Hi all,
why my video / animation of Rabbit Fox Hawk only plot 1 red and then become 1 blue till the end of step?
from:
https://juliadynamics.github.io/Agents.jl/stable/examples/rabbit_fox_hawk/
(it is a very great package anyway.)
I am using Julia REPL with this code:
using Agents, Agents.Pathfinding
using Random
import ImageMagick
using FileIO: load
@agent Animal ContinuousAgent{3} begin
type::Symbol # one of :rabbit, :fox or :hawk
energy::Float64
end
const v0 = (0.0, 0.0, 0.0) # we don't use the velocity field here
Rabbit(id, pos, energy) = Animal(id, pos, v0, :rabbit, energy)
Fox(id, pos, energy) = Animal(id, pos, v0, :fox, energy)
Hawk(id, pos, energy) = Animal(id, pos, v0, :hawk, energy)
eunorm(vec) = √sum(vec .^ 2)
# The environment is generated from a heightmap:
# a 2D matrix, where each value denotes the height of the terrain at that point.
function initialize_model(
heightmap_url =
"https://raw.githubusercontent.com/JuliaDynamics/" *
"JuliaDynamics/master/videos/agents/rabbit_fox_hawk_heightmap.png",
water_level = 8,
grass_level = 20,
mountain_level = 35;
n_rabbits = 160, ## initial number of rabbits
n_foxes = 30, ## initial number of foxes
n_hawks = 30, ## initial number of hawks
Δe_grass = 25, ## energy gained from eating grass
Δe_rabbit = 30, ## energy gained from eating one rabbit
rabbit_repr = 0.06, ## probability for a rabbit to (asexually) reproduce at any step
fox_repr = 0.03, ## probability for a fox to (asexually) reproduce at any step
hawk_repr = 0.02, ## probability for a hawk to (asexually) reproduce at any step
rabbit_vision = 6, ## how far rabbits can see grass and spot predators
fox_vision = 10, ## how far foxes can see rabbits to hunt
hawk_vision = 15, ## how far hawks can see rabbits to hunt
rabbit_speed = 1.3, ## movement speed of rabbits
fox_speed = 1.1, ## movement speed of foxes
hawk_speed = 1.2, ## movement speed of hawks
regrowth_chance = 0.03, ## probability that a patch of grass regrows at any step
dt = 0.1, ## discrete timestep each iteration of the model
seed = 42, ## seed for random number generator
)
# Download and load the heightmap. The grayscale value is converted to `Float64` and
# scaled from 1 to 40
heightmap = floor.(Int, convert.(Float64, load(download(heightmap_url))) * 39) .+ 1
# The x and y dimensions of the pathfinder are that of the heightmap
dims = (size(heightmap)..., 50)
# The region of the map that is accessible to each type of animal (land-based or flying)
# is defined using `BitArrays`
land_walkmap = BitArray(falses(dims...))
air_walkmap = BitArray(falses(dims...))
