Log posterior functions

Log posterior functions for transit modelling and parameter estimation.

A log posterior function (LPF) class creates a basis for Bayesian parameter estimation from transit light curves. In PyTransit, LPFs are a bit more than what the name implies. An LPF stores the observations, model priors, etc. It also contains methods for posterior optimisation and MCMC sampling.

class pytransit.lpf.BaseLPF(name: str, passbands: list, times: list = None, fluxes: Iterable[T_co] = None, errors: list = None, pbids: list = None, covariates: list = None, wnids: list = None, tm: pytransit.models.transitmodel.TransitModel = None, nsamples: tuple = 1, exptimes: tuple = 0.0, init_data=True, result_dir: pathlib.Path = None, tref: float = 0.0, lnlikelihood: str = 'wn')[source]
add_as_prior(mean: float, std: float) → None[source]

Add a normal prior on the scaled semi-major axis (a / R_\star).

Parameters:
  • mean (float) – Mean of the normal distribution.
  • std (float) – Standard deviation of the normal distribution
add_ldtk_prior(teff: tuple, logg: tuple, z: tuple, passbands: tuple, uncertainty_multiplier: float = 3, **kwargs) → None[source]

Add a LDTk-based prior on the limb darkening.

Parameters:
  • teff –
  • logg –
  • z –
  • passbands –
  • uncertainty_multiplier –
add_prior(prior)[source]
add_t14_prior(mean: float, std: float) → None[source]

Add a normal prior on the transit duration.

Parameters:
  • mean (float) – Mean of the normal distribution
  • std (float) – Standard deviation of the normal distribution.
baseline(pv)[source]
create_pv_population(npop=50)[source]
flux_model(pv)[source]
lnlikelihood(pvp)[source]

Log likelihood for a 1D or 2D array of model parameters.

Parameters:pvp (ndarray) – Either a 1D parameter vector or a 2D parameter array.
Returns:
Return type:Log likelihood for the given parameter vector(s)
plot_light_curves(method='de', ncol: int = 3, width: float = 2.0, max_samples: int = 1000, figsize=None, data_alpha=0.5, ylim=None)[source]
posterior_samples(burn: int = 0, thin: int = 1, derived_parameters: bool = True)[source]
remove_outliers(sigma=5)[source]
remove_transits(tids)[source]
residuals(pv)[source]
set_radius_ratio_prior(kmin, kmax)[source]

Set a uniform prior on all radius ratios.

transit_model(pv, copy=True)[source]
trends(pv)[source]

Additive trends