Python space.SingleGrid类代码示例

class WorldModel(Model):
    def __init__(self, N, width, height):
        self.grid = SingleGrid(height, width, True)

        self.schedule = RandomActivation(self)
        self.num_agents = N
        self.running = True
        
        for i in range(self.num_agents):
            ethnicity = random.choice(Ethnicities)
            a = PersonAgent(unique_id=i,
                            model=self,
                            ethnicity=int(ethnicity)
                            )
            self.schedule.add(a)
            # Add the agent to a random grid cell

            self.grid.position_agent(a)
            
        self.datacollector = DataCollector(
            agent_reporters={
                "Nationalism": lambda a: a.nationalism,
                "X": lambda a: a.pos[0],
                "Y": lambda a: a.pos[1]
            }
        )

    def step(self):
        self.datacollector.collect(self)
        self.schedule.step()

class ShapesModel(Model):
    def __init__(self, N, width=20, height=10):
        self.running = True
        self.N = N    # num of agents
        self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1))  # tuples are fast
        self.grid = SingleGrid(width, height, torus=False)
        self.schedule = RandomActivation(self)
        self.make_walker_agents()

    def make_walker_agents(self):
        unique_id = 0
        while True:
            if unique_id == self.N:
                break
            x = random.randrange(self.grid.width)
            y = random.randrange(self.grid.height)
            pos = (x, y)
            heading = random.choice(self.headings)
            # heading = (1, 0)
            if self.grid.is_cell_empty(pos):
                print("Creating agent {2} at ({0}, {1})"
                      .format(x, y, unique_id))
                a = Walker(unique_id, self, pos, heading)
                self.schedule.add(a)
                self.grid.place_agent(a, pos)
                unique_id += 1

    def step(self):
        self.schedule.step()

class Schelling(Model):
    '''
    Model class for the Schelling segregation model.
    '''

    def __init__(self, height=20, width=20, density=0.8, minority_pc=0.2, homophily=3):
        '''
        '''

        self.height = height
        self.width = width
        self.density = density
        self.minority_pc = minority_pc
        self.homophily = homophily

        self.schedule = RandomActivation(self)
        self.grid = SingleGrid(height, width, torus=True)

        self.happy = 0
        self.datacollector = DataCollector(
            {"happy": "happy"},  # Model-level count of happy agents
            # For testing purposes, agent's individual x and y
            {"x": lambda a: a.pos[0], "y": lambda a: a.pos[1]})

        # Set up agents
        # We use a grid iterator that returns
        # the coordinates of a cell as well as
        # its contents. (coord_iter)
        for cell in self.grid.coord_iter():
            x = cell[1]
            y = cell[2]
            if self.random.random() < self.density:
                if self.random.random() < self.minority_pc:
                    agent_type = 1
                else:
                    agent_type = 0

                agent = SchellingAgent((x, y), self, agent_type)
                self.grid.position_agent(agent, (x, y))
                self.schedule.add(agent)

        self.running = True
        self.datacollector.collect(self)

    def step(self):
        '''
        Run one step of the model. If All agents are happy, halt the model.
        '''
        self.happy = 0  # Reset counter of happy agents
        self.schedule.step()
        # collect data
        self.datacollector.collect(self)

        if self.happy == self.schedule.get_agent_count():
            self.running = False

class SchellingModel(Model):
    """
    Model class for the Schelling segregation model.
    """

    def __init__(self, height, width, density, minority_pc, homophily):
        """
        """

        self.height = height
        self.width = width
        self.density = density
        self.minority_pc = minority_pc
        self.homophily = homophily

        self.schedule = RandomActivation(self)
        self.grid = SingleGrid(height, width, torus=True)

        self.happy = 0
        self.total_agents = 0
        self.datacollector = DataCollector(
            {"unhappy": lambda m: m.total_agents - m.happy},
            # For testing purposes, agent's individual x and y
            {"x": lambda a: a.pos[X], "y": lambda a: a.pos[Y]},
        )

        self.running = True

        # Set up agents
        # We use a grid iterator that returns
        # the coordinates of a cell as well as
        # its contents. (coord_iter)
        for cell, x, y in self.grid.coord_iter():
            if random.random() < self.density:
                if random.random() < self.minority_pc:
                    agent_type = 1
                else:
                    agent_type = 0

                agent = SchellingAgent(self.total_agents, agent_type)
                self.grid.position_agent(agent, x, y)
                self.schedule.add(agent)
                self.total_agents += 1

    def step(self):
        """
        Run one step of the model. If All agents are happy, halt the model.
        """
        self.happy = 0  # Reset counter of happy agents
        self.schedule.step()
        self.datacollector.collect(self)

        if self.happy == self.total_agents:
            self.running = False

class PD_Model(Model):
    '''
    Model class for iterated, spatial prisoner's dilemma model.
    '''

    schedule_types = {"Sequential": BaseScheduler,
                      "Random": RandomActivation,
                      "Simultaneous": SimultaneousActivation}

    # This dictionary holds the payoff for this agent,
    # keyed on: (my_move, other_move)

    payoff = {("C", "C"): 1,
              ("C", "D"): 0,
              ("D", "C"): 1.6,
              ("D", "D"): 0}

    def __init__(self, height, width, schedule_type, payoffs=None):
        '''
        Create a new Spatial Prisoners' Dilemma Model.

