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
