On Samstag 01 Dezember 2007, Martin Spacek wrote: > Kurt Smith wrote: > > You might try numpy.memmap -- others have had success with it for > > large files (32 bit should be able to handle a 1.3 GB file, AFAIK). > > Yeah, I looked into numpy.memmap. Two issues with that. I need to > eliminate as much disk access as possible while my app is running. I'm > displaying stimuli on a screen at 200Hz, so I have up to 5ms for each > movie frame to load before it's too late and it drops a frame. I'm sort > of faking a realtime OS on windows by setting the process priority > really high. Disk access in the middle of that causes frames to drop. So > I need to load the whole file into physical RAM, although it need not be > contiguous. memmap doesn't do that, it loads on the fly as you index > into the array, which drops frames, so that doesn't work for me.
Sounds as if using memmap and then copying each frame into a separate
in-memory ndarray could help?
Ciao, / / .o.
/--/ ..o
/ / ANS ooo
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