Merge branch 'bf/fslpy_0_3_9_update' into 'master'

BF: update to fslpy 3.9.0

See merge request fsl/fsl_mrs!48

CHANGELOG.rst

 This document contains the FSL-MRS release history in reverse chronological order.
 
+1.1.12 (Wednesday 20th April)
+-----------------------------
+- Update to fslpy version (to 3.9.0) to substantially speed up MRSI preprocessing.
+- Fixes to NIFTI_MRS class for compatibility with new fslpy version.
+- Previous versions of FSL-MRS will not be compatible with fslpy >= 3.9.0
+
 1.1.11 (Monday 4th April 2022)
 ------------------------------
 - Now able to choose the number of workers in fsl_mrs_sim.

fsl_mrs/core/nifti_mrs.py

@@ -327,7 +327,7 @@ class NIFTI_MRS(Image):
             dim - None, dimension index (4, 5, 6) or tag. None iterates over all indices.'''
         if remove_dim:
             dim = self._dim_tag_to_index(remove_dim)
-            reduced_data = self.data.take(0, axis=dim)
+            reduced_data = self[:].take(0, axis=dim)
             new_obj = NIFTI_MRS(reduced_data, header=self.header)
             new_obj._filename = self.filename
 
@@ -350,7 +350,7 @@ class NIFTI_MRS(Image):
 
             return new_obj
         else:
-            return NIFTI_MRS(self.data, header=self.header)
+            return NIFTI_MRS(self[:], header=self.header)
 
     def iterate_over_dims(self, dim=None, iterate_over_space=False, reduce_dim_index=False, voxel_index=None):
         '''Return generator to iterate over all indices or one dimension (and FID).
@@ -364,7 +364,7 @@ class NIFTI_MRS(Image):
             index - data location slice object.
         '''
 
-        data = self.data
+        data = self[:]
         dim = self._dim_tag_to_index(dim)
 
         # Convert indicies to slices to preserve singleton dimensions
@@ -434,14 +434,14 @@ class NIFTI_MRS(Image):
         :yield: Complex FID data with any higher dimensions. Index to data.
         :rtype: tuple
         """
-        data = self.data
+        data = self[:]
 
         def calc_slice_idx(idx):
-            slice_obj = list(idx[:3]) + [slice(None), ] * (self.data.ndim - 3)
+            slice_obj = list(idx[:3]) + [slice(None), ] * (data.ndim - 3)
             return tuple(slice_obj)
 
         for idx in np.ndindex(data.shape[:3]):
-            yield self.data[idx], calc_slice_idx(idx)
+            yield self[idx], calc_slice_idx(idx)
 
     def generate_mrs(self, dim=None, basis_file=None, basis=None, ref_data=None):
         """Generator for MRS or MRSI objects from the data, optionally returning a whole dimension as a list.
@@ -578,9 +578,10 @@ class NIFTI_MRS(Image):
 
         return tvar_dict2, tvar_tuple2.reshape(self.shape[4:]), tvar_array
 
+    # As of move to fslpy 3.9.0 this is no longer required.
     # If save called do some hairy temporary conjugation
     # The save method of fsl.data.image.Image makes a call
     # to __getitem__ resulting in a final undesired conjugation.
-    def save(self, filename=None):
-        self[:] = self[:].conj()
-        super().save(filename=filename)
+    # def save(self, filename=None):
+    #     self[:] = self[:].conj()
+    #     super().save(filename=filename)

fsl_mrs/tests/test_core_nifti_mrs.py

@@ -6,6 +6,7 @@ Copyright Will Clarke, University of Oxford, 2021'''
 
 # Imports
 from pathlib import Path
+from copy import deepcopy
 
 import pytest
 import numpy as np
@@ -53,10 +54,16 @@ def test_nifti_mrs_filename():
 
 def test_nifti_mrs_save(tmp_path):
     obj = NIFTI_MRS(data['metab'])
-    obj.save(tmp_path / 'out')
+    original = deepcopy(obj[:])
 
+    obj.save(tmp_path / 'out')
     assert (tmp_path / 'out.nii.gz').exists()
 
