tests/test_atlases_query.py → fsl/tests/test_atlases_query.py

@@ -17,7 +17,7 @@ import fsl.transform.affine     as affine
 import fsl.utils.image.resample as resample
 import fsl.utils.cache          as cache
 
-from . import (testdir, make_random_mask)
+from fsl.tests import (testdir, make_random_mask)
 
 
 pytestmark = pytest.mark.fsltest
@@ -41,17 +41,16 @@ _atlases = cache.Cache()
 def _get_atlas(atlasID, res, summary=False):
     atlas = _atlases.get((atlasID, res, summary), default=None)
     if atlas is None:
+        atlas = fslatlases.loadAtlas(atlasID,
+                                     loadSummary=summary,
+                                     resolution=res)
+
+        # We need some atlases to be loaded into memory,
+        # so we can use boolean-mask-based indexing
         if summary or atlasID in ('talairach', 'striatum-structural',
                                   'jhu-labels', 'smatt'):
-            kwargs = {}
-        else:
-            kwargs = {'loadData'  : False,
-                      'calcRange' : False}
+            atlas.data
 
-        atlas = fslatlases.loadAtlas(atlasID,
-                                     loadSummary=summary,
-                                     resolution=res,
-                                     **kwargs)
         _atlases.put((atlasID, res, summary), atlas)
 
     return atlas
@@ -85,7 +84,7 @@ def _get_zero_mask(aimg):
         elif isinstance(aimg, fslatlases.ProbabilisticAtlas):
 
             # Keep memory usage down
-            zmask = np.ones(aimg.shape[:3], dtype=np.bool)
+            zmask = np.ones(aimg.shape[:3], dtype=bool)
             for vol in range(aimg.shape[-1]):
                 zmask = np.logical_and(zmask, aimg[..., vol] == 0)
 
@@ -284,7 +283,7 @@ def _eval_mask_query(atlas, query, qtype, qin):
         rmask = resample.resample(
             mask, atlas.shape[:3], dtype=np.float32, order=0)[0]
 
-    rmask = np.array(rmask, dtype=np.bool)
+    rmask = np.array(rmask, dtype=bool)
 
     def evalLabel():
 

tests/test_bids.py → fsl/tests/test_bids.py

@@ -18,8 +18,11 @@ import fsl.utils.bids  as     fslbids
 
 
 def test_parseFilename():
-    with pytest.raises(ValueError):
-        fslbids.parseFilename('bad.txt')
+    badtests = ['bad_file.txt']
+
+    for test in badtests:
+        with pytest.raises(ValueError):
+            fslbids.parseFilename(test)
 
     tests = [
         ('sub-01_ses-01_t1w.nii.gz',
@@ -62,12 +65,12 @@ def test_isBIDSFile():
         Path('sub-01_ses-01_t1w.nii'),
         Path('sub-01_ses-01_t1w.json'),
         Path('a-1_b-2_c-3_d-4_e.nii.gz'),
+        Path('sub-01_ses-01_t1w.txt'),
     ]
     badfiles = [
         Path('sub-01_ses-01.nii.gz'),
         Path('sub-01_ses-01_t1w'),
         Path('sub-01_ses-01_t1w.'),
-        Path('sub-01_ses-01_t1w.txt'),
         Path('sub_ses-01_t1w.nii.gz'),
         Path('sub-01_ses_t1w.nii.gz'),
     ]
@@ -105,3 +108,56 @@ def test_loadMetadata():
         assert fslbids.loadMetadata(t1) == {**meta2, **meta1}
         json4.write_text(json.dumps(meta4))
         assert fslbids.loadMetadata(t1) == {**meta4, **meta2, **meta1}
+
+
+def test_loadMetadata_control_characters():
+    dd     = Path('dataset_description.json')
+    t1     = Path('sub-01/func/sub-01_task-stim_bold.nii.gz')
+    json1  = Path('sub-01/func/sub-01_task-stim_bold.json')
+    meta1  =  {"a" : "1",  "b" : "2\x19\x20"}
+    smeta1 = '{"a" : "1",  "b" : "2\x19\x20"}'
+
+    with tempdir():
+        dd.touch()
+        Path(op.dirname(t1)).mkdir(parents=True)
+        t1.touch()
+        assert fslbids.loadMetadata(t1) == {}
+        json1.write_text(smeta1)
+        assert fslbids.loadMetadata(t1) == meta1
+
+
+def test_loadMetadata_symlinked():
+    ddreal = Path('a')
+    t1real = Path('b')
+    j1real = Path('c')
+    j2real = Path('d')
+    j3real = Path('e')
+    j4real = Path('f')
+    dd     = Path('data/dataset_description.json')
+    t1     = Path('data/sub-01/func/sub-01_task-stim_bold.nii.gz')
+    json1  = Path('data/sub-01/func/sub-01_task-stim_bold.json')
+    json2  = Path('data/sub-01/sub-01_bold.json')
+    json3  = Path('data/sub-01_t1w.json')
+    json4  = Path('data/sub-01/task-stim_bold.json')
+    meta1  = {'a' : '1',   'b' : '2'}
+    meta2  = {'a' : '10',  'c' : '3'}
+    meta3  = {'a' : '109', 'b' : '99'}
+    meta4  = {'c' : '9',   'd' : '5'}
+
+    with tempdir():
+        ddreal.touch()
+        t1real.touch()
+        j1real.write_text(json.dumps(meta1))
+        j2real.write_text(json.dumps(meta2))
+        j3real.write_text(json.dumps(meta3))
+        j4real.write_text(json.dumps(meta4))
+
+        Path(op.dirname(t1)).mkdir(parents=True)
+        dd   .symlink_to(op.join('..',             ddreal))
+        t1   .symlink_to(op.join('..', '..', '..', t1real))
+        json1.symlink_to(op.join('..', '..', '..', j1real))
+        json2.symlink_to(op.join('..', '..',       j2real))
+        json3.symlink_to(op.join('..',             j3real))
+        json4.symlink_to(op.join('..', '..',       j4real))
+
+        assert fslbids.loadMetadata(t1) == {**meta4, **meta2, **meta1}

