Using C libraries
=================

Apart from writing fast code, one of the main use cases of Cython is
to call external C libraries from Python code.  As Cython code
compiles down to C code itself, it is actually trivial to call C
functions directly in the code.  The following gives a complete
example for using (and wrapping) an external C library in Cython code,
including appropriate error handling and considerations about
designing a suitable API for Python and Cython code.

Imagine you need an efficient way to store integer values in a FIFO
queue.  Since memory really matters, and the values are actually
coming from C code, you cannot afford to create and store Python
``int`` objects in a list or deque.  So you look out for a queue
implementation in C.

After some web search, you find the C-algorithms library [CAlg]_ and
decide to use its double ended queue implementation.  To make the
handling easier, however, you decide to wrap it in a Python extension
type that can encapsulate all memory management.

The C API of the queue implementation, which is defined in the header
file ``libcalg/queue.h``, essentially looks like this::

    /* file: queue.h */

    typedef struct _Queue Queue;
    typedef void *QueueValue;

    Queue *queue_new(void);
    void queue_free(Queue *queue);

    int queue_push_head(Queue *queue, QueueValue data);
    QueueValue queue_pop_head(Queue *queue);
    QueueValue queue_peek_head(Queue *queue);

    int queue_push_tail(Queue *queue, QueueValue data);
    QueueValue queue_pop_tail(Queue *queue);
    QueueValue queue_peek_tail(Queue *queue);

    int queue_is_empty(Queue *queue);

To get started, the first step is to redefine the C API in a ``.pxd``
file, say, ``cqueue.pxd``::

    # file: cqueue.pxd

    cdef extern from "libcalg/queue.h":
        ctypedef struct Queue:
            pass
        ctypedef void* QueueValue

        Queue* queue_new()
        void queue_free(Queue* queue)

        int queue_push_head(Queue* queue, QueueValue data)
        QueueValue  queue_pop_head(Queue* queue)
        QueueValue queue_peek_head(Queue* queue)

        int queue_push_tail(Queue* queue, QueueValue data)
        QueueValue queue_pop_tail(Queue* queue)
        QueueValue queue_peek_tail(Queue* queue)

        bint queue_is_empty(Queue* queue)

Note how these declarations are almost identical to the header file
declarations, so you can often just copy them over.  However, you do
not need to provide *all* declarations as above, just those that you
use in your code or in other declarations, so that Cython gets to see
a sufficient and consistent subset of them.  Then, consider adapting
them somewhat to make them more comfortable to work with in Cython.

One noteworthy difference to the header file that we use above is the
declaration of the ``Queue`` struct in the first line.  ``Queue`` is
in this case used as an *opaque handle*; only the library that is
called knows what is really inside.  Since no Cython code needs to
know the contents of the struct, we do not need to declare its
contents, so we simply provide an empty definition (as we do not want
to declare the ``_Queue`` type which is referenced in the C header)
[#]_.

.. [#] There's a subtle difference between ``cdef struct Queue: pass``
       and ``ctypedef struct Queue: pass``.  The former declares a
       type which is referenced in C code as ``struct Queue``, while
       the latter is referenced in C as ``Queue``.  This is a C
       language quirk that Cython is not able to hide.  Most modern C
       libraries use the ``ctypedef`` kind of struct.

Another exception is the last line.  The integer return value of the
``queue_is_empty()`` function is actually a C boolean value, i.e. the
only interesting thing about it is whether it is non-zero or zero,
indicating if the queue is empty or not.  This is best expressed by
Cython's ``bint`` type, which is a normal ``int`` type when used in C
but maps to Python's boolean values ``True`` and ``False`` when
converted to a Python object.  This way of tightening declarations in
a ``.pxd`` file can often simplify the code that uses them.

It is good practice to define one ``.pxd`` file for each library that
you use, and sometimes even for each header file (or functional group)
if the API is large.  That simplifies their reuse in other projects.
Sometimes, you may need to use C functions from the standard C
library, or want to call C-API functions from CPython directly.  For
common needs like this, Cython ships with a set of standard ``.pxd``
files that provide these declarations in a readily usable way that is
adapted to their use in Cython.  The main packages are ``cpython``,
``libc`` and ``libcpp``.  The NumPy library also has a standard
``.pxd`` file ``numpy``, as it is often used in Cython code.  See
Cython's ``Cython/Includes/`` source package for a complete list of
provided ``.pxd`` files.

