This would certainly be useful in my case as well. I originally tried doing something similar:
fun = lambda x: (x.min(), x,max()) apply_along_axis(fun, -1, val_pts) It turned out to be much slower, which I guess isn't too surprising. Brad On Sat, Jun 19, 2010 at 4:45 PM, Warren Weckesser < warren.weckes...@enthought.com> wrote: > Benjamin Root wrote: > > Brad, I think you are doing it the right way, but I think what is > > happening is that the reshape() call on the sliced array is forcing a > > copy to be made first. The fact that the copy has to be made twice > > just worsens the issue. I would save a copy of the reshape result (it > > is usually a view of the original data, unless a copy is forced), and > > then perform a min/max call on that with the appropriate axis. > > > > On that note, would it be a bad idea to have a function that returns a > > min/max tuple? > > +1. More than once I've wanted exactly such a function. > > Warren > > > > Performing two iterations to gather the min and the max information > > versus a single iteration to gather both at the same time would be > > useful. I should note that there is a numpy.ptp() function that > > returns the difference between the min and the max, but I don't see > > anything that returns the actual values. > > > > Ben Root > > > > On Thu, Jun 17, 2010 at 4:50 PM, Brad Buran <bbu...@cns.nyu.edu > > <mailto:bbu...@cns.nyu.edu>> wrote: > > > > I have a 1D array with >100k samples that I would like to reduce by > > computing the min/max of each "chunk" of n samples. Right now, my > > code is as follows: > > > > n = 100 > > offset = array.size % downsample > > array_min = array[offset:].reshape((-1, n)).min(-1) > > array_max = array[offset:].reshape((-1, n)).max(-1) > > > > However, this appears to be running pretty slowly. The array is data > > streamed in real-time from external hardware devices and I need to > > downsample this and compute the min/max for plotting. I'd like to > > speed this up so that I can plot updates to the data as quickly as > new > > data comes in. > > > > Are there recommendations for faster ways to perform the > downsampling? > > > > Thanks, > > Brad > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > ------------------------------------------------------------------------ > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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