def test_nifti_dataset_from3_d():
"""Test NiftiDataset based on 3D volume(s)
"""
tssrc = os.path.join(pymvpa_dataroot, 'bold')
masrc = os.path.join(pymvpa_dataroot, 'mask')
# Test loading of 3D volumes
# by default we are enforcing 4D, testing here with the demo 3d mask
ds = fmri_dataset(masrc, mask=masrc, targets=1)
assert_equal(len(ds), 1)
plain_data = NiftiImage(masrc).data
# Lets check if mapping back works as well
assert_array_equal(plain_data,
map2nifti(ds).data.reshape(plain_data.shape))
# test loading from a list of filenames
# for now we should fail if trying to load a mix of 4D and 3D volumes
assert_raises(ValueError, fmri_dataset, (masrc, tssrc),
mask=masrc, targets=1)
# Lets prepare some custom NiftiImage
dsfull = fmri_dataset(tssrc, mask=masrc, targets=1)
ds_selected = dsfull[3]
nifti_selected = map2nifti(ds_selected)
# Load dataset from a mix of 3D volumes
# (given by filenames and NiftiImages)
labels = [123, 2, 123]
ds2 = fmri_dataset((masrc, masrc, nifti_selected),
mask=masrc, targets=labels)
assert_equal(ds2.nsamples, 3)
assert_array_equal(ds2.samples[0], ds2.samples[1])
assert_array_equal(ds2.samples[2], dsfull.samples[3])
assert_array_equal(ds2.targets, labels)
def test_nifti_mapper():
"""Basic testing of map2Nifti
"""
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d'),
targets=[1,2])
# test mapping of ndarray
vol = map2nifti(data, np.ones((294912,), dtype='int16'))
assert_equal(vol.data.shape, (24, 96, 128))
assert_true((vol.data == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.data.shape, (2, 24, 96, 128))
def test_nifti_dataset_from3_d():
"""Test NiftiDataset based on 3D volume(s)
"""
tssrc = os.path.join(pymvpa_dataroot, 'bold.nii.gz')
masrc = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
# Test loading of 3D volumes
# by default we are enforcing 4D, testing here with the demo 3d mask
ds = fmri_dataset(masrc, mask=masrc, targets=1)
assert_equal(len(ds), 1)
if externals.exists('nibabel'):
import nibabel
plain_data = nibabel.load(masrc).get_data()
# Lets check if mapping back works as well
assert_array_equal(plain_data,
map2nifti(ds).get_data().reshape(plain_data.shape))
else:
import nifti
plain_data = nifti.NiftiImage(masrc).data
# Lets check if mapping back works as well
assert_array_equal(plain_data,
map2nifti(ds).data.reshape(plain_data.shape))
# test loading from a list of filenames
# for now we should fail if trying to load a mix of 4D and 3D volumes
assert_raises(ValueError,
fmri_dataset, (masrc, tssrc),
mask=masrc,
targets=1)
# Lets prepare some custom NiftiImage
dsfull = fmri_dataset(tssrc, mask=masrc, targets=1)
ds_selected = dsfull[3]
nifti_selected = map2nifti(ds_selected)
# Load dataset from a mix of 3D volumes
# (given by filenames and NiftiImages)
labels = [123, 2, 123]
ds2 = fmri_dataset((masrc, masrc, nifti_selected),
mask=masrc,
targets=labels)
assert_equal(ds2.nsamples, 3)
assert_array_equal(ds2.samples[0], ds2.samples[1])
assert_array_equal(ds2.samples[2], dsfull.samples[3])
assert_array_equal(ds2.targets, labels)
def test_nifti_self_mapper():
"""Test map2Nifti facility ran without arguments
"""
example_path = os.path.join(pymvpa_dataroot, 'example4d')
example = NiftiImage(example_path)
data = fmri_dataset(samples=example_path,
targets=[1,2])
# Map read data to itself
vol = map2nifti(data)
assert_equal(vol.data.shape, example.data.shape)
assert_array_equal(vol.data, example.data)
data.samples[:] = 1
vol = map2nifti(data)
assert_true((vol.data == 1).all())
def test_nifti_mapper(filename):
"""Basic testing of map2Nifti
"""
import nibabel
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=[1,2])
# test mapping of ndarray
vol = map2nifti(data, np.ones((294912,), dtype='int16'))
assert_equal(vol.get_shape(), (128, 96, 24))
