hotfix

agent.py

@@ -103,7 +103,7 @@ class RandomWalkerAnt(Agent):
             """
             combined = res_weights * walk_weights
             normalized = combined / np.sum(combined)
-            return normalized
+            return list(normalized)
 
         def _pick_from_remaining_five(remaining_five):
             """
@@ -190,7 +190,9 @@ class RandomWalkerAnt(Agent):
                 res_weights = self._get_resistance_weights()
                 weights = _combine_weights(res_weights, sens_weights)
 
-                self._next_pos = np.random.choice(all_neighbors_cells, p=weights)
+                random_index = np.random.choice(range(6), p=weights)
+                self._next_pos = all_neighbors_cells[random_index]
+
                 self._prev_pos = self.pos
                 return
 
@@ -205,7 +207,9 @@ class RandomWalkerAnt(Agent):
         walk_weights[front_index] = self.model.alpha
 
         weights = _combine_weights(res_weights, walk_weights)
-        self._nex_pos = np.random.choice(all_neighbors_cells, p=weights)
+
+        random_index = np.random.choice(range(6), p=weights)
+        self._next_pos = all_neighbors_cells[random_index]
         self._prev_pos = self.pos
 
     def step(self):

main.py

@@ -180,21 +180,16 @@ if __name__ == "__main__":
     print(kwargs)
     model = ActiveWalkerModel(**kwargs)
 
-    # from hexplot import plot_hexagon
-    # a = np.zeros_like(model.grid.fields['food'])
-    # a[np.nonzero(model.grid.fields['food'])] = 1
-    # plot_hexagon(a, title="Nest locations")
-    # plot_hexagon(model.grid.fields['res'], title="Resistance Map")
+    from hexplot import plot_hexagon
+    a = np.zeros_like(model.grid.fields['food'])
+    a[np.nonzero(model.grid.fields['food'])] = 1
+    plot_hexagon(a, title="Nest locations")
+    plot_hexagon(model.grid.fields['res'], title="Resistance Map")
 
 
-    # from tqdm import tqdm as progress_bar
-    #for _ in progress_bar(range(model.max_steps)):
-    # model.step()
-    # agent_densities = model.datacollector.get_model_vars_dataframe()["agent_dens"]
-    # mean_dens = np.mean(agent_densities)
-    # norm_dens = mean_dens/np.max(mean_dens)
-    # plot_hexagon(norm_dens, title="Ant density overall")
-    # plt.show()
+    from tqdm import tqdm as progress_bar
+    for _ in progress_bar(range(model.max_steps)):
+        model.step()