move some logic into their own funcs(move,eat,reproduce)
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0165c839cd
commit
f0bfe3cb87
283
predator_prey.jl
283
predator_prey.jl
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@ -46,17 +46,63 @@
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# example, we could have only one type and one additional filed to separate them.
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# Nevertheless, for the sake of example, we will use two different types.)
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using Agents, Random
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using CairoMakie
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@agent struct Sheep(GridAgent{2})
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energy::Float64
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reproduction_prob::Float64
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Δenergy::Float64
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#perception::Int32
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#speed::Float64
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#endurance::Float64
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end
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function move!(sheep::Sheep,model)
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randomwalk!(sheep, model)
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sheep.energy -= 1
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end
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function eat!(sheep::Sheep, model)
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if model.fully_grown[sheep.pos...]
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sheep.energy += sheep.Δenergy
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model.fully_grown[sheep.pos...] = false
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end
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return
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end
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function reproduce!(sheep::Sheep, model)
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if rand(abmrng(model)) ≤ sheep.reproduction_prob
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sheep.energy /= 2
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replicate!(sheep, model)
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end
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end
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@agent struct Wolf(GridAgent{2})
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energy::Float64
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reproduction_prob::Float64
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Δenergy::Float64
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#perception::Int32
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#speed::Float64
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#endurance::Float64
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end
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function move!(wolf::Wolf,model)
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randomwalk!(wolf, model; ifempty=false)
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wolf.energy -= 1
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end
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function eat!(wolf::Wolf, model)
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dinner = first_sheep_in_position(wolf.pos, model)
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if !isnothing(dinner)
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remove_agent!(dinner, model)
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wolf.energy += wolf.Δenergy
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end
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end
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function reproduce!(wolf::Wolf, model)
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if rand(abmrng(model)) ≤ wolf.reproduction_prob
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wolf.energy /= 2
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replicate!(wolf, model)
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end
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end
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function first_sheep_in_position(pos, model)
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ids = ids_in_position(pos, model)
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j = findfirst(id -> model[id] isa Sheep, ids)
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isnothing(j) ? nothing : model[ids[j]]::Sheep
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end
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# The function `initialize_model` returns a new model containing sheep, wolves, and grass
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@ -119,56 +165,23 @@ end
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# Notice how the function `sheepwolf_step!`, which is our `agent_step!`,
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# is dispatched to the appropriate agent type via Julia's Multiple Dispatch system.
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function sheepwolf_step!(sheep::Sheep, model)
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randomwalk!(sheep, model)
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sheep.energy -= 1
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move!(sheep, model)
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if sheep.energy < 0
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remove_agent!(sheep, model)
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return
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end
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eat!(sheep, model)
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if rand(abmrng(model)) ≤ sheep.reproduction_prob
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sheep.energy /= 2
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replicate!(sheep, model)
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end
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reproduce!(sheep, model)
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end
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function sheepwolf_step!(wolf::Wolf, model)
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randomwalk!(wolf, model; ifempty=false)
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wolf.energy -= 1
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move!(wolf, model)
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if wolf.energy < 0
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remove_agent!(wolf, model)
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return
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end
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## If there is any sheep on this grid cell, it's dinner time!
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dinner = first_sheep_in_position(wolf.pos, model)
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!isnothing(dinner) && eat!(wolf, dinner, model)
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if rand(abmrng(model)) ≤ wolf.reproduction_prob
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wolf.energy /= 2
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replicate!(wolf, model)
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end
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end
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function first_sheep_in_position(pos, model)
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ids = ids_in_position(pos, model)
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j = findfirst(id -> model[id] isa Sheep, ids)
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isnothing(j) ? nothing : model[ids[j]]::Sheep
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end
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# Sheep and wolves have separate `eat!` functions. If a sheep eats grass, it will acquire
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# additional energy and the grass will not be available for consumption until regrowth time
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# has elapsed. If a wolf eats a sheep, the sheep dies and the wolf acquires more energy.
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function eat!(sheep::Sheep, model)
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if model.fully_grown[sheep.pos...]
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sheep.energy += sheep.Δenergy
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model.fully_grown[sheep.pos...] = false
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end
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return
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end
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function eat!(wolf::Wolf, sheep::Sheep, model)
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remove_agent!(sheep, model)
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wolf.energy += wolf.Δenergy
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return
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eat!(wolf, model)
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reproduce!(wolf, model)
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end
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# The behavior of grass function differently. If it is fully grown, it is consumable.
