def __init__(self, *args, **kwargs):
super(EllipseWithPriors, self).__init__(*args, **kwargs)
if self.ellipsePriors is None:
ellipsePriors = _GaussianPriors(None)
ellipsePriors.add('ee1', 0., self.ellipticityStd,
param=EllipseESoft(1.,0.,0.))
ellipsePriors.add('ee2', 0., self.ellipticityStd,
param=EllipseESoft(1.,0.,0.))
self.__class__.ellipsePriors = ellipsePriors
self.gpriors = self.ellipsePriors
self.uppers[0] = 5.
def fromStamp(stamp, N=3, P0=None, approx=1e-6, damp=0.):
'''
optional P0 = (list of floats): initial parameter guess.
(parameters of a GaussianMixtureEllipsePSF)
'''
from tractor.ellipses import EllipseESoft
w = np.ones(N) / float(N)
mu = np.zeros((N, 2))
ell = [EllipseESoft(np.log(2 * r), 0., 0.) for r in range(1, N + 1)]
psf = GaussianMixtureEllipsePSF(w, mu, ell)
if P0 is not None:
psf.setAllParams(P0)
tim = Image(data=stamp, invvar=1e6 * np.ones_like(stamp), psf=psf)
H, W = stamp.shape
src = PointSource(PixPos(W // 2, H // 2), Flux(1.))
tr = Tractor([tim], [src])
tr.freezeParam('catalog')
tim.freezeAllBut('psf')
# print 'Fitting:'
# tr.printThawedParams()
tim.modelMinval = approx
alphas = [0.1, 0.3, 1.0]
for step in range(50):
dlnp, X, alpha = tr.optimize(shared_params=False,
alphas=alphas,
damp=damp)
# print 'dlnp', dlnp, 'alpha', alpha
# print 'X', X
if dlnp < 1e-6:
break
# print 'psf', psf
return psf
def __init__(self, *args):
'''
args = (amp, mean, ell)
or
args = (a0,a1,..., mx0,my0,mx1,my1,..., logr0,ee1-0,ee2-0,logr1,ee1-2,...)
amp: np array (size K) of Gaussian amplitudes
mean: np array (size K,2) of means
ell: list (length K) of EllipseESoft objects
'''
if len(args) == 3:
amp, mean, ell = args
else:
from tractor.ellipses import EllipseESoft
assert (len(args) % 6 == 0)
K = len(args) // 6
amp = np.array(args[:K])
mean = np.array(args[K:3 * K]).reshape((K, 2))
args = args[3 * K:]
ell = [EllipseESoft(*args[3 * k:3 * (k + 1)]) for k in range(K)]
K = len(amp)
var = np.zeros((K, 2, 2))
for k in range(K):
var[k, :, :] = self.ellipseToVariance(ell[k])
self.ellipses = [e.copy() for e in ell]
super(GaussianMixtureEllipsePSF, self).__init__(amp, mean, var)
self.stepsizes = [0.001] * K + [0.001] * (K * 2) + [0.001] * (K * 3)
def __init__(self, *args, **kwargs):
super(EllipseWithPriors, self).__init__(*args, **kwargs)
if self.ellipsePriors is None:
ellipsePriors = _GaussianPriors(None)
ellipsePriors.add('ee1',
0.,
self.ellipticityStd,
param=EllipseESoft(1., 0., 0.))
ellipsePriors.add('ee2',
0.,
self.ellipticityStd,
param=EllipseESoft(1., 0., 0.))
self.__class__.ellipsePriors = ellipsePriors
self.gpriors = self.ellipsePriors
# MAGIC -- 30" default max r_e!
# SEE ALSO survey.py : class(LogRadius)!
self.uppers[0] = np.log(30.)
