Hi Wolfgang, I thought maybe there is a trick for your specific operation. Your array stacking is a simple case of the group-by operation and normalization is aggregation followed by update. I believe group-by and aggregation are on the NumPy todo-list. You may have to write a small extension module to speed up your operations. Val
On Thu, May 31, 2012 at 8:27 AM, Wolfgang Kerzendorf <wkerzend...@gmail.com>wrote: > Hey Val, > > Well it doesn't matter what I do, but specifically I do factor = > sum(data_array[start_point:start_point+length_data]) and then > data[array[start_point:start_point+length_data]) /= factor. and that for > every star_point and length data. > > How to do this fast? > > Cheers > Wolfgang > On 2012-05-31, at 1:43 AM, Val Kalatsky wrote: > > What do you mean by "normalized it"? > Could you give the output of your procedure for the sample input data. > Val > > On Thu, May 31, 2012 at 12:36 AM, Wolfgang Kerzendorf < > wkerzend...@gmail.com> wrote: > >> Dear all, >> >> I have an ndarray which consists of many arrays stacked behind each other >> (only conceptually, in truth it's a normal 1d float64 array). >> I have a second array which tells me the start of the individual data >> sets in the 1d float64 array and another one which tells me the length. >> Example: >> >> data_array = (conceptually) [[1,2], [1,2,3,4], [1,2,3]] = in reality >> [1,2,1,2,3,4,1,2,3, dtype=float64] >> start_pointer = [0, 2, 6] >> length_data = [2, 4, 3] >> >> I now want to normalize each of the individual data sets. I wrote a >> simple for loop over the start_pointer and length data grabbed the data and >> normalized it and wrote it back to the big array. That's slow. Is there an >> elegant numpy way to do that? Do I have to go the cython way? >> >> Cheers >> Wolfgang >> _______________________________________________ >> 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 > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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