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

Mark Wiebe mwwiebe at gmail.com
Thu May 10 19:06:15 EDT 2012


On Thu, May 10, 2012 at 5:47 PM, Dag Sverre Seljebotn <
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.


> 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.


>
> 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.

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.
> >
> >
> >
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion at scipy.org
> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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