+    obj_reloaded = NIFTI_MRS(tmp_path / 'out.nii.gz')
+
+    assert np.allclose(obj_reloaded[:], obj[:])
+    assert np.allclose(obj_reloaded[:], original)
+
 
 def test_nifti_mrs_generator():
     obj = NIFTI_MRS(data['unprocessed'])
@@ -189,6 +196,17 @@ def test_gen_new_nifti_mrs(tmp_path):
     assert (tmp_path / 'out.nii.gz').exists()
 
 
+def test_gen_new_nifti_mrs_conj(tmp_path):
+    obj_in = NIFTI_MRS(data['metab'])
+
+    nmrs = gen_new_nifti_mrs(obj_in[:],
+                             obj_in.dwelltime,
+                             obj_in.spectrometer_frequency[0],
+                             nucleus='1H')
+
+    assert np.allclose(nmrs[:], obj_in[:])
+
+
 def test_add_remove_field():
 
     nmrs = NIFTI_MRS(data['unprocessed'])

fsl_mrs/tests/test_scripts_proc.py

@@ -275,7 +275,7 @@ def test_coilcombine(svs_data_uncomb, mrsi_data_uncomb, tmp_path):
     # Run directly
     directRun = preproc.coilcombine(svsdata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil combination on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -290,7 +290,7 @@ def test_coilcombine(svs_data_uncomb, mrsi_data_uncomb, tmp_path):
     # Run directly
     directRun = preproc.coilcombine(mrsidata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_average(svs_data, mrsi_data, tmp_path):
@@ -310,7 +310,7 @@ def test_average(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.average(svsdata, 'DIM_DYN')
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil combination on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -326,7 +326,7 @@ def test_average(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.average(mrsidata, 'DIM_DYN')
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_align(svs_data, mrsi_data, tmp_path):
@@ -347,7 +347,7 @@ def test_align(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.align(svsdata, 'DIM_DYN', ppmlim=(-10, 10))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     subprocess.check_call(['fsl_mrs_proc',
                            'align',
@@ -384,7 +384,7 @@ def test_align_all(svs_data_uncomb_reps, tmp_path):
     # Run directly
     directRun = preproc.align(svsdata, 'all', ppmlim=(-10, 10))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_ecc(svs_data, mrsi_data, tmp_path):
@@ -404,7 +404,7 @@ def test_ecc(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.ecc(svsdata, svsdata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil ecc on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -420,7 +420,7 @@ def test_ecc(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.ecc(mrsidata, mrsidata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_remove(svs_data, mrsi_data, tmp_path):
@@ -440,7 +440,7 @@ def test_remove(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.remove_peaks(svsdata, (-10, 10))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil combination on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -456,7 +456,7 @@ def test_remove(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.remove_peaks(mrsidata, (-10, 10))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_model(svs_data, mrsi_data, tmp_path):
@@ -477,7 +477,7 @@ def test_model(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.hlsvd_model_peaks(svsdata, (-10, 10), components=5)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run model on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -494,7 +494,7 @@ def test_model(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.hlsvd_model_peaks(mrsidata, (-10, 10), components=5)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_align_diff(svs_data_diff, mrsi_data_diff, tmp_path):
@@ -518,7 +518,7 @@ def test_align_diff(svs_data_diff, mrsi_data_diff, tmp_path):
                                   'add',
                                   ppmlim=(-10, 10))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
     # TODO: finish MRSI test
 
 
@@ -538,7 +538,7 @@ def test_fshift(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.fshift(svsdata, 1.0 * 123.2)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     subprocess.check_call(['fsl_mrs_proc',
                            'fshift',
@@ -555,7 +555,7 @@ def test_fshift(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.shift_to_reference(svsdata, 4.0, (-5.0, 5.0))
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
     # TODO: finish MRSI test
 
 
@@ -576,7 +576,7 @@ def test_conj(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.conjugate(svsdata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil combination on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -591,7 +591,7 @@ def test_conj(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.conjugate(mrsidata)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
 
 def test_fixed_phase(svs_data, mrsi_data, tmp_path):
@@ -613,7 +613,7 @@ def test_fixed_phase(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.apply_fixed_phase(svsdata, 90, 0.001)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])
 
