"""
Build distance-based adjacency matrices and heatmap visualisations
for the Dianchi Water dataset.

Usage
-----
# Generate adjacency CSVs + combined heatmap at default thresholds:
    python build_adjacency.py

# Single threshold:
    python build_adjacency.py --threshold-km 20

# Custom thresholds and output directory:
    python build_adjacency.py --thresholds-km 5,10,15,20,25,30 --output-dir ./outputs

Requirements: numpy, pandas, matplotlib
"""

from __future__ import annotations

import argparse
from pathlib import Path
from typing import List, Tuple

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


# ── adjacency construction ───────────────────────────────────────────


def build_adjacency_matrix(
    dist_df: pd.DataFrame,
    threshold_km: float,
    self_loop: float = 1.0,
) -> pd.DataFrame:
    """Linear-decay adjacency: w_ij = max(0, 1 - d_ij / threshold).

    Parameters
    ----------
    dist_df : pd.DataFrame
        Square pairwise distance matrix (km) with station names as
        both index and columns.
    threshold_km : float
        Distance threshold in km.  Pairs farther than this receive
        weight 0.
    self_loop : float
        Diagonal value (default 1.0).  Set to 0.0 if your model adds
        self-loops separately.
    """
    if threshold_km <= 0:
        raise ValueError("threshold_km must be positive.")
    adj = (1.0 - dist_df / threshold_km).clip(lower=0.0, upper=1.0)
    np.fill_diagonal(adj.values, float(self_loop))
    return adj


# ── visualisation ────────────────────────────────────────────────────


def plot_single_heatmap(
    df: pd.DataFrame, title: str, save_path: Path
) -> None:
    n = len(df)
    fig, ax = plt.subplots(
        figsize=(max(10, n * 0.6), max(8, n * 0.5)), dpi=220
    )
    im = ax.imshow(df.values, cmap="viridis", vmin=0, vmax=1, aspect="auto")
    ax.set_title(title, fontsize=14)
    ax.set_xticks(range(n))
    ax.set_yticks(range(n))
    ax.set_xticklabels(df.columns, rotation=90, fontsize=7)
    ax.set_yticklabels(df.index, fontsize=7)
    cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
    cbar.set_label("Adjacency weight", rotation=90)
    fig.tight_layout()
    fig.savefig(save_path, bbox_inches="tight")
    plt.close(fig)
    print(f"  Saved: {save_path}")


def plot_combined_heatmaps(
    panels: List[Tuple[float, pd.DataFrame]], save_path: Path
) -> None:
    n_panels = len(panels)
    ncols = min(n_panels, 3)
    nrows = (n_panels + ncols - 1) // ncols
    fig, axes = plt.subplots(
        nrows, ncols, figsize=(7.5 * ncols, 7 * nrows),
        dpi=220, constrained_layout=True,
    )
    axes = np.atleast_1d(axes).flatten()

    im = None
    for idx, (threshold, df) in enumerate(panels):
        ax = axes[idx]
        im = ax.imshow(
            df.values, cmap="viridis", vmin=0, vmax=1, aspect="auto"
        )
        ax.set_title(f"threshold = {threshold:g} km", fontsize=12)
        ax.set_xticks(range(len(df)))
        ax.set_yticks(range(len(df)))
        ax.set_xticklabels(df.columns, rotation=90, fontsize=6)
        ax.set_yticklabels(df.index, fontsize=6)

    for idx in range(n_panels, len(axes)):
        axes[idx].axis("off")

    if im is not None:
        fig.colorbar(im, ax=axes.tolist(), fraction=0.02, pad=0.02,
                     label="Adjacency weight")
    fig.suptitle(
        "Distance-Based Adjacency Under Different Thresholds", fontsize=16
    )
    fig.savefig(save_path, bbox_inches="tight")
    plt.close(fig)
    print(f"Combined heatmap saved: {save_path}")


# ── CLI ──────────────────────────────────────────────────────────────


def parse_thresholds(text: str) -> List[float]:
    out = [float(t) for t in text.split(",") if t.strip()]
    if not out:
        raise ValueError("At least one threshold must be provided.")
    return out


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Build adjacency matrices and heatmaps from the "
                    "Dianchi Water station distance matrix."
    )
    parser.add_argument(
        "--distance-csv",
        default=str(Path(__file__).resolve().parent.parent
                    / "data" / "dianchi_station_distance_km.csv"),
        help="Path to dianchi_station_distance_km.csv "
             "(default: ../data/dianchi_station_distance_km.csv)",
    )
    parser.add_argument(
        "--output-dir", default=None,
        help="Output directory (default: same as --distance-csv).",
    )
    parser.add_argument(
        "--threshold-km", type=float, default=None,
        help="Single distance threshold in km.",
    )
    parser.add_argument(
        "--thresholds-km", default="10,15,20,25,30",
        help="Comma-separated distance thresholds (default: 10,15,20,25,30).",
    )
    parser.add_argument(
        "--self-loop", type=float, default=1.0,
        help="Diagonal value of adjacency matrix (default: 1.0).",
    )
    parser.add_argument(
        "--no-plot", action="store_true",
        help="Skip heatmap generation.",
    )
    args = parser.parse_args()

    dist_path = Path(args.distance_csv)
    output_dir = Path(args.output_dir) if args.output_dir else dist_path.parent
    output_dir.mkdir(parents=True, exist_ok=True)

    dist_df = pd.read_csv(dist_path, index_col=0)
    print(f"Loaded distance matrix: {dist_path}  ({len(dist_df)} stations)")

    thresholds = (
        [args.threshold_km] if args.threshold_km is not None
        else parse_thresholds(args.thresholds_km)
    )

    panels: List[Tuple[float, pd.DataFrame]] = []
    for t in thresholds:
        adj = build_adjacency_matrix(dist_df, t, self_loop=args.self_loop)
        out = output_dir / f"adjacency_threshold_{t:g}km.csv"
        adj.to_csv(out, encoding="utf-8-sig")
        print(f"Adjacency matrix saved: {out}")
        panels.append((t, adj))

    if not args.no_plot and panels:
        plot_combined_heatmaps(
            panels, output_dir / "adjacency_heatmaps_combined.png"
        )
        for t, adj in panels:
            plot_single_heatmap(
                adj,
                f"Adjacency (threshold = {t:g} km)",
                output_dir / f"adjacency_heatmap_{t:g}km.png",
            )


if __name__ == "__main__":
    main()