[Numpy-discussion] NA-mask interactions with existing C code
Dag Sverre Seljebotn
d.s.seljebotn at astro.uio.no
Thu May 10 20:15:18 EDT 2012
On 05/11/2012 01:06 AM, Mark Wiebe wrote:
> On Thu, May 10, 2012 at 5:47 PM, Dag Sverre Seljebotn
> <d.s.seljebotn at astro.uio.no <mailto:d.s.seljebotn at astro.uio.no>> wrote:
>
> On 05/11/2012 12:28 AM, Mark Wiebe wrote:
> > I did some searching for typical Cython and C code which accesses
> numpy
> > arrays, and added a section to the NEP describing how they behave
> in the
> > current implementation. Cython code which uses either straight Python
> > access or the buffer protocol is fine (after a bugfix in numpy, it
> > wasn't failing currently as it should in the pep3118 case). C
> code which
> > follows the recommended practice of using PyArray_FromAny or one
> of the
> > related macros is also fine, because these functions have been
> made to
> > fail on NA-masked arrays unless the flag NPY_ARRAY_ALLOWNA is
> provided.
> >
> > In general, code which follows the recommended numpy practices will
> > raise exceptions when encountering NA-masked arrays. This means
> > programmers don't have to worry about the NA unless they want to
> support
> > it. Having things go through PyArray_FromAny also provides a
> place where
> > lazy evaluation arrays could be evaluated, and other similar
> potential
> > future extensions can use to provide compatibility.
> >
> > Here's the section I added to the NEP:
> >
> > Interaction With Pre-existing C API Usage
> > =========================================
> >
> > Making sure existing code using the C API, whether it's written
> in C, C++,
> > or Cython, does something reasonable is an important goal of this
> > implementation.
> > The general strategy is to make existing code which does not
> explicitly
> > tell numpy it supports NA masks fail with an exception saying so.
> There are
> > a few different access patterns people use to get ahold of the numpy
> > array data,
> > here we examine a few of them to see what numpy can do. These
> examples are
> > found from doing google searches of numpy C API array access.
> >
> > Numpy Documentation - How to extend NumPy
> > -----------------------------------------
> >
> >
> http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html#dealing-with-array-objects
> >
> > This page has a section "Dealing with array objects" which has some
> > advice for how
> > to access numpy arrays from C. When accepting arrays, the first
> step it
> > suggests is
> > to use PyArray_FromAny or a macro built on that function, so code
> > following this
> > advice will properly fail when given an NA-masked array it
> doesn't know
> > how to handle.
> >
> > The way this is handled is that PyArray_FromAny requires a
> special flag,
> > NPY_ARRAY_ALLOWNA,
> > before it will allow NA-masked arrays to flow through.
> >
> >
> http://docs.scipy.org/doc/numpy/reference/c-api.array.html#NPY_ARRAY_ALLOWNA
> >
> > Code which does not follow this advice, and instead just calls
> > PyArray_Check() to verify
> > its an ndarray and checks some flags, will silently produce incorrect
> > results. This style
> > of code does not provide any opportunity for numpy to say "hey, this
> > array is special",
> > so also is not compatible with future ideas of lazy evaluation,
> derived
> > dtypes, etc.
>
> This doesn't really cover the Cython code I write that interfaces with C
> (and probably the code others write in Cython).
>
> Often I'd do:
>
> def f(arg):
> cdef np.ndarray arr = np.asarray(arg)
> c_func(np.PyArray_DATA(arr))
>
> So I mix Python np.asarray with C PyArray_DATA. In general, I think you
> use PyArray_FromAny if you're very concerned about performance or need
> some special flag, but it's certainly not the first thing you tgry.
>
>
> 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?
Dag
>
> Cheers,
> Mark
>
> Dag
>
> >
> > Tutorial From Cython Website
> > ----------------------------
> >
> > http://docs.cython.org/src/tutorial/numpy.html
> >
> > This tutorial gives a convolution example, and all the examples
> fail with
> > Python exceptions when given inputs that contain NA values.
> >
> > Before any Cython type annotation is introduced, the code
> functions just
> > as equivalent Python would in the interpreter.
> >
> > When the type information is introduced, it is done via numpy.pxd
> which
> > defines a mapping between an ndarray declaration and
> PyArrayObject \*.
> > Under the hood, this maps to __Pyx_ArgTypeTest, which does a direct
> > comparison of Py_TYPE(obj) against the PyTypeObject for the ndarray.
> >
> > Then the code does some dtype comparisons, and uses regular
> python indexing
> > to access the array elements. This python indexing still goes
> through the
> > Python API, so the NA handling and error checking in numpy still
> can work
> > like normal and fail if the inputs have NAs which cannot fit in
> the output
> > array. In this case it fails when trying to convert the NA into
> an integer
> > to set in in the output.
> >
> > The next version of the code introduces more efficient indexing. This
> > operates based on Python's buffer protocol. This causes Cython to
> call
> > __Pyx_GetBufferAndValidate, which calls __Pyx_GetBuffer, which calls
> > PyObject_GetBuffer. This call gives numpy the opportunity to raise an
> > exception if the inputs are arrays with NA-masks, something not
> supported
> > by the Python buffer protocol.
> >
> > Numerical Python - JPL website
> > ------------------------------
> >
> > http://dsnra.jpl.nasa.gov/software/Python/numpydoc/numpy-13.html
> >
> > This document is from 2001, so does not reflect recent numpy, but
> it is the
> > second hit when searching for "numpy c api example" on google.
> >
> > There first example, heading "A simple example", is in fact already
> > invalid for
> > recent numpy even without the NA support. In particular, if the
> data is
> > misaligned
> > or in a different byteorder, it may crash or produce incorrect
> results.
> >
> > The next thing the document does is introduce
> > PyArray_ContiguousFromObject, which
> > gives numpy an opportunity to raise an exception when NA-masked
> arrays
> > are used,
> > so the later code will raise exceptions as desired.
> >
> >
> >
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