small bugfix. forgot to add optimize
parent
dae76c3305
commit
4d24b75f30
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@ -0,0 +1,107 @@
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import Pkg
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Pkg.activate("./env")
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using Distributed
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@everywhere include("./predator_prey_generic.jl")
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using BlackBoxOptim, Random
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using Statistics: mean, median
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using Serialization
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function load(file)
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fh = open(file, "r")
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optctrlb, res = deserialize(fh);
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close(fh)
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return (optctrlb, res)
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end
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function generator(x,n)
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models = []
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rng = MersenneTwister(71758)
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for i in 1:n
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animal_defs = [
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#AnimalDefinition(trunc(Int,x[8]),'●',RGBAf(1.0, 1.0, 1.0, 1),x[3], x[3], 1, x[1], x[3], trunc(Int,x[6]), "Sheep", ["Wolf","Bear"], ["Grass"])
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#AnimalDefinition(trunc(Int,x[9]),'▲',RGBAf(0.2, 0.2, 0.3, 1),x[4], x[4], 1, x[2], x[4], trunc(Int,x[7]), "Wolf", [], ["Sheep"])
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AnimalDefinition(trunc(Int,x[6]),'●',RGBAf(1.0, 1.0, 1.0, 1),0, 0, 1, x[1], x[3], 0, "Sheep", ["Wolf","Bear"], ["Grass"])
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AnimalDefinition(trunc(Int,x[7]),'▲',RGBAf(0.2, 0.2, 0.3, 1),0, 0, 1, x[2], x[4], 0, "Wolf", [], ["Sheep"])
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]
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stable_params = (;
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events = [],
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animal_defs = animal_defs,
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dims = (30, 30),
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regrowth_time = 30,
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Δenergy_grass = x[5],
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#seed = 71758,
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)
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seed = rand(rng,1000:100000)
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push!(models,initialize_model(;seed,stable_params...))
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end
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return models
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end
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function cost(x)
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steps = 2000
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iterations = 10
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models = generator(x,iterations)
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sheep(a) = a.def.type == "Sheep"
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wolf(a) = a.def.type == "Wolf"
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eaten(a) = a.def.type == "Sheep" && a.death_cause == Predation
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starved(a) = a.def.type == "Sheep" && a.death_cause == Starvation
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count_grass(model) = count(model.fully_grown)
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adata = [(sheep, count), (wolf, count), (eaten, count), (starved, count)]
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mdata = [count_grass]
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df1,df2 = ensemblerun!(models, steps; adata, mdata, parallel=true, showprogress=true)
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println(x)
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fitness_scores = []
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for i in 1:iterations
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df = df1[df1.ensemble .== i,:]
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println(string(count(!iszero,df.count_sheep))*" "*string(count(!iszero,df.count_wolf)))
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score = count(iszero,df.count_sheep) + 2*count(iszero,df.count_wolf)
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push!(fitness_scores,score)
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end
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fitness = float(sum(fitness_scores))
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println(fitness)
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return fitness
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end
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#result = bboptimize(cost,SearchRange = [(0.0, 1.0),(0.0, 1.0),(0.0, 30.0),(0.0, 30.0),],NumDimensions = 4,MaxTime = 20,)
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SearchRange = [
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(0.01, 0.4),
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(0.01, 0.4),
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(5.0, 30.0),
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(5.0, 30.0),
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(5.0, 30.0),
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#(1, 3),
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#(1, 3),
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(3, 30),
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(3, 30),
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]
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optctrl = bbsetup(cost;SearchRange, MaxTime = 300, Method = :generating_set_search)#, TraceInterval=1.0, TraceMode=:verbose);
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#optctrl, res = load("SimpleModellOptimization900.tmp");
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res = bboptimize(optctrl)
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tempfilename = "./temp" * string(rand(1:Int(1e8))) * ".tmp"
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fh = open(tempfilename, "w")
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serialize(fh, (optctrl, res))
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close(fh)
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#cost([0.806586, 0.0481975, 18.5285, 22.329])
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#[0.165438, 0.0462449, 15.4501, 12.0382]
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#[0.571934, 0.74005, 4.22395, 24.9997, 15.4605, 1.09129, 2.18749, 24.3948, 11.3926]
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#Repro_Schaf, Repro_Wolf, Delta_Energie_Schaf, Delta_Energy_Wolf, Delta_Energy_Gras, n_Schaf, n_Wölfe
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#[0.26817737483789245, 0.027182763696826588, 14.440470034137558, 27.81279288508929, 15.785601397364756, 28.644469239080397, 13.471462703569484]
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#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
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# Schaf stirbt aus obwohl es mehr energy bekommt???
