fix movement, but updating model parameters with GLMakie broken
parent
fbf2453ccd
commit
2906c96aa4
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@ -30,14 +30,16 @@ end
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death_cause::Union{DeathCause,Nothing}
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nearby_dangers
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nearby_food
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food_scores
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danger_scores
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end
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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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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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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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a.nearby_food = [a.nearby_food; nearby_fully_grown(a, model)]
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end
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@ -64,22 +66,36 @@ function calculate_best_pos(a::Animal,model)
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positions = collect(nearby_positions(a, model, 1))
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# weight scores with utility functions
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for pos in positions
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if !isempty(a.nearby_dangers)
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danger_score = sum(map(danger -> findmax(abs.(pos.-danger))[1], a.nearby_dangers))
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push!(danger_scores,danger_score)
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danger_score = 0
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for danger in a.nearby_dangers
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distance = findmax(abs.(pos.-danger))[1]
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if distance != 0
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danger_score=danger_score-1/distance
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else
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danger_score-=2
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end
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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.-food))[1], a.nearby_food))
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push!(food_scores,food_score)
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food_score = 0
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for food in a.nearby_food
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distance = findmax(abs.(pos.-food))[1]
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if distance != 0
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food_score=food_score+1/distance
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else
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food_score+=2
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end
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end
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push!(danger_scores,danger_score)
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push!(food_scores,food_score)
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end
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a.danger_scores = danger_scores
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a.food_scores = food_scores
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#findall(==(minimum(x)),x) to find all mins
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#best to filter out all positions where there is already an agent and take into account the current position, so sheeps dont just stand still when the position is occupied
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if !isempty(a.nearby_dangers) #&& a.energy > a.def.energy_threshold
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safest_position = positions[findmax(danger_scores)[2]]
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return safest_position
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elseif !isempty(a.nearby_food) #&& a.energy < a.def.energy_threshold
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foodiest_position = positions[findmin(food_scores)[2]]
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foodiest_position = positions[findmax(food_scores)[2]]
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return foodiest_position
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else
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return nothing
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@ -106,12 +122,27 @@ function reproduce!(a::Animal, model)
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end
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function Agents.agent2string(agent::Animal)
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food_scores = ""
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danger_scores = ""
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f(x) = string(round(x,digits=2))
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if !isempty(agent.food_scores)
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food_scores = "\n" * f(agent.food_scores[6]) * "|" * f(agent.food_scores[7]) * "|" * f(agent.food_scores[8]) * "\n" *
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f(agent.food_scores[4]) * "|" * " " * "|" * f(agent.food_scores[5]) * "\n" *
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f(agent.food_scores[1]) * "|" * f(agent.food_scores[2]) * "|" * f(agent.food_scores[3])
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end
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if !isempty(agent.danger_scores)
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danger_scores = "\n" * f(agent.danger_scores[6]) * "|" * f(agent.danger_scores[7]) * "|" * f(agent.danger_scores[8]) * "\n" *
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f(agent.danger_scores[4]) * "|" * " " * "|" * f(agent.danger_scores[5]) * "\n" *
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f(agent.danger_scores[1]) * "|" * f(agent.danger_scores[2]) * "|" * f(agent.danger_scores[3])
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end
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"""
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Type = $(agent.def.type)
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ID = $(agent.id)
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energy = $(agent.energy)
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perception = $(agent.def.perception)
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death = $(agent.death_cause)
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food_scores = $(food_scores)
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danger_scores = $(danger_scores)
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"""
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end
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@ -174,14 +205,14 @@ function initialize_model(;
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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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model_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_grass => Δenergy_grass,
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)
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properties = merge(properties,animal_properties)
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properties = merge(model_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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@ -189,7 +220,7 @@ function initialize_model(;
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for start_def in start_defs
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for _ in 1:start_def.n
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energy = rand(abmrng(model), 1:(start_def.def.Δenergy*2)) - 1
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add_agent!(Animal, model, energy, start_def.def, nothing, [], [])
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add_agent!(Animal, model, energy, start_def.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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@ -217,7 +248,7 @@ function animal_step!(a::Animal, model)
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#remove_agent!(a, model)
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#return
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end
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perceive!(a, model)
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#perceive!(a, 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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@ -238,8 +269,12 @@ end
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function grass_step!(model)
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ids = collect(allids(model))
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dead_animals = filter(id -> !isnothing(model[id].death_cause), ids)
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for a in dead_animals
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remove_agent!(a, model)
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for id in ids
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if !isnothing(model[id].death_cause)
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remove_agent!(id, model)
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else
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perceive!(model[id], model)
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end
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end
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@inbounds for p in positions(model) # we don't have to enable bound checking
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if !(model.fully_grown[p...])
