Generate number array based on a range, std and mean condition

generate_distribution.py

import numpy as np
import scipy.stats
from pprint import pprint
import matplotlib.pyplot as plt
import logging
logging.basicConfig(level=logging.DEBUG)

# source: https://stackoverflow.com/a/50629604/4257991
def my_distribution(min_val, max_val, mean, std):
    scale = max_val - min_val
    location = min_val
    # Mean and standard deviation of the unscaled beta distribution
    unscaled_mean = (mean - min_val) / scale
    unscaled_var = (std / scale) ** 2
    # Computation of alpha and beta can be derived from mean and variance formulas
    t = unscaled_mean / (1 - unscaled_mean)
    beta = ((t / unscaled_var) - (t * t) - (2 * t) - 1) / ((t * t * t) + (3 * t * t) + (3 * t) + 1)
    alpha = beta * t
    # Not all parameters may produce a valid distribution
    if alpha <= 0 or beta <= 0:
        raise ValueError('Cannot create distribution for the given parameters.')
    # Make scaled beta distribution with computed parameters
    return scipy.stats.beta(alpha, beta, scale=scale, loc=location)

def generate_number(number_of_person):
    # np.random.seed(13)

    # stat value from real eye images
    min_val = 1
    max_val = 47
    mean = 8.369697
    std = 5.630085
    my_dist = my_distribution(min_val, max_val, mean, std)
    # # Plot distribution PDF
    # x = np.linspace(min_val, max_val, 165)
    #
    # # print(my_dist.pdf(x))
    # plt.plot(x, my_dist.pdf(x))
    # # Stats
    # print('mean:', my_dist.mean(), 'std:', my_dist.std())
    # Get a large sample to check bounds
    sample = my_dist.rvs(size=number_of_person)
    # print('min:', sample.min(), 'max:', sample.max())
    # print(sample)
    # plt.show()

    sample = np.round(sample).astype('int16')

    return sample

num_images = generate_number(50)