # 151
(1456, 1245, 1149, 1295, 1141, 512, 493, 490, 604, 245, 186, 116, 0, 1429, 1205, 964, 756, 1115, 643, 550, 322, 578, 392, 209, 91, 0), # 152
(1462, 1251, 1156, 1306, 1144, 517, 496, 492, 606, 246, 187, 116, 0, 1437, 1211, 972, 760, 1121, 646, 550, 325, 580, 393, 212, 92, 0), # 153
(1470, 1261, 1160, 1319, 1151, 521, 499, 495, 608, 247, 187, 116, 0, 1450, 1217, 977, 763, 1126, 649, 551, 327, 582, 399, 213, 92, 0), # 154
(1476, 1265, 1168, 1320, 1153, 522, 500, 496, 611, 247, 187, 116, 0, 1461, 1227, 984, 770, 1135, 653, 554, 328, 585, 400, 213, 92, 0), # 155
(1484, 1271, 1172, 1326, 1157, 524, 501, 501, 612, 248, 187, 116, 0, 1466, 1232, 986, 776, 1145, 660, 559, 331, 587, 402, 214, 94, 0), # 156
(1490, 1276, 1178, 1335, 1166, 526, 502, 502, 613, 249, 189, 117, 0, 1478, 1243, 988, 776, 1152, 664, 561, 336, 590, 405, 215, 94, 0), # 157
(1492, 1278, 1184, 1340, 1175, 529, 504, 507, 617, 250, 192, 117, 0, 1487, 1248, 996, 778, 1165, 670, 564, 338, 590, 407, 216, 94, 0), # 158
(1495, 1284, 1190, 1345, 1181, 532, 504, 511, 622, 250, 192, 118, 0, 1499, 1254, 999, 781, 1170, 675, 568, 341, 591, 409, 216, 94, 0), # 159
(1504, 1288, 1195, 1351, 1197, 537, 505, 515, 625, 251, 193, 118, 0, 1505, 1266, 1003, 785, 1181, 680, 575, 344, 593, 410, 217, 95, 0), # 160
(1507, 1293, 1201, 1360, 1207, 544, 509, 517, 629, 252, 193, 118, 0, 1512, 1271, 1006, 787, 1189, 683, 578, 348, 597, 413, 217, 95, 0), # 161
(1513, 1300, 1205, 1364, 1213, 546, 509, 521, 632, 253, 193, 119, 0, 1518, 1277, 1009, 790, 1197, 684, 580, 350, 599, 414, 220, 95, 0), # 162
(1518, 1304, 1214, 1370, 1222, 551, 511, 525, 635, 253, 195, 120, 0, 1530, 1281, 1011, 793, 1201, 687, 580, 351, 602, 415, 222, 96, 0), # 163
(1526, 1308, 1221, 1373, 1229, 552, 511, 525, 638, 255, 195, 123, 0, 1536, 1288, 1016, 795, 1207, 690, 582, 355, 603, 415, 226, 96, 0), # 164
(1530, 1313, 1230, 1377, 1235, 553, 512, 528, 641, 257, 197, 123, 0, 1539, 1292, 1019, 800, 1214, 691, 584, 360, 605, 423, 228, 97, 0), # 165
(1534, 1316, 1233, 1383, 1237, 554, 513, 528, 647, 258, 197, 123, 0, 1547, 1298, 1021, 805, 1220, 692, 585, 361, 606, 425, 228, 97, 0), # 166
(1545, 1323, 1240, 1387, 1246, 555, 514, 529, 649, 259, 198, 123, 0, 1552, 1306, 1023, 805, 1231, 694, 586, 364, 610, 429, 228, 100, 0), # 167
(1552, 1329, 1242, 1391, 1247, 557, 515, 531, 653, 260, 199, 123, 0, 1560, 1307, 1027, 810, 1234, 696, 589, 366, 614, 431, 229, 100, 0), # 168
(1560, 1334, 1244, 1395, 1253, 558, 515, 531, 654, 260, 199, 124, 0, 1570, 1311, 1032, 813, 1242, 700, 592, 367, 616, 433, 231, 100, 0), # 169
(1565, 1339, 1249, 1398, 1260, 559, 519, 531, 654, 265, 200, 124, 0, 1577, 1317, 1035, 815, 1247, 703, 594, 368, 617, 435, 231, 100, 0), # 170
(1571, 1342, 1252, 1404, 1265, 560, 520, 531, 658, 267, 202, 124, 0, 1582, 1318, 1041, 818, 1249, 704, 598, 369, 618, 435, 234, 100, 0), # 171
(1580, 1343, 1254, 1412, 1265, 563, 520, 533, 662, 267, 204, 124, 0, 1587, 1322, 1045, 822, 1255, 704, 600, 370, 622, 437, 235, 100, 0), # 172
(1584, 1347, 1261, 1414, 1266, 565, 523, 535, 662, 267, 207, 124, 0, 1594, 1326, 1049, 825, 1261, 711, 601, 370, 623, 439, 238, 100, 0), # 173
(1593, 1350, 1266, 1417, 1271, 566, 524, 536, 664, 270, 210, 124, 0, 1601, 1329, 1051, 828, 1263, 712, 603, 371, 625, 440, 239, 100, 0), # 174
(1595, 1353, 1277, 1421, 1272, 567, 525, 536, 666, 270, 212, 124, 0, 1607, 1333, 1053, 828, 1268, 716, 604, 372, 628, 441, 241, 100, 0), # 175
(1598, 1356, 1282, 1424, 1277, 569, 525, 537, 666, 270, 213, 125, 0, 1616, 1335, 1055, 829, 1269, 721, 605, 373, 630, 441, 241, 101, 0), # 176
(1600, 1358, 1286, 1427, 1280, 571, 527, 537, 667, 271, 213, 126, 0, 1621, 1338, 1057, 831, 1274, 722, 606, 374, 631, 444, 241, 102, 0), # 177
(1602, 1359, 1291, 1427, 1282, 572, 529, 538, 667, 272, 213, 127, 0, 1629, 1343, 1061, 833, 1279, 724, 606, 374, 633, 446, 241, 102, 0), # 178
(1602, 1359, 1291, 1427, 1282, 572, 529, 538, 667, 272, 213, 127, 0, 1629, 1343, 1061, 833, 1279, 724, 606, 374, 633, 446, 241, 102, 0), # 179
)
passenger_arriving_rate = (
(5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0
(5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 1
(5.686723008979731, 5.732269739983398, 4.915035237956178, 5.275490778498595, 4.192641982499829, 2.072282983465593, 2.345909253980352, 2.193593853293508, 2.297595602292516, 1.1197284437551367, 0.7933038581293855, 0.46193605433775464, 0.0, 5.75436482820969, 5.0812965977153, 3.9665192906469278, 3.3591853312654094, 4.595191204585032, 3.0710313946109116, 2.345909253980352, 1.480202131046852, 2.0963209912499146, 1.758496926166199, 0.9830070475912357, 0.5211154309075817, 0.0), # 2
(6.016757793146562, 6.062668793441743, 5.198342391099879, 5.579682305649055, 4.435107784001268, 2.191782029841316, 2.4810018208239777, 2.3197088156227115, 2.430045053640364, 1.1841956746065454, 0.8390580686378972, 0.4885571404108718, 0.0, 6.086272806254225, 5.374128544519589, 4.195290343189486, 3.5525870238196355, 4.860090107280728, 3.247592341871796, 2.4810018208239777, 1.5655585927437972, 2.217553892000634, 1.8598941018830188, 1.0396684782199759, 0.551151708494704, 0.0), # 3
(6.343136148415981, 6.389098099345293, 5.478244083085864, 5.880216481036927, 4.674763069197661, 2.3098432043158894, 2.6144718342542292, 2.444304942164548, 2.560900681860902, 1.24788897429192, 0.8842623557650959, 0.514858199362897, 0.0, 6.414188632939817, 5.6634401929918665, 4.42131177882548, 3.743666922875759, 5.121801363721804, 3.422026919030367, 2.6144718342542292, 1.6498880030827783, 2.3373815345988307, 1.9600721603456428, 1.095648816617173, 0.5808270999404813, 0.0), # 4
(6.66456271868351, 6.710236293698289, 5.753607444793765, 6.175877014290295, 4.910646611132853, 2.4259888147198754, 2.745778957362612, 2.566878236885247, 2.689633039327186, 1.310550451479666, 0.9287337544245222, 0.5407327839361791, 0.0, 6.736785359632827, 5.948060623297969, 4.64366877212261, 3.9316513544389973, 5.379266078654372, 3.593629531639346, 2.745778957362612, 1.7328491533713395, 2.4553233055664263, 2.058625671430099, 1.1507214889587531, 0.6100214812452991, 0.0), # 5
(6.979742147844666, 7.024762012504959, 6.023299607103222, 6.465447615037239, 5.141797182850695, 2.5397411688838374, 2.8743828532406313, 2.686924703751037, 2.8157126784122717, 1.3719222148381898, 0.9722892995297139, 0.5660744468730674, 0.0, 7.052736037699606, 6.22681891560374, 4.8614464976485685, 4.115766644514569, 5.631425356824543, 3.761694585251452, 2.8743828532406313, 1.8141008349170267, 2.5708985914253475, 2.1551492050124135, 1.2046599214206444, 0.6386147284095418, 0.0), # 6
(7.2873790797949685, 7.331353891769537, 6.286187700893863, 6.747711992905847, 5.367253557395036, 2.650622574638337, 2.9997431849797924, 2.8039403467281465, 2.9386101514892147, 1.4317463730358968, 1.0147460259942116, 0.5907767409159108, 0.0, 7.360713718506519, 6.498544150075018, 5.073730129971057, 4.2952391191076895, 5.877220302978429, 3.9255164854194056, 2.9997431849797924, 1.8933018390273837, 2.683626778697518, 2.249237330968616, 1.2572375401787725, 0.6664867174335943, 0.0), # 7
(7.586178158429934, 7.628690567496257, 6.54113885704533, 7.021453857524196, 5.586054507809724, 2.7581553398139356, 3.1213196156715988, 2.917421169782802, 3.0577960109310682, 1.4897650347411937, 1.0559209687315536, 0.6147332188070586, 0.0, 7.659391453419917, 6.762065406877643, 5.279604843657768, 4.469295104223581, 6.1155920218621365, 4.084389637695923, 3.1213196156715988, 1.970110957009954, 2.793027253904862, 2.3404846191747324, 1.3082277714090662, 0.6935173243178416, 0.0), # 8
(7.874844027645085, 7.915450675689353, 6.787020206437253, 7.285456918520376, 5.797238807138606, 2.861861772241199, 3.23857180840756, 3.0268631768812346, 3.1727408091108913, 1.5457203086224858, 1.0956311626552797, 0.6378374332888596, 0.0, 7.947442293806162, 7.016211766177453, 5.478155813276398, 4.637160925867456, 6.345481618221783, 4.237608447633728, 3.23857180840756, 2.044186980172285, 2.898619403569303, 2.4284856395067926, 1.3574040412874508, 0.7195864250626686, 0.0), # 9
(8.152081331335932, 8.190312852353056, 7.022698879949271, 7.538504885522466, 5.999845228425533, 2.961264179750688, 3.3509594262791773, 3.1317623719896712, 3.282915098401738, 1.599354303348179, 1.133693642678929, 0.6599829371036627, 0.0, 8.22353929103161, 7.259812308140289, 5.668468213394645, 4.798062910044536, 6.565830196803476, 4.384467320785539, 3.3509594262791773, 2.11518869982192, 2.9999226142127666, 2.5128349618408223, 1.4045397759898541, 0.7445738956684597, 0.0), # 10
(8.416594713398005, 8.451955733491605, 7.247042008461013, 7.779381468158547, 6.192912544714355, 3.055884870172965, 3.457942132377958, 3.2316147590743394, 3.3877894311766643, 1.6504091275866801, 1.1699254437160416, 0.6810632829938176, 0.0, 8.486355496462611, 7.491696112931993, 5.849627218580208, 4.951227382760039, 6.775578862353329, 4.524260662704076, 3.457942132377958, 2.1827749072664036, 3.0964562723571776, 2.5931271560528497, 1.4494084016922026, 0.7683596121356006, 0.0), # 11
(8.667088817726812, 8.699057955109222, 7.458916722852117, 8.006870376056709, 6.375479529048918, 3.1452461513385908, 3.5589795897954057, 3.325916342101467, 3.486834359808726, 1.6986268900063934, 1.2041436006801558, 0.7009720237016724, 0.0, 8.734563961465534, 7.710692260718395, 6.020718003400779, 5.095880670019179, 6.973668719617452, 4.656282878942054, 3.5589795897954057, 2.246604393813279, 3.187739764524459, 2.6689567920189035, 1.4917833445704234, 0.7908234504644749, 0.0), # 12
(8.902268288217876, 8.93029815321015, 7.657190154002218, 8.219755318845033, 6.546584954473067, 3.2288703310781304, 3.653531461623028, 3.414163125037284, 3.579520436670977, 1.7437496992757264, 1.2361651484848115, 0.7196027119695768, 0.0, 8.966837737406735, 7.915629831665344, 6.180825742424058, 5.2312490978271775, 7.159040873341954, 4.7798283750521975, 3.653531461623028, 2.306335950770093, 3.2732924772365335, 2.7399184396150114, 1.5314380308004438, 0.8118452866554684, 0.0), # 13
(9.120837768766716, 9.144354963798623, 7.840729432790956, 8.416820006151594, 6.705267594030659, 3.306279717222145, 3.7410574109523305, 3.4958511118480193, 3.6653182141364735, 1.785519664063084, 1.2658071220435476, 0.7368489005398801, 0.0, 9.181849875652563, 8.10533790593868, 6.329035610217737, 5.3565589921892505, 7.330636428272947, 4.894191556587227, 3.7410574109523305, 2.3616283694443894, 3.3526337970153297, 2.8056066687171985, 1.5681458865581912, 0.8313049967089657, 0.0), # 14
(9.321501903268855, 9.339907022878865, 8.008401690097953, 8.59684814760449, 6.850566220765538, 3.376996617601199, 3.821017100874813, 3.5704763064998986, 3.743698244578273, 1.823678893036873, 1.2928865562699035, 0.752604142154931, 0.0, 9.37827342756938, 8.27864556370424, 6.464432781349516, 5.471036679110618, 7.487396489156546, 4.998666829099858, 3.821017100874813, 2.4121404411437135, 3.425283110382769, 2.865616049201497, 1.6016803380195905, 0.8490824566253515, 0.0), # 15
(9.5029653356198, 9.51563296645512, 8.159074056802854, 8.758623452831788, 6.981519607721555, 3.4405433400458514, 3.892870194481988, 3.6375347129591504, 3.8141310803694286, 1.8579694948654994, 1.3172204860774188, 0.7667619895570784, 0.0, 9.554781444523545, 8.434381885127861, 6.586102430387094, 5.5739084845964975, 7.628262160738857, 5.092548598142811, 3.892870194481988, 2.4575309571756083, 3.4907598038607777, 2.9195411509439295, 1.6318148113605708, 0.8650575424050111, 0.0), # 16
(9.663932709715075, 9.670211430531618, 8.291613663785293, 8.900929631461583, 7.097166527942559, 3.4964421923866666, 3.9560763548653552, 3.6965223351920073, 3.8760872738829946, 1.8881335782173672, 1.3386259463796333, 0.7792159954886714, 0.0, 9.710046977881415, 8.571375950375383, 6.693129731898166, 5.6644007346521, 7.752174547765989, 5.17513126926881, 3.9560763548653552, 2.4974587088476192, 3.5485832639712793, 2.9669765438205284, 1.6583227327570589, 0.8791101300483289, 0.0), # 17
(9.803108669450204, 9.802321051112584, 8.404887641924901, 9.022550393121959, 7.1965457544723925, 3.5442154824542103, 4.010095245116426, 3.746935177164692, 3.929037377492032, 1.9139132517608846, 1.3569199720900849, 0.7898597126920597, 0.0, 9.842743079009345, 8.688456839612655, 6.784599860450424, 5.741739755282652, 7.858074754984064, 5.245709248030569, 4.010095245116426, 2.531582487467293, 3.5982728772361963, 3.0075167977073205, 1.6809775283849802, 0.8911200955556896, 0.0), # 18
(9.919197858720699, 9.910640464202265, 8.497763122101317, 9.122269447440985, 7.2786960603549105, 3.5833855180790386, 4.054386528326697, 3.7882692428434357, 3.9724519435695926, 1.9350506241644574, 1.3719195981223131, 0.7985866939095915, 0.0, 9.951542799273696, 8.784453633005505, 6.859597990611565, 5.80515187249337, 7.944903887139185, 5.30357693998081, 4.054386528326697, 2.55956108434217, 3.6393480301774552, 3.0407564824803295, 1.6995526244202632, 0.9009673149274788, 0.0), # 19
(10.010904921422082, 9.993848305804882, 8.569107235194169, 9.198870504046766, 7.342656218633962, 3.613474607091719, 4.088409867587681, 3.8200205361944657, 4.005801524488732, 1.95128780409649, 1.3834418593898585, 0.805290491883616, 0.0, 10.035119190040824, 8.858195410719775, 6.9172092969492915, 5.853863412289469, 8.011603048977465, 5.348028750672252, 4.088409867587681, 2.5810532907797996, 3.671328109316981, 3.0662901680155894, 1.713821447038834, 0.9085316641640803, 0.0), # 20
(10.076934501449866, 10.050623211924679, 8.6177871120831, 9.251137272567364, 7.387465002353392, 3.6340050573228124, 4.1116249259908795, 3.84168506118401, 4.028556672622507, 1.9623669002253892, 1.39130379080626, 0.8098646593564828, 0.0, 10.092145302677078, 8.90851125292131, 6.9565189540313, 5.887100700676166, 8.057113345245014, 5.378359085657614, 4.1116249259908795, 2.5957178980877234, 3.693732501176696, 3.0837124241891223, 1.72355742241662, 0.91369301926588, 0.0), # 21
(10.115991242699579, 10.079643818565883, 8.642669883647738, 9.277853462630876, 7.41216118455705, 3.644499176602881, 4.1234913666278, 3.852758821778298, 4.040187940343971, 1.968030021219561, 1.3953224272850568, 0.8122027490705409, 0.0, 10.121294188548827, 8.934230239775948, 6.976612136425284, 5.904090063658682, 8.080375880687942, 5.393862350489617, 4.1234913666278, 2.6032136975734863, 3.706080592278525, 3.09261782087696, 1.7285339767295478, 0.9163312562332622, 0.0), # 22
(10.13039336334264, 10.083079961133974, 8.645769318701419, 9.281198109567903, 7.418488037355065, 3.6458333333333335, 4.124902001129669, 3.8539557613168727, 4.0416420781893, 1.9686980681298587, 1.3958263395269568, 0.8124914647157445, 0.0, 10.125, 8.93740611187319, 6.9791316976347835, 5.906094204389575, 8.0832841563786, 5.395538065843622, 4.124902001129669, 2.604166666666667, 3.7092440186775324, 3.0937327031893016, 1.729153863740284, 0.9166436328303613, 0.0), # 23
(10.141012413034153, 10.08107561728395, 8.645262345679013, 9.280786458333335, 7.422071742409901, 3.6458333333333335, 4.124126906318083, 3.852291666666667, 4.041447222222222, 1.968287654320988, 1.39577076318743, 0.8124238683127573, 0.0, 10.125, 8.936662551440328, 6.978853815937151, 5.904862962962962, 8.082894444444443, 5.393208333333334, 4.124126906318083, 2.604166666666667, 3.7110358712049507, 3.0935954861111123, 1.7290524691358027, 0.9164614197530866, 0.0), # 24
(10.15140723021158, 10.077124771376313, 8.644261545496114, 9.279972029320987, 7.4255766303963355, 3.6458333333333335, 4.122599451303155, 3.8490226337448563, 4.041062242798354, 1.96747970964792, 1.3956605665710604, 0.8122904282883707, 0.0, 10.125, 8.935194711172077, 6.978302832855302, 5.902439128943758, 8.082124485596708, 5.388631687242799, 4.122599451303155, 2.604166666666667, 3.7127883151981678, 3.0933240097736636, 1.728852309099223, 0.9161022519433014, 0.0), # 25
(10.161577019048034, 10.071287780064015, 8.642780635573846, 9.278764081790122, 7.429002578947403, 3.6458333333333335, 4.120343359154361, 3.8442103909465026, 4.0404920781893, 1.9662876771833566, 1.3954967473084758, 0.8120929736320684, 0.0, 10.125, 8.933022709952752, 6.977483736542379, 5.898863031550069, 8.0809841563786, 5.381894547325103, 4.120343359154361, 2.604166666666667, 3.7145012894737013, 3.0929213605967085, 1.7285561271147696, 0.915571616369456, 0.0), # 26
(10.171520983716636, 10.063624999999998, 8.640833333333333, 9.277171874999999, 7.432349465696142, 3.6458333333333335, 4.117382352941177, 3.837916666666667, 4.039741666666666, 1.9647250000000003, 1.3952803030303031, 0.8118333333333335, 0.0, 10.125, 8.930166666666667, 6.976401515151515, 5.894175, 8.079483333333332, 5.373083333333334, 4.117382352941177, 2.604166666666667, 3.716174732848071, 3.0923906250000006, 1.7281666666666669, 0.914875, 0.0), # 27
(10.181238328390501, 10.054196787837219, 8.638433356195703, 9.275204668209877, 7.4356171682756, 3.6458333333333335, 4.113740155733075, 3.830203189300412, 4.038815946502057, 1.9628051211705537, 1.3950122313671698, 0.8115133363816492, 0.0, 10.125, 8.926646700198141, 6.9750611568358485, 5.88841536351166, 8.077631893004114, 5.3622844650205765, 4.113740155733075, 2.604166666666667, 3.7178085841378, 3.091734889403293, 1.7276866712391405, 0.9140178898033837, 0.0), # 28
(10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29
(10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30
(10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 31
(10.217825590600954, 10.00003675125743, 8.624581047096479, 9.263771026234568, 7.447893689561397, 3.6458333333333335, 4.092833918340999, 3.7863863168724285, 4.033458744855967, 1.951822450845908, 1.3934436234775742, 0.8096663618350862, 0.0, 10.125, 8.906329980185948, 6.96721811738787, 5.8554673525377225, 8.066917489711933, 5.3009408436214, 4.092833918340999, 2.604166666666667, 3.7239468447806985, 3.0879236754115236, 1.7249162094192958, 0.909094250114312, 0.0), # 32
(10.226397897897897, 9.98268672839506, 8.620123456790123, 9.260067708333333, 7.450763635790041, 3.6458333333333335, 4.086141612200436, 3.7725000000000004, 4.031730555555555, 1.9483182098765437, 1.392932379349046, 0.8090720164609053, 0.0, 10.125, 8.899792181069957, 6.96466189674523, 5.84495462962963, 8.06346111111111, 5.2815, 4.086141612200436, 2.604166666666667, 3.7253818178950207, 3.086689236111112, 1.724024691358025, 0.9075169753086421, 0.0), # 33
(10.23473881023881, 9.963933413351622, 8.615295496113397, 9.256044945987654, 7.453553663647644, 3.6458333333333335, 4.078910453481805, 3.7575643004115222, 4.029856687242798, 1.9445374256973027, 1.3923754936193207, 0.8084282883706753, 0.0, 10.125, 8.892711172077426, 6.961877468096604, 5.833612277091907, 8.059713374485597, 5.260590020576132, 4.078910453481805, 2.604166666666667, 3.726776831823822, 3.085348315329219, 1.7230590992226795, 0.9058121284865113, 0.0), # 34
(10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35
(10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36
(10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37
(10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38
(10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39
(10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 40
(10.286579288398128, 9.79843798582533, 8.57228166438043, 9.219718942901235, 7.4708358654371345, 3.6458333333333335, 4.015197208101347, 3.628816872427984, 4.0130754115226335, 1.9114579446730684, 1.3872831296504138, 0.8026931108062796, 0.0, 10.125, 8.829624218869075, 6.936415648252069, 5.734373834019204, 8.026150823045267, 5.0803436213991775, 4.015197208101347, 2.604166666666667, 3.7354179327185673, 3.073239647633746, 1.7144563328760862, 0.8907670896204848, 0.0), # 41
(10.293040391323, 9.770631172839506, 8.564984567901236, 9.213473958333335, 7.472982141115872, 3.6458333333333335, 4.004509259259259, 3.6077083333333335, 4.010213888888889, 1.9059487654320992, 1.3863970258136926, 0.8017201646090536, 0.0, 10.125, 8.818921810699589, 6.931985129068463, 5.717846296296297, 8.020427777777778, 5.050791666666667, 4.004509259259259, 2.604166666666667, 3.736491070557936, 3.0711579861111122, 1.7129969135802474, 0.8882391975308643, 0.0), # 42
(10.299262936849892, 9.741964277549155, 8.557440557841794, 9.206992862654321, 7.475047397120935, 3.6458333333333335, 3.993495965464375, 3.58610596707819, 4.007251131687243, 1.9002840306355744, 1.3854742590514195, 0.800714296601128, 0.0, 10.125, 8.807857262612407, 6.927371295257098, 5.700852091906722, 8.014502263374485, 5.020548353909466, 3.993495965464375, 2.604166666666667, 3.7375236985604676, 3.0689976208847747, 1.7114881115683587, 0.8856331161408324, 0.0), # 43
(10.305246129151927, 9.712497656607225, 8.549663351623229, 9.200284915123458, 7.477031511085363, 3.6458333333333335, 3.9821810497861696, 3.564071502057614, 4.0041920781893, 1.8944771833561962, 1.3845158269942222, 0.7996773357719861, 0.0, 10.125, 8.796450693491845, 6.92257913497111, 5.683431550068587, 8.0083841563786, 4.98970010288066, 3.9821810497861696, 2.604166666666667, 3.7385157555426813, 3.0667616383744867, 1.709932670324646, 0.8829543324188387, 0.0), # 44
(10.310989172402216, 9.682291666666666, 8.541666666666668, 9.193359375, 7.478934360642197, 3.6458333333333335, 3.9705882352941178, 3.541666666666667, 4.001041666666666, 1.8885416666666672, 1.3835227272727273, 0.798611111111111, 0.0, 10.125, 8.784722222222221, 6.917613636363637, 5.665625, 8.002083333333331, 4.958333333333334, 3.9705882352941178, 2.604166666666667, 3.7394671803210984, 3.064453125000001, 1.7083333333333335, 0.8802083333333335, 0.0), # 45
(10.31649127077388, 9.65140666438043, 8.533464220393233, 9.186225501543209, 7.480755823424477, 3.6458333333333335, 3.958741245057694, 3.518953189300412, 3.997804835390946, 1.8824909236396894, 1.3824959575175624, 0.7975174516079867, 0.0, 10.125, 8.772691967687852, 6.912479787587812, 5.647472770919067, 7.995609670781892, 4.926534465020577, 3.958741245057694, 2.604166666666667, 3.7403779117122387, 3.062075167181071, 1.7066928440786466, 0.8774006058527665, 0.0), # 46
(10.321751628440035, 9.619903006401461, 8.525069730224052, 9.178892554012345, 7.482495777065244, 3.6458333333333335, 3.9466638021463734, 3.4959927983539094, 3.994486522633745, 1.8763383973479657, 1.3814365153593549, 0.7963981862520958, 0.0, 10.125, 8.760380048773053, 6.9071825767967745, 5.629015192043896, 7.98897304526749, 4.894389917695474, 3.9466638021463734, 2.604166666666667, 3.741247888532622, 3.0596308513374493, 1.7050139460448106, 0.8745366369455876, 0.0), # 47
(10.326769449573796, 9.587841049382716, 8.516496913580248, 9.171369791666667, 7.48415409919754, 3.6458333333333335, 3.9343796296296296, 3.4728472222222226, 3.9910916666666667, 1.8700975308641978, 1.3803453984287317, 0.7952551440329219, 0.0, 10.125, 8.74780658436214, 6.901726992143659, 5.610292592592592, 7.982183333333333, 4.861986111111112, 3.9343796296296296, 2.604166666666667, 3.74207704959877, 3.05712326388889, 1.7032993827160496, 0.871621913580247, 0.0), # 48
(10.331543938348286, 9.555281149977136, 8.507759487882945, 9.163666473765433, 7.485730667454405, 3.6458333333333335, 3.9219124505769383, 3.4495781893004116, 3.987625205761317, 1.8637817672610888, 1.3792236043563206, 0.7940901539399483, 0.0, 10.125, 8.73499169333943, 6.896118021781603, 5.5913453017832655, 7.975250411522634, 4.829409465020577, 3.9219124505769383, 2.604166666666667, 3.7428653337272024, 3.054555491255145, 1.7015518975765893, 0.8686619227251944, 0.0), # 49
(10.336074298936616, 9.522283664837678, 8.49887117055327, 9.155791859567902, 7.4872253594688765, 3.6458333333333335, 3.909285988057775, 3.4262474279835393, 3.9840920781893, 1.85740454961134, 1.3780721307727481, 0.7929050449626583, 0.0, 10.125, 8.72195549458924, 6.89036065386374, 5.572213648834019, 7.9681841563786, 4.796746399176955, 3.909285988057775, 2.604166666666667, 3.7436126797344382, 3.051930619855968, 1.6997742341106543, 0.86566215134888, 0.0), # 50
(10.34035973551191, 9.488908950617283, 8.489845679012346, 9.147755208333333, 7.488638052873998, 3.6458333333333335, 3.896523965141612, 3.4029166666666666, 3.9804972222222226, 1.8509793209876546, 1.3768919753086422, 0.7917016460905352, 0.0, 10.125, 8.708718106995885, 6.884459876543211, 5.552937962962963, 7.960994444444445, 4.764083333333334, 3.896523965141612, 2.604166666666667, 3.744319026436999, 3.049251736111112, 1.6979691358024693, 0.8626280864197532, 0.0), # 51
(10.344399452247279, 9.455217363968908, 8.480696730681299, 9.139565779320987, 7.489968625302809, 3.6458333333333335, 3.883650104897926, 3.3796476337448556, 3.976845576131687, 1.8445195244627348, 1.3756841355946297, 0.7904817863130622, 0.0, 10.125, 8.695299649443683, 6.878420677973147, 5.533558573388203, 7.953691152263374, 4.731506687242798, 3.883650104897926, 2.604166666666667, 3.7449843126514044, 3.04652192644033, 1.69613934613626, 0.8595652149062645, 0.0), # 52
(10.348192653315843, 9.421269261545497, 8.471438042981255, 9.131232831790122, 7.491216954388353, 3.6458333333333335, 3.8706881303961915, 3.3565020576131688, 3.9731420781893005, 1.8380386031092826, 1.3744496092613379, 0.7892472946197227, 0.0, 10.125, 8.681720240816947, 6.872248046306688, 5.514115809327846, 7.946284156378601, 4.699102880658437, 3.8706881303961915, 2.604166666666667, 3.7456084771941764, 3.043744277263375, 1.694287608596251, 0.8564790237768635, 0.0), # 53
(10.351738542890716, 9.387125000000001, 8.462083333333332, 9.122765625, 7.492382917763668, 3.6458333333333335, 3.8576617647058824, 3.333541666666666, 3.9693916666666667, 1.8315500000000005, 1.3731893939393938, 0.788, 0.0, 10.125, 8.668, 6.865946969696969, 5.49465, 7.938783333333333, 4.666958333333333, 3.8576617647058824, 2.604166666666667, 3.746191458881834, 3.040921875000001, 1.6924166666666667, 0.8533750000000002, 0.0), # 54
(10.355036325145022, 9.352844935985367, 8.452646319158665, 9.114173418209877, 7.493466393061793, 3.6458333333333335, 3.844594730896474, 3.3108281893004117, 3.9655992798353905, 1.8250671582075908, 1.3719044872594257, 0.7867417314433777, 0.0, 10.125, 8.654159045877153, 6.859522436297127, 5.4752014746227715, 7.931198559670781, 4.6351594650205765, 3.844594730896474, 2.604166666666667, 3.7467331965308963, 3.0380578060699595, 1.6905292638317333, 0.8502586305441244, 0.0), # 55
(10.358085204251871, 9.31848942615455, 8.443140717878373, 9.105465470679011, 7.4944672579157725, 3.6458333333333335, 3.8315107520374405, 3.288423353909465, 3.961769855967078, 1.818603520804756, 1.3705958868520598, 0.7854743179393385, 0.0, 10.125, 8.640217497332722, 6.852979434260299, 5.455810562414267, 7.923539711934156, 4.603792695473251, 3.8315107520374405, 2.604166666666667, 3.7472336289578863, 3.035155156893005, 1.6886281435756747, 0.8471354023776865, 0.0), # 56
(10.360884384384383, 9.284118827160494, 8.433580246913582, 9.096651041666666, 7.495385389958644, 3.6458333333333335, 3.818433551198257, 3.2663888888888892, 3.957908333333333, 1.812172530864198, 1.369264590347924, 0.7841995884773663, 0.0, 10.125, 8.626195473251027, 6.8463229517396185, 5.436517592592593, 7.915816666666666, 4.572944444444445, 3.818433551198257, 2.604166666666667, 3.747692694979322, 3.0322170138888898, 1.6867160493827165, 0.844010802469136, 0.0), # 57
(10.36343306971568, 9.24979349565615, 8.423978623685414, 9.087739390432098, 7.496220666823449, 3.6458333333333335, 3.8053868514483984, 3.2447865226337447, 3.954019650205761, 1.8057876314586196, 1.367911595377645, 0.7829193720469442, 0.0, 10.125, 8.612113092516385, 6.8395579768882255, 5.417362894375858, 7.908039300411522, 4.5427011316872425, 3.8053868514483984, 2.604166666666667, 3.7481103334117245, 3.029246463477367, 1.684795724737083, 0.8408903177869229, 0.0), # 58
(10.36573046441887, 9.215573788294467, 8.414349565614998, 9.078739776234567, 7.49697296614323, 3.6458333333333335, 3.792394375857339, 3.2236779835390945, 3.9501087448559673, 1.799462265660723, 1.3665378995718502, 0.7816354976375554, 0.0, 10.125, 8.597990474013107, 6.83268949785925, 5.398386796982168, 7.900217489711935, 4.513149176954733, 3.792394375857339, 2.604166666666667, 3.748486483071615, 3.02624659207819, 1.6828699131229998, 0.8377794352994972, 0.0), # 59
(10.367775772667077, 9.181520061728396, 8.404706790123456, 9.069661458333334, 7.497642165551024, 3.6458333333333335, 3.779479847494553, 3.203125, 3.946180555555556, 1.7932098765432103, 1.3651445005611673, 0.7803497942386832, 0.0, 10.125, 8.583847736625515, 6.825722502805837, 5.37962962962963, 7.892361111111112, 4.484375, 3.779479847494553, 2.604166666666667, 3.748821082775512, 3.023220486111112, 1.6809413580246915, 0.8346836419753088, 0.0), # 60
(10.369568198633415, 9.147692672610884, 8.395064014631917, 9.060513695987654, 7.498228142679874, 3.6458333333333335, 3.7666669894295164, 3.183189300411523, 3.9422400205761314, 1.7870439071787843, 1.3637323959762233, 0.7790640908398111, 0.0, 10.125, 8.56970499923792, 6.818661979881115, 5.361131721536351, 7.884480041152263, 4.456465020576132, 3.7666669894295164, 2.604166666666667, 3.749114071339937, 3.0201712319958856, 1.6790128029263836, 0.8316084247828076, 0.0), # 61
(10.371106946491004, 9.114151977594878, 8.385434956561502, 9.051305748456791, 7.498730775162823, 3.6458333333333335, 3.753979524731703, 3.1639326131687247, 3.9382920781893, 1.7809778006401469, 1.3623025834476452, 0.7777802164304223, 0.0, 10.125, 8.555582380734645, 6.811512917238226, 5.3429334019204395, 7.8765841563786, 4.429505658436215, 3.753979524731703, 2.604166666666667, 3.7493653875814115, 3.0171019161522645, 1.6770869913123003, 0.8285592706904436, 0.0), # 62
(10.37239122041296, 9.080958333333333, 8.375833333333334, 9.042046875, 7.499149940632904, 3.6458333333333335, 3.741441176470588, 3.1454166666666667, 3.9343416666666666, 1.7750250000000003, 1.360856060606061, 0.7765000000000001, 0.0, 10.125, 8.5415, 6.804280303030303, 5.325075, 7.868683333333333, 4.403583333333334, 3.741441176470588, 2.604166666666667, 3.749574970316452, 3.014015625000001, 1.675166666666667, 0.8255416666666667, 0.0), # 63
(10.373420224572397, 9.048172096479195, 8.366272862368541, 9.032746334876544, 7.4994855167231655, 3.6458333333333335, 3.729075667715646, 3.127703189300412, 3.9303937242798352, 1.7691989483310475, 1.3593938250820965, 0.7752252705380279, 0.0, 10.125, 8.527477975918305, 6.796969125410483, 5.307596844993141, 7.8607874485596705, 4.378784465020577, 3.729075667715646, 2.604166666666667, 3.7497427583615828, 3.0109154449588487, 1.6732545724737085, 0.822561099679927, 0.0), # 64
