[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

examples/continuous_predator_prey/prey_predator/agents.py

@@ -1,16 +1,17 @@
 import math
+
 from mesa.experimental.continuous_space import ContinuousSpaceAgent
 
 
 class Prey(ContinuousSpaceAgent):
-    #a prey agent which has random motion in continuous space
+    # a prey agent which has random motion in continuous space
 
     def __init__(self, space, model, pos, speed=1.0, max_age=40):
         # unique_id is handled automatically by super()
         super().__init__(space, model)
         self.position = pos
         self.speed = speed
-        
+
         # we need give them random starting age so they don't all die on the exact same step!
         self.age = self.random.randint(0, max_age)
         self.max_age = max_age  # it's maximum lifespan of prey agent
@@ -37,7 +38,7 @@ def random_move(self):
 
     def step(self):
         # it calls the random move function to move the agent in each step of the simulation
-        
+
         # 1. DYING OLD: increase age, and die if too old
         self.age += 1  # age increasing each step
         if self.age > self.max_age:
@@ -47,27 +48,34 @@ def step(self):
         self.random_move()
 
         # check surroundings for mating and overcrowding
-        neighbors, _ = self.get_neighbors_in_radius(radius=2.5)  # it get nearby agents within radius
-        prey_neighbors = [n for n in neighbors if isinstance(n, Prey)]  # it filter to find only prey
+        neighbors, _ = self.get_neighbors_in_radius(
+            radius=2.5
+        )  # it get nearby agents within radius
+        prey_neighbors = [
+            n for n in neighbors if isinstance(n, Prey)
+        ]  # it filter to find only prey
 
         # 2. MATING & CROWDING: must have at least 1 mate nearby (>0),
         # but won't reproduce if it's too overcrowded (<6)
         # it check not too lonely and not too crowded
-        if 0 < len(prey_neighbors) < 6 and self.random.random() < self.model.prey_reproduce:
+        if (
+            0 < len(prey_neighbors) < 6
+            and self.random.random() < self.model.prey_reproduce
+        ):
             # create new prey at the exact same position (asexual reproduction)
             Prey(self.model.space, self.model, self.position, self.speed, self.max_age)
 
 
 class Predator(ContinuousSpaceAgent):
-    #a predator agent which moves randomly but also hunts nearby prey
+    # a predator agent which moves randomly but also hunts nearby prey
 
     def __init__(self, space, model, pos, speed=1.5, energy=0, max_age=60):
         # unique_id is handled automatically by super()
         super().__init__(space, model)
         self.position = pos
         self.speed = speed
         self.energy = energy  # energy level of the predator; it need for survival and reproduction
-        
+
         # dying old - we need give them random starting age
         self.age = self.random.randint(0, max_age)
         self.max_age = max_age  # it's maximum lifespan of predator agent
@@ -95,13 +103,17 @@ def step(self):
         self.energy -= 1  # predator lose energy each step; it's cost of living
 
         # get nearby agents within hunting radius (increased from 2.0 so they can find food easier)
-        neighbors, _ = self.get_neighbors_in_radius(radius=4.0)  
-        prey_neighbors = [n for n in neighbors if isinstance(n, Prey)]  # it filter the nearby agents to find the prey
+        neighbors, _ = self.get_neighbors_in_radius(radius=4.0)
+        prey_neighbors = [
+            n for n in neighbors if isinstance(n, Prey)
+        ]  # it filter the nearby agents to find the prey
 
         if prey_neighbors:
-            prey_to_eat = self.random.choice(prey_neighbors)  # choose random prey to eat
+            prey_to_eat = self.random.choice(
+                prey_neighbors
+            )  # choose random prey to eat
             self.energy += self.model.predator_gain_from_food  # gain energy from eating
-            
+
             # Modern Mesa 4.0 removal (replaces all the contextlib fallbacks)
             prey_to_eat.remove()  # prey agent removed from simulation
 
@@ -120,5 +132,5 @@ def step(self):
                 self.position,
                 self.speed,
                 int(self.energy),
-                self.max_age
+                self.max_age,
             )  # new predator starts with half parent's energy

examples/continuous_predator_prey/prey_predator/app.py

@@ -1,10 +1,7 @@
-from matplotlib.markers import MarkerStyle
-
+from agents import Predator, Prey
 from mesa.visualization import Slider, SolaraViz, SpaceRenderer, make_plot_component
 from mesa.visualization.components import AgentPortrayalStyle
-
 from model import PredatorPreyModel
-from agents import Prey, Predator
 
 
 def agent_draw(agent):
@@ -13,13 +10,13 @@ def agent_draw(agent):
         return AgentPortrayalStyle(
             color="blue",
             size=15,
-            marker="o"  # Circle for prey
+            marker="o",  # Circle for prey
         )
     elif isinstance(agent, Predator):
         return AgentPortrayalStyle(
             color="red",
             size=30,
-            marker="^"  # Triangle for predator
+            marker="^",  # Triangle for predator
         )
 
 
@@ -74,9 +71,7 @@ def agent_draw(agent):
 
 
 # Set up the Population Line Chart
-population_chart = make_plot_component(
-    {"Prey": "blue", "Predators": "red"}
-)
+population_chart = make_plot_component({"Prey": "blue", "Predators": "red"})
 
 
 # Create the Solara webpage

examples/continuous_predator_prey/prey_predator/model.py

@@ -1,6 +1,6 @@
 import mesa
-from mesa.experimental.continuous_space import ContinuousSpace
 from agents import Predator, Prey
+from mesa.experimental.continuous_space import ContinuousSpace
 
 
 class PredatorPreyModel(mesa.Model):
@@ -62,13 +62,21 @@ def __init__(
         # it keeps track of no. of prey and predators in each step of the simulation for analysis and visualization purposes
         self.datacollector = mesa.DataCollector(
             model_reporters={
-                "Prey": lambda m: sum(1 for a in m.agents if isinstance(a, Prey)),  # it's count the number of prey by checking instances of prey class in the agents
-                "Predators": lambda m: sum(1 for a in m.agents if isinstance(a, Predator)),  # it's count the number of predators by checking instances of predator class in the agents
+                "Prey": lambda m: sum(
+                    1 for a in m.agents if isinstance(a, Prey)
+                ),  # it's count the number of prey by checking instances of prey class in the agents
+                "Predators": lambda m: sum(
+                    1 for a in m.agents if isinstance(a, Predator)
+                ),  # it's count the number of predators by checking instances of predator class in the agents
             }
         )
 
     def step(self):
         # advancing to next step, model need to collect data before the agents take their actions
-        self.datacollector.collect(self)  # collect the data for the current step of the simulation
+        self.datacollector.collect(
+            self
+        )  # collect the data for the current step of the simulation
         # Mesa 4.0 native agent activation (replaces the old scheduler)
-        self.agents.shuffle_do("step")  # it step all agents during the scheduling process
+        self.agents.shuffle_do(
+            "step"
+        )  # it step all agents during the scheduling process