def test_gifti_dataset_with_anatomical_surface(fn, format_, include_nodes):
ds = _get_test_dataset(include_nodes)
nsamples, nfeatures = ds.shape
vertices = np.random.normal(size=(nfeatures, 3))
faces = np.asarray([i + np.arange(3)
for i in range(2 * nfeatures)]) % nfeatures
surf = Surface(vertices, faces)
img = map2gifti(ds, surface=surf)
arr_index = 0
if include_nodes:
# check node indices
node_arr = img.darrays[arr_index]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
arr_index += 1
for sample in ds.samples:
# check sample content
arr = img.darrays[arr_index]
data = arr.data
assert_almost_equal(data, sample)
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
arr_index += 1
# check vertices
vertex_arr = img.darrays[arr_index]
assert_almost_equal(vertex_arr.data, vertices)
assert_equal(vertex_arr.data.dtype, np.float32)
assert_equal(vertex_arr.datatype, data_type_codes['float32'])
# check faces
arr_index += 1
face_arr = img.darrays[arr_index]
assert_almost_equal(face_arr.data, faces)
assert_equal(face_arr.data.dtype, np.int32)
assert_equal(face_arr.datatype, data_type_codes['int32'])
# getting the functional data should ignore faces and vertices
ds_again = gifti_dataset(img)
assert_datasets_almost_equal(ds, ds_again)
def test_gifti_dataset_with_anatomical_surface(fn, format_, include_nodes):
ds = _get_test_dataset(include_nodes)
nsamples, nfeatures = ds.shape
vertices = np.random.normal(size=(nfeatures, 3))
faces = np.asarray([i + np.arange(3) for i in xrange(2 * nfeatures)]) % nfeatures
surf = Surface(vertices, faces)
img = map2gifti(ds, surface=surf)
arr_index = 0
if include_nodes:
# check node indices
node_arr = img.darrays[arr_index]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
arr_index += 1
for sample in ds.samples:
# check sample content
arr = img.darrays[arr_index]
data = arr.data
assert_almost_equal(data, sample)
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
arr_index += 1
# check vertices
vertex_arr = img.darrays[arr_index]
assert_almost_equal(vertex_arr.data, vertices)
assert_equal(vertex_arr.data.dtype, np.float32)
assert_equal(vertex_arr.datatype, data_type_codes['float32'])
# check faces
arr_index += 1
face_arr = img.darrays[arr_index]
assert_almost_equal(face_arr.data, faces)
assert_equal(face_arr.data.dtype, np.int32)
assert_equal(face_arr.datatype, data_type_codes['int32'])
# getting the functional data should ignore faces and vertices
ds_again = gifti_dataset(img)
assert_datasets_almost_equal(ds, ds_again)
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
# test float64 and int64, which must be converted to float32 and int32
fa = dict()
if include_nodes:
fa['node_indices'] = ds.fa.node_indices.astype(np.int64)
ds_float64 = Dataset(samples=ds.samples.astype(np.float64), fa=fa)
ds_float64_again = gifti_dataset(map2gifti(ds_float64))
assert_equal(ds_float64_again.samples.dtype, np.float32)
if include_nodes:
assert_equal(ds_float64_again.fa.node_indices.dtype, np.int32)
# test contents of GIFTI image
assert (isinstance(img2, nb_gifti.GiftiImage))
nsamples = ds.samples.shape[0]
if include_nodes:
node_arr = img2.darrays[0]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
first_data_array_pos = 1
narrays = nsamples + 1
else:
first_data_array_pos = 0
narrays = nsamples
assert_equal(len(img.darrays), narrays)
for i in xrange(nsamples):
arr = img2.darrays[i + first_data_array_pos]
# check intent code
illegal_intents = ['NIFTI_INTENT_NODE_INDEX',
'NIFTI_INTENT_GENMATRIX',
'NIFTI_INTENT_POINTSET',
'NIFTI_INTENT_TRIANGLE']
assert (arr.intent not in [intent_codes.code[s]
for s in illegal_intents])
