Matthew, That looks right. I'm concluding that the .astype(np.uint8) is applied after the array is constructed, instead of during the process. This random array is a test case. In the production analysis of radio telescope data this is how the data comes in, and there is no problem with 10GBy files. linearInputData = np.fromfile(dataFile, dtype = np.uint8, count = -1) spectrumArray = linearInputData.reshape(nSpectra,sizeSpectrum) On Sun, Dec 6, 2015 at 4:07 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.
Cheers,
Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
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