(10.374193163142438, 9.015853623685413, 8.35676726108825, 9.023413387345679, 7.499737381066645, 3.6458333333333335, 3.7169067215363514, 3.1108539094650207, 3.9264531893004113, 1.7635130887059902, 1.357916874506381, 0.7739578570339887, 0.0, 10.125, 8.513536427373873, 6.7895843725319045, 5.290539266117969, 7.852906378600823, 4.355195473251029, 3.7169067215363514, 2.604166666666667, 3.7498686905333223, 3.0078044624485605, 1.67135345221765, 0.819623056698674, 0.0), # 65
(10.374709240296196, 8.984063271604938, 8.34733024691358, 9.014057291666667, 7.499905411296382, 3.6458333333333335, 3.7049580610021784, 3.094930555555556, 3.9225250000000003, 1.7579808641975312, 1.3564262065095398, 0.7726995884773664, 0.0, 10.125, 8.499695473251029, 6.782131032547699, 5.273942592592592, 7.8450500000000005, 4.332902777777778, 3.7049580610021784, 2.604166666666667, 3.749952705648191, 3.0046857638888897, 1.6694660493827165, 0.8167330246913582, 0.0), # 66
(10.374967660206792, 8.952861396890716, 8.337975537265661, 9.004687307098765, 7.499989485045419, 3.6458333333333335, 3.693253409182603, 3.0799948559670787, 3.9186140946502057, 1.7526157178783728, 1.3549228187222018, 0.7714522938576437, 0.0, 10.125, 8.485975232434079, 6.774614093611008, 5.257847153635117, 7.837228189300411, 4.31199279835391, 3.693253409182603, 2.604166666666667, 3.7499947425227096, 3.001562435699589, 1.6675951074531323, 0.8138964906264289, 0.0), # 67
(10.374791614480825, 8.922144586043629, 8.328671624942844, 8.995231305354269, 7.499918636864896, 3.645765673423767, 3.681757597414823, 3.0659766041761927, 3.9146959495503735, 1.747405110411792, 1.3533809980900628, 0.770210835158312, 0.0, 10.124875150034294, 8.47231918674143, 6.766904990450313, 5.242215331235375, 7.829391899100747, 4.29236724584667, 3.681757597414823, 2.604118338159833, 3.749959318432448, 2.99841043511809, 1.6657343249885688, 0.8111040532766937, 0.0), # 68
(10.373141706924315, 8.890975059737157, 8.319157021604937, 8.985212635869564, 7.499273783587508, 3.6452307956104257, 3.6701340906733066, 3.052124485596708, 3.910599279835391, 1.7422015976761076, 1.3516438064859118, 0.7689349144466104, 0.0, 10.12388599537037, 8.458284058912714, 6.758219032429559, 5.226604793028321, 7.821198559670782, 4.272974279835391, 3.6701340906733066, 2.6037362825788755, 3.749636891793754, 2.9950708786231885, 1.6638314043209876, 0.8082704599761052, 0.0), # 69
(10.369885787558895, 8.859209754856408, 8.309390360653863, 8.974565343196456, 7.497999542752628, 3.6441773992785653, 3.658330067280685, 3.0383135192805977, 3.9063009640298736, 1.736979881115684, 1.3496914810876801, 0.7676185634410675, 0.0, 10.121932334533609, 8.44380419785174, 6.7484574054383994, 5.210939643347051, 7.812601928059747, 4.253638926992837, 3.658330067280685, 2.6029838566275467, 3.748999771376314, 2.991521781065486, 1.6618780721307727, 0.8053827049869463, 0.0), # 70
(10.365069660642929, 8.826867654542236, 8.299375071444901, 8.963305127818035, 7.496112052502757, 3.6426225549966977, 3.646350829769494, 3.0245482777015704, 3.9018074035970125, 1.7317400898356603, 1.347531228463977, 0.7662627447677263, 0.0, 10.119039887688615, 8.428890192444989, 6.737656142319885, 5.195220269506979, 7.803614807194025, 4.234367588782199, 3.646350829769494, 2.6018732535690696, 3.7480560262513785, 2.987768375939346, 1.6598750142889804, 0.8024425140492942, 0.0), # 71
(10.358739130434783, 8.793967741935482, 8.289114583333333, 8.95144769021739, 7.493627450980392, 3.6405833333333337, 3.634201680672269, 3.0108333333333333, 3.897125, 1.7264823529411768, 1.3451702551834133, 0.7648684210526316, 0.0, 10.115234375, 8.413552631578947, 6.7258512759170666, 5.179447058823529, 7.79425, 4.215166666666667, 3.634201680672269, 2.600416666666667, 3.746813725490196, 2.983815896739131, 1.6578229166666667, 0.7994516129032258, 0.0), # 72
(10.35094000119282, 8.760529000176998, 8.27861232567444, 8.939008730877617, 7.490561876328034, 3.638076804856983, 3.621887922521546, 2.9971732586495965, 3.8922601547020275, 1.7212067995373737, 1.3426157678145982, 0.7634365549218266, 0.0, 10.110541516632374, 8.397802104140093, 6.71307883907299, 5.163620398612119, 7.784520309404055, 4.196042562109435, 3.621887922521546, 2.598626289183559, 3.745280938164017, 2.979669576959206, 1.655722465134888, 0.7964117272888181, 0.0), # 73
(10.341718077175404, 8.726570412407629, 8.267871727823502, 8.926003950281803, 7.486931466688183, 3.6351200401361585, 3.609414857849861, 2.9835726261240665, 3.8872192691662857, 1.7159135587293908, 1.3398749729261428, 0.7619681090013557, 0.0, 10.104987032750344, 8.38164919901491, 6.699374864630713, 5.147740676188171, 7.774438538332571, 4.177001676573693, 3.609414857849861, 2.5965143143829703, 3.7434657333440917, 2.975334650093935, 1.6535743455647005, 0.7933245829461482, 0.0), # 74
(10.331119162640901, 8.692110961768218, 8.256896219135802, 8.912449048913043, 7.482752360203341, 3.6317301097393697, 3.59678778918975, 2.9700360082304527, 3.8820087448559666, 1.7106027596223679, 1.336955077086656, 0.7604640459172624, 0.0, 10.098596643518519, 8.365104505089885, 6.684775385433279, 5.131808278867102, 7.764017489711933, 4.158050411522634, 3.59678778918975, 2.594092935528121, 3.7413761801016703, 2.9708163496376816, 1.6513792438271604, 0.7901919056152927, 0.0), # 75
(10.319189061847677, 8.65716963139962, 8.245689228966622, 8.898359727254428, 7.478040695016003, 3.6279240842351275, 3.5840120190737474, 2.956567977442463, 3.876634983234263, 1.7052745313214452, 1.3338632868647486, 0.7589253282955902, 0.0, 10.091396069101508, 8.348178611251491, 6.669316434323743, 5.115823593964334, 7.753269966468526, 4.139195168419449, 3.5840120190737474, 2.5913743458822336, 3.7390203475080015, 2.96611990908481, 1.6491378457933243, 0.7870154210363293, 0.0), # 76
(10.305973579054093, 8.621765404442675, 8.234254186671238, 8.883751685789049, 7.472812609268672, 3.6237190341919425, 3.5710928500343897, 2.9431731062338065, 3.871104385764365, 1.699929002931763, 1.3306068088290313, 0.7573529187623839, 0.0, 10.083411029663925, 8.330882106386222, 6.653034044145156, 5.099787008795288, 7.74220877152873, 4.120442348727329, 3.5710928500343897, 2.58837073870853, 3.736406304634336, 2.9612505619296834, 1.6468508373342476, 0.7837968549493343, 0.0), # 77
(10.291518518518519, 8.585917264038233, 8.222594521604938, 8.868640625, 7.467084241103849, 3.6191320301783265, 3.5580355846042124, 2.9298559670781894, 3.8654233539094642, 1.6945663035584608, 1.327192849548113, 0.7557477799436866, 0.0, 10.074667245370371, 8.313225579380552, 6.635964247740564, 5.083698910675381, 7.7308467078189285, 4.101798353909466, 3.5580355846042124, 2.585094307270233, 3.7335421205519244, 2.956213541666667, 1.6445189043209878, 0.7805379330943849, 0.0), # 78
(10.275869684499314, 8.549644193327138, 8.210713663123, 8.85304224537037, 7.460871728664031, 3.61418014276279, 3.5448455253157505, 2.916621132449322, 3.859598289132754, 1.6891865623066789, 1.3236286155906039, 0.7541108744655421, 0.0, 10.065190436385459, 8.295219619120962, 6.618143077953018, 5.067559686920035, 7.719196578265508, 4.083269585429051, 3.5448455253157505, 2.5815572448305644, 3.7304358643320157, 2.951014081790124, 1.6421427326246, 0.7772403812115581, 0.0), # 79
(10.259072881254847, 8.51296517545024, 8.198615040580703, 8.836972247383253, 7.454191210091719, 3.6088804425138448, 3.5315279747015405, 2.9034731748209115, 3.853635592897424, 1.683789908281557, 1.3199213135251149, 0.7524431649539947, 0.0, 10.0550063228738, 8.27687481449394, 6.599606567625574, 5.05136972484467, 7.707271185794848, 4.064862444749276, 3.5315279747015405, 2.577771744652746, 3.7270956050458595, 2.945657415794418, 1.639723008116141, 0.7739059250409311, 0.0), # 80
(10.241173913043479, 8.475899193548386, 8.186302083333333, 8.82044633152174, 7.447058823529411, 3.60325, 3.5180882352941176, 2.890416666666667, 3.8475416666666664, 1.6783764705882358, 1.3160781499202554, 0.7507456140350878, 0.0, 10.044140624999999, 8.258201754385965, 6.580390749601277, 5.035129411764706, 7.695083333333333, 4.046583333333333, 3.5180882352941176, 2.57375, 3.7235294117647055, 2.940148777173914, 1.6372604166666667, 0.7705362903225808, 0.0), # 81
(10.222218584123576, 8.438465230762423, 8.17377822073617, 8.803480198268922, 7.43949070711961, 3.5973058857897686, 3.504531609626018, 2.8774561804602956, 3.841322911903673, 1.6729463783318543, 1.3121063313446355, 0.7490191843348656, 0.0, 10.03261906292867, 8.23921102768352, 6.560531656723177, 5.018839134995561, 7.682645823807346, 4.0284386526444145, 3.504531609626018, 2.5695042041355487, 3.719745353559805, 2.934493399422974, 1.634755644147234, 0.767133202796584, 0.0), # 82
(10.202252698753504, 8.400682270233196, 8.16104688214449, 8.78608954810789, 7.431502999004814, 3.591065170451659, 3.4908634002297765, 2.8645962886755068, 3.8349857300716352, 1.6674997606175532, 1.3080130643668657, 0.7472648384793719, 0.0, 10.020467356824417, 8.219913223273089, 6.540065321834328, 5.002499281852659, 7.6699714601432705, 4.01043480414571, 3.4908634002297765, 2.5650465503226134, 3.715751499502407, 2.9286965160359637, 1.632209376428898, 0.7636983882030178, 0.0), # 83
(10.181322061191626, 8.362569295101553, 8.14811149691358, 8.768290081521739, 7.423111837327523, 3.584544924554184, 3.477088909637929, 2.851841563786008, 3.8285365226337444, 1.6620367465504726, 1.3038055555555557, 0.7454835390946503, 0.0, 10.007711226851852, 8.200318930041153, 6.519027777777778, 4.986110239651417, 7.657073045267489, 3.9925781893004113, 3.477088909637929, 2.5603892318244172, 3.7115559186637617, 2.922763360507247, 1.629622299382716, 0.7602335722819594, 0.0), # 84
(10.159472475696308, 8.32414528850834, 8.13497549439872, 8.75009749899356, 7.414333360230238, 3.577762218665854, 3.463213440383012, 2.8391965782655086, 3.8219816910531925, 1.6565574652357518, 1.2994910114793157, 0.7436762488067449, 0.0, 9.994376393175584, 8.180438736874192, 6.497455057396579, 4.969672395707254, 7.643963382106385, 3.9748752095717124, 3.463213440383012, 2.5555444419041815, 3.707166680115119, 2.916699166331187, 1.626995098879744, 0.7567404807734855, 0.0), # 85
(10.136749746525913, 8.285429233594407, 8.121642303955191, 8.731527501006443, 7.405183705855455, 3.57073412335518, 3.44924229499756, 2.826665904587715, 3.815327636793172, 1.6510620457785314, 1.2950766387067558, 0.7418439302416996, 0.0, 9.98048857596022, 8.160283232658694, 6.475383193533778, 4.953186137335593, 7.630655273586344, 3.9573322664228017, 3.44924229499756, 2.550524373825129, 3.7025918529277275, 2.910509167002148, 1.6243284607910382, 0.7532208394176735, 0.0), # 86
(10.113199677938807, 8.246440113500597, 8.10811535493827, 8.712595788043478, 7.3956790123456795, 3.563477709190672, 3.4351807760141093, 2.8142541152263374, 3.8085807613168727, 1.645550617283951, 1.290569643806486, 0.7399875460255577, 0.0, 9.96607349537037, 8.139863006281134, 6.452848219032429, 4.936651851851852, 7.6171615226337455, 3.9399557613168725, 3.4351807760141093, 2.54534122085048, 3.6978395061728397, 2.904198596014493, 1.6216230709876542, 0.7496763739545999, 0.0), # 87
(10.088868074193357, 8.207196911367758, 8.094398076703246, 8.693318060587762, 7.385835417843406, 3.5560100467408424, 3.4210341859651954, 2.801965782655083, 3.8017474660874866, 1.6400233088571508, 1.2859772333471164, 0.7381080587843638, 0.0, 9.951156871570646, 8.119188646628, 6.429886166735582, 4.9200699265714505, 7.603494932174973, 3.9227520957171165, 3.4210341859651954, 2.540007176243459, 3.692917708921703, 2.897772686862588, 1.6188796153406495, 0.7461088101243417, 0.0), # 88
(10.063800739547922, 8.16771861033674, 8.080493898605397, 8.673710019122383, 7.375669060491138, 3.5483482065742016, 3.406807827383354, 2.7898054793476605, 3.794834152568206, 1.634480249603271, 1.2813066138972575, 0.7362064311441613, 0.0, 9.935764424725651, 8.098270742585774, 6.4065330694862865, 4.903440748809812, 7.589668305136412, 3.905727671086725, 3.406807827383354, 2.534534433267287, 3.687834530245569, 2.891236673040795, 1.6160987797210793, 0.7425198736669765, 0.0), # 89
(10.03804347826087, 8.128024193548386, 8.06640625, 8.653787364130435, 7.365196078431373, 3.5405092592592595, 3.3925070028011204, 2.7777777777777777, 3.7878472222222226, 1.6289215686274514, 1.2765649920255184, 0.7342836257309943, 0.0, 9.919921875, 8.077119883040936, 6.382824960127592, 4.886764705882353, 7.575694444444445, 3.888888888888889, 3.3925070028011204, 2.5289351851851856, 3.6825980392156863, 2.884595788043479, 1.6132812500000002, 0.7389112903225807, 0.0), # 90
(10.011642094590563, 8.088132644143545, 8.05213856024234, 8.63356579609501, 7.35443260980661, 3.532510275364528, 3.378137014751031, 2.7658872504191434, 3.780793076512727, 1.6233473950348318, 1.2717595743005101, 0.7323406051709063, 0.0, 9.903654942558298, 8.055746656879968, 6.35879787150255, 4.870042185104494, 7.561586153025454, 3.872242150586801, 3.378137014751031, 2.5232216252603767, 3.677216304903305, 2.8778552653650036, 1.6104277120484682, 0.7352847858312315, 0.0), # 91
(9.984642392795372, 8.048062945263066, 8.0376942586877, 8.613061015499195, 7.343394792759352, 3.524368325458518, 3.363703165765621, 2.754138469745466, 3.773678116902911, 1.6177578579305527, 1.2668975672908422, 0.7303783320899415, 0.0, 9.886989347565157, 8.034161652989356, 6.334487836454211, 4.853273573791657, 7.547356233805822, 3.8557938576436523, 3.363703165765621, 2.517405946756084, 3.671697396379676, 2.871020338499732, 1.6075388517375402, 0.7316420859330061, 0.0), # 92
(9.957090177133654, 8.00783408004779, 8.023076774691358, 8.592288722826089, 7.332098765432098, 3.5161004801097393, 3.349210758377425, 2.742536008230453, 3.766508744855967, 1.6121530864197533, 1.261986177565125, 0.7283977691141434, 0.0, 9.869950810185184, 8.012375460255576, 6.309930887825625, 4.836459259259259, 7.533017489711934, 3.839550411522634, 3.349210758377425, 2.5115003429355283, 3.666049382716049, 2.86409624094203, 1.6046153549382718, 0.727984916367981, 0.0), # 93
(9.92903125186378, 7.967465031638567, 8.008289537608597, 8.571264618558777, 7.320560665967347, 3.5077238098867043, 3.3346650951189805, 2.7310844383478132, 3.759291361835086, 1.6065332096075746, 1.2570326116919686, 0.7263998788695563, 0.0, 9.85256505058299, 7.990398667565118, 6.285163058459842, 4.819599628822722, 7.518582723670172, 3.823518213686939, 3.3346650951189805, 2.5055170070619317, 3.6602803329836733, 2.8570882061862592, 1.6016579075217197, 0.7243150028762335, 0.0), # 94
(9.90051142124411, 7.926974783176247, 7.993335976794697, 8.550004403180354, 7.308796632507598, 3.499255385357923, 3.320071478522822, 2.719788332571255, 3.7520323693034596, 1.6008983565991557, 1.2520440762399827, 0.7243856239822234, 0.0, 9.834857788923182, 7.968241863804456, 6.260220381199914, 4.8026950697974655, 7.504064738606919, 3.8077036655997567, 3.320071478522822, 2.4994681323985164, 3.654398316253799, 2.850001467726785, 1.5986671953589393, 0.7206340711978407, 0.0), # 95
(9.871576489533012, 7.886382317801674, 7.978219521604939, 8.528523777173913, 7.296822803195352, 3.4907122770919066, 3.3054352111214853, 2.708652263374486, 3.7447381687242793, 1.5952486564996373, 1.247027777777778, 0.7223559670781895, 0.0, 9.816854745370371, 7.945915637860083, 6.23513888888889, 4.785745969498911, 7.489476337448559, 3.7921131687242804, 3.3054352111214853, 2.4933659122085046, 3.648411401597676, 2.8428412590579715, 1.595643904320988, 0.7169438470728796, 0.0), # 96
(9.842272260988848, 7.845706618655694, 7.962943601394604, 8.506838441022543, 7.284655316173109, 3.482111555657166, 3.2907615954475067, 2.697680803231215, 3.7374151615607376, 1.589584238414159, 1.2419909228739638, 0.7203118707834976, 0.0, 9.798581640089164, 7.923430578618472, 6.209954614369819, 4.768752715242476, 7.474830323121475, 3.7767531245237014, 3.2907615954475067, 2.4872225397551184, 3.6423276580865545, 2.8356128136741816, 1.5925887202789208, 0.7132460562414268, 0.0), # 97
(9.812644539869984, 7.804966668879153, 7.947511645518976, 8.48496409520934, 7.272310309583368, 3.4734702916222124, 3.276055934033421, 2.68687852461515, 3.7300697492760246, 1.5839052314478608, 1.236940718097151, 0.7182542977241916, 0.0, 9.78006419324417, 7.900797274966106, 6.184703590485755, 4.751715694343581, 7.460139498552049, 3.7616299344612103, 3.276055934033421, 2.48105020830158, 3.636155154791684, 2.8283213650697805, 1.589502329103795, 0.7095424244435595, 0.0), # 98
(9.782739130434782, 7.764181451612902, 7.931927083333334, 8.462916440217391, 7.259803921568627, 3.464805555555556, 3.261323529411765, 2.67625, 3.7227083333333333, 1.5782117647058826, 1.2318843700159492, 0.7161842105263159, 0.0, 9.761328125, 7.878026315789473, 6.159421850079745, 4.734635294117647, 7.445416666666667, 3.7467500000000005, 3.261323529411765, 2.474861111111111, 3.6299019607843137, 2.820972146739131, 1.5863854166666669, 0.7058346774193549, 0.0), # 99
(9.752601836941611, 7.723369949997786, 7.916193344192958, 8.44071117652979, 7.247152290271389, 3.4561344180257074, 3.2465696841150726, 2.665799801859473, 3.715337315195854, 1.572503967293365, 1.2268290851989685, 0.714102571815914, 0.0, 9.742399155521262, 7.8551282899750525, 6.134145425994841, 4.717511901880093, 7.430674630391708, 3.732119722603262, 3.2465696841150726, 2.468667441446934, 3.6235761451356945, 2.8135703921765973, 1.5832386688385918, 0.7021245409088898, 0.0), # 100
(9.722278463648834, 7.682551147174654, 7.900313857453133, 8.41836400462963, 7.234371553834153, 3.4474739496011786, 3.231799700675881, 2.6555325026672763, 3.7079630963267793, 1.5667819683154474, 1.2217820702148188, 0.7120103442190294, 0.0, 9.723303004972564, 7.832113786409323, 6.108910351074094, 4.7003459049463405, 7.415926192653559, 3.7177455037341867, 3.231799700675881, 2.4624813925722706, 3.6171857769170765, 2.806121334876544, 1.5800627714906266, 0.6984137406522414, 0.0), # 101
(9.691814814814816, 7.641744026284349, 7.884292052469135, 8.395890625, 7.221477850399419, 3.4388412208504806, 3.217018881626725, 2.645452674897119, 3.7005920781893, 1.56104589687727, 1.2167505316321108, 0.7099084903617069, 0.0, 9.704065393518519, 7.808993393978774, 6.083752658160553, 4.683137690631809, 7.4011841563786, 3.703633744855967, 3.217018881626725, 2.4563151577503435, 3.6107389251997093, 2.798630208333334, 1.5768584104938272, 0.6947040023894864, 0.0), # 102
(9.661256694697919, 7.60096757046772, 7.8681313585962505, 8.373306738123993, 7.208487318109686, 3.430253302342123, 3.20223252950014, 2.63556489102271, 3.6932306622466085, 1.5552958820839726, 1.211741676019454, 0.7077979728699895, 0.0, 9.68471204132373, 7.785777701569883, 6.058708380097269, 4.6658876462519165, 7.386461324493217, 3.689790847431794, 3.20223252950014, 2.4501809302443736, 3.604243659054843, 2.7911022460413317, 1.5736262717192502, 0.6909970518607019, 0.0), # 103
(9.63064990755651, 7.560240762865614, 7.851835205189758, 8.350628044484703, 7.195416095107452, 3.421727264644617, 3.187445946828663, 2.6258737235177567, 3.685885249961896, 1.5495320530406955, 1.2067627099454585, 0.7056797543699213, 0.0, 9.665268668552812, 7.762477298069133, 6.033813549727292, 4.648596159122086, 7.371770499923792, 3.6762232129248593, 3.187445946828663, 2.4440909033175835, 3.597708047553726, 2.783542681494901, 1.5703670410379515, 0.687294614805965, 0.0), # 104
(9.600040257648953, 7.519582586618876, 7.835407021604938, 8.327870244565217, 7.182280319535221, 3.4132801783264752, 3.172664436144829, 2.6163837448559675, 3.6785622427983538, 1.5437545388525786, 1.201820839978735, 0.7035547974875461, 0.0, 9.64576099537037, 7.739102772363006, 6.009104199893674, 4.631263616557734, 7.3571244855967075, 3.662937242798354, 3.172664436144829, 2.4380572702331964, 3.5911401597676105, 2.775956748188406, 1.5670814043209877, 0.6835984169653525, 0.0), # 105
(9.569473549233614, 7.479012024868357, 7.818850237197074, 8.305049038848631, 7.1690961295354905, 3.404929113956206, 3.1578932999811724, 2.6070995275110502, 3.6712680422191735, 1.5379634686247616, 1.1969232726878927, 0.701424064848908, 0.0, 9.626214741941014, 7.715664713337986, 5.9846163634394625, 4.613890405874283, 7.342536084438347, 3.6499393385154706, 3.1578932999811724, 2.4320922242544327, 3.5845480647677452, 2.768349679616211, 1.5637700474394147, 0.6799101840789417, 0.0), # 106
(9.538995586568856, 7.438548060754901, 7.802168281321446, 8.282180127818036, 7.155879663250759, 3.3966911421023225, 3.1431378408702306, 2.5980256439567144, 3.6640090496875475, 1.532158971462385, 1.1920772146415421, 0.6992885190800504, 0.0, 9.606655628429355, 7.692173709880553, 5.96038607320771, 4.596476914387154, 7.328018099375095, 3.6372359015394005, 3.1431378408702306, 2.426207958644516, 3.5779398316253794, 2.760726709272679, 1.5604336562642893, 0.6762316418868093, 0.0), # 107
(9.508652173913044, 7.398209677419356, 7.785364583333334, 8.259279211956523, 7.1426470588235285, 3.3885833333333335, 3.1284033613445374, 2.589166666666667, 3.656791666666667, 1.5263411764705888, 1.1872898724082936, 0.6971491228070177, 0.0, 9.587109375, 7.668640350877193, 5.936449362041468, 4.579023529411765, 7.313583333333334, 3.624833333333334, 3.1284033613445374, 2.4204166666666667, 3.5713235294117642, 2.7530930706521746, 1.557072916666667, 0.6725645161290325, 0.0), # 108
(9.478489115524543, 7.358015858002567, 7.768442572588021, 8.23636199174718, 7.129414454396299, 3.3806227582177515, 3.113695163936631, 2.580527168114617, 3.6496222946197223, 1.5205102127545123, 1.1825684525567568, 0.6950068386558532, 0.0, 9.567601701817559, 7.645075225214384, 5.9128422627837836, 4.561530638263536, 7.299244589239445, 3.612738035360464, 3.113695163936631, 2.4147305415841083, 3.5647072271981495, 2.7454539972490606, 1.5536885145176043, 0.668910532545688, 0.0), # 109
(9.448552215661715, 7.317985585645383, 7.751405678440788, 8.213444167673108, 7.116197988111569, 3.3728264873240867, 3.0990185511790447, 2.5721117207742723, 3.6425073350099066, 1.5146662094192962, 1.177920161655542, 0.6928626292526012, 0.0, 9.54815832904664, 7.621488921778612, 5.8896008082777085, 4.543998628257887, 7.285014670019813, 3.600956409083981, 3.0990185511790447, 2.409161776660062, 3.5580989940557846, 2.737814722557703, 1.5502811356881578, 0.6652714168768531, 0.0), # 110
(9.41888727858293, 7.278137843488651, 7.7342573302469155, 8.190541440217391, 7.103013798111837, 3.365211591220851, 3.0843788256043156, 2.5639248971193416, 3.635453189300412, 1.5088092955700803, 1.173352206273259, 0.6907174572233054, 0.0, 9.528804976851852, 7.597892029456357, 5.866761031366295, 4.526427886710239, 7.270906378600824, 3.5894948559670783, 3.0843788256043156, 2.4037225651577505, 3.5515068990559184, 2.7301804800724643, 1.546851466049383, 0.6616488948626047, 0.0), # 111
(9.38954010854655, 7.238491614673214, 7.717000957361684, 8.167669509863124, 7.089878022539605, 3.357795140476554, 3.069781289744979, 2.5559712696235333, 3.628466258954427, 1.5029396003120044, 1.1688717929785184, 0.6885722851940093, 0.0, 9.509567365397805, 7.574295137134101, 5.844358964892591, 4.5088188009360115, 7.256932517908854, 3.5783597774729463, 3.069781289744979, 2.3984251003403956, 3.5449390112698027, 2.7225565032877084, 1.543400191472337, 0.6580446922430195, 0.0), # 112
(9.360504223703044, 7.1991320672204555, 7.699681523543391, 8.14487541186903, 7.076783786782469, 3.3505906987084666, 3.0552629818283847, 2.548271903658586, 3.6215709370862066, 1.4970761841531826, 1.1644873176921446, 0.6864327447087024, 0.0, 9.490443900843221, 7.550760191795725, 5.8224365884607225, 4.491228552459547, 7.243141874172413, 3.5675806651220205, 3.0552629818283847, 2.3932790705060474, 3.5383918933912346, 2.7149584706230105, 1.5399363047086783, 0.654466551565496, 0.0), # 113
(9.331480897900065, 7.16044741823174, 7.682538062518016, 8.122342065958001, 7.063595569710884, 3.343581854975776, 3.0410091042052896, 2.5409213581271333, 3.6148730119043533, 1.491328791978196, 1.1602073895188663, 0.684326014342748, 0.0, 9.471275414160035, 7.5275861577702265, 5.801036947594331, 4.473986375934587, 7.229746023808707, 3.557289901377987, 3.0410091042052896, 2.3882727535541255, 3.531797784855442, 2.7074473553193346, 1.5365076125036032, 0.6509497652937947, 0.0), # 114
(9.302384903003995, 7.122451598792792, 7.665580777256098, 8.100063378886334, 7.050271785259067, 3.3367503822909463, 3.027029825095781, 2.533917772616129, 3.6083749928895963, 1.4857063319970194, 1.1560257519045158, 0.6822531318799043, 0.0, 9.452006631660376, 7.5047844506789465, 5.7801287595225785, 4.457118995991058, 7.216749985779193, 3.5474848816625806, 3.027029825095781, 2.3833931302078186, 3.5251358926295335, 2.700021126295445, 1.5331161554512198, 0.647495599890254, 0.0), # 115
(9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116
(9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117
(9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 118
(9.184546916944742, 6.976249595477001, 7.598956538421437, 8.012700459908778, 6.99521932355948, 3.3108761573001524, 2.973403979075378, 2.5089860290900607, 3.5840533058483475, 1.4642565220650932, 1.1401204476261382, 0.6742234638017862, 0.0, 9.373322671729932, 7.416458101819647, 5.70060223813069, 4.392769566195279, 7.168106611696695, 3.5125804407260848, 2.973403979075378, 2.3649115409286803, 3.49760966177974, 2.670900153302927, 1.5197913076842873, 0.6342045086797276, 0.0), # 119
(9.154542089925162, 6.940867657993644, 7.582437490410635, 7.991098997720545, 6.980916464638998, 3.304690006307063, 2.9604566564951265, 2.5034269924163928, 3.578308313179186, 1.4591017540939766, 1.136309018248736, 0.6722623579111081, 0.0, 9.353098006322597, 7.394885937022188, 5.68154509124368, 4.377305262281929, 7.156616626358372, 3.50479778938295, 2.9604566564951265, 2.360492861647902, 3.490458232319499, 2.663699665906849, 1.516487498082127, 0.6309879689085133, 0.0), # 120
(9.124246399149268, 6.90584244374276, 7.565907561536823, 7.969513766646325, 6.966357543311279, 3.29858461294281, 2.94764751801299, 2.4980988152572112, 3.572664624881166, 1.4540091307330743, 1.1325473816071863, 0.6703121147461852, 0.0, 9.33259128356866, 7.373433262208036, 5.662736908035931, 4.362027392199222, 7.145329249762332, 3.497338341360096, 2.94764751801299, 2.356131866387721, 3.4831787716556395, 2.656504588882109, 1.5131815123073646, 0.6278038585220692, 0.0), # 121
(9.093623478839854, 6.871118601671464, 7.549333909028926, 7.947905028731892, 6.951522477071823, 3.292543874970886, 2.9349538278930587, 2.492982147528187, 3.5671058073216297, 1.4489681873851195, 1.1288274547309753, 0.6683689034441251, 0.0, 9.31177220987977, 7.352057937885375, 5.644137273654876, 4.346904562155357, 7.1342116146432595, 3.490175006539462, 2.9349538278930587, 2.351817053550633, 3.4757612385359113, 2.6493016762439643, 1.5098667818057854, 0.6246471456064968, 0.0), # 122
(9.062636963219719, 6.836640780726876, 7.532683690115864, 7.92623304602302, 6.936391183416127, 3.28655169015479, 2.9223528503994194, 2.4880576391449933, 3.5616154268679177, 1.443968459452847, 1.1251411546495909, 0.6664288931420351, 0.0, 9.290610491667572, 7.330717824562385, 5.625705773247954, 4.33190537835854, 7.123230853735835, 3.4832806948029904, 2.9223528503994194, 2.3475369215391355, 3.4681955917080636, 2.642077682007674, 1.5065367380231727, 0.621512798247898, 0.0), # 123
(9.031250486511654, 6.802353629856113, 7.515924062026559, 7.90445808056549, 6.920943579839691, 3.2805919562580144, 2.9098218497961597, 2.483305940023303, 3.5561770498873715, 1.4389994823389904, 1.1214803983925201, 0.664488252977023, 0.0, 9.269075835343711, 7.309370782747252, 5.6074019919625995, 4.316998447016971, 7.112354099774743, 3.476628316032624, 2.9098218497961597, 2.3432799687557244, 3.4604717899198456, 2.634819360188497, 1.5031848124053118, 0.618395784532374, 0.0), # 124
(8.999427682938459, 6.768201798006293, 7.499022181989936, 7.88254039440507, 6.905159583838015, 3.274648571044058, 2.8973380903473696, 2.478707700078788, 3.5507742427473308, 1.4340507914462837, 1.1178371029892504, 0.6625431520861957, 0.0, 9.247137947319828, 7.2879746729481525, 5.5891855149462515, 4.30215237433885, 7.1015484854946616, 3.470190780110303, 2.8973380903473696, 2.3390346936028985, 3.4525797919190073, 2.6275134648016905, 1.4998044363979874, 0.6152910725460268, 0.0), # 125
(8.967132186722928, 6.734129934124536, 7.481945207234916, 7.8604402495875405, 6.889019112906595, 3.2687054322764144, 2.884878836317135, 2.474243569227122, 3.545390571815139, 1.4291119221774609, 1.1142031854692689, 0.6605897596066612, 0.0, 9.224766534007578, 7.266487355673273, 5.571015927346345, 4.287335766532382, 7.090781143630278, 3.463940996917971, 2.884878836317135, 2.334789594483153, 3.4445095564532977, 2.620146749862514, 1.4963890414469831, 0.6121936303749579, 0.0), # 126
(8.93432763208786, 6.7000826871579555, 7.464660294990421, 7.838117908158674, 6.8725020845409315, 3.26274643771858, 2.872421351969547, 2.469894197383977, 3.5400096034581354, 1.4241724099352562, 1.1105705628620632, 0.6586242446755264, 0.0, 9.201931301818599, 7.244866691430789, 5.552852814310316, 4.272517229805768, 7.080019206916271, 3.457851876337568, 2.872421351969547, 2.3305331697989855, 3.4362510422704657, 2.612705969386225, 1.4929320589980841, 0.6090984261052688, 0.0), # 127
(8.900977653256046, 6.666004706053673, 7.447134602485375, 7.815533632164248, 6.855588416236526, 3.2567554851340508, 2.859942901568691, 2.465640234465026, 3.534614904043661, 1.4192217901224033, 1.1069311521971208, 0.6566427764298991, 0.0, 9.178601957164537, 7.223070540728888, 5.534655760985604, 4.257665370367209, 7.069229808087322, 3.4518963282510366, 2.859942901568691, 2.3262539179528936, 3.427794208118263, 2.6051778773880834, 1.4894269204970751, 0.6060004278230613, 0.0), # 128
(8.867045884450281, 6.631840639758805, 7.4293352869486995, 7.792647683650037, 6.838258025488874, 3.250716472286322, 2.8474207493786565, 2.4614623303859418, 3.529190039939058, 1.4142495981416365, 1.1032768705039286, 0.6546415240068865, 0.0, 9.154748206457038, 7.20105676407575, 5.516384352519642, 4.242748794424909, 7.058380079878116, 3.4460472625403185, 2.8474207493786565, 2.321940337347373, 3.419129012744437, 2.597549227883346, 1.4858670573897401, 0.6028946036144368, 0.0), # 129
