def test_slice_time_image():
# Test slice timing on image in memory
n_slices = 4
n_vols = 5
TR = 3.5
vol_times = np.arange(n_vols) * TR
# Add some random jitter per slice
slice_times = np.arange(n_slices) * TR / n_slices
slice_times = slice_times + np.random.normal(0, 0.05, size=(n_slices,))
# TEST AXIAL
data = np.random.normal(size=(2, 3, n_slices, n_vols))
img = nib.Nifti1Image(data, np.eye(4))
# Test against interpolation with interp_slice
for order in ('linear', 'cubic', 1, 4):
interped = np.zeros_like(data)
for slice_no in range(n_slices):
orig_times = vol_times + slice_times[slice_no]
interped[:, :, slice_no, :] = interp_slice(
orig_times, data[:, :, slice_no, :], vol_times, order)
interped_img = slice_time_image(img, slice_times, TR, order)
assert_almost_equal(interped_img.get_data(), interped)
# TEST SAGITTAL
data = np.random.normal(size=(n_slices, 2, 3, n_vols))
img = nib.Nifti1Image(data, np.eye(4))
for order in ('linear', 'cubic', 1, 4):
interped = np.zeros_like(data)
for slice_no in range(n_slices):
orig_times = vol_times + slice_times[slice_no]
interped[slice_no, ...] = interp_slice(
orig_times, data[slice_no, ...], vol_times, order)
interped_img = slice_time_image(img, slice_times, TR, order,
slice_axis=0)
assert_almost_equal(interped_img.get_data(), interped)
def test_interp_slice():
# Test interpolation over 3D slices
data = np.random.normal(size=(4, 5, 6))
old_times = np.arange(6) * 2.5
# Add some random jitter
new_times = old_times + np.random.normal(0, 0.1, size=(6, ))
# Do manual thing
pad_front = new_times[0] < old_times[0]
pad_back = new_times[-1] > old_times[-1]
if pad_front and pad_back:
pad_times = [new_times[0]] + list(old_times) + [new_times[-1]]
pad_data = np.concatenate((data[..., 0:1], data, data[..., 5:6]),
axis=-1)
elif pad_front:
pad_times = [new_times[0]] + list(old_times)
pad_data = np.concatenate((data[..., 0:1], data), axis=-1)
elif pad_back:
pad_times = list(old_times) + [new_times[-1]]
pad_data = np.concatenate((data, data[..., 5:6]), axis=-1)
else:
pad_times = old_times
pad_data = data
for order in ('linear', 'cubic', 1, 4):
interper = spi.interp1d(pad_times, pad_data, order, axis=-1)
interped = interper(new_times)
assert_almost_equal(interped,
interp_slice(old_times, data, new_times, order))
def test_interp_slice():
# Test interpolation over 3D slices
data = np.random.normal(size=(4, 5, 6))
old_times = np.arange(6) * 2.5
# Add some random jitter
new_times = old_times + np.random.normal(0, 0.1, size=(6,))
# Do manual thing
pad_front = new_times[0] < old_times[0]
pad_back = new_times[-1] > old_times[-1]
if pad_front and pad_back:
pad_times = [new_times[0]] + list(old_times) + [new_times[-1]]
pad_data = np.concatenate((data[..., 0:1], data, data[..., 5:6]),
axis=-1)
elif pad_front:
pad_times = [new_times[0]] + list(old_times)
pad_data = np.concatenate((data[..., 0:1], data), axis=-1)
elif pad_back:
pad_times = list(old_times) + [new_times[-1]]
pad_data = np.concatenate((data, data[..., 5:6]), axis=-1)
else:
pad_times = old_times
pad_data = data
for order in ('linear', 'cubic', 1, 4):
interper = spi.interp1d(pad_times, pad_data, order, axis=-1)
interped = interper(new_times)
assert_almost_equal(interped,
interp_slice(old_times, data, new_times, order))
def test_slice_time_image():
# Test slice timing on image in memory
n_slices = 4
n_vols = 5
TR = 3.5
vol_times = np.arange(n_vols) * TR
# Add some random jitter per slice
slice_times = np.arange(n_slices) * TR / n_slices
slice_times = slice_times + np.random.normal(0, 0.05, size=(n_slices, ))
# TEST AXIAL
data = np.random.normal(size=(2, 3, n_slices, n_vols))
img = nib.Nifti1Image(data, np.eye(4))
# Test against interpolation with interp_slice
for order in ('linear', 'cubic', 1, 4):
interped = np.zeros_like(data)
for slice_no in range(n_slices):
orig_times = vol_times + slice_times[slice_no]
interped[:, :, slice_no, :] = interp_slice(orig_times,
data[:, :, slice_no, :],
vol_times, order)
interped_img = slice_time_image(img, slice_times, TR, order)
assert_almost_equal(interped_img.get_data(), interped)
# TEST SAGITTAL
data = np.random.normal(size=(n_slices, 2, 3, n_vols))
img = nib.Nifti1Image(data, np.eye(4))
for order in ('linear', 'cubic', 1, 4):
interped = np.zeros_like(data)
for slice_no in range(n_slices):
orig_times = vol_times + slice_times[slice_no]
interped[slice_no, ...] = interp_slice(orig_times, data[slice_no,
...],
vol_times, order)
interped_img = slice_time_image(img,
slice_times,
TR,
order,
slice_axis=0)
assert_almost_equal(interped_img.get_data(), interped)
