Updated line 340 with: best_positions_npy = np.load('best_positions.npy’). Updated line 358 with: contour = plt.contour(X, Y, Z, levels=20, cmap='magma’). Updated line 455 with: contour_plot = gwo.plot_contour_and_wolves(best_positions)

app.py

@@ -337,7 +337,7 @@ class GWO:
         def objective_function(x, y):
             return -5.0*np.exp(-0.5*((x+2.2)**2/0.4+(y-4.3)**2/0.4)) + -2.0*np.exp(-0.5*((x-2.2)**2/0.4+(y+4.3)**2/0.4))
 
-        best_postions_npy = np.load('best_positions.npy')
+        best_positions_npy = np.load('best_positions.npy')
 
         wolf_positions = best_positions_npy
 
@@ -355,7 +355,7 @@ class GWO:
 
         # Plot the contour map
         plt.figure(figsize=(10, 8))
-        contour = plt.contour(X, Y, Z, levels=20, cmap='viridis')
+        contour = plt.contour(X, Y, Z, levels=20, cmap='magma')
         plt.colorbar(contour)
 
         # Plot the wolf positions
@@ -452,7 +452,7 @@ def optimize(npart, ndim, max_iter):
     best_positions_text = f"Best Fitness: {last_best_positions}"
 
     # Get the contour plot as a PIL Image
-    contour_plot = gwo.plot_contour_and_wolves(wolf_positions)
+    contour_plot = gwo.plot_contour_and_wolves(best_positions)
 
     # Calculate the dispersion
     dispersion = gwo.Dispersion()