(8.832495959893366, 6.5975351372204685, 7.411229505609316, 7.769420324661814, 6.820490829793475, 3.2446132969388883, 2.8348321596635313, 2.457341135062396, 3.5237185775116666, 1.4092453693956895, 1.0995996348119743, 0.6526166565435961, 0.0, 9.130339756107748, 7.178783221979556, 5.4979981740598705, 4.2277361081870675, 7.047437155023333, 3.4402775890873545, 2.8348321596635313, 2.3175809263849203, 3.4102454148967376, 2.589806774887272, 1.4822459011218634, 0.5997759215654973, 0.0), # 130
(8.797291513808094, 6.563032847385783, 7.392784415696151, 7.7458118172453565, 6.802266746645829, 3.238429856855247, 2.8221543966874045, 2.4532572984100627, 3.5181840831288285, 1.4041986392872965, 1.0958913621507447, 0.6505643431771354, 0.0, 9.105346312528312, 7.156207774948489, 5.479456810753724, 4.212595917861889, 7.036368166257657, 3.4345602177740875, 2.8221543966874045, 2.3131641834680337, 3.4011333733229145, 2.5819372724151193, 1.4785568831392302, 0.596639349762344, 0.0), # 131
(8.76139618041726, 6.528278419201865, 7.373967174438122, 7.72178242344644, 6.783565693541435, 3.2321500497988933, 2.8093647247143627, 2.449191470344614, 3.5125701231578845, 1.3990989432191914, 1.0921439695497275, 0.6484807530446118, 0.0, 9.079737582130376, 7.13328828349073, 5.460719847748638, 4.1972968296575734, 7.025140246315769, 3.4288680584824593, 2.8093647247143627, 2.3086786069992096, 3.3917828467707176, 2.573927474482147, 1.4747934348876244, 0.5934798562910787, 0.0), # 132
(8.724773593943663, 6.493216501615832, 7.354744939064153, 7.697292405310838, 6.764367587975791, 3.225757773533322, 2.7964404080084946, 2.445124300781722, 3.5068602639661752, 1.3939358165941083, 1.0883493740384103, 0.6463620552831327, 0.0, 9.053483271325586, 7.10998260811446, 5.44174687019205, 4.181807449782324, 7.0137205279323505, 3.4231740210944106, 2.7964404080084946, 2.3041126953809443, 3.3821837939878954, 2.5657641351036133, 1.4709489878128308, 0.590292409237803, 0.0), # 133
(8.687387388610095, 6.457791743574804, 7.33508486680317, 7.672302024884328, 6.7446523474443945, 3.2192369258220297, 2.7833587108338893, 2.44103643963706, 3.5010380719210428, 1.388698794814781, 1.0844994926462799, 0.6442044190298056, 0.0, 9.026553086525583, 7.0862486093278605, 5.422497463231399, 4.166096384444343, 7.0020761438420855, 3.417451015491884, 2.7833587108338893, 2.2994549470157355, 3.3723261737221972, 2.557434008294776, 1.4670169733606342, 0.5870719766886187, 0.0), # 134
(8.649201198639354, 6.421948794025897, 7.314954114884091, 7.646771544212684, 6.724399889442747, 3.212571404428512, 2.770096897454634, 2.4369085368263, 3.4950871133898262, 1.3833774132839443, 1.0805862424028239, 0.6420040134217377, 0.0, 8.99891673414202, 7.0620441476391145, 5.402931212014119, 4.150132239851832, 6.9901742267796525, 3.41167195155682, 2.770096897454634, 2.2946938603060802, 3.3621999447213735, 2.548923848070895, 1.4629908229768183, 0.583813526729627, 0.0), # 135
(8.610178658254235, 6.385632301916229, 7.294319840535841, 7.62066122534168, 6.703590131466344, 3.205745107116265, 2.7566322321348173, 2.4327212422651154, 3.4889909547398688, 1.3779612074043308, 1.0766015403375297, 0.6397570075960368, 0.0, 8.970543920586536, 7.037327083556404, 5.383007701687648, 4.133883622212991, 6.9779819094797375, 3.4058097391711617, 2.7566322321348173, 2.289817933654475, 3.351795065733172, 2.540220408447227, 1.4588639681071682, 0.58051202744693, 0.0), # 136
(8.570283401677534, 6.348786916192918, 7.273149200987342, 7.593931330317094, 6.682202991010689, 3.1987419316487826, 2.7429419791385277, 2.428455205869179, 3.4827331623385107, 1.3724397125786756, 1.0725373034798844, 0.63745957068981, 0.0, 8.941404352270776, 7.012055277587909, 5.362686517399421, 4.117319137736026, 6.965466324677021, 3.3998372882168506, 2.7429419791385277, 2.284815665463416, 3.3411014955053444, 2.5313104434390317, 1.4546298401974684, 0.577162446926629, 0.0), # 137
(8.529479063132047, 6.311357285803083, 7.251409353467515, 7.566542121184698, 6.660218385571278, 3.1915457757895624, 2.729003402729852, 2.4240910775541624, 3.4762973025530934, 1.3668024642097119, 1.0683854488593754, 0.6351078718401649, 0.0, 8.91146773560639, 6.986186590241813, 5.341927244296877, 4.100407392629135, 6.952594605106187, 3.3937275085758274, 2.729003402729852, 2.2796755541354017, 3.330109192785639, 2.5221807070615663, 1.450281870693503, 0.5737597532548258, 0.0), # 138
(8.487729276840568, 6.273288059693839, 7.229067455205284, 7.538453859990269, 6.63761623264361, 3.184140537302099, 2.7147937671728797, 2.4196095072357395, 3.469666941750957, 1.3610389977001744, 1.0641378935054902, 0.6326980801842089, 0.0, 8.880703777005019, 6.959678882026297, 5.32068946752745, 4.083116993100523, 6.939333883501914, 3.3874533101300353, 2.7147937671728797, 2.274386098072928, 3.318808116321805, 2.51281795333009, 1.4458134910410567, 0.5702989145176218, 0.0), # 139
(8.444997677025897, 6.234523886812306, 7.206090663429573, 7.509626808779583, 6.614376449723186, 3.176510113949888, 2.7002903367316984, 2.4149911448295818, 3.462825646299444, 1.3551388484527966, 1.0597865544477159, 0.6302263648590494, 0.0, 8.849082182878314, 6.932490013449542, 5.298932772238579, 4.0654165453583895, 6.925651292598888, 3.3809876027614147, 2.7002903367316984, 2.2689357956784915, 3.307188224861593, 2.5032089362598615, 1.4412181326859146, 0.5667748988011189, 0.0), # 140
(8.40124789791083, 6.195009416105602, 7.1824461353693, 7.480021229598415, 6.590478954305501, 3.1686384034964257, 2.6854703756703975, 2.4102166402513627, 3.455756982565893, 1.349091551870313, 1.0553233487155398, 0.6276888950017938, 0.0, 8.816572659637913, 6.904577845019731, 5.276616743577699, 4.047274655610939, 6.911513965131786, 3.3743032963519077, 2.6854703756703975, 2.26331314535459, 3.2952394771527507, 2.4933404098661387, 1.4364892270738603, 0.5631826741914184, 0.0), # 141
(8.356443573718156, 6.154689296520844, 7.158101028253392, 7.44959738449254, 6.565903663886058, 3.1605093037052074, 2.670311148253063, 2.4052666434167547, 3.448444516917647, 1.3428866433554572, 1.0507401933384497, 0.6250818397495496, 0.0, 8.783144913695466, 6.875900237245045, 5.253700966692247, 4.028659930066371, 6.896889033835294, 3.3673733007834565, 2.670311148253063, 2.2575066455037196, 3.282951831943029, 2.4831991281641805, 1.4316202056506786, 0.5595172087746222, 0.0), # 142
(8.310548338670674, 6.113508177005149, 7.133022499310772, 7.418315535507731, 6.540630495960352, 3.152106712339729, 2.6547899187437842, 2.4001218042414303, 3.4408718157220486, 1.3365136583109634, 1.0460290053459322, 0.6224013682394242, 0.0, 8.748768651462617, 6.846415050633665, 5.230145026729661, 4.009540974932889, 6.881743631444097, 3.360170525938002, 2.6547899187437842, 2.251504794528378, 3.270315247980176, 2.472771845169244, 1.4266044998621543, 0.5557734706368318, 0.0), # 143
(8.263525826991184, 6.071410706505636, 7.107177705770357, 7.386135944689768, 6.514639368023886, 3.1434145271634857, 2.6388839514066493, 2.3947627726410623, 3.4330224453464364, 1.3299621321395652, 1.0411817017674754, 0.619643649608525, 0.0, 8.713413579351014, 6.816080145693774, 5.205908508837376, 3.9898863964186946, 6.866044890692873, 3.3526678816974873, 2.6388839514066493, 2.245296090831061, 3.257319684011943, 2.4620453148965895, 1.4214355411540713, 0.5519464278641489, 0.0), # 144
(8.215339672902477, 6.0283415339694235, 7.080533804861075, 7.353018874084421, 6.487910197572155, 3.134416645939974, 2.6225705105057466, 2.3891701985313234, 3.424879972158151, 1.3232216002439972, 1.036190199632566, 0.6168048529939595, 0.0, 8.6770494037723, 6.784853382933553, 5.180950998162829, 3.969664800731991, 6.849759944316302, 3.344838277943853, 2.6225705105057466, 2.238869032814267, 3.2439550987860777, 2.451006291361474, 1.4161067609722149, 0.548031048542675, 0.0), # 145
(8.16595351062735, 5.984245308343629, 7.053057953811847, 7.318924585737469, 6.460422902100661, 3.1250969664326886, 2.605826860305165, 2.3833247318278863, 3.4164279625245353, 1.3162815980269928, 1.0310464159706916, 0.6138811475328351, 0.0, 8.639645831138118, 6.7526926228611845, 5.155232079853457, 3.948844794080978, 6.832855925049071, 3.3366546245590407, 2.605826860305165, 2.2322121188804918, 3.2302114510503306, 2.439641528579157, 1.4106115907623695, 0.5440223007585119, 0.0), # 146
(8.1153309743886, 5.93906667857537, 7.024717309851591, 7.283813341694685, 6.4321573991049, 3.1154393864051255, 2.5886302650689905, 2.3772070224464232, 3.40764998281293, 1.3091316608912866, 1.0257422678113395, 0.6108687023622593, 0.0, 8.601172567860118, 6.719555725984851, 5.1287113390566965, 3.9273949826738592, 6.81529996562586, 3.3280898314249923, 2.5886302650689905, 2.2253138474322327, 3.21607869955245, 2.4279377805648954, 1.4049434619703185, 0.5399151525977609, 0.0), # 147
(8.063435698409021, 5.892750293611764, 6.9954790302092364, 7.247645404001847, 6.403093606080374, 3.105427803620781, 2.5709579890613132, 2.3707977203026074, 3.398529599390676, 1.301761324239612, 1.0202696721839972, 0.6077636866193392, 0.0, 8.561599320349941, 6.68540055281273, 5.101348360919985, 3.905283972718835, 6.797059198781352, 3.3191168084236504, 2.5709579890613132, 2.2181627168719866, 3.201546803040187, 2.4158818013339496, 1.3990958060418472, 0.535704572146524, 0.0), # 148
(8.010231316911412, 5.845240802399927, 6.965310272113703, 7.210381034704727, 6.37321144052258, 3.0950461158431497, 2.5527872965462204, 2.3640774753121114, 3.3890503786251127, 1.2941601234747035, 1.0146205461181517, 0.6045622694411826, 0.0, 8.520895795019237, 6.650184963853008, 5.073102730590758, 3.88248037042411, 6.778100757250225, 3.3097084654369557, 2.5527872965462204, 2.21074722560225, 3.18660572026129, 2.403460344901576, 1.3930620544227408, 0.5313855274909026, 0.0), # 149
(7.955681464118564, 5.796482853886981, 6.934178192793912, 7.171980495849104, 6.342490819927017, 3.0842782208357287, 2.5340954517878003, 2.3570269373906068, 3.3791958868835836, 1.2863175939992944, 1.0087868066432906, 0.601260619964897, 0.0, 8.479031698279647, 6.6138668196138655, 5.043934033216452, 3.8589527819978824, 6.758391773767167, 3.2998377123468496, 2.5340954517878003, 2.2030558720255207, 3.1712454099635083, 2.390660165283035, 1.3868356385587826, 0.5269529867169983, 0.0), # 150
(7.899749774253275, 5.746421097020041, 6.902049949478785, 7.132404049480748, 6.310911661789184, 3.0731080163620113, 2.5148597190501416, 2.3496267564537683, 3.3689496905334293, 1.2782232712161197, 1.002760370788901, 0.5978549073275894, 0.0, 8.435976736542818, 6.576403980603482, 5.013801853944504, 3.8346698136483583, 6.737899381066859, 3.2894774590352753, 2.5148597190501416, 2.1950771545442938, 3.155455830894592, 2.377468016493583, 1.3804099898957571, 0.5224019179109128, 0.0), # 151
(7.842399881538343, 5.6950001807462245, 6.868892699397251, 7.091611957645439, 6.278453883604579, 3.0615194001854955, 2.4950573625973322, 2.3418575824172674, 3.3582953559419897, 1.2698666905279126, 0.9965331555844703, 0.5943413006663675, 0.0, 8.391700616220398, 6.537754307330042, 4.982665777922351, 3.809600071583737, 6.716590711883979, 3.2786006153841742, 2.4950573625973322, 2.1867995715610684, 3.1392269418022893, 2.36387065254848, 1.3737785398794504, 0.5177272891587478, 0.0), # 152
(7.78359542019656, 5.642164754012652, 6.834673599778224, 7.049564482388949, 6.245097402868703, 3.049496270069676, 2.4746656466934596, 2.333700065196776, 3.3472164494766075, 1.2612373873374074, 0.9900970780594861, 0.5907159691183387, 0.0, 8.346173043724027, 6.497875660301725, 4.95048539029743, 3.783712162012222, 6.694432898953215, 3.2671800912754865, 2.4746656466934596, 2.17821162147834, 3.1225487014343516, 2.3498548274629836, 1.3669347199556448, 0.5129240685466048, 0.0), # 153
(7.723300024450729, 5.587859465766439, 6.7993598078506325, 7.006221885757057, 6.210822137077053, 3.0370225237780484, 2.453661835602614, 2.325134854707968, 3.3356965375046217, 1.2523248970473384, 0.9834440552434354, 0.5869750818206104, 0.0, 8.299363725465357, 6.456725900026714, 4.917220276217177, 3.7569746911420143, 6.671393075009243, 3.2551887965911552, 2.453661835602614, 2.169301802698606, 3.1054110685385266, 2.335407295252353, 1.3598719615701265, 0.5079872241605854, 0.0), # 154
(7.6614773285236355, 5.532028964954703, 6.762918480843396, 6.961544429795533, 6.175608003725131, 3.0240820590741087, 2.4320231935888805, 2.316142600866515, 3.323719186393376, 1.2431187550604388, 0.9765660041658056, 0.5831148079102902, 0.0, 8.251242367856026, 6.414262887013191, 4.882830020829028, 3.7293562651813157, 6.647438372786752, 3.242599641213121, 2.4320231935888805, 2.160058613624363, 3.0878040018625654, 2.320514809931845, 1.3525836961686795, 0.5029117240867913, 0.0), # 155
(7.598090966638081, 5.474617900524564, 6.725316775985439, 6.915492376550157, 6.139434920308432, 3.0106587737213526, 2.40972698491635, 2.3067039535880913, 3.3112679625102084, 1.2336084967794434, 0.9694548418560842, 0.5791313165244852, 0.0, 8.201778677307685, 6.370444481769337, 4.84727420928042, 3.7008254903383295, 6.622535925020417, 3.2293855350233276, 2.40972698491635, 2.150470552658109, 3.069717460154216, 2.3051641255167192, 1.3450633551970879, 0.49769253641132405, 0.0), # 156
(7.533104573016862, 5.415570921423138, 6.686521850505682, 6.868025988066703, 6.102282804322456, 2.9967365654832747, 2.3867504738491094, 2.2967995627883675, 3.2983264322224626, 1.2237836576070855, 0.9621024853437583, 0.5750207768003032, 0.0, 8.150942360231976, 6.325228544803333, 4.810512426718791, 3.671350972821256, 6.596652864444925, 3.2155193879037145, 2.3867504738491094, 2.140526118202339, 3.051141402161228, 2.2893419960222348, 1.3373043701011365, 0.4923246292202853, 0.0), # 157
(7.464680946405239, 5.353748694041236, 6.644659961585297, 6.817327186238432, 6.062454070580665, 2.9814309445183143, 2.3625533604639286, 2.285748730145572, 3.2838873638663655, 1.213341479072786, 0.9542659587564906, 0.570633297016195, 0.0, 8.096485859415345, 6.276966267178143, 4.771329793782452, 3.640024437218358, 6.567774727732731, 3.200048222203801, 2.3625533604639286, 2.129593531798796, 3.0312270352903323, 2.2724423954128112, 1.3289319923170593, 0.48670442673102154, 0.0), # 158
(7.382286766978402, 5.282809876299521, 6.58894818200249, 6.7529828690913405, 6.010127539854418, 2.95965229467081, 2.334106381692858, 2.2696723053184926, 3.2621424204073812, 1.2005702485246865, 0.9445694892698324, 0.5651135436402591, 0.0, 8.025427646920194, 6.216248980042849, 4.722847446349162, 3.601710745574059, 6.5242848408147625, 3.17754122744589, 2.334106381692858, 2.114037353336293, 3.005063769927209, 2.250994289697114, 1.3177896364004982, 0.4802554432999565, 0.0), # 159
(7.284872094904309, 5.202172001162321, 6.51826746496324, 6.673933132806645, 5.94428008756453, 2.9308657560278157, 2.301121874191892, 2.248166328969728, 3.2324750757428835, 1.1853014129657236, 0.9328765847682567, 0.5583751624073207, 0.0, 7.93642060889358, 6.142126786480525, 4.664382923841283, 3.55590423889717, 6.464950151485767, 3.147432860557619, 2.301121874191892, 2.0934755400198686, 2.972140043782265, 2.2246443776022153, 1.3036534929926482, 0.47292472737839286, 0.0), # 160
(7.17322205458596, 5.11236079574043, 6.4333724765919245, 6.5809293778175455, 5.865595416188075, 2.895420057582683, 2.263840723003438, 2.2215002221290754, 3.1952765889996724, 1.1676645482927346, 0.9192902757666179, 0.5504806224089643, 0.0, 7.830374044819097, 6.055286846498606, 4.596451378833089, 3.5029936448782033, 6.390553177999345, 3.1101003109807053, 2.263840723003438, 2.0681571839876307, 2.9327977080940375, 2.1936431259391824, 1.2866744953183848, 0.46476007234003913, 0.0), # 161
(7.048121770426357, 5.013901987144635, 6.335017883012913, 6.474723004557244, 5.7747572282021356, 2.853663928328766, 2.2225038131699044, 2.1899434058263343, 3.150938219304545, 1.147789230402558, 0.9039135927797701, 0.5414923927367745, 0.0, 7.708197254180333, 5.956416320104519, 4.519567963898851, 3.4433676912076736, 6.30187643860909, 3.065920768156868, 2.2225038131699044, 2.03833137737769, 2.8873786141010678, 2.158241001519082, 1.2670035766025827, 0.4558092715586033, 0.0), # 162
(6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163
(6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164
(6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165
(6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166
(6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167
(6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168
(5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169
(5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170
(5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171
(5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172
(5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173
(4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174
(4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175
(4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176
(4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177
(3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 134
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 136
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 137
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 138
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 139
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 140
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 141
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 142
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 143
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 144
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 145
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 146
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 147
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 148
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 149
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 150
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 151
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 152
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 153
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 154
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 155
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 156
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 157
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 158
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 159
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 160
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
73, # 1
)
|
## 99 bottles using while loop..
n = 99
while n > 0:
print(n,"bottles of beer on the wall,","Take one down, pass it around,")
n = n - 1
print('No more bottles of beer on the wall, no more bottles of beer. Go to the store and buy some more, 99 bottles of beer on the wall…')
"""
# 99 bottles using for loop (range), if else condition...
for i in range(99, 0, -1):
if i == 1:
print('1 bottle of beer on the wall, 1 bottle of beer!')
print('So take it down, pass it around, no more bottles of beer on the wall!')
elif i == 2:
print('2 more bottles of beer on the wall, 2 more bottles of beer!')
print('So take one down, pass it around, 1 more bottle of beer on the wall!')
else:
print('{0} bottles of beer on the wall, {0} bottles of beer!'.format(i))
print('So take it down, pass it around, {0} more bottles of beer on the wall!'.format(i - 1))
"""
|
# model settings
_base_ = './hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py'
voxel_size = [0.16, 0.16, 4]
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
model = dict(
voxel_encoder=dict(
type='PillarFeatureNet',
in_channels=4,
feat_channels=[64],
with_distance=False,
voxel_size=voxel_size,
point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1]),
bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
anchor_generator=dict(
_delete_=True,
type='Anchor3DRangeGenerator',
ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
sizes=[[1.6, 3.9, 1.56]],
rotations=[0, 1.57],
reshape_out=True)),
neck=dict(
type='SECONDFPN',
in_channels=[64, 64, 128, 256],
upsample_strides=[0, 1, 2, 4],
out_channels=[128, 64, 64, 128]),
# model training and testing settings
train_cfg=dict(
_delete_=True,
assigner=dict(
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
allowed_border=0,
pos_weight=-1,
debug=False))
# dataset settings
dataset_type = 'KittiDataset'
data_root = '/home/dk/kitti_dataset/'
class_names = ['Car']
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
sample_groups=dict(Car=15),
classes=class_names)
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[0.25, 0.25, 0.25],
global_rot_range=[0.0, 0.0],
rot_range=[-0.15707963267, 0.15707963267]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(pipeline=train_pipeline, classes=class_names)),
val=dict(pipeline=test_pipeline, classes=class_names),
test=dict(pipeline=test_pipeline, classes=class_names))
lr = 0.001
# The optimizer follows the setting in SECOND.Pytorch, but here we use
# the offcial AdamW optimizer implemented by PyTorch.
optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4,
)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4,
)
# Although the max_epochs is 40, this schedule is usually used we
# RepeatDataset with repeat ratio N, thus the actual max epoch
# number could be Nx40
runner = dict(type='EpochBasedRunner', max_epochs=80) |
"""
This module contains third-party libraries that usually have different licensing than
Binary Refinery itself.
"""
|
def for_Q():
""" Pattern of Captital Alphabet: 'Q' using for loop"""
for i in range(7):
for j in range(6):
if i%4==0 and j in(1,2,3) or j%4==0 and i in(1,2,3) or i==j and i>1:
print('*',end=' ')
else:
print(' ',end=' ')
print()
def while_Q():
""" Pattern of Captital Alphabet: 'Q' using while loop"""
i=0
while i<7:
j=0
while j<6:
if i%4==0 and j in(1,2,3) or j%4==0 and i in(1,2,3) or i==j and i>1:
print('*',end=' ')
else:
print(' ',end=' ')
j+=1
i+=1
print()
|
numstr=''
sumnum=0
strng=input('Enter a string:')
for a in strng:
if a.isdigit():
numstr+=a
sumnum+=int(a)
if sumnum==0:
print(strng,'has no digits.')
else:
print(strng,'has digits',numstr,'that sum to',sumnum)
|
# -*- coding: utf-8 -*-
"""autoproto
A package for making the process of reading a binary packet much easier.
"""
__author__ = '[email protected] (Andreas Blixt)'
__license__ = 'MIT'
__version__ = '0.1a'
|
#!/usr/bin/env python2
#!coding=utf-8
basic_command = [
('help', 'Show this help'),
('login', 'Login using Baidu account'),
('download', 'Download file from the Baidu pan link'),
('show', 'Show the Baidu pan real link and filename'),
('export', 'export link to aria2 json-rpc'),
('config', 'save configuration to file')
]
extended_usage = ''
def join_commands(command):
n = max(len(x[0]) for x in command)
n = max(n, 10)
return ''.join(' %%-%ds %%s\n' % n % (h, k) for (h, k) in basic_command)
basic_usage = '''python bddown_cli.py <command> [<args>]
Basic commands:
''' + join_commands(basic_command)
def usage():
return basic_usage + '''
Use 'python bddown_cli.py help' for details
Use 'python bddown_cli.py help <command>' for more information on a specific command.
Check https://github.com/banbanchs/pan-baidu-download for details'''
def show_help():
return ''' Python script for Baidu pan
Basic usage:
''' + basic_usage + extended_usage + '\n'
login = '''python bddown_cli.py login [username] [password]
Baidu login.
Example:
python bddown_cli.py login XXXXX 123456
python bddown_cli.py login [email protected] 123456
'''
download = '''python bddown_cli.py download [options] [Baidupan-url]...
Download file from the Baidu pan link
Options:
--limit=[speed] Max download speed limit.
--output-dir=[dir] Download task to dir.'''
show = '''python bddown_cli.py show [Baidupan-url]...
Show the real download link and filename
Example:
python bddown_cli.py show http://pan.baidu.com/s/15lliC
'''
export = '''python bddown_cli.py export [Baidupan-url]...
export link to aria2 json-rpc
Example:
python bddown_cli.py show http://pan.baidu.com/s/15lliC
'''
config = '''python bddown_cli.py config key [value]
save configuration to config.ini
Examples:
python bddown_cli.py config username XXXXX
python bddown_cli.py config password 123456
python bddown_cli.py config limit 500k
python bddown_cli.py config dir /home/john/Downloads
python bddown_cli.py config save_vcode 1
'''
help_help = '''Get helps:
python bddown_cli.py help help
python bddown_cli.py help download
python bddown_cli.py help show
python bddown_cli.py help <command>'''
help = help_help
|
#! /usr/bin/python2.7
"""Base 41 Data encoding"""
def base41_decode(input):
"""Decode a Base41 string.
input is the string to decode.
The decoded data is returned. A TypeError is raised if input
is not valid (odd number of chars, non-alphabet character present,
invalid word).
"""
rslt = bytearray()
i = 0
while i + 2 < len(input):
x = (ord(input[i]) - 41) + 41 * (ord(input[i+1]) - 41) + 1681*(ord(input[i+2]) - 41)
rslt.extend([x % 256, x // 256])
i += 3
if i != len(input):
raise TypeError("Invalid Base41 string")
return rslt
def base41_encode(input):
"""Encode an array of bytes to a Base41 string.
input is the array of bytes to encode.
The encoded string is returned.
If input has an odd number of bytes, a TypeError is raised.
"""
rslt = ""
i = 0
while i + 1 < len(input):
x = input[i] + 256 * input[i+1]
rslt += chr((x % 41) + 41)
x //= 41
rslt += chr((x % 41) + 41) + chr((x // 41) + 41)
i += 2
if i != len(input):
raise TypeError("Invalid input length for Base41 encoding")
return rslt
def test():
"""Test the Base41 codec"""
data = bytearray([26,168,48,178,7,162,52,188,199,36])
s = "BABA.DEDA.DECA."
decoded = base41_decode(s)
if data != decoded:
raise RuntimeError("bad decoding")
encoded = base41_encode(data)
if s != encoded:
raise RuntimeError("bad decoding")
if __name__ == '__main__':
test()
|
class Solution:
def findTheDifference(self, s: str, t: str) -> str:
result = 0
for char in s:
result ^= ord(char)
for char in t:
result ^= ord(char)
return chr(result) |
## 1. Análise de Vendas
# Nesse exercício vamos fazer uma "análise simples" de atingimento de Meta.
# Temos uma lista com os vendedores e os valores de vendas e queremos identificar (printar) quais os vendedores que bateram a meta e qual foi o valor que eles venderam.
meta = 10000
vendas = [
('João', 15000),
('Julia', 27000),
('Marcus', 9900),
('Maria', 3750),
('Ana', 10300),
('Alon', 7870),
]
# Unpacking das tuplas direto no for (vendedor corresponde à vendas[0], qtde correspode à vendas[1])
for vendedor, qtde in vendas:
if qtde >= meta:
print(f'{vendedor} bateu a meta, vendeu {qtde} unidades.')
## 2. Comparação com Ano Anterior
# Digamos que você está analisando as vendas de produtos de um ecommerce e quer identificar quais produtos tiveram no ano de 2020 mais vendas do que no ano de 2019, para reportar isso para a diretoria.
# Sua resposta pode ser um print de cada produto, qual foi a venda de 2019, a venda de 2020 e o % de crescimento de 2020 para 2019.
# Lembrando, para calcular o % de crescimento de um produto de um ano para o outro, podemos fazer: (vendas_produto2020/vendas_produto2019 - 1)
# A lógica da tupla é: (produto, vendas2019, vendas2020)
vendas_produtos = [('iphone', 558147, 951642), ('galaxy', 712350, 244295), ('ipad', 573823, 26964), ('tv', 405252, 787604), ('máquina de café', 718654, 867660), ('kindle', 531580, 78830), ('geladeira', 973139, 710331), ('adega', 892292, 646016), ('notebook dell', 422760, 694913), ('notebook hp', 154753, 539704), ('notebook asus', 887061, 324831), ('microsoft surface', 438508, 667179), ('webcam', 237467, 295633), ('caixa de som', 489705, 725316), ('microfone', 328311, 644622), ('câmera canon', 591120, 994303)]
for produto, vendas2019, vendas2020 in vendas_produtos:
if vendas2019 < vendas2020:
crescimento = (vendas2020/vendas2019) - 1
print(f'{produto.capitalize()} vendeu {vendas2019} em 2019 e {vendas2020} em 2020, com um crescimento de {crescimento:.2%}') |
"""Guarde en lista `naturales` los primeros 100 números naturales (desde el 1)
usando el bucle while
"""
S = 1
naturales = []
while S < 101:
naturales.append(S)
S += 1
print("\n\tLos primeros 100 números naturales desde 1 ==> ",naturales)
"""Guarde en `acumulado` una lista con el siguiente patrón:
['1','1 2','1 2 3','1 2 3 4','1 2 3 4 5',...,'...47 48 49 50']
Hasta el número 50.
"""
accumulated = []
for S in range(2, 52):
cadena=''
for P in range(1, S):
cadena = cadena + ' ' + str(P)
accumulated.append(cadena[1:])
print("\t\nAcumulado de una lista con el siguiente patrón '1', '1 2', '1 2 3', '1 2 3 4', '1 2 3 4 5', '1 2 3 4 5 6' Hasta [50] : \n",accumulated)
"""Guarde en `suma100` el entero de la suma de todos los números entre 1 y 100:
"""
suma100 = 0
for S in naturales:
suma100 = S +suma100
print("\nLa suma Total de todos los números entre 1 y 100 es : " ,suma100)
"""Guarde en `tabla100` un string con los primeros 10 múltiplos del número 134,
separados por coma, así:
'134,268,...'
"""
table100 = ''
for i in range(1, 11):
table100 = table100 + ',' + str( i * 134)
table100 = str(table100[1:])
print("\n\tLos 10 Primeros Multiplos de 134\n\t" ,table100)
"""Guardar en `multiplos3` la cantidad de números que son múltiplos de 3 y
menores a 300 en la lista `lista1` que se define a continuación (la lista
está ordenada).
"""
lista1 = [12, 15, 20, 27, 32, 39, 42, 48, 55, 66, 75, 82, 89, 91, 93, 105, 123, 132, 150, 180, 201, 203, 231, 250, 260, 267, 300, 304, 310, 312, 321, 326]
Base = [S for S in lista1 if S % 3 == 0 and S < 300]
multiples3 = len(Base)
print("\n\tCantidad de números que son múltiplos de 3 y menores a 300",multiples3)
"""Guardar en `regresivo50` una lista con la cuenta regresiva desde el número
50 hasta el 1, así:
[
'50 49 48 47...',
'49 48 47 46...',
...
'5 4 3 2 1',
'4 3 2 1',
'3 2 1',
'2 1',
'1'
]
"""
regressive = []
for S in range(50, 0, -1):
cadena=''
for P in range(S, 0, -1):
cadena = cadena + ' ' + str(P)
regressive.append(cadena[1:])
print("\n\tLista de cuenta regresiva desde el número 50 hasta el 1",regressive)
"""Invierta la siguiente lista usando el bucle for y guarde el resultado en
`invertido` (sin hacer uso de la función `reversed` ni del método `reverse`)
"""
lista2 = list(range(1, 70, 5))
invested = []
for a in range(71, 0, -5):
invested.append(a)
invested = invested[1:]
print(invested)
"""Guardar en `primos` una lista con todos los números primos desde el 37 al 300
Nota: Un número primo es un número entero que no se puede calcular multiplicando
otros números enteros.