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#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 12.155473514486612, 9.568612576389182, 22.299369669891565]
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#Wolf stirbt aus weil Schaf sich zu wenig reproduziert
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#[0.016950722103029812, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
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#Wolf stirbt aus, da er sich viel zu stark reproduziert
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#[0.11524114234251756, 0.25901432860274654, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
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#Aber hier plötzlich wieder einigermaßen stabil
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#[0.11524114234251756, 0.2477989358947258, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
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#Wolf stirbt weil zu wenig Schafe am Anfang
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#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 3.3286125763891814, 22.299369669891565]
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#Einigermaßen stabil, aber Schaf stirbt oft aus, weil zu hohe reproduktion
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#[0.39570778081524405, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
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@ -23,17 +23,19 @@ mutable struct AnimalDefinition
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end
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# some helper functions to get generated model parameters for animals
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reproduction_prop(a) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_prob")]
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Δenergy(a) = abmproperties(model)[Symbol(a.def.type*"_"*"Δenergy")]
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perception(a) = abmproperties(model)[Symbol(a.def.type*"_"*"perception")]
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reproduction_energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_energy_threshold")]
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forage_energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"forage_energy_threshold")]
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energy_usage(a) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_usage")]
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reproduction_prop(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_prob")]
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Δenergy(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"Δenergy")]
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perception(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"perception")]
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reproduction_energy_threshold(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_energy_threshold")]
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forage_energy_threshold(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"forage_energy_threshold")]
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energy_usage(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_usage")]
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# Animal with AnimalDefinition and fields that change during simulation
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# might be better to use @multiagent and @subagent with predator prey as subtypes. Allows to dispatch different functions per kind and change execution order with schedulers.bykind
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@agent struct Animal(GridAgent{2})
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energy::Float64
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color::GLMakie.ColorTypes.RGBA{Float32}
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symbol::Char
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def::AnimalDefinition
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death_cause::Union{DeathCause,Nothing}
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nearby_dangers
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@ -43,8 +45,8 @@ end
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# get nearby food and danger for later when choosing the next position
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function perceive!(a::Animal,model)
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if perception(a) > 0
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nearby = collect(nearby_agents(a, model, perception(a)))
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if perception(a, model) > 0
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nearby = collect(nearby_agents(a, model, perception(a, model)))
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a.nearby_dangers = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.dangers, nearby))
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a.nearby_food = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.food, nearby))
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if "Grass" ∈ a.def.food
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@ -67,7 +69,7 @@ function move!(a::Animal,model)
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else
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randomwalk!(a, model)
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end
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a.energy -= energy_usage(a)
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a.energy -= energy_usage(a, model)
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end
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# choose best position based on scoring
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@ -86,7 +88,7 @@ function choose_position(a::Animal,model)
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end
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end
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for food in a.nearby_food
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if a.energy < forage_energy_threshold(a)
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if a.energy < forage_energy_threshold(a, model)
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distance = findmax(abs.(pos.-food))[1]
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if distance != 0
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score += 1/distance
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@ -107,7 +109,8 @@ function eat!(a::Animal, model)
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if !isnothing(prey)
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#remove_agent!(dinner, model)
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prey.death_cause = Predation
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a.energy += Δenergy(prey)
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prey.symbol = 'x'
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a.energy += Δenergy(prey, model)
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end
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if "Grass" ∈ a.def.food && model.fully_grown[a.pos...]
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model.fully_grown[a.pos...] = false
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@ -119,7 +122,7 @@ end
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# dublicate the animal, based on chance and if it has enough energy
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function reproduce!(a::Animal, model)
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if a.energy > reproduction_energy_threshold(a) && rand(abmrng(model)) ≤ reproduction_prop(a)
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if a.energy > reproduction_energy_threshold(a, model) && rand(abmrng(model)) ≤ reproduction_prop(a, model)
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a.energy /= 2
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replicate!(a, model)
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end
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@ -156,7 +159,7 @@ function move_towards!(agent, pos, model; ifempty=true)
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walk!(agent,direction,model; ifempty=ifempty)
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end
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function nearby_fully_grown(a::Animal, model)
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nearby_pos = nearby_positions(a.pos, model, perception(a))
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nearby_pos = nearby_positions(a.pos, model, perception(a, model))
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fully_grown_positions = filter(x -> model.fully_grown[x...], collect(nearby_pos))
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return fully_grown_positions
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end
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@ -207,7 +210,7 @@ function initialize_model(;
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for def in animal_defs
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for _ in 1:def.n
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energy = rand(abmrng(model), 1:(def.Δenergy*2)) - 1
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add_agent!(Animal, model, energy, def, nothing, [], [], [])
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add_agent!(Animal, model, energy, def.color, def.symbol, def, nothing, [], [], [])
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end
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end
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## Add grass with random initial growth
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@ -231,10 +234,16 @@ function animal_step!(a::Animal, model)
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move!(a, model)
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if a.energy < 0
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a.death_cause = Starvation
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a.symbol = 'x'
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return
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end
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eat!(a, model)
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reproduce!(a, model)
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#if a.energy < 10 && a.def.type != "Wolf"
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# a.color = RGBAf(a.energy/10,a.energy/10,a.energy/10,1)
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#elseif a.def.type != "Wolf"
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# a.color = RGBAf(1,1,1,1)
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#end
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end
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function model_step!(model)
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