Merge branch 'dynamic_editing' into 'master'

ENH: Dynamic editing enabled by allowing multiple basis sets.

See merge request fsl/fsl_mrs!28

CHANGELOG.rst

@@ -4,6 +4,8 @@ This document contains the FSL-MRS release history in reverse chronological orde
 -------------------------------
 - Fixed typos in fsl_mrs_proc help.
 - Fixed simulator bug for edited sequence coherence filters.
+- Modified API of syntheticFromBasis function.
+- Dynamic fitting now handles multiple different basis sets.
 
 1.1.8 (Tuesday 5th October 2021)
 -------------------------------

fsl_mrs/tests/test_utils_fitting.py

@@ -8,7 +8,6 @@ from fsl_mrs.utils.synthetic import syntheticFID
 from fsl_mrs.utils.synthetic.synthetic_from_basis import syntheticFromBasisFile
 from fsl_mrs.core import MRS
 from fsl_mrs.utils.fitting import fit_FSLModel
-from fsl_mrs.utils import mrs_io
 from pytest import fixture
 import numpy as np
 
@@ -142,14 +141,12 @@ def test_fit_FSLModel_lorentzian_MH(data):
 
 def test_fit_FSLModel_on_invivo_sim():
 
-    FIDs, hdr, trueconcs = syntheticFromBasisFile(basis_path,
+    FIDs, mrs, trueconcs = syntheticFromBasisFile(basis_path,
                                                   noisecovariance=[[1E-3]],
                                                   broadening=(9.0, 9.0),
                                                   concentrations={'Mac': 2.0})
 
-    basis = mrs_io.read_basis(basis_path)
-
-    mrs = MRS(FID=FIDs, header=hdr, basis=basis)
+    mrs.FID = FIDs
     mrs.processForFitting()
 
     metab_groups = [0] * mrs.numBasis
@@ -164,6 +161,6 @@ def test_fit_FSLModel_on_invivo_sim():
     fittedRelconcs = res.getConc(scaling='internal', metab=mrs.names)
 
     answers = np.asarray(trueconcs)
-    answers /= (answers[basis.names.index('Cr')] + trueconcs[basis.names.index('PCr')])
+    answers /= (answers[mrs.names.index('Cr')] + trueconcs[mrs.names.index('PCr')])
 
     assert np.allclose(fittedRelconcs, answers, atol=5E-2)

fsl_mrs/tests/test_utils_synthetic.py

@@ -7,7 +7,6 @@ Copyright Will Clarke, University of Oxford, 2021'''
 from fsl_mrs.utils import synthetic as syn
 from fsl_mrs.utils.misc import FIDToSpec
 from fsl_mrs.utils import mrs_io
-from fsl_mrs.core import MRS
 import numpy as np
 from pathlib import Path
 
@@ -61,33 +60,33 @@ def test_syntheticFID():
 
 
 def test_syntheticFromBasis():
-    fid, header, _ = syn.syntheticFromBasisFile(str(basis_path),
-                                                ignore=['Scyllo'],
-                                                baseline=[0.0, 0.0],
-                                                concentrations={'Mac': 2.0},
-                                                coilamps=[1.0, 1.0],
-                                                coilphase=[0.0, np.pi],
-                                                noisecovariance=[[0.1, 0.0], [0.0, 0.1]])
+    fid, _, _ = syn.syntheticFromBasisFile(str(basis_path),
+                                           ignore=['Scyllo'],
+                                           baseline=[0.0, 0.0],
+                                           concentrations={'Mac': 2.0},
+                                           coilamps=[1.0, 1.0],
+                                           coilphase=[0.0, np.pi],
+                                           noisecovariance=[[0.1, 0.0], [0.0, 0.1]])
 
     assert fid.shape == (2048, 2)
 
 
 def test_syntheticFromBasis_baseline():
 
-    fid, header, _ = syn.syntheticFromBasisFile(str(basis_path),
-                                                baseline=[0.0, 0.0],
-                                                concentrations={'Mac': 2.0},
-                                                noisecovariance=[[0.0]])
+    fid, mrs, _ = syn.syntheticFromBasisFile(str(basis_path),
+                                             baseline=[0.0, 0.0],
+                                             concentrations={'Mac': 2.0},
+                                             noisecovariance=[[0.0]])
 
-    mrs = MRS(FID=fid, header=header)
+    mrs.FID = fid
     mrs.conj_FID = True
 
-    fid, header, _ = syn.syntheticFromBasisFile(str(basis_path),
-                                                baseline=[1.0, 1.0],
-                                                concentrations={'Mac': 2.0},
-                                                noisecovariance=[[0.0]])
+    fid, mrs2, _ = syn.syntheticFromBasisFile(str(basis_path),
+                                              baseline=[1.0, 1.0],
+                                              concentrations={'Mac': 2.0},
+                                              noisecovariance=[[0.0]])
 
