[Numpy-discussion] Crash using "reshape"...

Terry J. Ligocki tjligocki@lbl....
Wed Nov 21 04:25:44 CST 2012


I just checked, "numpy.intp" is "<type 'numpy.int64'>" in my 
installation of Python and NumPy.  It was a good thing to check but it 
looks like there's still may be a signed 32-bit integer somewhere in the 
code (or my build(s))...

                                 Terry J.
> On 11/21/12 10:12 AM, Terry J. Ligocki wrote:
>> I am having a problem with "reshape" crashing:
>>
>>      > python
>>      Python 2.6.4 (r264:75706, Jan 16 2010, 21:11:47)
>>      [GCC 4.3.2] on linux2
>>      Type "help", "copyright", "credits" or "license" for more information.
>>      >>> import numpy
>>      >>> numpy.version.version
>>      '1.6.2'
>>      >>> npData = numpy.ones([701,701,7899],dtype=numpy.dtype('b'))
>>      >>> npDataSubset = npData[[slice(0,700),slice(0,700),slice(0,5000)]]
>>      >>> npDataOutput = npDataSubset.reshape([700*700*5000],order='F')
>>      Segmentation fault
>>
>> If I change the "5000" to a "4000", everything is fine.  I'm not
>> running out of memory - my system had 48 GB of memory and nothing else
>> is using a significant portion of this memory.
>>
>> Note:  700x700x4000 = 1,960,000,000 < 2^31 and 700x700x5000 =
>> 2450000000 > 2^31.  I suspect somewhere in the underlying code there
>> is a signed 32-bit integer being used for an index/pointer offset
>> (this is running on a 64-bit machine).
> Yes, looks like a 32-bit issue.  Sometimes you can have 32-bit software
> installed in 64-bit machines, so that might be your problem.  What's the
> equivalent of numpy.intp in your machine?  Mine is:
>
> In []: import numpy as np
>
> In []: np.intp
> Out[]: numpy.int64
>
> If you see 'numpy.int32' here then that is the problem.
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