Sync pyproject with mesa (#268)

* Sync the pre-commit config from the Mesa repository

* Add the codespellignore config from the Mesa repository

* refactor(pyproject.toml): update the file to match the mesa repo a bit closer

* refactor: various ruff fixes after syncing the rules with the mesa repo

* refactor: more ruff fixes

* chore: updated ruff fixes after merging with main

* chore: remove the hard-codes python version for ruff

Author: derkweijers

examples/aco_tsp/aco_tsp/model.py

@@ -79,14 +79,10 @@ def from_tsp_file(cls, file_path: str) -> "TSPGraph":
 
 
 class AntTSP(CellAgent):
-    """
-    An agent
-    """
+    """An agent"""
 
     def __init__(self, model, alpha: float = 1.0, beta: float = 5.0):
-        """
-        Customize the agent
-        """
+        """Customize the agent"""
         super().__init__(model)
         self.alpha = alpha
         self.beta = beta
@@ -141,11 +137,9 @@ def decide_next_city(self):
         return new_city
 
     def step(self):
-        """
-        Modify this method to change what an individual agent will do during each step.
+        """Modify this method to change what an individual agent will do during each step.
         Can include logic based on neighbors states.
         """
-
         for _ in range(self.model.num_cities - 1):
             # Pick a random city that isn't in the list of cities visited
             new_city = self.decide_next_city()
@@ -158,8 +152,7 @@ def step(self):
 
 
 class AcoTspModel(mesa.Model):
-    """
-    The model class holds the model-level attributes, manages the agents, and generally handles
+    """The model class holds the model-level attributes, manages the agents, and generally handles
     the global level of our model.
 
     There is only one model-level parameter: how many agents the model contains. When a new model
@@ -168,12 +161,16 @@ class AcoTspModel(mesa.Model):
 
     def __init__(
         self,
+        tsp_graph: TSPGraph | None,
         num_agents: int = 20,
-        tsp_graph: TSPGraph = TSPGraph.from_random(20),
         max_steps: int = int(1e6),
         ant_alpha: float = 1.0,
         ant_beta: float = 5.0,
     ):
+        # Don't create the TSPGraph.from_random as argument default: https://docs.astral.sh/ruff/rules/function-call-in-default-argument/
+        if tsp_graph is None:
+            tsp_graph = TSPGraph.from_random(20)
+
         super().__init__()
         self.num_agents = num_agents
         self.tsp_graph = tsp_graph
@@ -224,15 +221,13 @@ def update_pheromone(self, q: float = 100, ro: float = 0.5):
             # Evaporate
             tau_ij = (1 - ro) * self.grid.G[i][j]["pheromone"]
             # Add ant's contribution
-            for k, delta_tau_ij_k in delta_tau_ij.items():
+            for _, delta_tau_ij_k in delta_tau_ij.items():
                 tau_ij += delta_tau_ij_k.get((i, j), 0.0)
 
             self.grid.G[i][j]["pheromone"] = tau_ij
 
     def step(self):
-        """
-        A model step. Used for activating the agents and collecting data.
-        """
+        """A model step. Used for activating the agents and collecting data."""
         self.agents.shuffle_do("step")
         self.update_pheromone()
 

examples/aco_tsp/app.py

@@ -1,6 +1,4 @@
-"""
-Configure visualization elements and instantiate a server
-"""
+"""Configure visualization elements and instantiate a server"""
 
 import networkx as nx
 import solara

examples/bank_reserves/bank_reserves/agents.py

@@ -1,5 +1,4 @@
-"""
-The following code was adapted from the Bank Reserves model included in Netlogo
+"""The following code was adapted from the Bank Reserves model included in Netlogo
 Model information can be found at:
 http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
@@ -18,7 +17,8 @@ class Bank:
     class. This is because there is only one bank in this model, and it does not
     use any Mesa-specific features like the scheduler or the grid, and doesn't
     have a step method. It is just used to keep track of the bank's reserves and
-    the amount it can loan out, for Person agents to interact with."""
+    the amount it can loan out, for Person agents to interact with.
+    """
 
     def __init__(self, model, reserve_percent=50):
         self.model = model
@@ -63,8 +63,9 @@ def __init__(self, model, moore, bank, rich_threshold):
         self.bank = bank
 
     def do_business(self):
-        """check if person has any savings, any money in wallet, or if the
-        bank can loan them any money"""
+        """Check if person has any savings, any money in wallet, or if the
+        bank can loan them any money
+        """
         if (self.savings > 0 or self.wallet > 0 or self.bank.bank_to_loan > 0) and len(
             self.cell.agents
         ) > 1:
@@ -164,8 +165,9 @@ def repay_a_loan(self, amount):
 
