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()``
    """
    import matplotlib.pyplot as plt
    if isinstance(df,str):
        df = pd.read_csv(df,index_col=0)
    if ax is None and not subplots:
        fig = plt.figure(figsize=figsize)
        ax = plt.subplot(111)
        ax.grid()


    if "post_stdev" not in df.columns and "post_var" in df.columns:
        df.loc[:,"post_stdev"] = df.post_var.apply(np.sqrt)
    if "prior_stdev" not in df.columns and "prior_var" in df.columns:
        df.loc[:,"prior_stdev"] = df.prior_var.apply(np.sqrt)
    if "prior_expt" not in df.columns and "prior_mean" in df.columns:
        df.loc[:,"prior_expt"] = df.prior_mean
    if "post_expt" not in df.columns and "post_mean" in df.columns:
        df.loc[:,"post_expt"] = df.post_mean

    if subplots:
        fig = plt.figure(figsize=figsize)
        ax = plt.subplot(2,3,1)
        ax_per_page = 6
        ax_count = 0
        axes = []
        figs = []
    for name in df.index:
        x,y = gaussian_distribution(df.loc[name,"post_expt"],
                                    df.loc[name,"post_stdev"])
        ax.fill_between(x,0,y,facecolor=pt_color,edgecolor="none",alpha=0.25)
        if label_post:
            mx_idx = np.argmax(y)
            xtxt,ytxt = x[mx_idx],y[mx_idx] * 1.001
            ax.text(xtxt,ytxt,name,ha="center",alpha=0.5)

        x,y = gaussian_distribution(df.loc[name,"prior_expt"],
                                    df.loc[name,"prior_stdev"])
        ax.plot(x,y,color='0.5',lw=3.0,dashes=(2,1))
        if label_prior:
            mx_idx = np.argmax(y)
            xtxt,ytxt = x[mx_idx],y[mx_idx] * 1.001
            ax.text(xtxt,ytxt,name,ha="center",alpha=0.5)
        #ylim = list(ax.get_ylim())
        #ylim[1] *= 1.2
        #ylim[0] = 0.0
        #ax.set_ylim(ylim)
        if subplots:
            ax.set_title(name)
            ax_count += 1
            ax.set_yticklabels([])
            axes.append(ax)
            if name == df.index[-1]:
                break
            if ax_count >= ax_per_page:
                figs.append(fig)
                fig = plt.figure(figsize=figsize)
                ax_count = 0
            ax = plt.subplot(2,3,ax_count+1)
    if subplots:
        figs.append(fig)
        return figs, axes
    ylim = list(ax.get_ylim())
    ylim[1] *= 1.2
    ylim[0] = 0.0
    ax.set_ylim(ylim)
    ax.set_yticklabels([])
    return ax
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()``