"""
Training visualization — generates monitoring charts saved as PNGs.

Charts:
  1. Loss curves (train vs val, per epoch)
  2. Per-statistic R² comparison (Model A vs Model B bar chart)
  3. Learning rate schedule
  4. Predicted vs actual scatter plots (per statistic)

All charts are saved to the checkpoint/log directory for download.
"""

from __future__ import annotations

import json
import logging
import os
from pathlib import Path

import numpy as np

logger = logging.getLogger(__name__)


def plot_loss_curves(
    history_a: list[dict],
    history_b: list[dict],
    outdir: str,
) -> str:
    """Plot train/val loss curves for both models."""
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    for ax, history, label in [
        (axes[0], history_a, "Model A (plain HH)"),
        (axes[1], history_b, "Model B (HH+ACh)"),
    ]:
        epochs = [h["epoch"] for h in history]
        train_loss = [h["train_loss"] for h in history]
        val_loss = [h["val_loss"] for h in history]

        ax.plot(epochs, train_loss, label="Train", color="#2196F3", alpha=0.8)
        ax.plot(epochs, val_loss, label="Val", color="#FF9800", alpha=0.8)
        ax.set_xlabel("Epoch")
        ax.set_ylabel("MSE Loss")
        ax.set_title(label)
        ax.legend()
        ax.grid(True, alpha=0.3)

        # Mark best epoch
        best_idx = int(np.argmin(val_loss))
        ax.axvline(epochs[best_idx], color="green", linestyle="--", alpha=0.5,
                    label=f"Best: epoch {epochs[best_idx]}")
        ax.legend()

    fig.suptitle("Training Loss Curves", fontsize=14, fontweight="bold")
    fig.tight_layout()

    path = os.path.join(outdir, "loss_curves.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    logger.info(f"Saved loss curves to {path}")
    return path


def plot_r2_comparison(
    r2_a: dict[str, float],
    r2_b: dict[str, float],
    outdir: str,
) -> str:
    """Bar chart comparing per-statistic R² for Model A vs Model B."""
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    stats = list(r2_a.keys())
    vals_a = [r2_a[s] for s in stats]
    vals_b = [r2_b[s] for s in stats]

    x = np.arange(len(stats))
    width = 0.35

    fig, ax = plt.subplots(figsize=(14, 6))
    bars_a = ax.bar(x - width/2, vals_a, width, label="Model A (plain HH)",
                    color="#2196F3", alpha=0.8)
    bars_b = ax.bar(x + width/2, vals_b, width, label="Model B (HH+ACh)",
                    color="#FF9800", alpha=0.8)

    ax.set_ylabel("R² (validation set)")
    ax.set_title("Per-Statistic R² — Model A vs Model B", fontsize=14, fontweight="bold")
    ax.set_xticks(x)
    ax.set_xticklabels([s.replace("_", "\n") for s in stats], fontsize=8, rotation=45, ha="right")
    ax.legend()
    ax.grid(True, axis="y", alpha=0.3)
    ax.set_ylim(-0.1, 1.05)
    ax.axhline(0, color="black", linewidth=0.5)

    # Add value labels on bars
    for bar in bars_a:
        h = bar.get_height()
        if h > 0:
            ax.text(bar.get_x() + bar.get_width()/2., h + 0.02,
                    f"{h:.2f}", ha="center", va="bottom", fontsize=7, color="#1565C0")
    for bar in bars_b:
        h = bar.get_height()
        if h > 0:
            ax.text(bar.get_x() + bar.get_width()/2., h + 0.02,
                    f"{h:.2f}", ha="center", va="bottom", fontsize=7, color="#E65100")

    # Mean R² annotation
    mean_a = np.mean(vals_a)
    mean_b = np.mean(vals_b)
    ax.text(0.98, 0.95, f"Mean R²: A={mean_a:.3f}, B={mean_b:.3f}",
            transform=ax.transAxes, ha="right", va="top",
            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5))

    fig.tight_layout()
    path = os.path.join(outdir, "r2_comparison.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    logger.info(f"Saved R² comparison to {path}")
    return path


def plot_lr_schedule(
    history: list[dict],
    outdir: str,
    model_label: str = "",
) -> str:
    """Plot learning rate over training."""
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    epochs = [h["epoch"] for h in history]
    lrs = [h["lr"] for h in history]

    fig, ax = plt.subplots(figsize=(8, 4))
    ax.plot(epochs, lrs, color="#4CAF50", linewidth=2)
    ax.set_xlabel("Epoch")
    ax.set_ylabel("Learning Rate")
    ax.set_title(f"LR Schedule {model_label}", fontsize=12)
    ax.grid(True, alpha=0.3)
    ax.set_yscale("log")

    fig.tight_layout()
    path = os.path.join(outdir, f"lr_schedule{'_' + model_label.lower().replace(' ', '_') if model_label else ''}.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    logger.info(f"Saved LR schedule to {path}")
    return path


def plot_val_r2_over_time(
    history: list[dict],
    outdir: str,
    model_label: str = "",
) -> str:
    """Plot mean validation R² over epochs."""
    import matplotlib
    matplotlib.use("Agg")
    import matplotlib.pyplot as plt

    epochs = [h["epoch"] for h in history]
    r2s = [h["mean_val_r2"] for h in history]

    fig, ax = plt.subplots(figsize=(8, 4))
    ax.plot(epochs, r2s, color="#9C27B0", linewidth=2)
    ax.set_xlabel("Epoch")
    ax.set_ylabel("Mean Val R²")
    ax.set_title(f"Validation R² Over Training {model_label}", fontsize=12)
    ax.grid(True, alpha=0.3)
    ax.set_ylim(-0.1, 1.05)

    fig.tight_layout()
    suffix = f"_{'_'.join(model_label.lower().split())}" if model_label else ""
    path = os.path.join(outdir, f"val_r2_over_time{suffix}.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    logger.info(f"Saved val R² curve to {path}")
    return path


def generate_all_plots(
    results_a: dict,
    results_b: dict,
    log_dir: str,
) -> list[str]:
    """Generate all training monitoring plots.

    Args:
        results_a: Results dict from train_one_model("A")
        results_b: Results dict from train_one_model("B")
        log_dir: Directory containing history JSONs and for saving plots

    Returns:
        List of saved plot paths.
    """
    plot_dir = os.path.join(log_dir, "plots")
    os.makedirs(plot_dir, exist_ok=True)

    saved = []

    # Load histories
    hist_a_path = os.path.join(log_dir, "history_model_a.json")
    hist_b_path = os.path.join(log_dir, "history_model_b.json")

    if os.path.exists(hist_a_path) and os.path.exists(hist_b_path):
        with open(hist_a_path) as f:
            history_a = json.load(f)
        with open(hist_b_path) as f:
            history_b = json.load(f)

        # 1. Loss curves
        saved.append(plot_loss_curves(history_a, history_b, plot_dir))

        # 2. R² comparison
        r2_a = results_a.get("final_val_r2", {})
        r2_b = results_b.get("final_val_r2", {})
        if r2_a and r2_b:
            saved.append(plot_r2_comparison(r2_a, r2_b, plot_dir))

        # 3. LR schedules
        saved.append(plot_lr_schedule(history_a, plot_dir, "Model A"))
        saved.append(plot_lr_schedule(history_b, plot_dir, "Model B"))

        # 4. Val R² over time
        saved.append(plot_val_r2_over_time(history_a, plot_dir, "Model A"))
        saved.append(plot_val_r2_over_time(history_b, plot_dir, "Model B"))

    logger.info(f"Generated {len(saved)} plots in {plot_dir}")
    return saved