from numpy.random import randn
from scipy.stats.mstats import mode
from scipy.stats import skew, kurtosis, mannwhitneyu
from matplotlib.pyplot import hist, show, clf, title
import numpy as np


def z_test(mean, expected, std, n):
    z = ((mean - expected) / std) * np.sqrt(n)
    return z


x1 = np.zeros(31)
x2 = np.zeros(31)
a = 183
c = 10
m = 102
x1[0] = abs(randn()) / m
x2[0] = abs(randn()) / m

for i in range(1, 31):
    x1[i] = ((a * x1[i-1] + c) % m) / m
    x2[i] = ((a * x2[i - 1] + c) % m) / m

sample1 = x1[1:]
sample2 = x2[1:]
print(sample1, '\n', sample2)

mean1 = np.mean(sample1)
mode1 = mode(sample1)
med1 = np.median(sample1)
var1 = np.var(sample1)
std1 = np.sqrt(var1)
skew1 = skew(sample1)
kurt1 = kurtosis(sample1)
z1 = z_test(mean1, 0.5, std1, len(sample1))
print('Średnia: {}\nModa: {}\nMediana: {}\nWariancja: {}\nOdch.stand.: {}\nSkośność: {}\nKurtoza: {}\nZ-Test: {}\n'
      .format(mean1, mode1, med1, var1, std1, skew1, kurt1, z1))
title('Ciag 1')
hist(sample1, bins=5)
show()
clf()

mean2 = np.mean(sample2)
mode2 = mode(sample2)
med2 = np.median(sample2)
var2 = np.var(sample2)
std2 = np.sqrt(var2)
skew2 = skew(sample2)
kurt2 = kurtosis(sample2)
z2 = z_test(mean2, 0.5, std2, len(sample2))
print('Średnia: {}\nModa: {}\nMediana: {}\nWariancja: {}\nOdch.stand.: {}\nSkośność: {}\nKurtoza: {}\nZ-Test: {}\n'
      .format(mean2, mode2, med2, var2, std2, skew2, kurt2, z2))
title('Ciag 2')
hist(sample2, bins=5)
show()
print("Test Manna-Whitneya", mannwhitneyu(sample1, sample2))