def plot_summary_distributions(df,ax=None,label_post=False,label_prior=False,
                               subplots=False,figsize=(11,8.5),pt_color='b'):
    """ helper function to plot gaussian distrbutions from prior and posterior
    means and standard deviations

    Parameters
    ----------
    df : pandas.DataFrame
        a dataframe and csv file.  Must have columns named:
        'prior_mean','prior_stdev','post_mean','post_stdev'.  If loaded
        from a csv file, column 0 is assumed to tbe the index
    ax: matplotlib.pyplot.axis
        If None, and not subplots, then one is created
        and all distributions are plotted on a single plot
    label_post: bool
        flag to add text labels to the peak of the posterior
    label_prior: bool
        flag to add text labels to the peak of the prior
    subplots: (boolean)
        flag to use subplots.  If True, then 6 axes per page
        are used and a single prior and posterior is plotted on each
    figsize: tuple
        matplotlib figure size

    Returns
    -------
    figs : list
        list of figures
    axes : list
        list of axes

    Note
    ----
    This is useful for demystifying FOSM results

    if subplots is False, a single axis is returned

    Example
    -------
    ``>>>import matplotlib.pyplot as plt``

    ``>>>import pyemu``

    ``>>>pyemu.helpers.plot_summary_distributions("pest.par.usum.csv")``

    ``>>>plt.show()``
    """
    warnings.warn("pyemu.helpers.plot_summary_distributions() has moved to plot_utils",PyemuWarning)
    from pyemu import plot_utils
    return plot_utils.plot_summary_distributions(df=df,ax=ax,label_post=label_post,
                                                 label_prior=label_prior,subplots=subplots,
                                                 figsize=figsize,pt_color=pt_color)
helper function to plot gaussian distrbutions from prior and posterior
    means and standard deviations

    Parameters
    ----------
    df : pandas.DataFrame
        a dataframe and csv file.  Must have columns named:
        'prior_mean','prior_stdev','post_mean','post_stdev'.  If loaded
        from a csv file, column 0 is assumed to tbe the index
    ax: matplotlib.pyplot.axis
        If None, and not subplots, then one is created
        and all distributions are plotted on a single plot
    label_post: bool
        flag to add text labels to the peak of the posterior
    label_prior: bool
        flag to add text labels to the peak of the prior
    subplots: (boolean)
        flag to use subplots.  If True, then 6 axes per page
        are used and a single prior and posterior is plotted on each
    figsize: tuple
        matplotlib figure size

    Returns
    -------
    figs : list
        list of figures
    axes : list
        list of axes

    Note
    ----
    This is useful for demystifying FOSM results

    if subplots is False, a single axis is returned

    Example
    -------
    ``>>>import matplotlib.pyplot as plt``

    ``>>>import pyemu``

    ``>>>pyemu.helpers.plot_summary_distributions("pest.par.usum.csv")``

    ``>>>plt.show()``