tests/test_cache.py → fsl/tests/test_cache.py

@@ -146,3 +146,22 @@ def test_lru():
     c[3]
     c[4]
     assert len(c) == 3
+
+
+
+def test_accessors():
+    c = cache.Cache(maxsize=3)
+
+    c[0] = '0'
+    c[1] = '1'
+    c[2] = '2'
+    c[3] = '3'
+
+    assert list(c.keys())   == [ 1,        2,        3]
+    assert list(c.values()) == ['1',      '2',      '3']
+    assert list(c.items())  == [(1, '1'), (2, '2'), (3, '3')]
+
+    assert 0 not in c
+    assert 1     in c
+    assert 2     in c
+    assert 3     in c

fsl/tests/test_cifti.py

+from fsl.data import cifti
+import os.path as op
+import numpy as np
+import nibabel as nib
+from numpy import testing
+import fsl.tests as tests
+from nibabel.cifti2 import cifti2_axes
+
+
+def volumetric_brain_model():
+    mask = np.random.randint(2, size=(10, 10, 10)) > 0
+    return cifti2_axes.BrainModelAxis.from_mask(mask, affine=np.eye(4))
+
+
+def surface_brain_model():
+    mask = np.random.randint(2, size=100) > 0
+    return cifti2_axes.BrainModelAxis.from_mask(mask, name='cortex')
+
+
+def volumetric_parcels(return_mask=False):
+    mask = np.random.randint(5, size=(10, 10, 10))
+    axis = cifti2_axes.ParcelsAxis(
+        [f'vol_{idx}' for idx in range(1, 5)],
+        voxels=[np.stack(np.where(mask == idx), axis=-1) for idx in range(1, 5)],
+        vertices=[{} for _ in range(1, 5)],
+        volume_shape=mask.shape,
+        affine=np.eye(4),
+    )
+    if return_mask:
+        return axis, mask
+    else:
+        return axis
+
+
+def surface_parcels(return_mask=False):
+    mask = np.random.randint(5, size=100)
+    axis = cifti2_axes.ParcelsAxis(
+        [f'surf_{idx}' for idx in range(1, 5)],
+        voxels=[np.zeros((0, 3), dtype=int) for _ in range(1, 5)],
+        vertices=[{'CIFTI_STRUCTURE_CORTEX': np.where(mask == idx)[0]} for idx in range(1, 5)],
+        nvertices={'CIFTI_STRUCTURE_CORTEX': 100},
+    )
+    if return_mask:
+        return axis, mask
+    else:
+        return axis
+
+
+def gen_data(axes):
+    return np.random.randn(*(5 if ax is None else len(ax) for ax in axes))
+
+
+def test_read_gifti():
+    testdir = op.join(op.dirname(__file__), 'testdata')
+
+    shapefile = op.join(testdir, 'example.shape.gii')
+    ref_data = nib.load(shapefile).darrays[0].data
+
+    data = cifti.load(shapefile)
+    assert isinstance(data, cifti.DenseCifti)
+    assert data.arr.shape == (642, )
+    testing.assert_equal(data.arr, ref_data)
+    testing.assert_equal(data.brain_model_axis.vertex, np.arange(642))
+    assert len(data.brain_model_axis.nvertices) == 1
+    assert data.brain_model_axis.nvertices['CIFTI_STRUCTURE_OTHER'] == 642
+
+    data = cifti.load(shapefile, mask_values=(ref_data[0], ))
+    assert isinstance(data, cifti.DenseCifti)
+    assert data.arr.shape == (np.sum(ref_data != ref_data[0]), )
+    testing.assert_equal(data.arr, ref_data[ref_data != ref_data[0]])
+    testing.assert_equal(data.brain_model_axis.vertex, np.where(ref_data != ref_data[0])[0])
+    assert len(data.brain_model_axis.nvertices) == 1
+    assert data.brain_model_axis.nvertices['CIFTI_STRUCTURE_OTHER'] == 642
+
+    cifti.load(op.join(testdir, 'example'))
+
+
+def test_read_nifti():
+    mask = np.random.randint(2, size=(10, 10, 10)) > 0
+    values = np.random.randn(10, 10, 10)
+    for mask_val in (0, np.nan):
+        values[~mask] = mask_val
+        affine = np.concatenate((np.random.randn(3, 4), np.array([0, 0, 0, 1])[None, :]), axis=0)
+        with tests.testdir():
+            nib.Nifti1Image(values, affine).to_filename("masked_image.nii.gz")
+            data = cifti.load("masked_image")
+            assert isinstance(data, cifti.DenseCifti)
+            testing.assert_equal(data.arr, values[mask])
+            testing.assert_allclose(data.brain_model_axis.affine, affine)
+            assert len(data.brain_model_axis.nvertices) == 0
+
+
+def check_io(data: cifti.Cifti, extension):
+    with tests.testdir():
+        data.save("test")
+        assert op.isfile(f'test.{extension}.nii')
+        loaded = cifti.load("test")
+        if data.arr.ndim == 1:
+            testing.assert_equal(data.arr, loaded.arr[0])
+            assert data.axes == loaded.axes[1:]
+        else:
+            testing.assert_equal(data.arr, loaded.arr)
+            assert data.axes == loaded.axes
+
+
+def test_io_cifti():
+    for cifti_class, cifti_type, main_axis_options in (
+        (cifti.DenseCifti, 'd', (volumetric_brain_model(), surface_brain_model(),
+                                 volumetric_brain_model() + surface_brain_model())),
+        (cifti.ParcelCifti, 'p', (volumetric_parcels(), surface_parcels(),
+                                  volumetric_parcels() + surface_parcels())),
+    ):
+        for main_axis in main_axis_options:
+            with tests.testdir():
+                data_1d = cifti_class(gen_data([main_axis]), [main_axis])
+                check_io(data_1d, f'{cifti_type}scalar')
+
+                connectome = cifti_class(gen_data([main_axis, main_axis]), (main_axis, main_axis))
+                check_io(connectome, f'{cifti_type}conn')
+
+                scalar_axis = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+                scalar = cifti_class(gen_data([scalar_axis, main_axis]), (scalar_axis, main_axis))
+                check_io(scalar, f'{cifti_type}scalar')
+
+                label_axis = cifti2_axes.LabelAxis(['A', 'B', 'C'], {1: ('some parcel', (1, 0, 0, 1))})
+                label = cifti_class(gen_data([label_axis, main_axis]), (label_axis, main_axis))
+                check_io(label, f'{cifti_type}label')
+
+                series_axis = cifti2_axes.SeriesAxis(10, 3, 50, unit='HERTZ')
+                series = cifti_class(gen_data([series_axis, main_axis]), (series_axis, main_axis))
+                check_io(series, f'{cifti_type}tseries')
+
+                if cifti_type == 'd':
+                    parcel_axis = surface_parcels()
+                    dpconn = cifti_class(gen_data([parcel_axis, main_axis]), (parcel_axis, main_axis))
+                    check_io(dpconn, 'dpconn')
+                else:
+                    dense_axis = surface_brain_model()
+                    pdconn = cifti_class(gen_data([dense_axis, main_axis]), (dense_axis, main_axis))
+                    check_io(pdconn, 'pdconn')
+
+
+def test_extract_dense():
+    vol_bm = volumetric_brain_model()
+    surf_bm = surface_brain_model()
