#!/usr/bin/env python3

import numpy as np
import matplotlib.pyplot as plt
import argparse
from scipy import stats

parser = argparse.ArgumentParser(description="Raspberry Pi Pico Random Number Generator Test Analyzer")
parser.add_argument("file", help="File that contains a random sample of bytes.", metavar="FILENAME")
args = parser.parse_args()

def read_in_chunks(file_object, chunk_size=1024):
    """Lazy function (generator) to read a file piece by piece.
    Default chunk size: 1k."""
    while True:
        data = file_object.read(chunk_size)
        if not data:
            break
        yield data

with open(args.file, 'rb') as f:
    myhist = np.zeros(256, dtype='float64')
    chisqs = []
    chisps = []
    acumd = 0
    acumn = 0
    for data in read_in_chunks(f, 10000):
        n, jnk = np.histogram(list(data), list(range(257)))
        myhist += n
        chisq, chisp = stats.chisquare(n)
        chisqs.append(chisq)
        chisps.append(chisp*100)
        acumd += sum(data)
        acumn += len(data)

#    plt.subplot(1, 2, 1)
    plt.bar(range(256), myhist/np.sum(myhist), width=1)
    plt.ylabel('Probability')
    plt.title(f'Distribution of randomness [$\mu$={acumd/acumn:.4f}]')
    plt.grid(True)
    plt.show()

    plt.subplot(1, 2, 1)
    n, bins, _ = plt.hist(chisqs, 401, density=True)
    plt.ylabel('Probability')
    plt.title(f'Distribution of chi-square [$\mu$={np.mean(chisqs):.4f}, Mdn={np.median(chisqs):.4f}]')
    plt.grid(True)

    plt.subplot(1, 2, 2)
    n, bins, _ = plt.hist(chisps, list(range(101)), density=False)
    plt.ylabel('Probability')
    plt.title(f'Distribution of percentage excess [$\mu$={np.mean(chisps):.4f}, Mdn={np.median(chisps):.4f}]')
    plt.grid(True)
    plt.show()


