All tests passing.

fsl_mrs/core/__init__.py

-from fsl_mrs.core.MRS import MRS
-from fsl_mrs.core.MRSI import MRSI
-from fsl_mrs.core.NIFTI_MRS import NIFTI_MRS, is_nifti_mrs
+from .mrs import MRS
+from .mrsi import MRSI
+from .nifti_mrs import NIFTI_MRS
+from .utility import mrs_from_files, is_nifti_mrs, mrsi_from_files

fsl_mrs/core/MRS.py → fsl_mrs/core/mrs.py

@@ -9,7 +9,6 @@
 
 import warnings
 
-from fsl_mrs.utils import mrs_io as io
 from fsl_mrs.utils import misc
 from fsl_mrs.utils.constants import GYRO_MAG_RATIO, PPM_SHIFT, PPM_RANGE
 
@@ -59,22 +58,6 @@ class MRS(object):
         self.metab_groups = None
         self.scaling = {'FID': 1.0, 'basis': 1.0}
 
-    def from_files(self, FID_file, Basis_file, H2O_file=None):
-        '''Load data from files into empty MRS class object'''
-
-        FID, FIDheader = io.read_FID(FID_file)
-        basis, names, Bheader = io.read_basis(Basis_file)
-        if H2O_file is not None:
-            H2O, _ = io.read_FID(H2O_file)
-        else:
-            H2O = None
-
-        MRSArgs = {'header': FIDheader,
-                   'basis': basis, 'basis_hdr': Bheader[0],
-                   'names': names}
-
-        self.__init__(FID=FID, H2O=H2O, **MRSArgs)
-
     def __str__(self):
         cf_MHz = self.centralFrequency / 1e6
         cf_T = self.centralFrequency / self.gyromagnetic_ratio / 1e6
@@ -167,7 +150,7 @@ class MRS(object):
                (cf_MHz > sevent_range[0] and cf_MHz < sevent_range[1]) or \
                (cf_MHz > ninefourt_range[0] and cf_MHz < ninefourt_range[1]) or \
                (cf_MHz > elevensevent_range[0] and cf_MHz < elevensevent_range[1]):
-                #print(f'Identified as {key} nucleus data.'
+                # print(f'Identified as {key} nucleus data.'
                 #      f' Esitmated field: {cf_MHz/GYRO_MAG_RATIO[key]} T.')
                 return key
 

fsl_mrs/core/MRSI.py → fsl_mrs/core/mrsi.py

@@ -9,11 +9,10 @@
 # SHBASECOPYRIGHT
 
 import numpy as np
-from fsl_mrs.core import MRS
-from fsl_mrs.utils import mrs_io, misc
 import matplotlib.pyplot as plt
-import nibabel as nib
-from fsl_mrs.utils.mrs_io import fsl_io
+
+from fsl_mrs.core import MRS
+from fsl_mrs.utils import misc
 
 
 class MRSI(object):
@@ -267,8 +266,9 @@ class MRSI(object):
         self.gm = gm
         self.tissue_seg_loaded = True
 
-    def write_output(self, data_list, file_path_name, indicies=None, cleanup=True, dtype=float):
-        '''Write 3D or 4D array of data to nifti file with current orientation.'''
+    def list_to_matched_array(self, data_list, indicies=None, cleanup=True, dtype=float):
+        '''Convert 3D or 4D array of data indexed from an mrsi object
+        to a  numpy array matching the shape of the mrsi data.'''
         if indicies is None:
             indicies = self.get_indicies_in_order()
 
@@ -287,54 +287,4 @@ class MRSI(object):
             data[data < 1e-10]   = 0
             data[data > 1e10]    = 0
 
