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move some logic into their own funcs(move,eat,reproduce)

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Michael Brehm 2024-05-16 14:30:45 +02:00
parent 0165c839cd
commit f0bfe3cb87
1 changed files with 149 additions and 134 deletions
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predator_prey.jl
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@ -46,17 +46,63 @@
# example, we could have only one type and one additional filed to separate them. # example, we could have only one type and one additional filed to separate them.
# Nevertheless, for the sake of example, we will use two different types.) # Nevertheless, for the sake of example, we will use two different types.)
using Agents, Random using Agents, Random
using CairoMakie
@agent struct Sheep(GridAgent{2}) @agent struct Sheep(GridAgent{2})
energy::Float64 energy::Float64
reproduction_prob::Float64 reproduction_prob::Float64
Δenergy::Float64 Δenergy::Float64
#perception::Int32
#speed::Float64
#endurance::Float64
end
function move!(sheep::Sheep,model)
randomwalk!(sheep, model)
sheep.energy -= 1
end
function eat!(sheep::Sheep, model)
if model.fully_grown[sheep.pos...]
sheep.energy += sheep.Δenergy
model.fully_grown[sheep.pos...] = false
end
return
end
function reproduce!(sheep::Sheep, model)
if rand(abmrng(model)) ≤ sheep.reproduction_prob
sheep.energy /= 2
replicate!(sheep, model)
end
end end
@agent struct Wolf(GridAgent{2}) @agent struct Wolf(GridAgent{2})
energy::Float64 energy::Float64
reproduction_prob::Float64 reproduction_prob::Float64
Δenergy::Float64 Δenergy::Float64
#perception::Int32
#speed::Float64
#endurance::Float64
end
function move!(wolf::Wolf,model)
randomwalk!(wolf, model; ifempty=false)
wolf.energy -= 1
end
function eat!(wolf::Wolf, model)
dinner = first_sheep_in_position(wolf.pos, model)
if !isnothing(dinner)
remove_agent!(dinner, model)
wolf.energy += wolf.Δenergy
end
end
function reproduce!(wolf::Wolf, model)
if rand(abmrng(model)) ≤ wolf.reproduction_prob
wolf.energy /= 2
replicate!(wolf, model)
end
end
function first_sheep_in_position(pos, model)
ids = ids_in_position(pos, model)
j = findfirst(id -> model[id] isa Sheep, ids)
isnothing(j) ? nothing : model[ids[j]]::Sheep
end end
# The function `initialize_model` returns a new model containing sheep, wolves, and grass # The function `initialize_model` returns a new model containing sheep, wolves, and grass
@ -119,56 +165,23 @@ end
# Notice how the function `sheepwolf_step!`, which is our `agent_step!`, # Notice how the function `sheepwolf_step!`, which is our `agent_step!`,
# is dispatched to the appropriate agent type via Julia's Multiple Dispatch system. # is dispatched to the appropriate agent type via Julia's Multiple Dispatch system.
function sheepwolf_step!(sheep::Sheep, model) function sheepwolf_step!(sheep::Sheep, model)
randomwalk!(sheep, model) move!(sheep, model)
sheep.energy -= 1
if sheep.energy < 0 if sheep.energy < 0
remove_agent!(sheep, model) remove_agent!(sheep, model)
return return
end end
eat!(sheep, model) eat!(sheep, model)
if rand(abmrng(model)) ≤ sheep.reproduction_prob reproduce!(sheep, model)
sheep.energy /= 2
replicate!(sheep, model)
end
end end
function sheepwolf_step!(wolf::Wolf, model) function sheepwolf_step!(wolf::Wolf, model)
randomwalk!(wolf, model; ifempty=false) move!(wolf, model)
wolf.energy -= 1
if wolf.energy < 0 if wolf.energy < 0
remove_agent!(wolf, model) remove_agent!(wolf, model)
return return
end end
## If there is any sheep on this grid cell, it's dinner time! eat!(wolf, model)
dinner = first_sheep_in_position(wolf.pos, model) reproduce!(wolf, model)
!isnothing(dinner) && eat!(wolf, dinner, model)
if rand(abmrng(model)) ≤ wolf.reproduction_prob
wolf.energy /= 2
replicate!(wolf, model)
end
end
function first_sheep_in_position(pos, model)
ids = ids_in_position(pos, model)
j = findfirst(id -> model[id] isa Sheep, ids)
isnothing(j) ? nothing : model[ids[j]]::Sheep
end
# Sheep and wolves have separate `eat!` functions. If a sheep eats grass, it will acquire
# additional energy and the grass will not be available for consumption until regrowth time
# has elapsed. If a wolf eats a sheep, the sheep dies and the wolf acquires more energy.
function eat!(sheep::Sheep, model)
if model.fully_grown[sheep.pos...]
