 'ccdf', 'icdf', 'iccdf', 'logcdf', 'logccdf',
    'ilogcdf', 'ilogccdf'.
ax : `matplotlib.axes`, optional
    Axes on which to generate the plot. If not provided, use the
    current axes.

Returns
-------
ax : `matplotlib.axes`
    Axes on which the plot was generated.
    The plot can be customized by manipulating this object.

Examples
--------
Instantiate a distribution with the desired parameters:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> X = stats.Normal(mu=1., sigma=2.)

Plot the PDF over the central 99.9% of the distribution.
Compare against a histogram of a random sample.

>>> ax = X.plot()
>>> sample = X.sample(10000)
>>> ax.hist(sample, density=True, bins=50, alpha=0.5)
>>> plt.show()

Plot ``logpdf(x)`` as a function of ``x`` in the left tail,
where the log of the CDF is between -10 and ``np.log(0.5)``.

>>> X.plot('x', 'logpdf', t=('logcdf', -10, np.log(0.5)))
>>> plt.show()

Plot the PDF of the normal distribution as a function of the
CDF for various values of the scale parameter.

>>> X = stats.Normal(mu=0., sigma=[0.5, 1., 2])
>>> X.plot('cdf', 'pdf')
>>> plt.show()

>