def test_fmridataset():
# full-blown fmri dataset testing
import nibabel
maskimg = nibabel.load(pathjoin(pymvpa_dataroot, 'mask.nii.gz'))
data = maskimg.get_data().copy()
data[data > 0] = np.arange(1, np.sum(data) + 1)
maskimg = nibabel.Nifti1Image(data, None, maskimg.header)
ds = fmri_dataset(samples=pathjoin(pymvpa_dataroot, 'bold.nii.gz'),
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
ds_alt = preprocessed_dataset(pathjoin(pymvpa_dataroot, 'bold.nii.gz'),
nibabel.load,
fmri_dataset,
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
assert_datasets_almost_equal(ds, ds_alt)
# content
assert_equal(len(ds), 1452)
assert_true(ds.nfeatures, 530)
assert_array_equal(sorted(ds.sa.keys()), ['time_coords', 'time_indices'])
assert_array_equal(sorted(ds.fa.keys()), ['myintmask', 'subj1_indices'])
assert_array_equal(sorted(ds.a.keys()), [
'imgaffine', 'imghdr', 'imgtype', 'mapper', 'subj1_dim', 'subj1_eldim'
])
# vol extent
assert_equal(ds.a.subj1_dim, (40, 20, 1))
# check time
assert_equal(ds.sa.time_coords[-1], 3627.5)
# non-zero mask values
assert_array_equal(ds.fa.myintmask, np.arange(1, ds.nfeatures + 1))
# we know that imgtype must be:
ok_(getattr(nibabel, ds.a.imgtype) is nibabel.Nifti1Image)
def test_fmridataset():
# full-blown fmri dataset testing
import nibabel
maskimg = nibabel.load(pathjoin(pymvpa_dataroot, 'mask.nii.gz'))
data = maskimg.get_data().copy()
data[data > 0] = np.arange(1, np.sum(data) + 1)
maskimg = nibabel.Nifti1Image(data, None, maskimg.header)
ds = fmri_dataset(samples=pathjoin(pymvpa_dataroot, 'bold.nii.gz'),
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
ds_alt = preprocessed_dataset(
pathjoin(pymvpa_dataroot, 'bold.nii.gz'),
nibabel.load,
fmri_dataset,
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
assert_datasets_almost_equal(ds, ds_alt)
# content
assert_equal(len(ds), 1452)
assert_true(ds.nfeatures, 530)
assert_array_equal(sorted(ds.sa.keys()),
['time_coords', 'time_indices'])
assert_array_equal(sorted(ds.fa.keys()),
['myintmask', 'subj1_indices'])
assert_array_equal(
sorted(ds.a.keys()),
['imgaffine', 'imghdr', 'imgtype', 'mapper', 'subj1_dim', 'subj1_eldim'])
# vol extent
assert_equal(ds.a.subj1_dim, (40, 20, 1))
# check time
assert_equal(ds.sa.time_coords[-1], 3627.5)
# non-zero mask values
assert_array_equal(ds.fa.myintmask, np.arange(1, ds.nfeatures + 1))
# we know that imgtype must be:
ok_(getattr(nibabel, ds.a.imgtype) is nibabel.Nifti1Image)
def test_er_nifti_dataset():
# setup data sources
tssrc = pathjoin(pymvpa_dataroot, u'bold.nii.gz')
evsrc = pathjoin(pymvpa_dataroot, 'fslev3.txt')
masrc = pathjoin(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')
# or like this
def toevents(ds):
return eventrelated_dataset(ds, evs, time_attr='time_coords')
import nibabel
ds_alt = preprocessed_dataset(tssrc,
nibabel.load,
fmri_dataset,
preproc_ds=toevents)
assert_datasets_almost_equal(ds, ds_alt)
# 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.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.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 = pathjoin(pymvpa_dataroot, u'bold.nii.gz')
evsrc = pathjoin(pymvpa_dataroot, 'fslev3.txt')
masrc = pathjoin(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')
# or like this
def toevents(ds):
return eventrelated_dataset(ds, evs, time_attr='time_coords')
import nibabel
ds_alt = preprocessed_dataset(
tssrc,
nibabel.load,
fmri_dataset,
preproc_ds=toevents)
assert_datasets_almost_equal(ds, ds_alt)
# 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.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.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])
