add custom color map to visualization script

mu_map/polar_map/visualize.py

@@ -101,11 +101,15 @@ for _id in ids:
         return np.where(mask, _img, black)
 
     cm_plasma = mlp.colormaps["plasma"]
+
+    cm_prism_data = pd.read_csv("data/color_maps/PrismOeyn.cm")
+    cm_prism = mlp.colors.ListedColormap(cm_prism_data.values / 255.0)
+
     cm_divergent = mlp.colormaps["RdYlBu"].reversed()
-    axs[0, 0].imshow(colormap_and_mask(baseline, cm_plasma))
+    axs[0, 0].imshow(colormap_and_mask(baseline, cm_prism))
     axs[0, 0].set_title("AC" if args.baseline == "symbia" else "AC - CT")
     for i, (recon, diff, label) in enumerate(zip(recons, diffs, labels), start=1):
-        axs[0, i].imshow(colormap_and_mask(recon, cm_plasma))
+        axs[0, i].imshow(colormap_and_mask(recon, cm_prism))
         axs[0, i].set_title(label)
         axs[1, i].imshow(colormap_and_mask(diff, cm_divergent))
 
@@ -113,12 +117,12 @@ for _id in ids:
         mlp.cm.ScalarMappable(
             norm=mlp.colors.Normalize(vmin=diff_min, vmax=diff_max), cmap=cm_divergent
         ),
-        ax=axs[1, 1:4],
+        ax=axs[1],
     )
     fig.colorbar(
         mlp.cm.ScalarMappable(
-            norm=mlp.colors.Normalize(vmin=0, vmax=100), cmap=cm_plasma
+            norm=mlp.colors.Normalize(vmin=0, vmax=100), cmap=cm_prism
         ),
-        ax=axs[0, 1:4]
+        ax=axs[0]
     )
     plt.show()