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projectmesa · Dec 27, 2024 · c850d5f · c850d5f
1 parent 0142b18
commit c850d5f
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Showing 2 changed files with 66 additions and 10 deletions.
diff --git a/examples/city_walking_behaviour/app.py b/examples/city_walking_behaviour/app.py
@@ -1,4 +1,10 @@
-from city_walking_behaviour.agents import Human, GroceryStore, SocialPlace, NonFoodShop, Other
+from city_walking_behaviour.agents import (
+    Human,
+    GroceryStore,
+    SocialPlace,
+    NonFoodShop,
+    Other,
+)
 from city_walking_behaviour.model import WalkingModel
 from mesa.experimental.devs import ABMSimulator
 from mesa.visualization import (
@@ -208,20 +214,62 @@ def post_process_lines_leisure(ax):
 
 def post_process_buildings_legend(ax):
     import matplotlib.lines as mlines
+
     # Create legend entries for each building/agent
-    grocery_store = mlines.Line2D([], [], color="tab:green", marker="s", linestyle="None", markersize=10, label="Grocery Store")
-    social_place = mlines.Line2D([], [], color="tab:purple", marker="s", linestyle="None", markersize=10, label="Social Place")
-    non_food_shop = mlines.Line2D([], [], color="tab:olive", marker="s", linestyle="None", markersize=10, label="Non-Food Shop")
-    other_building = mlines.Line2D([], [], color="tab:brown", marker="s", linestyle="None", markersize=10, label="Other")
-    human = mlines.Line2D([], [], color="tab:red", marker="v", linestyle="None", markersize=10, label="Human")
+    grocery_store = mlines.Line2D(
+        [],
+        [],
+        color="tab:green",
+        marker="s",
+        linestyle="None",
+        markersize=10,
+        label="Grocery Store",
+    )
+    social_place = mlines.Line2D(
+        [],
+        [],
+        color="tab:purple",
+        marker="s",
+        linestyle="None",
+        markersize=10,
+        label="Social Place",
+    )
+    non_food_shop = mlines.Line2D(
+        [],
+        [],
+        color="tab:olive",
+        marker="s",
+        linestyle="None",
+        markersize=10,
+        label="Non-Food Shop",
+    )
+    other_building = mlines.Line2D(
+        [],
+        [],
+        color="tab:brown",
+        marker="s",
+        linestyle="None",
+        markersize=10,
+        label="Other",
+    )
+    human = mlines.Line2D(
+        [],
+        [],
+        color="tab:red",
+        marker="v",
+        linestyle="None",
+        markersize=10,
+        label="Human",
+    )
 
     ax.legend(
         handles=[grocery_store, social_place, non_food_shop, other_building, human],
         loc="center",
-        bbox_to_anchor=(0.5, 0.5)
+        bbox_to_anchor=(0.5, 0.5),
     )
     ax.axis("off")
 
+
 space_component = make_space_component(
     agent_portrayal, draw_grid=True, post_process=post_process_space
 )
@@ -270,7 +318,9 @@ def post_process_buildings_legend(ax):
     post_process=post_process_lines_leisure,
 )
 
-plot_component_legend = make_plot_component({}, post_process=post_process_buildings_legend)
+plot_component_legend = make_plot_component(
+    {}, post_process=post_process_buildings_legend
+)
 
 # Initialize and run the model
 simulator = ABMSimulator()

diff --git a/examples/city_walking_behaviour/city_walking_behaviour/model.py b/examples/city_walking_behaviour/city_walking_behaviour/model.py
@@ -1,6 +1,12 @@
 import math
 from mesa import Model
-from city_walking_behaviour.agents import Human, GroceryStore, SocialPlace, NonFoodShop, Other
+from city_walking_behaviour.agents import (
+    Human,
+    GroceryStore,
+    SocialPlace,
+    NonFoodShop,
+    Other,
+)
 from mesa.experimental.cell_space import OrthogonalVonNeumannGrid
 from mesa.experimental.cell_space.property_layer import PropertyLayer
 from mesa.experimental.devs import ABMSimulator
@@ -521,7 +527,7 @@ def calculate_safety(i, j):
             base_safety = min_distance * 0.8  # Maximum safety of 0.8
 
             # Add some local variation
-            local_variation = self.random.random() * 0.2 
+            local_variation = self.random.random() * 0.2
 
             # Add some urban-like patterns
             if min_distance < 0.3:  # Near centers