Hi, I am trying to implement efficient out-of-memory computations on large arrays. I have two questions:
1) My data is stored in binary files, which I read using numpy.memmap. Is there a way to efficiently copy from memmap to CArray without reading all data into memory first? I suppose I could use iterate over chunks, but then I would need to optimize the chunksizes. 2) In the data I want to find threshold crossings. In numpy I usually do it using nonzero function: import numpy as np a = np.random.randn(100) T = 0 i, = np.nonzero((a[:-1]<T) & (a[1:]>T)) How can I implement it with tables.Expr? Cheers, Bartosz ------------------------------------------------------------------------------ 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