"""
cousins = []
Inicio = 37
while Inicio <= 300:
cont =1
x = 0
while cont <= Inicio:
if Inicio % cont ==0:
x = x+1
cont = cont + 1
if x == 2:
cousins.append(Inicio)
Inicio = Inicio + 1
print("\n\tLista de todos los números primos desde el 37 al 300 ",cousins)
"""Guardar en `fibonacci` una lista con los primeros 60 términos de la serie de
Fibonacci.
Nota: En la serie de Fibonacci, los 2 primeros términos son 0 y 1, y a partir
del segundo cada uno se calcula sumando los dos anteriores términos de la serie.
[0, 1, 1, 2, 3, 5, 8, ...]
"""
fibonacci= [0,1]
for i in range(2, 60):
fibonacci.append(fibonacci[-1] + fibonacci[-2])
print("\n\tLista con los primeros 60 términos de la secuencia de Fibonacci ",fibonacci)
"""Guardar en `factorial` el factorial de 30
El factorial (símbolo:!) Significa multiplicar todos los números enteros desde
el 1 hasta el número elegido.
Por ejemplo, el factorial de 5 se calcula así:
5! = 5 × 4 × 3 × 2 × 1 = 120
"""
factorial = 1
for i in range(1, 31):
factorial = factorial * i
print("\n\tel factorial de Numero [ 30 ] = ",factorial)
"""Guarde en lista `pares` los elementos de la siguiente lista que esten
presentes en posiciones pares, pero solo hasta la posición 80.
"""
lista3 = [941, 149, 672, 208, 99, 562, 749, 947, 251, 750, 889, 596, 836, 742, 512, 19, 674, 142, 272, 773, 859, 598, 898, 930, 119, 107, 798, 447, 348, 402, 33, 678, 460, 144, 168, 290, 929, 254, 233, 563, 48, 249, 890, 871, 484, 265, 831, 694, 366, 499, 271, 123, 870, 986, 449, 894, 347, 346, 519, 969, 242, 57, 985, 250, 490, 93, 999, 373, 355, 466, 416, 937, 214, 707, 834, 126, 698, 268, 217, 406, 334, 285, 429, 130, 393, 396, 936, 572, 688, 765, 404, 970, 159, 98, 545, 412, 629, 361, 70, 602]
pairs = []
for S in range(0, 81):
if S % 2 == 0:
pairs.append(lista3[S])
print("\n\tlista que esten presentes en posiciones pares, pero solo hasta la posición 80",pairs)
"""Guarde en lista `cubos` el cubo (potencia elevada a la 3) de los números del 1 al 100.
"""
cubes = []
for S in range(1, 101):
cubes.append(S ** 3)
print("\n\tel cubo (potencia elevada a la 3) de los números del 1 al 100",cubes)
"""Encuentre la suma de la serie 2 +22 + 222 + 2222 + .. hasta sumar 10 términos y guardar resultado en variable `suma_2s`
"""
Inicio = 0
for S in range(0, 11):
P = 10 ** S * (10 - S) * 2
Inicio = P + Inicio
suma_2s = Inicio
print("\n\tSuma de la serie 2 + 22 + 222 + 2222 + .. hasta sumar 10 términos = ",suma_2s)
"""Guardar en un string llamado `patron` el siguiente patrón llegando a una cantidad máxima de asteriscos de 30.
*
**
***
****
*****
******
*******
********
*********
********
*******
******
*****
****
***
**
*
"""
A = '*\n'
B = '******************************\n'
C = '*'
for S in range(2, 30):
C = '*'
C = C * S
A = A + C + '\n'
print(A)
for S in range(29, 0, -1):
C = '*'
C = C * S
B = B + C + '\n'
print(B)
pattern = A + B
pattern = pattern[:-1]
print("\n\tpatrón llegando a una cantidad máxima de asteriscos de 30 ", pattern)
End = '100%'
print("Casi Que No ....!!!!: \"¿Acabo?\"")
print('En Verdad Termine: \'¡ Por Fin !\'')
print("\n\tTerminamos" ,End)
|
#Given the year number. You need to check if this year is a leap year. If it is, print LEAP, otherwise print COMMON.
year = int(input("year=?"))
if year % 4 == 0 and year % 100 != 0:
print("It is a leap year!")
elif year % 400 == 0:
print("it is a leap year!")
else:
print("It is not a leap year")
|
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 7 09:39:00 2020
@author: Andrea Gerardo Russo, Biomedical Engineer
PhD candidate in Neuroscience
University of Salerno, Fisciano, Italy
"""
|
def two_fer_va(name=None):
if name is None:
name = 'you'
return f'One for {name}, one for me.'
def two_fer_vb(name=None):
return f'One for {"you" if name is None else name}, one for me.'
def two_fer_vc(name=None):
return f'One for {name or "you"}, one for me.'
def two_fer_vd(name='you'):
return f'One for {name}, one for me.'
two_fer = two_fer_vd |
'''
You can think of a Bernoulli trial as a flip of a possibly biased coin. Specifically, each coin flip has a probability p of landing heads (success) and probability 1−p of landing tails (failure). In this exercise, you will write a function to perform n Bernoulli trials, perform_bernoulli_trials(n, p), which returns the number of successes out of n Bernoulli trials, each of which has probability p of success. To perform each Bernoulli trial, use the np.random.random() function, which returns a random number between zero and one.
'''
def perform_bernoulli_trials(n, p):
"""Perform n Bernoulli trials with success probability p
and return number of successes."""
# Initialize number of successes: n_success
n_success = 0
# Perform trials
for i in range(n):
# Choose random number between zero and one: random_number
random_number = np.random.random()
# If less than p, it's a success so add one to n_success
if random_number < p:
n_success += 1
return n_success
|
# Find the Ascii value of user input string.
# define ASCII_value() function with user-input string as argument.
def ASCII_value(charInput):
print("\nASCII value :", end = ' ')
#Getting each character of the user input string
for i in range(len(charInput)):
#Finding the ASCII value of each character using the ord() function and converting it to the string and printing it,
print(str(ord(charInput[i])),end=' ')
if __name__ == "__main__":
userInput = input("\nENTER ANY CHARACTER : ")
ASCII_value(userInput) |
"""
Support for local Luftdaten sensors.
Copyright (c) 2019 Mario Villavecchia
Licensed under MIT. All rights reserved.
https://github.com/lichtteil/local_luftdaten/
"""
|
n,k=map(int,input().split())
for i in range(1,10):
rem=(n*i)%10
if(rem==0 or rem==k):
print(i)
break
|
student = {
"name": "Mark",
"student_id": 15163,
"feedback": None
}
student["last_name"] = "Kowalski"
try:
last_name = student["last_name"]
numbered_last_name = 3 + last_name
except KeyError:
print
except TypeError as error:
print("I can't add these two together!")
print(error)
print("This code executes!") |
# use your username and password
USERNAME = ''
PASSWORD = ''
# URL = 'http://192.168.168.6/sess/Start.aspx'
URL = 'http://pershiess.fasau.ac.ir/sess/Start.aspx'
# # Changing user agent to trick websites (for headless use)
# # with phantomjs (for headless use)
# DCAP = dict("")
# DCAP["phantomjs.page.settings.userAgent"] = (
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/53 "
# "(KHTML, like Gecko) Chrome/15.0.87")
# PATH_TO_PHANTOMJS = ''
|
# (C) 2020 Tim Churchard
# Settings
DEFAULT_DEPTH = 10
# Example xpubs
EXAMPLE_XPUBS = (
'xpub6CPkcptFb3VQudKr4FytZfY1GumgV29pUjBqYyx2GPpX5qirZULBg5U7ynEFHriZU5LXvdoMGQWPMK8LBAeR35f32FQNEAZHG8mNsS3oFwJ',
'ypub6X9tfM9t5GxhZwZWdAepwURh3X6JYfwbi8JtpCQB1eQGbjzyXpagoqZRxsjowgzqUhdVpoZwyrEMXUxZvYxYxuYNSyrvpKVZNjjR8YfB4Gd',
'Ltub2YvKWEvSpbSoNtSCwQULZLs9Ct7Q7yDoQ21mkUkCLtFuKAj9DfBCkRiC6mpp8hGjmAY16soeDgEdHmRa2UQRpnnLXdQk4oTfNVXGtTVtsn7',
'dgub8rEUCjTRge3oCcY2joYqnG7UwZsrtcxKVat1KYct3Kkhcjys5XBfhGWsZ2xkuB4qbgSDxR21puUtDyrkJqC3EuqrvNM7C48jE3i4xnfEsE3',
'drkpRyUaAg9YzaY3GnRLKiTGydm5rhbsu6iWhr89QMoV8hsPuA91AHAkm1piRgmN34bkJqriScvSMVeuNSbHDSW2X7fiYxfuHfztPCV14LX2W3V'
)
# Coin symbols
BTC = 'BTC'
LTC = 'LTC'
DOGE = 'DOGE'
DASH = 'DASH'
# Coin names
COIN_NAMES = {
BTC: 'Bitcoin',
LTC: 'Litecoin',
DOGE: 'Dogecoin',
DASH: 'Dash'
}
# Supported coins by symbol
SUPPORTED_COINS = (BTC, LTC, DOGE, DASH)
# Fiats
USD = 'usd'
GBP = 'gbp'
EUR = 'eur'
DEFAULT_FIAT = GBP
# Supported fiat currencies (coingecko)
SUPPORTED_FIATS = (USD, GBP, EUR)
FIAT_SYMBOLS = {
USD: '$',
GBP: '£',
EUR: '€'
}
|
#!/usr/bin/env python
# encoding:utf-8
# file: homework.py
# 自己实现python自带的map、zip和filter函数
# 还没学到 yield语法不熟 先简单实现
# 实现map函数
def my_map(*args):
"""文档字符串位置
"""
if len(args) < 2:
# 先不用异常的方式处理 只是打印
print('map()至少需要两个参数')
else:
# 判断是否为可迭代对象 先不处理
fnc_nme = args[0]
new_tpl = args[1:]
min_len = len(min(new_tpl, key=len))
for idx in range(min_len):
# yield后的代码会继续执行 yield只要存在函数就变成生成器
yield fnc_nme(*[itr[idx] for itr in new_tpl])
# 实现zip函数
def my_zip(*args):
if not len(args):
return tuple()
min_len = len(min(args, key=len))
for idx in range(min_len):
yield tuple(itr[idx] for itr in args)
# 实现filter函数
def my_filter(func, itr):
if func is not None:
for it in itr:
if func(it):
yield it
else:
for it in itr:
if it:
yield it
# 测试函数 加法
def func1(x, y):
return x + y
# 测试函数 平方
def func2(x):
return x ** 2
# 测试函数 取大于100的数
def func3(x):
return True if x > 100 else False
if __name__ == '__main__':
l1 = [3, 2, 3]
l2 = [6, 5]
print(list(my_map(func1, l1, l2)))
print(list(my_zip([1, 2, 3], [4, 5], 'abcdefg')))
print(list(my_filter(func3, [0, 201, 1, 2, 3, 100, 101])))
print(list(my_zip()))
print(list(my_filter(None, [0, 201, 1, 2, 3, 100, 101])))
print('-------- 对照组 --------')
print(list(map(func1, l1, l2)))
print(list(zip([1, 2, 3], [4, 5], 'abcdefg')))
print(list(filter(func3, [0, 201, 1, 2, 3, 100, 101])))
print(list(zip()))
print(list(filter(None, [0, 201, 1, 2, 3, 100, 101])))
|
def generate_permutations(L,i=None,n=None):
if i==None:
i=0
if n==None:
n=len(L)-1
if i==n:
print(L)
else:
for j in range(i,n+1):
L[i],L[j]=L[j],L[i]
generate_permutations(L,i=i+1,n=n)
L[i],L[j]=L[j],L[i]
L=["a","b","c","d","e"]
generate_permutations(L)
|
def int_to_bit(n, nbits, lsb0=True):
'''
convert the integer into a bit string of the number of bits specified.
e.g. if n is in bit is "0111",
if nbits is 5 and lsb0 is True, then it's gonna be "00111".
if lsb0 is False, then its' gonna be "11100".
if nbits is less than n in bit, omit the bits. overflow in other word.
e.g. if n is in bit is "11110000" and nbits is 6, then it gonna be "110000".
'''
if lsb0:
return zfill(bin(n)[2:], nbits)[-nbits:]
else:
return zfill(bin(n)[2:], nbits)[-nbits:][::-1]
def rzfill(s, w):
'''
pycom doesn't support str.zfill()
'''
return s + "".join(["0" for i in range(w-len(s))])
def zfill(s, w):
'''
pycom doesn't support str.zfill()
'''
return "".join(["0" for i in range(w-len(s))]) + s
def bit_set(ba, pos, val=None, extend=False):
'''
set a bit or a series of bits at the position in the bytearray.
the position of the most left bit is 0.
ba: bytearray
pos: bit position from the head
i.e.
position: 0 1 2 3 4 5 6 7 8 9 10 11 12 13
bit: 0 0 0 0 0 0 0 0 0 0 0 0 0 0
bytearray: --------- 0 ----------|--------- 1 -----
val:
if the type of val is None, it sets a bit on at the position in the ba.
if the type is bool, then at the position,
it sets a bit off if the val is False,
otherwise sets a bit on.
if the type is int,
or if the type is str (it must consist of "0" and "1"),
this sets bits from the position.
e.g. the following operation is gonna be "0000 0011 1100 0000"
ba = bytearray(2)
bit_set(ba, 6, "1111")
bit_set(ba, 6, 15)
extend:
if True, it extends the size of ba when the position is over the size.
'''
p0 = pos >> 3
p1 = pos % 8
if extend == True:
# extend the buffer if needed.
bit_len = 1
if isinstance(val, str):
bit_len = len(val)
ext_len = (((pos+bit_len+7)&(~7))>>3) - len(ba)
if ext_len > 0:
# micropython is required to convert the list into the bytearray.
# CPython doesn't require so, and converting doesn't affect.
#ba.extend([0 for i in range(ext_len)])
ba.extend(bytearray([0 for i in range(ext_len)]))
elif not (p0 < len(ba)):
# just return if the buffer size is shoter than the position
return ba
#
if val is None:
b = zfill(bin(ba[p0])[2:], 8)
ba[p0] = int(b[:p1] + "1" + b[p1+1:], 2)
return ba
elif isinstance(val, bool):
b = zfill(bin(ba[p0])[2:], 8)
ba[p0] = int(b[:p1] + ("1" if val else "0") + b[p1+1:], 2)
return ba
elif isinstance(val, int):
return bit_set(ba, pos, bin(val)[2:])
elif isinstance(val, str):
guard_pos = len(ba) << 3
for i in val:
ba = bit_set(ba, pos, (False if i == "0" else True))
pos += 1
if guard_pos == pos:
# don't raise an error anyway.
break
return ba
else:
raise ValueError("invalid val, not allow %s" % (type(val)))
def bit_get(ba, pos, val=None, ret_type=bin):
'''
get a bit at the position in the bytearray.
pos: see bit_get().
val: if the type of val is None, it gets a value of the bit
from the position, and returns either "1" or "0" in string.
if the type is a number, it gets a series of the value of the bits
from the position toward right and return the bit string of the value.
if the position is greater than the length of ba, it returns None.
e.g. if ba in bit is "00001111", bit_get(ba, 2, 4) is gonna be "0011".
ret_type: specify the type of return value.
either 'int', 'bin', 'bytes', 'hex' is available.
'bin' means to return a binary string.
if the type is bytes, the bits are aligned to the left.
i.e. "0011" is gonna be 0x30.
default is bin.
'''
p0 = pos >> 3
p1 = pos % 8
if val is None:
if p0 < len(ba):
b = zfill(bin(ba[p0])[2:], 8)
ret = ("1" if b[p1] == "1" else "0")
else:
return None
elif isinstance(val, int):
# if the bit length is zero, it returns None.
if val == 0:
return None
# construct the bit string.
ret = ""
for i in range(val):
r = bit_get(ba, pos+i)
if r != None:
ret += r
else:
raise ValueError("invalid val, not allow %s" % (type(val)))
#
if ret_type == int:
return int(ret, 2)
elif ret_type == hex:
return hex(int(ret, 2))[2:]
elif ret_type == bytes:
return bit_set(bytearray(1), 0, ret, extend=True)
elif ret_type == bin:
return ret
else:
raise ValueError("invalid ret_type, not allow %s" % (ret_type))
def bit_find(n, bit_len=0, val=None):
'''
find a position where a bit is on bit in n.
return the number of the position where the bit set.
and return the number that the bit found in n is turned off.
however, note that the significant bits is taken from the most right in n.
the position of the most left bit is 0.
if there is no bit set, return None.
XXX not yet supported. but, if val is a bit string,
it try to find the series of bits in n.
'''
if not bit_len:
bit_len = len(bin(n)[2:])
for i in range(bit_len):
x = 2**(bit_len-i-1)
if n & x:
return i, n-x
return None, n
def bit_count(n, bit_len, zero=False):
'''
count the number of bits in the bit string.
if zero is False (default), it counts a bit which is "1".
if zero is True, it counts a bit which is "0".
bit_len indicates that the n has the bit width at minimum.
'''
b = "0" if zero else "1"
bs = int_to_bit(n, bit_len)
nb = 0
for i in range(len(bs)):
if bs[i] == b:
nb += 1
return nb
|
#py_binary_operations.py
base_10_number = 12
fmt = "{:<20} {:<30}"
print("Base 2 operations: left shift increases by powers of 2")
for i in range(3):
print(fmt.format("mybin_1000: ", bin(base_10_number << i)))
print()
print("Base 2 operations: right shift reduces by powers of 2")
for i in range(3):
print(fmt.format("mybin_1000: ", bin(base_10_number >> i)))
#This is like the base 10 operation of increasing the power of 10
print()
base = 12
print("Base 10 operations:")
fmt = "base number raised to 10**:{:<20} {}"
for i in range(-3,3):
print(fmt.format(i, round(base * 10**i, 3)))
|
def linhas():
print('='*30)
print('CADASTRE UMA PESSOA')
tot18 = masc = fem20 = 0
while True:
idade = int(input('Idade: '))
sexo = ' '
while sexo not in 'MF':
sexo = str(input('Sexo: [M/F]')).upper()[0].strip()
if idade >= 18:
tot18 += 1
if sexo == 'M':
masc += 1
if sexo == 'F' and idade < 20:
fem20 += 1
resp= ' '
while resp not in 'SN':
resp = str(input('Quer continuar? [S/N]')).upper()[0].strip()
linhas()
if resp == 'N':
break
print(f'O total de pessoas maiores de 18 anos é {tot18}')
print(f'O total de pessoas do sexo masculino é {masc}')
print(f'O total de mulheres menores de 20 anos é {fem20}')
linhas()
print('Acabou') |
#Crie um programa que tenha uma tupla com várias palavras (não usar acentos). Depois disso, você deve mostrar, para cada palavra, quais são as suas vogais.
p = ('cavalo', 'mamadeira', 'violino')
for c in p:
print(f'\nPara a palavra {c.upper()} temos ', end='')
for l in c:
if l in 'aeiou':
print(l.lower(), end= ' ') |
"""
Escriba una clase Flowers que tiene tres propiedades, de tipo int, str
y float, que respectivamente representa el nombre de la flor, su
cantidad de pétalos y su precio. La clase debe incluir un método
constructor que inicialice cada variable con su valor apropiado, y la
clase debe incluir métodos para establecer el valor de cada propiedad y
recuperar el valor de cada propiedad.
"""
class Flowers:
def __init__(self, name: str, petals: int, price: float) -> None:
self._name = name
self._petals = petals
self._price = price
@property
def name(self) -> str:
return self._name
@name.setter
def name(self, name) -> None:
self._name = name
@property
def petals(self) -> int:
return self._petals
@petals.setter
def petals(self, petals) -> None:
self._petals = petals
@property
def price(self) -> float:
return self._price
@price.setter
def price(self, price) -> None:
self._price = price |
{
"targets": [
# the only purpose of this target is to share settings between celt
# and silk targets.
{
"target_name": "opus_common_settings",
"type": "none",
"direct_dependent_settings" : {
"include_dirs": [
"1.1/opus-1.1/include"
],
"defines" : [
"USE_ALLOCA=", # one of 3 mem alloc variants (alt VAR_ARRAYS)
# does this choice have to match between celt & silk & opus?
# Probably.
"OPUS_BUILD"
]
}
},
{
"target_name": "celt",
"type": "static_library",
"include_dirs": [
"1.1/opus-1.1/celt"
],
"sources": [
"1.1/opus-1.1/celt/*.c"
],
"sources!": [
"1.1/opus-1.1/celt/*_demo.c"
],
"direct_dependent_settings": {
"include_dirs": [
"1.1/opus-1.1/include",
"1.1/opus-1.1/celt"
]
},
"dependencies": [ "opus_common_settings" ]
},
{
"target_name": "silk",
"type": "static_library",
"include_dirs": [
"1.1/opus-1.1/silk",
"1.1/opus-1.1/silk/float",
"1.1/opus-1.1/celt"
],
"sources": [
"1.1/opus-1.1/silk/*.c",
# for a no-FPU platform you'd prefer silk/fixed?
"1.1/opus-1.1/silk/float/*.c"
],
"direct_dependent_settings": {
"include_dirs": [
"1.1/opus-1.1/include",
"1.1/opus-1.1/silk"
]
},
"dependencies": [ "opus_common_settings" ]
},
{
"target_name": "opus",
"type": "static_library",
"sources": [
"1.1/opus-1.1/src/*.c"
],
"sources!": [
"1.1/opus-1.1/src/opus_compare.c",
"1.1/opus-1.1/src/*_demo.c"
],
"direct_dependent_settings": {
"include_dirs": [
"1.1/opus-1.1/include"
]
},
"dependencies" : [
"opus_common_settings",
"silk",
"celt"
]
},
# opus has multiple tests, the encode one here is pretty slow (runs
# several minutes)
{
"target_name" : "test_opus_encode",
"type" : "executable",
#disabling test for now due to being painfully slow
#"test": { "cwd": "1.1/opus-1.1/tests" },
"sources" : [
"1.1/opus-1.1/tests/test_opus_encode.c"
],
"include_dirs" : [
"1.1/opus-1.1/celt" # or should this be inherited from "celt"?
],
"dependencies": [
"opus"
],
# this disables building the example on iOS
"conditions": [
["OS=='iOS'",
{
"type": "none"
}
]
]
},
# this test runs pretty quickly
{
"target_name" : "test_opus_api",
"type" : "executable",
"test": {
"cwd": "1.1/opus-1.1/tests"
},
"sources" : [
"1.1/opus-1.1/tests/test_opus_api.c"
],
"include_dirs" : [
"1.1/opus-1.1/celt" # or should this be inherited from "celt"?
],
"dependencies": [
"opus"
],
# this disables building the example on iOS
"conditions": [
["OS=='iOS'",
{
"type": "none"
}
]
]
},
# test needs cmd line args, in fact it's not much of a test, it's
# doing a speexenc/speexdec equivalent, encoding pcm as opus and
# back. Format is 16bit PCM LittleEndian aka ffmpeg's s16le.
# Not much of a test, only verifies the codec won't crash/hang.
# Listen to output PCM to gauge codec quality 'manually'.
# Since the opus tar.gz doesn't include PCM files afaik we pretend
# some arbitrary file from this repo is s16le (terribly noisy :) PCM.
{
"target_name" : "opus_trivial_example",
"type" : "executable",
"test": {
"cwd": "1.1/opus-1.1",
"args": ["configure", "configure.pretend.s16le.pcm"]
},
"sources" : [
"1.1/opus-1.1/doc/trivial_example.c"
],
"dependencies": [
"opus"
],
# this disables building the example on iOS
"conditions": [
["OS=='iOS'",
{
"type": "none"
}
]
]
}
]
}
|
def max_point(strA, left, right):
if right < left:
return -1
mid = (left + right) // 2
if strA[mid] > strA[mid -1] and strA[mid] > strA[mid + 1]:
return strA[mid]
elif strA[mid] > strA[mid -1] and strA[mid] < strA[mid + 1]:
return max_point(strA, mid, right)
elif strA[mid] < strA[mid -1] and strA[mid] > strA[mid + 1]:
return max_point(strA, left, mid)
strA = [ 13, 31, 10, 15, 9, 11]
print(max_point(strA, 0, len(strA) - 1))
strA = [ 6, 14, 21, 7, 17]
print(max_point(strA, 0, len(strA) - 1))
strA = [ 11, 66, 41, 21 ]
print(max_point(strA, 0, len(strA) - 1)) |
palette = {
"none": "",
"text": "rgb(40, 40, 40)",
"comment": "rgb(130, 130, 130)",
"string": "rgb(30, 100, 0)",
"function": "rgb(50, 50, 200)",
"value": "rgb(200, 0, 0)",
"type": "rgb(0, 110, 120)",
"reserved": "rgb(80, 0, 0)",
"operator": "rgb(100, 100, 0)",
"call": "rgb(0, 50, 200)",
"bracket": "rgb(100, 100, 200)",
"number": "rgb(200, 50, 50)",
"field": "",
}
|
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:',
},
}
INSTALLED_APPS = [
'django.contrib.contenttypes',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.messages',
'django.contrib.sites',
'djangocms_cameraslider',
'cms',
'easy_thumbnails',
'filer',
'menus',
'sekizai',
'treebeard',
]
LANGUAGE_CODE = 'en'
MIDDLEWARE_CLASSES = []
SECRET_KEY = "cms999"
SITE_ID = 1
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'OPTIONS': {
'context_processors': [
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
'django.core.context_processors.i18n',
'django.core.context_processors.debug',
'django.core.context_processors.request',
'django.core.context_processors.media',
'django.core.context_processors.csrf',
'django.core.context_processors.tz',
'sekizai.context_processors.sekizai',
'django.core.context_processors.static',
'cms.context_processors.cms_settings',
],
'loaders': [
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
'django.template.loaders.eggs.Loader',
],
},
},
]
CMS_TEMPLATES = [
('djangocms_cameraslider/base.html', 'Default template'),
]
|
class Agent(object):
def train(self, batch):
raise NotImplementedError
def act(self, observation):
raise NotImplementedError
|
# WARNING: Do not edit by hand, this file was generated by Crank:
#
# https://github.com/gocardless/crank
#
class BankDetailsLookup(object):
"""A thin wrapper around a bank_details_lookup, providing easy access to its
attributes.
Example:
bank_details_lookup = client.bank_details_lookups.get()
bank_details_lookup.id
"""
def __init__(self, attributes, api_response):
self.attributes = attributes
self.api_response = api_response
@property
def available_debit_schemes(self):
return self.attributes.get('available_debit_schemes')
@property
def bank_name(self):
return self.attributes.get('bank_name')
@property
def bic(self):
return self.attributes.get('bic')
|
# this is a list of skills which are not one-lines
ACTIVE_SKILLS = [
"book_skill",
"christmas_new_year_skill",
"dff_coronavirus_skill",
"dummy_skill_dialog",
"emotion_skill",
"game_cooperative_skill",
"meta_script_skill",
"dff_movie_skill",
"news_api_skill",
"oscar_skill",
"personal_info_skill",
"reddit_ner_skill",
"short_story_skill",
"superbowl_skill",
"valentines_dat_skill",
"weather_skill",
"wikidata_dial_skill",
"comet_dialog_skill",
"dff_animals_skill",
"dff_food_skill",
"dff_music_skill",
"dff_sport_skill",
"dff_travel_skill",
"dff_bot_persona_skill",
"dff_gaming_skill",
"dff_science_skill",
"dff_gossip_skill",
"small_talk_skill",
"dff_wiki_skill, dff_art_skill",
]
ALMOST_ACTIVE_SKILLS = [
"friendship_skill",
"dff_friendship_skill",
]
UNPREDICTABLE_SKILLS = [
"convert_reddit",
"knowledge_grounding_skill",
]
CAN_NOT_BE_DISLIKED_SKILLS = ["meta_script_skill", "personal_info_skill"]
NOT_ADD_PROMPT_SKILLS = ["alexa_handler", "intent_responder", "misheard_asr", "program_y_dangerous"]
COMPLETELY_CHANGING_THE_SUBJECT_PHRASES = [
"Completely changing the subject,",
"This has nothing to do with what we were talking about, but",
"Not to change the subject, but",
"Changing gears a little bit,",
"Changing the topic slightly,",
"Totally unrelated,",
]
CHANGE_TOPIC_SUBJECT = [
"Speaking of SUBJECT,",
"Talking about SUBJECT,",
"Let's talk about SUBJECT,",
"I feel we need to discuss SUBJECT,",
"I wanted to talk with you about SUBJECT,",
"I wanted to tell you about SUBJECT,",
]
BY_THE_WAY = [
"By the way,",
"Anyway,",
"Oh, before I forget,",
"I wanted to mention that,",
]
|
in_min=136818
in_max=685979
def includes_duplicates(num):
str_num = str(num)
for n in range(len(str_num) - 1):
if str_num[n] == str_num[n + 1]:
return True
return False
def monotonic_increase(num):
str_num = str(num)
for n in range(len(str_num) - 1):
if str_num[n + 1] < str_num[n]:
return False
return True
def includes_exactly_a_pair(num):
str_num = str(num)
digits = []
for i in range(10):
tmp = str_num.count(str(i))
if tmp > 1:
digits.append(tmp)
for i in range(len(digits)):
if digits[i] == 2:
return True
return False
#print(len(filter(includes_duplicates, filter(monotonic_increase, range(in_min, in_max)))))
print(len(filter(includes_exactly_a_pair,filter(monotonic_increase, range(in_min, in_max))))) |
# 1. 변수 선언 및 사용
# name 변수에 "홍길동" 문자열 대입
name = "홍길동"
# age 변수에 정수 10 대입
age = 10
# height 변수에 150.24 실수 대입
height = 150.24
# family 변수에 리스트 저장
family = ["엄마", "아빠", "누나"]
# score 변수에 딕셔너리 저장
score = {
"국어": 98,
"수학": 100,
"영어": 82
}
# hands 변수에 튜플 저장
# 튜플은 값을 변경할 수 없습니다.
hands = (3, 7)
# ========== 출력하기 ========== #
# 쉼표로 구분하여 문자열, 변수 등을 출력할 수 있습니다
print("제 이름은", name, "이고, 나이는", age, "입니다.")
# 문자열의 format 메소드를 사용하여 간편하게 포맷이나 위치를 지정할 수 있습니다.
print("제 키는 {}cm 입니다.".format(height))
# end="" 는 print 후 어떤 글자를 출력할지 정할 수 있습니다.
# 기본값으로는 \n(개행) 문자입니다
print("그리고 저의 가족 구성원은 ", end="")
# 리스트나 튜플은 반복가능한 객체이므로 for문에서 사용할 수 있습니다.
for f in family:
print(f, end=" ")
# len() 함수는 해당 객체의 길이를 반환합니다
print("이고 총 {}명 입니다.".format(len(family)))
# 딕셔너리.keys() 메소드는 해당 딕셔너리의 키 리스트를 반환합니다.
print("저의 이번 시험 성적은,")
for k in score.keys():
print("{}점수: {}점".format(k, score[k]))
print("입니다.")
