Use pre-commit (#27)

* Use Pathlib

Code by Phil Robare (versilimidude2)

* Use pre-commit

* Dropped unused imports

* used pre-commit to run pyupgrade, trim whitespace

* Remove pathlib changes

These belong in a separate PR.

* remove redundant black, move comment

as per rht's suggestions

---------

Co-authored-by: Catherine Devlin <[email protected]>

Author: 2 people

.gitignore

@@ -79,3 +79,5 @@ dmypy.json
 
 # Virtual environment
 venv/
+
+examples/caching_and_replay/my_cache_file_path.cache

.pre-commit-config.yaml

@@ -0,0 +1,20 @@
+ci:
+    autoupdate_schedule: 'monthly'
+    autofix_prs: false
+
+repos:
+-   repo: https://github.com/psf/black
+    rev: 23.3.0
+    hooks:
+      - id: black-jupyter
+-   repo: https://github.com/asottile/pyupgrade
+    rev: v3.3.1
+    hooks:
+    -   id: pyupgrade
+        args: [--py38-plus]
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0  # Use the ref you want to point at
+    hooks:
+    -   id: trailing-whitespace
+    -   id: check-toml
+    -   id: check-yaml

CONTRIBUTING.rst

@@ -1,9 +1,9 @@
 Contributing
 =========================
 
-As an open source project, Mesa welcomes contributions of many forms, and from beginners to experts. 
+As an open source project, Mesa welcomes contributions of many forms, and from beginners to experts.
 
-Contributions can a full model or it could one of the following to an existing model: 
+Contributions can a full model or it could one of the following to an existing model:
 
 - Code patches
 - Bug reports and patch reviews

README.rst

@@ -1,6 +1,6 @@
 # mesa-examples
 
-This repository contains examples that work with Mesa and illustrate different features of Mesa. 
+This repository contains examples that work with Mesa and illustrate different features of Mesa.
 
 To contribute to this repository, see `CONTRIBUTING.rst`_
 

examples/bank_reserves/bank_reserves/agents.py

@@ -1,6 +1,7 @@
 """
 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
+Model information can be found at:
+http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
 Author of NetLogo code:
     Wilensky, U. (1998). NetLogo Bank Reserves model.
@@ -76,12 +77,14 @@ def do_business(self):
                 if self.random.randint(0, 1) == 0:
                     # 50% chance of trading $5
                     if self.random.randint(0, 1) == 0:
-                        # give customer $5 from my wallet (may result in negative wallet)
+                        # give customer $5 from my wallet
+                        # (may result in negative wallet)
                         customer.wallet += 5
                         self.wallet -= 5
                     # 50% chance of trading $2
                     else:
-                        # give customer $2 from my wallet (may result in negative wallet)
+                        # give customer $2 from my wallet
+                        # (may result in negative wallet)
                         customer.wallet += 2
                         self.wallet -= 2
 

examples/bank_reserves/bank_reserves/model.py

@@ -1,6 +1,7 @@
 """
 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
+Model information can be found at:
+http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
 Author of NetLogo code:
     Wilensky, U. (1998). NetLogo Bank Reserves model.

examples/bank_reserves/batch_run.py

@@ -1,6 +1,7 @@
 """
 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
+Model information can be found at:
+http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
 Author of NetLogo code:
     Wilensky, U. (1998). NetLogo Bank Reserves model.

examples/boltzmann_wealth_model_network/boltzmann_wealth_model_network/model.py

@@ -14,7 +14,6 @@ class BoltzmannWealthModelNetwork(mesa.Model):
     """A model with some number of agents."""
 
     def __init__(self, num_agents=7, num_nodes=10):
-
         self.num_agents = num_agents
         self.num_nodes = num_nodes if num_nodes >= self.num_agents else self.num_agents
         self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=0.5)
@@ -65,7 +64,6 @@ def move(self):
             self.model.grid.move_agent(self, new_position)
 
     def give_money(self):
-
         neighbors_nodes = self.model.grid.get_neighbors(self.pos, include_center=False)
         neighbors = self.model.grid.get_cell_list_contents(neighbors_nodes)
         if len(neighbors) > 0:

examples/caching_and_replay/README.md

@@ -9,7 +9,7 @@ It uses the [Mesa-Replay](https://github.com/Logende/mesa-replay) library and pu
 From the user's perspective, the new model behaves the same way as the original Schelling model, but additionally supports caching.
 
