Move reslist output to optional method of dynres results object. Add more...

Move reslist output to optional method of dynres results object. Add more tests for nifti mrs interator.

fsl_mrs/tests/test_core_nifti_mrs.py

@@ -70,6 +70,16 @@ def test_nifti_mrs_generator():
                              0, 0)
         break
 
+    for gen_data, slice_idx in obj.iterate_over_dims(dim='DIM_COIL'):
+        assert gen_data.shape == (1, 1, 1, 4096, 32)
+        assert slice_idx == (slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             0)
+        break
+
     for gen_data, slice_idx in obj.iterate_over_dims(dim='DIM_DYN'):
         assert gen_data.shape == (1, 1, 1, 4096, 64)
         assert slice_idx == (slice(None, None, None),
@@ -90,6 +100,32 @@ def test_nifti_mrs_generator():
         assert slice_idx == (0, 0, 0, slice(None, None, None), 0)
         break
 
+    obj2 = gen_new_nifti_mrs(
+        np.zeros((1, 1, 1, 100, 2, 10), dtype=complex),
+        0.0005,
+        120.0,
+        dim_tags=['DIM_EDIT', 'DIM_DYN', None])
+
+    for gen_data, slice_idx in obj2.iterate_over_dims(dim='DIM_DYN'):
+        assert gen_data.shape == (1, 1, 1, 100, 10)
+        assert slice_idx == (slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             0,
+                             slice(None, None, None))
+        break
+
+    for gen_data, slice_idx in obj2.iterate_over_dims(dim='DIM_EDIT'):
+        assert gen_data.shape == (1, 1, 1, 100, 2)
+        assert slice_idx == (slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             slice(None, None, None),
+                             0)
+        break
+
 
 def test_nifti_mrs_spatial_generator():
     obj = NIFTI_MRS(data['unprocessed'])

fsl_mrs/tests/test_utils_dynamic_dyn_results.py

@@ -50,12 +50,10 @@ def test_dynRes(fixed_ratio_mrs):
         baseline_order=0,
         metab_groups=[0, 0],
         rescale=False)
-    res = dyn_obj.fit()
+    res_obj = dyn_obj.fit()[0]
 
     resinit = dyn_obj.initialise()
 
-    res_obj = res['result']
-
     import plotly
     fig = res_obj.plot_spectra(init=True, fit_to_init=True)
     assert isinstance(fig, plotly.graph_objs._figure.Figure)
@@ -102,9 +100,8 @@ def test_dynRes_newton(fixed_ratio_mrs):
         baseline_order=0,
         metab_groups=[0, 0],
         rescale=False)
-    res = dyn_obj.fit()
+    res_obj = dyn_obj.fit()[0]
 
-    res_obj = res['result']
     assert isinstance(res_obj.cov_dyn, pd.DataFrame)
     assert res_obj.cov_dyn.shape == (10, 10)
 
@@ -122,6 +119,9 @@ def test_dynRes_newton(fixed_ratio_mrs):
     assert isinstance(res_obj.mapped_params, pd.DataFrame)
     assert res_obj.mapped_params.shape == (2, 8)
 
+    assert isinstance(res_obj.reslist, list)
+    assert len(res_obj.reslist) == 2
+
 
 def test_dynRes_mcmc(fixed_ratio_mrs):
     """Test mcmc optimiser specific components"""
@@ -136,9 +136,8 @@ def test_dynRes_mcmc(fixed_ratio_mrs):
         metab_groups=[0, 0],
         rescale=False)
 
-    res = dyn_obj.fit(method='MH', mh_jumps=20)
+    res_obj = dyn_obj.fit(method='MH', mh_jumps=20)[0]
 
-    res_obj = res['result']
     assert isinstance(res_obj.cov_dyn, pd.DataFrame)
     assert res_obj.cov_dyn.shape == (10, 10)
 
@@ -169,7 +168,7 @@ def test_load_save(fixed_ratio_mrs, tmp_path):
         metab_groups=[0, 0],
         rescale=False)
 
-    res = dyn_obj.fit()['result']
+    res = dyn_obj.fit()[0]
 
     res.save(tmp_path / 'res_save_test')
     res_loaded = dyn.load_dyn_result(tmp_path / 'res_save_test', dyn_obj)

fsl_mrs/tests/test_utils_dynamic_dynmrs.py

@@ -150,9 +150,9 @@ def test_dynMRS_fit(fixed_ratio_mrs):
         baseline_order=0,
         metab_groups=[0, 0],
         rescale=False)
-    res = dyn_obj.fit()
+    res, _ = dyn_obj.fit()
 
-    concs = res['result'].data_frame.filter(like='conc').to_numpy()
+    concs = res.data_frame.filter(like='conc').to_numpy()
     assert np.allclose(concs, [1, 1, 1, 1], atol=0.1)
 
