"""Compute metrics and render ClimODE forecast maps from saved outputs."""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

import numpy as np
import yaml

PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from scripts.metrics import evaluate, save_metrics


def _resolve(path: str | Path) -> Path:
    value = Path(path)
    return value if value.is_absolute() else PROJECT_ROOT / value


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--config", type=Path, default=PROJECT_ROOT / "conf/config.yaml")
    parser.add_argument("--predictions", type=Path, default=None)
    parser.add_argument("--targets", type=Path, default=None)
    parser.add_argument("--std", type=Path, default=None)
    parser.add_argument("--output-dir", type=Path, default=None)
    parser.add_argument("--stats-dir", type=Path, default=None)
    parser.add_argument("--static-file", type=Path, default=None)
    parser.add_argument("--sample", type=int, default=0)
    parser.add_argument("--lead", type=int, default=0)
    args = parser.parse_args()
    with args.config.open("r", encoding="utf-8") as handle:
        config = yaml.safe_load(handle)
    output_dir = args.output_dir or _resolve(config["data"]["output_dir"])
    predictions = np.load(args.predictions or output_dir / "predictions.npy")
    targets = np.load(args.targets or output_dir / "targets.npy")
    std_path = args.std or output_dir / "std.npy"
    std = np.load(std_path) if std_path.is_file() else None
    lengths_path = output_dir / "valid_lengths.npy"
    valid_lengths = np.load(lengths_path) if lengths_path.is_file() else None
    static_file = _resolve(args.static_file or config["data"]["static_file"])
    import h5py

    with h5py.File(static_file, "r") as handle:
        lat2d = handle["lat2d"][:]
    stats_dir = _resolve(
        args.stats_dir
        or config["data"].get("stats_dir", Path(config["data"]["data_dir"]) / "static")
    )
    minimum = np.load(stats_dir / "min_values.npy").reshape(1, 1, 1, 5, 1, 1)
    maximum = np.load(stats_dir / "max_values.npy").reshape(1, 1, 1, 5, 1, 1)
    scale = maximum - minimum
    metrics = evaluate(
        predictions * scale + minimum,
        targets * scale + minimum,
        lat2d,
        std * scale if std is not None else None,
        crps_predictions=predictions if std is not None else None,
        crps_targets=targets if std is not None else None,
        crps_std=std,
        valid_lengths=valid_lengths,
    )
    metrics_path = output_dir.parent / "metrics.json"
    save_metrics(metrics, metrics_path)

    figure_dir = output_dir / "figures"
    figure_dir.mkdir(parents=True, exist_ok=True)
    try:
        import matplotlib

        matplotlib.use("Agg")
        import matplotlib.pyplot as plt
    except ImportError as exc:
        raise RuntimeError("Visualization requires matplotlib in the active environment") from exc

    if not 0 <= args.sample < predictions.shape[0]:
        raise IndexError(f"sample must be in [0,{predictions.shape[0] - 1}]")
    if not 0 <= args.lead < predictions.shape[1]:
        raise IndexError(f"lead must be in [0,{predictions.shape[1] - 1}]")
    if valid_lengths is not None and args.lead >= int(valid_lengths[args.sample]):
        raise IndexError(
            f"lead {args.lead} is padding for sample {args.sample}; "
            f"valid length is {int(valid_lengths[args.sample])}"
        )
    names = ["z", "t", "t2m", "u10", "v10"]
    for channel, name in enumerate(names):
        prediction = predictions[args.sample, args.lead, 0, channel]
        target = targets[args.sample, args.lead, 0, channel]
        difference = prediction - target
        figure, axes = plt.subplots(1, 3, figsize=(12, 3.4), constrained_layout=True)
        for axis, image, title in zip(
            axes,
            (prediction, target, difference),
            ("prediction", "target", "difference"),
        ):
            cmap = "RdBu_r" if title == "difference" else "viridis"
            plot = axis.imshow(image, cmap=cmap, origin="upper", aspect="auto")
            axis.set_title(title)
            axis.set_xlabel("longitude index")
            axis.set_ylabel("latitude index")
            figure.colorbar(plot, ax=axis, shrink=0.8)
        figure.suptitle(f"ClimODE {name}, lead={(args.lead + 1) * 6} h")
        figure.savefig(figure_dir / f"{name}_lead_{(args.lead + 1) * 6:03d}h.png", dpi=150)
        plt.close(figure)
    print(json.dumps({"metrics": str(metrics_path), "figures": str(figure_dir)}))


if __name__ == "__main__":
    main()