Dec. 6, 2015
9:07 p.m.
Hi, On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student) <dps7802@rit.edu> wrote:
This works. A big array of eight bit random numbers is constructed:
import numpy as np
spectrumArray = np.random.randint(0,255, (2**20,2**12)).astype(np.uint8)
This fails. It eats up all 64GBy of RAM:
spectrumArray = np.random.randint(0,255, (2**21,2**12)).astype(np.uint8)
The difference is a factor of two, 2**21 rather than 2**20, for the extent of the first axis.
I think what's happening is that this: np.random.randint(0,255, (2**21,2**12)) creates 2**33 random integers, which (on 64-bit) will be of dtype int64 = 8 bytes, giving total size 2 ** (21 + 12 + 6) = 2 ** 39 bytes = 512 GiB. Cheers, Matthew