-        if nt == self.FID_points:
-            fsl_io.saveNIFTI(file_path_name, data, self.header)
-        else:
-            img = nib.Nifti1Image(data, self.header['nifti'].affine)
-            nib.save(img, file_path_name)
-
-    @classmethod
-    def from_files(cls, data_file,
-                   mask_file=None,
-                   basis_file=None,
-                   H2O_file=None,
-                   csf_file=None,
-                   gm_file=None,
-                   wm_file=None):
-        """ Load MRSI data directly from files """
-        data, hdr = mrs_io.read_FID(data_file)
-        if mask_file is not None:
-            nib_img = nib.load(mask_file)
-            mask = np.asanyarray(nib_img.dataobj)
-        else:
-            mask = None
-
-        if basis_file is not None:
-            basis, names, basisHdr = mrs_io.read_basis(basis_file)
-        else:
-            basis, names, basisHdr = None, None, [None, ]
-        if H2O_file is not None:
-            data_w, hdr_w = mrs_io.read_FID(H2O_file)
-        else:
-            data_w = None
-
-        out = cls(data, hdr,
-                  mask=mask,
-                  basis=basis,
-                  names=names,
-                  basis_hdr=basisHdr[0],
-                  H2O=data_w)
-
-        def loadNii(f):
-            nii = np.asanyarray(nib.load(f).dataobj)
-            if nii.ndim == 2:
-                nii = np.expand_dims(nii, 2)
-            return nii
-
-        if (csf_file is not None) and (gm_file is not None) and (wm_file is not None):
-            csf = loadNii(csf_file)
-            gm = loadNii(gm_file)
-            wm = loadNii(wm_file)
-            out.set_tissue_seg(csf, wm, gm)
-
-        return out
+        return data

fsl_mrs/core/NIFTI_MRS.py → fsl_mrs/core/nifti_mrs.py

@@ -10,27 +10,10 @@ import numpy as np
 import json
 from fsl.data.image import Image
 from fsl_mrs.core import MRS, MRSI
-import fsl.utils.path as fslpath
-from nibabel.nifti2 import Nifti2Header
 from nibabel.nifti1 import Nifti1Extension
-from fsl_mrs.utils.misc import checkCFUnits
-
-
-class NIFTIMRS_DimDoesntExist(Exception):
-    pass
 
-
-class NotNIFTI_MRS(Exception):
-    pass
-
-
-def is_nifti_mrs(file_path):
-    '''Check that a file is of the NIFTI-MRS format type.'''
-    try:
-        NIFTI_MRS(file_path)
-        return True
-    except fslpath.PathError:
-        raise NotNIFTI_MRS("File isn't NIFTI-MRS, wrong extension type.")
+from nibabel.nifti2 import Nifti2Header
+from fsl_mrs.utils.misc import checkCFUnits
 
 
 def gen_new_nifti_mrs(data, dwelltime, spec_freq, nucleus='1H', affine=None, dim_tags=[None, None, None]):
@@ -46,7 +29,7 @@ def gen_new_nifti_mrs(data, dwelltime, spec_freq, nucleus='1H', affine=None, dim
     :return: NIFTI_MRS object
     '''
 
-    if not np.iscomplex(data).all():
+    if not np.iscomplexobj(data):
         raise ValueError('data must be complex')
     if data.ndim < 4 or data.ndim > 7:
         raise ValueError(f'data must between 4 and 7 dimensions, currently has {data.ndim}')
@@ -74,6 +57,14 @@ def gen_new_nifti_mrs(data, dwelltime, spec_freq, nucleus='1H', affine=None, dim
     return NIFTI_MRS(data, header=header)
 
 
+class NIFTIMRS_DimDoesntExist(Exception):
+    pass
+
+
+class NotNIFTI_MRS(Exception):
+    pass
+
+
 class NIFTI_MRS(Image):
     """Load NIFTI MRS format data. Derived from nibabel's Nifti2Image."""
     def __init__(self, *args, **kwargs):
@@ -304,7 +295,15 @@ class NIFTI_MRS(Image):
             basis_hdr = basis_hdr[0]
 
         if ref_data is not None:
-            ref_data = ref_data.data
+            if isinstance(ref_data, str):
+                ref_data = NIFTI_MRS(ref_data).data
+            elif isinstance(ref_data, NIFTI_MRS):
+                ref_data = ref_data.data
+            elif isinstance(ref_data, np.ndarray):
+                pass
+            else:
+                raise TypeError('ref_data must be a path to a NIFTI-MRS file,'
+                                'a NIFTI_MRS object, or a numpy array.')
 
