code: def zplot(pvalue_dict, name='',
            format='png', path='./', fontmap=None, verbose=1):
      
  
      if verbose:
          print_('\nGenerating model validation plot')
  
      if fontmap is None:
          fontmap = {1: 10, 2: 8, 3: 6, 4: 5, 5: 4}
  
      x, y, labels = [], [], []
  
      for i, var in enumerate(pvalue_dict):
  
          # Get p-values
          pvals = pvalue_dict[var]
          # Take absolute values of inverse-standard normals
          zvals = abs(special.ndtri(pvals))
  
          x = append(x, zvals)
          y = append(y, ones(size(zvals)) * (i + 1))
  
          vname = var
          vname += " (%i)" % size(zvals)
          labels = append(labels, vname)
  
      # Spawn new figure
      figure()
      subplot(111)
      subplots_adjust(left=0.25, bottom=0.1)
      # Plot scores
      pyplot(x, y, 'o')
      # Set range on axes
      ylim(0, size(pvalue_dict) + 2)
      xlim(xmin=0)
      # Tick labels for y-axis
      yticks(arange(len(labels) + 2), append(append("", labels), ""))
      # X label
      xlabel("Absolute z transformation of p-values")
  
      if not os.path.exists(path):
          os.mkdir(path)
      if not path.endswith('/'):
          path += '/'
  
      if name:
          name += '-'
  
      savefig("%s%svalidation.%s" % (path, name, format))
docs: Plots absolute values of z-scores for model validation output from
      diagnostics.validate().