The library for generating random variates is in the file generateDistribution.py. By importing the library, we can create an object of the distGenerator class.

# Import the generateDistribution Library
import generateDistribution as genDist

# Create an object of the distGenerator class
dist = genDist.distGenerator()

Once the object is created, we can call the functions below:

• plotDist(): Plot histogram of the generated random numbers

• compPlotDist(): Side by Side Comparison of Distribution Histograms, usually the generated Random Variate (from the generateDistribution.py Library) and the corresponding scipy function

• validateDist(): Validates if the generated distribution matches that of the scipy reference distribution by conducting a Kolmogorov–Smirnov test (KS) Test, usually the generated Random Variate (from the generateDistribution.py Library) and the corresponding scipy function generated Random Variate

• generateExponential(Lambda, size, random_state): Generates size number of Exponential Random Variables based on Lambda parameter and random state seed

• generateWeibull(Alpha, Beta, size, random_state): Generates size number of Weibull Random Variables based on Alpha and Beta parameter and random state seed

• generateBernoulli(p, size, random_state): Generates size number of Bernoulli Random Variables based on p parameter and random state seed

• generateGeometric(p, size, random_state): Generates size number of Geometric Random Variables based on p parameter and random state seed

• generateNormal(Mu, Sigma, size, random_state1, random_state2): Generates size number of Normal Random Variables based on Mu, Sigma parameters and random state seeds using the Box-Mueller method

• generateGamma(Alpha, Beta, size, random_state): Generates size number of Gamma Random Variables based on Alpha and Beta parameter and random state seed

More examples of how to use the functions are in demo.ipynb