def test_nifti_mapper(filename):
"""Basic testing of map2Nifti
"""
skip_if_no_external('scipy')
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'))
if externals.versions['nibabel'] >= '1.2':
vol_shape = vol.shape
else:
vol_shape = vol.get_shape()
assert_equal(vol_shape, (128, 96, 24))
assert_true((vol.get_data() == 1).all())
# test mapping of the dataset
vol = map2nifti(data)
if externals.versions['nibabel'] >= '1.2':
vol_shape = vol.shape
else:
vol_shape = vol.get_shape()
assert_equal(vol_shape, (128, 96, 24, 2))
ok_(isinstance(vol, data.a.imgtype))
# test providing custom imgtypes
vol = map2nifti(data, imgtype=nibabel.Nifti1Pair)
if externals.versions['nibabel'] >= '1.2':
vol_shape = vol.shape
else:
vol_shape = vol.get_shape()
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 run(args):
try:
nii = nibabel.load(args.input)
img = numpy.squeeze(nii.get_data())
img_min = numpy.amin(img)
img_max = numpy.amax(img)
print 'Image type: {} {}-{}'.format(img.dtype,img_min,img_max)
hdr = nii.get_header()
q = hdr.get_best_affine();
ornt = nibabel.io_orientation(q)
print 'Orientation: {}'.format(ornt)
from StringIO import StringIO
file_map = nibabel.MincImage.make_file_map()
file_map['image'].fileobj = StringIO()
print sorted(file_map)
mnc = nibabel.MincImage(img,q)
mnc.file_map = file_map
mnc.to_file_map()
except:
print "Unexpected error:", sys.exc_info()[0]
raise