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)
# 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)
def test_assert_datasets_almost_equal(digits, attribute):
samples = np.random.standard_normal((2, 5))
args = dict(sa=dict(targets=np.asarray([1., 2])),
fa=dict(ids=np.asarray([0., 1, 2, 3, 4])),
a=dict(a_value=[66]))
ds = Dataset(samples=samples, **args)
def negate_assert(f):
def raiser(*args, **kwargs):
assert_raises(AssertionError, f, *args, **kwargs)
return raiser
assert_datasets_not_almost_equal = negate_assert(
assert_datasets_almost_equal)
assert_datasets_not_equal = negate_assert(assert_datasets_equal)
def change_attribute(name, how_much):
# change a single attribute in samples, a, fa, or sa.
ds2 = ds.copy(deep=True)
attr = ds2.__dict__[name]
if name == 'samples':
value = attr
else:
for key in attr:
break
value = attr[key].value
value[0] += how_much
return ds2
def remove_attribute(name):
ds2 = ds.copy(deep=True)
attr = ds2.__dict__[name]
for key in list(attr.keys()):
attr.pop(key)
return ds2
if digits is None:
ds2 = change_attribute(attribute, 0)
assert_datasets_equal(ds, ds2)
else:
ds2 = change_attribute(attribute, .5 * 10**-digits)
assert_datasets_not_equal(ds, ds2)
assert_datasets_not_almost_equal(ds, ds2, decimal=digits + 1)
if attribute == 'samples':
assert_datasets_almost_equal(ds, ds2, decimal=digits)
else:
assert_datasets_not_almost_equal(ds, ds2, decimal=digits - 1)
# test ignore_ options
args = {('ignore_' + attribute): list(args[attribute].keys())}
assert_datasets_equal(ds, ds2, **args)
assert_datasets_almost_equal(ds, ds2, **args)
ds3 = remove_attribute(attribute)
assert_datasets_not_equal(ds, ds3)
assert_datasets_not_almost_equal(ds, ds3)
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)
def test_assert_datasets_almost_equal(digits, attribute):
samples = np.random.standard_normal((2, 5))
args = dict(sa=dict(targets=np.asarray([1., 2])),
fa=dict(ids=np.asarray([0., 1, 2, 3, 4])),
a=dict(a_value=[66]))
ds = Dataset(samples=samples, **args)
def negate_assert(f):
def raiser(*args, **kwargs):
assert_raises(AssertionError, f, *args, **kwargs)
return raiser
assert_datasets_not_almost_equal = negate_assert(assert_datasets_almost_equal)
assert_datasets_not_equal = negate_assert(assert_datasets_equal)
def change_attribute(name, how_much):
# change a single attribute in samples, a, fa, or sa.
ds2 = ds.copy(deep=True)
attr = ds2.__dict__[name]
if name == 'samples':
value = attr
else:
for key in attr:
break
value = attr[key].value
value[0] += how_much
return ds2
def remove_attribute(name):
ds2 = ds.copy(deep=True)
attr = ds2.__dict__[name]
for key in attr.keys():
attr.pop(key)
return ds2
if digits is None:
ds2 = change_attribute(attribute, 0)
assert_datasets_equal(ds, ds2)
else:
ds2 = change_attribute(attribute, .5 * 10 ** -digits)
assert_datasets_not_equal(ds, ds2)
assert_datasets_not_almost_equal(ds, ds2, decimal=digits + 1)
if attribute == 'samples':
assert_datasets_almost_equal(ds, ds2, decimal=digits)
else:
assert_datasets_not_almost_equal(ds, ds2, decimal=digits - 1)
# test ignore_ options
args = {('ignore_' + attribute): args[attribute].keys()}
assert_datasets_equal(ds, ds2, **args)
assert_datasets_almost_equal(ds, ds2, **args)
ds3 = remove_attribute(attribute)
assert_datasets_not_equal(ds, ds3)
assert_datasets_not_almost_equal(ds, ds3)
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)
