def test_fit_function_identical_to_strategy_method(self):
model = SimpleModel()
strategy = NmpfitStrategy(seed=123)
strategy_result = strategy.fit(model, DATA)
strategy_result.time = None
model_result = fit(DATA, model, strategy=strategy)
model_result.time = None
self.assertEqual(strategy_result, model_result)
def test_model_fit_method_identical_to_strategy_method(self):
model = SimpleModel()
strategy = NmpfitStrategy(seed=123)
data = np.array(.5)
strategy_result = strategy.fit(model, data)
strategy_result.time = None
model_result = model.fit(data, strategy)
model_result.time = None
self.assertEqual(strategy_result, model_result)
def test_subset_data_fit_result_stores_model(self):
model = self._make_model()
holo = get_example_data('image0001')
np.random.seed(40)
fitter = NmpfitStrategy(npixels=100, maxiter=1)
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
# ignore not-converged warnings since we only do 2 iterations
fitted = fitter.fit(model, holo)
self.assertEqual(model, fitted.model)
def test_subset_data_fit_result_is_saveable(self):
model = self._make_model()
holo = get_example_data('image0001')
np.random.seed(40)
fitter = NmpfitStrategy(npixels=100, maxiter=1)
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
# ignore not-converged warnings since we only do 2 iterations
fitted = fitter.fit(model, holo)
result = fitted # was fix_flat(fitted)
assert_read_matches_write(result)
def test_fitted_uncertainties_similar_to_nmpfit(self):
data = make_fake_data()
model = make_model()
fitter_scipy = LeastSquaresScipyStrategy(npixels=300)
result_scipy = fitter_scipy.fit(model, data)
uncertainties_scipy = pack_uncertainties_into_dict(result_scipy)
fitter_nmp = NmpfitStrategy(npixels=300)
result_nmp = fitter_nmp.fit(model, data)
uncertainties_nmp = pack_uncertainties_into_dict(result_nmp)
for key in uncertainties_scipy.keys():
self.assertTrue(
np.isclose(
uncertainties_scipy[key],
uncertainties_nmp[key],
rtol=0.1, atol=0))
def test_fitted_parameters_similar_to_nmpfit(self):
data = make_fake_data()
model = make_model()
np.random.seed(40)
fitter_scipy = LeastSquaresScipyStrategy(npixels=300, ftol=1e-6)
result_scipy = fitter_scipy.fit(model, data)
params_scipy = result_scipy.parameters
np.random.seed(40)
fitter_nmp = NmpfitStrategy(npixels=300, ftol=1e-6)
result_nmp = fitter_nmp.fit(model, data)
params_nmp = result_nmp.parameters
for key in params_scipy.keys():
self.assertAlmostEqual(params_scipy[key],
params_nmp[key],
places=4)
def test_optimization_with_maxiter_of_2():
gold_fit_dict = {
'0:r': 0.52480509800531849,
'1:center.1': 14.003687569304704,
'alpha': 0.93045027963762217,
'0:center.2': 19.93177549652841,
'1:r': 0.56292664494653732,
'0:center.1': 15.000340621607815,
'1:center.0': 14.020984607646726,
'0:center.0': 15.000222185576494,
'1:center.2': 20.115613202192328
}
#calculate a hologram with known particle positions to do a fit against
schema = detector_grid(shape=100, spacing=.1)
s1 = Sphere(center=(15, 15, 20), n=1.59, r=0.5)
s2 = Sphere(center=(14, 14, 20), n=1.59, r=0.5)
cluster = Spheres([s1, s2])
holo = calc_holo(schema, cluster, 1.33, .66, illum_polarization=(1, 0))
#trying to do a fast fit:
guess1 = Sphere(center=(prior.Uniform(5, 25, guess=15),
prior.Uniform(5, 25, 15), prior.Uniform(5, 25,
20)),
r=(prior.Uniform(.4, .6, guess=.45)),
n=1.59)
guess2 = Sphere(center=(prior.Uniform(5, 25, guess=14),
prior.Uniform(5, 25, 14), prior.Uniform(5, 25,
20)),
r=(prior.Uniform(.4, .6, guess=.45)),
n=1.59)
par_s = Spheres([guess1, guess2])
model = AlphaModel(par_s,
medium_index=1.33,
illum_wavelen=.66,
illum_polarization=(1, 0),
alpha=prior.Uniform(.1, 1, .6))
optimizer = Nmpfit(maxiter=2)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
result = optimizer.fit(model, holo)
assert_obj_close(gold_fit_dict, result.parameters, rtol=1e-5)