def test_getNumComponents():
paths = ['analysis.ica/melodic_IC.nii.gz',
'analysis.ica/melodic_mix',
'analysis.ica/melodic_FTmix']
with tests.testdir(paths) as testdir:
meldir = op.join(testdir, 'analysis.ica')
icfile = op.join(meldir, 'melodic_IC.nii.gz')
tests.make_random_image(icfile, (10, 10, 10, 17))
assert mela.getNumComponents(meldir) == 17
with tests.testdir(paths) as testdir:
meldir = op.join(testdir, 'analysis.ica')
with pytest.raises(Exception):
mela.getNumComponents(meldir)
def test_VoxelwiseEVs():
with tempdir():
img = tests.make_random_image('image.nii.gz', (10, 10, 10, 10))
ev1 = featdesign.VoxelwiseEV(0, 0, 'ev1', 'image.nii.gz')
ev2 = featdesign.VoxelwiseConfoundEV(0, 0, 'ev2', 'image.nii.gz')
for xyz in tests.random_voxels((10, 10, 10), 10):
x, y, z = map(int, xyz)
exp = img.dataobj[x, y, z, :]
assert np.all(ev1.image[x, y, z, :] == exp)
assert np.all(ev2.image[x, y, z, :] == exp)
def test_looksLikeTensorImage():
# dtifit outputs one of two formats:
# - the eigenvectors/values of the tensor matrix
# - the raw values of the tensor matrix
#
# looksLikeTensorImage tests for the latter
testcases = [((10, 10, 10), False), ((10, 10, 10, 5), False),
((10, 10, 10, 6), True)]
with tests.testdir() as testdir:
fname = op.join(testdir, 'tensor_image.nii')
for dims, expected in testcases:
tests.make_random_image(fname, dims=dims)
img = fslimage.Image(fname)
assert dtifit.looksLikeTensorImage(img) == expected
img = None
def test_DTIFitTensor():
with tests.testdir() as testdir:
with pytest.raises(Exception):
dtifit.DTIFitTensor(testdir)
v1file = op.join(testdir, 'dti_V1.nii')
v2file = op.join(testdir, 'dti_V2.nii')
v3file = op.join(testdir, 'dti_V3.nii')
l1file = op.join(testdir, 'dti_L1.nii')
l2file = op.join(testdir, 'dti_L2.nii')
l3file = op.join(testdir, 'dti_L3.nii')
v1 = tests.make_random_image(v1file, (5, 5, 5, 3)).get_data()
v2 = tests.make_random_image(v2file, (5, 5, 5, 3)).get_data()
v3 = tests.make_random_image(v3file, (5, 5, 5, 3)).get_data()
l1 = tests.make_random_image(l1file, (5, 5, 5)) .get_data()
l2 = tests.make_random_image(l2file, (5, 5, 5)) .get_data()
l3 = tests.make_random_image(l3file, (5, 5, 5)) .get_data()
dtiobj = dtifit.DTIFitTensor(testdir)
assert np.all(np.isclose(dtiobj.V1()[:], v1))
assert np.all(np.isclose(dtiobj.V2()[:], v2))
assert np.all(np.isclose(dtiobj.V3()[:], v3))
assert np.all(np.isclose(dtiobj.L1()[:], l1))
assert np.all(np.isclose(dtiobj.L2()[:], l2))
assert np.all(np.isclose(dtiobj.L3()[:], l3))
v1 = fslimage.Image(v1file)
assert np.all(np.isclose(dtiobj.voxToWorldMat, v1.voxToWorldMat))
assert np.all(np.isclose(dtiobj.shape[:3], v1.shape[:3]))
assert np.all(np.isclose(dtiobj.pixdim[:3], v1.pixdim[:3]))
def test_compressed_voxelwise_ev():
testcases = [((1, 1, 10, 10), (0, 0, 5)), ((1, 10, 1, 10), (0, 5, 0)),
((10, 1, 1, 10), (5, 0, 0))]
with tempdir():
for shape, vox in testcases:
img = tests.make_random_image('vev.nii.gz', shape)
vev = featdesign.VoxelwiseEV(0, 0, 'ev1', 'vev.nii.gz')
x, y, z = vox
assert np.all(vev.getData(5, 5, 5) == img.dataobj[x, y, z, :])
def test_callfsl():
with tests.testdir() as testdir:
fname = op.join(testdir, 'myimage.nii.gz')
img = tests.make_random_image(fname)
img = img.get_data()
# Pass a single string
cmd = 'fslstats {} -m'.format(fname)
with mock.patch('fsl.utils.callfsl.sp.check_output',
mock_check_output):
result = callfsl.callFSL(cmd)
assert np.isclose(float(result), img.mean())
# Or pass a list of args
result = callfsl.callFSL(*cmd.split())
assert np.isclose(float(result), img.mean())
# Bad commands
badcmds = ['fslblob', 'fslstats notafile']
for cmd in badcmds:
with pytest.raises((OSError, sp.CalledProcessError)):
callfsl.callFSL(cmd)
# No FSL - should crash
cmd = 'fslinfo {}'.format(fname)
with mock.patch('fsl.utils.callfsl.sp.check_output',
mock_check_output):
callfsl.callFSL(cmd)
fslplatform.fsldir = None
with pytest.raises(Exception):
callfsl.callFSL(cmd)
