How to use the mesa.datacollection.DataCollector function in Mesa

def __init__(self):
        self.schedule = BaseScheduler(self)
        self.model_val = 100

        for i in range(10):
            a = MockAgent(i, self, val=i)
            self.schedule.add(a)
        self.datacollector = DataCollector(
            {"total_agents": lambda m: m.schedule.get_agent_count(),
             "model_value": "model_val"},
            {"value": lambda a: a.val, "value2": "val2"},
            {"Final_Values": ["agent_id", "final_value"]})

def __init__(self, num_nodes=10, avg_node_degree=3, initial_outbreak_size=1, virus_spread_chance=0.4,
                virus_check_frequency=0.4, recovery_chance=0.3, gain_resistance_chance=0.5):

        self.num_nodes = num_nodes
        prob = avg_node_degree / self.num_nodes
        self.G = nx.erdos_renyi_graph(n=self.num_nodes, p=prob)
        self.grid = NetworkGrid(self.G)
        self.schedule = RandomActivation(self)
        self.initial_outbreak_size = initial_outbreak_size if initial_outbreak_size <= num_nodes else num_nodes
        self.virus_spread_chance = virus_spread_chance
        self.virus_check_frequency = virus_check_frequency
        self.recovery_chance = recovery_chance
        self.gain_resistance_chance = gain_resistance_chance

        self.datacollector = DataCollector({"Infected": number_infected,
                                            "Susceptible": number_susceptible,
                                            "Resistant": number_resistant})

        # Create agents
        for i, node in enumerate(self.G.nodes()):
            a = VirusAgent(i, self, State.SUSCEPTIBLE, self.virus_spread_chance, self.virus_check_frequency,
                           self.recovery_chance, self.gain_resistance_chance)
            self.schedule.add(a)
            # Add the agent to the node
            self.grid.place_agent(a, node)

        # Infect some nodes
        infected_nodes = random.sample(self.G.nodes(), self.initial_outbreak_size)
        for a in self.grid.get_cell_list_contents(infected_nodes):
            a.state = State.INFECTED

def __init__(self, N=100, width=10, height=10):
        self.num_agents = N
        self.grid = MultiGrid(height, width, True)
        self.schedule = RandomActivation(self)
        self.datacollector = DataCollector(
            model_reporters={"Gini": compute_gini},
            agent_reporters={"Wealth": "wealth"}
        )
        # Create agents
        for i in range(self.num_agents):
            a = MoneyAgent(i, self)
            self.schedule.add(a)
            # Add the agent to a random grid cell
            x = random.randrange(self.grid.width)
            y = random.randrange(self.grid.height)
            self.grid.place_agent(a, (x, y))

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

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.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)
        for cell in self.grid.coord_iter():
            x = cell[1]
            y = cell[2]
            if random.random() < self.density:
                if random.random() < self.minority_pc:
                    agent_type = 1

def __init__(self, N, width, height):
        self.num_agents = N
        self.running = True
        self.grid = MultiGrid(height, width, True)
        self.schedule = RandomActivation(self)
        self.datacollector = DataCollector(
            model_reporters={"Gini": compute_gini},
            agent_reporters={"Wealth": lambda a: a.wealth}
        )
        # Create agents
        for i in range(self.num_agents):
            a = WealthAgent(i, self)
            self.schedule.add(a)
            # Add the agent to a random grid cell
            x = random.randrange(self.grid.width)
            y = random.randrange(self.grid.height)
            self.grid.place_agent(a, (x, y))

schedule_type: Can be "Sequential", "Random", or "Simultaneous".
                           Determines the agent activation regime.
            payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
        '''
        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 = PDAgent((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"])
        })

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

initial_population=100):
        '''
        Create a new Constant Growback model with the given parameters.

        Args:
            initial_population: Number of population to start with
        '''

        # Set parameters
        self.height = height
        self.width = width
        self.initial_population = initial_population

        self.schedule = RandomActivationByBreed(self)
        self.grid = MultiGrid(self.height, self.width, torus=False)
        self.datacollector = DataCollector({"SsAgent": lambda m: m.schedule.get_breed_count(SsAgent), })

        # Create sugar
        import numpy as np
        sugar_distribution = np.genfromtxt("sugarscape_cg/sugar-map.txt")
        for _, x, y in self.grid.coord_iter():
            max_sugar = sugar_distribution[x, y]
            sugar = Sugar((x, y), self, max_sugar)
            self.grid.place_agent(sugar, (x, y))
            self.schedule.add(sugar)

        # Create agent:
        for i in range(self.initial_population):
            x = random.randrange(self.width)
            y = random.randrange(self.height)
            sugar = random.randrange(6, 25)
            metabolism = random.randrange(2, 4)

br_params = {"init_people": [25, 100, 150, 200],
             "rich_threshold": [5, 10, 15, 20],
             "reserve_percent": [0, 50, 100]}

br = BatchRunner(BankReservesModel,
                 br_params,
                 iterations=1,
                 max_steps=1000,
                 model_reporters={"Data Collector": lambda m: m.datacollector})

if __name__ == '__main__':
    br.run_all()
    br_df = br.get_model_vars_dataframe()
    br_step_data = pd.DataFrame()
    for i in range(len(br_df["Data Collector"])):
        if isinstance(br_df["Data Collector"][i], DataCollector):
            i_run_data = br_df["Data Collector"][i].get_model_vars_dataframe()
            br_step_data = br_step_data.append(i_run_data, ignore_index=True)
    br_step_data.to_csv("BankReservesModel_Step_Data.csv")

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

self.true_votes = 0
        self.false_votes = 0

        self.disputes = 0
        self.successful = 0
        self.failed = 0

        #self.datacollector = DataCollector({"Gini": compute_gini} )

        for j in range(0,juror_count):
            juror = Juror(self.next_id(), self)
            self.schedule.add(juror)

        self.running = True

        self.datacollector = DataCollector({"Gini": compute_gini ,"total":"disputes", "successful":"successful", "failed":"failed"})