+    for bm in (vol_bm + surf_bm, surf_bm + vol_bm):
+        for ndim, no_other_axis in ((1, True), (2, False), (2, True)):
+            if ndim == 1:
+                data = cifti.DenseCifti(gen_data([bm]), [bm])
+            else:
+                scl = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+                data = cifti.DenseCifti(gen_data([scl, bm]),
+                                        [None if no_other_axis else scl, bm])
+
+            # extract volume
+            ref_arr = data.arr[..., data.brain_model_axis.volume_mask]
+            vol_image = data.to_image(fill=np.nan)
+            if ndim == 1:
+                assert vol_image.shape == data.brain_model_axis.volume_shape
+            else:
+                assert vol_image.shape == data.brain_model_axis.volume_shape + (3, )
+            assert np.isfinite(vol_image.data).sum() == len(vol_bm) * (3 if ndim == 2 else 1)
+            testing.assert_equal(vol_image.data[tuple(vol_bm.voxel.T)], ref_arr.T)
+
+            from_image = cifti.DenseCifti.from_image(vol_image)
+            assert from_image.brain_model_axis == vol_bm
+            testing.assert_equal(from_image.arr, ref_arr)
+
+            # extract surface
+            ref_arr = data.arr[..., data.brain_model_axis.surface_mask]
+            mask, surf_data = data.surface('cortex', partial=True)
+            assert surf_data.shape[-1] < 100
+            testing.assert_equal(ref_arr, surf_data)
+            testing.assert_equal(surf_bm.vertex, mask)
+
+            surf_data_full = data.surface('cortex', fill=np.nan)
+            assert surf_data_full.shape[-1] == 100
+            mask_full = np.isfinite(surf_data_full)
+            if ndim == 2:
+                assert (mask_full.any(0) == mask_full.all(0)).all()
+                mask_full = mask_full[0]
+            assert mask_full.sum() == len(surf_bm)
+            assert mask_full[..., mask].sum() == len(surf_bm)
+            testing.assert_equal(surf_data_full[..., mask_full], ref_arr)
+
+
+def test_extract_parcel():
+    vol_parcel, vol_mask = volumetric_parcels(return_mask=True)
+    surf_parcel, surf_mask = surface_parcels(return_mask=True)
+    parcel = vol_parcel + surf_parcel
+    for ndim, no_other_axis in ((1, True), (2, False), (2, True)):
+        if ndim == 1:
+            data = cifti.ParcelCifti(gen_data([parcel]), [parcel])
+        else:
+            scl = cifti2_axes.ScalarAxis(['A', 'B', 'C'])
+            data = cifti.ParcelCifti(gen_data([scl, parcel]),
+                                     [None if no_other_axis else scl, parcel])
+
+        # extract volume
+        vol_image = data.to_image(fill=np.nan)
+        if ndim == 1:
+            assert vol_image.shape == data.parcel_axis.volume_shape
+        else:
+            assert vol_image.shape == data.parcel_axis.volume_shape + (3, )
+        assert np.isfinite(vol_image.data).sum() == np.sum(vol_mask != 0) * (3 if ndim == 2 else 1)
+        if ndim == 1:
+            testing.assert_equal(vol_mask != 0, np.isfinite(vol_image.data))
+            for idx in range(1, 5):
+                testing.assert_allclose(vol_image.data[vol_mask == idx], data.arr[..., idx - 1])
+        else:
+            for idx in range(3):
+                testing.assert_equal(vol_mask != 0, np.isfinite(vol_image.data[..., idx]))
+                for idx2 in range(1, 5):
+                    testing.assert_allclose(vol_image.data[vol_mask == idx2, idx], data.arr[idx, idx2 - 1])
+
+        # extract surface
+        mask, surf_data = data.surface('cortex', partial=True)
+        assert surf_data.shape[-1] == (surf_mask != 0).sum()
+        assert (surf_mask[mask] != 0).all()
+        print(data.arr)
+        for idx in range(1, 5):
+            if ndim == 1:
+                testing.assert_equal(surf_data.T[surf_mask[mask] == idx], data.arr[idx + 3])
+            else:
+                for idx2 in range(3):
+                    testing.assert_equal(surf_data.T[surf_mask[mask] == idx, idx2], data.arr[idx2, idx + 3])
+
+        surf_data_full = data.surface('cortex', partial=False)
+        assert surf_data_full.shape[-1] == 100
+        if ndim == 1:
+            testing.assert_equal(np.isfinite(surf_data_full), surf_mask != 0)
+            for idx in range(1, 5):
+                testing.assert_equal(surf_data_full.T[surf_mask == idx], data.arr[idx + 3])
+        else:
+            for idx2 in range(3):
+                testing.assert_equal(np.isfinite(surf_data_full)[idx2], (surf_mask != 0))
+                for idx in range(1, 5):
+                    testing.assert_equal(surf_data_full.T[surf_mask == idx, idx2], data.arr[idx2, idx + 3])
+
+
+def test_brainstructure():
+    for primary in ['cortex', 'cerebellum']:
+        for secondary in [None, 'white', 'pial']:
+            for gtype in [None, 'volume', 'surface']:
+                for orientation in ['left', 'right', 'both']:
+                    bst = cifti.BrainStructure(primary, secondary, orientation, gtype)
+                    print(bst.cifti)
+                    assert bst.cifti == 'CIFTI_STRUCTURE_%s%s' % (primary.upper(), '' if orientation == 'both' else '_' + orientation.upper())
+                    assert bst.gifti['AnatomicalStructurePrimary'][:len(primary)] == primary.capitalize()
+                    assert len(bst.gifti) == (1 if secondary is None else 2)
+                    if secondary is not None:
+                        assert bst.gifti['AnatomicalStructureSecondary'] == secondary.capitalize()
+                    assert bst == cifti.BrainStructure(primary, secondary, orientation, gtype)
+                    assert bst == bst
+                    assert bst != cifti.BrainStructure('Thalamus', secondary, orientation, gtype)
+                    if secondary is None:
+                        assert bst == cifti.BrainStructure(primary, 'midplane', orientation, gtype)
+                    else:
+                        assert bst != cifti.BrainStructure(primary, 'midplane', orientation, gtype)
+                    if (gtype == 'volume' and primary == 'cortex') or (gtype == 'surface' and primary != 'cortex'):
+                        assert cifti.BrainStructure.from_string(bst.cifti) != bst
+                    else:
+                        assert cifti.BrainStructure.from_string(bst.cifti) == bst
+                    assert cifti.BrainStructure.from_string(bst.cifti).secondary is None