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@ -187,108 +200,110 @@ function grass_step!(model)
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end
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end
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sheepwolfgrass = initialize_model()
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function run()
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sheepwolfgrass = initialize_model()
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# ## Running the model
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# %% #src
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# We will run the model for 500 steps and record the number of sheep, wolves and consumable
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# grass patches after each step. First: initialize the model.
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# ## Running the model
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# %% #src
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# We will run the model for 500 steps and record the number of sheep, wolves and consumable
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# grass patches after each step. First: initialize the model.
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using CairoMakie
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CairoMakie.activate!() # hide
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CairoMakie.activate!() # hide
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# To view our starting population, we can build an overview plot using [`abmplot`](@ref).
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# We define the plotting details for the wolves and sheep:
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offset(a) = a isa Sheep ? (-0.1, -0.1*rand()) : (+0.1, +0.1*rand())
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ashape(a) = a isa Sheep ? :circle : :utriangle
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acolor(a) = a isa Sheep ? RGBAf(1.0, 1.0, 1.0, 0.8) : RGBAf(0.2, 0.2, 0.3, 0.8)
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# To view our starting population, we can build an overview plot using [`abmplot`](@ref).
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# We define the plotting details for the wolves and sheep:
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offset(a) = a isa Sheep ? (-0.1, -0.1*rand()) : (+0.1, +0.1*rand())
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ashape(a) = a isa Sheep ? :circle : :utriangle
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acolor(a) = a isa Sheep ? RGBAf(1.0, 1.0, 1.0, 0.8) : RGBAf(0.2, 0.2, 0.3, 0.8)
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# and instruct [`abmplot`](@ref) how to plot grass as a heatmap:
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grasscolor(model) = model.countdown ./ model.regrowth_time
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# and finally define a colormap for the grass:
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heatkwargs = (colormap = [:brown, :green], colorrange = (0, 1))
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# and instruct [`abmplot`](@ref) how to plot grass as a heatmap:
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grasscolor(model) = model.countdown ./ model.regrowth_time
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# and finally define a colormap for the grass:
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heatkwargs = (colormap = [:brown, :green], colorrange = (0, 1))
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# and put everything together and give it to [`abmplot`](@ref)
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plotkwargs = (;
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agent_color = acolor,
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agent_size = 25,
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agent_marker = ashape,
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offset,
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agentsplotkwargs = (strokewidth = 1.0, strokecolor = :black),
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heatarray = grasscolor,
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heatkwargs = heatkwargs,
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)
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# and put everything together and give it to [`abmplot`](@ref)
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plotkwargs = (;
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agent_color = acolor,
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agent_size = 25,
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agent_marker = ashape,
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offset,
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agentsplotkwargs = (strokewidth = 1.0, strokecolor = :black),
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heatarray = grasscolor,
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heatkwargs = heatkwargs,
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)
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sheepwolfgrass = initialize_model()
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sheepwolfgrass = initialize_model()
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fig, ax, abmobs = abmplot(sheepwolfgrass; plotkwargs...)
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fig
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fig, ax, abmobs = abmplot(sheepwolfgrass; plotkwargs...)
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fig
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# Now, lets run the simulation and collect some data. Define datacollection:
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sheep(a) = a isa Sheep
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wolf(a) = a isa Wolf
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count_grass(model) = count(model.fully_grown)
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# Run simulation:
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sheepwolfgrass = initialize_model()
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steps = 1000
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adata = [(sheep, count), (wolf, count)]
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mdata = [count_grass]
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adf, mdf = run!(sheepwolfgrass, steps; adata, mdata)
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# Now, lets run the simulation and collect some data. Define datacollection:
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sheep(a) = a isa Sheep
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wolf(a) = a isa Wolf
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count_grass(model) = count(model.fully_grown)
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# Run simulation:
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sheepwolfgrass = initialize_model()
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steps = 1000
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adata = [(sheep, count), (wolf, count)]
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mdata = [count_grass]
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adf, mdf = run!(sheepwolfgrass, steps; adata, mdata)
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# The following plot shows the population dynamics over time.
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# Initially, wolves become extinct because they consume the sheep too quickly.
