From 4f9d97c9918704da616a7240631171af9c027487 Mon Sep 17 00:00:00 2001
From: Jan Kwakkel <j.h.kwakkel@tudelft.nl>
Date: Thu, 14 Nov 2024 06:39:59 +0100
Subject: [PATCH] remove devs related examples from devs/examples (#2507)

---
 .../devs/examples/epstein_civil_violence.py   | 305 ------------------
 mesa/experimental/devs/examples/wolf_sheep.py | 250 --------------
 2 files changed, 555 deletions(-)
 delete mode 100644 mesa/experimental/devs/examples/epstein_civil_violence.py
 delete mode 100644 mesa/experimental/devs/examples/wolf_sheep.py

diff --git a/mesa/experimental/devs/examples/epstein_civil_violence.py b/mesa/experimental/devs/examples/epstein_civil_violence.py
deleted file mode 100644
index 6f32e061356..00000000000
--- a/mesa/experimental/devs/examples/epstein_civil_violence.py
+++ /dev/null
@@ -1,305 +0,0 @@
-"""Epstein civil violence example using ABMSimulator."""
-
-import enum
-import math
-
-from mesa import Agent, Model
-from mesa.experimental.devs.simulator import ABMSimulator
-from mesa.space import SingleGrid
-
-
-class EpsteinAgent(Agent):
-    """Epstein Agent."""
-
-    def __init__(self, model, vision, movement):
-        """Initialize the agent.
-
-        Args:
-            model: a model instance
-            vision: size of neighborhood
-            movement: boolean whether agent can move or not
-        """
-        super().__init__(model)
-        self.vision = vision
-        self.movement = movement
-
-
-class AgentState(enum.IntEnum):
-    """Agent states."""
-
-    QUIESCENT = enum.auto()
-    ARRESTED = enum.auto()
-    ACTIVE = enum.auto()
-
-
-class Citizen(EpsteinAgent):
-    """A member of the general population, may or may not be in active rebellion.
-
-    Summary of rule: If grievance - risk > threshold, rebel.
-
-    Attributes:
-        unique_id: unique int
-        model :
-        hardship: Agent's 'perceived hardship (i.e., physical or economic
-            privation).' Exogenous, drawn from U(0,1).
-        regime_legitimacy: Agent's perception of regime legitimacy, equal
-            across agents.  Exogenous.
-        risk_aversion: Exogenous, drawn from U(0,1).
-        threshold: if (grievance - (risk_aversion * arrest_probability)) >
-            threshold, go/remain Active
-        vision: number of cells in each direction (N, S, E and W) that agent
-            can inspect
-        condition: Can be "Quiescent" or "Active;" deterministic function of
-            greivance, perceived risk, and
-        grievance: deterministic function of hardship and regime_legitimacy;
-            how aggrieved is agent at the regime?
-        arrest_probability: agent's assessment of arrest probability, given
-            rebellion
-    """
-
-    def __init__(
-        self,
-        model,
-        vision,
-        movement,
-        hardship,
-        regime_legitimacy,
-        risk_aversion,
-        threshold,
-        arrest_prob_constant,
-    ):
-        """Create a new Citizen.
-
-        Args:
-            model : model instance
-            vision: number of cells in each direction (N, S, E and W) that
-                agent can inspect. Exogenous.
-            movement: whether agent can move or not
-            hardship: Agent's 'perceived hardship (i.e., physical or economic
-                privation).' Exogenous, drawn from U(0,1).
-            regime_legitimacy: Agent's perception of regime legitimacy, equal
-                across agents.  Exogenous.
-            risk_aversion: Exogenous, drawn from U(0,1).
-            threshold: if (grievance - (risk_aversion * arrest_probability)) >
-                threshold, go/remain Active
-            arrest_prob_constant : agent's assessment of arrest probability
-
-        """
-        super().__init__(model, vision, movement)
-        self.hardship = hardship
-        self.regime_legitimacy = regime_legitimacy
-        self.risk_aversion = risk_aversion
-        self.threshold = threshold
-        self.condition = AgentState.QUIESCENT
-        self.grievance = self.hardship * (1 - self.regime_legitimacy)
-        self.arrest_probability = None
-        self.arrest_prob_constant = arrest_prob_constant
-
-    def step(self):
-        """Decide whether to activate, then move if applicable."""
