def test_with_fitters_and_sigma_clip(self):
import scipy.stats as stats
np.random.seed(0)
c = stats.bernoulli.rvs(0.25, size=self.z.shape)
self.z += (np.random.normal(0., 0.2, self.z.shape) +
c*np.random.normal(self.z, 2.0, self.z.shape))
guess = self.initial_guess(self.z, np.array([self.y, self.x]))
g2_init = models.Gaussian2D(amplitude=guess[0], x_mean=guess[1],
y_mean=guess[2], x_stddev=0.75,
y_stddev=1.25)
# test with Levenberg-Marquardt Least Squares fitter
fit = FittingWithOutlierRemoval(LevMarLSQFitter(), sigma_clip,
niter=3, sigma=3.)
fitted_model, _ = fit(g2_init, self.x, self.y, self.z)
assert_allclose(fitted_model.parameters[0:5], self.model_params,
atol=1e-1)
# test with Sequential Least Squares Programming fitter
fit = FittingWithOutlierRemoval(SLSQPLSQFitter(), sigma_clip, niter=3,
sigma=3.)
fitted_model, _ = fit(g2_init, self.x, self.y, self.z)
assert_allclose(fitted_model.parameters[0:5], self.model_params,
atol=1e-1)
# test with Simplex LSQ fitter
fit = FittingWithOutlierRemoval(SimplexLSQFitter(), sigma_clip,
niter=3, sigma=3.)
fitted_model, _ = fit(g2_init, self.x, self.y, self.z)
assert_allclose(fitted_model.parameters[0:5], self.model_params,
atol=1e-1)
def test_with_fitters_and_sigma_clip(self):
import scipy.stats as stats
np.random.seed(0)
c = stats.bernoulli.rvs(0.25, size=self.x.shape)
self.y += (np.random.normal(0., 0.2, self.x.shape) +
c * np.random.normal(3.0, 5.0, self.x.shape))
g_init = models.Gaussian1D(amplitude=1., mean=0, stddev=1.)
# test with Levenberg-Marquardt Least Squares fitter
fit = FittingWithOutlierRemoval(LevMarLSQFitter(),
sigma_clip,
niter=3,
sigma=3.0)
fitted_model, _ = fit(g_init, self.x, self.y)
assert_allclose(fitted_model.parameters, self.model_params, rtol=1e-1)
# test with Sequential Least Squares Programming fitter
fit = FittingWithOutlierRemoval(SLSQPLSQFitter(),
sigma_clip,
niter=3,
sigma=3.0)
fitted_model, _ = fit(g_init, self.x, self.y)
assert_allclose(fitted_model.parameters, self.model_params, rtol=1e-1)
# test with Simplex LSQ fitter
fit = FittingWithOutlierRemoval(SimplexLSQFitter(),
sigma_clip,
niter=3,
sigma=3.0)
fitted_model, _ = fit(g_init, self.x, self.y)
assert_allclose(fitted_model.parameters, self.model_params, atol=1e-1)
def test_psf_photometry_uncertainties():
"""
Test an Astropy fitter that does not return a parameter
covariance matrix (param_cov). The output table should not
contain flux_unc, x_0_unc, and y_0_unc columns.
"""
psf = IntegratedGaussianPRF(sigma=GAUSSIAN_WIDTH)
basic_phot = BasicPSFPhotometry(group_maker=DAOGroup(2),
bkg_estimator=None, psf_model=psf,
fitter=SimplexLSQFitter(),
fitshape=7)
phot_tbl = basic_phot(image=image, init_guesses=INTAB)
columns = ('flux_unc', 'x_0_unc', 'y_0_unc')
for column in columns:
assert column not in phot_tbl.colnames
def test_fitters_interface():
"""
Test that ``**kwargs`` work with all optimizers.
This is a basic smoke test.
"""
levmar = LevMarLSQFitter()
slsqp = SLSQPLSQFitter()
simplex = SimplexLSQFitter()
kwargs = {'maxiter': 77, 'verblevel': 1, 'epsilon': 1e-2, 'acc': 1e-6}
simplex_kwargs = {'maxiter': 77, 'verblevel': 1, 'acc': 1e-6}
model = models.Gaussian1D(10, 4, .3)
x = np.arange(21)
y = model(x)
_ = slsqp(model, x, y, **kwargs)
_ = simplex(model, x, y, **simplex_kwargs)
kwargs.pop('verblevel')
_ = levmar(model, x, y, **kwargs)
def test_simplex_lsq_fitter(self):
"""A basic test for the `SimplexLSQ` fitter."""
class Rosenbrock(Fittable2DModel):
a = Parameter()
b = Parameter()
@staticmethod
def evaluate(x, y, a, b):
return (a - x)**2 + b * (y - x**2)**2
x = y = np.linspace(-3.0, 3.0, 100)
with NumpyRNGContext(_RANDOM_SEED):
z = Rosenbrock.evaluate(x, y, 1.0, 100.0)
z += np.random.normal(0., 0.1, size=z.shape)
fitter = SimplexLSQFitter()
r_i = Rosenbrock(1, 100)
r_f = fitter(r_i, x, y, z)
assert_allclose(r_f.parameters, [1.0, 100.0], rtol=1e-2)
Keyword arguments for the fitter (name and astropy default values)::
* `LinearLSQFitter`: `calc_uncertainties=False`
* `LevMarLSQFitter`: `calc_uncertainties=False`
* `SimplexLSQFitter`, `SLSQPLSQFitter`: N/A
* `JointFitter`: `models`, jointparameters`, `initvals` (must be given)
"""
if fitter_name.lower().startswith("lev") or fitter_name.lower().startswith(
"lm"):
return LevMarLSQFitter(**kwargs)
elif fitter_name.lower().startswith("lin"):
return LinearLSQFitter(**kwargs)
elif fitter_name.lower().startswith("sl"):
return SLSQPLSQFitter(**kwargs)
elif fitter_name.lower().startswith("sim"):
return SimplexLSQFitter(**kwargs)
elif fitter_name.lower().startswith("jo"):
return JointFitter(**kwargs)
else:
return fitter_name(**kwargs)
# ^ assume the `fitter_name` is already an astropy fitter
def get_model(model_name, *args, **kwargs):
""" Finds and returns the model with the given name.
For instance, it's customary to put degrees as args/kwargs for polynomial models:
`get_model("chebyshev2d", 2, 2)` or `get_model("chebyshev2d", x_degree=2,
y_degree=2)` return `Chebyshev2D(x_degree=2, y_degree=2)`.
For other cases, it depends: `get_model("gaussian1d")` returns
`Gaussian1D`, but sometimes you may want to initialize it with initial
values: `get_model("gaussian1d", amplitude=1, mean=0, stddev=1)`.