#using HDF5
include("aux_func.jl")
#using Plots
#using StatsPlots
using Statistics
using LinearAlgebra
using PlotlyJS, Random, StatsBase
using PlotlyJS: savefig


folder = "ini_exp_data/"

#fcnn = h5open("fcnn_watt_data.h5", "r") do file
#  read(file,"fcnn")
#end

#bcnn = h5open("bcnn_watt_data.h5", "r") do file
#  read(file,"bcnn")
#end

fcnn = load_pickle("$(folder)fcnn_wattdata.pkl")
bcnn = load_pickle("$(folder)bcnn_wattdata.pkl")

bcnn_w = removewatt(bcnn[:, 1])
mem_bcnn = removewatt(bcnn[:, 3])
#bcnn_ch = bcnn_w .> 28
#bcnn_start = findall(x -> x==1, bcnn_ch)[1]
#bcnn_end = findall(x -> x==1, bcnn_ch)[end]
#bcnn_w_tot = bcnn_w[bcnn_start:bcnn_end]
#bcnn_tot = sum(bcnn_w_tot)
#mean_bcnn_w = mean(bcnn_w_tot)
#bcnn_s = size(bcnn_w_tot)[1]
#mean_bcnn_w_v = mean_bcnn_w .* ones(bcnn_s)
#x_bcnn = collect(1:bcnn_s)

fcnn_w = removewatt(fcnn[:, 1])
mem_fcnn = removewatt(fcnn[:, 3])
#val_ch = fcnn_w .> 28
#fcnn_start = findall(x -> x==1, val_ch)[1]
#fcnn_end = findall(x -> x==1, val_ch)[end]
#fcnn_w_tot = fcnn_w[fcnn_start:fcnn_end]
#fcnn_tot = sum(fcnn_w_tot)
#mean_fcnn_w = mean(fcnn_w_tot)
#fcnn_s = size(fcnn_w_tot)[1]
#mean_fcnn_w_v = mean_fcnn_w .* ones(fcnn_s)
#x_fcnn = collect(1:fcnn_s)

mem_bcnn = parse.(Int64, replace.(bcnn[:, 3], r"[^0-9]+" => ""))
mem_fcnn = parse.(Int64, replace.(fcnn[:, 3], r"[^0-9]+" => ""))

#en_plot = boxplot(bcnn_w_tot,x_bcnn,title="Energy per sample",xlabel="Watts",ylabel="No of samples",label="BCNN",seriesalpha=0.5)
#boxplot!(fcnn_w_tot,x_fcnn,label="CNN",seriesalpha=0.5,color="green")
#savefig(en_plot,"energy.png")

#en_plot = histogram(bcnn_w,xlabel="Watts",ylabel="No of samples",label="BCNN",color="blue",normalize=:pdf,bar_position = :overlay)
#histogram!(fcnn_w,label="LeNet",seriesalpha=0.9,color="#ff9900",normalize=:pdf,bar_position=:overlay)
#savefig(en_plot,"energy.png")

#bcnn_w = incwzeros(bcnn_w,length(fcnn_w)-length(bcnn_w))

#bcnn_w = reshape(bcnn_w,:,1)
#fcnn_w = reshape(fcnn_w,:,1)
#w_mat = hcat(bcnn_w,fcnn_w)

#groupedbar(rand(length(bcnn_w),2),bar_width=0.5,bar_position = :dodge)

#w_mat = reshape(w_mat,2,:)


#mem_plot = plot(mem_fcnn,title="Memory usage, one epoch in MiB's",label="CNN Memory",lw=2,seriesalpha=0.5)
#plot!(mem_bcnn,label="BCNN Memory",lw=2,seriesalpha=0.5,fmt=:png)
#savefig(mem_plot,"memory.png")

#mem_plot = histogram(mem_bcnn,xlabel="MiB",ylabel="No of samples",label="BCNN",color="blue")
#histogram!(mem_fcnn,label="LeNet",seriesalpha=0.9,color="#ff9900")
#savefig(mem_plot,"memory.png")

#plot(bcnn_w_tot,title="BCNN energy in one epoch",label="Watt",lw=2);plot!(mean_bcnn_w_v,label="Mean")
#plot(fcnn_w_tot,title="CNN energy in one epoch",label="Watt",lw=2);plot!(mean_fcnn_w_v,label="Mean")
#scatter(bcnn[1,:],title="Energy usage in W",label="Bayesian CNN",lw=2);scatter!(fcnn[1,1:463],label="CNN",lw=2)


bcnn_d = countmap(bcnn_w)
bcnn_u = unique(bcnn_w)
bcnn_v = Vector()
for u in bcnn_u
    push!(bcnn_v, bcnn_d[u])

end

m_bcnn_d = countmap(mem_bcnn)
m_bcnn_u = unique(mem_bcnn)
m_bcnn_v = Vector()
for u in m_bcnn_u
    push!(m_bcnn_v, m_bcnn_d[u])

end


fcnn_d = countmap(fcnn_w)
fcnn_u = unique(fcnn_w)
fcnn_v = Vector()
for u in fcnn_u
    push!(fcnn_v, fcnn_d[u])

end

m_fcnn_d = countmap(mem_fcnn)
m_fcnn_u = unique(mem_fcnn)
m_fcnn_v = Vector()
for u in fcnn_u
    push!(m_fcnn_v, fcnn_d[u])

end


bcnn_v = bcnn_v / norm(bcnn_v)
fcnn_v = fcnn_v / norm(fcnn_v)

m_bcnn_v = m_bcnn_v / norm(m_bcnn_v)
m_fcnn_v = m_fcnn_v / norm(m_fcnn_v)

en_plot = plot(
    [
        bar(x = bcnn_u, y = bcnn_v, name = "BCNN", marker_color = "blue"),
        bar(x = fcnn_u, y = fcnn_v, name = "LeNet", marker_color = "#ff9900"),
    ],
    Layout(
        barmode = "group",
        xaxis_tickangle = -45,
        yaxis_title_text = "Samples",
        xaxis_title_text = "Watts",
        title = "Energy distribution experiment 1",
    ),
)
savefig(en_plot, "energy.png")

mem_plot = plot(
    [
        bar(x = m_bcnn_u, y = m_bcnn_v, name = "BCNN", marker_color = "blue"),
        bar(x = m_fcnn_u, y = m_fcnn_v, name = "LeNet", marker_color = "#ff9900"),
    ],
    Layout(
        barmode = "group",
        xaxis_tickangle = -45,
        yaxis_title_text = "Samples",
        xaxis_title_text = "MiB",
        title = "MRW distribution experiment 1",
    ),
)
savefig(mem_plot, "memory.png")