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_multiple_calls():
"""Test if doing exactly the same operation twice yields the same result
"""
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=1, sprefix='abc')
data2 = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=1, sprefix='abc')
assert_array_equal(data.a.abc_eldim, data2.a.abc_eldim)
def test_er_nifti_dataset_mapping():
"""Some mapping testing -- more tests is better
"""
# z,y,x
sample_size = (4, 3, 2)
# t,z,y,x
samples = np.arange(120).reshape((5, ) + sample_size)
dsmask = np.arange(24).reshape(sample_size) % 2
if externals.exists('nibabel'):
import nibabel
tds = fmri_dataset(nibabel.Nifti1Image(samples.T, None),
mask=nibabel.Nifti1Image(dsmask.T, None))
else:
import nifti
tds = fmri_dataset(nifti.NiftiImage(samples),
mask=nifti.NiftiImage(dsmask))
ds = eventrelated_dataset(tds,
events=[
Event(onset=0,
duration=2,
label=1,
chunk=1,
features=[1000, 1001]),
Event(onset=1,
duration=2,
label=2,
chunk=1,
features=[2000, 2001])
])
nfeatures = tds.nfeatures
mask = np.zeros(dsmask.shape, dtype='bool')
mask[0, 0, 0] = mask[1, 0, 1] = mask[0, 0, 1] = 1
fmask = ds.a.mapper.forward1(mask.T)
# select using mask in volume and all features in the other part
ds_sel = ds[:, fmask]
# now tests
assert_array_equal(mask.reshape(24).nonzero()[0], [0, 1, 7])
# two events, 2 orig features at 2 timepoints
assert_equal(ds_sel.samples.shape, (2, 4))
assert_array_equal(ds_sel.sa.features, [[1000, 1001], [2000, 2001]])
assert_array_equal(ds_sel.samples, [[1, 7, 25, 31], [25, 31, 49, 55]])
# reproducability
assert_array_equal(ds_sel.samples,
ds_sel.a.mapper.forward(np.rollaxis(samples.T, -1)))
# reverse-mapping
rmapped = ds_sel.a.mapper.reverse1(np.arange(10, 14))
assert_equal(np.rollaxis(rmapped, 0, 4).T.shape, (2, ) + sample_size)
expected = np.zeros((2, ) + sample_size, dtype='int')
expected[0, 0, 0, 1] = 10
expected[0, 1, 0, 1] = 11
expected[1, 0, 0, 1] = 12
expected[1, 1, 0, 1] = 13
assert_array_equal(np.rollaxis(rmapped, 0, 4).T, expected)
def test_multiple_calls():
"""Test if doing exactly the same operation twice yields the same result
"""
data = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'example4d.nii.gz'),
targets=1,
sprefix='abc')
data2 = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'example4d.nii.gz'),
targets=1,
sprefix='abc')
assert_array_equal(data.a.abc_eldim, data2.a.abc_eldim)
def test_er_nifti_dataset_mapping():
"""Some mapping testing -- more tests is better
"""
# z,y,x
sample_size = (4, 3, 2)
# t,z,y,x
samples = np.arange(120).reshape((5,) + sample_size)
dsmask = np.arange(24).reshape(sample_size) % 2
if externals.exists('nibabel'):
import nibabel
tds = fmri_dataset(nibabel.Nifti1Image(samples.T, None),
mask=nibabel.Nifti1Image(dsmask.T, None))
else:
import nifti
tds = fmri_dataset(nifti.NiftiImage(samples),
mask=nifti.NiftiImage(dsmask))
ds = eventrelated_dataset(
tds,
events=[Event(onset=0, duration=2, label=1,
chunk=1, features=[1000, 1001]),
Event(onset=1, duration=2, label=2,
chunk=1, features=[2000, 2001])])
nfeatures = tds.nfeatures
mask = np.zeros(dsmask.shape, dtype='bool')
mask[0, 0, 0] = mask[1, 0, 1] = mask[0, 0, 1] = 1
fmask = ds.a.mapper.forward1(mask.T)
# select using mask in volume and all features in the other part
ds_sel = ds[:, fmask]
# now tests
assert_array_equal(mask.reshape(24).nonzero()[0], [0, 1, 7])
# two events, 2 orig features at 2 timepoints
assert_equal(ds_sel.samples.shape, (2, 4))
assert_array_equal(ds_sel.sa.features,
[[1000, 1001], [2000, 2001]])
assert_array_equal(ds_sel.samples,
[[ 1, 7, 25, 31],
[ 25, 31, 49, 55]])
# reproducability
assert_array_equal(ds_sel.samples,
ds_sel.a.mapper.forward(np.rollaxis(samples.T, -1)))
# reverse-mapping
rmapped = ds_sel.a.mapper.reverse1(np.arange(10, 14))
assert_equal(np.rollaxis(rmapped, 0, 4).T.shape, (2,) + sample_size)
expected = np.zeros((2,)+sample_size, dtype='int')
expected[0,0,0,1] = 10
expected[0,1,0,1] = 11
expected[1,0,0,1] = 12
expected[1,1,0,1] = 13
assert_array_equal(np.rollaxis(rmapped, 0, 4).T, expected)
def test_fmridataset():
# full-blown fmri dataset testing
maskimg = NiftiImage(os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
