diff --git a/test.jl b/test.jl
index 3604034..69310bd 100644
--- a/test.jl
+++ b/test.jl
@@ -36,15 +36,15 @@ mean_5k_full = begin
   mean_params = vec(sum(params_pop; dims=1) / N_trials)
 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,
+# 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 = PLUV.reduce_to_free_parameters(meta, [100000.0000000, 7.6975592, 23.4041912, 9.7275630, 0.0000000, 1.0000000, 0.2900000, 0.2219937, 0.3000114,
+# 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 = PLUV.reduce_to_free_parameters(meta, best_params)
+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"])
@@ -57,12 +57,22 @@ 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)
+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_factor = fill(1e-1, length(simple_init))
+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)
 
@@ -74,13 +84,15 @@ function obj_χ2(P)
 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))
-  return factor * residuals
+  res = factor * residuals
+  return res
 end
 
-barriered_obj = Utils.add_log_barriers(obj_residuals, simple_bounds; padding_factor=padding_factor, mode=:outer)
+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()
@@ -89,16 +101,18 @@ 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 = PLUV.reduce_to_free_parameters(meta, collect(Float64, values(params_init)))
-  initial_guess = best_5k_params
+  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)
+  # _, result = GN.optimize(barriered_obj, initial_guess)
 
   if GN.has_converged(result)
     @info "Gauss-Newton converged"
@@ -130,6 +144,9 @@ if true
   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)")