sheep.energy += sheep.Δenergy
model.fully_grown[sheep.pos...] = false
end
return
end
function eat!(wolf::Wolf, sheep::Sheep, model)
remove_agent!(sheep, model)
wolf.energy += wolf.Δenergy
return
end end
# The behavior of grass function differently. If it is fully grown, it is consumable. # The behavior of grass function differently. If it is fully grown, it is consumable.
@ -187,108 +200,110 @@ function grass_step!(model)
end end
end end
sheepwolfgrass = initialize_model() function run()
sheepwolfgrass = initialize_model()
# ## Running the model # ## Running the model
# %% #src # %% #src
# We will run the model for 500 steps and record the number of sheep, wolves and consumable # We will run the model for 500 steps and record the number of sheep, wolves and consumable
# grass patches after each step. First: initialize the model. # grass patches after each step. First: initialize the model.
using CairoMakie CairoMakie.activate!() # hide
CairoMakie.activate!() # hide
# To view our starting population, we can build an overview plot using [`abmplot`](@ref). # To view our starting population, we can build an overview plot using [`abmplot`](@ref).
# We define the plotting details for the wolves and sheep: # We define the plotting details for the wolves and sheep:
offset(a) = a isa Sheep ? (-0.1, -0.1*rand()) : (+0.1, +0.1*rand()) offset(a) = a isa Sheep ? (-0.1, -0.1*rand()) : (+0.1, +0.1*rand())
ashape(a) = a isa Sheep ? :circle : :utriangle ashape(a) = a isa Sheep ? :circle : :utriangle
acolor(a) = a isa Sheep ? RGBAf(1.0, 1.0, 1.0, 0.8) : RGBAf(0.2, 0.2, 0.3, 0.8) acolor(a) = a isa Sheep ? RGBAf(1.0, 1.0, 1.0, 0.8) : RGBAf(0.2, 0.2, 0.3, 0.8)
# and instruct [`abmplot`](@ref) how to plot grass as a heatmap: # and instruct [`abmplot`](@ref) how to plot grass as a heatmap:
grasscolor(model) = model.countdown ./ model.regrowth_time grasscolor(model) = model.countdown ./ model.regrowth_time
# and finally define a colormap for the grass: # and finally define a colormap for the grass:
heatkwargs = (colormap = [:brown, :green], colorrange = (0, 1)) heatkwargs = (colormap = [:brown, :green], colorrange = (0, 1))
# and put everything together and give it to [`abmplot`](@ref) # and put everything together and give it to [`abmplot`](@ref)
plotkwargs = (; plotkwargs = (;
agent_color = acolor, agent_color = acolor,
agent_size = 25, agent_size = 25,
agent_marker = ashape, agent_marker = ashape,
offset, offset,
agentsplotkwargs = (strokewidth = 1.0, strokecolor = :black), agentsplotkwargs = (strokewidth = 1.0, strokecolor = :black),
heatarray = grasscolor, heatarray = grasscolor,
heatkwargs = heatkwargs, heatkwargs = heatkwargs,
) )
sheepwolfgrass = initialize_model() sheepwolfgrass = initialize_model()
fig, ax, abmobs = abmplot(sheepwolfgrass; plotkwargs...) fig, ax, abmobs = abmplot(sheepwolfgrass; plotkwargs...)
fig fig
# Now, lets run the simulation and collect some data. Define datacollection: # Now, lets run the simulation and collect some data. Define datacollection:
sheep(a) = a isa Sheep sheep(a) = a isa Sheep
wolf(a) = a isa Wolf wolf(a) = a isa Wolf
count_grass(model) = count(model.fully_grown) count_grass(model) = count(model.fully_grown)
# Run simulation: # Run simulation:
sheepwolfgrass = initialize_model() sheepwolfgrass = initialize_model()
steps = 1000 steps = 1000
adata = [(sheep, count), (wolf, count)] adata = [(sheep, count), (wolf, count)]
mdata = [count_grass] mdata = [count_grass]
adf, mdf = run!(sheepwolfgrass, steps; adata, mdata) adf, mdf = run!(sheepwolfgrass, steps; adata, mdata)
# The following plot shows the population dynamics over time. # The following plot shows the population dynamics over time.