         for data, _ in self.iterate_over_dims(dim=dim):
             if np.prod(data.shape[:3]) > 1:
@@ -332,6 +331,9 @@ class NIFTI_MRS(Image):
                                basis_hdr=basis_hdr,
                                H2O=ref_data)
             else:
+                if ref_data is not None:
+                    ref_data = ref_data.squeeze()
+
                 # Generate MRS objects
                 if data.ndim > 4:
                     out = []

fsl_mrs/core/utility.py

+'''utility.py -Module containing utility functions
+for creating MRS, MRSI and NIFTI_MRS objects
+Author: Saad Jbabdi <saad@fmrib.ox.ac.uk>
+        Will Clarke <william.clarke@ndcn.ox.ac.uk>
+
+Copyright (C) 2021 University of Oxford
+# SHBASECOPYRIGHT
+'''
+import nibabel as nib
+import numpy as np
+import fsl.utils.path as fslpath
+
+from fsl_mrs.utils import mrs_io
+from fsl_mrs.core.nifti_mrs import NIFTI_MRS, NotNIFTI_MRS
+
+
+def mrs_from_files(FID_file, Basis_file, H2O_file=None):
+    '''Construct an MRS object from FID, basis, and
+     (optionally) a reference file
+
+    :param FID_file: path to data file
+    :param Basis_file: path to basis file
+    :param H2O_file: Optional path to reference file
+
+    :return mrs: MRS object
+    '''
+
+    FID = mrs_io.read_FID(FID_file)
+    basis, names, Bheader = mrs_io.read_basis(Basis_file)
+    if H2O_file is not None:
+        H2O = mrs_io.read_FID(H2O_file).data
+    else:
+        H2O = None
+
+    return FID.mrs(basis=basis, names=names, basis_hdr=Bheader[0], ref_data=H2O)
+
+
+def mrsi_from_files(data_file,
+                    mask_file=None,
+                    basis_file=None,
+                    H2O_file=None,
+                    csf_file=None,
+                    gm_file=None,
+                    wm_file=None):
+    '''Construct an MRS object from data, and
+     (optionally) basis, mask, reference and segmentation files
+
+    :param FID_file: path to data file
+    :param mask_file: Optional path to basis file
+    :param basis_file: Optional path to reference file
+    :param H2O_file: Optional path to reference file
+    :param csf_file: Optional path to reference file
+    :param gm_file: Optional path to reference file
+    :param wm_file: Optional path to reference file
+
+    :return mrs: MRSI object
+    '''
+    data = mrs_io.read_FID(data_file)
+    if mask_file is not None:
+        nib_img = nib.load(mask_file)
+        mask = np.asanyarray(nib_img.dataobj)
+    else:
+        mask = None
+
+    if basis_file is not None:
+        basis, names, basisHdr = mrs_io.read_basis(basis_file)
+    else:
+        basis, names, basisHdr = None, None, [None, ]
+    if H2O_file is not None:
+        data_w = mrs_io.read_FID(H2O_file)
+    else:
+        data_w = None
+
+    out = data.mrs(basis=basis, names=names, basis_hdr=basisHdr[0], ref_data=data_w)
+
+    out.set_mask(mask)
+
+    def loadNii(f):
+        nii = np.asanyarray(nib.load(f).dataobj)
+        if nii.ndim == 2:
+            nii = np.expand_dims(nii, 2)
+        return nii
+
+    if (csf_file is not None) and (gm_file is not None) and (wm_file is not None):
+        csf = loadNii(csf_file)
+        gm = loadNii(gm_file)
+        wm = loadNii(wm_file)
+        out.set_tissue_seg(csf, wm, gm)
+
+    return out
+
+
+def is_nifti_mrs(file_path):
+    '''Check that a file is of the NIFTI-MRS format type.'''
+    try:
+        NIFTI_MRS(file_path)
+        return True
+    except fslpath.PathError:
+        raise NotNIFTI_MRS("File isn't NIFTI-MRS, wrong extension type.")