     # Run coil combination on both sets of data using the command line
     subprocess.check_call(['fsl_mrs_proc',
@@ -630,4 +630,4 @@ def test_fixed_phase(svs_data, mrsi_data, tmp_path):
     # Run directly
     directRun = preproc.apply_fixed_phase(mrsidata, 90, 0.001)
 
-    assert np.allclose(data.data, directRun.data)
+    assert np.allclose(data[:], directRun[:])

fsl_mrs/tests/test_utils_nifti_mrs_tools_misc.py

@@ -16,4 +16,4 @@ def test_conjugate():
 
     conjugated = nmrs_tools.conjugate(nmrs)
 
-    assert np.allclose(conjugated.data, np.conjugate(nmrs.data))
+    assert np.allclose(conjugated[:], np.conjugate(nmrs[:]))

fsl_mrs/tests/test_utils_nifti_mrs_tools_split_merge.py

@@ -365,22 +365,22 @@ def test_split():
     # Functionality testing
 
     out_1, out_2 = nmrs_tools.split(nmrs, 'DIM_DYN', 31)
-    assert out_1.data.shape == (1, 1, 1, 4096, 32, 32)
-    assert out_2.data.shape == (1, 1, 1, 4096, 32, 32)
-    assert np.allclose(out_1.data, nmrs.data[:, :, :, :, :, 0:32])
-    assert np.allclose(out_2.data, nmrs.data[:, :, :, :, :, 32:])
+    assert out_1[:].shape == (1, 1, 1, 4096, 32, 32)
+    assert out_2[:].shape == (1, 1, 1, 4096, 32, 32)
+    assert np.allclose(out_1[:], nmrs[:, :, :, :, :, 0:32])
+    assert np.allclose(out_2[:], nmrs[:, :, :, :, :, 32:])
     assert out_1.hdr_ext == nmrs.hdr_ext
     assert out_1.hdr_ext == nmrs.hdr_ext
     assert np.allclose(out_1.getAffine('voxel', 'world'), nmrs.getAffine('voxel', 'world'))
     assert np.allclose(out_2.getAffine('voxel', 'world'), nmrs.getAffine('voxel', 'world'))
 
     out_1, out_2 = nmrs_tools.split(nmrs, 'DIM_DYN', [0, 32, 63])
-    assert out_1.data.shape == (1, 1, 1, 4096, 32, 61)
-    assert out_2.data.shape == (1, 1, 1, 4096, 32, 3)
+    assert out_1[:].shape == (1, 1, 1, 4096, 32, 61)
+    assert out_2[:].shape == (1, 1, 1, 4096, 32, 3)
     test_list = np.arange(0, 64)
     test_list = np.delete(test_list, [0, 32, 63])
-    assert np.allclose(out_1.data, nmrs.data[:, :, :, :, :, test_list])
-    assert np.allclose(out_2.data, nmrs.data[:, :, :, :, :, [0, 32, 63]])
+    assert np.allclose(out_1[:], nmrs[:][:, :, :, :, :, test_list])
+    assert np.allclose(out_2[:], nmrs[:][:, :, :, :, :, [0, 32, 63]])
 
     # Split some synthetic data with header information
     nhdr_1 = gen_new_nifti_mrs(np.ones((1, 1, 1, 10, 4), dtype=complex),
@@ -461,9 +461,9 @@ def test_merge():
 
     # Functionality testing
     out = nmrs_tools.merge((nmrs_1, nmrs_2), 'DIM_DYN')
-    assert out.data.shape == (1, 1, 1, 4096, 32, 4)
-    assert np.allclose(out.data[:, :, :, :, :, 0:2], nmrs_1.data)
-    assert np.allclose(out.data[:, :, :, :, :, 2:], nmrs_2.data)
+    assert out[:].shape == (1, 1, 1, 4096, 32, 4)
+    assert np.allclose(out[:][:, :, :, :, :, 0:2], nmrs_1[:])
+    assert np.allclose(out[:][:, :, :, :, :, 2:], nmrs_2[:])
     assert out.hdr_ext == nmrs_1.hdr_ext
     assert np.allclose(out.getAffine('voxel', 'world'), nmrs_1.getAffine('voxel', 'world'))
 