# 튜플이나 리스트는 인덱스를 통해 접근하여 사용할 수 있습니다.
print("마지막으로 저는 왼손과 오른손을 {}:{} 만큼 씁니다.".format(hands[0], hands[1]))
|
class Solution:
def findMaximumXOR(self, nums: List[int]) -> int:
mx = 0
for i in reversed(range(31)):
prefixes = set(num >> i for num in nums)
mx <<= 1
cand = mx + 1
for prefix in prefixes:
if (cand ^ prefix) in prefixes:
mx = cand
break
return mx
|
class TreeNode:
def __init__(self, **kwargs):
self.children = dict()
def get(self, key):
return self.children.get(key, None)
def get_children(self, sort=False):
if sort:
return sorted(self.children.values())
return self.children.values()
def add(self, key, node=None, **kwargs):
if key not in self.children:
if node is None:
node = self.__class__(**kwargs)
self.children[key] = node
return self.children[key]
def set(self, key, val, **kwargs):
self.children[key] = val
return self.children[key]
class OrderedTreeNode:
def __init__(self, **kwargs):
self.children = list()
def get(self, index):
return self.children[index]
def get_children(self, sort=False):
if sort:
return sorted(self.children)
return self.children
def add(self, node=None, **kwargs):
if node is None:
node = self.__class__(**kwargs)
self.children.append(node)
return self.children[-1]
def set(self, index, val, **kwargs):
self.children[index] = val
return self.children[index]
class Tree:
def __init__(self, root=None, node_class=TreeNode, **kwargs):
if root is None:
root = node_class(**kwargs)
self.root = root
self.node_class = node_class
def get_children(self, node, sort=False):
return node.get_children(sort=sort)
def dfs(self, action=None, pre_action=None, post_action=None, sort=False):
assert(action is None or (pre_action is None and post_action is None))
if action is not None and pre_action is None and post_action is None:
pre_action = post_action = action
def visit(node, depth=0, parent=None):
if pre_action:
if pre_action(node, depth, parent):
return
for n in self.get_children(node, sort=sort):
visit(n, depth+1, node)
if post_action:
if post_action(node, depth, parent):
return
visit(self.root)
|
#add your reddit credentials here
username = "" #username
password = "" #password
client_id = "" #client id
client_secret = "" #client password
subreddits = "memes+dankmemes+me_irl" #subreddits you want to get memes from, it is important not to include spaces b/w '+'
min_upvotes = 150 #minimum number of upvotes
debug = True #change this to false when in production
|
# -*- coding: utf-8 -*-
def parseGroupWriting_3(lexicalItem):
#add indicator beginning (#) / end ($) of word
lexicalItem = "#." + lexicalItem + ".$"
lexicalItem = lexicalItem.replace("#.#", "#.").replace("$.$", ".$")
groupsRaw = lexicalItem.split(".")
itemCleaned = ""
# ] = no vowel after group CVC
# 0 = single consonant group
for group in groupsRaw:
newGroup = group
if len(newGroup) == 1 and "#" not in newGroup and "$" not in newGroup:
newGroup = newGroup #+ "A"
if len(newGroup) == 2 and "ʸ" in newGroup and "r" not in newGroup and "d" not in newGroup:
newGroup = newGroup #+ "A"
if newGroup == "rʸ":
newGroup = "rʸ-"
if len(newGroup) == 2 and not ("A" in newGroup) and not ("U" in newGroup) and not ("ʸ" in newGroup) and not ("-" in newGroup):
newGroup = newGroup[0] + "A." + newGroup[1] + "]"
if len(newGroup) > 2 and not ("ʸ" in newGroup):
if len(newGroup) == 3 and newGroup[1] == "A":
newGroup = newGroup[0] + newGroup[1] + "." + newGroup[2] + "]"
if len(newGroup) == 3 and newGroup[2] == "U":
newGroup = "[" + newGroup[0] + "U." + newGroup[1] + "]"
if len(newGroup) == 4 and newGroup[3] == "U":
newGroup = "[" + newGroup[0] + "U." + newGroup[2] + "]"
itemCleaned = itemCleaned + newGroup + "."
itemCleaned = itemCleaned.replace(".$.", ".$")
groups = itemCleaned.split(".")
parsedLexicalItem = []
# group
# cons
# vowelClass
# vowelHistorical
# group or single consonant (len>1 / len1)
# presence of y or not (+ʸ / -ʸ)
# position of the vowel
transcription = []
consGroup = []
consGroupIPA = []
vocGroup = []
vocGroupEdit = []
vocGroupRec = []
IDVowel = []
IDVowel_nr = 0
for group in groups:
if group == "#":
transcription.append("#")
consGroup.append("#")
consGroupIPA.append("#")
vocGroup.append("#")
vocGroupEdit.append("#")
vocGroupRec.append("#")
IDVowel.append("#")
elif group == "$":
if consGroup[-1] == "ʳ":
consGroup[-1] = "r"
vocGroup[-1] = "⤫"
vocGroupEdit[-1] = "⤫"
vocGroupRec[-1] = "|⤫"
IDVowel[-1] = "-"
transcription.append("$")
consGroup.append("$")
consGroupIPA.append("$")
vocGroup.append("$")
vocGroupEdit.append("$")
vocGroupRec.append("$")
IDVowel.append("$")
elif ("kA" in group):
#print("kA: " + group)
transcription.append(group)
consGroup.append(group.replace("A", ""))
consGroupIPA.append(group.replace("A", ""))
vocGroup.append("Ɔ")
vocGroupEdit.append("Ɔ")
vocGroupRec.append("|o|ō|ū")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
elif ("kU" in group):
#print("kU: " + group)
transcription.append(group)
consGroup.append(group.replace("U", ""))
consGroupIPA.append(group.replace("U", ""))
vocGroup.append("Ɔ")
vocGroupEdit.append("Ɔ")
vocGroupRec.append("|o|ō|ū")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
elif ("dʸ" in group):
#print("dʸ: " + group)
transcription.append(group)
consGroup.append(group.replace("ʸ", ""))
consGroupIPA.append(group.replace("ʸ", ""))
vocGroup.append("U")
vocGroupEdit.append("U")
vocGroupRec.append("|ū|ō|o")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
elif ("ḫʸ" in group):
#print("ḫʸ: " + group)
transcription.append(group)
consGroup.append(group.replace("ʸ", ""))
consGroupIPA.append(group.replace("ȟ", ""))
vocGroup.append("A")
vocGroupEdit.append("A")
vocGroupRec.append("|a|ā|e|ī|0")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
elif ("ʸr" in group):
#print("ʸr: " + group)
transcription.append(group)
consGroup.append("ʳ")
consGroupIPA.append("r")
vocGroup.append("-")
vocGroupEdit.append("-")
vocGroupRec.append("-")
if len(vocGroupRec) > 1:
vocGroupRec[-2] = vocGroupRec[-2].replace("|ā", "").replace("|ī", "").replace("|ū", "").replace("|ō",
"")
IDVowel.append("-")
elif ("n-" in group):
#print("n-: " + group)
transcription.append(group)
consGroup.append("ⁿ")
consGroupIPA.append("ⁿ")
vocGroup.append("ⁿ")
vocGroupEdit.append("ⁿ")
vocGroupRec.append("ⁿ")
elif "]" in group:
transcription.append(group)
consGroup.append(group.replace("]", ""))
consGroupIPA.append(group.replace("]", ""))
vocGroup.append("⤫")
vocGroupEdit.append("⤫]")
vocGroupRec.append("|⤫|]")
elif group == "":
transcription.append("")
consGroup.append("")
consGroupIPA.append("")
vocGroup.append("")
vocGroupEdit.append("")
vocGroupRec.append("")
IDVowel.append("")
else:
group = group.replace("ʸ", "A")
transcription.append(group)
consGroup.append(transcription[-1].replace("A","").replace("U",""))
consGroupIPA.append(transcription[-1].replace("A","").replace("U",""))
if "A" in group:
vocGroup.append("A")
vocGroupEdit.append("A")
vocGroupRec.append("|a|e|ī|0")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
else:
vocGroup.append("U")
vocGroupEdit.append("U")
vocGroupRec.append("|ū|ō|o")
IDVowel.append(IDVowel_nr)
IDVowel_nr = IDVowel_nr + 1
i = 0
while i < len(groups):
if "ⁿ" in consGroup[i]:
transcription[i+1] = "n-" + transcription[i+1]
consGroup[i+1] = "ⁿ" + consGroup[i+1]
consGroupIPA[i+1] = "ⁿ" + consGroupIPA[i+1]
consGroupIPA[i + 1] = consGroupIPA[i+1].replace("ⁿr", "l")
groups.pop(i)
transcription.pop(i)
consGroup.pop(i)
consGroupIPA.pop(i)
vocGroup.pop(i)
vocGroupEdit.pop(i)
vocGroupRec.pop(i)
IDVowel.pop(i)
i = i +1
for i in range(0, len(groups)):
if "[" in transcription[i]:
consGroup[i] = consGroup[i].replace("[", "")
consGroupIPA[i] = consGroupIPA[i].replace("[", "")
vocGroupRec[i] = "|[" + vocGroupRec[i]
vocGroupEdit[i] = "[" + vocGroupEdit[i]
for i in range(1, len(groups)):
if transcription[i-1] == transcription[i] and vocGroup[i] == "U":
vocGroupRec[i - 1] = "|[" + vocGroupRec[i - 1]
vocGroupRec[i] = "|]" + vocGroupRec[i]
vocGroupEdit[i - 1] = "[" + vocGroupEdit[i - 1]
vocGroupEdit[i] = vocGroupEdit[i] + "]"
if consGroup[i-1] == "ʳ":
vocGroup[i-1] = vocGroup[i]
vocGroupRec[i-1] = "|>" + vocGroupRec[i]
vocGroupRec[i] = "|<" + vocGroupRec[i]
vocGroupEdit[i - 1] = vocGroupEdit[i] + ">"
vocGroupEdit[i] = "<" + vocGroupEdit[i]
vocGroupRec[i - 1] = vocGroupRec[i - 1].replace("|ū", "").replace("|ā", "").replace("|ī", "").replace("|ō",
"")
IDVowel[i - 1] = IDVowel[i]
if "|⤫|]" in vocGroupRec[i]:
vocGroupRec[i - 1] = vocGroupRec[i - 1].replace("|ū", "").replace("|ā", "").replace("|ī", "").replace("|ō", "")
if not "|[" in vocGroupRec[1] and not vocGroupRec[1] == "":
vocGroupRec[1] = "|[" + vocGroupRec[1]
vocGroupEdit[1] = "[" + vocGroupEdit[1]
parsedLexicalItem = []
if len(transcription) == 3 and transcription[1] == "":
transcription[1] = "∅"
consGroup[1] = "∅"
consGroupIPA[1] = "∅"
vocGroup[1] = "∅"
vocGroupEdit[1] = "∅"
vocGroupRec[1] = "∅"
IDVowel[1] = "∅"
parsedLexicalItem.append(transcription)
parsedLexicalItem.append(consGroup)
parsedLexicalItem.append(consGroupIPA)
parsedLexicalItem.append(vocGroup)
parsedLexicalItem.append(vocGroupEdit)
parsedLexicalItem.append(vocGroupRec)
parsedLexicalItem.append(IDVowel)
parsedLexicalItem.insert(0, 3)
return(parsedLexicalItem) |
# 7. Perfect Number
# Write a function that receives an integer number and returns if this number is perfect or NOT.
# A perfect number is a positive integer that is equal to the sum of its proper positive divisors.
# That is the sum of its positive divisors excluding the number itself (also known as its aliquot sum).
# def check_if_number_perfect(num):
# aliquot_sum = 0
#
# for digit in range(1, num):
# if num % digit == 0:
# aliquot_sum += digit
#
# if aliquot_sum == num:
# return True
# return False
#
#
# number = int(input())
#
# result = check_if_number_perfect(number)
#
# if result:
# print('We have a perfect number!')
# else:
# print('It\'s not so perfect.')
def check_if_number_perfect(num):
proper_divisors = []
for digit in range(1, num):
if num % digit == 0:
proper_divisors.append(digit)
if sum(proper_divisors) == num:
return True
return False
number = int(input())
result = check_if_number_perfect(number)
if result:
print('We have a perfect number!')
else:
print('It\'s not so perfect.')
|
def conv_output_length(input_length, filter_size,
padding, stride, dilation=1):
if input_length is None:
return None
assert padding == "same"
output_length = input_length
return (output_length + stride - 1) // stride
def same_padding_length(input_length, filter_size, stride, dilation=1):
dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
output_length = (input_length + stride - 1) // stride
pad_length = max(0, (output_length - 1) * stride + dilated_filter_size - input_length)
return pad_length
def compute_output_shape(input_shape, filters, kernel_size, padding, strides, dilation):
space = input_shape[1:]
new_space = []
for i in range(len(space)):
new_dim = conv_output_length(
space[i],
self.kernel_size[i],
padding=padding,
stride=strides[i],
dilation=dilation[i])
new_space.append(new_dim)
return (filters,) + tuple(new_space)
def compute_same_padding2d(input_shape, kernel_size, strides, dilation):
space = input_shape[2:]
assert len(space) == 2, "{}".format(space)
new_space = []
new_input = []
for i in range(len(space)):
pad_length = same_padding_length(
space[i],
kernel_size[i],
stride=strides[i],
dilation=dilation[i])
new_space.append(pad_length)
new_input.append(pad_length % 2)
return tuple(new_space), tuple(new_input)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.cur_val = 0.
self.avg = 0.
self.sum = 0.
self.count = 0.
def update(self, cur_val):
self.cur_val = cur_val
self.sum += cur_val
self.count += 1
self.avg = self.sum / self.count
|
# This is sample in Python language for
# MCS - Most Common (Even/Uneven) Sum
# for given list, size and mode(even/uneven)
# created by https://github.com/trolit
# Press Shift+F10 to execute it.
def get_biggest_sum_depending_on_mode(list_numbers, var_size, var_mode):
# Use a breakpoint in the code line below to debug your script.
list_numbers_length = len(list_numbers)
if (var_size > list_numbers_length) or (var_size < 2) or (var_mode != 'even' and var_mode != 'uneven'):
print('Requirements not met.')
print()
return
list_numbers.sort(reverse=True)
var_is_sum_found = False
var_starting_pos = 0
while 1:
list_chosen_numbers = []
var_index = var_starting_pos
var_current_sum = 0
var_number_counter = 0
while (var_number_counter < var_size - 1) and (var_index < list_numbers_length):
var_current_sum += list_numbers[var_index]
list_chosen_numbers.append(list_numbers[var_index])
var_index += 1
var_number_counter += 1
if var_number_counter + 1 != var_size:
print('For: [', return_elements_of_list(list_numbers), ']')
print(f'Couldn\'t find {var_size} numbers for mode: {var_mode}')
print()
break
while var_index < list_numbers_length:
var_number = list_numbers[var_index]
var_test_sum = var_current_sum + var_number
if (var_test_sum % 2 == 0 and var_mode == 'even') or (var_test_sum % 2 != 0 and var_mode == 'uneven'):
list_chosen_numbers.append(var_number)
var_biggest_sum = var_test_sum
var_is_sum_found = True
print('For: [', return_elements_of_list(list_numbers), '],', var_mode, '(', var_size, ')')
print('selected:', return_elements_of_list(list_chosen_numbers))
print('which gives:', var_biggest_sum)
print()
break
var_index += 1
if var_starting_pos >= list_numbers_length:
print('For: [', return_elements_of_list(list_numbers), ']')
print(f'var_starting_pos reached unexpected position {var_starting_pos}')
print()
break
if not var_is_sum_found:
var_starting_pos += 1
elif var_is_sum_found:
break
def return_elements_of_list(user_list):
var_list_elements = ''
var_i = 0
for list_item in user_list:
if var_i == 0:
var_list_elements += str(list_item)
else:
var_list_elements += ' ' + str(list_item)
var_i += 1
return var_list_elements
# Examples
get_biggest_sum_depending_on_mode([1, 2, 9, 3, 6, 6, 7, 7, 3], 4, 'even')
get_biggest_sum_depending_on_mode([102, 15, 33, 4, 81, 2, 13, 6, 79], 4, 'even')
get_biggest_sum_depending_on_mode([4, 32, 15, 1, 6, 2, 8, 6, 9], 3, 'uneven')
get_biggest_sum_depending_on_mode([0, 0, 3, 2, 0, 5, 2, 7, 6], 7, 'uneven')
get_biggest_sum_depending_on_mode([3, 32, 5], 3, 'uneven')
get_biggest_sum_depending_on_mode([3, 32, 5], 2, 'uneven')
get_biggest_sum_depending_on_mode([1, 1, 1, 2, 3], 3, 'uneven')
get_biggest_sum_depending_on_mode([6, 0, 0, 4, 3], 5, 'even')
|
"""
可迭代对象工具集
"""
class IterableHelper:
"""
可迭代对象助手类
"""
# 静态方法：不需要操作实例与类成员
# 语义：工具函数(常用且独立)
@staticmethod
def find_all(iterable, condition):
"""
在可迭代对象中,根据任意条件查找满足的所有元素
:param iterable:可迭代对象
:param condition:函数类型,查找条件
:return:生成器,推算满足条件的元素
"""
for item in iterable:
if condition(item):
yield item
@staticmethod
def find_single(iterable, condition):
"""
在可迭代对象中,根据任意条件查找满足的单个元素
:param iterable:可迭代对象
:param condition:函数类型,查找条件
:return:生成器,推算满足条件的元素
"""
for item in iterable:
if condition(item):
return item
@staticmethod
def select(iterable, condition):
"""
在可迭代对象中,根据逻辑处理元素
:param iterable: 可迭代对象
:param condition: 函数类型的处理逻辑
:return:生成器,推算处理结果
"""
for item in iterable:
yield condition(item)
@staticmethod
def get_count(iterable, condition):
"""
在可迭代对象中计算满足条件的元素数量
:param iterable:可迭代对象
:param condition:函数类型的条件
:return:数量
"""
count = 0
for item in iterable:
if condition(item):
count += 1
return count
@staticmethod
def sum(iterable, condition):
"""
在可迭代对象中根据条件累计运算
:param iterable:可迭代对象
:param condition:函数类型的条件
:return:累计结果
"""
sum_value = 0
for item in iterable:
sum_value += condition(item)
return sum_value
@staticmethod
def delete_all(iterable, condition):
"""
在可迭代对象中删除满足条件的元素
:param iterable: 可迭代对象
:param condition: 函数类型的条件
:return:删除数量
"""
count = 0
for i in range(len(iterable) - 1, -1, -1):
if condition(iterable[i]):
del iterable[i]
count += 1
return count
@staticmethod
def get_max(iterable, condition):
"""
根据条件在可迭代对象中获取最大元素
:param iterable:可迭代对象
:param condition:函数类型的条件
:return:最大元素
"""
max_value = iterable[0]
for i in range(1, len(iterable)):
if condition(max_value) < condition(iterable[i]):
max_value = iterable[i]
return max_value
@staticmethod
def sort(iterable, condition, reverse=False):
"""
根据条件对象可迭代对象进行降序排列
:param iterable: 可迭代对象
:param condition: 函数类型的条件
:param reverse: 是否反转
"""
for r in range(len(iterable) - 1):
for c in range(r + 1, len(iterable)):
if reverse: # 是反转 降序排列(大-->小)
if condition(iterable[r]) < condition(iterable[c]):
iterable[r], iterable[c] = iterable[c], iterable[r]
else:
if condition(iterable[r]) > condition(iterable[c]):
iterable[r], iterable[c] = iterable[c], iterable[r]
|
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def print_color(code: int, message: str) -> None:
print(f"\033[{code}m{message}\033[00m")
def print_green(message: str) -> None:
print_color(92, message)
def print_yellow(message: str) -> None:
print_color(93, message)
def print_cyan(message: str) -> None:
print_color(96, message)
def print_red(message: str) -> None:
print_color(91, message)
def snake_to_camelcase(name: str) -> str:
"""Convert snake-case string to camel-case string."""
return "".join(n.capitalize() for n in name.split("_"))
|
# scoping.level.1.py
def my_function():
test = 1 # this is defined in the local scope of the function
print('my_function:', test)
test = 0 # this is defined in the global scope
my_function()
print('global:', test)
|
_base_ = [
'../_base_/models/lcgn_config.py',
'../_base_/datasets/gqa_dataset.py',
'../_base_/schedules/schedule_vqa.py',
'../_base_/default_runtime.py'
] # yapf:disable
|
#
class BaseConfig(object):
DEBUG = True
class TestConfig(BaseConfig):
DEBUG = False
TESTING = True
|
class ArpeggioError(Exception):
"""
Base class for arpeggio errors.
"""
def __init__(self, message: str) -> None:
self.message = message
def __str__(self) -> str:
return repr(self.message)
class GrammarError(ArpeggioError):
"""
Error raised during parser building phase used to indicate error in the
grammar definition.
"""
class SemanticError(ArpeggioError):
"""
Error raised during the phase of semantic analysis used to indicate
semantic error.
"""
|
class Makieta:
def __init__(self, dane):
self.dane = dane
|
# -*- coding: utf-8 -*-
# Kurs: Python: Grundlagen der Programmierung für Nicht-Informatiker
# Semester: Herbstsemester 2018
# Homepage: http://accaputo.ch/kurs/python-uzh-hs-2018/
# Author: Giuseppe Accaputo
# Aufgabe: 6.3
def durchschnitt(zahlen):
# Um den Durchschnitt zu berechnen, benötigen wir zuerst die
# Summe bestehnd aus allen Zahlen
summe = 0
for zahl in zahlen:
summe = summe + zahl
# Der Durchschnitt berechnet sich aus der Summe aller Zahlen
# dividiert durch die Anzahl Zahlen
anzahl_zahlen = len(zahlen)
avg = summe / float(anzahl_zahlen)
return avg
print(durchschnitt([1,2,3,4]))
print(durchschnitt([4,18,30,-20]))
print(durchschnitt([3,3,3,3])) |
class Walking_HabitsBaseException(Exception):
pass
class InvalidLayoutError(Walking_HabitsBaseException):
pass
|
"""
monobit.constants
(c) 2019--2021 Rob Hagemans
licence: https://opensource.org/licenses/MIT
"""
DEFAULT_FORMAT = 'yaff'
VERSION = '0.20'
CONVERTER_NAME = f'monobit v{VERSION}'
|
"""
Closes all the sub windows that are currently open in the Weasel GUI.
"""
def main(weasel):
weasel.close_subwindows()
|
"""
.. module:: check_is_version_string
:synopsis: Given a string, checks if it looks like a version string.
.. moduleauthor:: Scott W. Fleming <[email protected]>
"""
#--------------------
def check_is_version_string(istring):
"""
Checks if the string looks like a version.
:param istring: The string to check.
:type istring: str
:returns: bool - True if string looks like a version, false otherwise.
"""
return istring[0] == 'v'
#--------------------
|
text = """
Formula 1 is to abandon plans to race in the Americas this year, with new races at Imola, the Nurburgring and Algarve
now set to join the calendar. The sport's owner Liberty Media has been weighing up how best to fill out its schedule,
with the coronavirus pandemic still moving at different speeds around the world. But with the situation in America and
Brazil among the most concerning, it is understood a decision has been taken to call off the races on the American
continent. It means there will be no grands prix in the United States, Canada, Mexico nor Brazil this season. Instead,
F1 has slotted in three extra races in Europe, all at venues that have not held F1 recently. The Nurburgring and Imola
circuits will be returning to the schedule after a lengthy absence, while Algarve will hold an F1 race for the first
time. F1 still hopes to host between 15 and 18 races this season, with the end of the campaign likely to feature two
races in Bahrain and one event in Abu Dhabi. The latest announcements will lift the calendar up to 13 races, with the
events in the Middle East enough to fulfil the mimimum 15 grands prix that F1 needs to fulfil its television contracts.
There remains an outside possibility that F1 could look to hosting one or two events in Asia in November - potentially
in Vietnam or even Malaysia.
"""
question = "Who is the owner?"
|
def calcula_nota(conceito):
if conceito.upper() == 'A':
return 4*credito
elif conceito.upper() == 'B':
return 3*credito
elif conceito.upper() == 'C':
return 2*credito
elif conceito.upper() == 'D':
return 0
pergunta = input('Já possúi CR? (S/N)')
if pergunta.upper() == 'N':
credito_total = 0
nota_parcial = 0
n = int(input('Quantas matérias completará?'))
for i in range(n):
print(f'{i + 1}ª matéria:')
conceito = input('Conceito: ')
credito = int(input('Créditos: '))
print('')
credito_total = credito_total + credito
nota = calcula_nota(conceito)
nota_parcial = nota_parcial + nota
CR = print(f'Seu CR será {nota_parcial/credito_total}')
if pergunta.upper() == 'S':
cr_anterior = float(input('Digite seu Cr anterior: '))
creditos_completos = int(input('Quantos créditos já completou? '))
n = int(input('Quantas matérias comletará neste quad? '))
creditos_atuais = 0
nota_parcial = 0
for i in range(n):
print(f'{i + 1}ª matéria:')
conceito = input('Conceito: ')
credito = int(input('Créditos: '))
print('')
creditos_atuais = creditos_atuais + credito
nota = calcula_nota(conceito)
nota_parcial = nota_parcial + nota
CR = (cr_anterior*creditos_completos + nota_parcial)/(creditos_completos + creditos_atuais)
print(f'Seu Cr será {round(CR, 3)}') |
# encoding: utf-8
MOCK_QUERY_RESULT = "QUERY_RESULT"
def mock_connection(execution_function=lambda x, y: None,
fetch_function=lambda x: None):
cursor = type('cursor', (object, ), {
'execute': execution_function,
'fetchall': fetch_function
})
def cursor_call(*args, **kwargs):
return cursor()
connection = type('connection', (object, ), {
'cursor': cursor_call,
'json': lambda x: {},
'close': lambda x: {},
})
return connection()
|
b = bytes(range(20))
il = int.from_bytes(b, "little")
ib = int.from_bytes(b, "big")
print(il)
print(ib)
print(il.to_bytes(20, "little"))
|
'''
Now You Code 4: Reddit News Sentiment Analysis
In this assignment you're tasked with performing a sentiment analysis on top Reddit news articles. (https://www.reddit.com/r/news/top.json)
You should perform the analysis on the titles only.
Start by getting the Reddit API to work, and extracting a list of titles only. You'll have to research the Reddit API, and can do so here: https://www.reddit.com/dev/api/ The Reddit API requires a custom 'User-Agent' You must specify this in your headers, as explained here: https://github.com/reddit/reddit/wiki/API
After you get Reddit working move on to sentiment analysis. Once again, we will use (http://text-processing.com/api/sentiment/) like we did in the in-class coding lab.
Start by getting the sentiment analysis working for a single title. Then write your program to loop over each title runnning the sentiment analysis and printing both the sentiment and the title, like this:
Example Run (Your output will vary as news stories change...)
neutral : FBI Chief Comey 'Rejects' Phone Tap Allegation
pos : New Peeps-flavored Oreos reportedly turning people's poop pink
neutral : President Trump Signs Revised Travel Ban Executive Order
neutral : Police: Overdose survivors to be charged with misdemeanor
neutral : Struggling students forced to wait 3-4 weeks as Utah's public colleges don't have enough mental health therapists
neutral : Army Veteran Faces Possible Deportation to Mexico
neutral : Rep. Scott Taylor called out at town hall for ‘blocking’ constituents on social media
neutral : GM to suspend third shift at Delta Township plant, layoff 1,100 workers
neutral : American citizen Khizr Khan reportedly cancels trip to Canada after being warned his 'travel privileges are being reviewed'
neg : Mars far more likely to have had life than we thought, researchers find after new water discovery
neutral : Bird Flu Found at U.S. Farm That Supplies Chickens to Tyson
neutral : Investigation Reveals Huge Volume of Shark Fins Evading International Shipping Bans
neg : Sikh man's shooting in Washington investigated as hate crime
'''
# TODO: Write Todo list then beneath write your code
# Write code here
|
# coding: utf-8
class PVWattsError(Exception):
"""
Base class for PVWatts errors
"""
def __init__(self, message):
Exception.__init__(self, message)
class PVWattsValidationError(PVWattsError):
"""
Validation error on request
"""
def __init__(self, message):
PVWattsError.__init__(self, message)
|
# constants.py
# Copyright (c) 2013-2019 Pablo Acosta-Serafini
# See LICENSE for details
# pylint: disable=C0111,C0302,W0105
###
# Global variables
###
AXIS_TICKS_FONT_SIZE = 14
"""
Axis tick labels font size in points
:type: integer
"""
AXIS_LABEL_FONT_SIZE = 18
"""
Axis labels font size in points
:type: integer
"""
LEGEND_SCALE = 1.5
"""
Scale factor for panel legend. The legend font size in points is equal to the
axis font size divided by the legend scale
:type: number
"""
LINE_WIDTH = 2.5
"""
Series line width in points
:type: float
"""
MARKER_SIZE = 14
"""
Series marker size in points
:type: integer
"""
MIN_TICKS = 6
"""
Minimum number of ticks desired for the independent and dependent axis of
a panel
:type: integer
"""
PRECISION = 10
"""
Number of mantissa significant digits used in all computations
:type: integer
"""
SUGGESTED_MAX_TICKS = 10
"""
Maximum number of ticks desired for the independent and dependent axis of a
panel. It is possible for a panel to have more than SUGGESTED_MAX_TICKS in the
dependent axis if one or more series are plotted with an interpolation function
and at least one interpolated curve goes above or below the maximum and minimum
data points of the panel. In this case the panel will have
SUGGESTED_MAX_TICKS+1 ticks if some interpolation curve is above the maximum
data point of the panel or below the minimum data point of the panel; or the
panel will have SUGGESTED_MAX_TICKS+2 ticks if some interpolation curve(s)
is(are) above the maximum data point of the panel and below the minimum data
point of the panel
:type: integer
"""
TITLE_FONT_SIZE = 24
"""
Figure title font size in points
:type: integer
"""
|
# lst1 = [8,3,9,6,4,7,5,2,1]
# lst2 = [10,11,12,8,3,9,6,4,7,5,2,1]
# lst3 = [8,9,3,6,7,4,5,2,1]
# lst4 = [8,3,9,6,4,7,5,2,1]
# lst = [7, 2, 6, 4, 2, 3, 2, 1]
# lst5 = [14, 4, 8, 17, 16, 2, 12, 6, 18, 3, 10, 13, 9, 5, 1, 11, 19, 15, 7, 20]
# lst6 = [1, 17, 11, 20, 7, 15, 13, 10, 6, 16, 12, 19, 8, 18, 5, 3, 4, 14, 9, 2]
# lst7 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
# k3 = [2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
# #k = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,2,0]
# k1 = [0,0,0,0,4,0,2,0]
# k2 = [0,0,0,0,0,0,0,1]
#[9, 1, 1]
#[8, 3, 9, 6, 4, 7, 5, 2, 1]
# 9 1 1
# 8 3 9 6 4 7 5 2 1
def main():
variable1 = input()
variable2 = input()
a = variable1.split()
b = variable2.split()
first_line = []
second_line = []
# k = variable1.split()
# print(k)
#get inputs
for i in a:
first_line.append(int(i))
#print(int(i))
for i in b:
second_line.append(int(i))
#what kind of order
add = True
type = first_line[1]
if first_line[2] >= 0:
add = True
if first_line[2] < 0:
add = False
lst = second_line
k = abs(int(first_line[2]))
k = [int(i) for i in str(k)]
# missing_k = 0
missing_k = 20 - len(k)
k = k[::-1]
for i in range(missing_k):
k.append(0)
k = k[::-1]
if type == 1:
if add == True:
output = dict_order(add_1(second_line, k))
else:
output = dict_order(subtract_1(second_line, k))
if type == 2:
if add == True:
output = order_2(add_2(second_line, k))
else:
output = order_2(subtract_2(second_line, k))
if type == 3:
if add == True:
output = order_3(add_3(second_line,k))
else:
output = order_3(subtract_3(second_line, k))
final = ' '.join(str(i) for i in output)
print(final)
#print(lst)
#print(k)
#print(add)
#order_3(add_3(lst1 , k))
#order_2(add_2(lst7, k3))
#shift_3(lst1)
############### 字典
def shift_1(lst):
new_lst = lst
shifted_num = []
while new_lst:
count = 0
compare = new_lst[0]
for i in range(len(new_lst)):
if new_lst[i] < new_lst[0]:
count += 1
shifted_num.append(count)
del new_lst[0]
shifted_num.pop()
#print("shifted_num: ", shifted_num)
return shifted_num
def add_1(lst1, k):
shifted_num = shift_1(lst1)
limit = len(shifted_num) + 1
limit_list = []
added_list = []
k = k[::-1]
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
#the list i want to work with
true_list = shifted_num[::-1]
#print("here",true_list)
list_len = len(true_list)
for i in range(list_len):
if (true_list[i]+k[i]) > limit_list[i]:
true_list[i+1] += 1
a = (true_list[i] + k[i]) - (limit_list[i])
added_list.append(a)
elif (true_list[i]+k[i]) == limit_list[i]:
if len(true_list) != 1:
true_list[i+1] += 1
else:
true_list.append(1)
added_list.append(0)
else:
added_list.append(true_list[i] + k[i])
#print("here", true_list )
#print("limit_list: ", limit_list)
#print("added_list: ", added_list[::-1])
return added_list[::-1]
def helper1(lst, start):
new_lst = lst[start:]
index_of_zeros = []
index_of_carry = []
for i in range(len(new_lst)):
if new_lst[i] == 0:
index_of_zeros.append(i+ start)
elif new_lst[i] > 0:
index_of_carry.append(i)
#print(index_of_carry, start)
index_of_carry = index_of_carry[1] + start
a = []
a.append([index_of_carry])
a.append(index_of_zeros)
return a
def subtract_1(lst, k):
shifted_num = shift_1(lst)
limit_list = []
k = k[::-1]
limit = len(shifted_num) + 1
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
top = limit_list
lst = shifted_num[::-1]
subtract_list = []
list_len = len(lst)
next_borrow = 1
#print(top)
for i in range(list_len):
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
while (lst[i] - k[i]) < 0:
if lst[i+next_borrow] > 0:
lst[i+next_borrow] -= 1
lst[i] = lst[i] + top[i]
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
elif lst[i+next_borrow] == 0:
a = helper1(lst, i)
index_of_carry = a[0][0]
index_of_zeros = a[1][0]
temp = lst[:index_of_carry]
lst[index_of_carry] -= 1
for j in range(len(temp)):
lst[j] += top[j]
if (lst[i] - k[i]) > 0:
subtract_list.append(lst[i] - k[i])
#print("subtract_list:", subtract_list[::-1])
return subtract_list[::-1]
def dict_order(lst):
limit = len(lst) + 1
temp = []
for i in range(limit):
temp.append(-1)
for i in range(len(lst)):
bigger = False
current_carry = lst[i] + 1
if i == 0:
temp[0] = current_carry
for j in temp[:i]:
if j <= (current_carry):
current_carry += 1
temp[i] = current_carry
j = -10
elif current_carry not in temp[:i]:
temp[i] = current_carry
while (current_carry) in temp[:i]:
current_carry += 1
temp[i] = current_carry
left = []
no = []
for i in range(len(temp)):
left.append(i+1)
for i in left:
if i not in temp:
no.append(i)
for i in range(len(temp)):
if temp[i] == -1:
temp[i] = no[0]
print(temp)
return temp
###############
###############加
def shift_2(lst):
new_lst = lst
shifted_num = []
#print(new_lst)
while new_lst:
count = 0
biggest = max(new_lst)
biggest_index = new_lst.index(biggest)
# for i in range(len(new_lst[biggest_index:])):
# #print(new_lst[biggest_index:])
# if new_lst[i] < biggest:
# #print(new_lst[i],biggest)
# count += 1
for i in new_lst[biggest_index+1:]:
#print(new_lst[biggest_index:])
if i < biggest:
#print(new_lst[i],biggest)
count += 1
shifted_num.append(count)
del new_lst[biggest_index]
shifted_num.pop()
#print("shifted_num: ", shifted_num)
return shifted_num[::-1]
##### add them using the algorithm
def add_2(lst, k):
shifted_num = shift_2(lst)
limit = len(shifted_num) + 1
limit_list = []
added_list = []
k = k[::-1]
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
#the list i want to work with
true_list = shifted_num
limit_list = limit_list
#print("limit_list: ", limit_list)
#print("true_list: ", true_list)
#print("k: ", k)
list_len = len(true_list)
for i in range(list_len):
if (true_list[i]+k[i]) > limit_list[i]:
true_list[i+1] += 1
a = (true_list[i] + k[i]) - (limit_list[i])
added_list.append(a)
elif (true_list[i]+k[i]) == limit_list[i]:
if len(true_list) != 1:
true_list[i+1] += 1
else:
true_list.append(1)
added_list.append(0)
else:
added_list.append(true_list[i] + k[i])
#print(added_list[::-1])
return added_list[::-1]
def helper2(lst, start):
new_lst = lst[start:]
index_of_zeros = []
index_of_carry = []
for i in range(len(new_lst)):
if new_lst[i] == 0:
index_of_zeros.append(i+ start)
elif new_lst[i] > 0:
index_of_carry.append(i)
index_of_carry = index_of_carry[1] + start
a = []
a.append([index_of_carry])
a.append(index_of_zeros)
#print("a:", a)
return a
def subtract_2(lst, k):
shifted_num = shift_2(lst)
limit_list = []
shifted_num = shifted_num[::-1]
k = k[::-1]
limit = len(shifted_num) + 1
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
top = limit_list
lst = shifted_num[::-1]
subtract_list = []
list_len = len(lst)
next_borrow = 1
#print("top: ",top)
#print(k)
#print(lst)
for i in range(list_len):
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
while (lst[i] - k[i]) < 0:
if lst[i+next_borrow] > 0:
lst[i+next_borrow] -= 1
lst[i] = lst[i] + top[i]
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
elif lst[i+next_borrow] == 0:
a = helper1(lst, i)
index_of_carry = a[0][0]
index_of_zeros = a[1][0]
temp = lst[:index_of_carry]
lst[index_of_carry] -= 1
for j in range(len(temp)):
lst[j] += top[j]
if (lst[i] - k[i]) > 0:
subtract_list.append(lst[i] - k[i])
print("subtract_list:", subtract_list)
return subtract_list[::-1]
def find_it_2(new_list,index,value):
num = 0
for i in range(len(new_list)):
if new_list[i] == -1:
num+=1
if num == index+1:
new_list[i] = value
#print("new list: ", new_list )
return new_list
#return from 中介数
def order_2(lst):
top = []
limit = len(lst) + 1
for i in range(limit):
top.append(i + 1)
top.pop(0)
new_top = top[::-1]
new_lst = lst
temp = []
for i in range(len(new_lst)+1):
temp.append(-1)
#print("new_lst:", new_lst, "new_top: ", new_top, "temp: ", temp)
for i in range(len(new_lst)):
#print(new_lst:", new_lst, "new_top: ", new_top, "temp: ", temp)
temp = find_it_2(temp, new_lst[i], new_top[i])
for i in range(len(temp)):
if temp[i] == -1:
temp[i] = 1
#print(temp[::-1])
return temp[::-1]
###############增
###############减
#get 中介数
def shift_3(lst):
a = shift_2(lst)
a = a[::-1]
#print(a[::-1])
return a[::-1]
# print (lst)
#add 中介数
def add_3(lst, k):
shifted_num = shift_3(lst)
limit = len(shifted_num) + 1
limit_list = []
added_list = []
k = k[::-1]
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
true_list = shifted_num[::-1]
limit_list = limit_list[::-1]
list_len = len(true_list)
for i in range(list_len):
if (true_list[i]+k[i]) > limit_list[i]:
true_list[i+1] += 1
a = (true_list[i] + k[i]) - (limit_list[i])
added_list.append(a)
elif (true_list[i]+k[i]) == limit_list[i]:
if len(true_list) != 1:
true_list[i+1] += 1
else:
true_list.append(1)
added_list.append(0)
else:
added_list.append(true_list[i] + k[i])
#print(added_list[::-1])
return added_list[::-1]
#subtraction helper
def helper3(lst, start):
new_lst = lst[start:]
index_of_zeros = []
index_of_carry = []
for i in range(len(new_lst)):
if new_lst[i] == 0:
index_of_zeros.append(i+ start)
elif new_lst[i] > 0:
index_of_carry.append(i)
index_of_carry = index_of_carry[1] + start
a = []
a.append([index_of_carry])
a.append(index_of_zeros)
return a
#subtract 中介数
def subtract_3(lst, k):
shifted_num = shift_3(lst)
limit_list = []
k = k[::-1]
limit = len(shifted_num) + 1
for i in range(limit):
limit_list.append(i + 1)
limit_list.pop(0)
top = limit_list[::-1]
#reverse list to work with
lst = shifted_num[::-1]
subtract_list = []
list_len = len(lst)
next_borrow = 1
for i in range(list_len):
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
while (lst[i] - k[i]) < 0:
if lst[i+next_borrow] > 0:
lst[i+next_borrow] -= 1
lst[i] = lst[i] + top[i]
if (lst[i] - k[i]) >= 0:
subtract_list.append(lst[i] - k[i])
elif lst[i+next_borrow] == 0:
a = helper1(lst, i)
index_of_carry = a[0][0]
index_of_zeros = a[1][0]
temp = lst[:index_of_carry]
lst[index_of_carry] -= 1
for j in range(len(temp)):
lst[j] += top[j]
if (lst[i] - k[i]) > 0:
subtract_list.append(lst[i] - k[i])
#print("subtract_list:", subtract_list)
return subtract_list[::-1]
#find value helper
def find_it_3(new_list,index,value):
num = 0
for i in range(len(new_list)):
if new_list[i] == -1:
num+=1
if num == index+1:
new_list[i] = value
return new_list
#return from 中介数
def order_3(lst):
top = []
limit = len(lst) + 1
for i in range(limit):
top.append(i + 1)
top.pop(0)
new_top = top[::-1]
new_lst = lst[::-1]
temp = []
for i in range(len(new_lst)+1):
temp.append(-1)
# print("new_lst:", new_lst, "new_top: ", new_top, "temp: ", temp)
for i in range(len(new_lst)):
#print(new_lst:", new_lst, "new_top: ", new_top, "temp: ", temp)
temp = find_it_3(temp, new_lst[i], new_top[i])
for i in range(len(temp)):
if temp[i] == -1:
temp[i] = 1
#print(temp[::-1])
return temp[::-1]
###############加
###############邻排列
def shift_4(lst):
new_lst = lst
shifted_num = []
while new_lst:
count = 0
biggest = max(new_lst)
biggest_index = new_lst.index(biggest)
for i in range(len(new_lst[biggest_index:])):
if new_lst[i] < biggest:
count += 1
shifted_num.append(count)
del new_lst[biggest_index]
shifted_num.pop()
a = shifted_num[::-1]
return a
if __name__ == "__main__":
main()
|
# 입력
B = int(input())
# 출력
res = 5*B - 400
print(res)
if res < 100:
print(1)
elif res == 100:
print(0)
else:
print(-1)
|
a = "dog\ndog\tdog"
b = 'cat'
c = '🐱'
print(f"001 {a}{b}{c}")
print(f'002 {a}{b}{c}')
print(f"""003 {a}{b}{c}""")
print(f"005 string {a!s} {b!s} {c!s}")
#broken print(f"006 ascii {a!a} {b!a} {c!a}")
d = -177
print(f"int: {d}")
e = (1, 3, 5)
print(f'tuple: {e}')
print(f'tuple: {e!s}')
print(f'tuple: {e!r}')
#print(f'tuple: {e!a}')
|
#
# PySNMP MIB module CISCO-ENTITY-PROVISIONING-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-ENTITY-PROVISIONING-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:39:48 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ValueRangeConstraint, SingleValueConstraint, ValueSizeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueRangeConstraint", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsIntersection")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
entPhysicalIndex, = mibBuilder.importSymbols("ENTITY-MIB", "entPhysicalIndex")
ModuleCompliance, NotificationGroup, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "ObjectGroup")
NotificationType, iso, ObjectIdentity, Bits, ModuleIdentity, Integer32, Counter64, TimeTicks, Unsigned32, Counter32, IpAddress, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, Gauge32 = mibBuilder.importSymbols("SNMPv2-SMI", "NotificationType", "iso", "ObjectIdentity", "Bits", "ModuleIdentity", "Integer32", "Counter64", "TimeTicks", "Unsigned32", "Counter32", "IpAddress", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Gauge32")
TextualConvention, AutonomousType, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "AutonomousType", "DisplayString")
ciscoEntityProvMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 139))
if mibBuilder.loadTexts: ciscoEntityProvMIB.setLastUpdated('9907082052Z')
if mibBuilder.loadTexts: ciscoEntityProvMIB.setOrganization('Cisco Systems, Inc.')
ciscoEntityProvMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 1))
ceProvContainerTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 139, 1, 1), )
if mibBuilder.loadTexts: ceProvContainerTable.setStatus('current')
ceProvContainerEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 139, 1, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: ceProvContainerEntry.setStatus('current')
ceProvContainerStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 139, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("unequipped", 1), ("provisioned", 2), ("mismatched", 3), ("invalid", 4), ("equipped", 5), ("failed", 6)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ceProvContainerStatus.setStatus('current')
ceProvContainerEquipped = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 139, 1, 1, 1, 2), AutonomousType()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ceProvContainerEquipped.setStatus('current')
ceProvContainerDetected = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 139, 1, 1, 1, 3), AutonomousType()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ceProvContainerDetected.setStatus('current')
ceProvMIBNotificationsPrefix = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 2))
ceProvMIBNotifications = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 2, 0))
ceProvMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 3))
ceProvMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 3, 1))
ceProvMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 139, 3, 2))
ceProvMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 139, 3, 1, 1)).setObjects(("CISCO-ENTITY-PROVISIONING-MIB", "ceProvContainerGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ceProvMIBCompliance = ceProvMIBCompliance.setStatus('current')
ceProvContainerGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 139, 3, 2, 1)).setObjects(("CISCO-ENTITY-PROVISIONING-MIB", "ceProvContainerStatus"), ("CISCO-ENTITY-PROVISIONING-MIB", "ceProvContainerEquipped"), ("CISCO-ENTITY-PROVISIONING-MIB", "ceProvContainerDetected"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ceProvContainerGroup = ceProvContainerGroup.setStatus('current')
mibBuilder.exportSymbols("CISCO-ENTITY-PROVISIONING-MIB", ceProvContainerTable=ceProvContainerTable, ceProvContainerStatus=ceProvContainerStatus, ceProvContainerEquipped=ceProvContainerEquipped, ciscoEntityProvMIBObjects=ciscoEntityProvMIBObjects, PYSNMP_MODULE_ID=ciscoEntityProvMIB, ceProvContainerDetected=ceProvContainerDetected, ceProvMIBConformance=ceProvMIBConformance, ceProvMIBGroups=ceProvMIBGroups, ceProvContainerGroup=ceProvContainerGroup, ceProvContainerEntry=ceProvContainerEntry, ceProvMIBNotifications=ceProvMIBNotifications, ciscoEntityProvMIB=ciscoEntityProvMIB, ceProvMIBCompliances=ceProvMIBCompliances, ceProvMIBCompliance=ceProvMIBCompliance, ceProvMIBNotificationsPrefix=ceProvMIBNotificationsPrefix)
|
le = [1,12,2,3,1,1,2,3,1,3,4,3,1,5,0,3,2,1,9,19,1,19,5,23,1,13,23,27,1,27,6,31,2,31,6,35,2,6,35,39,1,39,5,43,1,13,43,47,1,6,47,51,2,13,51,55,1,10,55,59,1,59,5,63,1,10,63,67,1,67,5,71,1,71,10,75,1,9,75,79,2,13,79,83,1,9,83,87,2,87,13,91,1,10,91,95,1,95,9,99,1,13,99,103,2,103,13,107,1,107,10,111,2,10,111,115,1,115,9,119,2,119,6,123,1,5,123,127,1,5,127,131,1,10,131,135,1,135,6,139,1,10,139,143,1,143,6,147,2,147,13,151,1,5,151,155,1,155,5,159,1,159,2,163,1,163,9,0,99,2,14,0,0]
last = 0
def execute(l, n):
global last
pos = n * 4
tkn = l[pos]
if tkn == 99:
print("result", last)
return []
elif tkn == 1:
last = l[l[pos + 1]] + l[l[pos + 2]]
elif tkn == 2:
last = l[l[pos + 1]] * l[l[pos + 2]]
l[l[pos + 3]] = last
return l
def execute_all(l):
for x in range(0, len(l)):
l = execute(l, x)
if len(l) == 0:
break
print("example 1")
execute_all([1,9,10,3,2,3,11,0,99,30,40,50])
print("example 2")
execute_all([2,3,0,3,99])
print("example 3")
execute_all([1,1,1,4,99,5,6,0,99])
print("example 4")
execute_all([2,4,4,5,99,0])
print("input")
execute_all(le) |
def tri_recursion(k):
if(k > 0):
result = k + tri_recursion(k - 1)
print(result,k)
else:
result = 0
return result
print("\n\nRecursion Example Results")
tri_recursion(6) |
extracted_data = {"contractor": "",
"contractee": "",
"duration": None,
"intervalPayment": None,
"interval": None,
"amount": None,
"numberOfPerformanceObligations": 0,
"poi": []
}
|
# Copyright (c) 2009 - 2015 Tropo, now part of Cisco
# Released under the MIT license. See the file LICENSE
# for the complete license
# --------------------------------------
# Sample Tropo app
# --------------------------------------
answer()
event=ask("where are you heading?",
{'repeat':3,'choices':"1st Floor (first, house wares, 1), 2nd Floor (second, bed and bath, 2), 3rd Floor (third, sporting goods, 3)", 'timeout':10.03456789,
'onChoice':lambda event :
event.onChoice( "1st Floor", lambda : say("Your destination is 1st Floor" ) ) and
event.onChoice( "2nd Floor", lambda : say("Your destination is 2nd Floor" ) ) and
event.onChoice( "3rd Floor", lambda : say("Your destination is 3rd Floor" ) ) and
event.onBadChoice( lambda : say("I can not recognize you. Please input again. ") ),
'onTimeout':lambda : say("wait input time out" ),
'onError':lambda : say("You have an error!" ),
'onHangup':lambda : log(">>>>>>>>>>>>>>>>>>Disconnected by the peer!<<<<<<<<<<<<<<<<<"),
'onEvent':lambda event :
event.onError( lambda : say("You have an error! " ) ) and
event.onTimeout( lambda : say("wait input time out" ) ) and
event.onHangup( lambda : log(">>>>>>>>>>>>>>>>>>>>Disconnected by the peer!<<<<<<<<<<<<<<<<<") ) and
event.onChoice( "1st Floor", lambda : say("Your destination is 1st Floor" ) ) and
event.onChoice( "2nd Floor", lambda : say("Your destination is 2nd Floor" ) ) and
event.onChoice( "3rd Floor", lambda : say("Your destination is 3rd Floor" ) ) and
event.onBadChoice( lambda : say("I can not recognize you. Please input again. " ) )
}
)
if event.name!="hangup":
if event.value != None:
say("run outer call back for event [" + event.name + "," + event.value + "]")
else:
say("run outer call back for event [" + event.name + "]")
event.onError( lambda : say("You have an error! " ) )
event.onChoice( "1st Floor", lambda : say("Your destination is 1st Floor" ) )
event.onChoice( "2nd Floor", lambda : say("Your destination is 2nd Floor" ) )
event.onChoice( "3rd Floor", lambda : say("Your destination is 3rd Floor" ) )
event.onBadChoice( lambda : say("I can not recognize you" ) )
say("Thanks for testing Python on the Tropo platform")
hangup()
else:
log(">>>>>>>>>>>>>>>Disconnected by the peer!<<<<<<<<<<<<<<<<<") |
"""
Diccionario que almacena los diferentes mensajes que se mostrarán en formato Flash en la aplicación.
"""
msg = {
'INTERNAL_ERROR':"Ha ocurrido algún error interno.",
'FORMAT_ERROR':"El formato no es correcto.",
'JOB_NOT_FOUND_ERROR':"El job_id no existe o ha caducado.",
'PROCESSING_ERROR':"Se ha producido un error durante el procesamiento.",
'EMPTY_REQUEST':"Introduce datos para poder procesar la consulta.",
'FILE_TOO_BIG':"El fichero es demasiado grande.",
'NOT_ALLOWED_FILE':"No es un tipo de fichero permitido.",
'NOT_ALLOWED_POSITIONS':"Las posiciones introducidas no son válidas.",
'NON_EXISTENT_FILE':"El fichero no existe.",
'NOT_ALLOWED_ACCESS':"No tiene acceso!.",
'FORBIDDEN_DELETE':"Eso que estás intentando no está permitido."
} |
try:
t=int(input())
for i in range(t):
n=int(input())
a=list(map(int,input().split()))
set1=list(set(a))
print(len(set1))
except:
pass |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# (c) 2014, Chris Hoffman <[email protected]>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# this is a windows documentation stub. actual code lives in the .ps1
# file of the same name
DOCUMENTATION = '''
---
module: win_service
version_added: "1.7"
short_description: Manages Windows services
description:
- Manages Windows services
options:
name:
description:
- Name of the service
required: true
default: null
aliases: []
start_mode:
description:
- Set the startup type for the service
required: false
choices:
- auto
- manual
- disabled
state:
description:
- C(started)/C(stopped) are idempotent actions that will not run
commands unless necessary. C(restarted) will always bounce the
service.
required: false
choices:
- started
- stopped
- restarted
default: null
aliases: []
author: Chris Hoffman
'''
EXAMPLES = '''
# Restart a service
win_service:
name: spooler
state: restarted
# Set service startup mode to auto and ensure it is started
win_service:
name: spooler
start_mode: auto
state: started
'''
|
# File generated by contrib/scrape-ec2-sizes.py script - DO NOT EDIT manually
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
REGION_DETAILS = {
"af-south-1": {
"api_name": "ec2_af_south",
"country": "South Africa",
"endpoint": "ec2.af-south-1.amazonaws.com",
"id": "af-south-1",
"instance_types": [],
"signature_version": "4",
},
"ap-east-1": {
"api_name": "ec2_ap_east",
"country": "Hong Kong",
"endpoint": "ec2.ap-east-1.amazonaws.com",
"id": "ap-east-1",
"instance_types": [
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.18xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"x1.16xlarge",
"x1.32xlarge",
],
"signature_version": "2",
},
"ap-northeast-1": {
"api_name": "ec2_ap_northeast",
"country": "Japan",
"endpoint": "ec2.ap-northeast-1.amazonaws.com",
"id": "ap-northeast-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"cc2.8xlarge",
"cr1.8xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5g.16xlarge",
"g5g.2xlarge",
"g5g.4xlarge",
"g5g.8xlarge",
"g5g.xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p3dn.24xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"vt1.24xlarge",
"vt1.3xlarge",
"vt1.6xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"x2iezn.12xlarge",
"x2iezn.2xlarge",
"x2iezn.4xlarge",
"x2iezn.6xlarge",
"x2iezn.8xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"ap-northeast-2": {
"api_name": "ec2_ap_northeast",
"country": "South Korea",
"endpoint": "ec2.ap-northeast-2.amazonaws.com",
"id": "ap-northeast-2",
"instance_types": [
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5g.16xlarge",
"g5g.2xlarge",
"g5g.4xlarge",
"g5g.8xlarge",
"g5g.xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "4",
},
"ap-northeast-3": {
"api_name": "ec2_ap_northeast",
"country": "Japan",
"endpoint": "ec2.ap-northeast-3.amazonaws.com",
"id": "ap-northeast-3",
"instance_types": [],
"signature_version": "4",
},
"ap-south-1": {
"api_name": "ec2_ap_south_1",
"country": "India",
"endpoint": "ec2.ap-south-1.amazonaws.com",
"id": "ap-south-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "4",
},
"ap-southeast-1": {
"api_name": "ec2_ap_southeast",
"country": "Singapore",
"endpoint": "ec2.ap-southeast-1.amazonaws.com",
"id": "ap-southeast-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5g.16xlarge",
"g5g.2xlarge",
"g5g.4xlarge",
"g5g.8xlarge",
"g5g.xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-12tb1.112xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"u-9tb1.112xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"ap-southeast-2": {
"api_name": "ec2_ap_southeast_2",
"country": "Australia",
"endpoint": "ec2.ap-southeast-2.amazonaws.com",
"id": "ap-southeast-2",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"f1.16xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"ca-central-1": {
"api_name": "ec2_ca_central_1",
"country": "Canada",
"endpoint": "ec2.ca-central-1.amazonaws.com",
"id": "ca-central-1",
"instance_types": [
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
],
"signature_version": "4",
},
"cn-north-1": {
"api_name": "ec2_cn_north",
"country": "China",
"endpoint": "ec2.cn-north-1.amazonaws.com.cn",
"id": "cn-north-1",
"instance_types": [],
"signature_version": "4",
},
"cn-northwest-1": {
"api_name": "ec2_cn_northwest",
"country": "China",
"endpoint": "ec2.cn-northwest-1.amazonaws.com.cn",
"id": "cn-northwest-1",
"instance_types": [],
"signature_version": "4",
},
"eu-central-1": {
"api_name": "ec2_eu_central",
"country": "Frankfurt",
"endpoint": "ec2.eu-central-1.amazonaws.com",
"id": "eu-central-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"f1.16xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-12tb1.112xlarge",
"u-3tb1.56xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"u-9tb1.112xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "4",
},
"eu-north-1": {
"api_name": "ec2_eu_north_stockholm",
"country": "Stockholm",
"endpoint": "ec2.eu-north-1.amazonaws.com",
"id": "eu-north-1",
"instance_types": [
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"hpc6a.48xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
],
"signature_version": "4",
},
"eu-south-1": {
"api_name": "ec2_eu_south",
"country": "Italy",
"endpoint": "ec2.eu-south-1.amazonaws.com",
"id": "eu-south-1",
"instance_types": [
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-3tb1.56xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
],
"signature_version": "4",
},
"eu-west-1": {
"api_name": "ec2_eu_west",
"country": "Ireland",
"endpoint": "ec2.eu-west-1.amazonaws.com",
"id": "eu-west-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"cc2.8xlarge",
"cr1.8xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"d3en.12xlarge",
"d3en.2xlarge",
"d3en.4xlarge",
"d3en.6xlarge",
"d3en.8xlarge",
"d3en.xlarge",
"f1.16xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5.12xlarge",
"g5.16xlarge",
"g5.24xlarge",
"g5.2xlarge",
"g5.48xlarge",
"g5.4xlarge",
"g5.8xlarge",
"g5.xlarge",
"h1.16xlarge",
"h1.2xlarge",
"h1.4xlarge",
"h1.8xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"i4i.16xlarge",
"i4i.2xlarge",
"i4i.32xlarge",
"i4i.4xlarge",
"i4i.8xlarge",
"i4i.large",
"i4i.xlarge",
"im4gn.16xlarge",
"im4gn.2xlarge",
"im4gn.4xlarge",
"im4gn.8xlarge",
"im4gn.large",
"im4gn.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"is4gen.2xlarge",
"is4gen.4xlarge",
"is4gen.8xlarge",
"is4gen.large",
"is4gen.medium",
"is4gen.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p3dn.24xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-12tb1.112xlarge",
"u-3tb1.56xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"u-9tb1.112xlarge",
"vt1.24xlarge",
"vt1.3xlarge",
"vt1.6xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2gd.12xlarge",
"x2gd.16xlarge",
"x2gd.2xlarge",
"x2gd.4xlarge",
"x2gd.8xlarge",
"x2gd.large",
"x2gd.medium",
"x2gd.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"x2iezn.12xlarge",
"x2iezn.2xlarge",
"x2iezn.4xlarge",
"x2iezn.6xlarge",
"x2iezn.8xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"eu-west-2": {
"api_name": "ec2_eu_west_london",
"country": "United Kingdom",
"endpoint": "ec2.eu-west-2.amazonaws.com",
"id": "eu-west-2",
"instance_types": [
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i2.large",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"x1.16xlarge",
"x1.32xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "4",
},
"eu-west-3": {
"api_name": "ec2_eu_west_paris",
"country": "France",
"endpoint": "ec2.eu-west-3.amazonaws.com",
"id": "eu-west-3",
"instance_types": [
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.18xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"x1.16xlarge",
"x1.32xlarge",
],
"signature_version": "4",
},
"sa-east-1": {
"api_name": "ec2_sa_east",
"country": "Brazil",
"endpoint": "ec2.sa-east-1.amazonaws.com",
"id": "sa-east-1",
"instance_types": [
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
],
"signature_version": "2",
},
"us-east-1": {
"api_name": "ec2_us_east",
"country": "USA",
"endpoint": "ec2.us-east-1.amazonaws.com",
"id": "us-east-1",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"cc2.8xlarge",
"cr1.8xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"d3en.12xlarge",
"d3en.2xlarge",
"d3en.4xlarge",
"d3en.6xlarge",
"d3en.8xlarge",
"d3en.xlarge",
"dl1.24xlarge",
"f1.16xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5.12xlarge",
"g5.16xlarge",
"g5.24xlarge",
"g5.2xlarge",
"g5.48xlarge",
"g5.4xlarge",
"g5.8xlarge",
"g5.xlarge",
"g5g.16xlarge",
"g5g.2xlarge",
"g5g.4xlarge",
"g5g.8xlarge",
"g5g.xlarge",
"h1.16xlarge",
"h1.2xlarge",
"h1.4xlarge",
"h1.8xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"i4i.16xlarge",
"i4i.2xlarge",
"i4i.32xlarge",
"i4i.4xlarge",
"i4i.8xlarge",
"i4i.large",
"i4i.xlarge",
"im4gn.16xlarge",
"im4gn.2xlarge",
"im4gn.4xlarge",
"im4gn.8xlarge",
"im4gn.large",
"im4gn.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"is4gen.2xlarge",
"is4gen.4xlarge",
"is4gen.8xlarge",
"is4gen.large",
"is4gen.medium",
"is4gen.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p3dn.24xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-12tb1.112xlarge",
"u-3tb1.56xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"u-9tb1.112xlarge",
"vt1.24xlarge",
"vt1.3xlarge",
"vt1.6xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2gd.12xlarge",
"x2gd.16xlarge",
"x2gd.2xlarge",
"x2gd.4xlarge",
"x2gd.8xlarge",
"x2gd.large",
"x2gd.medium",
"x2gd.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"x2iezn.12xlarge",
"x2iezn.2xlarge",
"x2iezn.4xlarge",
"x2iezn.6xlarge",
"x2iezn.8xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"us-east-2": {
"api_name": "ec2_us_east_ohio",
"country": "USA",
"endpoint": "ec2.us-east-2.amazonaws.com",
"id": "us-east-2",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"h1.16xlarge",
"h1.2xlarge",
"h1.4xlarge",
"h1.8xlarge",
"hpc6a.48xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"i4i.16xlarge",
"i4i.2xlarge",
"i4i.32xlarge",
"i4i.4xlarge",
"i4i.8xlarge",
"i4i.large",
"i4i.xlarge",
"im4gn.16xlarge",
"im4gn.2xlarge",
"im4gn.4xlarge",
"im4gn.8xlarge",
"im4gn.large",
"im4gn.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"is4gen.2xlarge",
"is4gen.4xlarge",
"is4gen.8xlarge",
"is4gen.large",
"is4gen.medium",
"is4gen.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2gd.12xlarge",
"x2gd.16xlarge",
"x2gd.2xlarge",
"x2gd.4xlarge",
"x2gd.8xlarge",
"x2gd.large",
"x2gd.medium",
"x2gd.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "4",
},
"us-gov-west-1": {
"api_name": "ec2_us_govwest",
"country": "US",
"endpoint": "ec2.us-gov-west-1.amazonaws.com",
"id": "us-gov-west-1",
"instance_types": [],
"signature_version": "2",
},
"us-west-1": {
"api_name": "ec2_us_west",
"country": "USA",
"endpoint": "ec2.us-west-1.amazonaws.com",
"id": "us-west-1",
"instance_types": [
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
"us-west-2": {
"api_name": "ec2_us_west_oregon",
"country": "US",
"endpoint": "ec2.us-west-2.amazonaws.com",
"id": "us-west-2",
"instance_types": [
"a1.2xlarge",
"a1.4xlarge",
"a1.large",
"a1.medium",
"a1.xlarge",
"c1.medium",
"c1.xlarge",
"c3.2xlarge",
"c3.4xlarge",
"c3.8xlarge",
"c3.large",
"c3.xlarge",
"c4.2xlarge",
"c4.4xlarge",
"c4.8xlarge",
"c4.large",
"c4.xlarge",
"c5.12xlarge",
"c5.18xlarge",
"c5.24xlarge",
"c5.2xlarge",
"c5.4xlarge",
"c5.9xlarge",
"c5.large",
"c5.xlarge",
"c5a.12xlarge",
"c5a.16xlarge",
"c5a.24xlarge",
"c5a.2xlarge",
"c5a.4xlarge",
"c5a.8xlarge",
"c5a.large",
"c5a.xlarge",
"c5ad.12xlarge",
"c5ad.16xlarge",
"c5ad.24xlarge",
"c5ad.2xlarge",
"c5ad.4xlarge",
"c5ad.8xlarge",
"c5ad.large",
"c5ad.xlarge",
"c5d.12xlarge",
"c5d.18xlarge",
"c5d.24xlarge",
"c5d.2xlarge",
"c5d.4xlarge",
"c5d.9xlarge",
"c5d.large",
"c5d.xlarge",
"c5n.18xlarge",
"c5n.2xlarge",
"c5n.4xlarge",
"c5n.9xlarge",
"c5n.large",
"c5n.xlarge",
"c6a.12xlarge",
"c6a.16xlarge",
"c6a.24xlarge",
"c6a.2xlarge",
"c6a.32xlarge",
"c6a.48xlarge",
"c6a.4xlarge",
"c6a.8xlarge",
"c6a.large",
"c6a.xlarge",
"c6g.12xlarge",
"c6g.16xlarge",
"c6g.2xlarge",
"c6g.4xlarge",
"c6g.8xlarge",
"c6g.large",
"c6g.medium",
"c6g.xlarge",
"c6gd.12xlarge",
"c6gd.16xlarge",
"c6gd.2xlarge",
"c6gd.4xlarge",
"c6gd.8xlarge",
"c6gd.large",
"c6gd.medium",
"c6gd.xlarge",
"c6gn.12xlarge",
"c6gn.16xlarge",
"c6gn.2xlarge",
"c6gn.4xlarge",
"c6gn.8xlarge",
"c6gn.large",
"c6gn.medium",
"c6gn.xlarge",
"c6i.12xlarge",
"c6i.16xlarge",
"c6i.24xlarge",
"c6i.2xlarge",
"c6i.32xlarge",
"c6i.4xlarge",
"c6i.8xlarge",
"c6i.large",
"c6i.xlarge",
"cc2.8xlarge",
"cr1.8xlarge",
"d2.2xlarge",
"d2.4xlarge",
"d2.8xlarge",
"d2.xlarge",
"d3.2xlarge",
"d3.4xlarge",
"d3.8xlarge",
"d3.xlarge",
"d3en.12xlarge",
"d3en.2xlarge",
"d3en.4xlarge",
"d3en.6xlarge",
"d3en.8xlarge",
"d3en.xlarge",
"dl1.24xlarge",
"f1.16xlarge",
"f1.2xlarge",
"f1.4xlarge",
"g2.2xlarge",
"g2.8xlarge",
"g3.16xlarge",
"g3.4xlarge",
"g3.8xlarge",
"g3s.xlarge",
"g4ad.16xlarge",
"g4ad.2xlarge",
"g4ad.4xlarge",
"g4ad.8xlarge",
"g4ad.xlarge",
"g4dn.12xlarge",
"g4dn.16xlarge",
"g4dn.2xlarge",
"g4dn.4xlarge",
"g4dn.8xlarge",
"g4dn.xlarge",
"g5.12xlarge",
"g5.16xlarge",
"g5.24xlarge",
"g5.2xlarge",
"g5.48xlarge",
"g5.4xlarge",
"g5.8xlarge",
"g5.xlarge",
"g5g.16xlarge",
"g5g.2xlarge",
"g5g.4xlarge",
"g5g.8xlarge",
"g5g.xlarge",
"h1.16xlarge",
"h1.2xlarge",
"h1.4xlarge",
"h1.8xlarge",
"hs1.8xlarge",
"i2.2xlarge",
"i2.4xlarge",
"i2.8xlarge",
"i2.large",
"i2.xlarge",
"i3.16xlarge",
"i3.2xlarge",
"i3.4xlarge",
"i3.8xlarge",
"i3.large",
"i3.xlarge",
"i3en.12xlarge",
"i3en.24xlarge",
"i3en.2xlarge",
"i3en.3xlarge",
"i3en.6xlarge",
"i3en.large",
"i3en.xlarge",
"i4i.16xlarge",
"i4i.2xlarge",
"i4i.32xlarge",
"i4i.4xlarge",
"i4i.8xlarge",
"i4i.large",
"i4i.xlarge",
"im4gn.16xlarge",
"im4gn.2xlarge",
"im4gn.4xlarge",
"im4gn.8xlarge",
"im4gn.large",
"im4gn.xlarge",
"inf1.24xlarge",
"inf1.2xlarge",
"inf1.6xlarge",
"inf1.xlarge",
"is4gen.2xlarge",
"is4gen.4xlarge",
"is4gen.8xlarge",
"is4gen.large",
"is4gen.medium",
"is4gen.xlarge",
"m1.large",
"m1.medium",
"m1.small",
"m1.xlarge",
"m2.2xlarge",
"m2.4xlarge",
"m2.xlarge",
"m3.2xlarge",
"m3.large",
"m3.medium",
"m3.xlarge",
"m4.10xlarge",
"m4.16xlarge",
"m4.2xlarge",
"m4.4xlarge",
"m4.large",
"m4.xlarge",
"m5.12xlarge",
"m5.16xlarge",
"m5.24xlarge",
"m5.2xlarge",
"m5.4xlarge",
"m5.8xlarge",
"m5.large",
"m5.xlarge",
"m5a.12xlarge",
"m5a.16xlarge",
"m5a.24xlarge",
"m5a.2xlarge",
"m5a.4xlarge",
"m5a.8xlarge",
"m5a.large",
"m5a.xlarge",
"m5ad.12xlarge",
"m5ad.16xlarge",
"m5ad.24xlarge",
"m5ad.2xlarge",
"m5ad.4xlarge",
"m5ad.8xlarge",
"m5ad.large",
"m5ad.xlarge",
"m5d.12xlarge",
"m5d.16xlarge",
"m5d.24xlarge",
"m5d.2xlarge",
"m5d.4xlarge",
"m5d.8xlarge",
"m5d.large",
"m5d.xlarge",
"m5dn.12xlarge",
"m5dn.16xlarge",
"m5dn.24xlarge",
"m5dn.2xlarge",
"m5dn.4xlarge",
"m5dn.8xlarge",
"m5dn.large",
"m5dn.xlarge",
"m5n.12xlarge",
"m5n.16xlarge",
"m5n.24xlarge",
"m5n.2xlarge",
"m5n.4xlarge",
"m5n.8xlarge",
"m5n.large",
"m5n.xlarge",
"m5zn.12xlarge",
"m5zn.2xlarge",
"m5zn.3xlarge",
"m5zn.6xlarge",
"m5zn.large",
"m5zn.xlarge",
"m6a.12xlarge",
"m6a.16xlarge",
"m6a.24xlarge",
"m6a.2xlarge",
"m6a.32xlarge",
"m6a.48xlarge",
"m6a.4xlarge",
"m6a.8xlarge",
"m6a.large",
"m6a.xlarge",
"m6g.12xlarge",
"m6g.16xlarge",
"m6g.2xlarge",
"m6g.4xlarge",
"m6g.8xlarge",
"m6g.large",
"m6g.medium",
"m6g.xlarge",
"m6gd.12xlarge",
"m6gd.16xlarge",
"m6gd.2xlarge",
"m6gd.4xlarge",
"m6gd.8xlarge",
"m6gd.large",
"m6gd.medium",
"m6gd.xlarge",
"m6i.12xlarge",
"m6i.16xlarge",
"m6i.24xlarge",
"m6i.2xlarge",
"m6i.32xlarge",
"m6i.4xlarge",
"m6i.8xlarge",
"m6i.large",
"m6i.xlarge",
"p2.16xlarge",
"p2.8xlarge",
"p2.xlarge",
"p3.16xlarge",
"p3.2xlarge",
"p3.8xlarge",
"p3dn.24xlarge",
"p4d.24xlarge",
"r3.2xlarge",
"r3.4xlarge",
"r3.8xlarge",
"r3.large",
"r3.xlarge",
"r4.16xlarge",
"r4.2xlarge",
"r4.4xlarge",
"r4.8xlarge",
"r4.large",
"r4.xlarge",
"r5.12xlarge",
"r5.16xlarge",
"r5.24xlarge",
"r5.2xlarge",
"r5.4xlarge",
"r5.8xlarge",
"r5.large",
"r5.xlarge",
"r5a.12xlarge",
"r5a.16xlarge",
"r5a.24xlarge",
"r5a.2xlarge",
"r5a.4xlarge",
"r5a.8xlarge",
"r5a.large",
"r5a.xlarge",
"r5ad.12xlarge",
"r5ad.16xlarge",
"r5ad.24xlarge",
"r5ad.2xlarge",
"r5ad.4xlarge",
"r5ad.8xlarge",
"r5ad.large",
"r5ad.xlarge",
"r5b.12xlarge",
"r5b.16xlarge",
"r5b.24xlarge",
"r5b.2xlarge",
"r5b.4xlarge",
"r5b.8xlarge",
"r5b.large",
"r5b.xlarge",
"r5d.12xlarge",
"r5d.16xlarge",
"r5d.24xlarge",
"r5d.2xlarge",
"r5d.4xlarge",
"r5d.8xlarge",
"r5d.large",
"r5d.xlarge",
"r5dn.12xlarge",
"r5dn.16xlarge",
"r5dn.24xlarge",
"r5dn.2xlarge",
"r5dn.4xlarge",
"r5dn.8xlarge",
"r5dn.large",
"r5dn.xlarge",
"r5n.12xlarge",
"r5n.16xlarge",
"r5n.24xlarge",
"r5n.2xlarge",
"r5n.4xlarge",
"r5n.8xlarge",
"r5n.large",
"r5n.xlarge",
"r6g.12xlarge",
"r6g.16xlarge",
"r6g.2xlarge",
"r6g.4xlarge",
"r6g.8xlarge",
"r6g.large",
"r6g.medium",
"r6g.xlarge",
"r6gd.12xlarge",
"r6gd.16xlarge",
"r6gd.2xlarge",
"r6gd.4xlarge",
"r6gd.8xlarge",
"r6gd.large",
"r6gd.medium",
"r6gd.xlarge",
"r6i.12xlarge",
"r6i.16xlarge",
"r6i.24xlarge",
"r6i.2xlarge",
"r6i.32xlarge",
"r6i.4xlarge",
"r6i.8xlarge",
"r6i.large",
"r6i.xlarge",
"t1.micro",
"t2.2xlarge",
"t2.large",
"t2.medium",
"t2.micro",
"t2.nano",
"t2.small",
"t2.xlarge",
"t3.2xlarge",
"t3.large",
"t3.medium",
"t3.micro",
"t3.nano",
"t3.small",
"t3.xlarge",
"t3a.2xlarge",
"t3a.large",
"t3a.medium",
"t3a.micro",
"t3a.nano",
"t3a.small",
"t3a.xlarge",
"t4g.2xlarge",
"t4g.large",
"t4g.medium",
"t4g.micro",
"t4g.nano",
"t4g.small",
"t4g.xlarge",
"u-12tb1.112xlarge",
"u-3tb1.56xlarge",
"u-6tb1.112xlarge",
"u-6tb1.56xlarge",
"u-9tb1.112xlarge",
"vt1.24xlarge",
"vt1.3xlarge",
"vt1.6xlarge",
"x1.16xlarge",
"x1.32xlarge",
"x1e.16xlarge",
"x1e.2xlarge",
"x1e.32xlarge",
"x1e.4xlarge",
"x1e.8xlarge",
"x1e.xlarge",
"x2gd.12xlarge",
"x2gd.16xlarge",
"x2gd.2xlarge",
"x2gd.4xlarge",
"x2gd.8xlarge",
"x2gd.large",
"x2gd.medium",
"x2gd.xlarge",
"x2idn.16xlarge",
"x2idn.24xlarge",
"x2idn.32xlarge",
"x2iedn.16xlarge",
"x2iedn.24xlarge",
"x2iedn.2xlarge",
"x2iedn.32xlarge",
"x2iedn.4xlarge",
"x2iedn.8xlarge",
"x2iedn.xlarge",
"x2iezn.12xlarge",
"x2iezn.2xlarge",
"x2iezn.4xlarge",
"x2iezn.6xlarge",
"x2iezn.8xlarge",
"z1d.12xlarge",
"z1d.2xlarge",
"z1d.3xlarge",
"z1d.6xlarge",
"z1d.large",
"z1d.xlarge",
],
"signature_version": "2",
},
}
|
#!/usr/bin/env python
# encoding: utf-8
name = "R_Addition_MultipleBond/groups"
shortDesc = u""
longDesc = u"""
The reaction site *3 should be a triplet, otherwise it will react via the 1+2_Cycloaddition family instead.
