Merge remote-tracking branch 'upstream/main' into new_spaces

Author: quaquel

.github/workflows/test_examples.yml

@@ -25,7 +25,7 @@ jobs:
   #     with:
   #       python-version: "3.12"
   #   - name: Install dependencies
-  #     run: pip install mesa pytest
+  #     run: pip install mesa[network] pytest
   #   - name: Test with pytest
   #     run: pytest -rA -Werror test_examples.py
 
@@ -39,7 +39,7 @@ jobs:
         python-version: "3.12"
     - name: Install dependencies
       run: |
-        pip install mesa --pre
+        pip install mesa[network] --pre
         pip install .[test]
     - name: Test with pytest
       run: pytest -rA -Werror -Wdefault::FutureWarning test_examples.py
@@ -55,6 +55,6 @@ jobs:
     - name: Install dependencies
       run: |
         pip install .[test]
-        pip install -U git+https://github.com/projectmesa/mesa@main#egg=mesa
+        pip install -U git+https://github.com/projectmesa/mesa@main#egg=mesa[network]
     - name: Test with pytest
       run: pytest -rA -Werror -Wdefault::FutureWarning test_examples.py

examples/aco_tsp/aco_tsp/model.py

@@ -233,7 +233,7 @@ def step(self):
         """
         A model step. Used for activating the agents and collecting data.
         """
-        self.agents.shuffle().do("step")
+        self.agents.shuffle_do("step")
         self.update_pheromone()
 
         # Check len of cities visited by an agent

examples/aco_tsp/app.py

@@ -6,7 +6,7 @@
 import solara
 from aco_tsp.model import AcoTspModel, TSPGraph
 from matplotlib.figure import Figure
-from mesa.visualization import SolaraViz
+from mesa.visualization import SolaraViz, make_plot_measure
 
 
 def circle_portrayal_example(agent):
@@ -35,6 +35,8 @@ def circle_portrayal_example(agent):
     },
 }
 
+model = AcoTspModel()
+
 
 def make_graph(model):
     fig = Figure()
@@ -55,7 +57,7 @@ def make_graph(model):
         edge_color="gray",
     )
 
-    solara.FigureMatplotlib(fig)
+    return solara.FigureMatplotlib(fig)
 
 
 def ant_level_distances(model):
@@ -67,10 +69,8 @@ def ant_level_distances(model):
 
 
 page = SolaraViz(
-    AcoTspModel,
-    model_params,
-    space_drawer=None,
-    measures=["best_distance_iter", "best_distance", make_graph],
-    agent_portrayal=circle_portrayal_example,
+    model,
+    components=[make_plot_measure(["best_distance_iter", "best_distance"]), make_graph],
+    model_params=model_params,
     play_interval=1,
 )

examples/bank_reserves/bank_reserves/model.py

@@ -155,7 +155,7 @@ def __init__(
 
     def step(self):
         # tell all the agents in the model to run their step function
-        self.agents.shuffle().do("step")
+        self.agents.shuffle_do("step")
         # collect data
         self.datacollector.collect(self)
 

