Each element is assigned the result of a function based on it's neighbors.
Neighbors are selected based on a weight array.

It uses the new pad routines to pad arrays if neighboring values are required that would be off the edge of the input array.

Will be great to have the masked array settled because right now you can only sort of exclude from the neighborhood using a zero in the weight array. Zero or np.IGNORE don't affect np.sum, but functions like np.mean and np.std would give different answers. Because of this my early implementations of neighbor included an optional mask array along with the weight array, but I decided would be best to wait for the new masked arrays.

This in some ways could be considered a generalization of a convolution, and comparison with existing numpy/scipy convolution results are included in the tests. The advantage to neighbor is that any function that accepts a 1-d array, and returns a single result, can be used instead of convolution only using summation. The convolution functions require the weight array to be flipped to get the same answer as neighbor.