code: def plot_summary_distributions(df,ax=None,label_post=False,label_prior=False,
                                 subplots=False,figsize=(11,8.5),pt_color='b'):
      
      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)
docs: 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()``