After declaring our C library's API, we can start to design the Queue
class that should wrap the C queue.  It will live in a file called
``queue.pyx``. [#]_

.. [#] Note that the name of the ``.pyx`` file must be different from
       the ``cqueue.pxd`` file with declarations from the C library,
       as both do not describe the same code.  A ``.pxd`` file next to
       a ``.pyx`` file with the same name defines exported
       declarations for code in the ``.pyx`` file.  As the
       ``cqueue.pxd`` file contains declarations of a regular C
       library, there must not be a ``.pyx`` file with the same name
       that Cython associates with it.

Here is a first start for the Queue class::

    # file: queue.pyx

    cimport cqueue

    cdef class Queue:
        cdef cqueue.Queue *_c_queue
        def __cinit__(self):
            self._c_queue = cqueue.queue_new()

Note that it says ``__cinit__`` rather than ``__init__``.  While
``__init__`` is available as well, it is not guaranteed to be run (for
instance, one could create a subclass and forget to call the
ancestor's constructor).  Because not initializing C pointers often
leads to hard crashes of the Python interpreter, Cython provides
``__cinit__`` which is *always* called immediately on construction,
before CPython even considers calling ``__init__``, and which
therefore is the right place to initialise ``cdef`` fields of the new
instance.  However, as ``__cinit__`` is called during object
construction, ``self`` is not fully constructed yet, and one must
avoid doing anything with ``self`` but assigning to ``cdef`` fields.

Note also that the above method takes no parameters, although subtypes
may want to accept some.  A no-arguments ``__cinit__()`` method is a
special case here that simply does not receive any parameters that
were passed to a constructor, so it does not prevent subclasses from
adding parameters.  If parameters are used in the signature of
``__cinit__()``, they must match those of any declared ``__init__``
method of classes in the class hierarchy that are used to instantiate
the type.

Before we continue implementing the other methods, it is important to
understand that the above implementation is not safe.  In case
anything goes wrong in the call to ``queue_new()``, this code will
simply swallow the error, so we will likely run into a crash later on.
According to the documentation of the ``queue_new()`` function, the
only reason why the above can fail is due to insufficient memory.  In
that case, it will return ``NULL``, whereas it would normally return a
pointer to the new queue.

The Python way to get out of this is to raise a ``MemoryError`` [#]_.
We can thus change the init function as follows::

    cimport cqueue

    cdef class Queue:
        cdef cqueue.Queue *_c_queue
        def __cinit__(self):
            self._c_queue = cqueue.queue_new()
            if self._c_queue is NULL:
	        raise MemoryError()

.. [#] In the specific case of a ``MemoryError``, creating a new
   exception instance in order to raise it may actually fail because
   we are running out of memory.  Luckily, CPython provides a C-API
   function ``PyErr_NoMemory()`` that safely raises the right
   exception for us.  Since version 0.14.1, Cython automatically
   substitutes this C-API call whenever you write ``raise
   MemoryError`` or ``raise MemoryError()``.  If you use an older
   version, you have to cimport the C-API function from the standard
   package ``cpython.exc`` and call it directly.

The next thing to do is to clean up when the Queue instance is no
longer used (i.e. all references to it have been deleted).  To this
end, CPython provides a callback that Cython makes available as a
special method ``__dealloc__()``.  In our case, all we have to do is
to free the C Queue, but only if we succeeded in initialising it in
the init method::

        def __dealloc__(self):
            if self._c_queue is not NULL:
                cqueue.queue_free(self._c_queue)

At this point, we have a working Cython module that we can test.  To
compile it, we need to configure a ``setup.py`` script for distutils.
Here is the most basic script for compiling a Cython module::

    from distutils.core import setup
    from distutils.extension import Extension
    from Cython.Distutils import build_ext

    setup(
        cmdclass = {'build_ext': build_ext},
        ext_modules = [Extension("queue", ["queue.pyx"])]
    ) 

To build against the external C library, we must extend this script to
include the necessary setup.  Assuming the library is installed in the
usual places (e.g. under ``/usr/lib`` and ``/usr/include`` on a
Unix-like system), we could simply change the extension setup from

::

    ext_modules = [Extension("queue", ["queue.pyx"])]

to

::

    ext_modules = [
        Extension("queue", ["queue.pyx"],
                  libraries=["calg"])
        ]

If it is not installed in a 'normal' location, users can provide the
required parameters externally by passing appropriate C compiler
flags, such as::

    CFLAGS="-I/usr/local/otherdir/calg/include"  \
    LDFLAGS="-L/usr/local/otherdir/calg/lib"     \
        python setup.py build_ext -i

Once we have compiled the module for the first time, we can now import
it and instantiate a new Queue::

    $ export PYTHONPATH=.
    $ python -c 'import queue.Queue as Q ; Q()'

However, this is all our Queue class can do so far, so let's make it
more usable.

