[Numpy-discussion] reduce array by computing min/max every n samples

Benjamin Root ben.root@ou....
Sat Jun 19 15:37:15 CDT 2010

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?  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 <bburan@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
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20100619/95b5ebf1/attachment.html 

More information about the NumPy-Discussion mailing list