# although the GIFTI standard is not very clear about whether
# arrays with other intent than NODE_INDEX can have a
# GiftiCoordSystem, FreeSurfer's mris_convert
# does not seem to like its presence. Thus we make sure that
# it's not there.
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
# another test for map2gifti, setting the encoding explicitly
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
maskimg = {}
targets = {}
subimg = {}
subds = []
zdss = []
ind = {}
for ses in ['LR','RL']:
cname = '%s/%s/rfMRI_REST1_%s/rfMRI_REST1_%s_10k_Atlas_MSMAll_clean_24nuisance.reg_lin.trend_filt_sm6.dtseries.nii' % (tempdir,sub,ses,ses)
tmpimg =cifti.read(cname)
zds = tmpimg[0]
zscore(zds,chunks_attr=None)
nverts = zds.shape[1]
zdss.append(zds)
for hemi in ['L', 'R']:
# Mask:
tmpmask = gifti_dataset(
'%s/outgoing/%s.HCP_10k_mask.func.gii' % (tempdir,hemi))
maskimg[hemi] = np.where(tmpmask.samples != 0)[1]
# Connectivity Targets:
tmptargs = gifti_dataset(
'%s/resources/700_data/%s.700_to_10k_conn_targs.func.gii' % (tempdir, hemi))
tmptargs = tmptargs[:, maskimg[hemi]]
targimg[hemi] = np.where(tmptargs.samples != 0)[1]
# Extract hemi data:
ind[hemi] = [index for index in range(nverts) if tmpimg[1][1][index][2] == '%s' % brainstruct[hemi]]
subimg[hemi] = zds[:,ind[hemi]]
# Mask data and get connectivity target timeseries
tmp = subimg[hemi][:, maskimg[hemi]]
targets[hemi] = tmp[:, targimg[hemi]]
# Combine left and right target timeseries
targts.append(np.c_[targets['L'], targets['R']])
subds.append(np.c_[subimg['L'], subimg['R']])
def test_gifti_dataset(fn, format_, include_nodes):
expected_ds = _get_test_dataset(include_nodes)
expected_ds_sa = expected_ds.copy(deep=True)
expected_ds_sa.sa['chunks'] = [4, 3, 2, 1, 3, 2]
expected_ds_sa.sa['targets'] = ['t%d' % i for i in xrange(6)]
# build GIFTI file from scratch
gifti_string = _build_gifti_string(format_, include_nodes)
with open(fn, 'w') as f:
f.write(gifti_string)
# reading GIFTI file
ds = gifti_dataset(fn)
assert_datasets_almost_equal(ds, expected_ds)
# test GiftiImage input
img = nb_giftiio.read(fn)
ds2 = gifti_dataset(img)
assert_datasets_almost_equal(ds2, expected_ds)
# test using Nibabel's output from write
nb_giftiio.write(img, fn)
ds3 = gifti_dataset(fn)
assert_datasets_almost_equal(ds3, expected_ds)
# test targets and chunks arguments
ds3_sa = gifti_dataset(fn, targets=expected_ds_sa.targets,
chunks=expected_ds_sa.chunks)
assert_datasets_almost_equal(ds3_sa, expected_ds_sa)
# test map2gifti
img2 = map2gifti(ds)
ds4 = gifti_dataset(img2)
assert_datasets_almost_equal(ds4, expected_ds)
# test float64 and int64, which must be converted to float32 and int32
fa = dict()
if include_nodes:
fa['node_indices'] = ds.fa.node_indices.astype(np.int64)
ds_float64 = Dataset(samples=ds.samples.astype(np.float64), fa=fa)
ds_float64_again = gifti_dataset(map2gifti(ds_float64))
assert_equal(ds_float64_again.samples.dtype, np.float32)
if include_nodes:
assert_equal(ds_float64_again.fa.node_indices.dtype, np.int32)
# test contents of GIFTI image
assert (isinstance(img2, nb_gifti.GiftiImage))
nsamples = ds.samples.shape[0]
if include_nodes:
node_arr = img2.darrays[0]
assert_equal(node_arr.intent,
intent_codes.code['NIFTI_INTENT_NODE_INDEX'])
assert_equal(node_arr.coordsys, None)
assert_equal(node_arr.data.dtype, np.int32)
assert_equal(node_arr.datatype, data_type_codes['int32'])
first_data_array_pos = 1
narrays = nsamples + 1
else:
first_data_array_pos = 0
narrays = nsamples
assert_equal(len(img.darrays), narrays)
for i in xrange(nsamples):
arr = img2.darrays[i + first_data_array_pos]
# check intent code
illegal_intents = ['NIFTI_INTENT_NODE_INDEX',
'NIFTI_INTENT_GENMATRIX',
'NIFTI_INTENT_POINTSET',
'NIFTI_INTENT_TRIANGLE']
assert (arr.intent not in [intent_codes.code[s]
for s in illegal_intents])
# although the GIFTI standard is not very clear about whether
# arrays with other intent than NODE_INDEX can have a
# GiftiCoordSystem, FreeSurfer's mris_convert
# does not seem to like its presence. Thus we make sure that
# it's not there.
assert_equal(arr.coordsys, None)
assert_equal(arr.data.dtype, np.float32)
assert_equal(arr.datatype, data_type_codes['float32'])
# another test for map2gifti, setting the encoding explicitly
map2gifti(ds, fn, encoding=format_)
ds5 = gifti_dataset(fn)
assert_datasets_almost_equal(ds5, expected_ds)
# test map2gifti with array input; nodes are not stored
map2gifti(ds.samples, fn)
ds6 = gifti_dataset(fn)
if include_nodes:
assert_raises(AssertionError, assert_datasets_almost_equal,
ds6, expected_ds)
else:
assert_datasets_almost_equal(ds6, expected_ds)
assert_raises(TypeError, gifti_dataset, ds3_sa)
assert_raises(TypeError, map2gifti, img, fn)