        Args:
            height, width: Grid size. There will be one agent per grid cell.
            schedule_type: Can be "Sequential", "Random", or "Simultaneous".
                           Determines the agent activation regime.
            payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
        '''
        self.running = True
        self.grid = SingleGrid(height, width, torus=True)
        self.schedule_type = schedule_type
        self.schedule = self.schedule_types[self.schedule_type](self)

        # Create agents
        for x in range(width):
            for y in range(height):
                agent = PD_Agent((x, y), self)
                self.grid.place_agent(agent, (x, y))
                self.schedule.add(agent)

        self.datacollector = DataCollector({
            "Cooperating_Agents":
            lambda m: len([a for a in m.schedule.agents if a.move == "C"])
        })

    def step(self):
        self.datacollector.collect(self)
        self.schedule.step()

    def run(self, n):
        '''
        Run the model for a certain number of steps.
        '''
        for _ in range(n):
            self.step()

class SchellingModel(Model):
    '''
    Model class for the Schelling segregation model.
    '''

    def __init__(self, height, width, density, type_pcs=[.2, .2, .2, .2, .2]):
        '''
        '''

        self.height = height
        self.width = width
        self.density = density
        self.type_pcs = type_pcs

        self.schedule = RandomActivation(self)
        self.grid = SingleGrid(height, width, torus=False)

        self.happy = 0
        self.datacollector = DataCollector(
            {"happy": lambda m: m.happy},  # Model-level count of happy agents
            # For testing purposes, agent's individual x and y
            {"x": lambda a: a.pos[0], "y": lambda a: a.pos[1]})

        self.running = True

        # Set up agents
        # We use a grid iterator that returns
        # the coordinates of a cell as well as
        # its contents. (coord_iter)

        total_agents = self.height * self.width * self.density
        agents_by_type = [total_agents*val for val in self.type_pcs]

        for loc, types in enumerate(agents_by_type):
            for i in range(int(types)):
                pos = self.grid.find_empty()
                agent = SchellingAgent(pos, self, loc)
                self.grid.position_agent(agent, pos)
                self.schedule.add(agent)

    def step(self):
        '''
        Run one step of the model. If All agents are happy, halt the model.
        '''
        self.happy = 0  # Reset counter of happy agents
        self.schedule.step()
        self.datacollector.collect(self)

        if self.happy == self.schedule.get_agent_count():
            self.running = False

 def __init__(self, N, width=20, height=10):
     self.running = True
     self.N = N    # num of agents
     self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1))  # tuples are fast
     self.grid = SingleGrid(width, height, torus=False)
     self.schedule = RandomActivation(self)
     self.make_walker_agents()

    def __init__(self, height, width, schedule_type, payoffs=None):
        """
        Create a new Spatial Prisoners' Dilemma Model.

        Args:
            height, width: Grid size. There will be one agent per grid cell.
            schedule_type: Can be "Sequential", "Random", or "Simultaneous".
                           Determines the agent activation regime.
            payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
        """
        self.running = True
        self.grid = SingleGrid(height, width, torus=True)
        self.schedule_type = schedule_type
        self.schedule = self.schedule_types[self.schedule_type](self)

        # Create agents
        for x in range(width):
            for y in range(height):
                agent = PD_Agent((x, y))
                self.grid.place_agent(agent, (x, y))
                self.schedule.add(agent)

        self.datacollector = DataCollector(
            {"Cooperating_Agents": lambda m: len([a for a in m.schedule.agents if a.move == "C"])}
        )

 def setUp(self):
     self.space = SingleGrid(50, 50, False)
     self.agents = []
     for i, pos in enumerate(TEST_AGENTS_GRID):
         a = MockAgent(i, None)
         self.agents.append(a)
         self.space.place_agent(a, pos)

 def __init__(self, width=50, height=50, torus=True, num_bug=50, seed=42, strategy=None):
     super().__init__(seed=seed)
     self.number_of_bug = num_bug
     if not(strategy in ["stick", "switch"]):
         raise TypeError("'strategy' must be one of {stick, switch}")
     self.strategy = strategy
     
     self.grid = SingleGrid(width, height, torus)
     self.schedule = RandomActivation(self)
     data = {"Bean": lambda m: m.number_of_bean,
             "Corn": lambda m: m.number_of_corn,
             "Soy": lambda m: m.number_of_soy,
             "Bug": lambda m: m.number_of_bug,
             }
     self.datacollector = DataCollector(data)
     
