import numpy as np
import matplotlib.pyplot as plt

# %% Number of events (experiments; draws; trials):

n = 10

# %% Params of choice():

sample_space = np.array([1, 2, 3, 4, 5, 6])
sample_size = 1
replace = True
prob = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]

# %% Initialization:

population = np.zeros(n)

# %% For loop realizing the n trials:

for k in range(0, n):
    
    # Sample number k (event number k):
    sample_k = np.random.choice(
        sample_space,
        sample_size,
        replace,
        prob)

    population[k] = sample_k


# %% Generating histogram data:

num_bins = len(sample_space)
histogram_data = np.histogram(population, num_bins)
histogram_data_norm = histogram_data[0]/n

# %% Plotting:

plt.close('all')  # Closes all figures before plotting
fig1 = plt.figure(num=1, figsize=(30/2.54, 24/2.54))#Inch

plt.bar(sample_space, histogram_data_norm, width=0.95)
plt.plot(sample_space, prob, 'ro')
plt.xlabel('Samples (pips)')
plt.ylabel('Frequency')

# plt.savefig('plot_distr_dice_10.pdf')
# plt.savefig('plot_distr_dice_1000000.pdf')
plt.show()