def get_bins(values):
    """
    Automatically compute the number of bins for discrete variables.

    Parameters
    ----------
    values = numpy array
        values

    Returns
    -------
    array with the bins

    Notes
    -----
    Computes the width of the bins by taking the maximun of the Sturges and the Freedman-Diaconis
    estimators. Acording to numpy `np.histogram` this provides good all around performance.

    The Sturges is a very simplistic estimator based on the assumption of normality of the data.
    This estimator has poor performance for non-normal data, which becomes especially obvious for
    large data sets. The estimate depends only on size of the data.

    The Freedman-Diaconis rule uses interquartile range (IQR) to estimate the binwidth.
    It is considered a robusts version of the Scott rule as the IQR is less affected by outliers
    than the standard deviation. However, the IQR depends on fewer points than the standard
    deviation, so it is less accurate, especially for long tailed distributions.
    """
    x_min = values.min().astype(int)
    x_max = values.max().astype(int)

    # Sturges histogram bin estimator
    bins_sturges = (x_max - x_min) / (np.log2(values.size) + 1)

    # The Freedman-Diaconis histogram bin estimator.
    iqr = np.subtract(*np.percentile(values, [75, 25]))  # pylint: disable=assignment-from-no-return
    bins_fd = 2 * iqr * values.size ** (-1 / 3)

    width = round(np.max([1, bins_sturges, bins_fd])).astype(int)

    return np.arange(x_min, x_max + width + 1, width)