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

predator_prey.jl

@@ -46,17 +46,63 @@
 # 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.)
 using Agents, Random
-
+using CairoMakie
 @agent struct Sheep(GridAgent{2})
     energy::Float64
     reproduction_prob::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
 
 @agent struct Wolf(GridAgent{2})
     energy::Float64
     reproduction_prob::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
 
 # 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!`,
 # is dispatched to the appropriate agent type via Julia's Multiple Dispatch system.
 function sheepwolf_step!(sheep::Sheep, model)
-    randomwalk!(sheep, model)
-    sheep.energy -= 1
+    move!(sheep, model)
     if sheep.energy < 0
         remove_agent!(sheep, model)
         return
     end
     eat!(sheep, model)
-    if rand(abmrng(model)) ≤ sheep.reproduction_prob
-        sheep.energy /= 2
-        replicate!(sheep, model)
-    end
+    reproduce!(sheep, model)
 end
 
 function sheepwolf_step!(wolf::Wolf, model)
-    randomwalk!(wolf, model; ifempty=false)
-    wolf.energy -= 1
+    move!(wolf, model)
     if wolf.energy < 0
         remove_agent!(wolf, model)
         return
     end
-    ## If there is any sheep on this grid cell, it's dinner time!
-    dinner = first_sheep_in_position(wolf.pos, 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
+    eat!(wolf, model)
+    reproduce!(wolf, model)
 end
 
 # The behavior of grass function differently. If it is fully grown, it is consumable.
@@ -187,6 +200,7 @@ function grass_step!(model)
     end
 end
 
+function run()
     sheepwolfgrass = initialize_model()
 
     # ## Running the model
@@ -194,7 +208,6 @@ sheepwolfgrass = initialize_model()
     # 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.
 
-using CairoMakie
     CairoMakie.activate!() # hide
 
     # To view our starting population, we can build an overview plot using [`abmplot`](@ref).
@@ -292,3 +305,5 @@ abmvideo(
     # <source src="../sheepwolf.mp4" type="video/mp4">
     # </video>
     # ```
+end
+run()