[Numpy-discussion] array of random numbers fails to construct

[DAVID SAROFF (RIT Student)]

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.

[Matthew Brett]

Hi,

On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255, (2**20,2**12)).astype(np.uint8)
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

[Jaime Fernández del Río]

Post by Matthew Brett
Hi,
On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255, (2**20,2**12)).astype(np.uint8)
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

Post by DAVID SAROFF (RIT Student)
of the first axis.

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

Post by Matthew Brett
Cheers,
Matthew
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[DAVID SAROFF (RIT Student)]

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)

Post by Matthew Brett
Hi,
On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255, (2**20,2**12)).astype(np.uint8)
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

Post by DAVID SAROFF (RIT Student)
of the first axis.

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
https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Allan Haldane]

I've also often wanted to generate large datasets of random uint8 and
uint16. As a workaround, this is something I have used:

np.ndarray(100, 'u1', np.random.bytes(100))

It has also crossed my mind that np.random.randint and np.random.rand
could use an extra 'dtype' keyword. It didn't look easy to implement though.

Allan

Post by DAVID SAROFF (RIT Student)
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)
Hi,
On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255, (2**20,2**12)).astype(np.uint8)
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.

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
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--
David P. Saroff
Rochester Institute of Technology
54 Lomb Memorial Dr, Rochester, NY 14623
227-6242
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[DAVID SAROFF (RIT Student)]

Allan,

I see with a google search on your name that you are in the physics
department at Rutgers. I got my BA in Physics there. 1975. Biological
physics. A thought: Is there an entropy that can be assigned to the dna in
an organism? I don't mean the usual thing, coupled to the heat bath.
Evolution blindly explores metabolic and signalling pathways, and tends
towards disorder, as long as it functions. Someone working out signaling
pathways some years ago wrote that they were senselessly complex, branched
and interlocked. I think that is to be expected. Evolution doesn't find
minimalist, clear, rational solutions. Look at the amazon rain forest. What
are all those beetles and butterflies and frogs for? It is the wrong
question. I think some measure of the complexity could be related to the
amount of time that ecosystem has existed. Similarly for genomes.

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8 and
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and np.random.rand
could use an extra 'dtype' keyword. It didn't look easy to implement though.
Allan

Post by DAVID SAROFF (RIT Student)
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)
Hi,
On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255,

(2**20,2**12)).astype(np.uint8)

Post by DAVID SAROFF (RIT Student)
spectrumArray = np.random.randint(0,255,

(2**21,2**12)).astype(np.uint8)

Post by DAVID SAROFF (RIT Student)
The difference is a factor of two, 2**21 rather than 2**20, for the

extent

Post by DAVID SAROFF (RIT Student)
of the first axis.

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
https://mail.scipy.org/mailman/listinfo/numpy-discussion
--
David P. Saroff
Rochester Institute of Technology
54 Lomb Memorial Dr, Rochester, NY 14623
227-6242
_______________________________________________
NumPy-Discussion mailing list
https://mail.scipy.org/mailman/listinfo/numpy-discussion

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[Elliot Hallmark]

David,

I'm concluding that the .astype(np.uint8) is applied after the array is

constructed, instead of during the process.

That is how python works in general. astype is a method of an array, so
randint needs to return the array before there is something with an astype
method to call. A dtype keyword arg to randint, on the otherhand, would
influence the construction of the array.

Elliot

[Warren Weckesser]

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8 and
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and np.random.rand
could use an extra 'dtype' keyword.

+1. Not a high priority, but it would be nice.

Warren

Post by Allan Haldane
It didn't look easy to implement though.
Allan

Post by DAVID SAROFF (RIT Student)
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)
Hi,
On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)

Post by DAVID SAROFF (RIT Student)
import numpy as np
spectrumArray = np.random.randint(0,255,

(2**20,2**12)).astype(np.uint8)

Post by DAVID SAROFF (RIT Student)
spectrumArray = np.random.randint(0,255,

(2**21,2**12)).astype(np.uint8)

Post by DAVID SAROFF (RIT Student)
The difference is a factor of two, 2**21 rather than 2**20, for the

extent

Post by DAVID SAROFF (RIT Student)
of the first axis.

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
https://mail.scipy.org/mailman/listinfo/numpy-discussion
--
David P. Saroff
Rochester Institute of Technology
54 Lomb Memorial Dr, Rochester, NY 14623
227-6242
_______________________________________________
NumPy-Discussion mailing list
https://mail.scipy.org/mailman/listinfo/numpy-discussion

_______________________________________________
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[Sebastian]

Post by Allan Haldane
It has also crossed my mind that np.random.randint and
np.random.rand could use an extra 'dtype' keyword.
+1. Not a high priority, but it would be nice.

Opened an issue for this: https://github.com/numpy/numpy/issues/6790

Post by Allan Haldane
Warren

Sebastian

[Stephan Hoyer]

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8 and
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and np.random.rand
could use an extra 'dtype' keyword. It didn't look easy to implement though.

Another workaround that avoids creating a copy is to use the view method,
e.g.,
np.random.randint(np.iinfo(int).min, np.iinfo(int).max,
size=(1,)).view(np.uint8) # creates 8 random bytes

Cheers,
Stephan

[Matthew Brett]

Hi,

Post by Stephan Hoyer

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8 and
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and np.random.rand
could use an extra 'dtype' keyword. It didn't look easy to implement though.

Another workaround that avoids creating a copy is to use the view method,
e.g.,
np.random.randint(np.iinfo(int).min, np.iinfo(int).max,
size=(1,)).view(np.uint8) # creates 8 random bytes

I think that is not quite (pseudo) random because the second parameter
to randint is the max value plus 1 - and:

np.random.random_integers(np.iinfo(int).min, np.iinfo(int).max + 1,
size=(1,)).view(np.uint8)

gives:

OverflowError: Python int too large to convert to C long

Cheers,

Matthew

[Allan Haldane]

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and
np.random.rand could use an extra 'dtype' keyword. It didn't look
easy to implement though.
Another workaround that avoids creating a copy is to use the view
method, e.g.,
np.random.randint(np.iinfo(int).min, np.iinfo(int).max,
size=(1,)).view(np.uint8) # creates 8 random bytes

Just to note, the line I pasted doesn't copy either, according to the
OWNDATA flag.

Cheers,
Allan

[Allan Haldane]

Post by Allan Haldane

Post by Allan Haldane
I've also often wanted to generate large datasets of random uint8
np.ndarray(100, 'u1', np.random.bytes(100))
It has also crossed my mind that np.random.randint and
np.random.rand could use an extra 'dtype' keyword. It didn't look
easy to implement though.
Another workaround that avoids creating a copy is to use the view
method, e.g.,
np.random.randint(np.iinfo(int).min, np.iinfo(int).max,
size=(1,)).view(np.uint8) # creates 8 random bytes

Just to note, the line I pasted doesn't copy either, according to the
OWNDATA flag.
Cheers,
Allan

Oops, but I forgot my version is readonly. If you want to write to it
you do need to make a copy, that's true.

Allan