def test_optimize_params():
# load test data
test_img = nib.load(EXAMPLE_FULLPATH)
test_data = test_img.get_data()
# create translation in random volume (after first)
idx = np.random.randint(1, test_data.shape[-1])
trans = [1, 1, 1]
test_data[...,idx] = affine_transform(test_data[...,idx], np.eye(3), trans, order=1)
vol = test_data[...,idx]
ref = test_data[...,0]
vol_img = nib.Nifti1Image(vol, test_img.affine)
ref_img = nib.Nifti1Image(ref, test_img.affine)
# get motion correction params
mc_params0 = fmin_powell(cost_function, [0, 0, 0, 0, 0, 0, 1, 1, 1],
args=(vol_img, ref_img))
# apply mc_params
mc_data0 = apply_transform(vol_img, ref_img, mc_params0)
# get motion corrected data and params using optimize_params
vol_img = nib.Nifti1Image(test_data, test_img.affine)
ref_img = nib.Nifti1Image(np.reshape(ref, ref.shape + (1,)), test_img.affine)
mc_data1, mc_params1 = optimize_params(vol_img, ref_img, 0)
# assert same data and params
assert np.allclose(mc_data0, mc_data1[...,idx], rtol=1e-4)
assert np.allclose(mc_params0, mc_params1[idx,:], rtol=1e-4)
# test single image
vol_img = nib.Nifti1Image(vol, test_img.affine)
ref_img = nib.Nifti1Image(ref, test_img.affine)
mc_data2, mc_params2 = optimize_params(vol_img, ref_img)
# assert same data and params
assert np.allclose(mc_data0, mc_data2[...,0], rtol=1e-4)
assert np.allclose(mc_params0, mc_params2, rtol=1e-4)
def test_cost_function():
# get test data
test_img = nib.load(EXAMPLE_FULLPATH)
test_data = test_img.get_data()
vol = test_data[...,0]
ref = test_data[...,1]
vol_img = nib.Nifti1Image(vol, test_img.affine)
ref_img = nib.Nifti1Image(ref, test_img.affine)
# create random params
params = np.random.rand(9)
# apply transformation
resampled = apply_transform(vol_img, ref_img, params)
# get negative correlation
correl0 = -np.corrcoef(resampled.ravel(), ref.ravel())[0, 1]
# get negative correlation from cost_function
correl1 = cost_function(params, vol_img, ref_img)
# assert same
assert np.allclose(correl0, correl1, rtol=1e-4)
def test_apply_transform():
# create random params
params = np.random.rand(9)
P = transformation_matrix(params)
# get example volume
test_img = nib.load(EXAMPLE_FULLPATH)
test_data = test_img.get_data()
vol = test_data[...,0]
ref = test_data[...,1]
vol_img = nib.Nifti1Image(vol, test_img.affine)
ref_img = nib.Nifti1Image(ref, test_img.affine)
# concatenate matrices then get mat, vec
Q = npl.inv(vol_img.affine).dot(P).dot(ref_img.affine)
mat, vec = nib.affines.to_matvec(Q)
# apply affine to create resampled data
resampled0 = affine_transform(vol, mat, vec, order=1, output_shape=ref.shape)
# get resampled data from apply_transform
resampled1 = apply_transform(vol_img, ref_img, params)
# assert same
assert np.allclose(resampled0, resampled1, rtol=1e-4)