-        self.update_neighbors()
-        self.update_estimated_arrest_probability()
-        net_risk = self.risk_aversion * self.arrest_probability
-        if self.grievance - net_risk > self.threshold:
-            self.condition = AgentState.ACTIVE
-        else:
-            self.condition = AgentState.QUIESCENT
-        if self.movement and self.empty_neighbors:
-            new_pos = self.random.choice(self.empty_neighbors)
-            self.model.grid.move_agent(self, new_pos)
-
-    def update_neighbors(self):
-        """Look around and see who my neighbors are."""
-        self.neighborhood = self.model.grid.get_neighborhood(
-            self.pos, moore=True, radius=self.vision
-        )
-        self.neighbors = self.model.grid.get_cell_list_contents(self.neighborhood)
-        self.empty_neighbors = [
-            c for c in self.neighborhood if self.model.grid.is_cell_empty(c)
-        ]
-
-    def update_estimated_arrest_probability(self):
-        """Based on the ratio of cops to actives in my neighborhood, estimate the p(Arrest | I go active)."""
-        cops_in_vision = len([c for c in self.neighbors if isinstance(c, Cop)])
-        actives_in_vision = 1.0  # citizen counts herself
-        for c in self.neighbors:
-            if isinstance(c, Citizen) and c.condition == AgentState.ACTIVE:
-                actives_in_vision += 1
-        self.arrest_probability = 1 - math.exp(
-            -1 * self.arrest_prob_constant * (cops_in_vision / actives_in_vision)
-        )
-
-    def sent_to_jail(self, value):
-        """Sent agent to jail.
-
-        Args:
-            value: duration of jail sentence
-
-        """
-        self.model.active_agents.remove(self)
-        self.condition = AgentState.ARRESTED
-        self.model.simulator.schedule_event_relative(self.release_from_jail, value)
-
-    def release_from_jail(self):
-        """Release agent from jail."""
-        self.model.active_agents.add(self)
-        self.condition = AgentState.QUIESCENT
-
-
-class Cop(EpsteinAgent):
-    """A cop for life.  No defection.
-
-    Summary of rule: Inspect local vision and arrest a random active agent.
-
-    Attributes:
-        unique_id: unique int
-        x, y: Grid coordinates
-        vision: number of cells in each direction (N, S, E and W) that cop is
-            able to inspect
-    """
-
-    def __init__(self, model, vision, movement, max_jail_term):
-        """Initialize a Cop agent.
-
-        Args:
-            model: a model instance
-            vision: size of neighborhood
-            movement: whether agent can move or not
-            max_jail_term: maximum jail sentence
-        """
-        super().__init__(model, vision, movement)
-        self.max_jail_term = max_jail_term
-
-    def step(self):
-        """Inspect local vision and arrest a random active agent. Move if applicable."""
-        self.update_neighbors()
-        active_neighbors = []
-        for agent in self.neighbors:
-            if isinstance(agent, Citizen) and agent.condition == "Active":
-                active_neighbors.append(agent)
-        if active_neighbors:
-            arrestee = self.random.choice(active_neighbors)
-            arrestee.sent_to_jail(self.random.randint(0, self.max_jail_term))
-        if self.movement and self.empty_neighbors:
-            new_pos = self.random.choice(self.empty_neighbors)
-            self.model.grid.move_agent(self, new_pos)
-
-    def update_neighbors(self):
-        """Look around and see who my neighbors are."""
-        self.neighborhood = self.model.grid.get_neighborhood(
-            self.pos, moore=True, radius=self.vision
-        )
-        self.neighbors = self.model.grid.get_cell_list_contents(self.neighborhood)
-        self.empty_neighbors = [
-            c for c in self.neighborhood if self.model.grid.is_cell_empty(c)
-        ]
-
-
-class EpsteinCivilViolence(Model):
-    """Model 1 from "Modeling civil violence: An agent-based computational approach," by Joshua Epstein.
