On 11/02/2014 15:38, Sturla Molden wrote: > 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.
Thanks Sturla. That's more or less my current approach (except that I use the fact that the data is evenly samples to use np.where(np.diff(t1) != dt) to detect the regions of continuous data, to avoid the loop. Cheers, Daniele _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
