"""
Visualisation utilities — training curves and comparison plots.

Uses only Matplotlib (no Seaborn / Plotly dependency).
All plots are saved to disk (non-interactive backend).
"""

from __future__ import annotations

from pathlib import Path

import numpy as np

try:
    import matplotlib
    matplotlib.use("Agg")          # Non-interactive backend (safe on servers)
    import matplotlib.pyplot as plt
    _MPL_OK = True
except ImportError:
    _MPL_OK = False
    plt = None                     # type: ignore


# ── Helper ────────────────────────────────────────────────────────────────────

def _check_mpl():
    if not _MPL_OK:
        raise ImportError(
            "matplotlib is required for plotting.\n"
            "Install with:  pip install matplotlib"
        )


def _moving_average(values: list, window: int) -> list:
    """Simple unweighted moving average."""
    result = []
    for i in range(len(values)):
        start = max(0, i - window + 1)
        result.append(float(np.mean(values[start : i + 1])))
    return result


# ── Public functions ──────────────────────────────────────────────────────────

def plot_training_curves(metrics, save_path: str | Path | None = None) -> bool:
    """
    Plot four training metrics in a 2×2 grid and save to *save_path*.

    Args:
        metrics:   A :class:`MetricsTracker` instance.
        save_path: Destination PNG path.  Shown interactively if None.

    Returns:
        True on success, False on failure.
    """
    try:
        _check_mpl()

        panel_cfg = [
            ("episode_reward",       "Episode Reward",       "blue",   "Reward"),
            ("average_waiting_time", "Avg Waiting Time",     "orange", "Waiting Time (s)"),
            ("average_queue_length", "Avg Queue Length",     "red",    "Queue Length"),
            ("throughput",           "Throughput",           "green",  "Vehicles Passed"),
        ]

        has_any = any(metrics.has(k) for k, *_ in panel_cfg)
        if not has_any:
            print("[WARN] No data available for plotting.")
            return False

        fig, axes = plt.subplots(2, 2, figsize=(15, 10))
        fig.suptitle("Training Progress", fontsize=16, fontweight="bold")
        axes_flat = axes.flatten()

        for ax, (key, title, colour, ylabel) in zip(axes_flat, panel_cfg):
            ax.set_title(title, fontsize=12, fontweight="bold")
            ax.set_xlabel("Episode", fontsize=10)
            ax.set_ylabel(ylabel, fontsize=10)
            ax.grid(True, alpha=0.3)

            if not metrics.has(key):
                ax.text(0.5, 0.5, "No data", ha="center", va="center",
                        transform=ax.transAxes, color="grey")
                continue

            vals = metrics.get(key)
            eps = range(1, len(vals) + 1)
            ax.plot(eps, vals, alpha=0.4, color=colour, linewidth=1, label="Raw")

            if len(vals) >= 10:
                w = min(50, max(10, len(vals) // 10))
                ma = _moving_average(vals, w)
                ax.plot(eps, ma, color=colour, linewidth=2,
                        label=f"MA-{w}")
                ax.legend(loc="best", fontsize=8)

        plt.tight_layout()

        if save_path:
            save_path = Path(save_path)
            save_path.parent.mkdir(parents=True, exist_ok=True)
            plt.savefig(save_path, dpi=150, bbox_inches="tight", facecolor="white")
            print(f"[OK] Plot saved -> {save_path}")
        else:
            plt.show()

        plt.close(fig)
        return True

    except ImportError as exc:
        print(f"[WARN] {exc}")
        return False
    except Exception as exc:
        print(f"[WARN] Plotting error: {exc}")
        try:
            plt.close("all")
        except Exception:
            pass
        return False


def plot_comparison(
    results_dict: dict[str, list],
    metric_name: str,
    save_path: str | Path | None = None,
) -> bool:
    """
    Overlay multiple result series on a single axes.

    Args:
        results_dict: ``{"Method Name": [values, …], …}``
        metric_name:  Y-axis label / title suffix.
        save_path:    Destination PNG path.

    Returns:
        True on success.
    """
    try:
        _check_mpl()

        if not results_dict:
            print("[WARN] No data for comparison plot.")
            return False

        fig, ax = plt.subplots(figsize=(12, 6))

        colours = ["blue", "green", "red", "orange", "purple"]
        for i, (name, vals) in enumerate(results_dict.items()):
            if vals:
                ax.plot(range(1, len(vals) + 1), vals,
                        label=name, linewidth=2, alpha=0.8,
                        color=colours[i % len(colours)])

        ax.set_xlabel("Episode", fontsize=12)
        ax.set_ylabel(metric_name, fontsize=12)
        ax.set_title(f"{metric_name} - Method Comparison",
                     fontsize=14, fontweight="bold")
        ax.legend(loc="best")
        ax.grid(True, alpha=0.3)

        plt.tight_layout()

        if save_path:
            save_path = Path(save_path)
            save_path.parent.mkdir(parents=True, exist_ok=True)
            plt.savefig(save_path, dpi=150, bbox_inches="tight", facecolor="white")
            print(f"[OK] Comparison plot saved -> {save_path}")
        else:
            plt.show()

        plt.close(fig)
        return True

    except ImportError as exc:
        print(f"[WARN] {exc}")
        return False
    except Exception as exc:
        print(f"[WARN] Comparison plot error: {exc}")
        try:
            plt.close("all")
        except Exception:
            pass
        return False


def plot_bar_comparison(
    method_scores: dict[str, float],
    title: str = "Method Comparison",
    ylabel: str = "Mean Reward",
    save_path: str | Path | None = None,
) -> bool:
    """
    Bar chart comparing scalar scores for different methods.

    Args:
        method_scores: {"Method": score, ...}
        title:         Chart title.
        ylabel:        Y-axis label.
        save_path:     Destination PNG path.

    Returns:
        True on success.
    """
    try:
        _check_mpl()

        if not method_scores:
            return False

        names = list(method_scores.keys())
        scores = [method_scores[n] for n in names]
        colours = ["#4472C4", "#ED7D31", "#A9D18E"]

        fig, ax = plt.subplots(figsize=(8, 5))
        bars = ax.bar(names, scores,
                      color=colours[: len(names)],
                      edgecolor="white", linewidth=1.5)

        # Value labels
        for bar, score in zip(bars, scores):
            ax.text(
                bar.get_x() + bar.get_width() / 2,
                bar.get_height() + (max(scores) - min(scores)) * 0.01,
                f"{score:.2f}",
                ha="center", va="bottom", fontsize=11, fontweight="bold",
            )

        ax.set_title(title, fontsize=14, fontweight="bold")
        ax.set_ylabel(ylabel, fontsize=12)
        ax.grid(axis="y", alpha=0.3)
        ax.set_ylim(min(scores) * 1.05, max(scores) * 0.95)  # Tight y-range

        plt.tight_layout()

        if save_path:
            save_path = Path(save_path)
            save_path.parent.mkdir(parents=True, exist_ok=True)
            plt.savefig(save_path, dpi=150, bbox_inches="tight", facecolor="white")
            print(f"[OK] Bar chart saved -> {save_path}")
        else:
            plt.show()

        plt.close(fig)
        return True

    except ImportError as exc:
        print(f"[WARN] {exc}")
        return False
    except Exception as exc:
        print(f"[WARN] Bar chart error: {exc}")
        return False