def D_N(empiric_dist, space, ex=Lab1.Ex1()):
d_n = []
for i in range(len(empiric_dist)):
d_n.append(abs(empiric_dist[i] - ex.F(space[i])))
return max(d_n)
# F_real = [Lab1.Ex1().F(x) for x in space]
# for nr_of_samples in n:
#
# F_empiric = empiric_distribution(randoms[0:nr_of_samples], space)
#
# D.append(D_N(F_empiric,space))
#
# pyplot.plot(n,D,'r*')
#
# pyplot.xlabel('N')
# pyplot.ylabel('D(N)')
# pyplot.plot(space, F_real, 'o')
# pyplot.plot(space, F_empiric, '*')
nr_of_samples = 500
keys = [random.uniform(0, 1) for i in range(nr_of_samples)]
randoms = Lab1.Generator.generate_random_numbers(keys, Lab1.Ex1())
F_empiric = empiric_distribution(randoms, space)
fig = pyplot.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.title.set_text(r'Dystrybuanta empiryczna')
ax2.title.set_text(r'Estymator wariancji')
pyplot.subplot(2, 1, 1)
pyplot.plot(space, F_empiric)
pyplot.subplot(2, 1, 2)
pyplot.plot(space, empiric_variance(F_empiric, Lab1.Ex1().F, space))
pyplot.show()
