def test_determine_default_theory_for_layered_spheres(self):
layered_spheres = Spheres([Sphere(r=[0.5, 1], n=[1, 1.5])] * 2)
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
default_theory = determine_default_theory_for(layered_spheres)
correct_theory = Mie()
self.assertTrue(default_theory == correct_theory)
def test_propagate_e_field():
e = calc_field(detector_grid(100, 0.1),
Sphere(1.59, .5, (5, 5, 5)),
illum_wavelen=0.66,
medium_index=1.33,
illum_polarization=(1, 0),
theory=Mie(False))
prop_e = propagate(e, 10)
verify(prop_e, 'propagate_e_field')
def residfunct(p, fjac=None):
# nmpfit calls residfunct w/fjac as a kwarg, we ignore
sphere = Sphere(n=p[0] + n_particle_imag * 1j, r=p[1], center=p[2:5])
thry = Mie(False)
calculated = calc_holo(holo, sphere, scaling=p[5], theory=thry)
status = 0
derivates = holo - calculated
return ([status, get_values(flat(derivates))])
def _make_model(self):
sphere = Sphere(center=(Uniform(0, 1e-5, guess=.567e-5),
Uniform(0, 1e-5, .567e-5), Uniform(1e-5,
2e-5)),
r=Uniform(1e-8, 1e-5, 8.5e-7),
n=Uniform(1, 2, 1.59))
model = AlphaModel(sphere,
theory=Mie(False),
alpha=Uniform(0.1, 1, 0.6))
return model
def make_model():
# Makes a model with all the paramters used in make_fake_data(),
# but with the guesses slightly off from the true values
center_guess = [
Uniform(0, 1e-5, name='x', guess=5.6e-6),
Uniform(0, 1e-5, name='y', guess=5.8e-6),
Uniform(1e-5, 2e-5, name='z', guess=14e-6),
]
scatterer = Sphere(n=Uniform(1, 2, name='n', guess=1.55),
r=Uniform(1e-8, 1e-5, name='r', guess=8.5e-7),
center=center_guess)
alpha = Uniform(0.1, 1, name='alpha', guess=0.6)
theory = Mie(compute_escat_radial=False)
model = AlphaModel(scatterer, theory=theory, alpha=alpha)
return model
def test_fit_mie_par_scatterer():
holo = normalize(get_example_data('image0001'))
s = Sphere(center=(Uniform(0, 1e-5, guess=.567e-5),
Uniform(0, 1e-5, .567e-5), Uniform(1e-5, 2e-5)),
r=Uniform(1e-8, 1e-5, 8.5e-7),
n=ComplexPrior(Uniform(1, 2, 1.59), 1e-4))
thry = Mie(False)
model = AlphaModel(s, theory=thry, alpha=Uniform(.1, 1, .6))
result = fix_flat(NmpfitStrategy().fit(model, holo))
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
# TODO: see if we can get this back to 3 sig figs correct alpha
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_Shell():
s = Sphere(
center=[7.141442573813124, 7.160766866147957, 11.095409800342143],
n=[(1.27121212428 + 0j), (1.49 + 0j)],
r=[0.960957713253 - 0.0055, 0.960957713253])
t = detector_grid(200, .071333)
thry = Mie(False)
h = calc_holo(t,
s,
1.36,
.658,
illum_polarization=(1, 0),
theory=thry,
scaling=0.4826042444701572)
verify(h, 'shell')
def test_fit_random_subset():
holo = normalize(get_example_data('image0001'))
s = Sphere(center=(Uniform(0, 1e-5, guess=.567e-5),
Uniform(0, 1e-5, .567e-5), Uniform(1e-5, 2e-5)),
r=Uniform(1e-8, 1e-5, 8.5e-7),
n=ComplexPrior(Uniform(1, 2, 1.59), 1e-4))
model = AlphaModel(s, theory=Mie(False), alpha=Uniform(.1, 1, .6))
np.random.seed(40)
result = fix_flat(NmpfitStrategy(npixels=1000).fit(model, holo))
# TODO: this tolerance has to be rather large to pass, we should
# probably track down if this is a sign of a problem
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-2)
# TODO: figure out if it is a problem that alpha is frequently coming out
# wrong in the 3rd decimal place.
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
assert_read_matches_write(result)
def test_fit_mie_single():
holo = normalize(get_example_data('image0001'))
parameters = [
Uniform(0, 1e-5, name='x', guess=.567e-5),
Uniform(0, 1e-5, name='y', guess=.576e-5),
Uniform(1e-5, 2e-5, name='z', guess=15e-6),
Uniform(1, 2, name='n', guess=1.59),
Uniform(1e-8, 1e-5, name='r', guess=8.5e-7)
]
def make_scatterer(parlist):
return Sphere(n=parlist[3], r=parlist[4], center=parlist[0:3])
thry = Mie(False)
model = AlphaModel(make_scatterer(parameters),
theory=thry,
alpha=Uniform(.1, 1, name='alpha', guess=.6))
result = NmpfitStrategy().fit(model, holo)
assert_obj_close(result.scatterer, gold_sphere, rtol=1e-3)
assert_approx_equal(result.parameters['alpha'], gold_alpha, significant=3)
assert_equal(model, result.model)
def test_io():
model = ExactModel(Sphere(1), calc_holo)
assert_read_matches_write(model)
model = ExactModel(Sphere(1), calc_holo, theory=Mie(False))
assert_read_matches_write(model)
def make_1_parameter_model():
alpha = Uniform(0.1, 1, name='alpha', guess=0.6)
theory = Mie(compute_escat_radial=False)
model = AlphaModel(SPHERE, theory=theory, alpha=alpha)
return model
def test_determine_default_theory_for_sphere(self):
default_theory = determine_default_theory_for(Sphere())
correct_theory = Mie()
self.assertTrue(default_theory == correct_theory)