include("aux_func.jl")
using PlotlyJS: savefig
using Statistics
using PlotlyJS
using Random
using Printf

#=
	Obtain energy data
=#

#mni_folder_1 = "ini_exp_data/"


mni_folder_100 = "exp_100_epochs/";
cif_folder_100 = "CIFAR_100_epoch/";

mni_folder_acc = "data_bounded/";
cif_folder_acc = "CIFAR_acc_bound/";

mni_folder_wat = "data_budget/";
cif_folder_wat = "CIFAR_energy_bound/";

mni_folder_est = "early_stop_res/";
cif_folder_est = "CIFAR_early_stop/";

bayes_model = "bayes";
freq_model = "freq";
w_type = "watt";
e_type = "exp";

#=
    Load GPU data
=#
mni_100_bay_ene = getgpudata(mni_folder_100, bayes_model, w_type, "eo");
mni_100_fre_ene = getgpudata(mni_folder_100, freq_model, w_type, "eo");
cif_100_bay_ene = getgpudata(cif_folder_100, bayes_model, w_type, "eo");
cif_100_fre_ene = getgpudata(cif_folder_100, freq_model, w_type, "eo");



#=
	Define CPU paths
=#
cif = "cifar";
mni = "mnist";
bay = "bayes";
wat = "cpu_watts";
frq = "freq";
ram = "ram_use";
_100 = "100";
acc = "acc";
es = "es";
wbud = "wbud";

#=
    Load CPU data
=#
bay_cif_100_ene = readcpudata(cif, bay, wat, _100);
bay_mni_100_ene = readcpudata(mni, bay, wat, _100);
frq_cif_100_ene = readcpudata(cif, frq, wat, _100);
frq_mni_100_ene = readcpudata(mni, frq, wat, _100);


for s = 1:5
    bay_cif_100_ene[s] = round.(getcpuwatt(bay_cif_100_ene[s]))
    bay_mni_100_ene[s] = round.(getcpuwatt(bay_mni_100_ene[s]))
    frq_cif_100_ene[s] = round.(getcpuwatt(frq_cif_100_ene[s]))
    frq_mni_100_ene[s] = round.(getcpuwatt(frq_mni_100_ene[s]))
end

for s = 1:5
    mni_100_bay_ene[s]["Ene"] = vcat(mni_100_bay_ene[s]["Ene"], bay_mni_100_ene[s])
    mni_100_fre_ene[s]["Ene"] = vcat(mni_100_fre_ene[s]["Ene"], frq_mni_100_ene[s])
    cif_100_bay_ene[s]["Ene"] = vcat(cif_100_bay_ene[s]["Ene"], bay_cif_100_ene[s])
    cif_100_fre_ene[s]["Ene"] = vcat(cif_100_fre_ene[s]["Ene"], frq_cif_100_ene[s])
end

ene_data = Dict("mni" => Dict("bay" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
                              "fre" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])),
                "cif" => Dict("bay" => Dict(1 => [],2 => [],3 => [],4 => [],5 => []),
                              "fre" => Dict(1 => [],2 => [],3 => [],4 => [],5 => [])))

#ene_data = Dict("mni" => Dict("bay" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
#                              "fre" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
#                "cif" => Dict("bay" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
#                              "fre" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))

for s = 1:5
    ene_data["mni"]["bay"][s] = sum.(join_vectors(resize1(makechunks(shuffle(mni_100_bay_ene[s]["Ene"]), 100))[1:100]))
    ene_data["mni"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(mni_100_fre_ene[s]["Ene"]), 100))[1:100]))
    ene_data["cif"]["bay"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_bay_ene[s]["Ene"]), 100))[1:100]))
    ene_data["cif"]["fre"][s] = sum.(join_vectors(resize1(makechunks(shuffle(cif_100_fre_ene[s]["Ene"]), 100))[1:100]))
end

