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    <p>How about something like this...</p> <pre><code>import numpy as np # Create large arrays x and y. # Note they are 1e4 not 1e6 b/c of memory issues creating random numpy matrices (CookieOfFortune) # However, the same principles apply to larger arrays x = np.random.randn(10000, 10000) y = np.random.randn(10000, 10000) # Create memory maps for x and y arrays xmap = np.memmap('xfile.dat', dtype='float32', mode='w+', shape=x.shape) ymap = np.memmap('yfile.dat', dtype='float32', mode='w+', shape=y.shape) # Fill memory maps with data xmap[:] = x[:] ymap[:] = y[:] # Create memory map for out of core dot product result prodmap = np.memmap('prodfile.dat', dtype='float32', mode='w+', shape=x.shape) # Due out of core dot product and write data prodmap[:] = np.memmap.dot(xmap, ymap) # Create memory map for out of core addition result addmap = np.memmap('addfile.dat', dtype='float32', mode='w+', shape=x.shape) # Due out of core addition and write data addmap[:] = xmap + ymap # Create memory map for out of core scaling result scalemap = np.memmap('scalefile.dat', dtype='float32', mode='w+', shape=x.shape) # Define scaling constant scale = 1.3 # Do out of core scaling and write data scalemap[:] = scale * xmap </code></pre> <p>This code will create files xfile.dat, yfile.dat, ect that contain the arrays in binary format. To access them later you simply need to do <code>np.memmap(filename)</code>. Other arguments to <code>np.memmap</code> are optional, but reccomended (arguments like dtype, shape, ect.).</p>
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