 
@@ -167,9 +167,10 @@ def test_dynMRS_fit_mcmc(fixed_ratio_mrs):
         baseline_order=0,
         metab_groups=[0, 0],
         rescale=False)
-    res = dyn_obj.fit(method='MH', mh_jumps=50)
+    init = dyn_obj.initialise(indiv_init=None)
+    res, _ = dyn_obj.fit(method='MH', mh_jumps=50, init=init)
 
-    concs = res['result'].data_frame.filter(like='conc').mean(axis=0).to_numpy()
+    concs = res.data_frame.filter(like='conc').mean(axis=0).to_numpy()
     assert np.allclose(concs, [1, 1, 1, 1], atol=0.1)
 
 

fsl_mrs/utils/dynamic/dyn_results.py

@@ -528,6 +528,15 @@ class dynRes_mcmc(dynRes):
         """
         super().__init__(samples, dyn, init)
 
+    @property
+    def reslist(self):
+        """Generate a list of (independent) results objects.
+
+        :return: List of FitRes objects.
+        :rtype: list
+        """
+        return self._dyn.form_FitRes(self.dataframe.to_numpy(), 'MH')
+
     @property
     def cov_dyn(self):
         """Returns the covariance matrix of free parameters
@@ -611,6 +620,15 @@ class dynRes_newton(dynRes):
             std[name] = np.array(s).T
         self._std = std
 
+    @property
+    def reslist(self):
+        """Generate a list of (independent) results objects.
+
+        :return: List of FitRes objects.
+        :rtype: list
+        """
+        return self._dyn.form_FitRes(self.x, 'Newton')
+
     @property
     def cov_dyn(self):
         """Returns the covariance matrix of free parameters

fsl_mrs/utils/dynamic/dynmrs.py

@@ -228,7 +228,7 @@ class dynMRS(object):
         :type init: dict, optional
         :param verbose: Verbosity flag, defaults to False
         :type verbose: bool, optional
-        :return: Tuple containing dedicated results object, list of fitRes objects, optimisation output (Newton only)
+        :return: Tuple containing dedicated results object, and optimisation output (Newton only)
         :rtype: tuple
         """
         if verbose:
@@ -258,25 +258,18 @@ class dynMRS(object):
         # Results
         if verbose:
             print('Collect results')
-        # 1. Create dedicated dynamic fit results
+        # Create dedicated dynamic fit results
         if method.lower() == 'newton':
             results = dyn_results.dynRes_newton(sol.x, self, init)
         elif method.lower() == 'mh':
             results = dyn_results.dynRes_mcmc(x, self, init)
         else:
             raise (Exception(f'Unrecognised method {method}'))
-        # 2. Create a list of fitRes objects for each timepoint for fsl_mrs like capabilities
-        res_list = self._form_FitRes(
-            x,
-            self._fit_args['model'],
-            method,
-            self._fit_args['ppmlim'],
-            self._fit_args['baseline_order'])
 
         if verbose:
             print(f"...completed in {time.time()-end_fit_time} seconds.")
 
-        return {'result': results, 'resList': res_list, 'optimisation_sol': sol}
+        return results, sol
 
     def fit_mean_spectrum(self):
         """Return the parameters from the fit of the mean spectra stored in mrs_list."""
@@ -288,12 +281,12 @@ class dynMRS(object):
         mean_mrs.FID = mean_fid
         return fitting.fit_FSLModel(mean_mrs, method='Newton', **self._fit_args).params
 
-    def initialise(self, indiv_init=None, verbose=False):
+    def initialise(self, indiv_init='mean', verbose=False):
         """Initialise the dynamic fitting using seperate fits of each spectrum.
 
         :param indiv_init: Optional initilisation of individual fits.
             Can be a numpy array of mapped parameters, 'mean' (fits the mean spectrum), or None (independent).
-            Defaults to None
+            Defaults to 'mean'.
         :param verbose: Print information during fitting, defaults to False
         :type verbose: bool, optional
         :return: Dict containing free parameters and individual FitRes objects
@@ -460,7 +453,7 @@ class dynMRS(object):
             fwd[time_index, :] = self.forward[time_index](p)
         return fwd.flatten()
 
-    def _form_FitRes(self, x, model, method, ppmlim, baseline_order):
+    def form_FitRes(self, x, method):
         """Create list of FitRes object"""
         _, _, _, base_poly, metab_groups, _, _, _ = self._get_constants(
             self.mrs_list[0],
@@ -486,13 +479,13 @@ class dynMRS(object):
         dynresList = []
         for t in range(self.vm.ntimes):
             mrs = self.mrs_list[t]
-            results = FitRes(model,
+            results = FitRes(self._fit_args['model'],
                              method,
                              mrs.names,
                              metab_groups,
-                             baseline_order,
+                             self._fit_args['baseline_order'],
                              base_poly,
-                             ppmlim)
+                             self._fit_args['ppmlim'])
             results.loadResults(mrs, mapped[t])
             dynresList.append(results)
         return dynresList