tests/test_dicom.py → fsl/tests/test_dicom.py

@@ -22,6 +22,9 @@ import fsl.data.dicom    as fsldcm
 import fsl.utils.tempdir as tempdir
 
 
+from fsl.tests import mockFSLDIR, touch
+
+
 datadir = op.join(op.dirname(__file__), 'testdata')
 
 
@@ -29,15 +32,16 @@ pytestmark = pytest.mark.dicomtest
 
 
 @contextlib.contextmanager
-def install_dcm2niix(version='1.0.20190902'):
+def install_dcm2niix(version='1.0.20220720'):
     filenames = {
+        '1.0.20201102' : 'v1.0.20201102/dcm2niix_lnx.zip',
         '1.0.20190902' : 'v1.0.20190902/dcm2niix_lnx.zip',
-        '1.0.20190410' : 'v1.0.20190410/dcm2niix_11-Apr-2019_lnx.zip',
         '1.0.20181125' : 'v1.0.20181125/dcm2niix_25-Nov-2018_lnx.zip',
         '1.0.20171017' : 'v1.0.20171017/dcm2niix_18-Oct-2017_lnx.zip',
+        '1.0.20220720' : 'v1.0.20220720/dcm2niix_lnx.zip',
     }
     prefix = 'https://github.com/rordenlab/dcm2niix/releases/download/'
-    url    = prefix + filenames[version]
+    url    = prefix + filenames.get(version, f'v{version}/dcm2niix_lnx.zip')
 
     with tempdir.tempdir() as td:
         request.urlretrieve(url, 'dcm2niix.zip')
@@ -47,9 +51,9 @@ def install_dcm2niix(version='1.0.20190902'):
 
         os.chmod(op.join(td, 'dcm2niix'), 0o755)
 
-        path = op.pathsep.join((op.abspath('.'), os.environ['PATH']))
+        path = op.abspath('dcm2niix')
 
-        with mock.patch.dict('os.environ', {'PATH' : path}):
+        with mock.patch('fsl.data.dicom.dcm2niix', return_value=path):
             try:
                 yield
             finally:
@@ -64,6 +68,31 @@ def test_disabled():
             fsldcm.loadSeries({})
 
 
+def test_dcm2niix():
+    """
+    """
+    env = os.environ.copy()
+    env.pop('FSLDIR', None)
+    with tempdir.tempdir() as td:
+        env['PATH'] = td
+        with mock.patch('os.environ', env):
+            assert fsldcm.dcm2niix() == 'dcm2niix'
+
+        bindir   = op.join(td, 'bin')
+        dcm2niix = op.join(bindir, 'dcm2niix')
+        os.makedirs(bindir)
+        touch(dcm2niix)
+        os.chmod(dcm2niix, 0o755)
+        env['PATH'] = bindir
+        with mock.patch('os.environ', env):
+            assert fsldcm.dcm2niix() == dcm2niix
+
+        with mockFSLDIR(bin=['dcm2niix']) as fsldir:
+            env['FSLDIR'] = fsldir
+            dcm2niix      = op.join(fsldir, 'bin', 'dcm2niix')
+            with mock.patch('os.environ', env):
+                assert fsldcm.dcm2niix() == dcm2niix
+
 
 def test_installedVersion():
     tests = [
@@ -122,14 +151,16 @@ def test_scanDir():
         datafile = op.join(datadir, 'example_dicom.tbz2')
 
         with tarfile.open(datafile) as f:
-            f.extractall()
+            f.extractall(filter='data')
 
         series = fsldcm.scanDir('.')
         assert len(series) == 2
 
         for s in series:
-            assert (s['PatientName'] == 'MCCARTHY_PAUL' or
-                    s['PatientName'] == 'MCCARTHY_PAUL_2')
+            assert s['PatientName'] in ('MCCARTHY_PAUL',
+                                        'MCCARTHY^PAUL',
+                                        'MCCARTHY_PAUL_2',
+                                        'MCCARTHY^PAUL^2')
 
 
 def test_sersiesCRC():
@@ -158,7 +189,7 @@ def test_sersiesCRC():
 def test_loadSeries():
 
