Add option to select SSNR and MAX ABS DIFF from create_histogram_summary.py

        help="Summary image file, e.g. summary.png", 

        default = None

    )

    parser.add_argument(

        "--measure",

        type=str,

        nargs=1,

        help="Measure, any of: MLD, DIFF, SSNR, default: MLD",

        default = ['MLD']

    )

    args = parser.parse_args()

    csv_report = args.csv_report

    csv_summary = args.csv_summary

    csv_image = args.csv_image

    measure = args.measure[0]

    limits = [0, 5, 10, math.inf]

    limits_per_measure = {'MLD':('MLD',[0, 5, 10, math.inf]), 'DIFF':('MAXIMUM ABS DIFF',[0, 1024, 16384, 32769]), 'SSNR':('MIN_SSNR',[-math.inf, 0, 20, 40, 60, 100])}

    (measure_label, limits) = limits_per_measure[measure]

    # Load CSV report

    results_sorted = {}

    with open(csv_report,'r') as fp:

        reader = csv.reader(fp, delimiter=';')

        header = next(reader)

        keys = header[1:-1]

        keys = header[1:]

        for row in reader:

            testcase = row[0]

            results_sorted[testcase] = {}

            for k, val in zip(keys, row[1:-1]):

            for k, val in zip(keys, row[1:]):

                results_sorted[testcase][k] = val

    # Output CSV file

    with open(csv_summary, "w") as fp:

        limits_labels = ["0"] + [f"{str(a)} -- {str(b)}" for (a,b) in zip(limits[0:-1],limits[1:])] + ["None"]

        limits_labels = [f"{str(a)} -- {str(b)}" for (a,b) in zip(limits[0:-1],limits[1:])] + ["None"]

        # Zero difference is treated as a special category for MLD and MAXIMUM ABS DIFF

        if measure_label == "MLD" or measure_label == "MAXIMUM ABS DIFF":

            limits_labels = ["0"] + limits_labels

        headerline = f"Format;Category;" + ";".join(limits_labels) + "\n"

        fp.write(headerline)

        subplot = 0

        fig, ax = plt.subplots(len(FORMATS)//2, 2)

        fig, ax = plt.subplots(len(FORMATS)//4, 4)

        for fmt in FORMATS:

            bottom = np.zeros(len(limits)+1)

            bottom = np.zeros(len(limits_labels))

            row = subplot // 4

            col = subplot % 4

            for cat in CATEGORIES:

                mld_vals = [x for x in [m["MLD"] for m in results_sorted.values() if m["Format"] == fmt and m["Category"] == cat]]

                mld = [float(x) for x in mld_vals if x != 'None' and x != '0']

                zero = sum([1 for x in mld_vals if x == '0'])

                none = sum([1 for x in mld_vals if x == 'None'])

                hist, _ = np.histogram(mld,limits)

                data = np.array([zero] + list(hist) + [none])

                values = [x for x in [m[measure_label] for m in results_sorted.values() if m["Format"] == fmt and m["Category"] == cat]]

                # Zero difference is treated as a special category for MLD and MAXIMUM ABS DIFF

                if measure_label == "MLD" or measure_label == "MAXIMUM ABS DIFF":

                    val = [float(x) for x in values if x != 'None' and x != '0' and x != '']

                    zero = [sum([1 for x in values if x == '0'])]

                    none = [sum([1 for x in values if x == 'None' or x == ''])]

                else:

                    val = [float(x) for x in values if x != 'None' and x != '']

                    zero = []

                    none = [sum([1 for x in values if x == 'None' or x == ''])]

                hist, _ = np.histogram(val,limits)

                data = np.array(zero + list(hist) + none)

                # CSV output

                line = f"{fmt};{cat};{'; '.join(map(str,data))}\n"

                fp.write(line)

                # Matplotlib histogram

                a = subplot % 4

                b = subplot // 4

                ax[a][b].bar(limits_labels, data, 0.5, label=cat, bottom=bottom)

                ax[row][col].bar(limits_labels, data, 0.5, label=cat, bottom=bottom)

                bottom += data

            # Histogram layout

            ax[a][b].set_title(fmt)

            ax[a][b].legend(loc="best")

            ax[a][b].set_xlabel("MLD")

            ax[a][b].set_ylabel("Number of test cases")

            ax[row][col].set_title(fmt)

            ax[row][col].legend(loc="best")

            ax[row][col].set_xlabel(measure_label)

            ax[row][col].set_ylabel("Number of test cases")

            subplot += 1

        fig.set_figheight(20)

        fig.set_figwidth(16)

        fig.set_figheight(16)

        fig.set_figwidth(32)

        if csv_image:

            plt.savefig(csv_image)

import re

import math

import numpy as np

import matplotlib.pyplot as plt

from xml.etree import ElementTree

"""