for i in 1:dims[1], j in 1:dims[2]
# land animals can only walk on top of the terrain between water_level and grass_level
if water_level < heightmap[i, j] < grass_level
land_walkmap[i, j, heightmap[i, j]+1] = true
end
# air animals can fly at any height upto mountain_level
if heightmap[i, j] < mountain_level
air_walkmap[i, j, (heightmap[i, j]+1):mountain_level] .= true
end
end
# Generate the RNG for the model
rng = MersenneTwister(seed)
# Note that the dimensions of the space do not have to correspond to the dimensions
# of the pathfinder. Discretisation is handled by the pathfinding methods
space = ContinuousSpace((100., 100., 50.); periodic = false)
# Generate an array of random numbers, and threshold it by the probability of grass growing
# at that location. Although this causes grass to grow below `water_level`, it is
# effectively ignored by `land_walkmap`
grass = BitArray(
rand(rng, dims[1:2]...) .< ((grass_level .- heightmap) ./ (grass_level - water_level)),
)
properties = (
# The pathfinder for rabbits and foxes
landfinder = AStar(space; walkmap = land_walkmap),
# The pathfinder for hawks
airfinder = AStar(space; walkmap = air_walkmap, cost_metric = MaxDistance{3}()),
Δe_grass = Δe_grass,
Δe_rabbit = Δe_rabbit,
rabbit_repr = rabbit_repr,
fox_repr = fox_repr,
hawk_repr = hawk_repr,
rabbit_vision = rabbit_vision,
fox_vision = fox_vision,
hawk_vision = hawk_vision,
rabbit_speed = rabbit_speed,
fox_speed = fox_speed,
hawk_speed = hawk_speed,
heightmap = heightmap,
grass = grass,
regrowth_chance = regrowth_chance,
water_level = water_level,
grass_level = grass_level,
dt = dt,
)
model = ABM(Animal, space; rng, properties)
# spawn each animal at a random walkable position according to its pathfinder
for _ in 1:n_rabbits
add_agent_pos!(
Rabbit(
nextid(model), ## Using `nextid` prevents us from having to manually keep track
# of animal IDs
random_walkable(model, model.landfinder),
rand(model.rng, Δe_grass:2Δe_grass),
),
model,
)
end
for _ in 1:n_foxes
add_agent_pos!(
Fox(
nextid(model),
random_walkable(model, model.landfinder),
rand(model.rng, Δe_rabbit:2Δe_rabbit),
),
model,
)
end
for _ in 1:n_hawks
add_agent_pos!(
Hawk(
nextid(model),
random_walkable(model, model.airfinder),
rand(model.rng, Δe_rabbit:2Δe_rabbit),
),
model,
)
end
return model
end
model = initialize_model()
# Stepping functions
function animal_step!(animal, model)
if animal.type == :rabbit
rabbit_step!(animal, model)
elseif animal.type == :fox
fox_step!(animal, model)
else
hawk_step!(animal, model)
end
end
function rabbit_step!(rabbit, model)
# Eat grass at this position, if any
if get_spatial_property(rabbit.pos, model.grass, model) == 1
model.grass[get_spatial_index(rabbit.pos, model.grass, model)] = 0
rabbit.energy += model.Δe_grass
end
# The energy cost at each step corresponds to the amount of time that has passed
# since the last step
rabbit.energy -= model.dt
# All animals die if their energy reaches 0
if rabbit.energy <= 0
kill_agent!(rabbit, model, model.landfinder)
return
end
# Get a list of positions of all nearby predators
predators = [
x.pos for x in nearby_agents(rabbit, model, model.rabbit_vision) if
x.type == :fox || x.type == :hawk
]
# If the rabbit sees a predator and isn't already moving somewhere
if !isempty(predators) && is_stationary(rabbit, model.landfinder)
# Try and get an ideal direction away from predators
direction = (0., 0., 0.)
for predator in predators
# Get the direction away from the predator
away_direction = (rabbit.pos .- predator)
# In case there is already a predator at our location, moving anywhere is
# moving away from it, so it doesn't contribute to `direction`
all(away_direction .≈ 0.) && continue
# Add this to the overall direction, scaling inversely with distance.
# As a result, closer predators contribute more to the direction to move in
direction = direction .+ away_direction ./ eunorm(away_direction) ^ 2
end
# If the only predator is right on top of the rabbit
if all(direction .≈ 0.)
# Move anywhere
chosen_position = random_walkable(rabbit.pos, model, model.landfinder, model.rabbit_vision)
else
# Normalize the resultant direction, and get the ideal position to move it
direction = direction ./ eunorm(direction)
# Move to a random position in the general direction of away from predators
position = rabbit.pos .+ direction .* (model.rabbit_vision / 2.)
chosen_position = random_walkable(position, model, model.landfinder, model.rabbit_vision / 2.)