     # part of balance_books()
     def take_out_loan(self, amount):
-        """borrow from the bank to put money in my wallet, and increase my
-        outstanding loans"""
+        """Borrow from the bank to put money in my wallet, and increase my
+        outstanding loans
+        """
         self.loans += amount
         self.wallet += amount
         # decresae the amount the bank can loan right now

examples/bank_reserves/bank_reserves/model.py

@@ -1,5 +1,4 @@
-"""
-The following code was adapted from the Bank Reserves model included in Netlogo
+"""The following code was adapted from the Bank Reserves model included in Netlogo
 Model information can be found at:
 http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
@@ -25,39 +24,34 @@
 
 
 def get_num_rich_agents(model):
-    """return number of rich agents"""
-
+    """Return number of rich agents"""
     rich_agents = [a for a in model.agents if a.savings > model.rich_threshold]
     return len(rich_agents)
 
 
 def get_num_poor_agents(model):
-    """return number of poor agents"""
-
+    """Return number of poor agents"""
     poor_agents = [a for a in model.agents if a.loans > 10]
     return len(poor_agents)
 
 
 def get_num_mid_agents(model):
-    """return number of middle class agents"""
-
+    """Return number of middle class agents"""
     mid_agents = [
         a for a in model.agents if a.loans < 10 and a.savings < model.rich_threshold
     ]
     return len(mid_agents)
 
 
 def get_total_savings(model):
-    """sum of all agents' savings"""
-
+    """Sum of all agents' savings"""
     agent_savings = [a.savings for a in model.agents]
     # return the sum of agents' savings
     return np.sum(agent_savings)
 
 
 def get_total_wallets(model):
-    """sum of amounts of all agents' wallets"""
-
+    """Sum of amounts of all agents' wallets"""
     agent_wallets = [a.wallet for a in model.agents]
     # return the sum of all agents' wallets
     return np.sum(agent_wallets)
@@ -80,8 +74,7 @@ def get_total_loans(model):
 
 
 class BankReservesModel(mesa.Model):
-    """
-    This model is a Mesa implementation of the Bank Reserves model from NetLogo.
+    """This model is a Mesa implementation of the Bank Reserves model from NetLogo.
     It is a highly abstracted, simplified model of an economy, with only one
     type of agent and a single bank representing all banks in an economy. People
     (represented by circles) move randomly within the grid. If two or more people
@@ -160,5 +153,5 @@ def step(self):
         self.datacollector.collect(self)
 
     def run_model(self):
-        for i in range(self.run_time):
+        for _ in range(self.run_time):
             self.step()

examples/bank_reserves/batch_run.py

@@ -1,5 +1,4 @@
-"""
-The following code was adapted from the Bank Reserves model included in Netlogo
+"""The following code was adapted from the Bank Reserves model included in Netlogo
 Model information can be found at:
 http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017

examples/boltzmann_wealth_model_network/boltzmann_wealth_model_network/model.py

@@ -40,6 +40,6 @@ def step(self):
     def compute_gini(self):
         agent_wealths = [agent.wealth for agent in self.agents]
         x = sorted(agent_wealths)
-        N = self.num_agents
-        B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
-        return 1 + (1 / N) - 2 * B
+        num_agents = self.num_agents
+        B = sum(xi * (num_agents - i) for i, xi in enumerate(x)) / (num_agents * sum(x))  # noqa: N806
+        return 1 + (1 / num_agents) - 2 * B

examples/caching_and_replay/cacheablemodel.py

@@ -3,8 +3,7 @@
 
 
 class CacheableSchelling(CacheableModel):
-    """
-    A wrapper around the original Schelling model to make the simulation cacheable
+    """A wrapper around the original Schelling model to make the simulation cacheable
     and replay-able.  Uses CacheableModel from the Mesa-Replay library,
     which is a wrapper that can be put around any regular mesa model to make it
     "cacheable".

examples/caching_and_replay/model.py

@@ -5,13 +5,10 @@
 
 
 class SchellingAgent(CellAgent):
-    """
-    Schelling segregation agent
-    """
+    """Schelling segregation agent"""
 
     def __init__(self, model, agent_type):
-        """
-        Create a new Schelling agent.
+        """Create a new Schelling agent.
 
         Args:
            x, y: Agent initial location.
@@ -34,9 +31,7 @@ def step(self):
 
 
 class Schelling(mesa.Model):
-    """
-    Model class for the Schelling segregation model.
-    """
+    """Model class for the Schelling segregation model."""
 
     def __init__(
         self,
@@ -48,8 +43,7 @@ def __init__(
         minority_pc=0.3,
         seed=None,
     ):
-        """
-        Create a new Schelling model.
+        """Create a new Schelling model.
 
         Args:
             width, height: Size of the space.
@@ -59,7 +53,6 @@ def __init__(
             radius: Search radius for checking similarity
             seed: Seed for Reproducibility
         """
-
         super().__init__(seed=seed)
         self.height = height
         self.width = width
@@ -88,9 +81,7 @@ def __init__(
         self.datacollector.collect(self)
 
     def step(self):
-        """
-        Run one step of the model.
-        """
+        """Run one step of the model."""
         self.happy = 0  # Reset counter of happy agents
         self.agents.shuffle_do("step")
 

examples/caching_and_replay/run.py

@@ -10,9 +10,7 @@
 
 
 def get_cache_file_status(_):
-    """
-    Display an informational text about caching and the status of the cache file (existing versus not existing)
-    """
+    """Display an informational text about caching and the status of the cache file (existing versus not existing)"""
     cache_file = Path(model_params["cache_file_path"])
     return (
         f"Only activate the 'Replay cached run?' switch when a cache file already exists, otherwise it will fail. "

examples/caching_and_replay/server.py

@@ -5,16 +5,12 @@
 
 
 def get_happy_agents(model):
-    """
-    Display a text count of how many happy agents there are.
-    """
+    """Display a text count of how many happy agents there are."""
     return f"Happy agents: {model.happy}"
 
 
 def schelling_draw(agent):
-    """
-    Portrayal Method for canvas
-    """
+    """Portrayal Method for canvas"""
     if agent is None:
         return
     portrayal = {"Shape": "circle", "r": 0.5, "Filled": "true", "Layer": 0}