[Numpy-discussion] NA-mask interactions with existing C code

Jim Bosch talljimbo at gmail.com
Fri May 11 11:14:50 EDT 2012

On 05/11/2012 01:36 AM, Travis Oliphant wrote:
>>> I guess this mixture of Python-API and C-API is different from the way
>>> the API tries to protect incorrect access. From the Python API, it.
>>> should let everything through, because it's for Python code to use. From
>>> the C API, it should default to not letting things through, because
>>> special NA-mask aware code needs to be written. I'm not sure if there is
>>> a reasonable approach here which works for everything.
>> Does that mean you consider changing ob_type for masked arrays
>> unreasonable? They can still use the same object struct...
>>>     But in general, I will often be lazy and just do
>>>     def f(np.ndarray arr):
>>>          c_func(np.PyArray_DATA(arr))
>>>     It's an exception if you don't provide an array -- so who cares. (I
>>>     guess the odds of somebody feeding a masked array to code like that,
>>>     which doesn't try to be friendly, is relatively smaller though.)
>>> This code would already fail with non-contiguous strides or byte-swapped
>>> data, so the additional NA mask case seems to fit in an already-failing
>>> category.
>> Honestly! I hope you did't think I provided a full-fledged example?
>> Perhaps you'd like to point out to me that "c_func" is a bad name for a
>> function as well?
>> One would of course check that things are contiguous (or pass on the
>> strides), check the dtype and dispatch to different C functions in each
>> case, etc.
>> But that isn't the point. Scientific code most of the time does fall in
>> the "already-failing" category. That doesn't mean it doesn't count.
>> Let's focus on the number of code lines written and developer hours that
>> will be spent cleaning up the mess -- not the "validity" of the code in
>> question.
>>>     If you know the datatype, you can really do
>>>     def f(np.ndarray[double] arr):
>>>          c_func(&arr[0])
>>>     which works with PEP 3118. But I use PyArray_DATA out of habit (and
>>>     since it works in the cases without dtype).
>>>     Frankly, I don't expect any Cython code to do the right thing here;
>>>     calling PyArray_FromAny is much more typing. And really, nobody ever
>>>     questioned that if we had an actual ndarray instance, we'd be allowed to
>>>     call PyArray_DATA.
>>>     I don't know how much Cython code is out there in the wild for which
>>>     this is a problem. Either way, it would cause something of a reeducation
>>>     challenge for Cython users.
>>> Since this style of coding already has known problems, do you think the
>>> case with NA-masks deserves more attention here? What will happen is.
>>> access to array element data without consideration of the mask, which
>>> seems similar in nature to accessing array data with the wrong stride or
>>> byte order.
>> I don't agree with the premise of that paragraph. There's no reason to
>> assume that just because code doesn't call FromAny, it has problems.
>> (And I'll continue to assume that whatever array is returned from
>> "np.ascontiguousarray is really contiguous...)
>> Whether it requires attention or not is a different issue though. I'm
>> not sure. I think other people should weigh in on that -- I mostly write
>> code for my own consumption.
>> One should at least check pandas, scikits-image, scikits-learn, mpi4py,
>> petsc4py, and so on. And ask on the Cython users list. Hopefully it will
>> usually be PEP 3118. But now I need to turn in.
>> Travis, would such a survey be likely to affect the outcome of your
>> decision in any way? Or should we just leave this for now?
> This dialog gets at the heart of the matter, I think.   The NEP seems to want NumPy to have a "better" API that always protects downstream users from understanding what is actually under the covers.   It would prefer to push NumPy in the direction of an array object that is fundamentally more opaque.   However, the world NumPy lives in is decidedly not opaque.   There has been significant education and shared understanding of what a NumPy array actually *is* (a strided view of memory of a particular "dtype").   This shared understanding has even been pushed into Python as the buffer protocol.    It is very common for extension modules to go directly to the data they want by using this understanding.
> This is very different from the traditional "shield your users" from how things are actually done view of most object APIs.    It was actually intentional.      I'm not saying that different choices could not have been made or that some amount of shielding should never be contemplated.   I'm just saying that NumPy has been used as a nice bridge between the world of scientific computing codes that have chunks of memory allocated for processing and high-level code.   Part of the reason for this bridge has been the simple object model.
> I just don't think the NEP fully appreciates just how fundamental of a shift this is in the wider NumPy community and it is not something that can be done immediately or without careful attention.

Just chiming in as another regular user of the C-API that strongly 
shares this view.  NumPy arrays are useful to my project precisely 
because they are simply blocks of shared, strided memory, and they work 
so well for us because we know exactly how they're represented under the 
hood and we can map them straightforwardly to C and C++.

We also don't use PyArray_FromAny or its brethren to get arrays in C - 
by and large, we consider that too "magical"; we'd prefer to fail when 
the array we are given isn't exactly what we were expecting, rather than 
create arrays from nested sequences or do anything else that involves an 
implicit deep-copy of the data.

That said, I think we could pretty easily adapt to this change just by 
switching a lot of PyArray_Check calls to PyArray_CheckExact, but I 
think our usage of NumPy is another data point that says attaching masks 
to all arrays or making masked arrays a subclass of regular arrays is a 
step in the wrong direction design-wise.


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