assert_true((vol.get_data() == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.get_shape(), (128, 96, 24, 2))
ok_(isinstance(vol, data.a.imgtype))
# test providing custom imgtypes
vol = map2nifti(data, imgtype=nibabel.Nifti1Pair)
ok_(isinstance(vol, nibabel.Nifti1Pair))
# Lets generate a dataset using an alternative format (MINC)
# and see if type persists
volminc = nibabel.MincImage(vol.get_data(),
vol.get_affine(),
vol.get_header())
ok_(isinstance(volminc, nibabel.MincImage))
dsminc = fmri_dataset(volminc, targets=1)
ok_(dsminc.a.imgtype is nibabel.MincImage)
ok_(isinstance(dsminc.a.imghdr, nibabel.minc.MincImage.header_class))
# Lets test if we could save/load now into Analyze volume/dataset
if externals.versions['nibabel'] < '1.1.0':
raise SkipTest('nibabel prior 1.1.0 had an issue with types comprehension')
volanal = map2nifti(dsminc, imgtype=nibabel.AnalyzeImage) # MINC has no 'save' capability
ok_(isinstance(volanal, nibabel.AnalyzeImage))
volanal.to_filename(filename)
dsanal = fmri_dataset(filename, targets=1)
# this one is tricky since it might become Spm2AnalyzeImage
ok_('AnalyzeImage' in str(dsanal.a.imgtype))
ok_('AnalyzeHeader' in str(dsanal.a.imghdr.__class__))
volanal_ = map2nifti(dsanal)
ok_(isinstance(volanal_, dsanal.a.imgtype)) # type got preserved
def test_nifti_mapper():
"""Basic testing of map2Nifti
"""
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=[1,2])
# test mapping of ndarray
vol = map2nifti(data, np.ones((294912,), dtype='int16'))
if externals.exists('nibabel'):
assert_equal(vol.get_shape(), (128, 96, 24))
assert_true((vol.get_data() == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.get_shape(), (128, 96, 24, 2))
else:
assert_equal(vol.data.shape, (24, 96, 128))
assert_true((vol.data == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.data.shape, (2, 24, 96, 128))
def test_nifti_mapper():
"""Basic testing of map2Nifti
"""
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'example4d.nii.gz'),
targets=[1, 2])
# test mapping of ndarray
vol = map2nifti(data, np.ones((294912, ), dtype='int16'))
if externals.exists('nibabel'):
assert_equal(vol.get_shape(), (128, 96, 24))
assert_true((vol.get_data() == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.get_shape(), (128, 96, 24, 2))
else:
assert_equal(vol.data.shape, (24, 96, 128))
assert_true((vol.data == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
assert_equal(vol.data.shape, (2, 24, 96, 128))
def test_er_nifti_dataset():
# setup data sources
tssrc = os.path.join(pymvpa_dataroot, 'bold.nii.gz')
evsrc = os.path.join(pymvpa_dataroot, 'fslev3.txt')
masrc = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
evs = FslEV3(evsrc).to_events()
# load timeseries
ds_orig = fmri_dataset(tssrc)
# segment into events
ds = eventrelated_dataset(ds_orig, evs, time_attr='time_coords')
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 3200)
assert_equal(len(ds), len(evs))
# the voxel indices are reflattened after boxcaring , but still 3D
assert_equal(ds.fa.voxel_indices.shape, (ds.nfeatures, 3))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:800], ds.fa.voxel_indices[800:1600])
# each feature got an event offset value
assert_array_equal(ds.fa.event_offsetidx, np.repeat([0,1,2,3], 800))
# check for all event attributes
assert_true('onset' in ds.sa)
assert_true('duration' in ds.sa)
assert_true('features' in ds.sa)
# check samples
origsamples = _load_anyimg(tssrc)[0]
for i, onset in \
enumerate([value2idx(e['onset'], ds_orig.sa.time_coords, 'floor')
for e in evs]):
assert_array_equal(ds.samples[i], origsamples[onset:onset+4].ravel())
assert_array_equal(ds.sa.time_indices[i], np.arange(onset, onset + 4))
assert_array_equal(ds.sa.time_coords[i],
np.arange(onset, onset + 4) * 2.5)
for evattr in [a for a in ds.sa
