def test_cosmo_do_not_store_unsupported_datatype():
ds = Dataset(np.zeros((0, 0)))
class ArbitraryClass(object):
pass
ds.a['unused'] = ArbitraryClass()
c = cosmo.map2cosmo(ds)
assert_false('a' in c.keys())
ds.a['foo'] = np.zeros((1,))
c = cosmo.map2cosmo(ds)
assert_true('a' in c.keys())
def test_cosmo_dataset(fn):
skip_if_no_external('scipy', min_version='0.8')
mat = _create_small_mat_dataset_dict()
ds_mat = cosmo.from_any(mat)
savemat(fn, mat)
# test Dataset, filename, dict in matlab form, and input from loadmat
for input in (ds_mat, fn, mat, loadmat(fn)):
# check dataset creation
ds = cosmo.from_any(mat)
# ensure dataset has expected vlaues
assert_array_equal(ds.samples, mat['samples'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.a.keys(), ['name'])
assert_array_equal(ds.a.name, 'input')
assert_array_equal(ds.sa.chunks, [2, 2])
assert_array_equal(ds.sa.targets, [1, 2])
assert_array_equal(ds.sa.labels, ['yin', 'yan'])
assert_array_equal(ds.fa.i, [3, 2, 1])
assert_array_equal(ds.fa.j, [1, 2, 2])
# check mapping to matlab format
mat_mapped = cosmo.map2cosmo(ds)
for m in (mat, mat_mapped):
assert_array_equal(ds_mat.samples, m['samples'])
_assert_ds_mat_attributes_equal(ds_mat, m)
def test_fmri_to_cosmo():
skip_if_no_external('nibabel')
from mvpa2.datasets.mri import fmri_dataset
# test exporting an fMRI dataset to CoSMoMVPA
pymvpa_ds = fmri_dataset(
samples=pathjoin(pymvpa_dataroot, 'example4d.nii.gz'),
targets=[1, 2], sprefix='voxel')
cosmomvpa_struct = cosmo.map2cosmo(pymvpa_ds)
_assert_set_equal(cosmomvpa_struct.keys(), ['a', 'fa', 'sa', 'samples'])
a_dict = dict(_obj2tup(cosmomvpa_struct['a']))
mri_keys = ['imgaffine', 'voxel_eldim', 'voxel_dim']
_assert_subset(mri_keys, a_dict.keys())
for k in mri_keys:
c_value = a_dict[k]
p_value = pymvpa_ds.a[k].value
if isinstance(p_value, tuple):
c_value = c_value.ravel()
p_value = np.asarray(p_value).ravel()
assert_array_almost_equal(c_value, p_value)
fa_dict = dict(_obj2tup(cosmomvpa_struct['fa']))
fa_keys = ['voxel_indices']
_assert_set_equal(fa_dict.keys(), fa_keys)
for k in fa_keys:
assert_array_almost_equal(fa_dict[k].T, pymvpa_ds.fa[k].value)
def test_cosmo_dataset(fn):
skip_if_no_external('scipy', min_version='0.8')
mat = _create_small_mat_dataset_dict()
ds_mat = cosmo.from_any(mat)
savemat(fn, mat)
# test Dataset, filename, dict in matlab form, and input from loadmat
for input in (ds_mat, fn, mat, loadmat(fn)):
# check dataset creation
ds = cosmo.from_any(mat)
# ensure dataset has expected vlaues
assert_array_equal(ds.samples, mat['samples'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.sa.keys(), ['chunks', 'labels', 'targets'])
_assert_set_equal(ds.a.keys(), ['name', 'size'])
assert_array_equal(ds.a.name, 'input')
assert_array_equal(ds.a.size, [3, 2, 1])
assert_array_equal(ds.sa.chunks, [2, 2])
assert_array_equal(ds.sa.targets, [1, 2])
assert_array_equal(ds.sa.labels, ['yin', 'yan'])
assert_array_equal(ds.fa.i, [3, 2, 1])
assert_array_equal(ds.fa.j, [1, 2, 2])
for convert_tuples in (True, False):
ds_copy = ds.copy(deep=True)
if convert_tuples:
# use dataset with tuple data
ds_copy.a.size = tuple(ds_copy.a.size)
# check mapping to matlab format
mat_mapped = cosmo.map2cosmo(ds_copy)
for m in (mat, mat_mapped):
assert_array_equal(ds_mat.samples, m['samples'])
_assert_ds_mat_attributes_equal(ds_mat, m)
def load_save(fn):
print('Running on {0}'.format(fn))
ds = h5load(fn)
assert(len(mask.fa.voxel_indices) == len(ds.fa.center_ids))
ds.fa['voxel_indices'] = mask.fa.voxel_indices
_ = map2cosmo(ds, fn.replace('hdf5', 'mat'))
def test_cosmo_empty_dataset():
ds = Dataset(np.zeros((0, 0)))
c = cosmo.map2cosmo(ds)
assert_equal(c['samples'].shape, (0, 0))