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# The few remaining sheep reproduce and gradually reach an
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# equilibrium that can be supported by the amount of available grass.
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function plot_population_timeseries(adf, mdf)
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figure = Figure(size = (600, 400))
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ax = figure[1, 1] = Axis(figure; xlabel = "Step", ylabel = "Population")
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sheepl = lines!(ax, adf.time, adf.count_sheep, color = :cornsilk4)
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wolfl = lines!(ax, adf.time, adf.count_wolf, color = RGBAf(0.2, 0.2, 0.3))
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grassl = lines!(ax, mdf.time, mdf.count_grass, color = :green)
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figure[1, 2] = Legend(figure, [sheepl, wolfl, grassl], ["Sheep", "Wolves", "Grass"])
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figure
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# The following plot shows the population dynamics over time.
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# Initially, wolves become extinct because they consume the sheep too quickly.
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# The few remaining sheep reproduce and gradually reach an
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# equilibrium that can be supported by the amount of available grass.
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function plot_population_timeseries(adf, mdf)
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figure = Figure(size = (600, 400))
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ax = figure[1, 1] = Axis(figure; xlabel = "Step", ylabel = "Population")
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sheepl = lines!(ax, adf.time, adf.count_sheep, color = :cornsilk4)
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wolfl = lines!(ax, adf.time, adf.count_wolf, color = RGBAf(0.2, 0.2, 0.3))
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grassl = lines!(ax, mdf.time, mdf.count_grass, color = :green)
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figure[1, 2] = Legend(figure, [sheepl, wolfl, grassl], ["Sheep", "Wolves", "Grass"])
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figure
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end
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plot_population_timeseries(adf, mdf)
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# Altering the input conditions, we now see a landscape where sheep, wolves and grass
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# find an equilibrium
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# %% #src
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stable_params = (;
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n_sheep = 140,
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n_wolves = 20,
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dims = (30, 30),
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Δenergy_sheep = 5,
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sheep_reproduce = 0.31,
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wolf_reproduce = 0.06,
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Δenergy_wolf = 30,
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seed = 71758,
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)
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sheepwolfgrass = initialize_model(;stable_params...)
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adf, mdf = run!(sheepwolfgrass, 2000; adata, mdata)
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plot_population_timeseries(adf, mdf)
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# Finding a parameter combination that leads to long-term coexistence was
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# surprisingly difficult. It is for such cases that the
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# [Optimizing agent based models](@ref) example is useful!
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# %% #src
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# ## Video
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# Given that we have defined plotting functions, making a video is as simple as
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sheepwolfgrass = initialize_model(;stable_params...)
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abmvideo(
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"sheepwolf.mp4",
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sheepwolfgrass;
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frames = 100,
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framerate = 8,
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title = "Sheep Wolf Grass",
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plotkwargs...,
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)
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# ```@raw html
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# <video width="auto" controls autoplay loop>
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# <source src="../sheepwolf.mp4" type="video/mp4">
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# </video>
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# ```
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end
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plot_population_timeseries(adf, mdf)
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# Altering the input conditions, we now see a landscape where sheep, wolves and grass
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# find an equilibrium
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# %% #src
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stable_params = (;
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n_sheep = 140,
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n_wolves = 20,
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dims = (30, 30),
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Δenergy_sheep = 5,
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sheep_reproduce = 0.31,
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wolf_reproduce = 0.06,
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Δenergy_wolf = 30,
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seed = 71758,
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)
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sheepwolfgrass = initialize_model(;stable_params...)
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adf, mdf = run!(sheepwolfgrass, 2000; adata, mdata)
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plot_population_timeseries(adf, mdf)
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# Finding a parameter combination that leads to long-term coexistence was
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# surprisingly difficult. It is for such cases that the
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# [Optimizing agent based models](@ref) example is useful!
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# %% #src
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# ## Video
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# Given that we have defined plotting functions, making a video is as simple as
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sheepwolfgrass = initialize_model(;stable_params...)
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abmvideo(
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"sheepwolf.mp4",
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sheepwolfgrass;
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frames = 100,
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framerate = 8,
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title = "Sheep Wolf Grass",
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plotkwargs...,
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)
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# ```@raw html
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# <video width="auto" controls autoplay loop>
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# <source src="../sheepwolf.mp4" type="video/mp4">
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# </video>
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# ```
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run()
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