if a.count("event_attrs")
and not a.count('event_attrs_event')]:
assert_array_equal(evs[i]['_'.join(evattr.split('_')[2:])],
ds.sa[evattr].value[i])
# check offset: only the last one exactly matches the tr
assert_array_equal(ds.sa.orig_offset, [1, 1, 0])
# map back into voxel space, should ignore addtional features
nim = map2nifti(ds)
# origsamples has t,x,y,z
assert_equal(nim.get_shape(), origsamples.shape[1:] + (len(ds) * 4,))
# check shape of a single sample
nim = map2nifti(ds, ds.samples[0])
# pynifti image has [t,]z,y,x
assert_equal(nim.get_shape(), (40, 20, 1, 4))
# and now with masking
ds = fmri_dataset(tssrc, mask=masrc)
ds = eventrelated_dataset(ds, evs, time_attr='time_coords')
nnonzero = len(_load_anyimg(masrc)[0].nonzero()[0])
assert_equal(nnonzero, 530)
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 4 * 530)
assert_equal(len(ds), len(evs))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:nnonzero],
ds.fa.voxel_indices[nnonzero:2*nnonzero])
def test_er_nifti_dataset():
# setup data sources
tssrc = os.path.join(pymvpa_dataroot, 'bold.nii.gz')
evsrc = os.path.join(pymvpa_dataroot, 'fslev3.txt')
masrc = os.path.join(pymvpa_dataroot, 'mask.nii.gz')
evs = FslEV3(evsrc).to_events()
# load timeseries
ds_orig = fmri_dataset(tssrc)
# segment into events
ds = eventrelated_dataset(ds_orig, evs, time_attr='time_coords')
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 3200)
assert_equal(len(ds), len(evs))
# the voxel indices are reflattened after boxcaring , but still 3D
assert_equal(ds.fa.voxel_indices.shape, (ds.nfeatures, 3))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:800],
ds.fa.voxel_indices[800:1600])
# each feature got an event offset value
assert_array_equal(ds.fa.event_offsetidx, np.repeat([0, 1, 2, 3], 800))
# check for all event attributes
assert_true('onset' in ds.sa)
assert_true('duration' in ds.sa)
assert_true('features' in ds.sa)
# check samples
origsamples = _load_anyimg(tssrc)[0]
for i, onset in \
enumerate([value2idx(e['onset'], ds_orig.sa.time_coords, 'floor')
for e in evs]):
assert_array_equal(ds.samples[i], origsamples[onset:onset + 4].ravel())
assert_array_equal(ds.sa.time_indices[i], np.arange(onset, onset + 4))
assert_array_equal(ds.sa.time_coords[i],
np.arange(onset, onset + 4) * 2.5)
for evattr in [
a for a in ds.sa
if a.count("event_attrs") and not a.count('event_attrs_event')
]:
assert_array_equal(evs[i]['_'.join(evattr.split('_')[2:])],
ds.sa[evattr].value[i])
# check offset: only the last one exactly matches the tr
assert_array_equal(ds.sa.orig_offset, [1, 1, 0])
# map back into voxel space, should ignore addtional features
nim = map2nifti(ds)
if externals.exists('nibabel'):
# origsamples has t,x,y,z
assert_equal(nim.get_shape(), origsamples.shape[1:] + (len(ds) * 4, ))
# check shape of a single sample
nim = map2nifti(ds, ds.samples[0])
# pynifti image has [t,]z,y,x
assert_equal(nim.get_shape(), (40, 20, 1, 4))
else:
# origsamples has t,x,y,z but pynifti image has [t,]z,y,x
assert_equal(nim.data.T.shape, origsamples.shape[1:] + (len(ds) * 4, ))
# check shape of a single sample
nim = map2nifti(ds, ds.samples[0])
# pynifti image has [t,]z,y,x
assert_equal(nim.data.T.shape, (40, 20, 1, 4))
# and now with masking
ds = fmri_dataset(tssrc, mask=masrc)
ds = eventrelated_dataset(ds, evs, time_attr='time_coords')
nnonzero = len(_load_anyimg(masrc)[0].nonzero()[0])
assert_equal(nnonzero, 530)
# we ask for boxcars of 9s length, and the tr in the file header says 2.5s
# hence we should get round(9.0/2.4) * np.prod((1,20,40) == 3200 features
assert_equal(ds.nfeatures, 4 * 530)
assert_equal(len(ds), len(evs))
# and they have been broadcasted through all boxcars
assert_array_equal(ds.fa.voxel_indices[:nnonzero],
ds.fa.voxel_indices[nnonzero:2 * nnonzero])
