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_LSQ_SLSQP_with_constraints(self):
"""
Runs `LevMarLSQFitter` and `SLSQPLSQFitter` on a model with
constraints.
"""
g1 = models.Gaussian1D(100, 5, stddev=1)
g1.mean.fixed = True
fitter = LevMarLSQFitter()
fslsqp = SLSQPLSQFitter()
slsqp_model = fslsqp(g1, self.xdata, self.ydata)
model = fitter(g1, self.xdata, self.ydata)
assert_allclose(model.parameters, slsqp_model.parameters,
rtol=10 ** (-4))
def test_psf_photometry_uncertainties_warning_check():
psf = IntegratedGaussianPRF(sigma=GAUSSIAN_WIDTH)
with pytest.warns(AstropyUserWarning):
f = psf_photometry(image,
INTAB,
psf,
fitter=SLSQPLSQFitter(),
param_uncert=True)
assert_equal(
'flux_fit_uncertainty' in f.colnames
or 'y_0_fit_uncertainty' in f.colnames
or 'x_0_fit_uncertainty' in f.colnames, False)
# test that AstropyUserWarning is raised
warnings.warn(
'uncertainties on fitted parameters cannot be '
'computed because fitter does not contain '
'`param_cov` key in its `fit_info` dictionary.',
AstropyUserWarning)
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)
print(GrayBody)
print(GrayBody._supports_unit_fitting)
#exit()
'''
# In[52]:
# Fit graybody
from astropy.modeling.fitting import LevMarLSQFitter, SLSQPLSQFitter
from astropy.utils.console import ProgressBar
from scipy.optimize import curve_fit
#fitter = LevMarLSQFitter()
fitter = SLSQPLSQFitter()
waves = [53, 89, 155, 216]*u.micron
# dataiter holds 4-tuples of each pixel in all 4 wavelengths
dataiter = list(zip(a_orig.data.flat,c_repr.data.flat,d_repr.data.flat,e_repr.data.flat))
# This is the function that we apply to every pixel in the maps.
# Note that we first convert the data values from Jy to erg/cm2/s to match the output from GrayBody
def fit_gb(y):
y = (y*u.Jy).to(FNU, equivalencies=u.spectral_density(waves))
print(y)
print('wut')
return fitter(GrayBody, waves, y, maxiter=50, acc=1e-5)
#print(GrayBody)
**kwargs :
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
