Examples ============== .. _examples: A Simple Function ----------------- Suppose we want to write an image-processing function in Python. Here's how it might look. .. code-block:: python def filter2d(image, filt): M, N = image.shape Mf, Nf = filt.shape Mf2 = Mf // 2 Nf2 = Nf // 2 result = numpy.zeros_like(image) for i in range(Mf2, M - Mf2): for j in range(Nf2, N - Nf2): num = 0.0 for ii in range(Mf): for jj in range(Nf): num += (filt[Mf-1-ii, Nf-1-jj] * image[i-Mf2+ii, j-Nf2+jj]) result[i, j] = num return result This kind of quadruply-nested for-loop is going to be quite slow. Using Numba we can compile this code to LLVM which then gets compiled to machine code: .. code-block:: python from numba import double fastfilter_2d = jit(double[:,:](double[:,:], double[:,:]))(filter2d) # Now fastfilter_2d runs at speeds as if you had first translated # it to C, compiled the code and wrapped it with Python res = fastfilter_2d(image, filt) Numba actually produces two functions. The first function is the low-level compiled version of filter2d. The second function is the Python wrapper to that low-level function so that the function can be called from Python. The first function can be called from other numba functions to eliminate all python overhead in function calling.