Daniele Nicolodi <dani...@grinta.net> wrote: > I was probably not that clear: I have two 2xN arrays, one for each data > recording, one column for time (taken from the same clock for both > measurements) and one with data values. Each array has some gaps.
If you want all subarrays where both timeseries are sampled, a single loop through the data can fix that. First find the smallest common timestamp. This is the first starting point. Then loop and follow the timestamps. As long as they are in synch, do just continue. If one timeseries suddenly skips forward (i.e. there is a gap), you have an end point. Then slice between the start and the end point, and append the view array to a list. Follow the timeseries that did not skip until timestamps are synchronous again, and you have the next starting point. Then just continue like this until the the two timeseries are exhausted. It is an O(n) strategy, so it will not be inefficient. If you are worried about the loop and performance, both Numba and Cython can transform this into C speed. Numba takes less effort to use. But Python loops are actually much faster than most scientists used to Matlab and the like would expect. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