-
-    http://www.pnas.org/content/99/suppl_3/7243.full
-    Attributes:
-        height: grid height
-        width: grid width
-        citizen_density: approximate % of cells occupied by citizens.
-        cop_density: approximate % of cells occupied by cops.
-        citizen_vision: number of cells in each direction (N, S, E and W) that
-            citizen can inspect
-        cop_vision: number of cells in each direction (N, S, E and W) that cop
-            can inspect
-        legitimacy:  (L) citizens' perception of regime legitimacy, equal
-            across all citizens
-        max_jail_term: (J_max)
-        active_threshold: if (grievance - (risk_aversion * arrest_probability))
-            > threshold, citizen rebels
-        arrest_prob_constant: set to ensure agents make plausible arrest
-            probability estimates
-        movement: binary, whether agents try to move at step end
-        max_iters: model may not have a natural stopping point, so we set a
-            max.
-    """
-
-    def __init__(
-        self,
-        width=40,
-        height=40,
-        citizen_density=0.7,
-        cop_density=0.074,
-        citizen_vision=7,
-        cop_vision=7,
-        legitimacy=0.8,
-        max_jail_term=1000,
-        active_threshold=0.1,
-        arrest_prob_constant=2.3,
-        movement=True,
-        max_iters=1000,
-        seed=None,
-    ):
-        """Initialize the Eppstein civil violence model.
-
-        Args:
-            width: the width of the grid
-            height: the height of the grid
-            citizen_density: density of citizens
-            cop_density: density of cops
-            citizen_vision: size of citizen vision
-            cop_vision: size of cop vision
-            legitimacy: perceived legitimacy
-            max_jail_term: maximum jail term
-            active_threshold: threshold for citizen to become active
-            arrest_prob_constant: arrest probability
-            movement: allow agent movement or not
-            max_iters: number of iterations
-            seed: seed for random number generator
-        """
-        super().__init__(seed)
-        if cop_density + citizen_density > 1:
-            raise ValueError("Cop density + citizen density must be less than 1")
-
-        self.width = width
-        self.height = height
-        self.citizen_density = citizen_density
-        self.cop_density = cop_density
-
-        self.max_iters = max_iters
-
-        self.grid = SingleGrid(self.width, self.height, torus=True)
-
-        for _, pos in self.grid.coord_iter():
-            if self.random.random() < self.cop_density:
-                agent = Cop(
-                    self,
-                    cop_vision,
-                    movement,
-                    max_jail_term,
-                )
-            elif self.random.random() < (self.cop_density + self.citizen_density):
-                agent = Citizen(
-                    self,
-                    citizen_vision,
-                    movement,
-                    hardship=self.random.random(),
-                    regime_legitimacy=legitimacy,
-                    risk_aversion=self.random.random(),
-                    threshold=active_threshold,
-                    arrest_prob_constant=arrest_prob_constant,
-                )
-            else:
-                continue
-            self.grid.place_agent(agent, pos)
-
-        self.active_agents = self.agents
-
-    def step(self):
-        """Run one step of the model."""
-        self.active_agents.shuffle_do("step")
-
-
-if __name__ == "__main__":
-    model = EpsteinCivilViolence(seed=15)
-    simulator = ABMSimulator()
-
-    simulator.setup(model)
-
-    simulator.run_for(time_delta=100)
diff --git a/mesa/experimental/devs/examples/wolf_sheep.py b/mesa/experimental/devs/examples/wolf_sheep.py
deleted file mode 100644
index 74318ef88af..00000000000
--- a/mesa/experimental/devs/examples/wolf_sheep.py
+++ /dev/null
@@ -1,250 +0,0 @@
-"""Example of using ABM simulator for Wolf-Sheep Predation Model."""
