docs: def plot_stat_summary(df, fig=None):
      '''
      Plot stats grouped by test capacitor load _and_ frequency.
  
      In other words, we calculate the mean of all samples in the data
      frame for each test capacitance and frequency pairing, plotting
      the following stats:
  
       - Root mean squared error
       - Coefficient of variation
       - Bias
  
      ## [Coefficient of variation][1] ##
  
      > In probability theory and statistics, the coefficient of
      > variation (CV) is a normalized measure of dispersion of a
      > probability distribution or frequency distribution. It is defined
      > as the ratio of the standard deviation to the mean.
  
      [1]: http://en.wikipedia.org/wiki/Coefficient_of_variation
      '''
      if fig is None:
          fig = plt.figure(figsize=(8, 8))
  
      # Define a subplot layout, 3 rows, 2 columns
      grid = GridSpec(3, 2)
      stats = calculate_stats(df, groupby=['test_capacitor',
                                           'frequency']).dropna()
  
      for i, stat in enumerate(['RMSE %', 'cv %', 'bias %']):
          axis = fig.add_subplot(grid[i, 0])
          axis.set_title(stat)
          # Plot a colormap to show how the statistical value changes
          # according to frequency/capacitance pairs.
          plot_colormap(stats, stat, axis=axis, fig=fig)
          axis = fig.add_subplot(grid[i, 1])
          axis.set_title(stat)
          # Plot a histogram to show the distribution of statistical
          # values across all frequency/capacitance pairs.
          try:
              axis.hist(stats[stat].values, bins=50)
          except AttributeError:
              print stats[stat].describe()
      fig.tight_layout()