import GLMakie as M import Metaheuristics as MH import DelimitedFiles: readdlm 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") best_5k = 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] @show idx_min_χ2 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), ' ')) @show best_params PLUV.reduce_to_free_parameters(meta, best_params) end mean_5k = 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) PLUV.reduce_to_free_parameters(meta, mean_params) end # best_5k = PLUV.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 = PLUV.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]) 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 = PLUV.reduce_to_free_parameters(meta, params_init, lower_bounds, upper_bounds, q) bounds = MH.boxconstraints(lb=lb_reduced, ub=ub_reduced) function obj(P) I_model = intensity_reduced(P) return Utils.chi2(I_data, I_model, err) end information = MH.Information(f_optimum=0.0) options = MH.Options(f_calls_limit=1_000_000, f_tol=1e-5); algorithm = MH.ECA(information=information, options=options) I_best_5k = intensity_reduced(best_5k) I_mean_5k = intensity_reduced(mean_5k) if false result = MH.optimize(obj, bounds, algorithm) 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) 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, 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