Okay, if I pass the pickles through pickletools.optimize, they look
identical except for the very first line (and a resulting systematic
shift in offset):
>>> pt.dis(pt.optimize(open('cp123.pkl').read()))
0: c GLOBAL 'numpy.core.multiarray _reconstruct'
>>> pt.dis(pt.optimize(open('pp123.pkl').read()))
0: c GLOBAL '_numpypy.multiarray _reconstruct'
So I suspect that simply lying about what class we just pickled would
do the trick.
I have no idea how acceptable that would be as a general solution,
though. Thoughts?
Eli
On Fri, Jun 24, 2016 at 2:29 PM, Eli Stevens (Gmail)
<[email protected]> wrote:
> Doesn't look like they are exactly the same:
>
> https://gist.github.com/elistevens/03e22f4684fb77d3edfe13ffcd406ef4
>
> Certainly some similarities, though.
>
> I'm not familiar with the pickle format, and I haven't yet had time to
> dig in beyond this, though. Hoping I can tonight.
>
> Cheers,
> Eli
>
>
> On Fri, Jun 24, 2016 at 1:21 PM, matti picus <[email protected]> wrote:
>> The first step would be to pickle the same dtype/shape/data ndarray once
>> from numpy and again from _numpypy, and to compare the binary result. The
>> only difference should be the class name, if the difference goes deeper that
>> difference must be fixed. Then it it just a matter of patching pickle.py to
>> use the desired class instead of the class name encoded into the pickled
>> binary result.
>> Matti
>>
>>> On 24 Jun 2016, at 10:43 PM, Eli Stevens (Gmail) <[email protected]>
>>> wrote:
>>>
>>> Yeah, looks like that's still the case:
>>>
>>>>>>> z = np.zeros((2,3), dtype=np.float32)
>>>>>>> z.tofile
>>> Traceback (most recent call last):
>>> File "<stdin>", line 1, in <module>
>>> AttributeError: 'numpy.ndarray' object has no attribute 'tofile'
>>>
>>> What would it take to get cross-interpreter numpy array pickles working?
>>>
>>> Thanks,
>>> Eli
>>>
>>>
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