>>> """
====================================
Automatically setting tick positions
====================================

Setting the behavior of tick auto-placement.

By default, Matplotlib will choose the number of ticks and tick positions so
that there is a reasonable number of ticks on the axis and they are located
at "round" numbers.

As a result, there may be no ticks on the edges of the plot.
"""
... 
... import matplotlib.pyplot as plt
... import numpy as np
... np.random.seed(19680801)
... 
... fig, ax = plt.subplots()
... dots = np.linspace(0.3, 1.2, 10)
... X, Y = np.meshgrid(dots, dots)
... x, y = X.ravel(), Y.ravel()
... ax.scatter(x, y, c=x+y)
... plt.show()
... 
... ###############################################################################
... # If you want to keep ticks at round numbers, and also have ticks at the edges
... # you can switch :rc:`axes.autolimit_mode` to 'round_numbers'. This expands the
... # axis limits to the next round number.
... 
... plt.rcParams['axes.autolimit_mode'] = 'round_numbers'
... 
... # Note: The limits are calculated at draw-time. Therefore, when using
... # :rc:`axes.autolimit_mode` in a context manager, it is important that
... # the ``show()`` command is within the context.
... 
... fig, ax = plt.subplots()
... ax.scatter(x, y, c=x+y)
... plt.show()
... 
... ###############################################################################
... # The round numbers autolimit_mode is still respected if you set an additional
... # margin around the data using `.Axes.set_xmargin` / `.Axes.set_ymargin`:
... 
... fig, ax = plt.subplots()
... ax.scatter(x, y, c=x+y)
... ax.set_xmargin(0.8)
... plt.show()
...