fsl_mrs/scripts/fsl_mrs

@@ -101,10 +101,6 @@ def main():
                                ' Defaults to tCr (Cr+PCr).')
     optional.add_argument('--h2o_scale', type=float, default=1.0,
                           help='Additional scaling modifier for external water referencing.')
-    optional.add_argument('--central_frequency', default=None, type=float,
-                          help='central frequency in Hz')
-    optional.add_argument('--dwell_time', default=None, type=float,
-                          help='dwell time in seconds')
     optional.add_argument('--report', action="store_true",
                           help='output html report')
     optional.add_argument('--verbose', action="store_true",
@@ -143,7 +139,6 @@ def main():
     import os
     import shutil
     import warnings
-    from fsl_mrs.core import MRS
     from fsl_mrs.utils import mrs_io
     from fsl_mrs.utils import report
     from fsl_mrs.utils import fitting
@@ -185,49 +180,23 @@ def main():
         print(f'  {args.data}')
         print(f'  {args.basis}\n')
 
-    FID, dataheader = mrs_io.read_FID(args.data)
+    FID = mrs_io.read_FID(args.data)
     basis, names, basisheader = mrs_io.read_basis(args.basis)
 
     if args.h2o is not None:
-        H2O, _ = mrs_io.read_FID(args.h2o)
+        H2O = mrs_io.read_FID(args.h2o)
     else:
         H2O = None
 
-    # Collect useful info
-    if args.central_frequency is not None:
-        cf = args.central_frequency
-    elif dataheader['centralFrequency'] is not None:
-        cf = dataheader['centralFrequency']
-        if args.verbose:
-            print(f'     Detected central frequency'
-                  f' in header info cf = {cf:0.6f} MHz')
-    else:
-        raise(Exception('Cannot determine central frequency.'
-                        'Please either set it or include it in data header'))
-
-    if args.dwell_time is not None:
-        bw = 1 / args.dwell_time
-    elif dataheader['bandwidth'] is not None:
-        bw = dataheader['bandwidth']
-        if args.verbose:
-            print(f'      Detected bandwidth in header info bw = {bw:0.1f} Hz')
-    else:
-        raise(Exception('Cannot determine bandwidth.'
-                        'Please either set it or include it in data header'))
-
-    # Fix case where basis file contains no header info (e.g. .RAW)
-    if basisheader is None:
-        basisheader = {'bandwidth': bw,
-                       'dwelltime': 1 / bw,
-                       'centralFrequency': cf}
-    else:
-        basisheader = basisheader[0]
-
     # Instantiate MRS object
-    MRSargs = {'FID': FID, 'basis': basis,
-               'basis_hdr': basisheader, 'names': names,
-               'H2O': H2O, 'cf': cf, 'bw': bw}
-    mrs = MRS(**MRSargs)
+    mrs = FID.mrs(basis=basis,
+                  names=names,
+                  basis_hdr=basisheader[0],
+                  ref_data=H2O)
+
+    if isinstance(mrs, list):
+        raise ValueError('fsl_mrs only handles a single FID at a time.'
+                         ' Please preprocess data.')
 
     # Check the FID and basis / conjugate
     if args.conjfid is not None:
@@ -268,7 +237,7 @@ def main():
     if args.verbose:
         print('--->> Start fitting\n\n')
         print('    Algorithm = [{}]\n'.format(args.algo))
-        start = time.time()
+    start = time.time()
 
