fix: error in fit error msg when using scipy instead of lmfit

CHAP/edd/processor.py

@@ -2362,15 +2362,12 @@ def cost_function_combined(
             axs[1,1].set_ylabel('Energy (keV)')
             if calibration_method in ('fix_tth_to_tth_init', 'iterate_tth'):
                 e_fit = e_bragg_fit
-                e_unconstrained = e_bragg_unconstrained
                 if quadratic_energy_calibration:
                     label = 'Unconstrained: quadratic fit'
                 else:
                     label = 'Unconstrained: linear fit'
             else:
                 e_fit = np.concatenate((e_xrf, e_bragg_fit))
-                e_unconstrained = np.concatenate(
-                    (e_xrf, e_bragg_unconstrained))
                 if quadratic_energy_calibration:
                     label = 'Quadratic fit'
                 else:

CHAP/utils/fit.py

@@ -2435,6 +2435,7 @@ def fit(self, config=None, **kwargs):
                 'maximum allowed number of processors, num_proc reduced to '
                 f'{num_proc_max}')
             num_proc = num_proc_max
+        logger.debug(f'Using {num_proc} processors to fit the data')
         self._redchi_cutoff *= self._y_range**2
 
         # Setup the fit
@@ -2799,7 +2800,12 @@ def _fit(self, n, current_best_values, return_result=False, **kwargs):
                 if par.vary:
                     current_best_values[par.name] = par.value
         else:
-            logger.warning(f'Fit for n = {n} failed: {result.lmdif_message}')
+            errortxt = f'Fit for n = {n} failed'
+            if hasattr(result, 'lmdif_message'):
+                errortxt += f'\n\t{result.lmdif_message}'
+            if hasattr(result, 'message'):
+                errortxt += f'\n\t{result.message}'
+            logger.warning(f'{errortxt}')
 
         # Renormalize the data and results
         self._renormalize(n, result)