Fixes for new stateless tresults class format.

fsl_mrs/dynamic/dynmrs.py

@@ -483,13 +483,13 @@ class dynMRS(object):
         dynresList = []
         for t in range(self.vm.ntimes):
             mrs = self.mrs_list[t]
-            results = FitRes(self._fit_args['model'],
+            results = FitRes(mrs,
+                             mapped[t],
+                             self._fit_args['model'],
                              method,
-                             mrs.names,
                              metab_groups,
                              self._fit_args['baseline_order'],
                              base_poly,
                              self._fit_args['ppmlim'])
-            results.loadResults(mrs, mapped[t])
             dynresList.append(results)
         return dynresList

fsl_mrs/scripts/results_to_spectrum.py

@@ -90,9 +90,16 @@ def main():
     B = prepare_baseline_regressor(mrs, baseline_order, ppmlim)
 
     # Generate results object
-    res = results.FitRes(model, method, mrs.names, metab_groups, baseline_order, B, ppmlim)
-    res.loadResults(mrs, param_df['mean'].to_numpy())
-    # res.params = param_df['mean'].to_numpy()
+    print(metab_groups)
+    res = results.FitRes(
+        mrs,
+        param_df['mean'].to_numpy(),
+        model,
+        method,
+        metab_groups,
+        baseline_order,
+        B,
+        ppmlim)
 
     if orig_args['combine'] is not None:
         res.combine(orig_args['combine'])

fsl_mrs/utils/fitting.py

@@ -317,7 +317,7 @@ def fit_FSLModel(mrs,
         results = FitRes(mrs, res.x, model, method, metab_groups, baseline_order, B, ppmlim)
 
     elif method == 'init':
-        results.loadResults(mrs, x0)
+        results = FitRes(mrs, x0, model, method, metab_groups, baseline_order, B, ppmlim)
 
     elif method == 'MH':
         def forward_mh(p):
@@ -406,7 +406,7 @@ def fit_FSLModel(mrs,
         samples = mcmc.fit(p0, LB=LB, UB=UB, verbose=False, mask=mask)
 
         # collect results
-        results.loadResults(mrs, samples)
+        results = FitRes(mrs, samples, model, method, metab_groups, baseline_order, B, ppmlim)
 
     elif method == 'VB':
         import warnings
@@ -470,7 +470,7 @@ def fit_FSLModel(mrs,
             x = p2x(np.exp(logcon), np.exp(loggamma), np.exp(logsigma), eps, phi0, phi1, b)
 
         # collect results
-        results.loadResults(mrs, x)
+        results = FitRes(mrs, x, model, method, metab_groups, baseline_order, B, ppmlim, vb_optim=res_vb)
 
     else:
         raise Exception('Unknown optimisation method.')

fsl_mrs/utils/results.py

@@ -23,7 +23,7 @@ class FitRes(object):
        Collects fitting results
     """
 
-    def __init__(self, mrs, results, model, method, metab_groups, baseline_order, B, ppmlim, runqc=True):
+    def __init__(self, mrs, results, model, method, metab_groups, baseline_order, B, ppmlim, runqc=True, vb_optim=None):
         """_summary_
 
         _extended_summary_
@@ -116,8 +116,8 @@ class FitRes(object):
 
         # VB metrics
         if self.method == 'VB':
-            self.vb_cov = self.optim_out.cov
-            self.vb_var = self.optim_out.var
+            self.vb_cov = vb_optim.cov
+            self.vb_var = vb_optim.var
             std = np.sqrt(self.vb_var)
             self.vb_corr = self.vb_cov / (std[:, np.newaxis] * std[np.newaxis, :])