def prior_sample(self, src_type, u=None):
params = SrcParams(u=u)
if src_type == 'star':
# TODO SET a with atoken
params.a = 0
color = self.star_flux_prior.rvs(size=1)[0]
logprob = self.star_flux_prior.logpdf(color)
params.fluxes = np.exp(self.star_flux_prior.to_fluxes(color))
return params, logprob
elif src_type == 'galaxy':
params.a = 1
color = self.galaxy_flux_prior.rvs(size=1)[0]
logprob = self.galaxy_flux_prior.logpdf(color)
params.fluxes = np.exp(self.galaxy_flux_prior.to_fluxes(color))
sample_ab = self.galaxy_ab_prior.rvs(size=1)[0,0]
sample_ab = np.exp(sample_ab) / (1.+np.exp(sample_ab))
params.shape = np.array([np.random.random(),
np.exp(self.galaxy_re_prior.rvs(size=1)[0,0]),
np.random.random() * np.pi,
sample_ab])
logprob_re = self.galaxy_re_prior.logpdf(params.sigma)
logprob_ab = self.galaxy_ab_prior.logpdf(params.rho)
logprob_shape = -np.log(np.pi) + logprob_re + logprob_ab
return params, logprob
def linear_propose_other_type(self):
""" based on linear regression of fluxes and conditional distribution
of galaxy shapes, propose parameters of the other type and report
the log probability of generating that proposal
Returns:
- proposal params
- log prob of proposal
- log prob of implied reverse proposal
- log determinant of the transformation |d(x',u')/d(x,u)|
"""
params = SrcParams(u=self.params.u)
if self.is_star():
params.a = 1
# fluxes
residual = mvn.rvs(cov=star_mag_proposal.res_covariance)
ll_prop_fluxes = mvn.logpdf(residual, mean=None, cov=star_mag_proposal.res_covariance)
gal_mag = star_mag_proposal.predict(self.params.mags.reshape((1,-1))) + residual
params.fluxes = du.mags2nanomaggies(gal_mag).flatten()
# compute reverse ll
res = gal_mag_proposal.predict(gal_mag) - self.params.mags
llrev = mvn.logpdf(res, mean=None, cov=gal_mag_proposal.res_covariance)
# shape
sample_re = star_rad_proposal.rvs(size=1)[0]
ll_shape = star_rad_proposal.logpdf(sample_re)
params.shape = np.array([np.random.rand(),
np.exp(sample_re),
np.random.rand() * np.pi,
np.random.rand()])
_, logdet = np.linalg.slogdet(star_mag_proposal.coef_)
return params, ll_prop_fluxes + ll_shape, llrev, logdet
elif self.is_galaxy():
params.a = 0
# fluxes
residual = mvn.rvs(cov=gal_mag_proposal.res_covariance)
llprob = mvn.logpdf(residual, mean=None, cov=gal_mag_proposal.res_covariance)
star_mag = gal_mag_proposal.predict(self.params.mags.reshape((1, -1))) + residual
params.fluxes = du.mags2nanomaggies(star_mag).flatten()
res = star_mag_proposal.predict(star_mag) - self.params.mags
llrev = mvn.logpdf(res, mean=None, cov=star_mag_proposal.res_covariance)
ll_re = star_rad_proposal.logpdf(np.log(self.params.sigma))
_, logdet = np.linalg.slogdet(gal_mag_proposal.coef_)
return params, llprob, llrev + ll_re, logdet