# Initially, wolves become extinct because they consume the sheep too quickly. # Initially, wolves become extinct because they consume the sheep too quickly.
# The few remaining sheep reproduce and gradually reach an # The few remaining sheep reproduce and gradually reach an
# equilibrium that can be supported by the amount of available grass. # equilibrium that can be supported by the amount of available grass.
function plot_population_timeseries(adf, mdf) function plot_population_timeseries(adf, mdf)
figure = Figure(size = (600, 400)) figure = Figure(size = (600, 400))
ax = figure[1, 1] = Axis(figure; xlabel = "Step", ylabel = "Population") ax = figure[1, 1] = Axis(figure; xlabel = "Step", ylabel = "Population")
sheepl = lines!(ax, adf.time, adf.count_sheep, color = :cornsilk4) sheepl = lines!(ax, adf.time, adf.count_sheep, color = :cornsilk4)
wolfl = lines!(ax, adf.time, adf.count_wolf, color = RGBAf(0.2, 0.2, 0.3)) wolfl = lines!(ax, adf.time, adf.count_wolf, color = RGBAf(0.2, 0.2, 0.3))
grassl = lines!(ax, mdf.time, mdf.count_grass, color = :green) grassl = lines!(ax, mdf.time, mdf.count_grass, color = :green)
figure[1, 2] = Legend(figure, [sheepl, wolfl, grassl], ["Sheep", "Wolves", "Grass"]) figure[1, 2] = Legend(figure, [sheepl, wolfl, grassl], ["Sheep", "Wolves", "Grass"])
figure figure
end
plot_population_timeseries(adf, mdf)
# Altering the input conditions, we now see a landscape where sheep, wolves and grass
# find an equilibrium
# %% #src
stable_params = (;
n_sheep = 140,
n_wolves = 20,
dims = (30, 30),
Δenergy_sheep = 5,
sheep_reproduce = 0.31,
wolf_reproduce = 0.06,
Δenergy_wolf = 30,
seed = 71758,
)
sheepwolfgrass = initialize_model(;stable_params...)
adf, mdf = run!(sheepwolfgrass, 2000; adata, mdata)
plot_population_timeseries(adf, mdf)
# Finding a parameter combination that leads to long-term coexistence was
# surprisingly difficult. It is for such cases that the
# [Optimizing agent based models](@ref) example is useful!
# %% #src
# ## Video
# Given that we have defined plotting functions, making a video is as simple as
sheepwolfgrass = initialize_model(;stable_params...)
abmvideo(
"sheepwolf.mp4",
sheepwolfgrass;
frames = 100,
framerate = 8,
title = "Sheep Wolf Grass",
plotkwargs...,
)
# ```@raw html
# <video width="auto" controls autoplay loop>
# <source src="../sheepwolf.mp4" type="video/mp4">
# </video>
# ```
end end
run()
plot_population_timeseries(adf, mdf)
# Altering the input conditions, we now see a landscape where sheep, wolves and grass
# find an equilibrium
# %% #src
stable_params = (;
n_sheep = 140,
n_wolves = 20,
dims = (30, 30),
Δenergy_sheep = 5,
sheep_reproduce = 0.31,
wolf_reproduce = 0.06,
Δenergy_wolf = 30,
seed = 71758,
)
sheepwolfgrass = initialize_model(;stable_params...)
adf, mdf = run!(sheepwolfgrass, 2000; adata, mdata)
plot_population_timeseries(adf, mdf)
# Finding a parameter combination that leads to long-term coexistence was
# surprisingly difficult. It is for such cases that the
# [Optimizing agent based models](@ref) example is useful!
# %% #src
# ## Video
# Given that we have defined plotting functions, making a video is as simple as
sheepwolfgrass = initialize_model(;stable_params...)
abmvideo(
"sheepwolf.mp4",
sheepwolfgrass;
frames = 100,
framerate = 8,
title = "Sheep Wolf Grass",
plotkwargs...,
)
# ```@raw html
# <video width="auto" controls autoplay loop>
# <source src="../sheepwolf.mp4" type="video/mp4">
# </video>
# ```