def test_AlphaModelholo_likelihood():
holo = get_example_data('image0001')
s = Sphere(
prior.Gaussian(.5, .1), prior.Gaussian(1.6, .1),
(prior.Gaussian(5, 1), prior.Gaussian(5, 1), prior.Gaussian(5, 1)))
model = AlphaModel(s, alpha=prior.Gaussian(.7, .1), noise_sd=.01)
assert_pickle_roundtrip(model)
def __new__(self, scatterer, calc_func, medium_index=None,
illum_wavelen=None, illum_polarization=None, theory='auto',
alpha=None, constraints=[]):
if calc_func is calc_holo:
fit_warning('hp.inference.AlphaModel', 'Model')
if alpha is None:
alpha = 1.0
model = AlphaModel(scatterer, alpha, None, medium_index,
illum_wavelen, illum_polarization, theory, constraints)
elif alpha is None:
fit_warning('hp.inference.ExactModel','Model')
model = ExactModel(scatterer, calc_func, None, medium_index,
illum_wavelen, illum_polarization, theory, constraints)
else:
raise ValueError("Cannot interpret alpha for non-hologram scattering \
calculations")
def residual(pars, data):
return model._residuals(pars, data, 1/np.sqrt(2)).flatten()
setattr(model, '_calc', model.forward)
setattr(model, 'residual', residual)
def get_alpha(pars):
try:
return model._get_parameter('alpha', pars)
except MissingParameter:
return 1.0
setattr(model, 'get_alpha', get_alpha)
return model
def test_alpha_subset_tempering(self):
holo = normalize(get_example_data('image0001'))
scat = Sphere(r=0.65e-6, n=1.58, center=[5.5e-6, 5.8e-6, 14e-6])
mod = AlphaModel(scat, noise_sd=.1, alpha=prior.Gaussian(0.7, 0.1))
strat = TemperedStrategy(nwalkers=4, nsamples=10, stages=1,
stage_len=10, parallel=None, seed=40)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
inference_result = strat.sample(mod, holo)
desired_alpha = np.array([0.650348])
assert_allclose(inference_result._parameters, desired_alpha, rtol=5e-3)
def make_default_model(base_scatterer, fitting_parameters=None):
if fitting_parameters is None:
fitting_parameters = base_scatterer.parameters.keys()
scatterer = parameterize_scatterer(base_scatterer, fitting_parameters)
alpha_prior = Uniform(0, 1, guess=0.7, name='alpha')
return AlphaModel(scatterer, noise_sd=1, alpha=alpha_prior)