Source code for pyretlife.retrieval.likelihood_validation

import numpy as np
from numpy import ndarray




def validate_pt_profile(
    settings: dict, temp_vars: dict, phys_vars: dict
) -> bool:
    # Check: Return -inf if pressures for madhuseager models are not
    # monotonically increasing.
    result = False
    if settings["parameterization"] == "madhuseager":
        if (
            not settings["log_top_pressure"]
            < temp_vars["log_P1"]
            < temp_vars["log_P2"]
            < temp_vars["log_P3"]
            < phys_vars["log_P0"]
        ):
            result = True
    elif settings["parameterization"] == "mod_madhuseager":
        if (
            not settings["log_top_pressure"]
            < temp_vars["log_P1"]
            < temp_vars["log_P2"]
            < phys_vars["log_P0"]
        ):
            result = True

    # Check: Return -inf if parameters for the Guillot model are bad
    elif settings["parameterization"] == "guillot":
        if temp_vars["alpha"] < -1:
            result = True

    return result





def validate_cube_finite(cube: list) -> bool:
    if not np.isfinite(cube).all():
        return True
    else:
        return False




def validate_positive_mass(phys_vars: dict) -> bool:
    if 'M_pl' in phys_vars:
        if phys_vars['M_pl'] <= 0.:
            return True
        else:
            return False
    return False




def validate_positive_temperatures(temp: ndarray) -> bool:
    if any((temp < 0).tolist()):
        return True
    else:
        return False




def validate_clouds(press: ndarray, temp: ndarray, cloud_vars: dict) -> bool:
    if 'Pcloud' in cloud_vars.keys():
        if cloud_vars['Pcloud']>press[-1]:
            return True
        if temp[-1] < temp[np.argmin(np.abs(np.log10(press)-np.log10(cloud_vars['Pcloud'])))]:
            return True
    else:
        return False




def validate_abundances(abundances_VMR: ndarray, chem_vars_VMR: dict) -> bool:
    if 'H2O' in abundances_VMR.keys():
        if abundances_VMR['H2O'][-1] != chem_vars_VMR['H2O']:
            return True
        else:
            return False
    else:
        return False




def validate_sum_of_abundances(abundances_VMR: dict) -> bool:
    total = np.zeros_like(abundances_VMR[list(abundances_VMR.keys())[0]])
    for abundance_VMR in abundances_VMR.keys():
        total = total + abundances_VMR[abundance_VMR]

    if np.any(total>1.0):
        return True
    else:
        return False




def validate_spectrum_goodness(flux: ndarray) -> bool:
    if np.sum(np.isnan(flux)) > 0:
        return True
    else:
        return False