GUIBRUSHR.Retrieval.ModelCalculation.Classes.Bestpars module

Best parameters class for atmospheric retrieval calculations.

This module contains the Bestpars class which manages the best-fit parameters for atmospheric retrieval using Differential Evolution algorithms.

class GUIBRUSHR.Retrieval.ModelCalculation.Classes.Bestpars.Bestpars(list_bestpars, list_bestpars_initial_value, multiplier_chains=2, multiplier_cores=1, use_pool=False, use_parallel_init=False, mode_jump_threshold=0.05)

Bases: object

Manages best-fit parameters for atmospheric retrieval calculations.

This class handles the storage and configuration of best-fit parameters used in atmospheric retrieval algorithms, particularly for Differential Evolution Retrieval methods.

Variables:

• list_bestpars – List of best-fit parameter values

• list_bestpars_initial_value – List of initial parameter values

• nfit – Number of fitted parameters

• nchains – Number of chains for the retrieval algorithm

• ncores – Number of cores for the retrieval algorithm

• gamma_coeff – Gamma coefficient for Differential Evolution

• multiplier_chains – Multiplier for the number of chains

• multiplier_cores – Multiplier for the number of cores

• use_pool – Flag indicating whether to use multiprocessing pool

• use_parallel_init – Flag indicating whether to use parallel initialization

__init__(list_bestpars, list_bestpars_initial_value, multiplier_chains=2, multiplier_cores=1, use_pool=False, use_parallel_init=False, mode_jump_threshold=0.05)

Initialize the Bestpars object.

Parameters:

• list_bestpars – List containing the best-fit parameter values

• list_bestpars_initial_value – List containing initial parameter values

• multiplier_chains – Multiplier for the number of chains

• multiplier_cores – Multiplier for the number of cores

• use_pool – Flag indicating whether to use multiprocessing pool

• use_parallel_init – Flag indicating whether to use parallel initialization

• mode_jump_threshold – Probability of using gamma=1 mode-jumping (ter Braak 2006). Default 0.05 (5%). Set lower for high-dimensional problems.