On Tue, Jan 4, 2011 at 8:06 AM, Sebastian Haase <seb.ha...@gmail.com> wrote: > On Mon, Jan 3, 2011 at 5:32 PM, Erik Rigtorp <e...@rigtorp.com> wrote: >> On Mon, Jan 3, 2011 at 11:26, Eric Firing <efir...@hawaii.edu> wrote: >>> Instead of calculating statistics independently each time the window is >>> advanced one data point, the statistics are updated. I have not done >>> any benchmarking, but I expect this approach to be quick. >> >> This might accumulate numerical errors. But could be fine for many >> applications. >> >>> The code is old; I have not tried to update it to take advantage of >>> cython's advances over pyrex. If I were writing it now, I might not >>> bother with the C level at all; it could all be done in cython, probably >>> with no speed penalty, and maybe even with reduced overhead. >>> >> >> No doubt this would be faster, I just wanted to offer a general way to >> this in NumPy. >> _______________________________________________ > > BTW, some of these operations can be done using scipy's ndimage - right ? > Any comments ? How does the performance compare ? > ndimage might have more options regarding edge handling, or ?
Take a look at the moving window function in the development version of the la package: https://github.com/kwgoodman/la/blob/master/la/farray/mov.py Many of the moving window functions offer three calculation methods: filter (ndimage), strides (the strides trick discussed in this thread), and loop (a simple python loop). For example: >> a = np.random.rand(500,2000) >> timeit la.farray.mov_max(a, window=252, axis=-1, method='filter') 1 loops, best of 3: 336 ms per loop >> timeit la.farray.mov_max(a, window=252, axis=-1, method='strides') 1 loops, best of 3: 609 ms per loop >> timeit la.farray.mov_max(a, window=252, axis=-1, method='loop') 1 loops, best of 3: 638 ms per loop No one method is best for all situations. That is one of the reasons I started the Bottleneck package. I figured Cython could beat them all. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
