A description of the general.ini config.

[output]
    log_to_file -> bool
        If `True`, output is logged to a file as opposed to the command line.
    log_file -> str
        The file name the logged output is written to (in the `NonLinearSearch` output folder).
    log_level -> str
        The level of logging.
    model_results_decimal_places -> int
        The number of decimal places the estimated values and errors of all parameters in the model.results file are
        output to.
    remove_files -> bool
        If True, all output files of a `NonLinearSearch` (e.g. samples, samples_backup, model.results, images, etc.)
        are deleted once the model-fit has completed.
        A .zip file of all output is always created before files are removed, thus results are not lost with this
        option turned on. If PyAutoFit does not find the output files of a model-fit (because they were removed) but
        does find this .zip file, it will unzip the contents and continue the analysis as if the files were
        there all along.
        This feature was implemented because super-computers often have a limit on the number of files allowed per
        user and the large number of files output by PyAutoFit can exceed this limit. By removing files the
        number of files is restricted only to the .zip files.
    force_pickle_overwrite -> bool
        If `True`, the .pickle files of a resumed PyAutoFit run are overwritten for a fit even if the model-fit is
        completed and skipped. This is used so that results can be refreshed with new pickles, so that changes to
        source-code are refelected in `Aggregator` use.

[hoc]
    hpc_mode -> bool
        If `True`, high performance computing mode is used, which adjusts many settings automatically which are
        desireable for using PyAutoFit on super computers (e.g. minimizes file output, minimizes visualization, etc.).
    iterations_per_update -> int
        The number of iterations used per update in hpc mode, as it may be desireable to perform less iterations for
        runs on super computpers that can often have much longer run times.