#!/usr/bin/env python
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "matplotlib",
#     "pyqt6",
#     "numpy",
# ]
# ///

"""This program shows `hyperfine` benchmark results as a histogram."""

import argparse

import matplotlib.pyplot as plt
import numpy as np

from plot_utils import METRICS, add_metric_argument, load_results


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    add_metric_argument(parser)
    parser.add_argument("file", help="JSON file with benchmark results")
    parser.add_argument("--title", help="Plot title")
    parser.add_argument(
        "--labels", help="Comma-separated list of entries for the plot legend"
    )
    parser.add_argument("--bins", help="Number of bins (default: auto)")
    parser.add_argument(
        "--legend-location",
        help="Location of the legend on plot (default: upper center)",
        choices=[
            "upper center",
            "lower center",
            "right",
            "left",
            "best",
            "upper left",
            "upper right",
            "lower left",
            "lower right",
            "center left",
            "center right",
            "center",
        ],
        default="upper center",
    )
    parser.add_argument(
        "--type", help="Type of histogram (*bar*, barstacked, step, stepfilled)"
    )
    parser.add_argument("-o", "--output", help="Save image to the given filename.")
    parser.add_argument(
        "--min",
        "--t-min",
        dest="value_min",
        type=float,
        help="Minimum metric value to display",
    )
    parser.add_argument(
        "--max",
        "--t-max",
        dest="value_max",
        type=float,
        help="Maximum metric value to display",
    )
    parser.add_argument(
        "--log-count",
        help="Use a logarithmic y-axis for the event count",
        action="store_true",
    )

    args = parser.parse_args()
    metric, [results] = load_results(parser, [args.file], args.metric)
    metric_info = METRICS[metric]

    if args.labels:
        labels = args.labels.split(",")
    else:
        labels = [b.get("name", b["command"]) for b in results]
    all_values = [
        [m[metric]["value"] / metric_info.scale for m in b["measurements"]]
        for b in results
    ]

    value_min = (
        args.value_min
        if args.value_min is not None
        else np.min(list(map(np.min, all_values)))
    )
    value_max = (
        args.value_max
        if args.value_max is not None
        else np.max(list(map(np.max, all_values)))
    )

    bins = int(args.bins) if args.bins else "auto"
    histtype = args.type if args.type else "bar"

    plt.figure(figsize=(10, 5))
    plt.hist(
        all_values,
        label=labels,
        bins=bins,
        histtype=histtype,
        range=(value_min, value_max),
    )
    plt.legend(
        loc=args.legend_location,
        fancybox=True,
        shadow=True,
        prop={"size": 10, "family": ["Source Code Pro", "Fira Mono", "Courier New"]},
    )

    plt.xlabel(metric_info.axis_label)
    if args.title:
        plt.title(args.title)

    if args.log_count:
        plt.yscale("log")
    else:
        plt.ylim(0, None)

    if args.output:
        plt.savefig(args.output, dpi=600)
    else:
        plt.show()


if __name__ == "__main__":
    main()