 Note that the main purpose of this example is to demonstrate that caching and replaying simulation runs is possible.
-The example is designed to be accessible. 
+The example is designed to be accessible.
 In practice, someone who wants to replay their simulation might not necessarily embed a replay button into the web view, but instead have a dedicated script to run a simulation that is being cached, separate from a script to replay a simulation run from a given cache file.
 More examples of caching and replay can be found in the [Mesa-Replay Repository](https://github.com/Logende/mesa-replay/tree/main/examples).
 
@@ -31,7 +31,7 @@ To run the model interactively, run ``mesa runserver`` in this directory. e.g.
 
 Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press Reset, then Run.
 
-First, run the **simulation** with the 'Replay' switch disabled. 
+First, run the **simulation** with the 'Replay' switch disabled.
 When the simulation run is finished (e.g. all agents are happy, no more new steps are simulated), the run will automatically be stored in a cache file.
 
 Next, **replay** your latest cached simulation run by enabling the Replay switch and then pressing Reset.

examples/caching_and_replay/cacheablemodel.py

@@ -3,11 +3,15 @@
 
 
 class CacheableSchelling(CacheableModel):
-    """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". From outside, a CacheableSchelling instance can be treated like any regular Mesa model.
-    The only difference is that the model will write the state of every simulation step to a cache file or when in
-    replay mode use a given cache file to replay that cached simulation run."""
+    """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".
+    From outside, a CacheableSchelling instance can be treated like any
+    regular Mesa model.
+    The only difference is that the model will write the state of every simulation step
+    to a cache file or when in replay mode use a given cache file to replay that cached
+    simulation run."""
 
     def __init__(
         self,
@@ -16,7 +20,8 @@ def __init__(
         density=0.8,
         minority_pc=0.2,
         homophily=3,
-        # Note that this is an additional parameter we add to our model, which decides whether to simulate or replay
+        # Note that this is an additional parameter we add to our model,
+        # which decides whether to simulate or replay
         replay=False,
     ):
         actual_model = Schelling(width, height, density, minority_pc, homophily)

examples/charts/charts/agents.py

@@ -1,6 +1,7 @@
 """
 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
+Model information can be found at:
+http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
 Author of NetLogo code:
     Wilensky, U. (1998). NetLogo Bank Reserves model.
@@ -76,12 +77,14 @@ def do_business(self):
                 if self.random.randint(0, 1) == 0:
                     # 50% chance of trading $5
                     if self.random.randint(0, 1) == 0:
-                        # give customer $5 from my wallet (may result in negative wallet)
+                        # give customer $5 from my wallet
+                        # (may result in negative wallet)
                         customer.wallet += 5
                         self.wallet -= 5
                     # 50% chance of trading $2
                     else:
-                        # give customer $2 from my wallet (may result in negative wallet)
+                        # give customer $2 from my wallet
+                        # (may result in negative wallet)
                         customer.wallet += 2
                         self.wallet -= 2
 

examples/charts/charts/model.py

@@ -1,6 +1,7 @@
 """
 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
+Model information can be found at:
+http://ccl.northwestern.edu/netlogo/models/BankReserves
 Accessed on: November 2, 2017
 Author of NetLogo code:
     Wilensky, U. (1998). NetLogo Bank Reserves model.
@@ -80,7 +81,6 @@ def get_total_loans(model):
 
 
 class Charts(mesa.Model):
-
     # grid height
     grid_h = 20
     # grid width

examples/color_patches/color_patches/model.py

@@ -80,7 +80,7 @@ def __init__(self, width=20, height=20):
         #  -->but only col & row
         # for (contents, col, row) in self._grid.coord_iter():
         # replaced content with _ to appease linter
-        for (_, row, col) in self._grid.coord_iter():
+        for _, row, col in self._grid.coord_iter():
             cell = ColorCell(
                 (row, col), self, ColorCell.OPINIONS[self.random.randrange(0, 16)]
             )
@@ -115,7 +115,7 @@ def grid(self):
              80                 cell_objects = model.grid.get_cell_list_contents([(x, y)])
 
         AttributeError: 'ColorPatches' object has no attribute 'grid'
-        """
+        """  # noqa: E501
         return self._grid
 
     @property

examples/conways_game_of_life/conways_game_of_life/model.py

@@ -27,7 +27,7 @@ def __init__(self, width=50, height=50):
 
