convert animal parameters to model parameters to make them adjustable in GLMakie
parent
78552bb044
commit
fbf2453ccd
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@ -15,6 +15,10 @@ mutable struct AnimalDefinition
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dangers::Vector{String}
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food::Vector{String}
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end
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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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energy_threshold(a) = abmproperties(model)[Symbol(a.def.type*"_"*"energy_threshold")]
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struct StartDefinition
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n::Int32
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def::AnimalDefinition
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@ -30,8 +34,8 @@ end
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function perceive!(a::Animal,model)
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if a.def.perception > 0
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nearby = collect(nearby_agents(a, model, a.def.perception))
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if perception(a) > 0
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nearby = collect(nearby_agents(a, model, perception(a)))
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a.nearby_dangers = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.dangers && isnothing(x.death_cause), nearby))
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a.nearby_food = map(x -> x.pos, filter(x -> isa(x, Animal) && x.def.type ∈ a.def.food && isnothing(x.death_cause), nearby))
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if "Grass" ∈ a.def.food
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@ -65,7 +69,7 @@ function calculate_best_pos(a::Animal,model)
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push!(danger_scores,danger_score)
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end
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if !isempty(a.nearby_food)
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food_score = sum(map(food -> findmax(abs.(pos.-danger))[1], a.nearby_food))
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food_score = sum(map(food -> findmax(abs.(pos.-food))[1], a.nearby_food))
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push!(food_scores,food_score)
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end
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end
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@ -86,7 +90,7 @@ 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 += prey.def.Δenergy
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a.energy += Δenergy(prey)
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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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@ -95,7 +99,7 @@ function eat!(a::Animal, model)
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return
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end
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function reproduce!(a::Animal, model)
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if a.energy > a.def.energy_threshold && rand(abmrng(model)) ≤ a.def.reproduction_prob
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if a.energy > energy_threshold(a) && rand(abmrng(model)) ≤ reproduction_prop(a)#a.def.reproduction_prob
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a.energy /= 2
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replicate!(a, model)
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end
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@ -122,7 +126,7 @@ 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, a.def.perception)
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nearby_pos = nearby_positions(a.pos, model, perception(a))
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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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@ -165,19 +169,19 @@ function initialize_model(;
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## Notice how the properties are a `NamedTuple` to ensure type stability.
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## define as dictionary(mutable) instead of tuples(immutable) as per https://github.com/JuliaDynamics/Agents.jl/issues/727
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## maybe instead of AnimalDefinition we build the properties dict dynamically and use model properties during the simulation
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animal_defs = Vector{AnimalDefinition}()
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for start_def in start_defs
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push!(animal_defs,start_def.def)
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end
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animal_properties = generate_animal_parameters(animal_defs)
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properties = Dict(
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:events => events,
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:fully_grown => falses(dims),
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:countdown => zeros(Int, dims),
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:regrowth_time => regrowth_time,
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:Δenergy_sheep => Δenergy_sheep,
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:Δenergy_wolf => Δenergy_wolf,
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:Δenergy_grass => Δenergy_grass,
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:sheep_reproduce => sheep_reproduce,
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:wolf_reproduce => wolf_reproduce,
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:sheep_perception => sheep_perception,
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:wolf_perception => wolf_perception
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)
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properties = merge(properties,animal_properties)
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model = StandardABM(Animal, space;
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agent_step! = animal_step!, model_step! = model_step!,
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properties, rng, scheduler = Schedulers.Randomly(), warn = false, agents_first = false
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@ -317,6 +321,28 @@ function event_handler!(model)
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end
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end
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function generate_animal_parameters(defs::Vector{AnimalDefinition})
