>>> """
=================================
Artist customization in box plots
=================================

This example demonstrates how to use the various keyword arguments to fully
customize box plots. 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. It also demonstrates how to set the limit of the whiskers to
specific percentiles (lower right axes)

A good general reference on boxplots and their history can be found here:
https://vita.had.co.nz/papers/boxplots.pdf

"""
... 
... import numpy as np
... import matplotlib.pyplot as plt
... 
... # fake data
... np.random.seed(19680801)
... data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
... labels = list('ABCD')
... 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].boxplot(data, labels=labels)
... axs[0, 0].set_title('Default', fontsize=fs)
... 
... axs[0, 1].boxplot(data, labels=labels, showmeans=True)
... axs[0, 1].set_title('showmeans=True', fontsize=fs)
... 
... axs[0, 2].boxplot(data, labels=labels, showmeans=True, meanline=True)
... axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
... 
... axs[1, 0].boxplot(data, labels=labels, 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].boxplot(data, labels=labels, notch=True, bootstrap=10000)
... axs[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs)
... 
... axs[1, 2].boxplot(data, labels=labels, 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,
...                   markeredgecolor='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=3, figsize=(6, 6), sharey=True)
... axs[0, 0].boxplot(data, boxprops=boxprops)
... axs[0, 0].set_title('Custom boxprops', fontsize=fs)
... 
... axs[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)
... axs[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)
... 
... axs[0, 2].boxplot(data, whis=(0, 100))
... axs[0, 2].set_title('whis=(0, 100)', fontsize=fs)
... 
... axs[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,
...                   showmeans=True)
... axs[1, 0].set_title('Custom mean\nas point', fontsize=fs)
... 
... axs[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,
...                   showmeans=True)
... axs[1, 1].set_title('Custom mean\nas line', fontsize=fs)
... 
... axs[1, 2].boxplot(data, whis=[15, 85])
... axs[1, 2].set_title('whis=[15, 85]\n#percentiles', 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.boxplot` / `matplotlib.pyplot.boxplot`
...