-    mrs2 = MRS(FID=fid, header=header)
+    mrs2.FID = fid
     mrs2.conj_FID = True
 
     assert np.allclose(mrs2.get_spec(), mrs.get_spec() + complex(1.0, -1.0))

fsl_mrs/utils/dynamic/dyn_results.py

@@ -235,8 +235,8 @@ class dynRes:
 
         def calc_fit_from_flatmapped(mapped):
             fwd = []
-            for mp in mapped:
-                fwd.append(self._dyn.forward(mp))
+            for idx, mp in enumerate(mapped):
+                fwd.append(self._dyn.forward[idx](mp))
             return np.asarray(fwd)
 
         init_fit = calc_fit_from_flatmapped(self.mapped_parameters_init)

fsl_mrs/utils/dynamic/dynmrs.py

@@ -33,7 +33,8 @@ class dynMRS(object):
             model='voigt',
             ppmlim=(.2, 4.2),
             baseline_order=2,
-            metab_groups=None):
+            metab_groups=None,
+            rescale=True):
         """Create a dynMRS class object
 
         :param mrs_list: List of MRS objects, one per time_var
@@ -50,25 +51,27 @@ class dynMRS(object):
         :type baseline_order: int, optional
         :param metab_groups: Metabolite group list, defaults to None
         :type metab_groups: list, optional
+        :param rescale: Apply basis and FID rescaling, defaults to True
+        :type rescale: bool, optional
         """
 
         self.time_var = time_var
         self.mrs_list = mrs_list
-        self._process_mrs_list()
+        if rescale:
+            self._process_mrs_list()
 
         if metab_groups is None:
             metab_groups = [0] * len(self.metabolite_names)
-        self.data = self._prepare_data(ppmlim)
-        self.constants = self._get_constants(model, ppmlim, baseline_order, metab_groups)
-        self.forward = self._get_forward(model)
-        self.gradient = self._get_gradient(model)
 
         self._fit_args = {'model': model,
-                          'baseline_order': baseline_order,
                           'metab_groups': metab_groups,
                           'baseline_order': baseline_order,
                           'ppmlim': ppmlim}
 
+        self.data = self._prepare_data(ppmlim)
+        self.forward = self._get_forward()
+        self.gradient = self._get_gradient()
+
         numBasis, numGroups = self.mrs_list[0].numBasis, max(metab_groups) + 1
         varNames, varSizes = models.FSLModel_vars(model, numBasis, numGroups, baseline_order)
         self.vm = self._create_vm(model, config_file, varNames, varSizes)
@@ -217,9 +220,8 @@ class dynMRS(object):
         return {'x': init, 'resList': resList}
 
     # Utility methods
-    def _get_constants(self, model, ppmlim, baseline_order, metab_groups):
+    def _get_constants(self, mrs, ppmlim, baseline_order, metab_groups):
         """collect constants for forward model"""
-        mrs = self.mrs_list[0]
         first, last = mrs.ppmlim_to_range(ppmlim)  # data range
         freq, time, basis = mrs.frequencyAxis, mrs.timeAxis, mrs.basis
         base_poly = fitting.prepare_baseline_regressor(mrs, baseline_order, ppmlim)
@@ -241,28 +243,54 @@ class dynMRS(object):
         data = [mrs.get_spec().copy()[first:last] for mrs in self.mrs_list]
         return data
 
-    def _get_forward(self, model):
+    def _get_forward(self):
         """Get forward model"""
-        forward = models.getModelForward(model)
-        first, last = self.constants[-2:]
-        return lambda x: forward(x, *self.constants[:-2])[first:last]
+        fwd_lambdas = []
+        for mrs in self.mrs_list:
+            forward = models.getModelForward(self._fit_args['model'])
+
+            constants = self._get_constants(
+                mrs,
+                self._fit_args['ppmlim'],
+                self._fit_args['baseline_order'],
+                self._fit_args['metab_groups'])
+
+            def raiser(const):
+                first, last = const[-2:]
+                return lambda x: forward(x, *const[:-2])[first:last]
+            fwd_lambdas.append(raiser(constants))
 
-    def _get_gradient(self, model):
+        return fwd_lambdas
+
+    def _get_gradient(self):
         """Get gradient"""
-        gradient = models.getModelJac(model)
-        return lambda x: gradient(x, *self.constants)
+        gradient = models.getModelJac(self._fit_args['model'])
+        fwd_grads = []
+        for mrs in self.mrs_list:
+            constants = self._get_constants(
+                mrs,
+                self._fit_args['ppmlim'],
+                self._fit_args['baseline_order'],
+                self._fit_args['metab_groups'])
+
+            def raiser(const):
+                return lambda x: gradient(x, *const)
+
+            fwd_grads.append(raiser(constants))
+
+        return fwd_grads
 
