def propose_number_of_bins(
    values,
    binning_logic_system = None,
    ):

    """
    This function returns a proposal for binning for a histogram of a specified
    list using an optional specified binning logic system.

    Freedman-Diaconis: bin width is proportional to the interquartile range of
                       the data divided by the cube root of the size of the data

    Scott:             bin width is proportional to the standard deviation of
                       the values divided by the cube root of the size of the
                       data
    """

    # Set the default binning logic system.
    if binning_logic_system is None:
        binning_logic_system = "Scott"
    # Engage the requested logic system.
    if binning_logic_system == "Freedman-Diaconis":
        #log.debug("engage Freedman-Diaconis binning logic")
        bin_size =\
            2 * interquartile_range(values) * \
            len(values) ** (-1/3)
    elif binning_logic_system == "Scott":
        #log.debug("engage Scott binning logic")
        bin_size =\
            3.5 * standard_deviation(values) * \
            len(values) ** (-1/3)
    else:
        log.error("undefined binning logic system requested")
        raise(ValueError)
    number_of_bins = (max(values) - min(values)) / bin_size
    if numpy.isinf(number_of_bins) or numpy.isnan(number_of_bins):
        number_of_bins = len(set(values)) # number of unique values
        #log.debug(
        #    "binning algorithms ineffective -- " +
        #    "propose binning by unique values"
        #)
    return int(round(number_of_bins))
This function returns a proposal for binning for a histogram of a specified
    list using an optional specified binning logic system.

    Freedman-Diaconis: bin width is proportional to the interquartile range of
                       the data divided by the cube root of the size of the data

    Scott:             bin width is proportional to the standard deviation of
                       the values divided by the cube root of the size of the
                       data