def test_nii_bo_nii():
bo_nii = se.Brain(_gray(20))
nii = _brain_to_nifti(bo_nii, _gray(20))
nii_0 = _gray(20).get_data().flatten()
nii_0[np.isnan(nii_0)] = 0
assert np.allclose(nii_0, nii.get_data().flatten())
def test_resample_nii():
nii = _resample_nii(_gray(), 20, precision=5)
assert isinstance(nii, se.Nifti)
def test_gray():
nii = _gray(20)
assert isinstance(nii, se.Nifti)
def test_bo_nii_bo():
nii = _brain_to_nifti(bo, _gray(20))
b_d, b_l, b_h = _nifti_to_brain(nii)
assert np.allclose(bo.get_locs(), b_l)
def test_brain_to_nifti():
nii = _brain_to_nifti(bo, _gray(20))
assert isinstance(nii, se.Nifti)
def test_nifti_to_brain():
b_d, b_l, b_h = _nifti_to_brain(_gray(20))
assert isinstance(b_d, np.ndarray)
assert isinstance(b_l, np.ndarray)
assert isinstance(b_h, dict)
# create brain object from subset of locations
sub_locs = bo_full.locs.iloc[6:]
sub_data = bo_full.data.iloc[:, sub_locs.index]
bo = se.Brain(data=sub_data.as_matrix(),
sessions=bo_full.sessions,
locs=sub_locs,
sample_rate=10,
meta={'brain object locs sampled': 2})
# simulate correlation matrix
data = [
se.simulate_model_bos(n_samples=10, locs=locs, sample_locs=n_elecs)
for x in range(n_subs)
]
# test model to compare
test_model = se.Model(data=data, locs=locs, rbf_width=100)
bo_nii = se.Brain(_gray(20))
nii = _brain_to_nifti(bo_nii, _gray(20))
a = np.array([[1, 2, 3], [4, 5, 6], [
7,
8,
9,
]])
b = np.array([[-1, 2, 2], [-4, 5, 5], [
-7,
8,
8,
]])
c = np.array([[0, 4, 5], [0, 10, 11], [0, 16, 17]])
add_log = _to_log_complex(a)
a_log = _to_log_complex(a)
def test_bo_nii_bo():
nii = _brain_to_nifti(bo, _gray(20))
print(type(str(nii.header['descrip'])))
b_d, b_l, b_h, affine = _nifti_to_brain(nii)
assert np.allclose(bo.get_locs(), b_l)
