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extract, process, store in c(c_offset (1:bt)). Temp = textscan(fid, '%d,%d:%d.%d,%f,%d,%c', block) īt = size(temp, 1) % first dimension - should be `block`, except for last loop initialize structure array or other storage for c.
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That would look something like this: block = 1000 įid = fopen('C:\Program Files\MATLAB\R2013a\EDU13.csv','r') Ĭ = struct(field1, field2, fieldn, value) %. I'm pretty sure it will be much faster - but you would have to experiment a bit with the block size. If instead you allocate a fixed amount of memory for the final result, and process in smaller batches, you avoid all that overhead. That is a very slow operation - if it happens every 1MB, say, then it copies 1 MB once, next 2 MB, then again 3 MB, etc - as you can see it is quadratic in the size of the array. One problem with reading large files is the fact that you don't know ahead of time how big it will be - and that very likely means that Matlab guesses the amount of memory it needs, and frequently has to rescale.
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It is quite likely that this would be faster if you include a loop that allows you to use a smaller, fixed amount of memory for much of the operation. whether it's worth converting that to another shape really depends on how you want to access the data afterwards. Your code would look like this: fid = fopen('C:\Program Files\MATLAB\R2013a\EDU13.csv','r') Ĭ = textscan(fid, '%d,%d:%d.%d,%f,%d,%c')
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