# assign some values we can check later on
maskimg.data[maskimg.data>0] = np.arange(1, np.sum(maskimg.data) + 1)
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'bold'),
targets=attr.targets, chunks=attr.chunks,
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
# content
assert_equal(len(ds), 1452)
assert_true(ds.nfeatures, 530)
assert_array_equal(sorted(ds.sa.keys()),
['chunks', 'targets', 'time_coords', 'time_indices'])
assert_array_equal(sorted(ds.fa.keys()),
['myintmask', 'subj1_indices'])
assert_array_equal(sorted(ds.a.keys()),
['imghdr', 'mapper', 'subj1_dim', 'subj1_eldim'])
# vol extent
assert_equal(ds.a.subj1_dim, (1, 20, 40))
# 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))
def test_fmridataset():
# full-blown fmri dataset testing
import nibabel
maskimg = nibabel.load(os.path.join(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.get_header())
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
# content
assert_equal(len(ds), 1452)
assert_true(ds.nfeatures, 530)
assert_array_equal(sorted(ds.sa.keys()),
['chunks', 'targets', 'time_coords', 'time_indices'])
assert_array_equal(sorted(ds.fa.keys()),
['myintmask', 'subj1_indices'])
assert_array_equal(sorted(ds.a.keys()),
['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_(ds.a.imgtype is nibabel.Nifti1Image)
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_dataset():
"""Basic testing of NiftiDataset
"""
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'example4d.nii.gz'),
targets=[1, 2],
sprefix='voxel')
assert_equal(ds.nfeatures, 294912)
assert_equal(ds.nsamples, 2)
assert_array_equal(ds.a.voxel_eldim, ds.a.imghdr['pixdim'][1:4])
assert_true(ds.a['voxel_dim'].value == (128, 96, 24))
# XXX move elsewhere
#check that mapper honours elementsize
#nb22 = np.array([i for i in data.a.mapper.getNeighborIn((1, 1, 1), 2.2)])
#nb20 = np.array([i for i in data.a.mapper.getNeighborIn((1, 1, 1), 2.0)])
#self.failUnless(nb22.shape[0] == 7)
#self.failUnless(nb20.shape[0] == 5)
merged = ds.copy()
merged.append(ds)
assert_equal(merged.nfeatures, 294912)
assert_equal(merged.nsamples, 4)
# check that the header survives
for k in merged.a.imghdr.keys():
assert_array_equal(merged.a.imghdr[k], ds.a.imghdr[k])
# throw away old dataset and see if new one survives
del ds
assert_array_equal(merged.samples[3], merged.samples[1])
# check whether we can use a plain ndarray as mask
mask = np.zeros((128, 96, 24), dtype='bool')
mask[40, 20, 12] = True
nddata = fmri_dataset(samples=os.path.join(pymvpa_dataroot,
'example4d.nii.gz'),
targets=[1, 2],
mask=mask)
assert_equal(nddata.nfeatures, 1)
rmap = nddata.a.mapper.reverse1(np.array([44]))
assert_equal(rmap.shape, (128, 96, 24))
assert_equal(np.sum(rmap), 44)
assert_equal(rmap[40, 20, 12], 44)
def test_nifti_dataset():
"""Basic testing of NiftiDataset
"""
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=[1,2], sprefix='voxel')
assert_equal(ds.nfeatures, 294912)
assert_equal(ds.nsamples, 2)
assert_array_equal(ds.a.voxel_eldim, ds.a.imghdr['pixdim'][1:4])
assert_true(ds.a['voxel_dim'].value == (128,96,24))
# XXX move elsewhere
#check that mapper honours elementsize
#nb22 = np.array([i for i in data.a.mapper.getNeighborIn((1, 1, 1), 2.2)])
#nb20 = np.array([i for i in data.a.mapper.getNeighborIn((1, 1, 1), 2.0)])
#self.failUnless(nb22.shape[0] == 7)
#self.failUnless(nb20.shape[0] == 5)
merged = ds.copy()
merged.append(ds)
assert_equal(merged.nfeatures, 294912)
assert_equal(merged.nsamples, 4)
# check that the header survives
for k in merged.a.imghdr.keys():
assert_array_equal(merged.a.imghdr[k], ds.a.imghdr[k])
# throw away old dataset and see if new one survives
del ds
assert_array_equal(merged.samples[3], merged.samples[1])
# check whether we can use a plain ndarray as mask
mask = np.zeros((128, 96, 24), dtype='bool')
mask[40, 20, 12] = True
nddata = fmri_dataset(samples=os.path.join(pymvpa_dataroot,'example4d.nii.gz'),
targets=[1,2],
mask=mask)
assert_equal(nddata.nfeatures, 1)
rmap = nddata.a.mapper.reverse1(np.array([44]))
assert_equal(rmap.shape, (128, 96, 24))
assert_equal(np.sum(rmap), 44)
assert_equal(rmap[40, 20, 12], 44)
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 load_example_fmri_dataset():
"""Load minimal fMRI dataset that is shipped with PyMVPA."""
