Other Libraries or Packages of interest

Numba

Numba gives you the power to speed up your applications with high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran, without having to switch languages or Python interpreters.

Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware, and is designed to integrate with the Python scientific software stack.

from numba import jit
from numpy import arange

# jit decorator tells Numba to compile this function.
# The argument types will be inferred by Numba when function is called.
@jit
def sum2d(arr):
    M, N = arr.shape
    result = 0.0
    for i in range(M):
        for j in range(N):
            result += arr[i,j]
    return result

a = arange(9).reshape(3,3)
print(sum2d(a))

See also this article on using Cython vs Numba for speeding up one's code.

Pyresample

Pyresample is a Python package for resampling (reprojection) of earth observing satellite data. Pyresample handles both resampling of gridded data (e.g. geostationary satellites) and swath data (polar orbiting satellites). Pyresample can use multiple processor cores for resampling. Pyresample supports masked arrays.

This can either be used explicitly or just for inspiration. The reason to use this would be to enable us to do correct regridding of geospatial data that takes into account that a lat/lon grid is projected on a sphere as opposed to approximating it to Euclidian space.