-- @example Example that uses MultipleRuns to compute wow many cells agents moving randomly can
-- reach. It uses a model that starts with a given number of agents randomly distributed in
-- space and runs a small number of steps. The output shows a logistic growth of cells
-- where at least one agent has passed during each simulation. Note that the curve sometimes
-- has a negative derivative. As we increase the initial number of agents, sometimes the number
-- of covered cells diminishes due to randomness.
SingleAgentModel = Model{
	finalTime = 20,
	quantity = 20,
	init = function(model)
		model.cell = Cell{
			state = "empty",
			execute = function(self)
				if not self:isEmpty() then
					self.state = "full"
				end
			end
		}

		model.cs = CellularSpace{
			xdim = 40,
			instance = model.cell
		}

		model.cs:createNeighborhood{}

		model.agent = Agent{
			execute = function(self)
				local cell = self:getCell():getNeighborhood():sample()

				if cell:isEmpty() then
					self:move(cell)
				end
			end
		}

		model.society = Society{
			instance = model.agent,
			quantity = model.quantity
		}

		model.env = Environment{
			model.cs,
			model.society
		}

		model.env:createPlacement{}

		model.map1 = Map{
			target = model.cs,
			grouping = "placement",
			max = 1,
			min = 0,
			slices = 2,
			color = "YlOrRd"
		}

		model.map2 = Map{
			target = model.cs,
			select = "state",
			value = {"empty", "full"},
			color = {"white", "blue"}
		}

		model.timer = Timer{
			Event{action = model.society},
			Event{action = model.cs},
			Event{action = model.map1},
			Event{action = model.map2}
		}

	end
}

import("calibration")

mr = MultipleRuns{
	model = SingleAgentModel,
	parameters = {
		quantity = Choice{min = 5, max = 455, step = 5},
	},
	total = function(model)
		return #model.cs:split("state").full
	end
}

Chart{
	target = mr.output,
	select = "total",
	xAxis = "quantity"
}