using Plots

training_ref = -0.72763
exact_ref = 4.0766890719636635 - 0.01275892774109674im

training_c = [2.0, 1.9, 1.8]
extrapolating_c = 0.0 : 0.2 : 1.2

include("../ho_basis_3body_resonance.jl")
H0 = H

# Vp = perturbation to make the state artificially bound
Vp_of_r(r) = -exp(-(r/3)^2)
@time "Vp" Vp =  get_src_V_matrix(Vp_of_r, basis, μω, μω_global)

exact = ComplexF64[]
training = ComplexF64[]
extrapolated = ComplexF64[]
training_vecs = Vector{ComplexF64}[]

current_E = training_ref

for c in training_c
    println("Training for c = $c")
    local H = H0 + c .* Vp
    local evals, evecs = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=true, check=1)

    global current_E = nearest(evals, current_E)
    push!(training, current_E)
    push!(training_vecs, evecs[:, nearestIndex(evals, current_E)])
end

# CA-EC
training_vecs = vcat(training_vecs, conj(training_vecs))

println("Original EC dimensionality = $(length(training_vecs))")
@time "Gram-Schmidt" training_vecs = gram_schmidt!(training_vecs; verbose=true) # orthonormalization

EC_basis = hcat(training_vecs...)
H0_EC = transpose(EC_basis) * H0 * EC_basis
Vp_EC = transpose(EC_basis) * Vp * EC_basis

current_E = exact_ref

for c in extrapolating_c
    println("Extrapolating for c = $c")
    local H = H0 + c .* Vp
    local evals, _ = eigs(H, nev=3, ncv=24, which=:LI, maxiter=5000, tol=1e-5, ritzvec=false, check=1)

    global current_E = nearest(evals, current_E)
    push!(exact, current_E)

    # extrapolation
    H_EC = H0_EC + c .* Vp_EC
    evals = eigvals(H_EC)
    push!(extrapolated, nearest(evals, current_E))
end

exportCSV("temp/HO_B2R.csv", (training, exact, extrapolated), ("training", "exact", "extrapolated"))

scatter(real.(training),imag.(training), label="training")
scatter!(real.(exact),imag.(exact), label="exact")
scatter!(real.(extrapolated),imag.(extrapolated), label="extrapolated")
savefig("temp/HO_B2R.pdf")