Hi again, > Choosing the slice size should be not difficult, just something that is > not too large or too small (anything between 1 MB ~ 10 MB should do > fine). The only thing to have in mind is that your slices should not > exceed your available memory. PyTables will automatically determine an > adequate HDF5 chunk size for your on-disk datasets. > > > Uh, no. tables.Expr only supports simple element-wise operations whose > output has the same shape than operands (so `nonzero` is not supported). > Also, it cannot carry out operations that makes use of different indices > in operands for computing some element (so `diff` is be supported > either). Rather, you need to think about tables.Expr (and numexpr in > general) as a virtual machine that only accepts vectors (matrices) and > can perform operations only among elements in the same positions (mostly > like a SIMD processor).
Now I got it. Thanks. Would you recommend to use the same chunk approach as for data copy above to perform the "on-disk" threshold detection? Best, Barte ------------------------------------------------------------------------------ Special Offer-- Download ArcSight Logger for FREE (a $49 USD value)! Finally, a world-class log management solution at an even better price-free! Download using promo code Free_Logger_4_Dev2Dev. Offer expires February 28th, so secure your free ArcSight Logger TODAY! http://p.sf.net/sfu/arcsight-sfd2d _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
