# Suppose we have global data on a coarse grid (the input data does not
# have to be on a grid):

theta = np.linspace(0., np.pi, 7)
phi = np.linspace(0., 2*np.pi, 9)
data = np.empty((theta.shape[0], phi.shape[0]))
data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
data[1:-1,1], data[1:-1,-1] = 1., 1.
data[1,1:-1], data[-2,1:-1] = 1., 1.
data[2:-2,2], data[2:-2,-2] = 2., 2.
data[2,2:-2], data[-3,2:-2] = 2., 2.
data[3,3:-2] = 3.
data = np.roll(data, 4, 1)

# We need to set up the interpolator object

lats, lons = np.meshgrid(theta, phi)
from scipy.interpolate import SmoothSphereBivariateSpline
lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
                                  data.T.ravel(), s=3.5)

# As a first test, we'll see what the algorithm returns when run on the
# input coordinates

data_orig = lut(theta, phi)

# Finally we interpolate the data to a finer grid

fine_lats = np.linspace(0., np.pi, 70)
fine_lons = np.linspace(0., 2 * np.pi, 90)

data_smth = lut(fine_lats, fine_lons)

import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(131)
ax1.imshow(data, interpolation='nearest')
ax2 = fig.add_subplot(132)
ax2.imshow(data_orig, interpolation='nearest')
ax3 = fig.add_subplot(133)
ax3.imshow(data_smth, interpolation='nearest')
plt.show()