add initial version of pytest parsing script to get MLD stats

import argparse

from pathlib import Path

import os

import uuid

import requests

import pandas as pd

from glob import glob

import subprocess

import re

import platform

import numpy as np

import shutil

import matplotlib.pyplot as pplt

import json

import math

def parse_pytest_json(filename):

    mld_thresh = 1.0

    ssnr_thresh = 50.0

    num_tests = 0

    mld_bel_th = 0

    ssnr_above_th = 0

    all_mld = np.empty(0)

    all_ssnr = np.empty(0)

    with open(filename) as jd:

        data = json.load(jd)

        df = pd.DataFrame(data['tests'])

        #breakpoint()

        df1 = pd.DataFrame(df['user_properties'])

        dt = df1['user_properties']

        for i in range(0, len(dt)):

            ent = dt[i]

            if isinstance(ent, float) == False:

                for i in range(0, len(ent)):

                    val = ent[i]

                    if 'MLD' in val:

                        num_tests = num_tests + 1

                        mld = val['MLD']

                        all_mld = np.append(all_mld, float(mld))

                    if 'MIN_SSNR' in val:

                        ssnr = val['MIN_SSNR']                  

                        if ssnr != 'inf' and ssnr != 'nan':

                            all_ssnr = np.append(all_ssnr, float(ssnr))

                        else:

                            ssnr_above_th = ssnr_above_th + 1

                            all_ssnr = np.append(all_ssnr, 1000.0)

    print('MAX MLD is ', str(np.max(all_mld)))        

    print('MIN SSNR is ', str(np.min(all_ssnr))) 

    ##  print MLD stats

    mld_th = np.array([1.0, 2.0, 3.0, 4.0, 5.0])

    mld_hist = np.empty(0)    

    for i in range(0, mld_th.size):

       all_mld_1 = all_mld[all_mld < mld_th[i]]

       mld_hist = np.append(mld_hist, all_mld_1.size)

    mld_perc = 100*mld_hist/num_tests

    print(mld_hist)

    print(mld_perc)

    ##  print SSNR stats

    ssnr_th = np.array([50.0, 55.0, 60.0, 65.0, 70.0])

    ssnr_hist = np.empty(0)    

    for i in range(0, ssnr_th.size):

       all_ssnr_1 = all_ssnr[all_ssnr > ssnr_th[i]]

       ssnr_hist = np.append(ssnr_hist, all_ssnr_1.size)

    ssnr_perc = 100*ssnr_hist/num_tests

    print(ssnr_hist)

    print(ssnr_perc)    

    # Save to CSV

    filename2 = os.path.basename(filename)

    idx2 = filename2.find('.json');

    csv_file = filename2[:idx2] + '_mld_stats' + '.csv';  

    mld_perc = np.append(mld_perc, np.max(all_mld))

    np.savetxt(csv_file, mld_perc.T, delimiter=",", fmt="%f", header=', '.join(map(str, mld_th)), comments="")

    csv_file = filename2[:idx2] + '_ssnr_stats' + '.csv'; 

    ssnr_perc = np.append(ssnr_perc, np.max(np.min(all_ssnr)))

    np.savetxt(csv_file, ssnr_perc.T, delimiter=",", fmt="%f", header=', '.join(map(str, ssnr_th)), comments="")    

if __name__ == "__main__":

    parser = argparse.ArgumentParser("Parse pytests json script")

    parser.add_argument("--filename", type=Path, required=True, help=".json file with pytest output. ")

    args = parser.parse_args()

    filename:Path = args.filename

    parse_pytest_json(args.filename)