Interpolation with scipy's SmoothSphereBivariateSpline

Question

I want to use scipy.interpolate.SmoothSphereBivariateSpline to interpolate temperature on a map (I'm not familiar with data interpolation, so this maybe not a good choice but I'd like to give it a go).

These are what I did:

• Load data from a tsv file, which looks like this:

latitude longitude temperature city

30.22 120.14 39 2caves

30.26 120.13 39 3caves

30.23 120.13 39 Anlong

33.48 108.5 30 Anda

37.2 100.74 15 Anan

...

• into pandas

  data = pandas.read_table('temp.tsv')
  

• Get radians from lat, lon:

  theta = numpy.array(data.latitude) / 180 * numpy.pi # the lat, lon domain is safe here so
  phi = numpy.array(data.longitude) / 180 * numpy.pi  # I won't adjust for the output range
  temp = numpy.array(data.temperature)
  

• Feed them into scipy:

  lut = SmoothSphereBivariateSpline(theta, phi, temps)
  

Then the function throws a ValueError:

ValueError: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots.

I've tried to adjust the s parameter with different values, from 1, 2, 3 to 7000, 8000, it just kept being too small. What should I do to make the interpolation work?

Answer 1

Just for the records: following the documentation there is a smoothing parameter s to be chosen; the default value is s=0, but sometimes it is critical to select it manually as suggested in the documentation.