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

"""This program shows parametrized `hyperfine` benchmark results as an
errorbar plot."""

import argparse
import sys

import matplotlib.pyplot as plt

from plot_utils import METRICS, add_metric_argument, load_results

parser = argparse.ArgumentParser(description=__doc__)
add_metric_argument(parser)
parser.add_argument("file", help="JSON file with benchmark results", nargs="+")
parser.add_argument(
    "--parameter-name",
    metavar="name",
    type=str,
    help="Deprecated; parameter names are now inferred from benchmark files",
)
parser.add_argument(
    "--log-x", help="Use a logarithmic x (parameter) axis", action="store_true"
)
parser.add_argument(
    "--log-y",
    "--log-time",
    dest="log_y",
    help="Use a logarithmic metric axis",
    action="store_true",
)
parser.add_argument(
    "--titles", help="Comma-separated list of titles for the plot legend"
)
parser.add_argument("-o", "--output", help="Save image to the given filename.")

args = parser.parse_args()
metric, datasets = load_results(parser, args.file, args.metric)
metric_info = METRICS[metric]
if args.parameter_name is not None:
    sys.stderr.write(
        "warning: --parameter-name is deprecated; names are inferred from "
        "benchmark results\n"
    )


def die(msg):
    sys.stderr.write(f"fatal: {msg}\n")
    sys.exit(1)


def extract_parameters(results):
    """Return `(parameter_name: str, parameter_values: List[float])`."""
    if not results:
        die("no benchmark data to plot")
    (names, values) = zip(*(unique_parameter(b) for b in results))
    names = frozenset(names)
    if len(names) != 1:
        die(
            f"benchmarks must all have the same parameter name, but found: {sorted(names)}"
        )
    return (next(iter(names)), list(values))


def unique_parameter(benchmark):
    """Return the unique parameter `(name: str, value: float)`, or die."""
    params_dict = benchmark.get("parameters", {})
    if not params_dict:
        die("benchmarks must have exactly one parameter, but found none")
    if len(params_dict) > 1:
        die(
            f"benchmarks must have exactly one parameter, but found multiple: {sorted(params_dict)}"
        )
    [(name, value)] = params_dict.items()
    return (name, float(value["value"]))


parameter_name = None

for results in datasets:
    (this_parameter_name, parameter_values) = extract_parameters(results)
    if parameter_name is not None and this_parameter_name != parameter_name:
        die(
            f"files must all have the same parameter name, but found {parameter_name!r} vs. {this_parameter_name!r}"
        )
    parameter_name = this_parameter_name

    summaries = [b["summary"][metric] for b in results]
    means = [s["mean"] / metric_info.scale for s in summaries]
    stddevs = [
        s["stddev"] / metric_info.scale if s["stddev"] is not None else float("nan")
        for s in summaries
    ]

    plt.errorbar(x=parameter_values, y=means, yerr=stddevs, capsize=2)

plt.xlabel(parameter_name)
plt.ylabel(metric_info.axis_label)

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

if args.log_x:
    plt.xscale("log")

if args.titles:
    plt.legend(args.titles.split(","))

if args.output:
    plt.savefig(args.output)
else:
    plt.show()