Updated the optimize method with removal of contour_plot functionality. Updated plot_contour_and_wolves method lines 344-346 with: best_positions_loaded_array = np.load('best_positions_array.npy') wolf_positions = best_positions_loaded_array. And modified the optimize function with: # Load best_positions_loaded_array best_positions_loaded_array = np.load('best_positions_array.npy') # Plot the contour_and_wolves plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_loaded_array) Removed the contour_plot from gwo.optimize also only the other two variables remain

app.py

@@ -222,16 +222,16 @@ class GWO:
 
         # Convert the list of best positions to a NumPy array
         best_positions_array = np.array(best_positions)
+        np.save('best_positions_array', best_positions_array)
 
-        # Get the contour plot as a PIL Image
-        contour_plot = self.plot_contour_and_wolves(best_positions_array)
+       
 
         # Print the best positions and fitness found
         print("Best Positions:", best_positions)
         print("Best Fitness:", best_fitness)
 
         # Return the best positions and fitness after the optimization
-        return best_positions, best_fitness, contour_plot
+        return best_positions, best_fitness
 
     def Step(self):
         """Do one swarm step"""
@@ -341,9 +341,9 @@ class GWO:
             }
     def plot_contour_and_wolves(self, wolf_positions):
          # Ensure wolf_positions is a 2D array
-        wolf_positions = np.array(wolf_positions)
-        if wolf_positions.ndim == 1:
-            wolf_positions = wolf_positions.reshape(-1, 1)
+        best_positions_loaded_array = np.load('best_positions_array.npy')
+
+        wolf_positions = best_positions_loaded_array
             
         # Define the objective function
         def objective_function(x, y):
@@ -491,7 +491,7 @@ def optimize(npart, ndim, max_iter):
     # Initialize the GWO algorithm with the provided parameters
     gwo = GWO(obj=obj, npart=npart, ndim=ndim, max_iter=max_iter, init=init, bounds=bounds)
     
-    best_positions, best_fitness, contour_plot = gwo.optimize()
+    best_positions, best_fitness= gwo.optimize()
     
     # Convert best_fitness and best_positions to NumPy arrays if necessary
     best_fitness_npy = np.array(best_fitness)
@@ -501,6 +501,12 @@ def optimize(npart, ndim, max_iter):
     dispersion = gwo.Dispersion()
     dispersion_text = f"Dispersion: {dispersion}"
 
+    # Load best_positions_loaded_array
+    best_positions_loaded_array = np.load('best_positions_array.npy')
+    
+    # Plot the contour_and_wolves 
+    plot_contour_and_wolves = gwo.plot_contour_and_wolves(best_positions_loaded_array)
+
     # Plot the dispersion over time
     dispersion_plot = gwo.plot_dispersion()