ng `gaussian_kde` with the transformed data.

References
----------
.. [1] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and
       Visualization", John Wiley & Sons, New York, Chicester, 1992.
.. [2] B.W. Silverman, "Density Estimation for Statistics and Data
       Analysis", Vol. 26, Monographs on Statistics and Applied Probability,
       Chapman and Hall, London, 1986.
.. [3] B.A. Turlach, "Bandwidth Selection in Kernel Density Estimation: A
       Review", CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.
.. [4] D.M. Bashtannyk and R.J. Hyndman, "Bandwidth selection for kernel
       conditional density estimation", Computational Statistics & Data
       Analysis, Vol. 36, pp. 279-298, 2001.
.. [5] Gray P. G., 1969, Journal of the Royal Statistical Society.
       Series A (General), 132, 272

Examples
--------
Generate some random two-dimensional data:

>>> import numpy as np
>>> from scipy import stats
>>> def measure(n):
...     "Measurement model, return two coupled measurements."
...     m1 = np.random.normal(size=n)
...     m2 = np.random.normal(scale=0.5, size=n)
...     return m1+m2, m1-m2

>>> m1, m2 = measure(2000)
>>> xmin = m1.min()
>>> xmax = m1.max()
>>> ymin = m2.min()
>>> ymax = m2.max()

Perform a kernel density estimate on the data:

>>> X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
>>> positions = np.vstack([X.ravel(), Y.ravel()])
>>> values = np.vstack([m1, m2])
>>> kernel = stats.gaussian_kde(values)
>>> Z = np.reshape(kernel(positions).T, X.shape)

Plot the results:

>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots()
>>> ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth_r,
...           extent=[xmin, xmax, ymin, ymax])
>>> ax.plot(m1, m2, 'k.', markersize=2)
>>> ax.set_xlim([xmin, xmax])
>>> ax.set_ylim([ymin, ymax])
>>> plt.show()

Nc