add new histogram script

#!/usr/bin/env python3

import argparse

import math

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

BINS_4_COLS = {

    "MLD": [0, 1, 2, 3, 4, 5, 10, 20, math.inf],

    "MAXIMUM ABS DIFF": [0, 16, 256, 1024, 2048, 4096, 8192, 16384, 32769],

    "SSNR": [-math.inf, 0, 10, 20, 30, 40, 40, 50, 60, 100],

    "ODG": [-5, -4, -3, -2, -1, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.5],

    "DELTA_ODG": [-5, -4, -3, -2, -1, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.5],

}

if __name__ == "__main__":

    parser = argparse.ArgumentParser(

        description="Parses a CSV report and creates a summary report."

    )

    parser.add_argument(

        "csv_report",

        type=str,

        help="CSV report file of test cases, e.g. report.csv",

    )

    args = parser.parse_args()

    df = pd.read_csv(args.csv_report)

    measures = ["MAXIMUM ABS DIFF"]

    formats = df["format"].unique()

    categories = df["category"].unique()

    for measure in measures:

        bins = BINS_4_COLS[measure]

        x = [f"{x}" for x in bins] + ["", "ERROR"]

        for fmt in formats:

            fig, ax = plt.subplots()

            bottom = np.zeros(len(x))

            for cat in categories:

                data_mask = np.logical_and(df["format"] == fmt, df["category"] == cat)

                df_slice = df[data_mask]

                error_mask = df_slice["result"] == "ERROR"

                n_errors = np.sum(error_mask)

                df_hist = df_slice[np.logical_not(error_mask)]

                counts, _ = np.histogram(df_hist[measure], bins)

                data = np.concat([counts, [0], [n_errors], [0]])

                ax.bar(

                    x,

                    data,

                    1,

                    align="edge",

                    edgecolor="black",

                    linewidth=0.5,

                    label=cat,

                    bottom=bottom,

                )

                bottom += data

            # Histogram layout

            ax.set_title(fmt)

            ax.legend(loc="best")

            ax.set_xlabel(measure)

            if "DIFF" in measure:

                ax.set_xticks(range(len(x)), x, rotation=35)

            ax.set_ylabel("Number of test cases")

            fig.set_figheight(4)

            fig.set_figwidth(6)

            plt.tight_layout()

    plt.show()