Use data-driven heatmap color ranges

scripts/make_figures.py

@@ -353,7 +353,7 @@ def fig1_disparity_heatmap(suite: dict[str, dict], output_dir: Path):
     fig, ax = plt.subplots(figsize=(7.0, 3.6), constrained_layout=True)
     cmap = plt.cm.RdYlBu_r.copy()
     cmap.set_bad("#efefef")
-    im = ax.imshow(np.ma.masked_invalid(matrix), aspect="auto", cmap=cmap, vmin=0.0, vmax=0.85)
+    im = ax.imshow(np.ma.masked_invalid(matrix), aspect="auto", cmap=cmap)
 
     ax.set_xticks(range(len(DGP_SPECS)))
     ax.set_xticklabels([label for _, label in DGP_SPECS])
@@ -386,7 +386,8 @@ def fig1_disparity_heatmap(suite: dict[str, dict], output_dir: Path):
     highlight_best_cells(ax, matrix, methods, exclude={"oracle"})
 
     cbar = fig.colorbar(im, ax=ax, shrink=0.95, pad=0.02)
-    cbar.set_label("Max disparity")
+    cbar.set_label("Max disparity (low → high)")
+    cbar.set_ticks([])
 
     save_figure(fig, output_dir, "fig1_synthetic_disparity_heatmap.pdf")
     plt.close(fig)
@@ -554,7 +555,7 @@ def fig5_real_disparity_heatmap(real_suite: dict[str, dict], output_dir: Path):
     fig, ax = plt.subplots(figsize=(7.0, 4.0), constrained_layout=True)
     cmap = plt.cm.RdYlBu_r.copy()
     cmap.set_bad("#efefef")
-    im = ax.imshow(np.ma.masked_invalid(matrix), aspect="auto", cmap=cmap, vmin=0.0, vmax=0.9)
+    im = ax.imshow(np.ma.masked_invalid(matrix), aspect="auto", cmap=cmap)
 
     ax.set_xticks(range(len(tasks)))
     ax.set_xticklabels([task_labels.get(task, task) for task in tasks])
@@ -587,7 +588,8 @@ def fig5_real_disparity_heatmap(real_suite: dict[str, dict], output_dir: Path):
     highlight_best_cells(ax, matrix, methods)
 
     cbar = fig.colorbar(im, ax=ax, shrink=0.95, pad=0.02)
-    cbar.set_label("Max disparity")
+    cbar.set_label("Max disparity (low → high)")
+    cbar.set_ticks([])
 
     save_figure(fig, output_dir, "fig5_real_disparity_heatmap.pdf")
     plt.close(fig)