end
plan_route!(rabbit, chosen_position, model.landfinder)
end
# Reproduce with a random probability, scaling according to the time passed each
# step
rand(model.rng) <= model.rabbit_repr * model.dt && reproduce!(rabbit, model)
# If the rabbit isn't already moving somewhere, move to a random spot
if is_stationary(rabbit, model.landfinder)
plan_route!(
rabbit,
random_walkable(rabbit.pos, model, model.landfinder, model.rabbit_vision),
model.landfinder
)
end
# Move along the route planned above
move_along_route!(rabbit, model, model.landfinder, model.rabbit_speed, model.dt)
end
function fox_step!(fox, model)
# Look for nearby rabbits that can be eaten
food = [x for x in nearby_agents(fox, model) if x.type == :rabbit]
if !isempty(food)
kill_agent!(rand(model.rng, food), model, model.landfinder)
fox.energy += model.Δe_rabbit
end
# The energy cost at each step corresponds to the amount of time that has passed
# since the last step
fox.energy -= model.dt
# All animals die once their energy reaches 0
if fox.energy <= 0
kill_agent!(fox, model, model.landfinder)
return
end
# Random chance to reproduce every step
rand(model.rng) <= model.fox_repr * model.dt && reproduce!(fox, model)
# If the fox isn't already moving somewhere
if is_stationary(fox, model.landfinder)
# Look for any nearby rabbits
prey = [x for x in nearby_agents(fox, model, model.fox_vision) if x.type == :rabbit]
if isempty(prey)
# Move anywhere if no rabbits were found
plan_route!(
fox,
random_walkable(fox.pos, model, model.landfinder, model.fox_vision),
model.landfinder,
)
else
# Move toward a random rabbit
plan_route!(fox, rand(model.rng, map(x -> x.pos, prey)), model.landfinder)
end
end
move_along_route!(fox, model, model.landfinder, model.fox_speed, model.dt)
end
function hawk_step!(hawk, model)
# Look for rabbits nearby
food = [x for x in nearby_agents(hawk, model) if x.type == :rabbit]
if !isempty(food)
# Eat (kill) the rabbit
kill_agent!(rand(model.rng, food), model, model.airfinder)
hawk.energy += model.Δe_rabbit
# Fly back up
plan_route!(hawk, hawk.pos .+ (0., 0., 7.), model.airfinder)
end
# The rest of the stepping function is similar to that of foxes, except hawks use a
# different pathfinder
hawk.energy -= model.dt
if hawk.energy <= 0
kill_agent!(hawk, model, model.airfinder)
return
end
rand(model.rng) <= model.hawk_repr * model.dt && reproduce!(hawk, model)
if is_stationary(hawk, model.airfinder)
prey = [x for x in nearby_agents(hawk, model, model.hawk_vision) if x.type == :rabbit]
if isempty(prey)
plan_route!(
hawk,
random_walkable(hawk.pos, model, model.airfinder, model.hawk_vision),
model.airfinder,
)
else
plan_route!(hawk, rand(model.rng, map(x -> x.pos, prey)), model.airfinder)
end
end
move_along_route!(hawk, model, model.airfinder, model.hawk_speed, model.dt)
end
function reproduce!(animal, model)
animal.energy = Float64(ceil(Int, animal.energy / 2))
add_agent_pos!(Animal(nextid(model), animal.pos, v0, animal.type, animal.energy), model)
end
function model_step!(model)
# To prevent copying of data, obtain a view of the part of the grass matrix that
# doesn't have any grass, and grass can grow there
growable = view(
model.grass,
model.grass .== 0 .& model.water_level .< model.heightmap .<= model.grass_level,
)
# Grass regrows with a random probability, scaling with the amount of time passing
# each step of the model
growable .= rand(model.rng, length(growable)) .< model.regrowth_chance * model.dt
end
# Visualization
using InteractiveDynamics
using GLMakie # CairoMakie doesn't do 3D plots
animalcolor(a) =
if a.type == :rabbit
:brown
elseif a.type == :fox
:orange
else
:blue
end
function static_preplot!(ax, model)
surface!(
ax,
(100/205):(100/205):100,
(100/205):(100/205):100,
model.heightmap;
colormap = :terrain
)
end
abmvideo(
"rabbit_fox_hawk.mp4",
model, animal_step!, model_step!;
figure = (resolution = (800, 700),),
frames = 300,
framerate = 15,
ac = animalcolor,
as = 1.0,
static_preplot!,
title = "Rabbit Fox Hawk with pathfinding"
)
Is it wrong to use REPL?
