Exemple #1
0
 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.
Exemple #2
0
    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
Exemple #3
0
    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)
Exemple #4
0
 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.)