Before implementing the public interface of this class, it is good
practice to look at what interfaces Python offers, e.g. in its
``list`` or ``collections.deque`` classes.  Since we only need a FIFO
queue, it's enough to provide the methods ``append()``, ``peek()`` and
``pop()``, and additionally an ``extend()`` method to add multiple
values at once.  Also, since we already know that all values will be
coming from C, it's best to provide only ``cdef`` methods for now, and
to give them a straight C interface.

In C, it is common for data structures to store data as a ``void*`` to
whatever data item type.  Since we only want to store ``int`` values,
which usually fit into the size of a pointer type, we can avoid
additional memory allocations through a trick: we cast our ``int`` values
to ``void*`` and vice versa, and store the value directly as the
pointer value.

Here is a simple implementation for the ``append()`` method::

        cdef append(self, int value):
            cqueue.queue_push_tail(self._c_queue, <void*>value)

Again, the same error handling considerations as for the
``__cinit__()`` method apply, so that we end up with this
implementation instead::

        cdef append(self, int value):
            if not cqueue.queue_push_tail(self._c_queue,
                                          <void*>value):
                raise MemoryError()

Adding an ``extend()`` method should now be straight forward::

    cdef extend(self, int* values, size_t count):
        """Append all ints to the queue.
        """
        cdef size_t i
        for i in range(count):
            if not cqueue.queue_push_tail(
                    self._c_queue, <void*>values[i]):
                raise MemoryError()

This becomes handy when reading values from a NumPy array, for
example.

So far, we can only add data to the queue.  The next step is to write
the two methods to get the first element: ``peek()`` and ``pop()``,
which provide read-only and destructive read access respectively::

    cdef int peek(self):
        return <int>cqueue.queue_peek_head(self._c_queue)

    cdef int pop(self):
        return <int>cqueue.queue_pop_head(self._c_queue)

Simple enough.  Now, what happens when the queue is empty?  According
to the documentation, the functions return a ``NULL`` pointer, which
is typically not a valid value.  Since we are simply casting to and
from ints, we cannot distinguish anymore if the return value was
``NULL`` because the queue was empty or because the value stored in
the queue was ``0``.  However, in Cython code, we would expect the
first case to raise an exception, whereas the second case should
simply return ``0``.  To deal with this, we need to special case this
value, and check if the queue really is empty or not::

    cdef int peek(self) except? -1:
        cdef int value = \
          <int>cqueue.queue_peek_head(self._c_queue)
        if value == 0:
            # this may mean that the queue is empty, or
            # that it happens to contain a 0 value
            if cqueue.queue_is_empty(self._c_queue):
                raise IndexError("Queue is empty")
        return value

Note how we have effectively created a fast path through the method in
the hopefully common cases that the return value is not ``0``.  Only
that specific case needs an additional check if the queue is empty.

The ``except? -1`` declaration in the method signature falls into the
same category.  If the function was a Python function returning a
Python object value, CPython would simply return ``NULL`` internally
instead of a Python object to indicate an exception, which would
immediately be propagated by the surrounding code.  The problem is
that the return type is ``int`` and any ``int`` value is a valid queue
item value, so there is no way to explicitly signal an error to the
calling code.  In fact, without such a declaration, there is no
obvious way for Cython to know what to return on exceptions and for
calling code to even know that this method *may* exit with an
exception.

The only way calling code can deal with this situation is to call
``PyErr_Occurred()`` when returning from a function to check if an
exception was raised, and if so, propagate the exception.  This
obviously has a performance penalty.  Cython therefore allows you to
declare which value it should implicitly return in the case of an
exception, so that the surrounding code only needs to check for an
exception when receiving this exact value.

