Generate N random numbers in range(A, B) with an average mean of X

generateRandomInRangeAroundNormal.py

import numpy as np
from math import ceil, floor

"""
The idea is to assume a normal Gaussian distribution around the given
average for a given range. Random numbers will be generated on either
side of the normal.
"""

def randomCeilOrFloor(X):
    """
    Randomly choose ceil or float for a given number on a coin toss
    """
    toss = np.random.randint(2)
    return ceil(X) if toss == 1 else floor(X)


def generate(N, A, B, average):
    """
    Given a range A, B and a standard deviation, we can construct a
    normal distribution of random variables of size(N)
    Here, N should be less than 10^8

    numpy.random.normal(loc = 0.0, scale = 1.0, size = None) :
    creates an array of specified shape and fills it with random
     values which is actually a part of Normal(Gaussian)Distribution.
    """
    mean = A + (B - A) / 2
    if average > B or average < A:
        return []

    std_deviation = abs(mean - average)
    return np.random.normal(average, std_deviation, size=(N))


if __name__ == "__main__":
    rand_range = generate(N=40, A=0.66, B=5, average=2.4)
    # rand_range = generate(N=100, A=100, B=200, average=181)
    sum_random = sum(rand_range)
    print("Average numbers: {0}".format(sum_random / 40))
    for i in rand_range:
        print(randomCeilOrFloor(i)),
    pass