from mvpa.datasets.mri import fmri_dataset
from mvpa.misc.io import SampleAttributes
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
return ds
def load_example_fmri_dataset():
"""Load minimal fMRI dataset that is shipped with PyMVPA."""
from mvpa.datasets.mri import fmri_dataset
from mvpa.misc.io import SampleAttributes
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
return ds
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_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 load_datadb_tutorial_data(path=os.path.join(
pymvpa_datadbroot, 'tutorial_data', 'tutorial_data', 'data'),
roi='brain'):
"""Loads the block-design demo dataset from PyMVPA dataset DB.
Parameters
----------
path : str
Path of the directory containing the dataset files.
roi : str or int or tuple or None
Region Of Interest to be used for masking the dataset. If a string is
given a corresponding mask image from the demo dataset will be used
(mask_<str>.nii.gz). If an int value is given, the corresponding ROI
is determined from the atlas image (mask_hoc.nii.gz). If a tuple is
provided it may contain int values that a processed as explained
before, but the union of a ROIs is taken to produce the final mask.
If None, no masking is performed.
"""
import nibabel as nb
from mvpa.datasets.mri import fmri_dataset
from mvpa.misc.io import SampleAttributes
if roi is None:
mask = None
elif isinstance(roi, str):
mask = os.path.join(path, 'mask_' + roi + '.nii.gz')
elif isinstance(roi, int):
nimg = nb.load(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = nimg.get_data() == roi
mask = nb.Nifti1Image(tmpmask.astype(int), nimg.get_affine(),
nimg.get_header())
elif isinstance(roi, tuple) or isinstance(roi, list):
nimg = nb.load(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = np.zeros(nimg.get_shape(), dtype='bool')
for r in roi:
tmpmask = np.logical_or(tmpmask, nimg.get_data() == r)
mask = nb.Nifti1Image(tmpmask.astype(int), nimg.get_affine(),
nimg.get_header())
else:
raise ValueError("Got something as mask that I cannot handle.")
attr = SampleAttributes(os.path.join(path, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(path, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=mask)
return ds
def _run_core(self,):
"""
Core routine for detecting outliers
Parameters
----------
imgfile :
motionfile :
"""
attr = SampleAttributes(self.inputs.attributes_file)
dataset = fmri_dataset(
samples=self.inputs.samples_file,
labels=attr.labels,
chunks=attr.chunks,
mask=self.inputs.mask_file)
if 'rest' in dataset.uniquelabels:
dataset = dataset[dataset.sa.labels != 'rest']
# zscore dataset relative to baseline ('rest') mean
zscore(dataset, chunks_attr=True, dtype='float32')
# choose classifier
clf = LinearCSVMC()
# setup measure to be computed by Searchlight
# cross-validated mean transfer using an N-fold dataset splitter
cv = CrossValidatedTransferError(TransferError(clf),
NFoldSplitter())
sl = sphere_searchlight(cv, radius=self.inputs.radius,
space='voxel_indices',
nproc=2, mapper=mean_sample())
ds = dataset.copy(deep=False,
sa=['labels', 'chunks'], fa=['voxel_indices'], a=[])
sl_map = sl(ds)
# map sensitivity map into original dataspace
orig_sl_map = dataset.map2nifti(sl_map)
orig_sl_map.save(self._get_output_filename())
def load_datadb_tutorial_data(path=os.path.join(
pymvpa_datadbroot, 'tutorial_data', 'tutorial_data', 'data'),
roi='brain'):
"""Loads the block-design demo dataset from PyMVPA dataset DB.
