Pkg.add("JLD2")
using JLD2, Statistics, DataFrames

A = load("data.jld2", "A")
Size_A = length(A)
println("Size A: $(Size_A)")
plot(A[1:300])

function pn_gen(init_state::Vector{Int}, poly_exp::Vector{Int}, n::Int)
    N = length(init_state)
    reg = copy(init_state)
    taps = Int[]
    for e in poly_exp
        if e == 0
            push!(taps, N)
        elseif e != N
            push!(taps, N - e)
        end
    end
    outputs = Bool[]
    for _ in 1:n
        push!(outputs, reg[end] == 1)
        fb = 0
        for t in taps
            fb = xor(fb, reg[t])
        end
        reg = [fb; reg[1:end-1]]
    end
    return Int.(outputs)
end

function pn_gen_walsh(init_state::Vector{Int}, poly_exp::Vector{Int}, n::Int)
    raw = pn_gen(init_state, poly_exp, n)
    return [x == 0 ? 1 : -1 for x in raw]
end

function walsh_hadamard(n::Int, size::Int)
    H = [1 1 1 1; 1 -1 1 -1; 1 1 -1 -1; 1 -1 -1 1]
    for i in 1:2
        H = [H H; H -H]
    end
    code = H[n+1, :]
    return repeat(code, inner=div(size, 16))
end

pn_I = pn_gen_walsh([1; zeros(Int, 14)], [15, 13, 9, 8, 7, 5, 1], Size_A)
pn_Q = pn_gen_walsh([1; zeros(Int, 14)], [15, 12, 11, 10, 6, 5, 4, 3, 1], Size_A)
walsh_pilot = walsh_hadamard(0, Size_A)  # [1,1,1,1...]
walsh_user1 = walsh_hadamard(1, Size_A)  # [1,-1,1,-1...]

pilot_I = pn_I .* walsh_pilot
pilot_Q = pn_Q .* walsh_pilot
user1_I = pn_I .* walsh_user1
user1_Q = pn_Q .* walsh_user1

code_list = [A, pilot_I, pilot_Q, user1_I, user1_Q]
code_names = ["A (engee)", "Pilot I", "Pilot Q", "User1 I", "User1 Q"]

df_stats = DataFrame(
    代码=code_names,
    长度=[code_list中c的长度(c)],
    Average=[round(mean(c),digits=4)for c in code_list],
    Std = [round(std(c), digits=4) for c in code_list],
    Min=[code_list中c的最小值(c)],
    Max=[code_list中c的最大值(c)],
    Percent_1=[code_list中c的round(count(==(1),c)/length(c)*100,digits=2)],
    Percent_minis1=[code_list中c的round(count(==(-1),c)/length(c)*100,digits=2)]
)
println(df_stats)

corr_data = [
    ("Pilot I × User1 I", round(sum(pilot_I .* user1_I)/length(pilot_I), digits=4)),
    ("Pilot Q × User1 Q", round(sum(pilot_Q .* user1_Q)/length(pilot_Q), digits=4))
]

df_corr = DataFrame(
    Pair=[x[1]for x in corr_data],
    Corr_data中x的相关性=[x[2]]
)
println(df_corr)

function run_model( name_model)
    Path = (@__DIR__) * "/" * name_model * ".engee"
    if name_model in [m.name for m in engee.get_all_models()] 
        model = engee.open( name_model ) 
        model_output = engee.run( model, verbose=true ); 
    else
        model = engee.load( Path, force=true ) 
        model_output = engee.run( model, verbose=true );
        engee.close( name_model, force=true );
    end
    sleep(0.1)
    return model_output
end
run_model("CDMA_MAI")
BER = collect(simout["CDMA_MAI/Error Rate Calculation.1"]).value[end]
println("误码率(BER):△(BER[1])")
println("错误数:$(BER[2])")
println("总位数：$(BER[3])")