         # Place a cell at each location, with some initialized to
         # ALIVE and some to DEAD.
-        for (contents, x, y) in self.grid.coord_iter():
+        for contents, x, y in self.grid.coord_iter():
             cell = Cell((x, y), self)
             if self.random.random() < 0.1:
                 cell.state = cell.ALIVE

examples/epstein_civil_violence/epstein_civil_violence/model.py

@@ -79,7 +79,7 @@ def __init__(
         unique_id = 0
         if self.cop_density + self.citizen_density > 1:
             raise ValueError("Cop density + citizen density must be less than 1")
-        for (contents, x, y) in self.grid.coord_iter():
+        for contents, x, y in self.grid.coord_iter():
             if self.random.random() < self.cop_density:
                 cop = Cop(unique_id, self, (x, y), vision=self.cop_vision)
                 unique_id += 1

examples/forest_fire/forest_fire/model.py

@@ -29,7 +29,7 @@ def __init__(self, width=100, height=100, density=0.65):
         )
 
         # Place a tree in each cell with Prob = density
-        for (contents, x, y) in self.grid.coord_iter():
+        for contents, x, y in self.grid.coord_iter():
             if self.random.random() < density:
                 # Create a tree
                 new_tree = TreeCell((x, y), self)

examples/hex_snowflake/hex_snowflake/cell.py

@@ -36,7 +36,9 @@ def step(self):
         changed here, but is just computed and stored in self._nextState,
         because our current state may still be necessary for our neighbors
         to calculate their next state.
-        When a cell is made alive, its neighbors are able to be considered in the next step. Only cells that are considered check their neighbors for performance reasons.
+        When a cell is made alive, its neighbors are able to be considered
+        in the next step. Only cells that are considered check their neighbors
+        for performance reasons.
         """
         # assume no state change
         self._nextState = self.state

examples/hex_snowflake/hex_snowflake/model.py

@@ -5,7 +5,8 @@
 
 class HexSnowflake(mesa.Model):
     """
-    Represents the hex grid of cells. The grid is represented by a 2-dimensional array of cells with adjacency rules specific to hexagons.
+    Represents the hex grid of cells. The grid is represented by a 2-dimensional array
+    of cells with adjacency rules specific to hexagons.
     """
 
     def __init__(self, width=50, height=50):
@@ -25,7 +26,7 @@ def __init__(self, width=50, height=50):
         self.grid = mesa.space.HexGrid(width, height, torus=True)
 
         # Place a dead cell at each location.
-        for (contents, x, y) in self.grid.coord_iter():
+        for contents, x, y in self.grid.coord_iter():
             cell = Cell((x, y), self)
             self.grid.place_agent(cell, (x, y))
             self.schedule.add(cell)

examples/pd_grid/pd_grid/agent.py

@@ -28,7 +28,9 @@ def isCooroperating(self):
         return self.move == "C"
 
     def step(self):
-        """Get the best neighbor's move, and change own move accordingly if better than own score."""
+        """Get the best neighbor's move, and change own move accordingly
+        if better than own score."""
+
         neighbors = self.model.grid.get_neighbors(self.pos, True, include_center=True)
         best_neighbor = max(neighbors, key=lambda a: a.score)
         self.next_move = best_neighbor.move

examples/shape_example/shape_example/server.py

@@ -17,7 +17,6 @@ def agent_draw(agent):
             "Layer": 2,
             "Color": ["#00FF00", "#99FF99"],
             "stroke_color": "#666666",
-            "Filled": "true",
             "heading_x": agent.heading[0],
             "heading_y": agent.heading[1],
             "text": agent.unique_id,

examples/sugarscape_cg/sugarscape_cg/model.py

@@ -75,7 +75,6 @@ def step(self):
             print([self.schedule.time, self.schedule.get_type_count(SsAgent)])
 
     def run_model(self, step_count=200):
-
         if self.verbose:
             print(
                 "Initial number Sugarscape Agent: ",

examples/sugarscape_g1mt/Readme.md

@@ -5,19 +5,19 @@
 This is Epstein & Axtell's Sugarscape model with Traders, a detailed description is in Chapter four of
 *Growing Artificial Societies: Social Science from the Bottom Up.* (1996)
 
-This code generally matches the code in the Complexity Explorer Tutorial, but in `.py` instead of `.ipynb` format.   
+This code generally matches the code in the Complexity Explorer Tutorial, but in `.py` instead of `.ipynb` format.
 
