On Sun, Dec 6, 2015 at 10:07 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
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.
8 is only 2**3, so it is "just" 64 GiB, which also explains why the half sized array does work, but yes, that is most likely what's happening. Jaime
Cheers,
Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
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