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import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
npoints = 20 # number of integer support points of the distribution minus 1
npointsh = npoints // 2
npointsf = float(npoints)
nbound = 4 # bounds for the truncated normal
normbound = (1 + 1/npointsf) * nbound # actual bounds of truncated normal
grid = np.arange(-npointsh, npointsh+2,1) # integer grid
gridlimitsnorm = (grid - 0.5) / npointsh * nbound # bin limits for the truncnorm
gridlimits = grid - 0.5
grid = grid[:-1]
probs = np.diff(stats.truncnorm.cdf(gridlimitsnorm, -normbound, normbound))
gridint = grid
rng = np.random.default_rng()
normdiscrete = stats.rv_discrete(
values=(gridint, np.round(probs, decimals=7)),
name='normdiscrete')
n_sample = 500
rvs = normdiscrete.rvs(size=n_sample, random_state=rng)
f, l = np.histogram(rvs,bins=gridlimits)
sfreq = np.vstack([gridint,f,probs*n_sample]).T
fs = sfreq[:,1] / float(n_sample)
ft = sfreq[:,2] / float(n_sample)
fs = sfreq[:,1].cumsum() / float(n_sample)
ft = sfreq[:,2].cumsum() / float(n_sample)
nd_std = np.sqrt(normdiscrete.stats(moments='v'))
ind = gridint # the x locations for the groups
width = 0.35 # the width of the bars
plt.figure()
plt.subplot(111)
rects1 = plt.bar(ind, ft, width, color='b')
rects2 = plt.bar(ind+width, fs, width, color='r')
normline = plt.plot(ind+width/2.0, stats.norm.cdf(ind+0.5, scale=nd_std),
color='b')
plt.ylabel('cdf')
plt.title('Cumulative Frequency and CDF of normdiscrete')
plt.xticks(ind+width, ind)
plt.legend((rects1[0], rects2[0]), ('true', 'sample'))
plt.show()
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