global_minimization

despasito.parameter_fitting.fit_functions.global_minimization

global_minimization(global_method, *args, **kwargs)[source]

Fit defined parameters for equation of state object with given experimental data.

Each set of experimental data is converted to an object with the build in ability to evaluate its part of objective function. To add another type of supported experimental data, see Parameter Fitting Features.

Parameters:

  • global_method (str) – Global optimization method used to fit parameters. See supported global_methods.

  • parameters_guess (numpy.ndarray,) – An array of initial guesses for parameters, these will be optimized throughout the process.

  • bounds (list[tuple]) – List of length equal to fit_parameter_names with lists of pairs for minimum and maximum bounds of parameter being fit. Defaults are broad, recommend specification.

  • fit_bead (str) – Name of bead whose parameters are being fit, should be in bead list of bead_configuration

  • fit_parameter_names (list[str]) – This list contains the name of the parameter being fit (e.g. epsilon). See Eos object documentation for supported parameter names. Cross interaction parameter names should be composed of parameter name and the other bead type, separated by an underscore (e.g. epsilon_CO2).

  • exp_dict (dict) – Dictionary of experimental data objects.

  • global_opts (dict, Optional) – Keyword arguments of global optimization algorithm. See specific options in global_methods. Note that unless in the keyword, workers is provided, the thermodynamic calculations will we split among the cores. Check the global optimization method to ensure it uses the workers keyword.

  • minimizer_opts (dict, Optional) – Dictionary used to define minimization type and the associated options.

    • method (str) - Method available to scipy.optimize.minimize

    • options (dict) - This dictionary contains the kwargs available to the chosen

    method

  • constraints (dict, Optional) – This dictionary of constraint types and their arguments will be converted into the appropriate form for the chosen optimization method.

Returns:

Objective – Sum of objective values according to appropriate weights. Output file saved in current working directory.

Return type:

float