def test_precomputed_angular_spectrum_functions():
data = np.random.rand(2, 2)
wf = propagation.Wavefront(cmplx_field=data, dx=1, wavelength=.6328)
tf = propagation.angular_spectrum_transfer_function(
2, wf.wavelength, wf.dx, 1)
wf2 = wf.free_space(tf=tf)
assert wf2
def test_can_mul_wavefronts():
data = np.random.rand(2, 2).astype(np.complex128)
x = np.array([1, 2])
y = np.array([1, 2])
wf = propagation.Wavefront(x=x, y=y, fcn=data, wavelength=.6328)
wf2 = wf * 2
assert wf2
def test_obj_oriented_wavefront_focusing_reverses():
x = y = np.arange(128, dtype=np.float32)
z = np.random.rand(128, 128)
wf = propagation.Wavefront(x=x, y=y, fcn=z, wavelength=HeNe)
wf2 = wf.focus(1, 1).unfocus(
1, 1
) # first is efl, meaningless. second is Q, we neglect padding at the moment
assert np.allclose(wf.fcn, wf2.fcn)
def test_obj_oriented_wavefront_focusing_reverses():
z = np.random.rand(128, 128)
dx = 1
wf = propagation.Wavefront(dx=dx, cmplx_field=z, wavelength=HeNe)
wf2 = wf.focus(1, 1).unfocus(
1,
1) # first is efl, meaningless. second is Q, we neglect padding here
assert np.allclose(wf.data, wf2.data)
def test_frespace_functions():
dx = 1
z = np.random.rand(SAMPLES, SAMPLES)
wf = propagation.Wavefront(dx=dx,
cmplx_field=z,
wavelength=HeNe,
space='pupil')
wf = wf.free_space(1, 1)
assert wf
def test_focus_fft_mdft_equivalent_Wavefront():
x = y = np.linspace(-1, 1, SAMPLES)
z = np.random.rand(SAMPLES, SAMPLES)
wf = propagation.Wavefront(x=x, y=y, fcn=z, wavelength=HeNe, space='pupil')
unfocus_fft = wf.focus(Q=2, efl=1)
unfocus_mdft = wf.focus_fixed_sampling(
efl=1,
sample_spacing=unfocus_fft.sample_spacing,
samples=unfocus_fft.samples_x)
assert np.allclose(unfocus_fft.data, unfocus_mdft.data)
def test_focus_fft_mdft_equivalent_Wavefront():
dx = 1
z = np.random.rand(SAMPLES, SAMPLES)
wf = propagation.Wavefront(dx=dx,
cmplx_field=z,
wavelength=HeNe,
space='pupil')
unfocus_fft = wf.focus(Q=2, efl=1)
unfocus_mdft = wf.focus_fixed_sampling(efl=1,
dx=unfocus_fft.dx,
samples=unfocus_fft.data.shape[1])
assert np.allclose(unfocus_fft.data, unfocus_mdft.data)
def test_unfocus_fft_mdft_equivalent_Wavefront():
x = y = np.linspace(-1, 1, SAMPLES)
z = np.random.rand(SAMPLES, SAMPLES)
wf = propagation.Wavefront(x=x, y=y, fcn=z, wavelength=HeNe, space='psf')
unfocus_fft = wf.unfocus(Q=2, efl=1)
# magic number 4 - a bit unclear, but accounts for non-energy
# conserving fft; sf is to satisfy parseval's theorem
sf = fttools.mdft._norm(wf.data, 2, unfocus_fft.samples_x) * 4
unfocus_mdft = wf.unfocus_fixed_sampling(
efl=1,
sample_spacing=unfocus_fft.sample_spacing,
samples=unfocus_fft.samples_x)
assert np.allclose(unfocus_fft.data, unfocus_mdft.data/sf)
def test_unfocus_fft_mdft_equivalent_Wavefront():
z = np.random.rand(128, 128)
dx = 1
wf = propagation.Wavefront(dx=dx,
cmplx_field=z,
wavelength=HeNe,
space='psf')
unfocus_fft = wf.unfocus(Q=2, efl=1)
# magic number 4 - a bit unclear, but accounts for non-energy
# conserving fft; sf is to satisfy parseval's theorem
unfocus_mdft = wf.unfocus_fixed_sampling(efl=1,
dx=unfocus_fft.dx,
samples=unfocus_fft.data.shape[1])
assert np.allclose(unfocus_fft.data, unfocus_mdft.data)
def test_frespace_functions():
x = y = np.linspace(-1, 1, SAMPLES)
z = np.random.rand(SAMPLES, SAMPLES)
wf = propagation.Wavefront(x=x, y=y, fcn=z, wavelength=HeNe, space='pupil')
wf = wf.free_space(1, 1)
assert wf
def test_can_div_wavefronts():
data = np.random.rand(2, 2).astype(np.complex128)
wf = propagation.Wavefront(cmplx_field=data, dx=1, wavelength=.6328)
wf2 = wf / 2
assert wf2