#for s = 1:5
#    ene_data["mni"]["bay"][s] = sum(mni_100_bay_ene[s]["Ene"]) / 100
#    ene_data["mni"]["fre"][s] = sum(mni_100_fre_ene[s]["Ene"]) / 100
#    ene_data["cif"]["bay"][s] = sum(cif_100_bay_ene[s]["Ene"]) / 100
#    ene_data["cif"]["fre"][s] = sum(cif_100_fre_ene[s]["Ene"]) / 100
#end


#=
  Load Accuracy data
=#

mni_100_bay_exp = getgpudata(mni_folder_100, bayes_model, e_type, "eo");
mni_100_fre_exp = getgpudata(mni_folder_100, freq_model, e_type, "eo");
cif_100_bay_exp = getgpudata(cif_folder_100, bayes_model, e_type, "eo");
cif_100_fre_exp = getgpudata(cif_folder_100, freq_model, e_type, "eo");


for i = 1:5
    mni_100_bay_exp[i] = expdatatodict(mni_100_bay_exp[i])
    mni_100_fre_exp[i] = expdatatodict(mni_100_fre_exp[i])
    cif_100_bay_exp[i] = expdatatodict(cif_100_bay_exp[i])
    cif_100_fre_exp[i] = expdatatodict(cif_100_fre_exp[i])
end


original_vectors_acc = Dict("LeNet" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
                                "CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
                     "BCNN" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
                                "CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))


for i in 1:5
    original_vectors_acc["LeNet"]["MNIST"][i] = mean(mni_100_fre_exp[i]["acc"][1:100])
    @printf "LeNet-5 on MNIST acc size %i Metric: %-.8f\n" i original_vectors_acc["LeNet"]["MNIST"][i]
    original_vectors_acc["BCNN"]["MNIST"][i] = mean(mni_100_bay_exp[i]["acc"][1:100])
    @printf "BCNN-5 on MNIST acc size %i Metric: %-.8f\n" i original_vectors_acc["BCNN"]["MNIST"][i]
    original_vectors_acc["LeNet"]["CIFAR"][i] = mean(cif_100_fre_exp[i]["acc"][1:100])
    @printf "LeNet-5 on CIFAR acc size %i Metric: %-.8f\n" i original_vectors_acc["LeNet"]["CIFAR"][i]
    original_vectors_acc["BCNN"]["CIFAR"][i] = mean(cif_100_bay_exp[i]["acc"][1:100])
    @printf "BCNN-5 on CIFAR acc size %i Metric: %-.8f\n" i original_vectors_acc["BCNN"]["CIFAR"][i]
end

println("\n\n\n")

original_vectors_pre = Dict("LeNet" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
                                "CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)),
                     "BCNN" => Dict("MNIST" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0),
                                "CIFAR" => Dict(1 => 0.0,2 => 0.0,3 => 0.0,4 => 0.0,5 => 0.0)))


for i in 1:5
    original_vectors_pre["LeNet"]["MNIST"][i] = mean(mni_100_fre_exp[i]["pre"][1:100])
    @printf "LeNet-5 on MNIST pre size %i Metric: %-.8f\n" i original_vectors_pre["LeNet"]["MNIST"][i]
    original_vectors_pre["BCNN"]["MNIST"][i] = mean(mni_100_bay_exp[i]["pre"][1:100])
    @printf "BCNN-5 on MNIST pre size %i Metric: %-.8f\n" i original_vectors_pre["BCNN"]["MNIST"][i]
    original_vectors_pre["LeNet"]["CIFAR"][i] = mean(cif_100_fre_exp[i]["pre"][1:100])
    @printf "LeNet-5 on CIFAR pre size %i Metric: %-.8f\n" i original_vectors_pre["LeNet"]["CIFAR"][i]
    original_vectors_pre["BCNN"]["CIFAR"][i] = mean(cif_100_bay_exp[i]["pre"][1:100])
    @printf "BCNN-5 on CIFAR pre size %i Metric: %-.8f\n" i original_vectors_pre["BCNN"]["CIFAR"][i]
end