def test_prob_2_priors(self):
real = Uniform(*(np.random.rand(2) + np.array([0, 1])))
imag = Uniform(*(np.random.rand(2) + np.array([0, 1])))
cp = ComplexPrior(real, imag)
self.assertEqual(cp.prob(1), 0)
self.assertEqual(cp.prob(1.0j), 0)
self.assertAlmostEqual(cp.prob(1 + 1.0j), real.prob(1) * imag.prob(1))
def test_lnprob_2_priors(self):
real = Uniform(*np.random.rand(2) + np.array([0, 1]))
imag = Uniform(*np.random.rand(2) + np.array([0, 1]))
cp = ComplexPrior(real, imag)
self.assertEqual(cp.lnprob(1), -np.inf)
self.assertEqual(cp.lnprob(1.0j), -np.inf)
self.assertEqual(cp.lnprob(1 + 1.0j), real.lnprob(1) + imag.lnprob(1))
def test_lnprob_fixed_real(self):
real = np.random.rand()
imag = Uniform(*(np.random.rand(2) + np.array([0, 1])))
cp = ComplexPrior(real, imag)
self.assertEqual(cp.lnprob(1), -np.inf)
self.assertEqual(cp.lnprob(1.0j), imag.lnprob(1))
self.assertEqual(cp.lnprob(1 + 1.0j), imag.lnprob(1))
def test_sample_fixed_imag(self):
n_samples = 10
real = Uniform(*(np.random.rand(2) + np.array([0, 1])))
imag = np.random.rand()
cp = ComplexPrior(real, imag)
samples = cp.sample(n_samples)
self.assertTrue(np.all(samples.real < real.upper_bound))
self.assertTrue(np.all(samples.real > real.lower_bound))
self.assertTrue(np.all(samples.imag == imag))
def test_sample_fixed_real(self):
n_samples = 10
real = np.random.rand()
imag = Uniform(*(np.random.rand(2) + np.array([0, 1])))
cp = ComplexPrior(real, imag)
samples = cp.sample(n_samples)
self.assertTrue(np.all(samples.real == real))
self.assertTrue(np.all(samples.imag < imag.upper_bound))
self.assertTrue(np.all(samples.imag > imag.lower_bound))
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=ComplexPrior(Uniform(1, 2, 1.59), 1e-4))
model = AlphaModel(sphere,
theory=Mie(False),
alpha=Uniform(0.1, 1, 0.6))
return model
def test_complex_prior(self):
self.assertEqual(self.c + 2 + 1j, ComplexPrior(self.u + 2, self.g + 1))
self.assertEqual(self.c + 2, ComplexPrior(self.u + 2, self.g))
self.assertEqual(self.c * 2, ComplexPrior(self.u * 2, self.g * 2))
self.assertEqual(-self.c, ComplexPrior(-self.u, -self.g))
cp = ComplexPrior(2, self.g)
self.assertEqual(self.c + cp, ComplexPrior(self.u + 2,
self.g + self.g))
def test_expand_parameters(self):
array_f = xr.DataArray([[11, 12, 13], [14, 15, 16]],
dims=['d2', 'd3'],
coords={
'd3': ['H', 'He', 'Li'],
'd2': ['Left', 'Right']
})
compressed = {
'a': 0,
'b': [0.5, 1, 2],
'c': {
'c1': 3,
'c2': 4
},
'd': ComplexPrior(5, 6),
'e': {
'e1': ComplexPrior(7, 8),
'e2': [9, 10]
},
'f': array_f
}
expansion = dict(_expand_parameters(compressed.items()))
self.assertTrue(expansion == self.expanded)
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_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 __new__(self, real, imag, name=None):
fit_warning('hp.inference.prior.ComplexPrior', 'ComplexParameter')
if isinstance(real, Prior) or isinstance(imag, Prior):
return ComplexPrior(real, imag, name)
else:
return real + 1.0j * imag
def test_my_properties(self):
self.assertEqual(self.u, Uniform(1, 2))
self.assertEqual(self.g, Gaussian(1, 2))
self.assertEqual(self.b, BoundedGaussian(1, 2, 0, 3))
self.assertEqual(self.c, ComplexPrior(self.u, self.g))
def c(self):
return ComplexPrior(self.u, self.g)
def test_guess_fixed_real(self):
real = np.random.rand()
imag = Uniform(*(np.random.rand(2) + np.array([0, 1])))
cp = ComplexPrior(real, imag)
self.assertEqual(cp.guess, real + 1.0j * imag.guess)
def test_construction(self):
parameters = {'real': Uniform(1, 2), 'imag': 3, 'name': 'a'}
cp = ComplexPrior(**parameters)
self.assertTrue(isinstance(cp, Prior))
for key, val in parameters.items():
self.assertEqual(getattr(cp, key), val)