     ppmlim = args.ppmlim
     if ppmlim is not None:
@@ -312,15 +281,14 @@ def main():
     # Echo time
     if args.TE is not None:
         echotime = args.TE * 1E-3
-    elif 'meta' in basisheader:
-        if 'TE' in basisheader['meta']:
-            echotime = basisheader['meta']['TE']
-            if echotime > 1.0:  # Assume in ms.
-                echotime *= 1E-3
-            else:
-                echotime = None
-    elif 'TE' in dataheader:
-        echotime = dataheader['TE']
+    elif 'meta' in basisheader and 'TE' in basisheader['meta']:
+        echotime = basisheader['meta']['TE']
+        if echotime > 1.0:  # Assume in ms.
+            echotime *= 1E-3
+        else:
+            echotime = None
+    elif 'EchoTime' in FID.hdr_ext:
+        echotime = FID.hdr_ext['TE']
     else:
         echotime = None
 

fsl_mrs/scripts/fsl_mrs_preproc

@@ -62,8 +62,6 @@ def main():
                           help='spit out verbose info')
     optional.add_argument('--conjugate', action="store_true",
                           help='apply conjugate to FID')
-    optional.add_argument('--no_conjugate', action="store_true",
-                          help='Forbid automatic conjugation')
     optional.add_argument('--overwrite', action="store_true",
                           help='overwrite existing output folder')
     optional.add_argument('--report', action="store_true",

fsl_mrs/scripts/fsl_mrsi

@@ -11,7 +11,7 @@ from fsl_mrs.auxiliary import configargparse
 
 from fsl_mrs import __version__
 from fsl_mrs.utils.splash import splash
-from fsl_mrs.utils import fitting, misc
+from fsl_mrs.utils import fitting, misc, mrs_io
 import time
 # NOTE!!!! THERE ARE MORE IMPORTS IN THE CODE BELOW (AFTER ARGPARSING)
 
@@ -124,8 +124,8 @@ def main():
     import shutil
     import warnings
     import numpy as np
-    from fsl_mrs import core as fslmrs
     from fsl_mrs.utils import report
+    from fsl_mrs.core import NIFTI_MRS
     import datetime
     import nibabel as nib
     from functools import partial
@@ -158,9 +158,14 @@ def main():
     # ######  Do the work #######
 
     # Read files
-    mrsi = fslmrs.MRSI.from_files(args.data,
-                                  basis_file=args.basis,
-                                  H2O_file=args.h2o)
+    mrsi_data = mrs_io.read_FID(args.data)
+    if args.h2o is not None:
+        H2O = mrs_io.read_FID(args.h2o)
+    else:
+        H2O = None
+
+    mrsi = mrsi_data.mrs(basis_file=args.basis,
+                         ref_data=H2O)
 
     def loadNii(f):
         nii = np.asanyarray(nib.load(f).dataobj)
@@ -207,13 +212,12 @@ def main():
     # Echo time
     if args.TE is not None:
         echotime = args.TE * 1E-3
-    elif 'meta' in mrsi.basis_hdr:
-        if 'TE' in mrsi.basis_hdr['meta']:
-            echotime = mrsi.basis_hdr['meta']['TE']
-            if echotime > 1.0:  # Assume in ms.
-                echotime *= 1E-3
-            else:
-                echotime = None
+    elif 'meta' in mrsi.basis_hdr and 'TE' in mrsi.basis_hdr['meta']:
+        echotime = mrsi.basis_hdr['meta']['TE']
+        if echotime > 1.0:  # Assume in ms.
+            echotime *= 1E-3
+        else:
+            echotime = None
     elif 'TE' in mrsi.header:
         echotime = mrsi.header['TE']
     else:
@@ -306,6 +310,13 @@ def main():
     if results[0][0].concScalings['molality'] is not None:
         scalings.append('molality')
 
