>>> """
=======================
Boxplot drawer function
=======================

This example demonstrates how to pass pre-computed box plot
statistics to the box plot drawer. The first figure demonstrates
how to remove and add individual components (note that the
mean is the only value not shown by default). The second
figure demonstrates how the styles of the artists can
be customized.

A good general reference on boxplots and their history can be found
here: http://vita.had.co.nz/papers/boxplots.pdf
"""
... 
... import numpy as np
... import matplotlib.pyplot as plt
... import matplotlib.cbook as cbook
... 
... # fake data
... np.random.seed(19680801)
... data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
... labels = list('ABCD')
... 
... # compute the boxplot stats
... stats = cbook.boxplot_stats(data, labels=labels, bootstrap=10000)
... 
... ###############################################################################
... # After we've computed the stats, we can go through and change anything.
... # Just to prove it, I'll set the median of each set to the median of all
... # the data, and double the means
... 
... for n in range(len(stats)):
...     stats[n]['med'] = np.median(data)
...     stats[n]['mean'] *= 2
... 
... print(list(stats[0]))
... 
... fs = 10  # fontsize
... 
... ###############################################################################
... # Demonstrate how to toggle the display of different elements:
... 
... fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
... axs[0, 0].bxp(stats)
... axs[0, 0].set_title('Default', fontsize=fs)
... 
... axs[0, 1].bxp(stats, showmeans=True)
... axs[0, 1].set_title('showmeans=True', fontsize=fs)
... 
... axs[0, 2].bxp(stats, showmeans=True, meanline=True)
... axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
... 
... axs[1, 0].bxp(stats, showbox=False, showcaps=False)
... tufte_title = 'Tufte Style\n(showbox=False,\nshowcaps=False)'
... axs[1, 0].set_title(tufte_title, fontsize=fs)
... 
... axs[1, 1].bxp(stats, shownotches=True)
... axs[1, 1].set_title('notch=True', fontsize=fs)
... 
... axs[1, 2].bxp(stats, showfliers=False)
... axs[1, 2].set_title('showfliers=False', fontsize=fs)
... 
... for ax in axs.flat:
...     ax.set_yscale('log')
...     ax.set_yticklabels([])
... 
... fig.subplots_adjust(hspace=0.4)
... plt.show()
... 
... ###############################################################################
... # Demonstrate how to customize the display different elements:
... 
... boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
... flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
...                   linestyle='none')
... medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
... meanpointprops = dict(marker='D', markeredgecolor='black',
...                       markerfacecolor='firebrick')
... meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
... 
... fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(6, 6), sharey=True)
... axs[0, 0].bxp(stats, boxprops=boxprops)
... axs[0, 0].set_title('Custom boxprops', fontsize=fs)
... 
... axs[0, 1].bxp(stats, flierprops=flierprops, medianprops=medianprops)
... axs[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)
... 
... axs[1, 0].bxp(stats, meanprops=meanpointprops, meanline=False,
...               showmeans=True)
... axs[1, 0].set_title('Custom mean\nas point', fontsize=fs)
... 
... axs[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True,
...               showmeans=True)
... axs[1, 1].set_title('Custom mean\nas line', fontsize=fs)
... 
... for ax in axs.flat:
...     ax.set_yscale('log')
...     ax.set_yticklabels([])
... 
... fig.suptitle("I never said they'd be pretty")
... fig.subplots_adjust(hspace=0.4)
... plt.show()
... 
... #############################################################################
... #
... # .. admonition:: References
... #
... #    The use of the following functions, methods, classes and modules is shown
... #    in this example:
... #
... #    - `matplotlib.axes.Axes.bxp`
... #    - `matplotlib.cbook.boxplot_stats`
...