"""
template(reactants=["R_R", "YJ"], products=["RJ_R_Y"], ownReverse=False)
reverse = "Beta_Scission"
recipe(actions=[
['CHANGE_BOND', '*1', -1, '*2'],
['FORM_BOND', '*1', 1, '*3'],
['GAIN_RADICAL', '*2', '1'],
['LOSE_RADICAL', '*3', '1'],
])
entry(
index = 1,
label = "R_R",
group = "OR{Cd_R, Ct_R, Od_R, Sd_R, Nd_R, Nt_R}",
kinetics = None,
)
entry(
index = 2,
label = "YJ",
group = "OR{HJ, Y_1centerquadrad, Y_1centertrirad, Y_1centerbirad, CJ, OJ, SJ, NJ}",
kinetics = None,
)
entry(
index = 3,
label = "Cd_R",
group =
"""
1 *1 C u0 {2,D}
2 *2 R!H u0 {1,D}
""",
kinetics = None,
)
entry(
index = 838,
label = "Ct_R",
group =
"""
1 *1 Ct u0 {2,T}
2 *2 R!H u0 {1,T}
""",
kinetics = None,
)
entry(
index = 873,
label = "Od_R",
group =
"""
1 *1 Od u0 {2,D}
2 *2 R!H u0 {1,D}
""",
kinetics = None,
)
entry(
index = 314,
label = "Nd_R",
group = "OR{N1d_R, N3d_R}",
kinetics = None,
)
entry(
index = 394,
label = "N1d_R",
group =
"""
1 *1 N1d u0 p2 {2,D}
2 *2 R!H u0 {1,D}
""",
kinetics = None,
)
entry(
index = 316,
label = "N3d_R",
group =
"""
1 *1 N3d u0 {2,D}
2 *2 R!H u0 {1,D}
""",
kinetics = None,
)
entry(
index=314,
label="Nt_R",
group="OR{N3t_R, N5t_R}",
kinetics=None,
)
entry(
index=338,
label="N3t_R",
group=
"""
1 *1 N3t u0 {2,T}
2 *2 R!H u0 {1,T}
""",
kinetics=None,
)
entry(
index = 343,
label = "N5t_R",
group =
"""
1 *1 N5t u0 {2,T}
2 *2 R!H u0 {1,T}
""",
kinetics = None,
)
entry(
index = 878,
label = "Sd_R",
group =
"""
1 *1 Sd u0 {2,D}
2 *2 R!H u0 {1,D}
""",
kinetics = None,
)
entry(
index = 915,
label = "HJ",
group =
"""
1 *3 H u1
""",
kinetics = None,
)
entry(
index = 916,
label = "CJ",
group =
"""
1 *3 C u1 p0
""",
kinetics = None,
)
entry(
index = 917,
label = "CbJ",
group =
"""
1 *3 Cb u1 p0
""",
kinetics = None,
)
entry(
index = 918,
label = "CtJ",
group =
"""
1 *3 Ct u1 p0 {2,T}
2 R!H u0 {1,T}
""",
kinetics = None,
)
entry(
index = 919,
label = "C2b",
group =
"""
1 *3 C u1 p0 {2,T}
2 C u1 {1,T}
""",
kinetics = None,
)
entry(
index = 920,
label = "C=SJ",
group =
"""
1 *3 CS u1 p0 {2,S}
2 R u0 {1,S}
""",
kinetics = None,
)
entry(
index = 930,
label = "CO_rad",
group =
"""
1 *3 C u1 p0 {2,D} {3,S}
2 O u0 {1,D}
3 R u0 {1,S}
""",
kinetics = None,
)
entry(
index = 935,
label = "CsJ",
group =
"""
1 *3 C u1 p0 {2,S} {3,S} {4,S}
2 R u0 {1,S}
3 R u0 {1,S}
4 R u0 {1,S}
""",
kinetics = None,
)
entry(
index = 1025,
label = "OJ",
group = "OR{OJ_pri, OJ_sec, O2b}",
kinetics = None,
)
entry(
index = 1026,
label = "OJ_pri",
group =
"""
1 *3 O u1 {2,S}
2 H u0 {1,S}
""",
kinetics = None,
)
entry(
index = 1027,
label = "OJ_sec",
group =
"""
1 *3 O u1 {2,S}
2 R!H u0 {1,S}
""",
kinetics = None,
)
entry(
index = 1032,
label = "O2b",
group =
"""
1 *3 O u1 {2,S}
2 O u1 {1,S}
""",
kinetics = None,
)
entry(
index = 1037,
label = "SJ",
group =
"""
1 *3 S u1
""",
kinetics = None,
)
entry(
index = 1038,
label = "SsJ",
group =
"""
1 *3 Ss u1 {2,S}
2 R u0 {1,S}
""",
kinetics = None,
)
entry(
index = 395,
label = "NJ",
group = "OR{N3J}",
kinetics = None,
)
entry(
index = 364,
label = "N3J",
group =
"""
1 *3 [N3s,N3d] u1
""",
kinetics = None,
)
entry(
index = 365,
label = "N3sJ",
group =
"""
1 *3 N3s u1
""",
kinetics = None,
)
entry(
index = 377,
label = "N3dJ",
group =
"""
1 *3 N3d u1
""",
kinetics = None,
)
entry(
index = 350,
label = "Y_1centerbirad",
group =
"""
1 *3 R!H u2
""",
kinetics = None,
)
entry(
index = 344,
label = "Y_1centertrirad",
group = "OR{N_atom_quartet, N_atom_doublet, CH_quartet, CH_doublet}",
kinetics = None,
)
entry(
index = 386,
label = "N_atom_quartet",
group =
"""
1 *3 N u3 p1
""",
kinetics = None,
)
entry(
index = 387,
label = "N_atom_doublet",
group =
"""
1 *3 N u1 p2
""",
kinetics = None,
)
entry(
index = 388,
label = "CH_quartet",
group =
"""
1 *3 Cs u3 p0 {2,S}
2 H u0 {1,S}
""",
kinetics = None,
)
entry(
index = 389,
label = "CH_doublet",
group =
"""
1 *3 C u1 p1 {2,S}
2 H u0 {1,S}
""",
kinetics = None,
)
entry(
index = 393,
label = "Y_1centerquadrad",
group = "OR{C_quintet, C_triplet}",
kinetics = None,
)
entry(
index = 383,
label = "C_quintet",
group =
"""
1 *3 C u4 p0
""",
kinetics = None,
)
entry(
index = 384,
label = "C_triplet",
group =
"""
1 *3 C u2 p1
""",
kinetics = None,
)
tree(
"""
L1: R_R
L2: Cd_R
L2: Ct_R
L2: Od_R
L2: Nd_R
L3: N1d_R
L3: N3d_R
L2: Nt_R
L3: N3t_R
L3: N5t_R
L2: Sd_R
L1: YJ
L2: HJ
L2: Y_1centerquadrad
L3: C_quintet
L3: C_triplet
L2: Y_1centertrirad
L3: N_atom_quartet
L3: N_atom_doublet
L3: CH_quartet
L3: CH_doublet
L2: Y_1centerbirad
L2: CJ
L3: CbJ
L3: CtJ
L3: C2b
L3: C=SJ
L3: CO_rad
L3: CsJ
L2: OJ
L3: OJ_pri
L3: OJ_sec
L3: O2b
L2: SJ
L3: SsJ
L2: NJ
L3: N3J
L4: N3sJ
L4: N3dJ
"""
)
forbidden(
label = "O2d",
group =
"""
1 *1 O u0 {2,D}
2 *2 O u0 {1,D}
""",
shortDesc = u"""""",
longDesc =
u"""
""",
)
|
# SPDX-License-Identifier: GPL-3.0-only
def make_parser_struct(cpp_struct_value, all_enums, all_bitfields, all_used_structs, all_used_groups, hpp, struct_name, read_only, struct_title):
hpp.write(" private:\n".format(struct_name))
hpp.write(" std::vector<ParserStructValue> get_values_internal() override;\n".format(struct_name))
hpp.write(" public:\n".format(struct_name))
cpp_struct_value.write("std::vector<ParserStructValue> {}::get_values_internal() {{\n".format(struct_name))
cpp_struct_value.write(" std::vector<ParserStructValue> values;\n")
cpp_struct_value.write(" values.reserve({});\n".format(len(all_used_structs)))
for struct in all_used_structs:
if "hidden" in struct and struct["hidden"]:
continue
if ("cache_only" in struct and struct["cache_only"]) or ("endian" in struct and struct["endian"] == "little") or ("unused" in struct and struct["unused"]):
continue
def make_cpp_string(what):
return "\"{}\"".format(what.replace("\"", "\\\"").replace("\n", "\\n"))
name = "\"{}\"".format(struct["display_name"])
member_name = struct["member_name"]
member_name_q = "\"{}\"".format(member_name)
comment = "nullptr" if "comment" not in struct else make_cpp_string(struct["comment"])
struct_read_only = "true" if ((read_only or ("read_only" in struct and struct["read_only"])) and not ("read_only" in struct and struct["read_only"] == False)) else "false"
unit = "nullptr" if "unit" not in struct else "\"{}\"".format(struct["unit"].replace("\"", "\\\""))
# If this is the start of a group, add a group
for i in all_used_groups:
if i["first"] == struct["name"]:
cpp_struct_value.write(" values.emplace_back(\"{}\", {});\n".format(i["name"], make_cpp_string(i["description"])))
break
first_arguments = "{},{},{},&this->{}".format(name, member_name_q, comment, struct["member_name"])
type = struct["type"]
if type == "TagDependency":
classes = struct["classes"]
classes_len = len(classes)
if classes[0] == "*":
cpp_struct_value.write(" values.emplace_back({}, nullptr, 0, {});\n".format(first_arguments, struct_read_only))
else:
cpp_struct_value.write(" TagFourCC {}_types[] = {{".format(member_name));
for c in range(0, classes_len):
if c != 0:
cpp_struct_value.write(", ")
cpp_struct_value.write("TagFourCC::TAG_FOURCC_{}".format(classes[c].upper()))
cpp_struct_value.write("};\n");
cpp_struct_value.write(" values.emplace_back({}, {}_types, {}, {});\n".format(first_arguments, member_name, classes_len, struct_read_only))
elif type == "TagReflexive":
minimum = 0 if not ("minimum" in struct) else struct["minimum"]
maximum = 0xFFFFFFFF
if "extended_maximum" in struct:
maximum = struct["extended_maximum"]
elif "maximum" in struct:
maximum = struct["maximum"]
vstruct = "std::vector<{}>".format(struct["struct"])
cpp_struct_value.write(" values.emplace_back({}, ParserStructValue::get_object_in_array_template<{}>, ParserStructValue::get_array_size_template<{}>, ParserStructValue::delete_objects_in_array_template<{}>, ParserStructValue::insert_object_in_array_template<{}>, ParserStructValue::duplicate_object_in_array_template<{}>, ParserStructValue::swap_object_in_array_template<{}>, static_cast<std::size_t>({}), static_cast<std::size_t>({}), {});\n".format(first_arguments, vstruct, vstruct, vstruct, vstruct, vstruct, vstruct, minimum, maximum, struct_read_only))
elif type == "TagDataOffset" or type == "TagString":
cpp_struct_value.write(" values.emplace_back({}, {});\n".format(first_arguments, struct_read_only))
elif type == "ScenarioScriptNodeValue" or type == "ScenarioStructureBSPArrayVertex":
pass
else:
found = False
for b in all_bitfields:
if type == b["name"]:
found = True
type = "{}Flag".format(type)
# Base mask (all existing fields)
mask = 2**len(b["fields"]) - 1
# Hide unused cache-only stuff
if "cache_only" in b:
mask_cache_only = 0
for a in b["cache_only"]:
for n in range(0, len(b["fields"])):
if b["fields"][n] == a:
mask_cache_only = mask_cache_only | (1 << n)
break
mask = (~mask_cache_only) & mask
# Hide unused bitmasks
if "__excluded" in struct and struct["__excluded"] is not None:
mask = (~struct["__excluded"]) & mask
# If mask is 0, disregard
if mask == 0:
break
cpp_struct_value.write(" values.emplace_back({}, ParserStructValue::list_bitmask_template<HEK::{}, HEK::{}_to_string, {}, 0x{:X}>, ParserStructValue::list_bitmask_template<HEK::{}, HEK::{}_to_string_pretty, {}, 0x{:X}>, ParserStructValue::read_bitfield_template<HEK::{}, HEK::{}_from_string>, ParserStructValue::write_bitfield_template<HEK::{}, HEK::{}_from_string>, {});\n".format(first_arguments, type, type, len(b["fields_formatted"]), mask, type, type, len(b["fields_formatted"]), mask, type, type, type, type, struct_read_only))
break
if found:
continue
for e in all_enums:
if type == e["name"]:
found = True
cpp_struct_value.write(" {\n")
ignorelist_params = ""
# Make an ignorelist to hold stuff we don't want to list
if "__excluded" in struct and struct["__excluded"] is not None:
cpp_struct_value.write(" static HEK::{} ignorelist[] = {{\n".format(e["name"]))
for x in struct["__excluded"]:
cpp_struct_value.write(" static_cast<HEK::{}>({}),\n".format(e["name"], x))
cpp_struct_value.write(" };\n")
ignorelist_params = ", ignorelist, {}".format(len(struct["__excluded"]))
# Do it!
list_enum_invocation = "ParserStructValue::list_enum_template<HEK::{}, HEK::{}_to_string{{}}, {}{}>".format(type, type, len(e["options_formatted"]), ignorelist_params)
cpp_struct_value.write(" values.emplace_back({}, {}, {}, ParserStructValue::read_enum_template<HEK::{}, HEK::{}_to_string>, ParserStructValue::write_enum_template<HEK::{}, HEK::{}_from_string>, {});\n".format(first_arguments, list_enum_invocation.format(""), list_enum_invocation.format("_pretty"), type, type, type, type, struct_read_only))
cpp_struct_value.write(" }\n")
break
if found:
continue
bounds_b = "bounds" in struct and struct["bounds"]
bounds = "true" if bounds_b else "false"
count = 1 * (2 if bounds_b else 1)
minimum = "static_cast<ParserStructValue::Number>({})".format(struct["minimum"]) if "minimum" in struct else "std::nullopt"
maximum = "static_cast<ParserStructValue::Number>({})".format(struct["maximum"]) if "maximum" in struct else "std::nullopt"
volatile = "true" if ("volatile" in struct and struct["volatile"]) else "false"
cpp_struct_value.write(" values.emplace_back({}, ParserStructValue::ValueType::VALUE_TYPE_{}, {}, {}, {}, {}, {}, {}, {});\n".format(first_arguments, type.upper(), unit, count, bounds, volatile, struct_read_only, minimum, maximum))
cpp_struct_value.write(" return values;\n")
cpp_struct_value.write("}\n")
hpp.write(" const char *struct_name() const override;\n")
cpp_struct_value.write("const char *{}::struct_name() const {{\n".format(struct_name))
cpp_struct_value.write(" return \"{}\";\n".format(struct_name))
cpp_struct_value.write("}\n")
if struct_title is not None:
hpp.write(" bool has_title() const override;\n")
cpp_struct_value.write("bool {}::has_title() const {{\n".format(struct_name))
cpp_struct_value.write(" return true;\n")
cpp_struct_value.write("}\n")
hpp.write(" const char *title() const override;\n")
cpp_struct_value.write("const char *{}::title() const {{\n".format(struct_name))
for struct in all_used_structs:
if "name" in struct and struct["name"] == struct_title:
if struct["type"] == "TagString":
cpp_struct_value.write(" return this->{}.string;\n".format(struct["member_name"]))
elif struct["type"] == "TagDependency":
cpp_struct_value.write(" const auto *start = this->{}.path.c_str();\n".format(struct["member_name"]))
cpp_struct_value.write(" for(const char *q = start; q && *q; q++) {\n")
cpp_struct_value.write(" if(*q == '\\\\') {\n")
cpp_struct_value.write(" start = q + 1;\n")
cpp_struct_value.write(" }\n")
cpp_struct_value.write(" }\n")
cpp_struct_value.write(" return start;\n")
else:
raise Exception("ohno")
cpp_struct_value.write("}\n")
|
# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
model_cfgs = {
# patch models (my experiments)
'swin_base_patch4_window12_384': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0),
'swin_base_patch4_window7_224': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',
),
'swin_large_patch4_window12_384': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
input_size=(3, 384, 384), crop_pct=1.0),
'swin_large_patch4_window7_224': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',
),
'swin_small_patch4_window7_224': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',
),
'swin_tiny_patch4_window7_224': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
),
'swin_base_patch4_window12_384_in22k': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
'swin_base_patch4_window7_224_in22k': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
num_classes=21841),
'swin_large_patch4_window12_384_in22k': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),
'swin_large_patch4_window7_224_in22k': _cfg(
url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
num_classes=21841),
}
def build_configs(name, **kwargs):
config = model_cfgs[name]
model_architectures = {
'swin_base_patch4_window12_384' : dict(
patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs),
'swin_base_patch4_window7_224' : dict(
patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs),
'swin_large_patch4_window12_384' : dict(
patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs),
'swin_large_patch4_window7_224' : dict(
patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs),
'swin_small_patch4_window7_224' : dict(
patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs),
'swin_tiny_patch4_window7_224' : dict(
patch_size=4, window_size=7, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs),
'swin_base_patch4_window12_384_in22k' : dict(
patch_size=4, window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs),
'swin_base_patch4_window7_224_in22k' : dict(
patch_size=4, window_size=7, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs),
'swin_large_patch4_window12_384_in22k' : dict(
patch_size=4, window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs),
'swin_large_patch4_window7_224_in22k' : dict(
patch_size=4, window_size=7, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
}
return model_architectures[name], config
|
lychrels = 0
for i in range(0, 10001):
s = str(i)
n = i
indicator = 0
for j in range(0, 50):
n = n + int(s[::-1])
s = str(n)
l = len(s)
if l % 2 == 0:
p1 = s[0:int(l/2)]
p2 = s[int(l/2):][::-1]
else:
p1 = s[0:int(l/2)+1]
p2 = s[int(l/2):][::-1]
if p1 == p2:
indicator += 1
break
if indicator == 0:
lychrels += 1
print(lychrels) # 249 |
# lowercased special tokens
UNK = '<unk>'
PAD = '<pad>'
START = '<bos>'
STOP = '<eos>'
# special tokens id (don't edit this order)
UNK_ID = 0
PAD_ID = 1
# this should be set later after building fields
TAGS_PAD_ID = 0
# output_dir
OUTPUT_DIR = 'runs'
# default filenames
CONFIG = 'config.json'
DATASET = 'dataset.torch'
MODEL = 'model.torch'
OPTIMIZER = 'optim.torch'
SCHEDULER = 'scheduler.torch'
TRAINER = 'trainer.torch'
VOCAB = 'vocab.torch'
PREDICTIONS = 'predictions.txt'
|
DETAIL_FILE = "詳細はhttps://github.com/Kdy0115/agent-simulation-system を参照して下さい。"
ENV_ERROR = """Error：実行環境エラー
マルチプロセスと非バッチ処理は対応していません。マルチプロセスを行う場合はバッチ処理に設定してください。
./controllers/env.pyの実行環境設定ファイルを編集してください。
{}""".format(DETAIL_FILE)
FILE_NOT_FIND_ERROR = """ファイルが正しく読み込まれませんでした。正しいパス名を選択してください。
./config/config.ini内のパスを正しい値に編集してください。
{}""".format(DETAIL_FILE)
SPACE_DEFINITION_ERROR = """シミュレーション空間定義に問題があります。
レイアウト情報、熱源情報、model.py内の空間定義アルゴリズムを見直してください。
{}""".format(DETAIL_FILE)
DATA_CLUMN_NAME_REFFERENCE_ERROR = """カラム名の対応が間違っています。
読み込みデータを確認するかカラム名に対応したプログラムに書き換えて下さい。
{}
""".format(DETAIL_FILE) |
def test_mysql_process(host):
assert host.service("my_db-mysql").is_running
assert host.service("my_db-mysql").is_enabled
def test_check_mysql_access(host):
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u1 -P 13306 -ppassword1 db1").succeeded
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u1 -P 13306 -ppassword1 db2").failed
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u1 -P 13306 -ppassword2 db1").failed
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u3 -P 13306 -ppassword3 db1").succeeded
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u3 -P 13306 -ppassword3 db2").succeeded
assert host.run("mysql --protocol=tcp -h 127.0.0.1 -u u3 -P 13306 -ppassword3 db3").failed
def test_db_encoding(host):
assert host.run("""echo "SELECT DEFAULT_CHARACTER_SET_NAME FROM information_schema.SCHEMATA where SCHEMA_NAME='db1';" | mysql --protocol=tcp -h 127.0.0.1 -u u1 -P 13306 -ppassword1 db1 -N""").stdout.strip() == "utf8"
|
# This file contains all the constants in use by the tetration modules
# defining tetration constants
TETRATION_API_INVENTORY_TAG = '/inventory/tags'
TETRATION_API_ROLE = '/roles'
TETRATION_API_USER = '/users'
TETRATION_API_SENSORS = '/sensors'
TETRATION_API_INVENTORY_FILTER = '/filters/inventories'
TETRATION_API_SCOPES = '/app_scopes'
TETRATION_API_APPLICATIONS = '/applications'
TETRATION_API_APPLICATION_POLICIES = '/policies'
TETRATION_API_AGENT_CONFIG_PROFILES = '/inventory_config/profiles'
TETRATION_API_AGENT_CONFIG_INTENTS = '/inventory_config/intents'
TETRATION_COLUMN_NAMES = '/assets/cmdb/attributenames'
TETRATION_API_APP_SCOPE_CAPABILITIES = ['SCOPE_READ', 'SCOPE_WRITE', 'EXECUTE',
'ENFORCE', 'SCOPE_OWNER', 'DEVELOPER']
TETRATION_API_SUCCESS_CODES = [200, 202]
# 422 is for the SCOPE Delete API
TETRATION_API_FAILURE_CODES_THAT_RETURN_DATA = [422]
TETRATION_API_PAGINATION_SIZE = 100
TETRATION_PROVIDER_SPEC = {
'server_endpoint': dict(type='str', required=True, aliases=['endpoint', 'host']),
'api_key': dict(type='str', required=True),
'api_secret': dict(type='str', required=True, no_log=True),
'verify': dict(type='bool', default=False),
'timeout': dict(type='int', default=10),
'max_retries': dict(type='int', default=3),
'api_version': dict(type='str', default='v1')
}
TETRATION_API_PROTOCOLS = [
dict(name='ANY', value=None),
dict(name='TCP', value=6),
dict(name='UDP', value=17),
dict(name='ICMP', value=1),
dict(name='Other', value=0),
dict(name='A/N', value=107),
dict(name='AH', value=51),
dict(name='ARGUS', value=13),
dict(name='ARIS', value=104),
dict(name='AX.25', value=93),
dict(name='BBN-RCC-MON', value=10),
dict(name='BNA', value=49),
dict(name='BR-SAT-MON', value=76),
dict(name='CARP', value=112),
dict(name='CBT', value=7),
dict(name='CFTP', value=62),
dict(name='CHAOS', value=16),
dict(name='CPHB', value=73),
dict(name='CPNX', value=72),
dict(name='CRTP', value=126),
dict(name='CRUDP', value=127),
dict(name='Compaq-Peer', value=110),
dict(name='DCCP', value=33),
dict(name='DCN-MEAS', value=19),
dict(name='DDP', value=37),
dict(name='DDX', value=116),
dict(name='DGP', value=86),
dict(name='DIVERT', value=258),
dict(name='DSR', value=48),
dict(name='EGP', value=8),
dict(name='EIGRP', value=88),
dict(name='EMCON', value=14),
dict(name='ENCAP', value=98),
dict(name='ESP', value=50),
dict(name='ETHERIP', value=97),
dict(name='FC', value=133),
dict(name='FIRE', value=125),
dict(name='GGP', value=3),
dict(name='GMTP', value=100),
dict(name='GRE', value=47),
dict(name='HIP', value=139),
dict(name='HMP', value=20),
dict(name='I-NLSP', value=52),
dict(name='IATP', value=117),
dict(name='IDPR', value=35),
dict(name='IDPR-CMTP', value=38),
dict(name='IDRP', value=45),
dict(name='IFMP', value=101),
dict(name='IGMP', value=2),
dict(name='IGP', value=9),
dict(name='IL', value=40),
dict(name='IP-ENCAP', value=4),
dict(name='IPCV', value=71),
dict(name='IPComp', value=108),
dict(name='IPIP', value=94),
dict(name='IPLT', value=129),
dict(name='IPPC', value=67),
dict(name='IPV6', value=41),
dict(name='IPV6-FRAG', value=44),
dict(name='IPV6-ICMP', value=58),
dict(name='IPV6-NONXT', value=59),
dict(name='IPV6-OPTS', value=60),
dict(name='IPV6-ROUTE', value=43),
dict(name='IPX-in-IP', value=111),
dict(name='IRTP', value=28),
dict(name='ISIS', value=124),
dict(name='ISO-IP', value=80),
dict(name='ISO-TP4', value=29),
dict(name='KRYPTOLAN', value=65),
dict(name='L2TP', value=115),
dict(name='LARP', value=91),
dict(name='LEAF-1', value=25),
dict(name='LEAF-2', value=26),
dict(name='MANET', value=138),
dict(name='MERIT-INP', value=32),
dict(name='MFE-NSP', value=31),
dict(name='MICP', value=95),
dict(name='MOBILE', value=55),
dict(name='MPLS-IN-IP', value=137),
dict(name='MTP', value=92),
dict(name='MUX', value=18),
dict(name='Mobility-Header', value=135),
dict(name='NARP', value=54),
dict(name='NETBLT', value=30),
dict(name='NSFNET-IGP', value=85),
dict(name='NVP-II', value=11),
dict(name='OSPFIGP', value=89),
dict(name='PFSYNC', value=240),
dict(name='PGM', value=113),
dict(name='PIM', value=103),
dict(name='PIPE', value=131),
dict(name='PNNI', value=102),
dict(name='PRM', value=21),
dict(name='PTP', value=123),
dict(name='PUP', value=12),
dict(name='PVP', value=75),
dict(name='QNX', value=106),
dict(name='RDP', value=27),
dict(name='ROHC', value=142),
dict(name='RSVP', value=46),
dict(name='RSVP-E2E-IGNORE', value=134),
dict(name='RVD', value=66),
dict(name='SAT-EXPAK', value=64),
dict(name='SAT-MON', value=69),
dict(name='SCC-SP', value=96),
dict(name='SCPS', value=105),
dict(name='SCTP', value=132),
dict(name='SDRP', value=42),
dict(name='SECURE-VMTP', value=82),
dict(name='SHIM6', value=140),
dict(name='SKIP', value=57),
dict(name='SM', value=122),
dict(name='SMP', value=121),
dict(name='SNP', value=109),
dict(name='SPS', value=130),
dict(name='SRP', value=119),
dict(name='SSCOPMCE', value=128),
dict(name='ST2', value=5),
dict(name='STP', value=118),
dict(name='SUN-ND', value=77),
dict(name='SWIPE', value=53),
dict(name='Sprite-RPC', value=90),
dict(name='TCF', value=87),
dict(name='TLSP', value=56),
dict(name='TP++', value=39),
dict(name='TRUNK-1', value=23),
dict(name='TRUNK-2', value=24),
dict(name='TTP', value=84),
dict(name='UDPLite', value=136),
dict(name='UTI', value=120),
dict(name='VINES', value=83),
dict(name='VISA', value=70),
dict(name='VMTP', value=81),
dict(name='WB-EXPAK', value=79),
dict(name='WB-MON', value=78),
dict(name='WESP', value=141),
dict(name='WSN', value=74),
dict(name='XNET', value=15),
dict(name='XNS-IDP', value=22),
dict(name='XTP', value=36),
]
|
# -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2010-Today OpenERP S.A. (<http://www.openerp.com>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
{
'name': 'Social Network',
'version': '1.0',
'category': 'Social Network',
'sequence': 2,
'summary': 'Discussions, Mailing Lists, News',
'description': """
Business oriented Social Networking
===================================
The Social Networking module provides a unified social network abstraction layer allowing applications to display a complete
communication history on documents with a fully-integrated email and message management system.
It enables the users to read and send messages as well as emails. It also provides a feeds page combined to a subscription mechanism that allows to follow documents and to be constantly updated about recent news.
Main Features
-------------
* Clean and renewed communication history for any OpenERP document that can act as a discussion topic
* Subscription mechanism to be updated about new messages on interesting documents
* Unified feeds page to see recent messages and activity on followed documents
* User communication through the feeds page
* Threaded discussion design on documents
* Relies on the global outgoing mail server - an integrated email management system - allowing to send emails with a configurable scheduler-based processing engine
* Includes an extensible generic email composition assistant, that can turn into a mass-mailing assistant and is capable of interpreting simple *placeholder expressions* that will be replaced with dynamic data when each email is actually sent.
