Hi, I'm storing large datasets (5760 x 2880 x ~150) in a compressed EArray (the last dimension represents time, and once per month there'll be one more 5760x2880 array to add to the end).
Now, extracting timeseries at one index location is slow; e.g., for four indices, it takes several seconds: In [19]: idx = ((5000, 600, 800, 900), (1000, 2000, 500, 1)) In [20]: %time AA = np.vstack([_a[i,j] for i,j in zip(*idx)]) CPU times: user 4.31 s, sys: 0.07 s, total: 4.38 s Wall time: 7.17 s I have the feeling that this performance could be improved, but I'm not sure about how to properly use the `chunkshape` parameter in my case. Any help is greatly appreciated :) Cheers, Andreas. ------------------------------------------------------------------------------ Get 100% visibility into Java/.NET code with AppDynamics Lite It's a free troubleshooting tool designed for production Get down to code-level detail for bottlenecks, with <2% overhead. Download for free and get started troubleshooting in minutes. http://p.sf.net/sfu/appdyn_d2d_ap2 _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users