small bugfix. forgot to add optimize

optimize.jl

@@ -0,0 +1,107 @@
+import Pkg
+Pkg.activate("./env")
+using Distributed
+@everywhere include("./predator_prey_generic.jl")
+using BlackBoxOptim, Random
+using Statistics: mean, median
+using Serialization
+function load(file)
+    fh = open(file, "r")
+    optctrlb, res = deserialize(fh);
+    close(fh)
+    return (optctrlb, res)
+end 
+function generator(x,n)
+    models = []
+    rng = MersenneTwister(71758)
+    for i in 1:n
+        animal_defs = [
+        #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"])
+        #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"])
+        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"])
+        AnimalDefinition(trunc(Int,x[7]),'▲',RGBAf(0.2, 0.2, 0.3, 1),0, 0, 1, x[2], x[4], 0, "Wolf", [], ["Sheep"])
+        ]
+        stable_params = (;
+            events = [],
+            animal_defs = animal_defs,
+            dims = (30, 30),
+            regrowth_time = 30,
+            Δenergy_grass = x[5],
+            #seed = 71758,
+        )
+        seed = rand(rng,1000:100000)
+        push!(models,initialize_model(;seed,stable_params...))
+    end
+    return models
+end
+function cost(x)
+    steps = 2000
+    iterations = 10
+    models = generator(x,iterations)
+    sheep(a) = a.def.type == "Sheep"
+    wolf(a) = a.def.type == "Wolf"
+    eaten(a) = a.def.type == "Sheep" && a.death_cause == Predation
+    starved(a) = a.def.type == "Sheep" && a.death_cause == Starvation
+    count_grass(model) = count(model.fully_grown)
+    adata = [(sheep, count), (wolf, count), (eaten, count), (starved, count)]
+    mdata = [count_grass]
+    df1,df2 = ensemblerun!(models, steps; adata, mdata, parallel=true, showprogress=true)
+    println(x)
+    fitness_scores = []
+    for i in 1:iterations
+        df = df1[df1.ensemble .== i,:]
+        println(string(count(!iszero,df.count_sheep))*"   "*string(count(!iszero,df.count_wolf)))
+        score = count(iszero,df.count_sheep) + 2*count(iszero,df.count_wolf)
+        push!(fitness_scores,score)
+    end
+    fitness = float(sum(fitness_scores))
+    println(fitness)
+    return fitness
+end
+
+#result = bboptimize(cost,SearchRange = [(0.0, 1.0),(0.0, 1.0),(0.0, 30.0),(0.0, 30.0),],NumDimensions = 4,MaxTime = 20,)
+SearchRange = [
+                (0.01, 0.4),
+                (0.01, 0.4),
+                (5.0, 30.0),
+                (5.0, 30.0),
+                (5.0, 30.0),
+                #(1, 3),
+                #(1, 3),
+                (3, 30),
+                (3, 30),
+            ]
+optctrl = bbsetup(cost;SearchRange, MaxTime = 300, Method = :generating_set_search)#, TraceInterval=1.0, TraceMode=:verbose);
+#optctrl, res = load("SimpleModellOptimization900.tmp");
+res = bboptimize(optctrl)
+tempfilename = "./temp" * string(rand(1:Int(1e8))) * ".tmp"
+fh = open(tempfilename, "w")
+serialize(fh, (optctrl, res))
+close(fh)
+
+#cost([0.806586, 0.0481975, 18.5285, 22.329])
+#[0.165438, 0.0462449, 15.4501, 12.0382]
+#[0.571934, 0.74005, 4.22395, 24.9997, 15.4605, 1.09129, 2.18749, 24.3948, 11.3926]
+
+#Repro_Schaf, Repro_Wolf, Delta_Energie_Schaf, Delta_Energy_Wolf, Delta_Energy_Gras, n_Schaf, n_Wölfe
+#[0.26817737483789245, 0.027182763696826588, 14.440470034137558, 27.81279288508929, 15.785601397364756, 28.644469239080397, 13.471462703569484]
+#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
+
+# Schaf stirbt aus obwohl es mehr energy bekommt???
+#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 12.155473514486612, 9.568612576389182, 22.299369669891565]
+
+#Wolf stirbt aus weil Schaf sich zu wenig reproduziert
+#[0.016950722103029812, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
+
+
+#Wolf stirbt aus, da er sich viel zu stark reproduziert
+#[0.11524114234251756, 0.25901432860274654, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
+
+#Aber hier plötzlich wieder einigermaßen stabil
+#[0.11524114234251756, 0.2477989358947258, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]
+
+#Wolf stirbt weil zu wenig Schafe am Anfang
+#[0.11524114234251756, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 3.3286125763891814, 22.299369669891565]
+
+#Einigermaßen stabil, aber Schaf stirbt oft aus, weil zu hohe reproduktion
+#[0.39570778081524405, 0.07378121226251827, 29.31006871020899, 20.47494251025892, 5.915473514486612, 9.568612576389182, 22.299369669891565]

