`POSEIDON.retrieval`

Functions related to atmospheric retrieval.

Module Contents

`run_retrieval`(planet, star, model, opac, data, priors, ...)	ADD DOCSTRING
`forward_model`(param_vector, planet, star, model, opac, ...)	ADD DOCSTRING
`CLR_Prior`(chem_params_drawn[, limit])	Implements the centred-log-ratio (CLR) prior for chemical mixing ratios.
`PyMultiNest_retrieval`(planet, star, model, opac, data, ...)	Main function for conducting atmospheric retrievals with PyMultiNest.
`retrieved_samples`(planet, star, model, opac, data, ...)	ADD DOCSTRING
`Z_to_sigma`(ln_Z1, ln_Z2)	Convert the log-evidences of two models to a sigma confidence level.
`Bayesian_model_comparison`(planet_name, model_1, model_2)	Conduct Bayesian model comparison between the outputs of two retrievals.

POSEIDON.retrieval.run_retrieval(planet, star, model, opac, data, priors, wl, P, P_ref=None, R_p_ref=None, P_param_set=0.01, R=None, retrieval_name=None, He_fraction=0.17, N_slice_EM=2, N_slice_DN=4, constant_gravity=False, spectrum_type='transmission', y_p=np.array([0.0]), stellar_T_step=20, stellar_log_g_step=0.1, N_live=400, ev_tol=0.5, sampling_algorithm='MultiNest', resume=False, verbose=True, sampling_target='parameter', chem_grid='fastchem', N_output_samples=1000): ADD DOCSTRING

POSEIDON.retrieval.forward_model(param_vector, planet, star, model, opac, data, wl, P, P_ref_set, R_p_ref_set, P_param_set, He_fraction, N_slice_EM, N_slice_DN, spectrum_type, T_phot_grid, T_het_grid, log_g_phot_grid, log_g_het_grid, I_phot_grid, I_het_grid, y_p, F_s_obs, constant_gravity, chemistry_grid): ADD DOCSTRING

POSEIDON.retrieval.CLR_Prior(chem_params_drawn, limit=-12.0)

Implements the centred-log-ratio (CLR) prior for chemical mixing ratios.

CLR[i] here is the centred log-ratio transform of the mixing ratio, X[i]

POSEIDON.retrieval.PyMultiNest_retrieval(planet, star, model, opac, data, prior_types, prior_ranges, spectrum_type, wl, P, P_ref_set, R_p_ref_set, P_param_set, He_fraction, N_slice_EM, N_slice_DN, N_params, T_phot_grid, T_het_grid, log_g_phot_grid, log_g_het_grid, I_phot_grid, I_het_grid, y_p, F_s_obs, constant_gravity, chemistry_grid, **kwargs): Main function for conducting atmospheric retrievals with PyMultiNest.

POSEIDON.retrieval.retrieved_samples(planet, star, model, opac, data, retrieval_name, wl, P, P_ref_set, R_p_ref_set, P_param_set, He_fraction, N_slice_EM, N_slice_DN, spectrum_type, T_phot_grid, T_het_grid, log_g_phot_grid, log_g_het_grid, I_phot_grid, I_het_grid, y_p, F_s_obs, constant_gravity, chemistry_grid, N_output_samples): ADD DOCSTRING

POSEIDON.retrieval.Z_to_sigma(ln_Z1, ln_Z2): Convert the log-evidences of two models to a sigma confidence level.

POSEIDON.retrieval.Bayesian_model_comparison(planet_name, model_1, model_2, ln_Z_format='{:.2f}', B_format='{:.2e}', ln_B_format='{:.2f}', sigma_format='{:.1f}'): Conduct Bayesian model comparison between the outputs of two retrievals. This function outputs the Bayes factor and equivalent sigma significance comparing the two models.