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

Λ = 0
m = 1.0
V_of_r(r) =  2 * exp(-(r-3)^2 / (1.5)^2)

vertices = [0, 2 - 0.2im, 3, 4]
subdivisions = [15, 10, 10]
jmax = 4

E_max = 40
μω_global = 0.5

H0, weights = get_3b_H_matrix(jacobi, V_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m)

# Vp = perturbation to make the state artificially bound
Vp_of_r(r) = -exp(-(r/3)^2)
Vp, _ = get_3b_H_matrix(jacobi, Vp_of_r, vertices, subdivisions, jmax, μω_global, E_max, Λ, m, false, true)

training_c = [2.6, 2.4, 2.2, 2.0, 1.8]
extrapolating_c = 0.0 : 0.2 : 1.2

training_ref = -2.22 # complete list not needed because identification is simple

extrapolating_ref = [4.076662025307587-0.012709842443350328im,
    3.613318119833891-0.007335804709990623im,
    3.1453431847006783-0.004030580410326795im,
    2.672967129943755-0.00211498327461944im,
    2.196542557810288-0.0010719835443437104im,
    1.7164583929199813-0.0005455212208182736im,
    1.233088227541505-0.0003070320106485624im]

EC = affine_EC(H0, Vp, weights)
train!(EC, training_c; ref_eval=training_ref, CAEC=true, orthonormalize_threshold=0, pseudo_inv_rtol=1e-6)
extrapolate!(EC, extrapolating_c; ref_eval=extrapolating_ref)

exportCSV(EC, "temp/Berggren_B2R.csv")
plot(EC, "temp/Berggren_B2R.pdf")