     # test a pre-CRC and a post-CRC version
-    for version in ('1.0.20190410', '1.0.20190902'):
+    for version in ('1.0.20181125', '1.0.20201102'):
 
         with install_dcm2niix(version):
 
@@ -170,23 +201,26 @@ def test_loadSeries():
             dcmdir   = os.getcwd()
             series   = fsldcm.scanDir(dcmdir)
             expShape = (512, 512, 1)
-            explens  = [1, 1]
 
-            for s, explen in zip(series, explens):
+            for s in series:
 
                 imgs = fsldcm.loadSeries(s)
 
-                assert len(imgs) == explen
-
                 for img in imgs:
 
                     assert img.dicomDir               == dcmdir
                     assert img.shape                  == expShape
                     assert img[:].shape               == expShape
-                    assert img.getMeta('PatientName') == 'MCCARTHY_PAUL' or \
-                           img.getMeta('PatientName') == 'MCCARTHY_PAUL_2'
+                    assert img.getMeta('PatientName') in ('MCCARTHY_PAUL',
+                                                          'MCCARTHY^PAUL',
+                                                          'MCCARTHY_PAUL_2',
+                                                          'MCCARTHY^PAUL^2')
                     assert 'PatientName'                      in img.metaKeys()
                     assert 'MCCARTHY_PAUL'                    in img.metaValues() or \
-                           'MCCARTHY_PAUL_2'                  in img.metaValues()
+                           'MCCARTHY^PAUL'                    in img.metaValues() or \
+                           'MCCARTHY_PAUL_2'                  in img.metaValues() or \
+                           'MCCARTHY^PAUL^2'                  in img.metaValues()
                     assert ('PatientName', 'MCCARTHY_PAUL')   in img.metaItems() or \
-                           ('PatientName', 'MCCARTHY_PAUL_2') in img.metaItems()
+                           ('PatientName', 'MCCARTHY^PAUL')   in img.metaItems() or \
+                           ('PatientName', 'MCCARTHY_PAUL_2') in img.metaItems() or \
+                           ('PatientName', 'MCCARTHY^PAUL^2') in img.metaItems()

tests/test_dtifit.py → fsl/tests/test_dtifit.py

@@ -10,7 +10,7 @@ import numpy   as np
 
 import pytest
 
-import tests
+import fsl.tests       as tests
 import fsl.data.dtifit as dtifit
 import fsl.data.image  as fslimage
 

tests/test_ensure.py → fsl/tests/test_ensure.py

@@ -5,6 +5,7 @@
 # Author: Paul McCarthy <pauldmccarthy@gmail.com>
 #
 
+import pathlib
 
 import numpy   as np
 import nibabel as nib
@@ -12,7 +13,7 @@ import nibabel as nib
 import fsl.utils.tempdir as tempdir
 import fsl.utils.ensure  as ensure
 
-from . import make_random_image
+from fsl.tests import make_random_image
 
 
 def test_ensureIsImage():
@@ -22,8 +23,10 @@ def test_ensureIsImage():
 
         assert ensure.ensureIsImage(img) is img
 
-        loaded = [ensure.ensureIsImage('image.nii'),
-                  ensure.ensureIsImage('image')]
+        loaded = [ensure.ensureIsImage(             'image.nii'),
+                  ensure.ensureIsImage(             'image'),
+                  ensure.ensureIsImage(pathlib.Path('image')),
+                  ensure.ensureIsImage(pathlib.Path('image.nii'))]
 
         for l in loaded:
             assert isinstance(l, nib.nifti1.Nifti1Image)

tests/test_featanalysis.py → fsl/tests/test_featanalysis.py

@@ -17,7 +17,7 @@ import numpy     as np
 
 import pytest
 
-import tests
+import fsl.tests             as tests
 import fsl.data.featanalysis as featanalysis
 import fsl.data.featdesign   as featdesign
 import fsl.data.image        as fslimage
@@ -59,7 +59,7 @@ def test_isFEATDir():
     # it's not a feat directory
     assert not featanalysis.isFEATDir('nonexistent.feat')
 
-    # If any of the above files are not 
+    # If any of the above files are not
     # present, it is not a FEAT directory
     perms = it.chain(it.combinations(paths, 1),
                      it.combinations(paths, 2),
@@ -69,7 +69,7 @@ def test_isFEATDir():
             assert not featanalysis.isFEATDir(
                 op.join(testdir, 'analysis.feat'))
 
-    
+
 def test_hasStats():
 
     with tests.testdir(['analysis.feat/stats/zstat1.nii.gz']) as testdir:
@@ -78,7 +78,7 @@ def test_hasStats():
 
     with tests.testdir(['analysis.feat/stats/zstat1.txt']) as testdir:
         featdir = op.join(testdir, 'analysis.feat')
-        assert not featanalysis.hasStats(featdir) 
+        assert not featanalysis.hasStats(featdir)
 
 
 def test_hasMelodicDir():
@@ -94,7 +94,7 @@ def test_getAnalysisDir():
              'analysis.feat/design.fsf',
              'analysis.feat/design.mat',
              'analysis.feat/design.con']
-    
+
     testpaths = ['analysis.feat/filtered_func_data.nii.gz',
                  'analysis.feat/stats/zstat1.nii.gz',
                  'analysis.feat/logs/feat4_post',
@@ -106,7 +106,7 @@ def test_getAnalysisDir():
             t = op.join(testdir, t)
             assert featanalysis.getAnalysisDir(t) == expected
 
