Add tests for niftimrs proc and start of processing provenance.

fsl_mrs/core/nifti_mrs.py

@@ -76,12 +76,20 @@ class NIFTI_MRS(Image):
         if isinstance(args[0], np.ndarray):
             args = list(args)
             args[0] = args[0].conj()
+            filename = None
         elif isinstance(args[0], Path):
             args = list(args)
+            filename = args[0].name
             args[0] = str(args[0])
+        elif isinstance(args[0], str):
+            args = list(args)
+            filename = Path(args[0]).name
 
         super().__init__(*args, **kwargs)
 
+        # Store original filename for reports etc
+        self._filename = filename
+
         # Check that file meets minimum requirements
         try:
             nmrs_version = self.mrs_nifti_version
@@ -173,6 +181,15 @@ class NIFTI_MRS(Image):
         self.header.extensions.append(extension)
         self._set_dim_tags()
 
+    @property
+    def filename(self):
+        '''Name of file object was generated from.
+        Returns empty string if N/A.'''
+        if self._filename:
+            return self._filename
+        else:
+            return ''
+
     def dim_position(self, dim_tag):
         '''Return position of dim if it exists.'''
         if dim_tag in self.dim_tags:
@@ -273,6 +290,7 @@ class NIFTI_MRS(Image):
             dim = self._dim_tag_to_index(remove_dim)
             reduced_data = self.data.take(0, axis=dim)
             new_obj = NIFTI_MRS(reduced_data, header=self.header)
+            new_obj._filename = self.filename
 
             # Modify the dim information in
             hdr_ext = new_obj.hdr_ext

fsl_mrs/tests/test_core_nifti_mrs.py

@@ -43,6 +43,14 @@ def test_nifti_mrs():
     assert obj.copy(remove_dim='DIM_DYN').shape == (1, 1, 1, 4096, 32)
 
 
+def test_nifti_mrs_filename():
+    obj = NIFTI_MRS(data['unprocessed'])
+    assert obj.filename == 'metab_raw.nii.gz'
+
+    obj = gen_new_nifti_mrs(np.zeros((1, 1, 1, 2), dtype=complex), 0.0005, 120.0)
+    assert obj.filename == ''
+
+
 def test_nifti_mrs_save(tmp_path):
     obj = NIFTI_MRS(data['metab'])
     obj.save(tmp_path / 'out')

fsl_mrs/tests/test_utils_nifti_mrs_proc.py

+'''FSL-MRS test script
+
+Test NIfTI-MRS processing
+
+Copyright Will Clarke, University of Oxford, 2021
+'''
+from pathlib import Path
+
+from fsl_mrs.utils.preproc import nifti_mrs_proc as nproc
+from fsl_mrs.utils.mrs_io import read_FID
+from fsl_mrs import __version__
+
+
+testsPath = Path(__file__).parent
+data = testsPath / 'testdata' / 'fsl_mrs_preproc'
+metab = data / 'metab_raw.nii.gz'
+wrefc = data / 'wref_raw.nii.gz'
+wrefq = data / 'quant_raw.nii.gz'
+ecc = data / 'ecc.nii.gz'
+
+
+def test_update_processing_prov():
+    nmrs_obj = read_FID(metab)
+
+    assert 'ProcessingApplied' not in nmrs_obj.hdr_ext
+
+    nproc.update_processing_prov(nmrs_obj, 'test', 'test_str')
+
+    assert 'ProcessingApplied' in nmrs_obj.hdr_ext
+    assert isinstance(nmrs_obj.hdr_ext['ProcessingApplied'], list)
+    assert 'Time' in nmrs_obj.hdr_ext['ProcessingApplied'][0]
+    assert nmrs_obj.hdr_ext['ProcessingApplied'][0]['Program'] == 'FSL-MRS'
+    assert nmrs_obj.hdr_ext['ProcessingApplied'][0]['Version'] == __version__
+    assert nmrs_obj.hdr_ext['ProcessingApplied'][0]['Method'] == 'test'
+    assert nmrs_obj.hdr_ext['ProcessingApplied'][0]['Details'] == 'test_str'
+
+
+def test_coilcombine():
+    nmrs_obj = read_FID(metab)
+    nmrs_ref_obj = read_FID(wrefc)
+    nmrs_ref_obj = nproc.average(nmrs_ref_obj, 'DIM_DYN')
+
+    combined = nproc.coilcombine(nmrs_obj, reference=nmrs_ref_obj)
+
+    assert combined.hdr_ext['ProcessingApplied'][0]['Method'] == 'RF coil combination'
+    assert combined.hdr_ext['ProcessingApplied'][0]['Details']\
+        == 'Coil combination, reference data used (wref_raw.nii.gz), prewhitening applied.'

fsl_mrs/utils/preproc/nifti_mrs_proc.py

@@ -6,9 +6,13 @@ Author: Saad Jbabdi <saad@fmrib.ox.ac.uk>
 
 Copyright (C) 2021 University of Oxford
 SHBASECOPYRIGHT'''
-from fsl_mrs.utils import preproc
+from datetime import datetime
+
 import numpy as np
+
+from fsl_mrs.utils import preproc
 from fsl_mrs.core import NIFTI_MRS
+from fsl_mrs import __version__
 
 
 class DimensionsDoNotMatch(Exception):
@@ -19,6 +23,35 @@ class OnlySVS(Exception):
     pass
 
 
+def update_processing_prov(nmrs_obj: NIFTI_MRS, method, details):
+    """Insert appropriate processing provenance information into the
+    NIfTI-MRS header extension.
+
+    :param nmrs_obj: NIFTI-MRS object which has been modified
+    :type nmrs_obj: fsl_mrs.core.NIFTI_MRS
+    :param method: [description]
+    :type method: str
+    :param details: [description]
+    :type details: str
+    """
+    # 1. Check for ProcessingApplied key and create if not present
+    if 'ProcessingApplied' not in nmrs_obj.hdr_ext:
+        nmrs_obj.add_hdr_field('ProcessingApplied', [])
+
+    # 2. Form object to append.
+    prov_dict = {
+        'Time': datetime.now().isoformat(sep='T', timespec='milliseconds'),
+        'Program': 'FSL-MRS',
+        'Version': __version__,
+        'Method': method,
+        'Details': details}
+
+    # 3. Append
+    current_processing = nmrs_obj.hdr_ext['ProcessingApplied']
+    current_processing.append(prov_dict)
+    nmrs_obj.add_hdr_field('ProcessingApplied', current_processing)
+
+
 def first_index(idx):
     return all([ii == slice(None, None, None) or ii == 0 for ii in idx])
 
@@ -71,6 +104,20 @@ def coilcombine(data, reference=None, no_prewhiten=False, figure=False, report=N
                                       html=report)
             if figure:
                 fig.show()
+
+    # Update processing prov
+    processing_info = 'Coil combination, '
+    if reference is None:
+        processing_info += 'no reference data, '
+    else:
+        processing_info += f'reference data used ({reference.filename}), '
+    if no_prewhiten:
+        processing_info += 'no prewhitening.'
+    else:
+        processing_info += 'prewhitening applied.'
+
+    update_processing_prov(combinedc_obj, 'RF coil combination', processing_info)
+
     return combinedc_obj
 
 
@@ -183,7 +230,7 @@ def aligndiff(data,
     :param report: Provide output location as path to generate report
     :param report_all: True to output all indicies
 
-    :return: Combined data in NIFTI_MRS format.
+    :return: Aligned data in NIFTI_MRS format.
     '''
     if data.shape[data.dim_position(dim_diff)] != 2:
         raise DimensionsDoNotMatch('Diff dimension must be of length 2')