-### Agents: 
+### Agents:
 
 - **Sugar**:  Sugar agents grow back at one unit per time step and can be harvested and traded by the trader agents. Sugar
 is unequally distributed across the landscape with sugar hills in the upper left and lower right of the space.
   (green if you do the interactive run)
 - **Spice**: Spice agents grow back at one unit per time step and can be harvested and traded by the trader agents. Spice
-is unequally distributed across the landscape with spice hills in the upper right and lower left of the space. 
+is unequally distributed across the landscape with spice hills in the upper right and lower left of the space.
 (yellow if you do the interactive run)
 - **Traders**: Trader agents have the following attributes: (1) metabolism for sugar, (2) metabolism for spice, (3) vision,
-  (4) initial sugar endowment and (5) initial spice endowment. The traverse the landscape harvesting sugar and spice and 
-trading with other agents. If they run out of sugar or spice then they are removed from the model. 
+  (4) initial sugar endowment and (5) initial spice endowment. The traverse the landscape harvesting sugar and spice and
+trading with other agents. If they run out of sugar or spice then they are removed from the model.
 
 The trader agents traverse the landscape according to rule **M**:
 - Look out as far as vision permits in the four principal lattice directions and identify the unoccupied site(s).
@@ -26,15 +26,15 @@ The trader agents traverse the landscape according to rule **M**:
 - Collect all the resources (sugar and spice) at that location
 (Epstein and Axtell, 1996, p. 99)
 
-The traders trade according to rule **T**: 
+The traders trade according to rule **T**:
 - Agents and potential trade partner compute their marginal rates of substitution (MRS), if they are equal *end*.
-- Exchange resources, with spice flowing from the agent with the higher MRS to the agent with the lower MRS and sugar 
+- Exchange resources, with spice flowing from the agent with the higher MRS to the agent with the lower MRS and sugar
 flowing the opposite direction.
 - The price (p) is calculated by taking the geometric mean of the agents' MRS.
 - If p > 1 then p units of spice are traded for 1 unit of sugar; if p < 1 then 1/p units of sugar for 1 unit of spice
-- The trade occurs if it will (a) make both agent better off (increases MRS) and (b) does not cause the agents' MRS to 
+- The trade occurs if it will (a) make both agent better off (increases MRS) and (b) does not cause the agents' MRS to
 cross over one another otherwise *end*.
-- This process then repeats until an *end* condition is met. 
+- This process then repeats until an *end* condition is met.
 (Epstein and Axtell, 1996, p. 105)
 
 The model demonstrates several Mesa concepts and features:
@@ -54,13 +54,13 @@ To install the dependencies use pip and the requirements.txt in this directory.
 
 ## How to Run
 
-To run the model a single instance of the model: 
+To run the model a single instance of the model:
 
 ```
   $ python run.py -s
 ```
 
-To run the model with BatchRunner: 
+To run the model with BatchRunner:
 
 ```
   $ python run.py -b
@@ -85,5 +85,5 @@ Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and p
 
 ## Additional Resources
 
-- [Growing Artificial Societies](https://mitpress.mit.edu/9780262550253/growing-artificial-societies/) 
+- [Growing Artificial Societies](https://mitpress.mit.edu/9780262550253/growing-artificial-societies/)
 - [Complexity Explorer Sugarscape with Traders Tutorial](https://www.complexityexplorer.org/courses/172-agent-based-models-with-python-an-introduction-to-mesa)

examples/sugarscape_g1mt/run.py

@@ -44,9 +44,9 @@ def assess_results(results, single_agent):
                 G.add_edge(row["AgentID"], agent)
 
     # Get Basic Network Statistics
-    print("Node Connectivity {}".format(nx.node_connectivity(G)))
-    print("Average Clustering {}".format(nx.average_clustering(G)))
-    print("Global Efficiency {}".format(nx.global_efficiency(G)))
+    print(f"Node Connectivity {nx.node_connectivity(G)}")
+    print(f"Average Clustering {nx.average_clustering(G)}")
+    print(f"Global Efficiency {nx.global_efficiency(G)}")
 
     # Plot histogram of degree distribution
     degree_sequence = sorted((d for n, d in G.degree()), reverse=True)