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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))...<br>
<br>
Terry J.<br>
<blockquote cite="mid:50ACA9A2.3070707@continuum.io" type="cite">
<pre wrap="">On 11/21/12 10:12 AM, Terry J. Ligocki wrote:
</pre>
<blockquote type="cite">
<pre wrap="">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).
</pre>
</blockquote>
<pre wrap="">
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.
</pre>
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