examples/bank_reserves/batch_run.py

@@ -37,6 +37,78 @@ def track_run(model):
     return model.uid
 
 
+class BankReservesModel(mesa.Model):
+    # id generator to track run number in batch run data
+    id_gen = itertools.count(1)
+
+    # grid height
+    grid_h = 20
+    # grid width
+    grid_w = 20
+
+    """init parameters "init_people", "rich_threshold", and "reserve_percent"
+       are all set via Slider"""
+
+    def __init__(
+        self,
+        height=grid_h,
+        width=grid_w,
+        init_people=2,
+        rich_threshold=10,
+        reserve_percent=50,
+    ):
+        super().__init__()
+        self.uid = next(self.id_gen)
+        self.height = height
+        self.width = width
+        self.init_people = init_people
+
+        self.grid = mesa.space.MultiGrid(self.width, self.height, torus=True)
+        # rich_threshold is the amount of savings a person needs to be considered "rich"
+        self.rich_threshold = rich_threshold
+        self.reserve_percent = reserve_percent
+        # see datacollector functions above
+        self.datacollector = mesa.DataCollector(
+            model_reporters={
+                "Rich": get_num_rich_agents,
+                "Poor": get_num_poor_agents,
+                "Middle Class": get_num_mid_agents,
+                "Savings": get_total_savings,
+                "Wallets": get_total_wallets,
+                "Money": get_total_money,
+                "Loans": get_total_loans,
+                "Model Params": track_params,
+                "Run": track_run,
+            },
+            agent_reporters={"Wealth": "wealth"},
+        )
+
+        # create a single bank object for the model
+        self.bank = Bank(self, self.reserve_percent)
+
+        # create people for the model according to number of people set by user
+        for i in range(self.init_people):
+            # set x coordinate as a random number within the width of the grid
+            x = self.random.randrange(self.width)
+            # set y coordinate as a random number within the height of the grid
+            y = self.random.randrange(self.height)
+            p = Person(i, (x, y), self, True, self.bank, self.rich_threshold)
+            # place the Person object on the grid at coordinates (x, y)
+            self.grid.place_agent(p, (x, y))
+
+        self.running = True
+
+    def step(self):
+        # collect data
+        self.datacollector.collect(self)
+        # tell all the agents in the model to run their step function
+        self.agents.shuffle_do("step")
+
+    def run_model(self):
+        for i in range(self.run_time):
+            self.step()
+
+
 # parameter lists for each parameter to be tested in batch run
 br_params = {
     "init_people": [25, 100],

examples/boid_flockers/app.py

@@ -1,5 +1,5 @@
 from boid_flockers.model import BoidFlockers
-from mesa.visualization import SolaraViz
+from mesa.visualization import SolaraViz, make_space_matplotlib
 
 
 def boid_draw(agent):
@@ -15,11 +15,12 @@ def boid_draw(agent):
     "separation": 2,
 }
 
+model = BoidFlockers(100, 100, 100, 5, 10, 2)
+
 page = SolaraViz(
-    model_class=BoidFlockers,
+    model,
+    [make_space_matplotlib(agent_portrayal=boid_draw)],
     model_params=model_params,
-    measures=[],
     name="BoidFlockers",
-    agent_portrayal=boid_draw,
 )
 page  # noqa

examples/boid_flockers/boid_flockers/model.py

@@ -143,4 +143,4 @@ def make_agents(self):
             self.space.place_agent(boid, pos)
 
     def step(self):
-        self.agents.shuffle().do("step")
+        self.agents.shuffle_do("step")

examples/boltzmann_wealth_model/boltzmann_wealth_model/model.py

@@ -40,7 +40,7 @@ def __init__(self, N=100, width=10, height=10):
         self.datacollector.collect(self)
 
     def step(self):
-        self.agents.shuffle().do("step")
+        self.agents.shuffle_do("step")
         # collect data
         self.datacollector.collect(self)
 

examples/boltzmann_wealth_model_experimental/app.py

@@ -1,4 +1,8 @@
-from mesa.visualization import SolaraViz, make_plot_measure, make_space_matplotlib
+from mesa.visualization import (
+    SolaraViz,
+    make_plot_measure,
+    make_space_matplotlib,
+)
 from model import BoltzmannWealthModel
 
 
@@ -29,9 +33,9 @@ def agent_portrayal(agent):
 
 # Create visualization elements. The visualization elements are solara components
 # that receive the model instance as a "prop" and display it in a certain way.
-# Under the hood these are just functions that receive the model instance.
-# You can also author your own visualization elements, they just have to return
-# a valid solara component or an ipywidget.
+# Under the hood these are just classes that receive the model instance.
+# You can also author your own visualization elements, which can also be functions
+# that receive the model instance and return a valid solara component.
 SpaceGraph = make_space_matplotlib(agent_portrayal)
 GiniPlot = make_plot_measure("Gini")
 

examples/boltzmann_wealth_model_experimental/model.py

@@ -40,7 +40,7 @@ def __init__(self, N=100, width=10, height=10):
         self.datacollector.collect(self)
 
     def step(self):
-        self.agents.shuffle().do("step")
+        self.agents.shuffle_do("step")
         # collect data
         self.datacollector.collect(self)