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@ -261,25 +296,25 @@ function event_handler!(model)
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if event.name == "Drought"
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model.regrowth_time = event.value
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for id in ids
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model[id].def.perception += 1
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abmproperties(model)[Symbol(model[id].def.type*"_"*"perception")] += 1
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end
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elseif event.name == "Flood"
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model.regrowth_time = event.value
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for id in ids
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model[id].def.Δenergy -= 1
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abmproperties(model)[Symbol(model[id].def.type*"_"*"Δenergy")] -= 1
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end
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elseif event.name == "PreyReproduceSeasonal"
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prey = filter(id -> "Grass" ∈ model[id].def.food, ids)
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for id in prey
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model[id].def.reproduction_prob = event.value
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abmproperties(model)[Symbol(model[id].def.type*"_"*"reproduction_prob")] = event.value
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end
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elseif event.name == "PredatorReproduceSeasonal"
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predators = filter(id -> !("Grass" ∈ model[id].def.food), ids)
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for id in predators
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model[id].def.reproduction_prob = event.value
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abmproperties(model)[Symbol(model[id].def.type*"_"*"reproduction_prob")] = event.value
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end
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end
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@ -289,25 +324,25 @@ function event_handler!(model)
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if event.name == "Drought"
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model.regrowth_time = event.pre_value
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for id in ids
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model[id].def.perception -= 1
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abmproperties(model)[Symbol(model[id].def.type*"_"*"perception")] -= 1
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end
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elseif event.name == "Flood"
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model.regrowth_time = event.pre_value
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for id in ids
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model[id].def.Δenergy += 1
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abmproperties(model)[Symbol(model[id].def.type*"_"*"Δenergy")] += 1
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end
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elseif event.name == "PreyReproduceSeasonal"
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prey = filter(id -> "Grass" ∈ model[id].def.food, ids)
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for id in prey
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model[id].def.reproduction_prob = event.pre_value
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abmproperties(model)[Symbol(model[id].def.type*"_"*"reproduction_prob")] = event.pre_value
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end
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elseif event.name == "PredatorReproduceSeasonal"
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predators = filter(id -> !("Grass" ∈ model[id].def.food), ids)
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for id in predators
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model[id].def.reproduction_prob = event.pre_value
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abmproperties(model)[Symbol(model[id].def.type*"_"*"reproduction_prob")] = event.pre_value
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end
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end
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@ -9,28 +9,7 @@
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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 `~/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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"\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `~/Studium/SCJ-PredatorPrey/env`\n"
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]
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}
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],
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@ -81,7 +60,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -101,7 +80,11 @@
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"\n",
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"stable_params = (;\n",
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" events = events,\n",
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" start_defs = [StartDefinition(30,sheep_def),StartDefinition(3,wolf_def),StartDefinition(3,bear_def)],\n",
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" start_defs = [\n",
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" StartDefinition(30,sheep_def),\n",
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" StartDefinition(3,wolf_def),\n",
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" #StartDefinition(3,bear_def)\n",
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" ],\n",
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" dims = (30, 30),\n",
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" regrowth_time = 30,\n",
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" Δenergy_grass = 6,\n",
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@ -133,7 +116,7 @@
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"#fig, ax, abmobs = abmplot(model; add_controls=true, plotkwargs...)\n",
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"\n",
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"fig\n",
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"#run!(model, 100)"
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"#run!(model, 2)"
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]
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}
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],
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