Parameters
----------
path : str
Path of the directory containing the dataset files.
roi : str or int or tuple or None
Region Of Interest to be used for masking the dataset. If a string is
given a corresponding mask image from the demo dataset will be used
(mask_<str>.nii.gz). If an int value is given, the corresponding ROI
is determined from the atlas image (mask_hoc.nii.gz). If a tuple is
provided it may contain int values that a processed as explained
before, but the union of a ROIs is taken to produce the final mask.
If None, no masking is performed.
"""
from nifti import NiftiImage
from mvpa.datasets.mri import fmri_dataset
from mvpa.misc.io import SampleAttributes
if roi is None:
mask = None
elif isinstance(roi, str):
mask = os.path.join(path, 'mask_' + roi + '.nii.gz')
elif isinstance(roi, int):
nimg = NiftiImage(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = nimg.data == roi
mask = NiftiImage(tmpmask.astype(int), nimg.header)
elif isinstance(roi, tuple) or isinstance(roi, list):
nimg = NiftiImage(os.path.join(path, 'mask_hoc.nii.gz'))
tmpmask = np.zeros(nimg.data.shape, dtype='bool')
for r in roi:
tmpmask = np.logical_or(tmpmask, nimg.data == r)
mask = NiftiImage(tmpmask.astype(int), nimg.header)
else:
raise ValueError("Got something as mask that I cannot handle.")
attr = SampleAttributes(os.path.join(path, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(path, 'bold.nii.gz'),
targets=attr.targets, chunks=attr.chunks,
mask=mask)
return ds
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_fmridataset():
# full-blown fmri dataset testing
if externals.exists('nibabel'):
import nibabel
maskimg = nibabel.load(os.path.join(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.get_header())
else:
import nifti
maskimg = nifti.NiftiImage(os.path.join(pymvpa_dataroot,
'mask.nii.gz'))
# assign some values we can check later on
maskimg.data.T[maskimg.data.T > 0] = np.arange(
1,
np.sum(maskimg.data) + 1)
attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
ds = fmri_dataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
targets=attr.targets,
chunks=attr.chunks,
mask=maskimg,
sprefix='subj1',
add_fa={'myintmask': maskimg})
# content
assert_equal(len(ds), 1452)
assert_true(ds.nfeatures, 530)
assert_array_equal(sorted(ds.sa.keys()),
['chunks', 'targets', 'time_coords', 'time_indices'])
assert_array_equal(sorted(ds.fa.keys()), ['myintmask', 'subj1_indices'])
assert_array_equal(sorted(ds.a.keys()),
['imghdr', '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))
###################### Piero #########################################
mask = '/media/DATA/fmri/ica_classification/ICA/mask.nii.gz'
img_s00 = ni.load('/media/DATA/fmri/ica_classification/shuffled_IC/shuffled_dr_stage2_subject00000_Z.nii.gz')
img_s01 = ni.load('/media/DATA/fmri/ica_classification/shuffled_IC/shuffled_dr_stage2_subject00001_Z.nii.gz')
img_s02 = ni.load('/media/DATA/fmri/ica_classification/shuffled_IC/shuffled_dr_stage2_subject00002_Z.nii.gz')
img_s03 = ni.load('/media/DATA/fmri/ica_classification/shuffled_IC/shuffled_dr_stage2_subject00003_Z.nii.gz')
img_s00 = ni.load('/media/DATA/fmri/ica_classification/ICA/dr_stage2_subject00000_Z.nii.gz')
img_s01 = ni.load('/media/DATA/fmri/ica_classification/ICA/dr_stage2_subject00001_Z.nii.gz')
img_s02 = ni.load('/media/DATA/fmri/ica_classification/ICA/dr_stage2_subject00002_Z.nii.gz')
img_s03 = ni.load('/media/DATA/fmri/ica_classification/ICA/dr_stage2_subject00003_Z.nii.gz')
ds0 = fmri_dataset(img_s00, mask=mask)
ds1 = fmri_dataset(img_s01, mask=mask)
ds2 = fmri_dataset(img_s02, mask=mask)
ds3 = fmri_dataset(img_s03, mask=mask)
d0 = fmri_dataset(img_00, mask=mask)
d1 = fmri_dataset(img_01, mask=mask)
d2 = fmri_dataset(img_02, mask=mask)
d3 = fmri_dataset(img_03, mask=mask)
ts0 = TimeSeries(ds0.samples, sampling_interval=1)
ts1 = TimeSeries(ds1.samples, sampling_interval=1)
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])