def stack():
rings = masks.radial_bins(centerX=cx,
centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y,
radius=ro,
radius_inner=ri,
n_bins=n_bins,
use_sparse=use_sparse,
dtype=np.complex64)
orders = np.arange(max_order + 1)
r, phi = masks.polar_map(centerX=cx,
centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y)
modulator = np.exp(phi * orders[:, np.newaxis, np.newaxis] * 1j)
if use_sparse:
rings = rings.reshape((rings.shape[0], 1, *rings.shape[1:]))
ring_stack = [rings] * len(orders)
ring_stack = sparse.concatenate(ring_stack, axis=1)
ring_stack *= modulator
else:
ring_stack = rings[:, np.newaxis, ...] * modulator
return ring_stack.reshape((-1, detector_y, detector_x))
def test_radial_bins():
bins = m.radial_bins(35, 37, 80, 80, n_bins=42)
assert np.allclose(1, bins.sum(axis=0).todense())
assert bins.shape == (42, 80, 80)
def test_radial_bins_dense():
bins = m.radial_bins(40, 41, 80, 80, n_bins=2)
assert np.allclose(1, bins.sum(axis=0))
assert bins.shape == (2, 80, 80)
def test_radial_bins():
bins = m.radial_bins(20, 20, 80, 80, n_bins=23)
assert np.allclose(1, bins.sum(axis=0).todense())
assert bins.shape == (23, 80, 80)