@@ -471,7 +471,7 @@ def test_merge():
     nmrs_1_e = nmrs_tools.reorder(nmrs_1, ['DIM_COIL', 'DIM_DYN', 'DIM_EDIT'])
     nmrs_2_e = nmrs_tools.reorder(nmrs_2, ['DIM_COIL', 'DIM_DYN', 'DIM_EDIT'])
     out = nmrs_tools.merge((nmrs_1_e, nmrs_2_e), 'DIM_EDIT')
-    assert out.data.shape == (1, 1, 1, 4096, 32, 2, 2)
+    assert out[:].shape == (1, 1, 1, 4096, 32, 2, 2)
     assert out.hdr_ext['dim_7'] == 'DIM_EDIT'
 
     # Merge some synthetic data with header information
@@ -486,7 +486,7 @@ def test_merge():
     nhdr_2.set_dim_header('DIM_DYN', {'RepetitionTime': [1, 2, 3, 4]})
 
     out = nmrs_tools.merge((nhdr_1, nhdr_2, nhdr_2), 'DIM_DYN')
-    assert out.data.shape == (1, 1, 1, 10, 12)
+    assert out[:].shape == (1, 1, 1, 10, 12)
     assert out.hdr_ext['dim_5'] == 'DIM_DYN'
     assert out.hdr_ext['dim_5_header'] == {'RepetitionTime': [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]}
 
@@ -494,7 +494,7 @@ def test_merge():
     nhdr_2.set_dim_header('DIM_DYN', {'RepetitionTime': [5, 6, 7, 8]})
 
     out = nmrs_tools.merge((nhdr_1, nhdr_2), 'DIM_DYN')
-    assert out.data.shape == (1, 1, 1, 10, 8)
+    assert out[:].shape == (1, 1, 1, 10, 8)
     assert out.hdr_ext['dim_5'] == 'DIM_DYN'
     assert out.hdr_ext['dim_5_header'] == {'RepetitionTime': {'start': 1, 'increment': 1}}
 
@@ -514,7 +514,7 @@ def test_merge():
     nhdr_2_e.set_dim_header('DIM_EDIT', {'OtherTime': [0.2, ]})
 
     out = nmrs_tools.merge((nhdr_1_e, nhdr_2_e), 'DIM_EDIT')
-    assert out.data.shape == (1, 1, 1, 10, 4, 2)
+    assert out[:].shape == (1, 1, 1, 10, 4, 2)
     assert out.hdr_ext['dim_6'] == 'DIM_EDIT'
     assert out.hdr_ext['dim_6_header'] == {'OtherTime': [0.1, 0.2, ]}
 
@@ -536,21 +536,21 @@ def test_reorder():
     # Functionality testing
     # Swap order of dimensions
     out = nmrs_tools.reorder(nmrs, ['DIM_DYN', 'DIM_COIL'])
-    assert out.data.shape == (1, 1, 1, 4096, 64, 32)
-    assert np.allclose(np.swapaxes(nmrs.data, 4, 5), out.data)
+    assert out[:].shape == (1, 1, 1, 4096, 64, 32)
+    assert np.allclose(np.swapaxes(nmrs[:], 4, 5), out[:])
     assert out.hdr_ext['dim_5'] == 'DIM_DYN'
     assert out.hdr_ext['dim_6'] == 'DIM_COIL'
 
     # # Add an additional singleton at end (not reported in shape)
     out = nmrs_tools.reorder(nmrs, ['DIM_COIL', 'DIM_DYN', 'DIM_EDIT'])
-    assert out.data.shape == (1, 1, 1, 4096, 32, 64)
+    assert out[:].shape == (1, 1, 1, 4096, 32, 64)
     assert out.hdr_ext['dim_5'] == 'DIM_COIL'
     assert out.hdr_ext['dim_6'] == 'DIM_DYN'
     assert out.hdr_ext['dim_7'] == 'DIM_EDIT'
 
