matplotlib.pyplot as plt
>>> from scipy.stats import vonmises

Define distribution parameters.

>>> loc = 0.5 * np.pi  # circular mean
>>> kappa = 1  # concentration

Compute the probability density at ``x=0`` via the ``pdf`` method.

>>> vonmises.pdf(0, loc=loc, kappa=kappa)
0.12570826359722018

Verify that the percentile function ``ppf`` inverts the cumulative
distribution function ``cdf`` up to floating point accuracy.

>>> x = 1
>>> cdf_value = vonmises.cdf(x, loc=loc, kappa=kappa)
>>> ppf_value = vonmises.ppf(cdf_value, loc=loc, kappa=kappa)
>>> x, cdf_value, ppf_value
(1, 0.31489339900904967, 1.0000000000000004)

Draw 1000 random variates by calling the ``rvs`` method.

>>> sample_size = 1000
>>> sample = vonmises(loc=loc, kappa=kappa).rvs(sample_size)

Plot the von Mises density on a Cartesian and polar grid to emphasize
that it is a circular distribution.

>>> fig = plt.figure(figsize=(12, 6))
>>> left = plt.subplot(121)
>>> right = plt.subplot(122, projection='polar')
>>> x = np.linspace(-np.pi, np.pi, 500)
>>> vonmises_pdf = vonmises.pdf(x, loc=loc, kappa=kappa)
>>> ticks = [0, 0.15, 0.3]

The left image contains the Cartesian plot.

>>> left.plot(x, vonmises_pdf)
>>> left.set_yticks(ticks)
>>> number_of_bins = int(np.sqrt(sample_size))
>>> left.hist(sample, density=True, bins=number_of_bins)
>>> left.set_title("Cartesian plot")
>>> left.set_xlim(-np.pi, np.pi)
>>> left.grid(True)

The right image contains the polar plot.

>>> right.plot(x, vonmises_pdf, label="PDF")
>>> right.set_yticks(ticks)
>>> right.hist(sample, density=True, bins=number_of_bins,
...            label="Histogram")
>>> right.set_title("Polar plot")
>>> right.legend(bbox_to_anchor=(0.15, 1.06))

c