Updated line 414 with: dispersion_values = np.array([self.Dispersion() for _ in positions]) [plot_dispersion_heatmap] method

app.py

@@ -403,6 +403,34 @@ class GWO:
         dy = y.max() - y.min()
         return (dx + dy) / 2.0
 
+    def plot_dispersion_heatmap(self, x_range, y_range, resolution=100):
+        # Create a grid of points within the specified range
+        x = np.linspace(*x_range, resolution)
+        y = np.linspace(*y_range, resolution)
+        X, Y = np.meshgrid(x, y)
+        positions = np.vstack([X.ravel(), Y.ravel()]).T
+
+        # Calculate the dispersion for each position in the grid
+        dispersion_values = np.array([self.Dispersion() for _ in positions])
+        Z = dispersion_values.reshape(X.shape)
+
+        # Plot the dispersion heatmap
+        plt.figure(figsize=(10, 8))
+        plt.pcolormesh(X, Y, Z, cmap='viridis')
+        plt.colorbar(label='Dispersion')
+
+        # Set plot title and labels
+        plt.title('Dispersion Heatmap')
+        plt.xlabel('x')
+        plt.ylabel('y')
+
+        # Convert the plot to a PIL Image and return it
+        buf = BytesIO()
+        plt.savefig(buf, format='png')
+        buf.seek(0)
+        plt.close()  # Close the figure to free up memory
+        return Image.open(buf)
+
     def plot_dispersion(self):
         """Plot the dispersion over time"""
         # Assuming self.giter stores the iteration number at which each best position was found
@@ -450,33 +478,6 @@ class GWO:
         #plt.close()  # Close the figure to free up memory
         #return Image.open(buf)
 
-    def plot_dispersion_heatmap(self, x_range, y_range, resolution=100):
-        # Create a grid of points within the specified range
-        x = np.linspace(*x_range, resolution)
-        y = np.linspace(*y_range, resolution)
-        X, Y = np.meshgrid(x, y)
-        positions = np.vstack([X.ravel(), Y.ravel()]).T
-
-        # Calculate the dispersion for each position in the grid
-        dispersion_values = np.array([self.Dispersion(pos) for pos in positions])
-        Z = dispersion_values.reshape(X.shape)
-
-        # Plot the dispersion heatmap
-        plt.figure(figsize=(10, 8))
-        plt.pcolormesh(X, Y, Z, cmap='viridis', norm=LogNorm())
-        plt.colorbar(label='Dispersion')
-
-        # Set plot title and labels
-        plt.title('Dispersion Heatmap')
-        plt.xlabel('x')
-        plt.ylabel('y')
-
-        # Convert the plot to a PIL Image and return it
-        buf = BytesIO()
-        plt.savefig(buf, format='png')
-        buf.seek(0)
-        plt.close()  # Close the figure to free up memory
-        return Image.open(buf)
 
 
 def optimize(npart, ndim, max_iter):