Hello all,

I'd like to see functions for calculating the relative extrema in a set of data included in numpy. I use that functionality frequently, and always seem to be writing my own version. It seems like this functionality would be useful to the community at large, as it's a fairly common operation.

For numeric data (which is presumably noisy), the definition of a relative extrema isn't completely obvious. The implementation I am proposing finds a point in an ndarray along an axis which is larger (or smaller) than it's `order` nearest neighbors (`order` being an optional parameter, default 1). This is likely to find more points than may be desired,  which I believe is preferable to the alternative. The output is formatted the same as numpy.where.

Code available here: https://github.com/numpy/numpy/pull/154

I'm not sure whether this belongs in numpy or scipy, that question is somewhat debatable. More sophisticated peak-finding functions (in N dimensions, as opposed to 1) may also be useful to the community, and those would definitely belong in scipy.

An alternative implementation would be to require that function be continuously descending (or ascending) for `order` points, which would enforce a minimum width on a peak.

-Jacob Silterra