From aa0d14a322363d35512f12c5098e8e9e5af192a8 Mon Sep 17 00:00:00 2001
From: Dominik Demuth <dominik.demuth@physik.tu-darmstadt.de>
Date: Thu, 30 Nov 2023 18:16:07 +0100
Subject: [PATCH] Make FitResults from all fixed parameters; closes #151

---
 src/nmreval/fit/minimizer.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/nmreval/fit/minimizer.py b/src/nmreval/fit/minimizer.py
index 40a7574..9ed33fa 100644
--- a/src/nmreval/fit/minimizer.py
+++ b/src/nmreval/fit/minimizer.py
@@ -227,6 +227,8 @@ class FitRoutine(object):
         if mode is None:
             mode = self.fitmethod
 
+        print('run')
+
         fit_groups, linked_parameter = self.prepare_links()
         for data_groups in fit_groups:
             if len(data_groups) == 1 and not self.linked:
@@ -234,6 +236,8 @@ class FitRoutine(object):
                 # get variable parameter for fitter
                 p0_k, lb_k, ub_k, var_pars_k = self._prep_data(data)
 
+                print(p0_k, var_pars_k)
+
                 if mode == 'lsq':
                     self._least_squares_single(data, p0_k, lb_k, ub_k, var_pars_k)
 
@@ -248,6 +252,7 @@ class FitRoutine(object):
                 data_pars, p0, lb, ub, var_pars = self._prep_global(data_groups, linked_parameter)
 
                 if mode == 'lsq':
+                    print(data_pars, p0,var_pars)
                     self._least_squares_global(data_groups, p0, lb, ub, var_pars, data_pars)
 
                 elif mode == 'nm':
@@ -287,8 +292,8 @@ class FitRoutine(object):
         vals = []
         var_pars = []
         for p_k, v_k in parameter.items():
+            vals.append([v_k.scaled_value, v_k.lb / v_k.scale, v_k.ub / v_k.scale])
             if v_k.var:
-                vals.append([v_k.scaled_value, v_k.lb / v_k.scale, v_k.ub / v_k.scale])
                 var_pars.append(p_k)
 
         pp, lb, ub = zip(*vals)
@@ -318,10 +323,10 @@ class FitRoutine(object):
 
                 actual_pars.append(p_k_used)
                 # parameter is variable and was not found before as shared parameter
+                p0.append(v_k_used.scaled_value)
+                lb.append(v_k_used.lb / v_k_used.scale)
+                ub.append(v_k_used.ub / v_k_used.scale)
                 if v_k_used.var and p_k_used not in var:
-                    p0.append(v_k_used.scaled_value)
-                    lb.append(v_k_used.lb / v_k_used.scale)
-                    ub.append(v_k_used.ub / v_k_used.scale)
                     var.append(p_k_used)
 
             data_pars.append(actual_pars)
@@ -472,7 +477,7 @@ class FitRoutine(object):
                     pass
 
         # reshape the correlation matrices
-        if corr is None:
+        if corr is None or not corr_idx:
             actual_corr = None
             actual_pcorr = None
         else: