September 2014
Intermediate to advanced
512 pages
12h 39m
English
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Processing huge NumPy arrays with memory mapping
Sometimes, we need to deal with NumPy arrays that are too big to fit in the system memory. A common solution is to use memory mapping and implement out-of-core computations. The array is stored in a file on the hard drive, and we create a memory-mapped object to this file that can be used as a regular NumPy array. Accessing a portion of the array results in the corresponding data being automatically fetched from the hard drive. Therefore, we only consume what we use.
How to do it...
- Let's create a memory-mapped array:
In [1]: import numpy as np In [2]: nrows, ncols = 1000000, 100 In [3]: f = np.memmap('memmapped.dat', dtype=np.float32, mode='w+', shape=(nrows, ncols)) - Let's feed the array with random ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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