def test_find_noise():
noise = 0.5
s = Sphere(n=prior.Uniform(1.5, 1.7), r=2, center=[1, 2, 3])
data_base = detector_grid(10, spacing=0.5)
data_noise = update_metadata(data_base, noise_sd=noise)
model_u = AlphaModel(s, alpha=prior.Uniform(0.7, 0.9))
model_g = AlphaModel(s, alpha=prior.Gaussian(0.8, 0.1))
pars = {'n': 1.6, 'alpha': 0.8}
assert_equal(model_u._find_noise(pars, data_noise), noise)
assert_equal(model_g._find_noise(pars, data_noise), noise)
assert_equal(model_u._find_noise(pars, data_base), 1)
assert_raises(MissingParameter, model_g._find_noise, pars, data_base)
pars.update({'noise_sd': noise})
assert_equal(model_g._find_noise(pars, data_base), noise)
def test_no_noise_if_all_uniform(self):
sphere = Sphere(r=prior.Uniform(0, 1), n=prior.Uniform(1, 2))
model = AlphaModel(sphere)
schema = detector_grid(2, 2)
found_noise = model._find_noise([0.5, 0.5], schema)
self.assertEqual(found_noise, 1)
def test_noise_from_schema(self):
model = AlphaModel(Sphere(), noise_sd=None)
schema = detector_grid(2, 2)
schema.attrs['noise_sd'] = 0.5
found_noise = model._find_noise([], schema)
self.assertEqual(found_noise, 0.5)
def test_model_noise_takes_precedence(self):
model = AlphaModel(Sphere(), noise_sd=0.8)
schema = detector_grid(2, 2)
schema.attrs['noise_sd'] = 0.5
found_noise = model._find_noise([], schema)
self.assertEqual(found_noise, 0.8)
def test_reads_noise_map(self):
noise = {'red': 0.5, 'green': prior.Uniform(0, 1)}
model = AlphaModel(Sphere(), noise_sd=noise)
found_noise = model._find_noise([0.7], None)
self.assertEqual(found_noise, {'red': 0.5, 'green': 0.7})