+    def save_img_output(fname, data):
+        if data.ndim > 3 and data.shape[3] == mrsi.FID_points:
+            NIFTI_MRS(data, header=mrsi_data.header).save(fname)
+        else:
+            img = nib.Nifti1Image(data, mrsi_data.voxToWorldMat)
+            nib.save(img, fname)
+
     metabs = results[0][0].metabs
     for scale in scalings:
         cur_fldr = os.path.join(concs_folder, scale)
@@ -314,42 +325,46 @@ def main():
             metab_conc_list = [res[0].getConc(scaling=scale, metab=metab)
                                for res in results]
             file_nm = os.path.join(cur_fldr, metab + '.nii.gz')
-            mrsi.write_output(metab_conc_list,
-                              file_nm,
-                              indicies=indicies,
-                              cleanup=True,
-                              dtype=float)
+            save_img_output(file_nm,
+                            mrsi.list_to_matched_array(
+                                metab_conc_list,
+                                indicies=indicies,
+                                cleanup=True,
+                                dtype=float))
 
     # Uncertainties
     for metab in results[0][0].metabs:
         metab_sd_list = [res[0].getUncertainties(metab=metab)
                          for res in results]
         file_nm = os.path.join(uncer_folder, metab + '_sd.nii.gz')
-        mrsi.write_output(metab_sd_list,
-                          file_nm,
-                          indicies=indicies,
-                          cleanup=True,
-                          dtype=float)
+        save_img_output(file_nm,
+                        mrsi.list_to_matched_array(
+                            metab_sd_list,
+                            indicies=indicies,
+                            cleanup=True,
+                            dtype=float))
 
     # qc - SNR & FWHM
     for metab in results[0][0].original_metabs:
         metab_fwhm_list = [res[0].getQCParams(metab=metab)[1]
                            for res in results]
         file_nm = os.path.join(qc_folder, metab + '_fwhm.nii.gz')
-        mrsi.write_output(metab_fwhm_list,
-                          file_nm,
-                          indicies=indicies,
-                          cleanup=True,
-                          dtype=float)
+        save_img_output(file_nm,
+                        mrsi.list_to_matched_array(
+                            metab_fwhm_list,
+                            indicies=indicies,
+                            cleanup=True,
+                            dtype=float))
 
         metab_snr_list = [res[0].getQCParams(metab=metab)[0]
                           for res in results]
         file_nm = os.path.join(qc_folder, metab + '_snr.nii.gz')
-        mrsi.write_output(metab_snr_list,
-                          file_nm,
-                          indicies=indicies,
-                          cleanup=True,
-                          dtype=float)
+        save_img_output(file_nm,
+                        mrsi.list_to_matched_array(
+                            metab_snr_list,
+                            indicies=indicies,
+                            cleanup=True,
+                            dtype=float))
 
     # fit
     # TODO: check if data has been conjugated, if so conjugate the predictions
@@ -358,31 +373,34 @@ def main():
     for res, scale in zip(results, mrs_scale):
         pred_list.append(res[0].pred / scale['FID'])
     file_nm = os.path.join(fit_folder, 'fit.nii.gz')
-    mrsi.write_output(pred_list,
-                      file_nm,
-                      indicies=indicies,
-                      cleanup=False,
-                      dtype=np.complex128)
+    save_img_output(file_nm,
+                    mrsi.list_to_matched_array(
+                        pred_list,
+                        indicies=indicies,
+                        cleanup=False,
+                        dtype=np.complex64))
 
     res_list = []
     for res, scale in zip(results, mrs_scale):
         res_list.append(res[0].residuals / scale['FID'])
     file_nm = os.path.join(fit_folder, 'residual.nii.gz')
-    mrsi.write_output(res_list,
-                      file_nm,
-                      indicies=indicies,
-                      cleanup=False,
-                      dtype=np.complex128)
+    save_img_output(file_nm,
+                    mrsi.list_to_matched_array(
+                        res_list,
+                        indicies=indicies,
+                        cleanup=False,
+                        dtype=np.complex64))
 
     baseline_list = []
     for res, scale in zip(results, mrs_scale):
         baseline_list.append(res[0].baseline / scale['FID'])
     file_nm = os.path.join(fit_folder, 'baseline.nii.gz')
-    mrsi.write_output(baseline_list,
-                      file_nm,
-                      indicies=indicies,
-                      cleanup=False,
-                      dtype=np.complex128)
+    save_img_output(file_nm,
+                    mrsi.list_to_matched_array(
+                        baseline_list,