-
-import mesa
-from mesa.experimental.cell_space import FixedAgent
-from mesa.experimental.devs.simulator import ABMSimulator
-
-
-class Animal(mesa.Agent):
-    """Base Animal class."""
-
-    def __init__(self, model, moore, energy, p_reproduce, energy_from_food):
-        """Initialize Animal instance.
-
-        Args:
-            model: a model instance
-            moore: using moore grid or not
-            energy: initial energy
-            p_reproduce: probability of reproduction
-            energy_from_food: energy gained from 1 unit of food
-        """
-        super().__init__(model)
-        self.energy = energy
-        self.p_reproduce = p_reproduce
-        self.energy_from_food = energy_from_food
-        self.moore = moore
-
-    def random_move(self):
-        """Move to random neighboring cell."""
-        next_moves = self.model.grid.get_neighborhood(self.pos, self.moore, True)
-        next_move = self.random.choice(next_moves)
-        # Now move:
-        self.model.grid.move_agent(self, next_move)
-
-    def spawn_offspring(self):
-        """Create offspring."""
-        self.energy /= 2
-        offspring = self.__class__(
-            self.model,
-            self.moore,
-            self.energy,
-            self.p_reproduce,
-            self.energy_from_food,
-        )
-        self.model.grid.place_agent(offspring, self.pos)
-
-    def feed(self): ...  # noqa: D102
-
-    def die(self):
-        """Die."""
-        self.model.grid.remove_agent(self)
-        self.remove()
-
-    def step(self):
-        """Execute one step of the agent."""
-        self.random_move()
-        self.energy -= 1
-
-        self.feed()
-
-        if self.energy < 0:
-            self.die()
-        elif self.random.random() < self.p_reproduce:
-            self.spawn_offspring()
-
-
-class Sheep(Animal):
-    """A sheep that walks around, reproduces (asexually) and gets eaten."""
-
-    def feed(self):
-        """Eat grass and gain energy."""
-        # If there is grass available, eat it
-        agents = self.model.grid.get_cell_list_contents(self.pos)
-        grass_patch = next(obj for obj in agents if isinstance(obj, GrassPatch))
-        if grass_patch.fully_grown:
-            self.energy += self.energy_from_food
-            grass_patch.fully_grown = False
-
-
-class Wolf(Animal):
-    """A wolf that walks around, reproduces (asexually) and eats sheep."""
-
-    def feed(self):
-        """Eat wolf and gain energy."""
-        agents = self.model.grid.get_cell_list_contents(self.pos)
-        sheep = [obj for obj in agents if isinstance(obj, Sheep)]
-        if len(sheep) > 0:
-            sheep_to_eat = self.random.choice(sheep)
-            self.energy += self.energy
-
-            # Kill the sheep
-            sheep_to_eat.die()
-
-
-class GrassPatch(FixedAgent):
-    """A patch of grass that grows at a fixed rate and it is eaten by sheep."""
-
-    @property
-    def fully_grown(self) -> bool:  # noqa: D102
-        return self._fully_grown
-
-    @fully_grown.setter
-    def fully_grown(self, value: bool):
-        self._fully_grown = value
-
-        if not value:
-            self.model.simulator.schedule_event_relative(
-                setattr,
-                self.grass_regrowth_time,
-                function_args=[self, "fully_grown", True],
-            )
-
-    def __init__(self, model, fully_grown, countdown, grass_regrowth_time):
-        """Creates a new patch of grass.
-
-        Args:
-            model: a model instance
-            fully_grown: (boolean) Whether the patch of grass is fully grown or not
-            countdown: Time for the patch of grass to be fully grown again
-            grass_regrowth_time: regrowth time for the grass
-        """
-        super().__init__(model)
-        self._fully_grown = fully_grown
-        self.grass_regrowth_time = grass_regrowth_time
-
-        if not self.fully_grown:
-            self.model.simulator.schedule_event_relative(
-                setattr, countdown, function_args=[self, "fully_grown", True]
-            )
-
-    def set_fully_grown(self):  # noqa
-        self.fully_grown = True
-
-
-class WolfSheep(mesa.Model):
-    """Wolf-Sheep Predation Model.
