Solution for architecure dependence in Numeric ?

[JohannesNix|Johannes.Nix at uni-oldenburg.de]

Hi,

I have a tricky problem with Numeric. Some time ago, I have generated
a huge and complex data structure, and stored it using the cPickle
module. Now I want to evaluate it quickly again on a workstation
cluster with 64-Bit Opteron CPUs - I have no more than three days to
do this. Compiling Python and running Numeric has been no problem at
all. However, I get an error message when accessing the data pickled
before. (I can load it regularly on 32 bit computers, but it is a
quite complex data object, so I really don't want to store every
element as ASCII data).  The problem seems to be with 64 Bit integers
(with 32-bit-floats, no problem was observed).

This looks like that (from the Unix command shell):


jnix at 32bithost:~> python ~/python/test_npickle.py  -dump test.pck
jnix at 32bithost:~> python ~/python/test_npickle.py  test.pck
[0 1 2 3 4 5 6 7 8 9]
jnix at 32bithost:~> ssh 64bithost python ~/python/test_npickle.py  test.pck
Traceback (most recent call last):
  File "/home/jnix/python/test_npickle.py", line 16, in ?
    a = cPickle.load(file(filename))
  File "/home/jnix/lib/python2.4/SuSE-9.0/x86_64/Numeric/Numeric.py", line 520, in array_constructor
    x.shape = shape
ValueError: ('total size of new array must be unchanged', <function array_constructor at 0x2a960b0b18>, ((10,), 'l', '\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00', 1))


also I get:
jnix at 32bithost:~> python -c "import Numeric; print Numeric.arange(0).itemsize()"
4
jnix at 64bithost:~>  python -c "import Numeric; print Numeric.arange(0).itemsize()"
8


The script used to produce the example above is:

-------------------------------------------------------------------------
#/usr/bin/python
# -*- coding: latin1 -*-

import Numeric
import cPickle
import sys

if len(sys.argv) > 1 and sys.argv[1] == '-dump':
    filename = sys.argv[2]
    binary=1
    a = Numeric.arange(10) 
    cPickle.dump(a, file(filename,'w',binary))

else:
    filename = sys.argv[1]
    a = cPickle.load(file(filename))
    print a
    

---------------------------------------------------------------------


So what would you suggest ? Can I hack Numeric to assume non-native
32 bit integer numbers ?

Many thanks for any help,

Johannes