# -*- coding: utf-8 -*-
"""
Created 2022
Get random samples from a random number generator ('rng')
and plot the density of a 'normal' or guassian random variable
@author: neil
"""

import numpy as np
import matplotlib.pyplot as plt

nsamples = 1000000   # this is the number of random samples we want
Q = np.random.default_rng().normal(0,1,nsamples) # guassian centeres on 0, sigma of 1
#Q = np.random.normal(0,1,nsamples) # a simpler call, if you don't care which random generator is used

fig = plt.figure()          # set up a plot window
ax1 = fig.add_subplot(1,1,1)
ax1.grid(True)

ax1.hist(Q,bins=100)        # this calls np.histogram to calculate the bins
ax1.set_ylabel("Numbers in bins")
ax1.set_xlabel("Values of gaussian random values")
ax1.set_title("Filling bins with the Random Variables")

plt.show()