-    
+
 def test_getTopLevelAnalysisDir():
     testcases = [
         ('analysis.feat/filtered_func_data.ica/melodic_IC.nii.gz', 'analysis.feat'),
@@ -127,7 +127,7 @@ def test_getReportFile():
 
     for paths, expected in testcases:
         with tests.testdir(paths) as testdir:
-        
+
             featdir = op.join(testdir, 'analysis.feat')
 
             if expected:
@@ -145,7 +145,7 @@ def test_loadContrasts():
          /ContrastName1 c1
          /ContrastName2 c2
          /ContrastName3 c3
-         /NumContrasts 3 
+         /NumContrasts 3
          /Matrix
          1 0 0
          0 1 0
@@ -188,9 +188,9 @@ def test_loadContrasts():
         0 1 1
         """,
     ]
-    
+
     with pytest.raises(Exception):
-        featanalysis.loadContrasts('no file') 
+        featanalysis.loadContrasts('no file')
 
     with tests.testdir() as testdir:
         featdir = op.join(testdir, 'analysis.feat')
@@ -210,6 +210,77 @@ def test_loadContrasts():
                 featanalysis.loadContrasts(featdir)
 
 
+def test_loadFTests():
+
+    goodtests = [
+        ("""
+         /NumWaves 4
+         /NumContrasts 3
+         /Matrix
+         0 1 0 1
+         0 0 1 1
+         1 1 1 1
+         """,
+         [[0, 1, 0, 1],
+          [0, 0, 1, 1],
+          [1, 1, 1, 1]]),
+        ("""
+         /NumWaves 10
+         /NumContrasts 2
+         /Matrix
+         0 1 0 1 0 1 1 0 0 1
+         0 0 1 1 1 0 0 1 0 0
+         """,
+         [[0, 1, 0, 1, 0, 1, 1, 0, 0, 1],
+          [0, 0, 1, 1, 1, 0, 0, 1, 0, 0]]),
+    ]
+    badtests  = [
+        """
+        /NumWaves 10
+        /NumContrasts 2
+        """,
+        """
+        /NumContrasts 2
+        /Matrix
+        1 0
+        0 1
+        """,
+        """
+        /NumWaves Burgers
+        /NumContrasts 2
+        /Matrix
+        1 0
+        0 1
+        """,
+        """
+        /Matrix
+        1 0
+        0 1
+        """,
+        """
+        /NumWaves  4
+        /NumContrasts 3
+        /Matrix
+        1 0 0 0 1 0 0
+        0 1 0 0 1 0 0
+        """,
+    ]
+
+    with tests.testdir() as testdir:
+        featdir = op.join(testdir, 'analysis.feat')
+        for contents, expect in goodtests:
+            designcon = op.join(featdir, 'design.fts')
+            tests.make_dummy_file(designcon, textwrap.dedent(contents).strip())
+            assert featanalysis.loadFTests(featdir) == expect
+
+        for contents in badtests:
+            designcon = op.join(featdir, 'design.fts')
+            tests.make_dummy_file(designcon, textwrap.dedent(contents).strip())
+            with pytest.raises(Exception):
+                featanalysis.loadFTests(featdir)
+
+
+
 def test_loadSettings():
 
     contents = """
@@ -240,9 +311,10 @@ def test_loadSettings():
 
     with tests.testdir() as testdir:
         featdir = op.join(testdir, 'analysis.feat')
-        tests.make_dummy_file(op.join(featdir, 'design.fsf'), contents)
-        result = featanalysis.loadSettings(featdir)
-        assert result == expected
+        designfsf = op.join(featdir, 'design.fsf')
+        tests.make_dummy_file(designfsf, contents)
+        assert featanalysis.loadSettings(featdir) == expected
+        assert featanalysis.loadFsf(designfsf) == expected
 
 
 def test_loadDesign():
@@ -274,7 +346,7 @@ def test_isFirstLevelAnalysis():
                 '2ndlevel_1.gfeat', '2ndlevel_2.gfeat']
 
     for featdir in featdirs:
-        
+
         expected = featdir.startswith('1')
         featdir  = op.join(datadir, featdir)
         settings = featanalysis.loadSettings(featdir)
@@ -288,7 +360,10 @@ def test_loadClusterResults():
                 '2ndlevel_1.gfeat/cope1.feat', '2ndlevel_1.gfeat/cope2.feat',
                 '2ndlevel_2.gfeat/cope1.feat', '2ndlevel_2.gfeat/cope2.feat']
     ncontrasts = [2, 2, 2, 1, 1, 1, 1]
-    nclusters  = [[1, 5], [2, 2], [3, 5], [7], [1], [10], [27]]
+    nftests    = [0, 0, 1, 0, 0, 1, 1]
+
+    # nclusters = [contrastclusters] + [ftestclusters]
+    nclusters  = [[1, 5], [2, 2], [3, 5, 3], [7], [1], [10, 8], [27, 21]]
 
     with pytest.raises(Exception):
         featanalysis.loadClusterResults('notafeatdir')
@@ -300,17 +375,18 @@ def test_loadClusterResults():
 
         with tests.testdir() as testdir:
 
-            # For higher level analyses, the
-            # loadClusterResults function peeks
-            # at the FEAT input data file
-            # header, so we have to generate it.
+            # work from a copy of the test data directory
             newfeatdir = op.join(testdir, 'analysis.feat')
             shutil.copytree(op.join(datadir, featdir), newfeatdir)
             featdir = newfeatdir
 
