tsa_saxs/test.jl

import GLMakie as M
import GaussNewton as GN
import Metaheuristics as MH
import DelimitedFiles: readdlm
import LinearAlgebra: norm

include("PLUV.jl")
include("Utils.jl")

# filtered_idx = d[:, 2] .> 0.0
# @views q, I, err = d[filtered_idx, 1], d[filtered_idx, 2], d[filtered_idx, 3]

meta, params_init, lower_bounds, upper_bounds, qie_data = Utils.load_config("Data/PAR_POPC-test.toml")
param_names = keys(params_init)

best_5k_full = begin
  f_χ2 = open("POPC-test-5k/Results_collection-X2.dat", "r")

  d_χ2 = readdlm(f_χ2)
  close(f_χ2)

  idx_min_χ2 = argmin(d_χ2)[1]

  f_params = open("POPC-test-5k/Results_collection.dat", "r")
  for i in 1:(idx_min_χ2-1)
    readline(f_params)
  end
  best_params = map(x -> parse(Float64, x), split(readline(f_params), ' '))
end

mean_5k_full = begin
  f_params = open("POPC-test-5k/Results_collection.dat", "r")
  params_pop = readdlm(f_params)
  N_trials, _ = size(params_pop)

  mean_params = vec(sum(params_pop; dims=1) / N_trials)
end

# best_5k = Utils.reduce_to_free_parameters(meta, [100000.0000000, 7.5004085, 23.5074449, 9.8664991, 0.0000000, 1.0000000, 0.2900000, 0.2203793,
#   0.2990402, 0.4122816, 0.3266636, 0.2276763, 0.2481895, 0.6642123, 0.1203572, 0.2629861, 0.9000000, 0.3050000,
#   0.2945315, 0.2762371, 0.4777401, 0.8100000, 2.0149998, 37.0000000, 0.0299139, 0.0002171])
#
# best_5k = Utils.reduce_to_free_parameters(meta, [100000.0000000, 7.6975592, 23.4041912, 9.7275630, 0.0000000, 1.0000000, 0.2900000, 0.2219937, 0.3000114,
#   0.4158804, 0.3278631, 0.2296156, 0.2475607, 0.6664143, 0.1191859, 0.2618609, 0.9000000, 0.3050000, 0.2963982, 0.2770345, 0.4762528,
#   0.8100000, 1.9706651, 37.0000000, 0.0299179, 0.0002167])

best_5k_params = Utils.reduce_to_free_parameters(meta, best_params)

q, I_data, err = Utils.select_columns(qie_data, meta["q_min"], meta["q_max"], meta["binning"], meta["binning"])
q_all, I_all, err_all = Utils.select_columns(qie_data, meta["q_min"], meta["q_max"], 1, meta["binning"])

I_data_lp = Utils.lowpass_filter(I_data; σ=2)
I_all_lp = Utils.lowpass_filter(I_all; σ=2)

w = Utils.compute_logscale_weights(q)
w_all = Utils.compute_logscale_weights(q_all)

# I_init = PLUV.intensity(params_init, q)

intensity_reduced, P_reduced, lb_reduced, ub_reduced = Utils.reduce_to_free_parameters(meta, PLUV.intensity, params_init, lower_bounds, upper_bounds, q)

simple_bounds = collect(zip(lb_reduced, ub_reduced))

simple_init = 0.5 * (lb_reduced .+ ub_reduced)
padding_factors = fill(1e-1, length(simple_init))
scaling_factors = fill(0.0, length(simple_init))

#padding_factors[4] = 1e0
#padding_factors[7] = 1e0
#padding_factors[17] = 1e0
#padding_factors[18] = 1e0

# scaling_factors[4] = 1e1
# scaling_factors[6] = 1e1
#scaling_factors[7] = 1e3

bounds = MH.boxconstraints(lb=lb_reduced, ub=ub_reduced)

function obj_χ2(P)
  I_model, neg_H20 = intensity_reduced(P)
  χ2 = Utils.chi2(I_data, I_model, err)
  factor = (neg_H20 == 0 ? 1.0 : 5.0 * (neg_H20 + 1))
  return factor * χ2
end

function obj_residuals(P)
  print(".")
  I_model, neg_H20 = intensity_reduced(P)
  residuals = Utils.residuals(I_data, I_model, err)
  factor = (neg_H20 == 0 ? 1.0 : 5.0 * (neg_H20 + 1))
  res = factor * residuals
  return res
end

barriered_obj = Utils.add_log_barriers(obj_residuals, simple_bounds; padding_factors=padding_factors, mode=:inner)

information = MH.Information(f_optimum=0.0)
information = MH.Information()
options = MH.Options(f_calls_limit=10_000, f_tol=1e-5);
algorithm = MH.ECA(information=information, options=options)
#algorithm = MH.PSO()

I_best_5k, _ = PLUV.intensity(best_5k_full, q)
I_best_5k_reduced, _ = intensity_reduced(best_5k_params)

I_mean_5k, _ = PLUV.intensity(mean_5k_full, q_all)

# Gauss-Newton
if true
  initial_guess = Utils.reduce_to_free_parameters(meta, collect(Float64, values(params_init)))
  initial_guess = simple_init
  # initial_guess = best_5k_params

  _, result = GN.optimize(barriered_obj, initial_guess; show_trace=true, iscale=1, ZCP=1e-2, ZCPMIN=1e-2)
  # _, result = GN.optimize(barriered_obj, initial_guess)

  if GN.has_converged(result)
    @info "Gauss-Newton converged"
    @show result
    P_best = result.minimizer
    I_best, _ = intensity_reduced(P_best)

    @info "Simulated Annealing 5k"
    Utils.print_check_bounds(param_names, best_5k_params, lb_reduced, ub_reduced)

    @info "Our result"
    Utils.print_check_bounds(param_names, P_best, lb_reduced, ub_reduced)
  else
    @error "Gauss-Newton did not converge"
  end
end

# Metaheuristics
if false
  result = MH.optimize(obj, bounds, algorithm)
  @show MH.minimum(result)

  P_best = MH.minimizer(result)
  I_best, _ = intensity_reduced(P_best)
end

if true
  fig = M.Figure()
  ax = M.Axis(fig[1, 1]; xscale=log10, yscale=log10)
  #ax = M.Axis(fig[1, 1])

  I_initial, _ = intensity_reduced(initial_guess)

  M.lines!(ax, q, I_initial, label="initial", linestyle=:dash, linewidth=2)
  M.lines!(ax, q, I_best, label="MH best (julia)")
  M.scatter!(ax, q, I_data, label="data")
  M.lines!(ax, q, I_best_5k, label="TSA best (5k)")
  M.lines!(ax, q_all, I_mean_5k, label="TSA mean (5k)")

  M.axislegend()

  display(fig)
end


if false
  fig = M.Figure()
  ax = M.Axis(fig[1, 1]; xscale=log10, yscale=log10)

  M.scatter!(ax, q, I)
  M.lines!(ax, q_all, I_all)
  M.lines!(ax, q_all, I_all_lp)
  #M.lines!(ax, q, I_init)

  display(fig)
end

if false
  fig = M.Figure()
  ax = M.Axis(fig[1, 1]; xscale=log10)

  #M.scatter!(ax, q, w)
  M.scatter!(ax, q_all, w_all)

  display(fig)
end