def test_BaseModel_lnprior(self):
scat = Sphere(r=prior.Gaussian(1, 1), n=prior.Gaussian(1, 1),
center=[10, 10, 10])
mod = Model(scat, noise_sd=0.1)
# Desired: log(sqrt(0.5/pi))-1/2
desired_sigma = -1.4189385332
assert_obj_close(mod.lnprior({'n': 0, 'r': 0}), desired_sigma * 2)
def test_yaml_round_trip_with_dict(self):
sphere = make_sphere()
for key in self.model_keywords:
value = {'red': 1, 'green': 0}
kwargs = {key: value}
model = Model(sphere, **kwargs)
with self.subTest(key=key):
reloaded = take_yaml_round_trip(model)
self.assertEqual(reloaded, model)
def test_yaml_round_trip_with_xarray(self):
sphere = make_sphere()
for key in self.model_keywords:
value = xr.DataArray([1, 0.],
dims=['illumination'],
coords={'illumination': ['red', 'green']})
kwargs = {key: value}
model = Model(sphere, **kwargs)
with self.subTest(key=key):
reloaded = take_yaml_round_trip(model)
self.assertEqual(reloaded, model)
def test_default_fit_strategy_is_nmpfit(self):
model = Model(Sphere())
default_strategy = model.validate_strategy(None, 'fit')
self.assertEqual(NmpfitStrategy(), default_strategy)
def test_initializable(self):
scatterer = make_sphere()
model = Model(scatterer)
self.assertTrue(model is not None)
def test_parallel_tempering_not_implemented(self):
model = Model(Sphere())
self.assertRaises(ValueError, model.validate_strategy,
'parallel tempering', 'sample')
def test_sample_strategy_names(self):
model = Model(Sphere())
for name, strategy in available_sampling_strategies.items():
if strategy is not NotImplemented:
strategy_by_name = model.validate_strategy(name, 'sample')
self.assertEqual(strategy(), strategy_by_name)
def test_fit_strategy_names(self):
model = Model(Sphere())
for name, strategy in available_fit_strategies.items():
strategy_by_name = model.validate_strategy(name, 'fit')
self.assertEqual(strategy(), strategy_by_name)
def test_default_sampling_strategy_is_emcee(self):
model = Model(Sphere())
default_strategy = model.validate_strategy(None, 'sample')
self.assertTrue(isinstance(default_strategy, EmceeStrategy))