/
calc.py
56 lines (50 loc) · 1.58 KB
/
calc.py
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from numpy.random import exponential, normal, triangular, uniform, beta, \
weibull, chisquare, gamma, lognormal, pareto, standard_t
from random import randrange
def get_dist_num(args):
dist = args[0]
for i in range(len(args[1:])):
args[i+1] = float(args[1:][i])
if dist == 'EXP':
return exponential(args[1])
elif dist == 'NOR':
return normal(loc=args[1], scale=args[2]) # loc = média , scale = desvio
elif dist == 'TRI':
return triangular(args[1], args[2], args[3])
elif dist == 'UNI':
return uniform(low=args[1], high=args[2])
elif dist == 'BET':
return beta(args[1], args[2])
elif dist == 'WEI':
return weibull(args[1])
elif dist == 'CAU': # CAU: Cauchy
return 0
elif dist == 'CHI':
return chisquare(args[1])
elif dist == 'ERL': # ERL: Erlang
return 0
elif dist == 'GAM':
return gamma(args[1], scale=args[2])
elif dist == 'LOG':
return lognormal(mean=args[1], sigma=args[2])
elif dist == 'PAR':
return pareto(args[1])
elif dist == 'STU':
return standard_t(args[1])
def get_priority(args):
list = []
for i in range(len(args)):
if i != 0:
list.append(int(args[i]) + list[-1])
else:
list.append(int(args[i]))
p = randrange(0,100)+1
for i in range(len(list)):
if p <= list[i]:
return 5-i
def get_exam_medicine(probability) -> bool:
em = randrange(0,100)+1
if em <= float(probability[0]):
return True
else:
return False