OK, I think I’ve managed to track the problem down a bit further:

```
the sort() method is failing for arrays pickled on another machine!
```

That is, it’s definitely not sorting the array, but changing to a very strange order (neither the way it started nor sorted).

Again, the array seems to otherwise behave fine (indeed, it even satisfies all(a==a1) for a pair that behave differently in this circumstance).

Hmmm…

A

## ···

On 4/5/06, **Andrew Jaffe** <a.h.jaffe@…287…> wrote:

Hi All,

I’ve encountered a strange problem: I’ve been running some python code

on both a linux box and OS X, both with python 2.4.1 and the latest

numpy and matplotlib from svn.I have found that when I transfer pickled numpy arrays from one machine

to the other (in either direction), the resulting data

looksall right

(i.e., it is a numpy array of the correct type with the correct values

at the correct indices), but it seems to produce the wrong result in (atleast) one circumstance: matplotlib.hist() gives the completely wrong

picture (and set of bins).This can be ameliorated by running the array through

arr=numpy.asarray(arr, dtype=numpy.float64)

but this seems like a complete kludge (and is only needed when you dothe transfer between machines).

I’ve attached a minimal code that exhibits the problem: try

test_pickle_hist.test(write=True)

on one machine, transfer the output file to another machine, and run`test_pickle_hist.test(write=False)`

on another, and you should see a very strange result (and it should be

fixed if you set asarray=True).Any ideas?

Andrew

import cPickle

import numpyimport pylab

def test(write=True,asarray=False):

`a = numpy.linspace(-3,3,num=100) if write: f1 = file("a.cpkl", 'w') cPickle.dump(a, f1) f1.close`

()

`f1 = open("a.cpkl", 'r') a1 = cPickle.load(f1) f1.close() pylab.subplot(1,2,1) h = pylab.hist(a) if asarray: a1 = numpy.asarray(a1, dtype=numpy.float64`

)

`pylab.subplot(1,2,2) h1 = pylab.hist(a1) return a, a1`