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parameter_dict = Dict()
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for def in defs
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parameter_dict[Symbol(def.type*"_"*"Δenergy")]=def.Δenergy
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parameter_dict[Symbol(def.type*"_"*"reproduction_prob")]=def.reproduction_prob
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parameter_dict[Symbol(def.type*"_"*"perception")]=def.perception
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parameter_dict[Symbol(def.type*"_"*"energy_threshold")]=def.energy_threshold
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end
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return parameter_dict
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end
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function generate_animal_parameter_ranges(defs::Vector{AnimalDefinition})
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parameter_range_dict = Dict()
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for def in defs
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parameter_range_dict[Symbol(def.type*"_"*"Δenergy")]=0:1:100
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parameter_range_dict[Symbol(def.type*"_"*"reproduction_prob")]=0:0.01:1
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parameter_range_dict[Symbol(def.type*"_"*"perception")]=0:1:10
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parameter_range_dict[Symbol(def.type*"_"*"energy_threshold")]=0:1:100
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end
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return parameter_range_dict
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end
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mutable struct RecurringEvent
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name::String
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@ -9,7 +9,28 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/SCJ/Projekt/SCJ-PredatorPrey/env`\n"
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"\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/Studium/SCJ-PredatorPrey/env`\n",
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"\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mSpecialFunctions\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mColorVectorSpace → SpecialFunctionsExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mSpecialFunctions → SpecialFunctionsChainRulesCoreExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mDualNumbers\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mHypergeometricFunctions\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mStatsFuns\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mStatsFuns → StatsFunsInverseFunctionsExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mStatsFuns → StatsFunsChainRulesCoreExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mDistributions\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mDistributions → DistributionsTestExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mDistributions → DistributionsChainRulesCoreExt\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mKernelDensity\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mStreamSampling\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39mAgents\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mMakie\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39m\u001b[90mAgents → AgentsVisualizations\u001b[39m\n",
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"\u001b[32m ✓ \u001b[39mAgentsExampleZoo\n",
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"\u001b[32m ✓ \u001b[39mCairoMakie\n",
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"\u001b[32m ✓ \u001b[39mGLMakie\n",
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" 19 dependencies successfully precompiled in 272 seconds. 270 already precompiled.\n"
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]
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}
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],
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@ -60,22 +81,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `~/SCJ/Projekt/SCJ-PredatorPrey/env/Manifest.toml`\n",
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" \u001b[90m[46ada45e] \u001b[39mAgents v6.0.13\n",
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" \u001b[90m[e9467ef8] \u001b[39mGLMakie v0.10.2\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"include(\"./predator_prey_generic.jl\")\n",
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"Pkg.status([\"Agents\",\"GLMakie\"]; mode = Pkg.Types.PKGMODE_MANIFEST, io=stdout)\n",
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"#Pkg.status([\"Agents\",\"GLMakie\"]; mode = Pkg.Types.PKGMODE_MANIFEST, io=stdout)\n",
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"using GLMakie\n",
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"GLMakie.activate!()\n",
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"events = RecurringEvent[]\n",
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@ -86,6 +97,7 @@
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"sheep_def = AnimalDefinition('●',RGBAf(1.0, 1.0, 1.0, 0.8),20, 0.3, 20, 3, \"Sheep\", [\"Wolf\",\"Bear\"], [\"Grass\"])\n",
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"wolf_def = AnimalDefinition('▲',RGBAf(0.2, 0.2, 0.3, 0.8),20, 0.07, 20, 1, \"Wolf\", [], [\"Sheep\"])\n",
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"bear_def = AnimalDefinition('■',RGBAf(1.0, 0.8, 0.5, 0.8),20, 0.07, 20, 1, \"Bear\", [], [\"Sheep\"])\n",
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"parameter_ranges = generate_animal_parameter_ranges([sheep_def,wolf_def,bear_def])\n",
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"\n",
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"stable_params = (;\n",
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" events = events,\n",
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@ -100,7 +112,7 @@
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" :regrowth_time => 0:1:100,\n",
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" :Δenergy_grass => 0:1:50,\n",
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")\n",
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"\n",
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"params = merge(params,parameter_ranges)\n",
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"sheep(a) = a.def.type == \"Sheep\"\n",
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"wolf(a) = a.def.type == \"Wolf\"\n",
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"eaten(a) = a.def.type == \"Sheep\" && a.death_cause == Predation\n",
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