predator_prey_generic.jl

@@ -23,17 +23,19 @@ mutable struct AnimalDefinition
 end
 
 # some helper functions to get generated model parameters for animals 
-reproduction_prop(a) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_prob")]
+reproduction_prop(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_prob")]
-Δenergy(a) = abmproperties(model)[Symbol(a.def.type*"_"*"Δenergy")]
+Δenergy(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"Δenergy")]
-perception(a) = abmproperties(model)[Symbol(a.def.type*"_"*"perception")]
+perception(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"perception")]
-reproduction_energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_energy_threshold")]
+reproduction_energy_threshold(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"reproduction_energy_threshold")]
-forage_energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"forage_energy_threshold")]
+forage_energy_threshold(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"forage_energy_threshold")]
-energy_usage(a) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_usage")]
+energy_usage(a, model) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_usage")]
 
 # Animal with AnimalDefinition and fields that change during simulation
 # 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
 @agent struct Animal(GridAgent{2})
     energy::Float64
+    color::GLMakie.ColorTypes.RGBA{Float32}
+    symbol::Char
     def::AnimalDefinition
     death_cause::Union{DeathCause,Nothing}
     nearby_dangers
@@ -43,8 +45,8 @@ end
 
 # get nearby food and danger for later when choosing the next position
 function perceive!(a::Animal,model)
-    if perception(a) > 0
+    if perception(a, model) > 0
-        nearby = collect(nearby_agents(a, model, perception(a)))
+        nearby = collect(nearby_agents(a, model, perception(a, model)))
         a.nearby_dangers = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.dangers, nearby))
         a.nearby_food = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.food, nearby))
         if "Grass" ∈ a.def.food
@@ -67,7 +69,7 @@ function move!(a::Animal,model)
     else
         randomwalk!(a, model)
     end
-    a.energy -= energy_usage(a)
+    a.energy -= energy_usage(a, model)
 end
 
 # choose best position based on scoring
@@ -86,7 +88,7 @@ function choose_position(a::Animal,model)
             end
         end
         for food in a.nearby_food
-            if a.energy < forage_energy_threshold(a) 
+            if a.energy < forage_energy_threshold(a, model) 
                 distance = findmax(abs.(pos.-food))[1]
                 if distance != 0
                     score += 1/distance
@@ -107,7 +109,8 @@ function eat!(a::Animal, model)
     if !isnothing(prey)
         #remove_agent!(dinner, model)
         prey.death_cause = Predation
-        a.energy += Δenergy(prey)
+        prey.symbol = 'x'
+        a.energy += Δenergy(prey, model)
     end
     if "Grass" ∈ a.def.food && model.fully_grown[a.pos...]
         model.fully_grown[a.pos...] = false
@@ -119,7 +122,7 @@ end
 
 # dublicate the animal, based on chance and if it has enough energy
 function reproduce!(a::Animal, model)
-    if a.energy > reproduction_energy_threshold(a) && rand(abmrng(model)) ≤ reproduction_prop(a)
+    if a.energy > reproduction_energy_threshold(a, model) && rand(abmrng(model)) ≤ reproduction_prop(a, model)
         a.energy /= 2
         replicate!(a, model)
     end
@@ -156,7 +159,7 @@ function move_towards!(agent, pos, model; ifempty=true)
     walk!(agent,direction,model; ifempty=ifempty)
 end
 function nearby_fully_grown(a::Animal, model)
-    nearby_pos = nearby_positions(a.pos, model, perception(a))
+    nearby_pos = nearby_positions(a.pos, model, perception(a, model))
     fully_grown_positions = filter(x -> model.fully_grown[x...], collect(nearby_pos))
     return fully_grown_positions
 end
@@ -207,7 +210,7 @@ function initialize_model(;
     for def in animal_defs
         for _ in 1:def.n
             energy = rand(abmrng(model), 1:(def.Δenergy*2)) - 1
-            add_agent!(Animal, model, energy, def, nothing, [], [], [])
+            add_agent!(Animal, model, energy, def.color, def.symbol, def, nothing, [], [], [])
         end
     end
     ## Add grass with random initial growth
@@ -231,10 +234,16 @@ function animal_step!(a::Animal, model)
     move!(a, model)
     if a.energy < 0
         a.death_cause = Starvation
+        a.symbol = 'x'
         return
     end
     eat!(a, model)
     reproduce!(a, model)
+    #if a.energy < 10 && a.def.type != "Wolf"
+    #    a.color = RGBAf(a.energy/10,a.energy/10,a.energy/10,1)
+    #elseif a.def.type != "Wolf"
+    #    a.color = RGBAf(1,1,1,1)
+    #end
 end
 
 function model_step!(model)