On Thu, Jul 6, 2017 at 10:55 AM, <paul.carr...@free.fr> wrote: > > It's is just a reflexion, but for huge files one solution might be to > split/write/build first the array in a dedicated file (2x o(n) iterations - > one to identify the blocks size - additional one to get and write), and > then to load it in memory and work with numpy - >
I may have your use case confused, but if you have a huge file with multiple "blocks" in it, there shouldn't be any problem with loading it in one go -- start at the top of the file and load one block at a time (accumulating in a list) -- then you only have the memory overhead issues for one block at a time, should be no problem. at this stage the dimension is known and some packages will be fast and > more adapted (pandas or astropy as suggested). > pandas at least is designed to read variations of CSV files, not sure you could use the optimized part to read an array out of part of an open file from a particular point or not. -CHB -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov
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