ad_helpme.py 文件源码

def lnprobfn(theta):
    """Given a parameter vector, a dictionary of observational data 
    and a model object, return the ln of the posterior. 
    This requires that an sps object (and if using spectra 
    and gaussian processes, a GP object) be instantiated.
    """

    print('lnprobfn loves pizza')
    # Calculate prior probability and exit if not within prior
    lnp_prior = model.prior_product(theta)
    if not np.isfinite(lnp_prior):
        return -np.infty

    # Generate mean model
    t1 = time.time()
    try:
        spec, phot, x = model.mean_model(theta, obs, sps=sps)
    except(ValueError):
        return -np.infty
    d1 = time.time() - t1
    vectors = {}  # This would be used for noise model weight functions

    # Calculate likelihoods
    t2 = time.time()
    lnp_spec = lnlike_spec(spec, obs=obs, spec_noise=spec_noise)
    lnp_phot = lnlike_phot(phot, obs=obs, phot_noise=phot_noise)
    d2 = time.time() - t2
    if verbose:
        write_log(theta, lnp_prior, lnp_spec, lnp_phot, d1, d2)

    return lnp_prior + lnp_phot + lnp_spec

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