""",
'author': 'OpenERP SA',
'website': 'https://www.odoo.com/page/enterprise-social-network',
'depends': ['base', 'base_setup'],
'data': [
'wizard/invite_view.xml',
'wizard/mail_compose_message_view.xml',
'mail_message_subtype.xml',
'res_config_view.xml',
'mail_message_view.xml',
'mail_mail_view.xml',
'mail_followers_view.xml',
'mail_thread_view.xml',
'mail_group_view.xml',
'res_partner_view.xml',
'data/mail_data.xml',
'data/mail_group_data.xml',
'security/mail_security.xml',
'security/ir.model.access.csv',
'mail_alias_view.xml',
'res_users_view.xml',
'views/mail.xml',
],
'demo': [
'data/mail_demo.xml',
'data/mail_group_demo_data.xml',
],
'installable': True,
'application': True,
'images': [
'images/inbox.jpeg',
'images/messages_form.jpeg',
'images/messages_list.jpeg',
'images/email.jpeg',
'images/join_a_group.jpeg',
'images/share_a_message.jpeg',
],
'qweb': [
'static/src/xml/mail.xml',
'static/src/xml/mail_followers.xml',
'static/src/xml/announcement.xml',
'static/src/xml/suggestions.xml',
],
}
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
# Definition for a binary tree node.
class TreeNode(object):
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def levelOrder(self, root):
"""
:type root: TreeNode
:rtype: List[List[int]]
"""
if not root:
return []
a = [root]
b = []
r = [[root.val]]
while a:
c = []
for n in a:
if n.left:
b.append(n.left)
c.append(n.left.val)
if n.right:
b.append(n.right)
c.append(n.right.val)
if c:
r.append(c)
a = b
b = []
return r
def test_level_order_1():
assert Solution().levelOrder(None) == []
def test_level_order_2():
a = TreeNode(3)
assert Solution().levelOrder(a) == [[3]]
def test_level_order_3():
a = TreeNode(1)
b = TreeNode(1)
a.left = b
assert Solution().levelOrder(a) == [[1], [1]]
def test_level_order_4():
a = TreeNode(2)
b = TreeNode(1)
c = TreeNode(3)
a.left = b
a.right = c
assert Solution().levelOrder(a) == [[2], [1, 3]]
def test_level_order_5():
a = TreeNode(5)
b = TreeNode(1)
c = TreeNode(7)
a.left = b
a.right = c
d = TreeNode(3)
e = TreeNode(8)
c.left = d
c.right = e
assert Solution().levelOrder(a) == [[5], [1, 7], [3, 8]]
def test_level_order_6():
a = TreeNode(5)
b = TreeNode(3)
c = TreeNode(9)
a.left = b
a.right = c
d = TreeNode(1)
e = TreeNode(4)
b.left = d
b.right = e
f = TreeNode(6)
g = TreeNode(10)
c.left = f
c.right = g
assert Solution().levelOrder(a) == [[5], [3, 9], [1, 4, 6, 10]]
|
def main():
print("I am a webscraper")
if __name__ == "__main__":
return main()
class Website(object):
#create a function that gets URL (def URL)
#create a function that turns HTML elements into an array of strings based on white space or '<> (def elements)
#create a function that uses REGEX to find links that matches URL through <href> (def Pages)
pass
class Page(Website):
#set URL to one of the stuff that will come out from def Pages (inherit def Pages)
#create a function that gets all elements from the page (inherit def elements)
pass
class Email(Page):
#source the elements from page and find elements with '@' through regex
pass
class Name(Page):
#set to find a page that has %ABOUT% (use REGEX)
#set to find %string string% (use REGEX)
pass
class Contact(Page):
#set to find a page that has %Contact% (use REGEX)
#filter to find digits (use REGEX)
pass
|
# from https://linked.data.gov.au/dataset/bdr/conservation-status-taxa-wa
# in the sop_recipe_abis_model datagraphs
CONSERVATION_STATUS_TAXA = [
"https://test-idafd.biodiversity.org.au/name/afd/70162908",
"https://test-idafd.biodiversity.org.au/name/afd/70162916",
"https://test-idafd.biodiversity.org.au/name/afd/70164586",
"https://test-idafd.biodiversity.org.au/name/afd/70165201",
"https://test-idafd.biodiversity.org.au/name/afd/70165203",
"https://test-idafd.biodiversity.org.au/name/afd/70167551",
"https://test-idafd.biodiversity.org.au/name/afd/70167566",
"https://test-idafd.biodiversity.org.au/name/afd/70167571",
"https://test-idafd.biodiversity.org.au/name/afd/70168226",
"https://test-idafd.biodiversity.org.au/name/afd/70171645",
"https://test-idafd.biodiversity.org.au/name/afd/70174584",
"https://test-idafd.biodiversity.org.au/name/afd/70174588",
"https://test-idafd.biodiversity.org.au/name/afd/70174589",
"https://test-idafd.biodiversity.org.au/name/afd/70174590",
"https://test-idafd.biodiversity.org.au/name/afd/70174591",
"https://test-idafd.biodiversity.org.au/name/afd/70174593",
"https://test-idafd.biodiversity.org.au/name/afd/70174596",
"https://test-idafd.biodiversity.org.au/name/afd/70174597",
"https://test-idafd.biodiversity.org.au/name/afd/70174598",
"https://test-idafd.biodiversity.org.au/name/afd/70174599",
"https://test-idafd.biodiversity.org.au/name/afd/70174601",
"https://test-idafd.biodiversity.org.au/name/afd/70174604",
"https://test-idafd.biodiversity.org.au/name/afd/70174605",
"https://test-idafd.biodiversity.org.au/name/afd/70174608",
"https://test-idafd.biodiversity.org.au/name/afd/70174611",
"https://test-idafd.biodiversity.org.au/name/afd/70174612",
"https://test-idafd.biodiversity.org.au/name/afd/70175999",
"https://test-idafd.biodiversity.org.au/name/afd/70176189",
"https://test-idafd.biodiversity.org.au/name/afd/70176351",
"https://test-idafd.biodiversity.org.au/name/afd/70176993",
"https://test-idafd.biodiversity.org.au/name/afd/70177704",
"https://test-idafd.biodiversity.org.au/name/afd/70177902",
"https://test-idafd.biodiversity.org.au/name/afd/70177903",
"https://test-idafd.biodiversity.org.au/name/afd/70179310",
"https://test-idafd.biodiversity.org.au/name/afd/70179311",
"https://test-idafd.biodiversity.org.au/name/afd/70179312",
"https://test-idafd.biodiversity.org.au/name/afd/70179313",
"https://test-idafd.biodiversity.org.au/name/afd/70179314",
"https://test-idafd.biodiversity.org.au/name/afd/70179315",
"https://test-idafd.biodiversity.org.au/name/afd/70179345",
"https://test-idafd.biodiversity.org.au/name/afd/70180670",
"https://test-idafd.biodiversity.org.au/name/afd/70180673",
"https://test-idafd.biodiversity.org.au/name/afd/70180674",
"https://test-idafd.biodiversity.org.au/name/afd/70180678",
"https://test-idafd.biodiversity.org.au/name/afd/70180682",
"https://test-idafd.biodiversity.org.au/name/afd/70181348",
"https://test-idafd.biodiversity.org.au/name/afd/70181350",
"https://test-idafd.biodiversity.org.au/name/afd/70181355",
"https://test-idafd.biodiversity.org.au/name/afd/70181379",
"https://test-idafd.biodiversity.org.au/name/afd/70181382",
"https://test-idafd.biodiversity.org.au/name/afd/70182008",
"https://test-idafd.biodiversity.org.au/name/afd/70182161",
"https://test-idafd.biodiversity.org.au/name/afd/70182790",
"https://test-idafd.biodiversity.org.au/name/afd/70182791",
"https://test-idafd.biodiversity.org.au/name/afd/70182814",
"https://test-idafd.biodiversity.org.au/name/afd/70183770",
"https://test-idafd.biodiversity.org.au/name/afd/70184890",
"https://test-idafd.biodiversity.org.au/name/afd/70185407",
"https://test-idafd.biodiversity.org.au/name/afd/70188407",
"https://test-idafd.biodiversity.org.au/name/afd/70188409",
"https://test-idafd.biodiversity.org.au/name/afd/70188412",
"https://test-idafd.biodiversity.org.au/name/afd/70188414",
"https://test-idafd.biodiversity.org.au/name/afd/70188418",
"https://test-idafd.biodiversity.org.au/name/afd/70195524",
"https://test-idafd.biodiversity.org.au/name/afd/70195525",
"https://test-idafd.biodiversity.org.au/name/afd/70195863",
"https://test-idafd.biodiversity.org.au/name/afd/70195981",
"https://test-idafd.biodiversity.org.au/name/afd/70195982",
"https://test-idafd.biodiversity.org.au/name/afd/70195984",
"https://test-idafd.biodiversity.org.au/name/afd/70195985",
"https://test-idafd.biodiversity.org.au/name/afd/70195987",
"https://test-idafd.biodiversity.org.au/name/afd/70195988",
"https://test-idafd.biodiversity.org.au/name/afd/70195989",
"https://test-idafd.biodiversity.org.au/name/afd/70195990",
"https://test-idafd.biodiversity.org.au/name/afd/70195991",
"https://test-idafd.biodiversity.org.au/name/afd/70195992",
"https://test-idafd.biodiversity.org.au/name/afd/70197086",
"https://test-idafd.biodiversity.org.au/name/afd/70197169",
"https://test-idafd.biodiversity.org.au/name/afd/70197172",
"https://test-idafd.biodiversity.org.au/name/afd/70197262",
"https://test-idafd.biodiversity.org.au/name/afd/70198528",
"https://test-idafd.biodiversity.org.au/name/afd/70199327",
"https://test-idafd.biodiversity.org.au/name/afd/70199426",
"https://test-idafd.biodiversity.org.au/name/afd/70199430",
"https://test-idafd.biodiversity.org.au/name/afd/70199663",
"https://test-idafd.biodiversity.org.au/name/afd/70199746",
"https://test-idafd.biodiversity.org.au/name/afd/70202632",
"https://test-idafd.biodiversity.org.au/name/afd/70202636",
"https://test-idafd.biodiversity.org.au/name/afd/70202637",
"https://test-idafd.biodiversity.org.au/name/afd/70202993",
"https://test-idafd.biodiversity.org.au/name/afd/70202998",
"https://test-idafd.biodiversity.org.au/name/afd/70203007",
"https://test-idafd.biodiversity.org.au/name/afd/70203256",
"https://test-idafd.biodiversity.org.au/name/afd/70204649",
"https://test-idafd.biodiversity.org.au/name/afd/70204650",
"https://test-idafd.biodiversity.org.au/name/afd/70204655",
"https://test-idafd.biodiversity.org.au/name/afd/70204658",
"https://test-idafd.biodiversity.org.au/name/afd/70207034",
"https://test-idafd.biodiversity.org.au/name/afd/70208223",
"https://test-idafd.biodiversity.org.au/name/afd/70208224",
"https://test-idafd.biodiversity.org.au/name/afd/70208227",
"https://test-idafd.biodiversity.org.au/name/afd/70214460",
"https://test-idafd.biodiversity.org.au/name/afd/70214469",
"https://test-idafd.biodiversity.org.au/name/afd/70214498",
"https://test-idafd.biodiversity.org.au/name/afd/70214833",
"https://test-idafd.biodiversity.org.au/name/afd/70215602",
"https://test-idafd.biodiversity.org.au/name/afd/70216090",
"https://test-idafd.biodiversity.org.au/name/afd/70216176",
"https://test-idafd.biodiversity.org.au/name/afd/70216838",
"https://test-idafd.biodiversity.org.au/name/afd/70216839",
"https://test-idafd.biodiversity.org.au/name/afd/70216888",
"https://test-idafd.biodiversity.org.au/name/afd/70217012",
"https://test-idafd.biodiversity.org.au/name/afd/70217319",
"https://test-idafd.biodiversity.org.au/name/afd/70217838",
"https://test-idafd.biodiversity.org.au/name/afd/70220134",
"https://test-idafd.biodiversity.org.au/name/afd/70220995",
"https://test-idafd.biodiversity.org.au/name/afd/70222918",
"https://test-idafd.biodiversity.org.au/name/afd/70222925",
"https://test-idafd.biodiversity.org.au/name/afd/70223068",
"https://test-idafd.biodiversity.org.au/name/afd/70223300",
"https://test-idafd.biodiversity.org.au/name/afd/70224616",
"https://test-idafd.biodiversity.org.au/name/afd/70226370",
"https://test-idafd.biodiversity.org.au/name/afd/70227478",
"https://test-idafd.biodiversity.org.au/name/afd/70227609",
"https://test-idafd.biodiversity.org.au/name/afd/70227610",
"https://test-idafd.biodiversity.org.au/name/afd/70227612",
"https://test-idafd.biodiversity.org.au/name/afd/70227613",
"https://test-idafd.biodiversity.org.au/name/afd/70227615",
"https://test-idafd.biodiversity.org.au/name/afd/70227616",
"https://test-idafd.biodiversity.org.au/name/afd/70227617",
"https://test-idafd.biodiversity.org.au/name/afd/70227618",
"https://test-idafd.biodiversity.org.au/name/afd/70227620",
"https://test-idafd.biodiversity.org.au/name/afd/70227621",
"https://test-idafd.biodiversity.org.au/name/afd/70227622",
"https://test-idafd.biodiversity.org.au/name/afd/70227623",
"https://test-idafd.biodiversity.org.au/name/afd/70227624",
"https://test-idafd.biodiversity.org.au/name/afd/70227625",
"https://test-idafd.biodiversity.org.au/name/afd/70227905",
"https://test-idafd.biodiversity.org.au/name/afd/70228973",
"https://test-idafd.biodiversity.org.au/name/afd/70229034",
"https://test-idafd.biodiversity.org.au/name/afd/70229898",
"https://test-idafd.biodiversity.org.au/name/afd/70229899",
"https://test-idafd.biodiversity.org.au/name/afd/70229924",
"https://test-idafd.biodiversity.org.au/name/afd/70229928",
"https://test-idafd.biodiversity.org.au/name/afd/70230322",
"https://test-idafd.biodiversity.org.au/name/afd/70230324",
"https://test-idafd.biodiversity.org.au/name/afd/70231054",
"https://test-idafd.biodiversity.org.au/name/afd/70231058",
"https://test-idafd.biodiversity.org.au/name/afd/70231088",
"https://test-idafd.biodiversity.org.au/name/afd/70232375",
"https://test-idafd.biodiversity.org.au/name/afd/70232451",
"https://test-idafd.biodiversity.org.au/name/afd/70232583",
"https://test-idafd.biodiversity.org.au/name/afd/70233031",
"https://test-idafd.biodiversity.org.au/name/afd/70234546",
"https://test-idafd.biodiversity.org.au/name/afd/70235188",
"https://test-idafd.biodiversity.org.au/name/afd/70239683",
"https://test-idafd.biodiversity.org.au/name/afd/70241451",
"https://test-idafd.biodiversity.org.au/name/afd/70244527",
"https://test-idafd.biodiversity.org.au/name/afd/70244528",
"https://test-idafd.biodiversity.org.au/name/afd/70246906",
"https://test-idafd.biodiversity.org.au/name/afd/70246921",
"https://test-idafd.biodiversity.org.au/name/afd/70248191",
"https://test-idafd.biodiversity.org.au/name/afd/70248815",
"https://test-idafd.biodiversity.org.au/name/afd/70248846",
"https://test-idafd.biodiversity.org.au/name/afd/70250437",
"https://test-idafd.biodiversity.org.au/name/afd/70251591",
"https://test-idafd.biodiversity.org.au/name/afd/70251592",
"https://test-idafd.biodiversity.org.au/name/afd/70251593",
"https://test-idafd.biodiversity.org.au/name/afd/70251594",
"https://test-idafd.biodiversity.org.au/name/afd/70251595",
"https://test-idafd.biodiversity.org.au/name/afd/70251596",
"https://test-idafd.biodiversity.org.au/name/afd/70251813",
"https://test-idafd.biodiversity.org.au/name/afd/70252582",
"https://test-idafd.biodiversity.org.au/name/afd/70252585",
"https://test-idafd.biodiversity.org.au/name/afd/70252798",
"https://test-idafd.biodiversity.org.au/name/afd/70253122",
"https://test-idafd.biodiversity.org.au/name/afd/70253249",
"https://test-idafd.biodiversity.org.au/name/afd/70254508",
"https://test-idafd.biodiversity.org.au/name/afd/70256724",
"https://test-idafd.biodiversity.org.au/name/afd/70256790",
"https://test-idafd.biodiversity.org.au/name/afd/70256793",
"https://test-idafd.biodiversity.org.au/name/afd/70258140",
"https://test-idafd.biodiversity.org.au/name/afd/70258503",
"https://test-idafd.biodiversity.org.au/name/afd/70260063",
"https://test-idafd.biodiversity.org.au/name/afd/70260064",
"https://test-idafd.biodiversity.org.au/name/afd/70260071",
"https://test-idafd.biodiversity.org.au/name/afd/70261170",
"https://test-idafd.biodiversity.org.au/name/afd/70261287",
"https://test-idafd.biodiversity.org.au/name/afd/70263603",
"https://test-idafd.biodiversity.org.au/name/afd/70263610",
"https://test-idafd.biodiversity.org.au/name/afd/70264561",
"https://test-idafd.biodiversity.org.au/name/afd/70264569",
"https://test-idafd.biodiversity.org.au/name/afd/70264662",
"https://test-idafd.biodiversity.org.au/name/afd/70265111",
"https://test-idafd.biodiversity.org.au/name/afd/70265123",
"https://test-idafd.biodiversity.org.au/name/afd/70265124",
"https://test-idafd.biodiversity.org.au/name/afd/70265184",
"https://test-idafd.biodiversity.org.au/name/afd/70266881",
"https://test-idafd.biodiversity.org.au/name/afd/70268851",
"https://test-idafd.biodiversity.org.au/name/afd/70270743",
"https://test-idafd.biodiversity.org.au/name/afd/70271780",
"https://test-idafd.biodiversity.org.au/name/afd/70271857",
"https://test-idafd.biodiversity.org.au/name/afd/70271859",
"https://test-idafd.biodiversity.org.au/name/afd/70272009",
"https://test-idafd.biodiversity.org.au/name/afd/70273308",
"https://test-idafd.biodiversity.org.au/name/afd/70273330",
"https://test-idafd.biodiversity.org.au/name/afd/70273907",
"https://test-idafd.biodiversity.org.au/name/afd/70274243",
"https://test-idafd.biodiversity.org.au/name/afd/70274706",
"https://test-idafd.biodiversity.org.au/name/afd/70274719",
"https://test-idafd.biodiversity.org.au/name/afd/70277849",
"https://test-idafd.biodiversity.org.au/name/afd/70277851",
"https://test-idafd.biodiversity.org.au/name/afd/70285187",
"https://test-idafd.biodiversity.org.au/name/afd/70285194",
"https://test-idafd.biodiversity.org.au/name/afd/70285198",
"https://test-idafd.biodiversity.org.au/name/afd/70285584",
"https://test-idafd.biodiversity.org.au/name/afd/70285746",
"https://test-idafd.biodiversity.org.au/name/afd/70285750",
"https://test-idafd.biodiversity.org.au/name/afd/70286324",
"https://test-idafd.biodiversity.org.au/name/afd/70287749",
"https://test-idafd.biodiversity.org.au/name/afd/70287751",
"https://test-idafd.biodiversity.org.au/name/afd/70287816",
"https://test-idafd.biodiversity.org.au/name/afd/70287833",
"https://test-idafd.biodiversity.org.au/name/afd/70288732",
"https://test-idafd.biodiversity.org.au/name/afd/70289475",
"https://test-idafd.biodiversity.org.au/name/afd/70289476",
"https://test-idafd.biodiversity.org.au/name/afd/70289479",
"https://test-idafd.biodiversity.org.au/name/afd/70289480",
"https://test-idafd.biodiversity.org.au/name/afd/70289481",
"https://test-idafd.biodiversity.org.au/name/afd/70289486",
"https://test-idafd.biodiversity.org.au/name/afd/70290449",
"https://test-idafd.biodiversity.org.au/name/afd/70290501",
"https://test-idafd.biodiversity.org.au/name/afd/70292085",
"https://test-idafd.biodiversity.org.au/name/afd/70292086",
"https://test-idafd.biodiversity.org.au/name/afd/70292087",
"https://test-idafd.biodiversity.org.au/name/afd/70292088",
"https://test-idafd.biodiversity.org.au/name/afd/70295160",
"https://test-idafd.biodiversity.org.au/name/afd/70295161",
"https://test-idafd.biodiversity.org.au/name/afd/70296349",
"https://test-idafd.biodiversity.org.au/name/afd/70297596",
"https://test-idafd.biodiversity.org.au/name/afd/70297628",
"https://test-idafd.biodiversity.org.au/name/afd/70298163",
"https://test-idafd.biodiversity.org.au/name/afd/70298233",
"https://test-idafd.biodiversity.org.au/name/afd/70298740",
"https://test-idafd.biodiversity.org.au/name/afd/70299515",
"https://test-idafd.biodiversity.org.au/name/afd/70299516",
"https://test-idafd.biodiversity.org.au/name/afd/70299517",
"https://test-idafd.biodiversity.org.au/name/afd/70299518",
"https://test-idafd.biodiversity.org.au/name/afd/70299519",
"https://test-idafd.biodiversity.org.au/name/afd/70299520",
"https://test-idafd.biodiversity.org.au/name/afd/70299521",
"https://test-idafd.biodiversity.org.au/name/afd/70300122",
"https://test-idafd.biodiversity.org.au/name/afd/70300340",
"https://test-idafd.biodiversity.org.au/name/afd/70300451",
"https://test-idafd.biodiversity.org.au/name/afd/70300457",
"https://test-idafd.biodiversity.org.au/name/afd/70300965",
"https://test-idafd.biodiversity.org.au/name/afd/70302014",
"https://test-idafd.biodiversity.org.au/name/afd/70302016",
"https://test-idafd.biodiversity.org.au/name/afd/70302020",
"https://test-idafd.biodiversity.org.au/name/afd/70302022",
"https://test-idafd.biodiversity.org.au/name/afd/70302024",
"https://test-idafd.biodiversity.org.au/name/afd/70302102",
"https://test-idafd.biodiversity.org.au/name/afd/70302288",
"https://test-idafd.biodiversity.org.au/name/afd/70303806",
"https://test-idafd.biodiversity.org.au/name/afd/70303807",
"https://test-idafd.biodiversity.org.au/name/afd/70304409",
"https://test-idafd.biodiversity.org.au/name/afd/70305109",
"https://test-idafd.biodiversity.org.au/name/afd/70305487",
"https://test-idafd.biodiversity.org.au/name/afd/70306547",
"https://test-idafd.biodiversity.org.au/name/afd/70311479",
"https://test-idafd.biodiversity.org.au/name/afd/70311489",
"https://test-idafd.biodiversity.org.au/name/afd/70312855",
"https://test-idafd.biodiversity.org.au/name/afd/70312859",
"https://test-idafd.biodiversity.org.au/name/afd/70312865",
"https://test-idafd.biodiversity.org.au/name/afd/70312866",
"https://test-idafd.biodiversity.org.au/name/afd/70312867",
"https://test-idafd.biodiversity.org.au/name/afd/70312876",
"https://test-idafd.biodiversity.org.au/name/afd/70312878",
"https://test-idafd.biodiversity.org.au/name/afd/70312882",
"https://test-idafd.biodiversity.org.au/name/afd/70313621",
"https://test-idafd.biodiversity.org.au/name/afd/70313626",
"https://test-idafd.biodiversity.org.au/name/afd/70314474",
"https://test-idafd.biodiversity.org.au/name/afd/70315958",
"https://test-idafd.biodiversity.org.au/name/afd/70316947",
"https://test-idafd.biodiversity.org.au/name/afd/70316953",
"https://test-idafd.biodiversity.org.au/name/afd/70317053",
"https://test-idafd.biodiversity.org.au/name/afd/70317056",
"https://test-idafd.biodiversity.org.au/name/afd/70317058",
"https://test-idafd.biodiversity.org.au/name/afd/70317059",
"https://test-idafd.biodiversity.org.au/name/afd/70317254",
"https://test-idafd.biodiversity.org.au/name/afd/70322776",
"https://test-idafd.biodiversity.org.au/name/afd/70322779",
"https://test-idafd.biodiversity.org.au/name/afd/70323779",
"https://test-idafd.biodiversity.org.au/name/afd/70324406",
"https://test-idafd.biodiversity.org.au/name/afd/70324452",
"https://test-idafd.biodiversity.org.au/name/afd/70324453",
"https://test-idafd.biodiversity.org.au/name/afd/70324458",
"https://test-idafd.biodiversity.org.au/name/afd/70325324",
"https://test-idafd.biodiversity.org.au/name/afd/70325733",
"https://test-idafd.biodiversity.org.au/name/afd/70327447",
"https://test-idafd.biodiversity.org.au/name/afd/70329302",
"https://test-idafd.biodiversity.org.au/name/afd/70330861",
"https://test-idafd.biodiversity.org.au/name/afd/70331275",
"https://test-idafd.biodiversity.org.au/name/afd/70333752",
"https://test-idafd.biodiversity.org.au/name/afd/70334439",
"https://test-idafd.biodiversity.org.au/name/afd/70335813",
"https://test-idafd.biodiversity.org.au/name/afd/70335929",
"https://test-idafd.biodiversity.org.au/name/afd/70342130",
"https://test-idafd.biodiversity.org.au/name/afd/70345235",
"https://test-idafd.biodiversity.org.au/name/afd/70345238",
"https://test-idafd.biodiversity.org.au/name/afd/70345433",
"https://test-idafd.biodiversity.org.au/name/afd/70346174",
"https://test-idafd.biodiversity.org.au/name/afd/70346208",
"https://test-idafd.biodiversity.org.au/name/afd/70346234",
"https://test-idafd.biodiversity.org.au/name/afd/70346268",
"https://test-idafd.biodiversity.org.au/name/afd/70346281",
"https://test-idafd.biodiversity.org.au/name/afd/70346329",
"https://test-idafd.biodiversity.org.au/name/afd/70346335",
"https://test-idafd.biodiversity.org.au/name/afd/70347205",
"https://test-idafd.biodiversity.org.au/name/afd/70347698",
"https://test-idafd.biodiversity.org.au/name/afd/70349617",
"https://test-idafd.biodiversity.org.au/name/afd/70349619",
"https://test-idafd.biodiversity.org.au/name/afd/70349808",
"https://test-idafd.biodiversity.org.au/name/afd/70350130",
"https://test-idafd.biodiversity.org.au/name/afd/70350988",
"https://test-idafd.biodiversity.org.au/name/afd/70352881",
"https://test-idafd.biodiversity.org.au/name/afd/70352885",
"https://test-idafd.biodiversity.org.au/name/afd/70352894",
"https://test-idafd.biodiversity.org.au/name/afd/70355354",
"https://test-idafd.biodiversity.org.au/name/afd/70355444",
"https://test-idafd.biodiversity.org.au/name/afd/70356809",
"https://test-idafd.biodiversity.org.au/name/afd/70356810",
"https://test-idafd.biodiversity.org.au/name/afd/70357156",
"https://test-idafd.biodiversity.org.au/name/afd/70360348",
"https://test-idafd.biodiversity.org.au/name/afd/70360885",
"https://test-idafd.biodiversity.org.au/name/afd/70360892",
"https://test-idafd.biodiversity.org.au/name/afd/70361265",
"https://test-idafd.biodiversity.org.au/name/afd/70362757",
"https://test-idafd.biodiversity.org.au/name/afd/70365077",
"https://test-idafd.biodiversity.org.au/name/afd/70366346",
"https://test-idafd.biodiversity.org.au/name/afd/70367067",
"https://test-idafd.biodiversity.org.au/name/afd/70368015",
"https://test-idafd.biodiversity.org.au/name/afd/70368163",
"https://test-idafd.biodiversity.org.au/name/afd/70368935",
"https://test-idafd.biodiversity.org.au/name/afd/70373790",
"https://test-idafd.biodiversity.org.au/name/afd/70376631",
"https://test-idafd.biodiversity.org.au/name/afd/70377818",
"https://test-idafd.biodiversity.org.au/name/afd/70378180",
"https://test-idafd.biodiversity.org.au/name/afd/70378181",
"https://test-idafd.biodiversity.org.au/name/afd/70378420",
"https://test-idafd.biodiversity.org.au/name/afd/70379814",
"https://test-idafd.biodiversity.org.au/name/afd/70380413",
"https://test-idafd.biodiversity.org.au/name/afd/70381780",
"https://test-idafd.biodiversity.org.au/name/afd/70381781",
"https://test-idafd.biodiversity.org.au/name/afd/70381782",
"https://test-idafd.biodiversity.org.au/name/afd/70382107",
"https://test-idafd.biodiversity.org.au/name/afd/70382989",
"https://test-idafd.biodiversity.org.au/name/afd/70383316",
"https://test-idafd.biodiversity.org.au/name/afd/70384107",
"https://test-idafd.biodiversity.org.au/name/afd/70384108",
"https://test-idafd.biodiversity.org.au/name/afd/70384110",
"https://test-idafd.biodiversity.org.au/name/afd/70384115",
"https://test-idafd.biodiversity.org.au/name/afd/70385776",
"https://test-idafd.biodiversity.org.au/name/afd/70386439",
"https://test-idafd.biodiversity.org.au/name/afd/70386757",
"https://test-idafd.biodiversity.org.au/name/afd/70386758",
"https://test-idafd.biodiversity.org.au/name/afd/70387748",
"https://test-idafd.biodiversity.org.au/name/afd/70387753",
"https://test-idafd.biodiversity.org.au/name/afd/70387758",
"https://test-idafd.biodiversity.org.au/name/afd/70387777",
"https://test-idafd.biodiversity.org.au/name/afd/70387785",
"https://test-idafd.biodiversity.org.au/name/afd/70387786",
"https://test-idafd.biodiversity.org.au/name/afd/70387790",
"https://test-idafd.biodiversity.org.au/name/afd/70387796",
"https://test-idafd.biodiversity.org.au/name/afd/70387802",
"https://test-idafd.biodiversity.org.au/name/afd/70387814",
"https://test-idafd.biodiversity.org.au/name/afd/70387825",
"https://test-idafd.biodiversity.org.au/name/afd/70387827",
"https://test-idafd.biodiversity.org.au/name/afd/70387830",
"https://test-idafd.biodiversity.org.au/name/afd/70387831",
"https://test-idafd.biodiversity.org.au/name/afd/70387832",
"https://test-idafd.biodiversity.org.au/name/afd/70387834",
"https://test-idafd.biodiversity.org.au/name/afd/70387849",
"https://test-idafd.biodiversity.org.au/name/afd/70387854",
"https://test-idafd.biodiversity.org.au/name/afd/70388323",
"https://test-idafd.biodiversity.org.au/name/afd/70388364",
"https://test-idafd.biodiversity.org.au/name/afd/70388365",
"https://test-idafd.biodiversity.org.au/name/afd/70389001",
"https://test-idafd.biodiversity.org.au/name/afd/70389257",
"https://test-idafd.biodiversity.org.au/name/afd/70389258",
"https://test-idafd.biodiversity.org.au/name/afd/70389259",
"https://test-idafd.biodiversity.org.au/name/afd/70389524",
"https://test-idafd.biodiversity.org.au/name/afd/70389533",
"https://test-idafd.biodiversity.org.au/name/afd/70393015",
"https://test-idafd.biodiversity.org.au/name/afd/70393185",
"https://test-idafd.biodiversity.org.au/name/afd/70393188",
"https://test-idafd.biodiversity.org.au/name/afd/70393533",
"https://test-idafd.biodiversity.org.au/name/afd/70393537",
"https://test-idafd.biodiversity.org.au/name/afd/70395745",
"https://test-idafd.biodiversity.org.au/name/afd/70397406",
"https://test-idafd.biodiversity.org.au/name/afd/70397409",
"https://test-idafd.biodiversity.org.au/name/afd/70401805",
"https://test-idafd.biodiversity.org.au/name/afd/70404092",
"https://test-idafd.biodiversity.org.au/name/afd/70404580",
"https://test-idafd.biodiversity.org.au/name/afd/70404975",
"https://test-idafd.biodiversity.org.au/name/afd/70404977",
"https://test-idafd.biodiversity.org.au/name/afd/70404979",
"https://test-idafd.biodiversity.org.au/name/afd/70404980",
"https://test-idafd.biodiversity.org.au/name/afd/70404981",
"https://test-idafd.biodiversity.org.au/name/afd/70404982",
"https://test-idafd.biodiversity.org.au/name/afd/70404984",
"https://test-idafd.biodiversity.org.au/name/afd/70404985",
"https://test-idafd.biodiversity.org.au/name/afd/70406086",
"https://test-idafd.biodiversity.org.au/name/afd/70407584",
"https://test-idafd.biodiversity.org.au/name/afd/70408085",
"https://test-idafd.biodiversity.org.au/name/afd/70408100",
"https://test-idafd.biodiversity.org.au/name/afd/70408131",
"https://test-idafd.biodiversity.org.au/name/afd/70408134",
"https://test-idafd.biodiversity.org.au/name/afd/70408595",
"https://test-idafd.biodiversity.org.au/name/afd/70408935",
"https://test-idafd.biodiversity.org.au/name/afd/70408956",
"https://test-idafd.biodiversity.org.au/name/afd/70409735",
"https://test-idafd.biodiversity.org.au/name/afd/70409767",
"https://test-idafd.biodiversity.org.au/name/afd/70411788",
"https://test-idafd.biodiversity.org.au/name/afd/70411799",
"https://test-idafd.biodiversity.org.au/name/afd/70412646",
"https://test-idafd.biodiversity.org.au/name/afd/70413908",
"https://test-idafd.biodiversity.org.au/name/afd/70417610",
"https://test-idafd.biodiversity.org.au/name/afd/70418247",
"https://test-idafd.biodiversity.org.au/name/afd/70418975",
"https://test-idafd.biodiversity.org.au/name/afd/70421266",
"https://test-idafd.biodiversity.org.au/name/afd/70421274",
"https://test-idafd.biodiversity.org.au/name/afd/70422141",
"https://test-idafd.biodiversity.org.au/name/afd/70422144",
"https://test-idafd.biodiversity.org.au/name/afd/70422212",
"https://test-idafd.biodiversity.org.au/name/afd/70422292",
"https://test-idafd.biodiversity.org.au/name/afd/70422369",
"https://test-idafd.biodiversity.org.au/name/afd/70422374",
"https://test-idafd.biodiversity.org.au/name/afd/70422561",
"https://test-idafd.biodiversity.org.au/name/afd/70423636",
"https://test-idafd.biodiversity.org.au/name/afd/70424033",
"https://test-idafd.biodiversity.org.au/name/afd/70424035",
"https://test-idafd.biodiversity.org.au/name/afd/70424571",
"https://test-idafd.biodiversity.org.au/name/afd/70425463",
"https://test-idafd.biodiversity.org.au/name/afd/70427304",
"https://test-idafd.biodiversity.org.au/name/afd/70427979",
"https://test-idafd.biodiversity.org.au/name/afd/70427980",
"https://test-idafd.biodiversity.org.au/name/afd/70428447",
"https://test-idafd.biodiversity.org.au/name/afd/70430090",
"https://test-idafd.biodiversity.org.au/name/afd/70430093",
"https://test-idafd.biodiversity.org.au/name/afd/70430554",
"https://test-idafd.biodiversity.org.au/name/afd/70432923",
"https://test-idafd.biodiversity.org.au/name/afd/70433252",
"https://test-idafd.biodiversity.org.au/name/afd/70433493",
"https://test-idafd.biodiversity.org.au/name/afd/70433509",
"https://test-idafd.biodiversity.org.au/name/afd/70433515",
"https://test-idafd.biodiversity.org.au/name/afd/70433519",
"https://test-idafd.biodiversity.org.au/name/afd/70434370",
"https://test-idafd.biodiversity.org.au/name/afd/70437941",
"https://test-idafd.biodiversity.org.au/name/afd/70438048",
"https://test-idafd.biodiversity.org.au/name/afd/70440034",
"https://test-idafd.biodiversity.org.au/name/afd/70440498",
"https://test-idafd.biodiversity.org.au/name/afd/70444906",
"https://test-idafd.biodiversity.org.au/name/afd/70445431",
"https://test-idafd.biodiversity.org.au/name/afd/70448887",
"https://test-idafd.biodiversity.org.au/name/afd/70449358",
"https://test-idafd.biodiversity.org.au/name/afd/70449359",
"https://test-idafd.biodiversity.org.au/name/afd/70449361",
"https://test-idafd.biodiversity.org.au/name/afd/70449558",
"https://test-idafd.biodiversity.org.au/name/afd/70449562",
"https://test-idafd.biodiversity.org.au/name/afd/70451548",
"https://test-idafd.biodiversity.org.au/name/afd/70451556",
"https://test-idafd.biodiversity.org.au/name/afd/70456220",
"https://test-idafd.biodiversity.org.au/name/afd/70456222",
"https://test-idafd.biodiversity.org.au/name/afd/70456224",
"https://test-idafd.biodiversity.org.au/name/afd/70456226",
"https://test-idafd.biodiversity.org.au/name/afd/70456227",
"https://test-idafd.biodiversity.org.au/name/afd/70456228",
"https://test-idafd.biodiversity.org.au/name/afd/70456237",
"https://test-idafd.biodiversity.org.au/name/afd/70457940",
"https://test-idafd.biodiversity.org.au/name/afd/70463103",
"https://test-idafd.biodiversity.org.au/name/afd/70464994",
"https://test-idafd.biodiversity.org.au/name/afd/70465807",
"https://test-idafd.biodiversity.org.au/name/afd/70465833",
"https://test-idafd.biodiversity.org.au/name/afd/70465843",
"https://test-idafd.biodiversity.org.au/name/afd/70466502",
"https://test-idafd.biodiversity.org.au/name/afd/70467686",
"https://test-idafd.biodiversity.org.au/name/afd/70467687",
"https://test-idafd.biodiversity.org.au/name/afd/70467688",
"https://test-idafd.biodiversity.org.au/name/afd/70468229",
"https://test-idafd.biodiversity.org.au/name/afd/70496191",
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.