"""Evaluate complete spatial predictions independently at every scale."""

import json
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import yaml


ROOT = Path(__file__).resolve().parents[1]


def scores(target, prediction):
    residual = target - prediction
    rmse = np.sqrt(np.mean(np.square(residual), axis=0))
    denominator = np.sum(np.square(target - target.mean(axis=0)), axis=0)
    r2 = 1.0 - np.sum(np.square(residual), axis=0) / np.maximum(denominator, 1e-20)
    return r2, rmse


def main():
    config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
    data = np.load(ROOT / config["paths"]["inference"])
    output = ROOT / config["paths"]["evaluation_dir"]
    output.mkdir(parents=True, exist_ok=True)
    report = {"format_version": config["data"]["format_version"], "per_scale": {}}
    plotted = []
    scales = [str(scale) for scale in data["scales"]]
    if scales != list(config["data"]["scales"]):
        raise ValueError("prediction scales do not match configuration")
    for scale_index, scale in enumerate(scales):
        report["per_scale"][scale] = {"grid": data[f"{scale}_grid_shape"].tolist()}
        for task, names in (("rf_tend", data["tend_output_names"]), ("rf_diff", data["diff_output_names"])):
            target = data[f"{scale}_{task[3:]}_targets"]
            prediction = data[f"{scale}_{task[3:]}_predictions"]
            r2, rmse = scores(target, prediction)
            if not np.isfinite(r2).all() or not np.isfinite(rmse).all():
                raise FloatingPointError(f"non-finite offline metrics for {scale}/{task}")
            report["per_scale"][scale][task] = {
                "outputs": [{"name": str(name), "r2": float(r2[i]), "rmse": float(rmse[i])}
                            for i, name in enumerate(names)],
                "mean_r2": float(r2.mean()), "mean_rmse": float(rmse.mean())}
            plotted.append((scale, task, float(r2.mean()), float(rmse.mean())))
    ny, nx = map(int, config["evaluation"]["online_proxy_grid"])
    target = data["online_x32_native_precipitation_target_3h"].reshape(ny, nx).mean(axis=1)
    prediction = data["online_x32_native_precipitation_prediction_3h"].reshape(ny, nx).mean(axis=1)
    proxy_r2, proxy_rmse = scores(target[:, None], prediction[:, None])
    report["online_engineering_proxy"] = {
        "scale": "x32", "grid": [ny, nx], "time_steps": 1,
        "metric": "zonal-mean 3h precipitation",
        "r2": float(proxy_r2[0]), "rmse": float(proxy_rmse[0]),
        "target_p99_9_3h": float(np.percentile(data["online_x32_native_precipitation_target_3h"], 99.9)),
        "prediction_p99_9_3h": float(np.percentile(data["online_x32_native_precipitation_prediction_3h"], 99.9)),
        "note": "Engineering proxy for online coupling, not the paper's 600-day SAM simulation."}
    numeric = [scale[task]["mean_r2"] for scale in report["per_scale"].values()
               for task in ("rf_tend", "rf_diff")]
    numeric += [report["online_engineering_proxy"][key] for key in ("r2", "rmse", "target_p99_9_3h", "prediction_p99_9_3h")]
    if not np.isfinite(numeric).all():
        raise FloatingPointError("evaluation report contains non-finite values")
    (output / "metrics.json").write_text(json.dumps(report, indent=2) + "\n")
    figure, axes = plt.subplots(1, 2, figsize=(12, 4.5))
    labels = [f"{scale}\n{task[3:]}" for scale, task, _, _ in plotted]
    axes[0].bar(labels, [item[2] for item in plotted], color=["#275d6c", "#d98b3a"] * 4)
    axes[0].axhline(0, color="black", linewidth=0.7); axes[0].set_ylabel("Mean output R2")
    axes[0].tick_params(axis="x", labelsize=8); axes[0].set_title("Complete-grid column prediction")
    latitude = np.linspace(-90, 90, ny)
    axes[1].plot(latitude, target, label="target", color="#202020", linewidth=2)
    axes[1].plot(latitude, prediction, label="RF prediction", color="#c44e52", linewidth=2)
    axes[1].set(xlabel="Latitude (degrees)", ylabel="3 h precipitation (proxy units)",
                title=f"x32 native zonal mean on {ny}x{nx} grid")
    axes[1].legend(); figure.tight_layout(); figure.savefig(output / "comparison.png", dpi=150)
    plt.close(figure)
    print(f"evaluation={output.relative_to(ROOT)} online_proxy_r2={proxy_r2[0]:.3f}")


if __name__ == "__main__":
    main()