Add new module and tests for nifti-mrs operations. Implemented split.

fsl_mrs/tests/test_utils_proc_nifti_mrs_tools.py

+"""Test the split, merge and reorder tools for NIFTI-MRS
+
+Author: Will Clarke <william.clarke@ndcn.ox.ac.uk>
+Copyright (C) 2021 University of Oxford
+"""
+
+from pathlib import Path
+import pytest
+
+import numpy as np
+
+from fsl_mrs.utils import mrs_io
+from fsl_mrs.utils.preproc import nifti_mrs_tools as nmrs_tools
+
+testsPath = Path(__file__).parent
+test_data_split = testsPath / 'testdata' / 'fsl_mrs_preproc' / 'metab_raw.nii.gz'
+test_data_merge = testsPath / 'testdata' / 'fsl_mrs_preproc' / 'wref_raw.nii.gz'
+
+
+def test_split():
+    """Test the split functionality
+    """
+    nmrs = mrs_io.read_FID(test_data_split)
+
+    # Error testing
+    # Wrong dim tag
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_EDIT', 1)
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "DIM_EDIT not found as dimension tag."\
+                                     " This data contains ['DIM_COIL', 'DIM_DYN', None]."
+
+    # Wrong dim index (no dim in this data)
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 6, 1)
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "Dimension must be one of 4, 5, or 6 (or DIM_TAG string)."\
+                                     " This data has 6 dimensions,"\
+                                     " i.e. a maximum dimension value of 5."
+
+    # Wrong dim index (too low)
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 3, 1)
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "Dimension must be one of 4, 5, or 6 (or DIM_TAG string)."\
+                                     " This data has 6 dimensions,"\
+                                     " i.e. a maximum dimension value of 5."
+
+    # Wrong dim index type
+    with pytest.raises(TypeError) as exc_info:
+        nmrs_tools.split(nmrs, [3, ], 1)
+
+    assert exc_info.type is TypeError
+    assert exc_info.value.args[0] == "Dimension must be an int (4, 5, or 6) or string (DIM_TAG string)."
+
+    # Single index - out of range low
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_DYN', -1)
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "index_or_indicies must be between 0 and N-1,"\
+                                     " where N is the size of the specified dimension (64)."
+
+    # Single index - out of range high
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_DYN', 64)
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "index_or_indicies must be between 0 and N-1,"\
+                                     " where N is the size of the specified dimension (64)."
+
+    # List of indicies - out of range low
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_DYN', [-1, 0, 1])
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "index_or_indicies must have elements between 0 and N,"\
+                                     " where N is the size of the specified dimension (64)."
+
+    # List of indicies - out of range high
+    with pytest.raises(ValueError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_DYN', [0, 65])
+
+    assert exc_info.type is ValueError
+    assert exc_info.value.args[0] == "index_or_indicies must have elements between 0 and N,"\
+                                     " where N is the size of the specified dimension (64)."
+
+    # List of indicies - wrong type
+    with pytest.raises(TypeError) as exc_info:
+        nmrs_tools.split(nmrs, 'DIM_DYN', '1')
+
+    assert exc_info.type is TypeError
+    assert exc_info.value.args[0] == "index_or_indicies must be single index or list of indicies"
+
+    # Functionality testing
+
+    out_1, out_2 = nmrs_tools.split(nmrs, 'DIM_DYN', 32)
+    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.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)
+    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]])

fsl_mrs/utils/preproc/nifti_mrs_tools.py

+"""Tools for merging, splitting and reordering the dimensions of NIfTI-MRS
+
+    Author: Will Clarke <william.clarke@ndcn.ox.ac.uk>
+    Copyright (C) 2021 University of Oxford
+"""
+
+import numpy as np
+from fsl_mrs.core.nifti_mrs import NIFTI_MRS, NIFTIMRS_DimDoesntExist
+
+
+def split(nmrs, dimension, index_or_indicies):
+    """Splits, or extracts indices from, a specified dimension of a
+    NIFTI_MRS object. Output is two NIFTI_MRS objects. Header information preserved.
+
+    :param nmrs: Input nifti_mrs object to split
+    :type nmrs: fsl_mrs.core.nifti_mrs.NIFTI_MRS
+    :param dimension: Dimension tag or one of 4, 5, 6 (for 0-indexed 5th, 6th, and 7th)
+    :type dimension: str or int
+    :param index_or_indicies: Single integer index to split after,
+        or list of interger indices to insert into second array.
+        E.g. '0' will place the first index into the first output
+        and 1 -> N in the second.
+        '[1, 5, 10]' will place 1, 5 and 10 into the second output
+        and all other will remain in the first.
+    :type index_or_indicies: int or [int]
+    :return: Two NIFTI_MRS object containing the split files
+    :rtype: fsl_mrs.core.nifti_mrs.NIFTI_MRS
+    """
+
+    if isinstance(dimension, str):
+        try:
+            dim_index = nmrs.dim_position(dimension)
+        except NIFTIMRS_DimDoesntExist:
+            raise ValueError(f'{dimension} not found as dimension tag. This data contains {nmrs.dim_tags}.')
+    elif isinstance(dimension, int):
+        if dimension > (nmrs.ndim - 1) or dimension < 4:
+            raise ValueError('Dimension must be one of 4, 5, or 6 (or DIM_TAG string).'
+                             f' This data has {nmrs.ndim} dimensions,'
+                             f' i.e. a maximum dimension value of {nmrs.ndim-1}.')
+        dim_index = dimension
+    else:
+        raise TypeError('Dimension must be an int (4, 5, or 6) or string (DIM_TAG string).')
+
+    # Construct indexing
+    if isinstance(index_or_indicies, int):
+        if index_or_indicies < 0\
+                or index_or_indicies >= nmrs.shape[dim_index]:
+            raise ValueError('index_or_indicies must be between 0 and N-1,'
+                             f' where N is the size of the specified dimension ({nmrs.shape[dim_index]}).')
+        index = np.arange(index_or_indicies, nmrs.shape[dim_index])
+
+    elif isinstance(index_or_indicies, list):
+        if not np.logical_and(np.asarray(index_or_indicies) >= 0,
+                              np.asarray(index_or_indicies) <= nmrs.shape[dim_index]).all():
+            raise ValueError('index_or_indicies must have elements between 0 and N,'
+                             f' where N is the size of the specified dimension ({nmrs.shape[dim_index]}).')
+        index = index_or_indicies
+
+    else:
+        raise TypeError('index_or_indicies must be single index or list of indicies')
+
+    nmrs_1 = NIFTI_MRS(np.delete(nmrs.data, index, axis=dim_index), header=nmrs.header)
+    nmrs_2 = NIFTI_MRS(np.take(nmrs.data, index, axis=dim_index), header=nmrs.header)
+
+    return nmrs_1, nmrs_2