We chose to use ``-1`` as the exception return value as we expect it
to be an unlikely value to be put into the queue.  The question mark
in the ``except? -1`` declaration indicates that the return value is
ambiguous (there *may* be a ``-1`` value in the queue, after all) and
that an additional exception check using ``PyErr_Occurred()`` is
needed in calling code.  Without it, Cython code that calls this
method and receives the exception return value would silently (and
sometimes incorrectly) assume that an exception has been raised.  In
any case, all other return values will be passed through almost
without a penalty, thus again creating a fast path for 'normal'
values.

Now that the ``peek()`` method is implemented, the ``pop()`` method
also needs adaptation.  Since it removes a value from the queue,
however, it is not enough to test if the queue is empty *after* the
removal.  Instead, we must test it on entry::

    cdef int pop(self) except? -1:
        if cqueue.queue_is_empty(self._c_queue):
            raise IndexError("Queue is empty")
        return <int>cqueue.queue_pop_head(self._c_queue)

The return value for exception propagation is declared exactly as for
``peek()``.

Lastly, we can provide the Queue with an emptiness indicator in the
normal Python way by implementing the ``__bool__()`` special method
(note that Python 2 calls this method ``__nonzero__``, whereas Cython
code can use either name)::

    def __bool__(self):
        return not cqueue.queue_is_empty(self._c_queue)

Note that this method returns either ``True`` or ``False`` as we
declared the return type of the ``queue_is_empty`` function as
``bint`` in ``cqueue.pxd``.

Now that the implementation is complete, you may want to write some
tests for it to make sure it works correctly.  Especially doctests are
very nice for this purpose, as they provide some documentation at the
same time.  To enable doctests, however, you need a Python API that
you can call.  C methods are not visible from Python code, and thus
not callable from doctests.

A quick way to provide a Python API for the class is to change the
methods from ``cdef`` to ``cpdef``.  This will let Cython generate two
entry points, one that is callable from normal Python code using the
Python call semantics and Python objects as arguments, and one that is
callable from C code with fast C semantics and without requiring
intermediate argument conversion from or to Python types.

The following listing shows the complete implementation that uses
``cpdef`` methods where possible::

    cimport cqueue

    cdef class Queue:
        """A queue class for C integer values.

        >>> q = Queue()
        >>> q.append(5)
        >>> q.peek()
        5
        >>> q.pop()
        5
        """
        cdef cqueue.Queue *_c_queue
        def __cinit__(self):
            self._c_queue = cqueue.queue_new()
            if self._c_queue is NULL:
                raise MemoryError()

        def __dealloc__(self):
            if self._c_queue is not NULL:
                cqueue.queue_free(self._c_queue)

        cpdef append(self, int value):
            if not cqueue.queue_push_tail(self._c_queue,
                                          <void*>value):
                raise MemoryError()

        cdef extend(self, int* values, size_t count):
            cdef size_t i
            for i in xrange(count):
                if not cqueue.queue_push_tail(
                        self._c_queue, <void*>values[i]):
                    raise MemoryError()

        cpdef int peek(self) except? -1:
            cdef int value = \
                <int>cqueue.queue_peek_head(self._c_queue)
            if value == 0:
                # this may mean that the queue is empty,
                # or that it happens to contain a 0 value
                if cqueue.queue_is_empty(self._c_queue):
                    raise IndexError("Queue is empty")
            return value

        cdef int pop(self) except? -1:
            if cqueue.queue_is_empty(self._c_queue):
                raise IndexError("Queue is empty")
            return <int>cqueue.queue_pop_head(self._c_queue)

        def __bool__(self):
            return not cqueue.queue_is_empty(self._c_queue)

The ``cpdef`` feature is obviously not available for the ``extend()``
method, as the method signature is incompatible with Python argument
types.  However, if wanted, we can rename the C-ish ``extend()``
method to e.g. ``c_extend()``, and write a new ``extend()`` method
instead that accepts an arbitrary Python iterable::

        cdef c_extend(self, int* values, size_t count):
            cdef size_t i
            for i in range(count):
                if not cqueue.queue_push_tail(
                        self._c_queue, <void*>values[i]):
                    raise MemoryError()

        cpdef extend(self, values):
            for value in values:
                self.append(value)

As a quick test with 10000 numbers on the author's machine indicates,
using this Queue from Cython code with C ``int`` values is about five
times as fast as using it from Cython code with Python object values,
almost eight times faster than using it from Python code in a Python
loop, and still more than twice as fast as using Python's highly
optimised ``collections.deque`` type from Cython code with Python
integers.

.. [CAlg] Simon Howard, C Algorithms library, http://c-algorithms.sourceforge.net/