+            # For higher level analyses, the
+            # loadClusterResults function peeks
+            # at the FEAT input data file
+            # header, so we have to generate it.
             if not firstlevel:
                 datafile = op.join(featdir, 'filtered_func_data.nii.gz')
-                data  = np.random.randint(1, 10, (91, 109, 91))
+                data  = np.random.randint(1, 10, (91, 109, 91), dtype=np.int32)
                 xform = np.array([[-2, 0, 0,   90],
                                   [ 0, 2, 0, -126],
                                   [ 0, 0, 2,  -72],
@@ -318,20 +394,55 @@ def test_loadClusterResults():
                 fslimage.Image(data, xform=xform).save(datafile)
 
             settings = featanalysis.loadSettings(featdir)
+            # contrasts
             for c in range(ncontrasts[i]):
                 clusters = featanalysis.loadClusterResults(
                     featdir, settings, c)
-
                 assert len(clusters) == nclusters[i][c]
+            # f-tests
+            for c in range(nftests[i]):
+                clusters = featanalysis.loadClusterResults(
+                    featdir, settings, c, ftest=True)
+                assert len(clusters) == nclusters[i][c + ncontrasts[i]]
 
             # Test calling the function on a feat dir
             # which doesn't have any cluster results
+            # (2ndlevel_2.gfeat)
             if i == len(featdirs) - 1:
                 for clustfile in glob.glob(op.join(featdir, 'cluster*txt')):
                     os.remove(clustfile)
                 assert featanalysis.loadClusterResults(
                     featdir, settings, 0) is None
 
+    # The above loop just checks that the number of
+    # clusters loaded for each analysis was correct.
+    # Below we check that the cluster data was loaded
+    # correctly, just for one analysis
+    featdir  = op.join(datadir, '1stlevel_1.feat')
+    settings = featanalysis.loadSettings(featdir)
+    cluster  = featanalysis.loadClusterResults(featdir, settings, 0)[0]
+    expected = {
+        'index'    : 1,
+        'nvoxels'  : 296,
+        'p'        : 1.79e-27,
+        'logp'     : 26.7,
+        'zmax'     : 6.03,
+        'zmaxx'    : 34,
+        'zmaxy'    : 10,
+        'zmaxz'    : 1,
+        'zcogx'    : 31.4,
+        'zcogy'    : 12.3,
+        'zcogz'    : 1.72,
+        'copemax'  : 612,
+        'copemaxx' : 34,
+        'copemaxy' : 10,
+        'copemaxz' : 1,
+        'copemean' : 143
+    }
+
+    for k, v in expected.items():
+        assert np.isclose(v, getattr(cluster, k))
+
 
 def test_getDataFile():
     paths = ['analysis.feat/filtered_func_data.nii.gz',
@@ -392,9 +503,9 @@ def test_getResidualFile():
                 assert featanalysis.getResidualFile(featdir) == expect
             else:
                 with pytest.raises(fslpath.PathError):
-                    featanalysis.getResidualFile(featdir) 
+                    featanalysis.getResidualFile(featdir)
+
 
-    
 def test_getPEFile():
     testcases = [
         (['analysis.feat/stats/pe1.nii.gz',
@@ -416,7 +527,7 @@ def test_getPEFile():
                     assert featanalysis.getPEFile(featdir, pei) == expect
                 else:
                     with pytest.raises(fslpath.PathError):
-                        featanalysis.getPEFile(featdir, pei) 
+                        featanalysis.getPEFile(featdir, pei)
 
 
 def test_getCOPEFile():
@@ -440,8 +551,32 @@ def test_getCOPEFile():
                     assert featanalysis.getCOPEFile(featdir, ci) == expect
                 else:
                     with pytest.raises(fslpath.PathError):
-                        featanalysis.getCOPEFile(featdir, ci) 
- 
+                        featanalysis.getCOPEFile(featdir, ci)
+
+
+def test_getZStatFile():
+    testcases = [
+        (['analysis.feat/stats/zstat1.nii.gz',
+          'analysis.feat/stats/zstat2.nii.gz'], True),
+        (['analysis.feat/stats/zstat1.nii.gz'], True),
+        (['analysis.feat/stats/zstat0.nii.gz'], False),
+        (['analysis.feat/stats/zstat1.txt'],    False),
+    ]
+
+    for paths, shouldPass in testcases:
+        with tests.testdir(paths) as testdir:
+            featdir = op.join(testdir, 'analysis.feat')
+
+            for zi in range(len(paths)):
+                expect = op.join(
+                    featdir, 'stats', 'zstat{}.nii.gz'.format(zi + 1))
+
+                if shouldPass:
+                    assert featanalysis.getZStatFile(featdir, zi) == expect
+                else:
+                    with pytest.raises(fslpath.PathError):
+                        featanalysis.getZStatFile(featdir, zi)
+
 
 def test_getZStatFile():
     testcases = [
@@ -464,8 +599,31 @@ def test_getZStatFile():
                     assert featanalysis.getZStatFile(featdir, zi) == expect
                 else:
                     with pytest.raises(fslpath.PathError):
-                        featanalysis.getZStatFile(featdir, zi) 
- 
+                        featanalysis.getZStatFile(featdir, zi)
+
+
+def test_getZFStatFile():
+    testcases = [
+        (['analysis.feat/stats/zfstat1.nii.gz',
+          'analysis.feat/stats/zfstat2.nii.gz'], True),
+        (['analysis.feat/stats/zfstat1.nii.gz'], True),
+        (['analysis.feat/stats/zfstat0.nii.gz'], False),
+        (['analysis.feat/stats/zfstat1.txt'],    False),
+    ]
+    for paths, shouldPass in testcases:
+        with tests.testdir(paths) as testdir:
+            featdir = op.join(testdir, 'analysis.feat')
+
+            for zi in range(len(paths)):
+                expect = op.join(
+                    featdir, 'stats', 'zfstat{}.nii.gz'.format(zi + 1))
+
+                if shouldPass:
+                    assert featanalysis.getZFStatFile(featdir, zi) == expect
+                else:
+                    with pytest.raises(fslpath.PathError):
+                        featanalysis.getZFStatFile(featdir, zi)
+
 