     # create foods
     self._populate(Bean)
     self._populate(Corn)
     self._populate(Soy)
     
     # create bugs
     for i in range(self.number_of_bug):
         pos = self.grid.find_empty()
         bug = Bug(i, self)
         bug.strategy = self.strategy
         self.grid.place_agent(bug, pos)
         self.schedule.add(bug)

class Foraging(Model):
    
    number_of_bean = 0
    number_of_corn = 0
    number_of_soy = 0
    
    def __init__(self, width=50, height=50, torus=True, num_bug=50, seed=42, strategy=None):
        super().__init__(seed=seed)
        self.number_of_bug = num_bug
        if not(strategy in ["stick", "switch"]):
            raise TypeError("'strategy' must be one of {stick, switch}")
        self.strategy = strategy
        
        self.grid = SingleGrid(width, height, torus)
        self.schedule = RandomActivation(self)
        data = {"Bean": lambda m: m.number_of_bean,
                "Corn": lambda m: m.number_of_corn,
                "Soy": lambda m: m.number_of_soy,
                "Bug": lambda m: m.number_of_bug,
                }
        self.datacollector = DataCollector(data)
        
        # create foods
        self._populate(Bean)
        self._populate(Corn)
        self._populate(Soy)
        
        # create bugs
        for i in range(self.number_of_bug):
            pos = self.grid.find_empty()
            bug = Bug(i, self)
            bug.strategy = self.strategy
            self.grid.place_agent(bug, pos)
            self.schedule.add(bug)
    
    def step(self):
        self.schedule.step()
        self.datacollector.collect(self)
        
        if not(self.grid.exists_empty_cells()):
            self.running = False
    
    def _populate(self, food_type):
        prefix = "number_of_{}"
        
        counter = 0
        while counter < food_type.density * (self.grid.width * self.grid.height):
            pos = self.grid.find_empty()
            food = food_type(counter, self)
            self.grid.place_agent(food, pos)
            self.schedule.add(food)
            food_name = food_type.__name__.lower()
            attr_name = prefix.format(food_name)
            val = getattr(self, attr_name)
            val += 1
            setattr(self, attr_name, val)
            counter += 1

    def __init__(self, height, width, palestinian_density, settlement_density,
                 settlers_violence_rate, settlers_growth_rate, suicide_rate, greed_level,
                 settler_vision=1, palestinian_vision=1, 
                 movement=True, max_iters=1000):

        super(SeparationBarrierModel, self).__init__()
        self.height = height
        self.width = width
        self.palestinian_density = palestinian_density
        self.settler_vision = settler_vision
        self.palestinian_vision = palestinian_vision
        self.settlement_density = settlement_density
        self.movement = movement
        self.running = True
        self.max_iters = max_iters
        self.iteration = 0
        self.schedule = RandomActivation(self)
        self.settlers_violence_rate = settlers_violence_rate
        self.settlers_growth_rate = settlers_growth_rate
        self.suicide_rate = suicide_rate
        self.greed_level = greed_level

        self.total_violence = 0

        self.grid = SingleGrid(height, width, torus=False)

        model_reporters = {
        }
        agent_reporters = {
#           "x": lambda a: a.pos[0],
#           "y": lambda a: a.pos[1],
        }
        self.dc = DataCollector(model_reporters=model_reporters,
                                agent_reporters=agent_reporters)
        self.unique_id = 0

        # Israelis and palestinans split the region in half
        for (contents, x, y) in self.grid.coord_iter():
            if random.random() < self.palestinian_density:
                palestinian = Palestinian(self.unique_id, (x, y), vision=self.palestinian_vision, breed="Palestinian",
                          model=self)
                self.unique_id += 1
                self.grid.position_agent(palestinian, x,y)
                self.schedule.add(palestinian)
            elif ((y > (self.grid.height) * (1-self.settlement_density)) and random.random() < self.settlement_density):
                settler = Settler(self.unique_id, (x, y),
                                  vision=self.settler_vision, model=self, breed="Settler")
                self.unique_id += 1
                self.grid.position_agent(settler, x,y)
                self.schedule.add(settler)

 def setUp(self):
     '''
     Create a test non-toroidal grid and populate it with Mock Agents
     '''
     self.grid = SingleGrid(3, 5, True)
     self.agents = []
     counter = 0
     for y in range(3):
         for x in range(5):
             if TEST_GRID[y][x] == 0:
                 continue
             counter += 1
             # Create and place the mock agent
             a = MockAgent(counter, None)
             self.agents.append(a)
             self.grid.place_agent(a, (x, y))

    def __init__(self, height=20, width=20, density=.8, group_ratio=.66, minority_ratio=.5, homophily=3):
        self.height = height
        self.width = width
        self.density = density
        self.group_ratio = group_ratio
        self.minority_ratio = minority_ratio
        self.homophily = homophily
        self.happy = 0
        self.segregated = 0

        self.schedule = RandomActivation(self)
        self.grid = SingleGrid(height, width, torus=False)

        self.place_agents()
        self.datacollector = DataCollector( {'happy': (lambda m: m.happy), 'segregated': (lambda m: m.segregated)})
        self.running = True

 def setUp(self):
     '''
     Create a test non-toroidal grid and populate it with Mock Agents
     '''
     width = 3
     height = 5
     self.grid = SingleGrid(width, height, True)
     self.agents = []
     counter = 0
     for x in range(width):
         for y in range(height):
             if TEST_GRID[x][y] == 0:
                 continue
             counter += 1
             # Create and place the mock agent
             a = MockAgent(counter, None)
             self.agents.append(a)
             self.grid.place_agent(a, (x, y))