-
-    A model for simulating wolf and sheep (predator-prey) ecosystem modelling.
-    """
-
-    def __init__(
-        self,
-        height,
-        width,
-        initial_sheep,
-        initial_wolves,
-        sheep_reproduce,
-        wolf_reproduce,
-        grass_regrowth_time,
-        wolf_gain_from_food=13,
-        sheep_gain_from_food=5,
-        moore=False,
-        simulator=None,
-        seed=None,
-    ):
-        """Create a new Wolf-Sheep model with the given parameters.
-
-        Args:
-            height: height of the grid
-            width: width of the grid
-            initial_sheep: Number of sheep to start with
-            initial_wolves: Number of wolves to start with
-            sheep_reproduce: Probability of each sheep reproducing each step
-            wolf_reproduce: Probability of each wolf reproducing each step
-            wolf_gain_from_food: Energy a wolf gains from eating a sheep
-            grass: Whether to have the sheep eat grass for energy
-            grass_regrowth_time: How long it takes for a grass patch to regrow
-                                 once it is eaten
-            sheep_gain_from_food: Energy sheep gain from grass, if enabled.
-            moore: whether to use moore or von Neumann grid
-            simulator: Simulator to use for simulating wolf and sheep
-            seed: Random seed
-        """
-        super().__init__(seed=seed)
-        # Set parameters
-        self.height = height
-        self.width = width
-        self.initial_sheep = initial_sheep
-        self.initial_wolves = initial_wolves
-        self.simulator = simulator
-
-        # self.sheep_reproduce = sheep_reproduce
-        # self.wolf_reproduce = wolf_reproduce
-        # self.grass_regrowth_time = grass_regrowth_time
-        # self.wolf_gain_from_food = wolf_gain_from_food
-        # self.sheep_gain_from_food = sheep_gain_from_food
-        # self.moore = moore
-
-        self.grid = mesa.space.MultiGrid(self.height, self.width, torus=False)
-
-        for _ in range(self.initial_sheep):
-            pos = (
-                self.random.randrange(self.width),
-                self.random.randrange(self.height),
-            )
-            energy = self.random.randrange(2 * sheep_gain_from_food)
-            sheep = Sheep(
-                self,
-                moore,
-                energy,
-                sheep_reproduce,
-                sheep_gain_from_food,
-            )
-            self.grid.place_agent(sheep, pos)
-
-        # Create wolves
-        for _ in range(self.initial_wolves):
-            pos = (
-                self.random.randrange(self.width),
-                self.random.randrange(self.height),
-            )
-            energy = self.random.randrange(2 * wolf_gain_from_food)
-            wolf = Wolf(
-                self,
-                moore,
-                energy,
-                wolf_reproduce,
-                wolf_gain_from_food,
-            )
-            self.grid.place_agent(wolf, pos)
-
-        # Create grass patches
-        possibly_fully_grown = [True, False]
-        for _agent, pos in self.grid.coord_iter():
-            fully_grown = self.random.choice(possibly_fully_grown)
-            if fully_grown:
-                countdown = grass_regrowth_time
-            else:
-                countdown = self.random.randrange(grass_regrowth_time)
-            patch = GrassPatch(self, fully_grown, countdown, grass_regrowth_time)
-            self.grid.place_agent(patch, pos)
-
-    def step(self):
-        """Perform one step of the model."""
-        self.agents_by_type[Sheep].shuffle_do("step")
-        self.agents_by_type[Wolf].shuffle_do("step")
-
-
-if __name__ == "__main__":
-    import time
-
-    simulator = ABMSimulator()
-
-    model = WolfSheep(25, 25, 60, 40, 0.2, 0.1, 20, simulator=simulator, seed=15)
-
-    simulator.setup(model)
-
-    start_time = time.perf_counter()
-    simulator.run(100)
-    print(simulator.time)
-    print("Time:", time.perf_counter() - start_time)