     # Loss functions
     def loss(self, x, i):
         """Calc loss function"""
-        loss_real = .5 * np.mean(np.real(self.forward(x) - self.data[i]) ** 2)
-        loss_imag = .5 * np.mean(np.imag(self.forward(x) - self.data[i]) ** 2)
+        loss_real = .5 * np.mean(np.real(self.forward[i](x) - self.data[i]) ** 2)
+        loss_imag = .5 * np.mean(np.imag(self.forward[i](x) - self.data[i]) ** 2)
         return loss_real + loss_imag
 
     def loss_grad(self, x, i):
         """Calc gradient of loss function"""
-        g = self.gradient(x)
-        e = self.forward(x) - self.data[i]
+        g = self.gradient[i](x)
+        e = self.forward[i](x) - self.data[i]
         grad_real = np.mean(np.real(g) * np.real(e[:, None]), axis=0)
         grad_imag = np.mean(np.imag(g) * np.imag(e[:, None]), axis=0)
         return grad_real + grad_imag
@@ -326,12 +354,16 @@ class dynMRS(object):
         mapped = self.vm.free_to_mapped(x)
         for time_index in range(self.vm.ntimes):
             p = np.hstack(mapped[time_index, :])
-            fwd[time_index, :] = self.forward(p)
+            fwd[time_index, :] = self.forward[time_index](p)
         return fwd.flatten()
 
     def _form_FitRes(self, x, model, method, ppmlim, baseline_order):
         """Create list of FitRes object"""
-        _, _, _, base_poly, metab_groups, _, _, _ = self.constants
+        _, _, _, base_poly, metab_groups, _, _, _ = self._get_constants(
+            self.mrs_list[0],
+            self._fit_args['ppmlim'],
+            self._fit_args['baseline_order'],
+            self._fit_args['metab_groups'])
         if method.lower() == 'mh':
             mapped = []
             for xx in x:

fsl_mrs/utils/example.py

@@ -176,7 +176,6 @@ def FMRS(smooth=False, path='/Users/saad/Desktop/Spectroscopy/'):
     basis, names, basisheader = mrs_io.read_basis(str(basisfile))
 
     # # Resample basis
-    from fsl_mrs.utils import misc
     basis = misc.ts_to_ts(basis,
                           basisheader[0]['dwelltime'],
                           FIDheader['dwelltime'],
@@ -214,33 +213,20 @@ def MPRESS(noise=1, path='/Users/saad/Desktop/Spectroscopy/'):
         mrs Object list
         list (time variable)
     """
-    from fsl_mrs.utils import mrs_io
     from fsl_mrs.utils.synthetic.synthetic_from_basis import syntheticFromBasisFile
 
     path = Path(path)
     mpress_on = path / 'mpress_basis/ON'
     mpress_off = path / 'mpress_basis/OFF'
 
-    basis, names, basis_hdr = mrs_io.read_basis(mpress_on)
-    FIDs, header, conc = syntheticFromBasisFile(mpress_on, noisecovariance=[[noise]])
-    mrs1 = MRS(FID=FIDs,
-               header=header,
-               basis=basis,
-               basis_hdr=basis_hdr[0],
-               names=names)
+    FIDs, mrs1, conc = syntheticFromBasisFile(mpress_on, noisecovariance=[[noise]])
+    mrs1.FID = FIDs
     mrs1.check_FID(repair=True)
-    mrs1.Spec = misc.FIDToSpec(mrs1.FID)
     mrs1.check_Basis(repair=True)
 
-    basis, names, basis_hdr = mrs_io.read_basis(mpress_off)
-    FIDs, header, conc = syntheticFromBasisFile(mpress_off, noisecovariance=[[noise]])
-    mrs2 = MRS(FID=FIDs,
-               header=header,
-               basis=basis,
-               basis_hdr=basis_hdr[0],
-               names=names)
+    FIDs, mrs2, conc = syntheticFromBasisFile(mpress_off, noisecovariance=[[noise]])
+    mrs2.FID = FIDs
     mrs2.check_FID(repair=True)
-    mrs2.Spec = misc.FIDToSpec(mrs2.FID)
     mrs2.check_Basis(repair=True)
 
     return [mrs1, mrs2], [0, 1]

fsl_mrs/utils/synthetic/synthetic_from_basis.py

@@ -218,7 +218,7 @@ def syntheticFromBasisFile(basisFile,
                                     ppmlim=baseline_ppm)
 
     return FIDs, \
-        {'bandwidth': bandwidth, 'centralFrequency': empty_mrs.centralFrequency}, \
+        empty_mrs, \
         concentrations