Merge branch 'new_spaces' of https://github.com/quaquel/mesa-examples into new_spaces

Author: quaquel

examples/hotelling_law/hotelling_law/model.py

@@ -140,7 +140,12 @@ def get_store_price_lambda(unique_id):
         """Return a lambda function that gets the
         price of a store by its unique ID."""
         return lambda m: next(
-            (agent.price for agent in m.agents_by_type[StoreAgent] if agent.unique_id == unique_id), 0
+            (
+                agent.price
+                for agent in m.agents_by_type[StoreAgent]
+                if agent.unique_id == unique_id
+            ),
+            0,
         )
 
     @staticmethod
@@ -211,10 +216,10 @@ def step(self):
 
     def recalculate_market_share(self):
         # Reset market share for all stores directly
-        for store in  self.agents_by_type[StoreAgent]:
+        for store in self.agents_by_type[StoreAgent]:
             store.market_share = 0
 
-        for consumer in  self.agents_by_type[ConsumerAgent]:
+        for consumer in self.agents_by_type[ConsumerAgent]:
             preferred_store = consumer.determine_preferred_store()
             if preferred_store:
                 preferred_store.market_share += 1
@@ -238,15 +243,17 @@ def export_data(self):
     def compute_average_price(self):
         if len(self.store_agents) == 0:
             return 0
-        return np.mean([agent.price for agent in  self.agents_by_type[StoreAgent]])
+        return np.mean([agent.price for agent in self.agents_by_type[StoreAgent]])
 
     # Function to compute the average market share for all store agents,
     def compute_average_market_share(self):
         if not self.store_agents:
             return 0
 
-        total_consumers = sum(agent.market_share for agent in  self.agents_by_type[StoreAgent])
-        average_market_share = total_consumers / len( self.agents_by_type[StoreAgent])
+        total_consumers = sum(
+            agent.market_share for agent in self.agents_by_type[StoreAgent]
+        )
+        average_market_share = total_consumers / len(self.agents_by_type[StoreAgent])
         return average_market_share
 
     def compute_price_variance(self):

examples/pd_grid/pd_grid/agent.py

@@ -2,6 +2,7 @@
 
 from mesa.experimental.cell_space import CellAgent
 
+
 class PDAgent(CellAgent):
     """Agent member of the iterated, spatial prisoner's dilemma model."""
 
@@ -31,7 +32,9 @@ def step(self):
         if better than own score."""
 
         # neighbors = self.model.grid.get_neighbors(self.pos, True, include_center=True)
-        neighbors = list(self.cell.neighborhood.agents) + [self,]
+        neighbors = list(self.cell.neighborhood.agents) + [
+            self,
+        ]
         best_neighbor = max(neighbors, key=lambda a: a.score)
         self.next_move = best_neighbor.move
 

examples/pd_grid/pd_grid/model.py

@@ -3,6 +3,7 @@
 
 from .agent import PDAgent
 
+
 class PdGrid(mesa.Model):
     """Model class for iterated, spatial prisoner's dilemma model."""
 

examples/schelling_experimental/model.py

@@ -2,6 +2,7 @@
 
 from mesa.experimental.cell_space import CellAgent, OrthogonalMooreGrid
 
+
 class SchellingAgent(CellAgent):
     """
     Schelling segregation agent

examples/shape_example/shape_example/model.py

@@ -3,6 +3,7 @@
 
 from mesa.experimental.cell_space import OrthogonalMooreGrid
 
+
 class Walker(mesa.Agent):
     def __init__(self, model, heading=(1, 0)):
         super().__init__(model)