 def test_getClusterMaskFile():
     testcases = [
@@ -488,4 +646,28 @@ def test_getClusterMaskFile():
                     assert featanalysis.getClusterMaskFile(featdir, ci) == expect
                 else:
                     with pytest.raises(fslpath.PathError):
-                        featanalysis.getClusterMaskFile(featdir, ci) 
+                        featanalysis.getClusterMaskFile(featdir, ci)
+
+
+def test_getFClusterMaskFile():
+    testcases = [
+        (['analysis.feat/cluster_mask_zfstat1.nii.gz',
+          'analysis.feat/cluster_mask_zfstat2.nii.gz'], True),
+        (['analysis.feat/cluster_mask_zfstat1.nii.gz'], True),
+        (['analysis.feat/cluster_mask_zfstat0.nii.gz'], False),
+        (['analysis.feat/cluster_mask_zfstat1.txt'],    False),
+    ]
+
+    for paths, shouldPass in testcases:
+        with tests.testdir(paths) as testdir:
+            featdir = op.join(testdir, 'analysis.feat')
+
+            for ci in range(len(paths)):
+                expect = op.join(
+                    featdir, 'cluster_mask_zfstat{}.nii.gz'.format(ci + 1))
+
+                if shouldPass:
+                    assert featanalysis.getFClusterMaskFile(featdir, ci) == expect
+                else:
+                    with pytest.raises(fslpath.PathError):
+                        featanalysis.getFClusterMaskFile(featdir, ci)

tests/test_featdesign.py → fsl/tests/test_featdesign.py

@@ -103,12 +103,13 @@ with the following commands:
 """
 
 
-import os.path as op
-import numpy   as np
+import os.path  as op
+import textwrap as tw
+import numpy    as np
 
 import pytest
 
-import tests
+import fsl.tests             as     tests
 from   fsl.utils.tempdir     import tempdir
 import fsl.data.featdesign   as     featdesign
 import fsl.data.featanalysis as     featanalysis
@@ -400,6 +401,45 @@ def test_loadDesignMat():
         featdesign.loadDesignMat(badfile)
 
 
+# fsl/fslpy!469
+def test_loadFEATDesignFile():
+    with tempdir():
+        with open('design1.con', 'wt') as f:
+            f.write(tw.dedent("""
+            /ContrastName1  mycontrast
+            /NumWaves       2
+            /NumContrasts   1
+
+            /Matrix
+            10 20
+            """).strip())
+
+        with open('design2.con', 'wt') as f:
+            f.write(tw.dedent("""
+            /ContrastName1
+            /NumWaves       2
+            /NumContrasts   1
+
+            /Matrix
+            10 20
+            """).strip())
+
+        des1 = featanalysis.loadFEATDesignFile('design1.con')
+        exp1 = {'ContrastName1': 'mycontrast',
+                'NumWaves':      '2',
+                'NumContrasts':  '1',
+                'Matrix':        '10 20'}
+
+        des2 = featanalysis.loadFEATDesignFile('design2.con')
+        exp2 = {'ContrastName1': '',
+                'NumWaves':      '2',
+                'NumContrasts':  '1',
+                'Matrix':        '10 20'}
+
+        assert des1 == exp1
+        assert des2 == exp2
+
+
 def test_VoxelwiseEVs():
     with tempdir():
         img = tests.make_random_image('image.nii.gz', (10, 10, 10, 10))

tests/test_featimage.py → fsl/tests/test_featimage.py

@@ -16,7 +16,7 @@ import numpy   as np
 
 import pytest
 
-import tests
+import fsl.tests             as tests
 import fsl.data.featimage    as featimage
 import fsl.data.featdesign   as featdesign
 import fsl.data.featanalysis as featanalysis
@@ -88,7 +88,8 @@ def test_FEATImage_attributes():
                     copes=False,
                     zstats=False,
                     residuals=False,
-                    clustMasks=False)
+                    clustMasks=False,
+                    zfstats=False)
             else:
                 featdir = op.join(datadir, featdir)
 
@@ -100,6 +101,7 @@ def test_FEATImage_attributes():
             design   = featdesign.FEATFSFDesign(featdir, settings)
             desmat   = design.getDesign()
             evnames  = [ev.title for ev in design.getEVs()]
+            ftests   = featanalysis.loadFTests(featdir)
             contrastnames, contrasts = featanalysis.loadContrasts(featdir)
 
             assert np.all(np.isclose(fi.shape,         shape))
@@ -115,8 +117,10 @@ def test_FEATImage_attributes():
             assert fi.numEVs()                 == desmat.shape[1]
             assert fi.evNames()                == evnames
             assert fi.numContrasts()           == len(contrasts)
+            assert fi.numFTests()              == len(ftests)
             assert fi.contrastNames()          == contrastnames
             assert fi.contrasts()              == contrasts
+            assert fi.ftests()                 == ftests
             assert np.all(np.isclose(fi.getDesign(), desmat))
 
             assert fi.thresholds() == featanalysis.getThresholds(settings)
@@ -138,7 +142,7 @@ def test_FEATImage_imageAccessors():
 
         shape = TEST_ANALYSES[featdir]['shape']
         xform = TEST_ANALYSES[featdir]['xform']
-        
+
         with tests.testdir() as testdir:
 
             if 'realdata' not in featdir:
@@ -153,9 +157,10 @@ def test_FEATImage_imageAccessors():
             shape4D = shape
             shape   = shape4D[:3]
 
-            fi    = featimage.FEATImage(featdir)
-            nevs  = fi.numEVs()
-            ncons = fi.numContrasts()
+            fi      = featimage.FEATImage(featdir)
+            nevs    = fi.numEVs()
+            ncons   = fi.numContrasts()
+            nftests = fi.numFTests()
 
             # Testing the FEATImage internal cache
             for i in range(2):
@@ -166,6 +171,9 @@ def test_FEATImage_imageAccessors():
                     assert fi.getCOPE(       con).shape == shape
                     assert fi.getZStats(     con).shape == shape
                     assert fi.getClusterMask(con).shape == shape
+                for ft in range(nftests):
+                    assert fi.getZFStats(     ft).shape == shape
+                    assert fi.getFClusterMask(ft).shape == shape
             del fi
             fi = None