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.
On Sun, Dec 6, 2015 at 6:55 PM, Allan Haldane <allanhald...@gmail.com>
wrote:
>
> 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
>
> On 12/06/2015 04:55 PM, DAVID SAROFF (RIT Student) wrote:
>
>> 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.br...@gmail.com
>> <mailto:matthew.br...@gmail.com>> wrote:
>>
>> Hi,
>>
>> On Sun, Dec 6, 2015 at 12:39 PM, DAVID SAROFF (RIT Student)
>> <dps7...@rit.edu <mailto:dps7...@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
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>>
>>
>> --
>> David P. Saroff
>> Rochester Institute of Technology
>> 54 Lomb Memorial Dr, Rochester, NY 14623
>> david.sar...@mail.rit.edu <mailto:david.sar...@mail.rit.edu> | (434)
>> 227-6242
>>
>>
>>
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--
David P. Saroff
Rochester Institute of Technology
54 Lomb Memorial Dr, Rochester, NY 14623
david.sar...@mail.rit.edu | (434) 227-6242
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