include("../ho_basis.jl")
include("../EC.jl")

Λ = 0
m = 1.0

# Distinguishable particles: V12 = bound and V13 & V23 = resonant
Va_of_r(r) = -2 * exp(-r^2/4)
Vb_of_r(r) = -exp(-r^2 / 3) + exp(-r^2 / 10)

E_max = 40
μω_global = 0.4 * exp(-2im * pi / 9)

# due to Jacobi coordinates
μ1ω1 = μω_global * 1/2
μ2ω2 = μω_global * 2
μ1 = m * 1/2
μ2 = m * 2/3

println("No of threads = ", Threads.nthreads())

basis = ho_basis_2B(E_max, Λ)
println("Basis size = ", basis.dim)

@time "T1" T1 = get_sp_T_matrix(basis.n1s, basis.l1s, [basis.n2s, basis.l2s]; μω_gen=μ1ω1, μ=μ1)
@time "T2" T2 = get_sp_T_matrix(basis.n2s, basis.l2s, [basis.n1s, basis.l1s]; μω_gen=μ2ω2, μ=μ2)

@time "Va" Va = get_jacobi_V1_matrix(Va_of_r, basis, μ1ω1)
@time "Vb" Vb = get_jacobi_V2_matrix(Vb_of_r, basis, μω_global)

@time "Ha" Ha = T1 + T2 + Va
@time "Eigenvalues" target_evals, _ = eigs(Ha, nev=5, ncv=50, which=:SR, maxiter=5000, tol=1e-5, ritzvec=false, check=1)
display(target_evals)

training_c = [-0.5, -0.65, -0.8, -1, -1.2]
extrapolating_c = [0.8, 0.6, 0.4, 0.2, 0.1, 0.0, -0.1, -0.2, -0.3]

ref_E = -0.5173809356244544

EC = affine_EC(Ha, Vb)
train!(EC, training_c; ref_eval=ref_E, CAEC=true) # try CAEC=false !!!
extrapolate!(EC, extrapolating_c)

exportCSV(EC, "temp/dis_HO_B2R.csv")
plot(EC, "temp/dis_HO_B2R.pdf")