main.jl

include("utils.jl")

### ABS material properties
α_ABS = 63e-6
E_ABS = 2.5e9    # 1.19e9 - 2.9e9
σ̄ = 6e7          # mega
Tcutoff = 100 # temperature above which strain isn't happening
σ̄_ABS = [
    [σ̄       σ̄*0.5   σ̄*0.375]
    [σ̄*0.5   σ̄       σ̄*0.375]
    [σ̄*0.375 σ̄*0.375 σ̄*0.75 ]
    ]
ABS = material(α_ABS, E_ABS, σ̄_ABS, Tcutoff)
td = tempdecay(215, 25, 0.04) # extrusion temp, room temp, decay rate
tdslow = tempdecay(215, 25, 0.01)
tdfast = tempdecay(215, 25, 0.08)
# visualise_tempdecay(tdfast)


add = "/Users/jayant/phd/tempaware/models/"
#stress_multiplier!(vd.voxels, 10)
#rl = random_rollout(cdata)


# single local search
#rl = random_rollout(cdata)
#cost_f(rl)
#@time local_search!(rl, max_iterations)


### MAIN LOOP
results = JSON.parse(open(add * "results.json"))
n_local_searches = 10
n_models = 10
max_iterations = 50
k = 150
for i in 1:n_models
    s = time()
    obj = "M" * string(i)
    # if obj in keys(results) || obj == "M4" || obj == "M9"
    if obj == "M4" || obj == "M9"
        continue # skip is already done
    end
    println("Starting " * obj)

    # Load data
    f_name = Symbol("cost_" * obj)
    try
        cost_func = getfield(Main, f_name)
    catch
        println("Loading data...")
        obj_add =  add * obj
        contours = clean_contour.(contour.(JSON.parse(open(obj_add * "contours.json"))))
        cdata = contourdata(contours, 40, 10) # contour data
        println(string(length(cdata.contours)) * " contours")
        vd = voxdata(obj_add * "_voxels.csv", cdata)
        println("Constructing Cost function...")
        construct_cost(cdata, vd, ABS, td, f_name)
        cost_func = getfield(Main, f_name)
        println(cost_func)
        construct_best_neighbor(cdata, cost_func, k)
    end
    cost_func = getfield(Main, f_name)

    # default rollout cost
    rl_d = Vector(1:length(cdata.contours))
    c = cost_func(rl_d)
    update_result(results, obj, rl_d, c, :default)

    # greedy rollout cost
    rl_g = greedy_rollout(cdata)
    c = cost_func(rl_g)
    update_result(results, obj, rl_g, c, :greedy)

    # local search
    # println("Doing local search " * string(n_local_searches) * " times")
    # opt_costs = zeros(n_local_searches)
    # Threads.@threads for i in 1:n_local_searches
    #     rl = random_rollout(cdata)
    #     random_cost = cost_f(rl)
    #     update_result(results, obj, rl, random_cost, :random)
    #     local_cost = local_search!(rl, max_iterations)
    #     update_result(results, obj, rl, local_cost, :local)
    # end
    println("Completed in " * string(time()-s) * " seconds")
end
save_result(results, add * "results.json")

### Load individual models for the plots
obj = "M1"
obj_add = add * obj
contours = clean_contour.(contour.(JSON.parse(open(obj_add * "contours.json"))))
cdata = contourdata(contours, 40, 10) # contour data
vd = voxdata(obj_add * "_voxels.csv", cdata)


### Visualisations
# distribution of stresses
data_stress = .√( vd.voxels.Sx.^2 + vd.voxels.Sy.^2 + vd.voxels.Sz.^2)
data_strain = .√(vd.voxels.Txy.^2 + vd.voxels.Tyz.^2 + vd.voxels.Txz.^2)
data = data_stress
histogram(data) # vast majority of voxels near 0 stress - can ignore
histogram!(sort(data, rev=true)[1:4000]) # vast majority of voxels near 0 stress - can ignore

# voxmap sanity check
plot(vd, 1500, cdata)

# rollout order 3d
rl_d = Vector(1:length(cdata.contours))
rl_r = random_rollout(cdata)
rl_g = greedy_rollout(cdata)
rl_l = Vector{Int}(results[obj]["best_rollout"])

plot(rl_d, cdata)
plot(rl_r, cdata)
plot(rl_g, cdata)
plot(rl_l, cdata)

# rollout order animated
@time plot_animate(rl_g, cdata, rate=5)

# plot results
plot(results)