| """ |
| 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 |
|
|
|
|
| |
|
|
|
|
| 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 |
|
|
|
|
| |
|
|
|
|
| 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}") |
|
|
|
|
| |
|
|
|
|
| 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() |
|
|