     # Add an additional singleton at 5 (not reported in shape)
     out = nmrs_tools.reorder(nmrs, ['DIM_EDIT', 'DIM_COIL', 'DIM_DYN'])
-    assert out.data.shape == (1, 1, 1, 4096, 1, 32, 64)
+    assert out[:].shape == (1, 1, 1, 4096, 1, 32, 64)
     assert out.hdr_ext['dim_5'] == 'DIM_EDIT'
     assert out.hdr_ext['dim_6'] == 'DIM_COIL'
     assert out.hdr_ext['dim_7'] == 'DIM_DYN'

fsl_mrs/utils/nifti_mrs_tools/misc.py

@@ -18,4 +18,4 @@ def conjugate(nmrs):
     :rtype: NIFTI_MRS
     """
 
-    return NIFTI_MRS(np.conjugate(nmrs.data), header=nmrs.header)
+    return NIFTI_MRS(np.conjugate(nmrs[:]), header=nmrs.header)

fsl_mrs/utils/nifti_mrs_tools/reshape.py

@@ -33,9 +33,9 @@ def reshape(nmrs, reshape, d5=None, d6=None, d7=None):
     :type d7: str, optional
     """
 
-    shape = nmrs.data.shape[0:4]
+    shape = nmrs[:].shape[0:4]
     shape += reshape
-    nmrs_reshaped = NIFTI_MRS(np.reshape(nmrs.data, shape), header=nmrs.header)
+    nmrs_reshaped = NIFTI_MRS(np.reshape(nmrs[:], shape), header=nmrs.header)
 
     # reshpaed_hrd = _reshape_hdr(nmrs_reshaped.dynamic_hdr_vals[2],)
 

fsl_mrs/utils/nifti_mrs_tools/split_merge.py

@@ -76,8 +76,8 @@ def split(nmrs, dimension, index_or_indicies):
     out_hdr_1 = utils.modify_hdr_ext(split_hdr_ext_1, nmrs.header)
     out_hdr_2 = utils.modify_hdr_ext(split_hdr_ext_2, nmrs.header)
 
-    nmrs_1 = NIFTI_MRS(np.delete(nmrs.data, index, axis=dim_index), header=out_hdr_1)
-    nmrs_2 = NIFTI_MRS(np.take(nmrs.data, index, axis=dim_index), header=out_hdr_2)
+    nmrs_1 = NIFTI_MRS(np.delete(nmrs[:], index, axis=dim_index), header=out_hdr_1)
+    nmrs_2 = NIFTI_MRS(np.take(nmrs[:], index, axis=dim_index), header=out_hdr_2)
 
     return nmrs_1, nmrs_2
 
@@ -198,9 +198,9 @@ def merge(array_of_nmrs, dimension):
 
         if nmrs.ndim == dim_index:
             # If a squeezed singleton on the end.
-            to_concat.append(np.expand_dims(nmrs.data, -1))
+            to_concat.append(np.expand_dims(nmrs[:], -1))
         else:
-            to_concat.append(nmrs.data)
+            to_concat.append(nmrs[:])
 
         # Merge header extension
         if idx == 0:
@@ -317,7 +317,7 @@ def reorder(nmrs, dim_tag_list):
     original_dims = nmrs.ndim
     new_dim = sum(x is not None for x in nmrs.dim_tags) + 4
     dims_to_add = tuple(range(original_dims, new_dim + 1))
-    data_with_singleton = np.expand_dims(nmrs.data, dims_to_add)
+    data_with_singleton = np.expand_dims(nmrs[:], dims_to_add)
 
     # Create list of source indicies
     # Create list of destination indicies

requirements.txt

@@ -7,7 +7,7 @@ jinja2
 beautifulsoup4
 nibabel
 hlsvdpropy
-fslpy>=3.0
+fslpy>=3.9.0
 pillow
 spec2nii>=0.3.0
 dill