def test_scatterer_from_parameters_dict(self):
sphere = Sphere(n=prior.Uniform(1, 2), r=prior.Uniform(0, 1))
model = AlphaModel(sphere)
pars = {'r': 0.8, 'n': 1.6}
expected = Sphere(n=1.6, r=0.8)
out_scatterer = model.scatterer_from_parameters(pars)
self.assertEqual(out_scatterer, expected)
def test_serialization():
par_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=Uniform(1, 2, 1.59))
alpha = Uniform(.1, 1, .6, 'alpha')
schema = update_metadata(detector_grid(shape=100, spacing=.1151e-6),
illum_wavelen=.66e-6,
medium_index=1.33,
illum_polarization=(1, 0))
model = AlphaModel(par_s,
medium_index=schema.medium_index,
illum_wavelen=schema.illum_wavelen,
alpha=alpha)
initial_guess = model.scatterer_from_parameters(model.initial_guess)
holo = calc_holo(schema, initial_guess, scaling=model.alpha.guess)
result = NmpfitStrategy().fit(model, holo)
temp = tempfile.NamedTemporaryFile(suffix='.h5', delete=False)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
save(temp.name, result)
loaded = load(temp.name)
assert_obj_close(result, loaded, context='serialized_result')
def test_scatterer_from_parameters_list(self):
sphere = Sphere(n=prior.Uniform(1, 2), r=prior.Uniform(0, 1))
model = AlphaModel(sphere)
pars = [1.6, 0.8]
expected = Sphere(n=1.6, r=0.8)
self.assertEqual(model.scatterer_from_parameters(pars), expected)
