.. This is a comment. Note how any initial comments are moved by
   transforms to after the document title, subtitle, and docinfo.

.. Need intro and conclusion
.. Exposing classes
    .. Constructors
    .. Overloading
    .. Properties and data members
    .. Inheritance
    .. Operators and Special Functions
    .. Virtual Functions
.. Call Policies

++++++++++++++++++++++++++++++++++++++++++++++
 Introducing Boost.Python (Extended Abstract)
++++++++++++++++++++++++++++++++++++++++++++++


.. bibliographic fields (which also require a transform):

:Author: David Abrahams
:Address: 45 Walnut Street
          Somerville, MA 02143
:Contact: dave@boost-consulting.com
:organization: `Boost Consulting`_
:date: $Date$
:status: This is a "work in progress"
:version: 1
:copyright: Copyright David Abrahams 2002. All rights reserved

:Dedication:

    For my girlfriend, wife, and partner Luann

:abstract:

    This paper describes the Boost.Python library, a system for
    C++/Python interoperability.

.. meta::
   :keywords: Boost,python,Boost.Python,C++
   :description lang=en: C++/Python interoperability with Boost.Python

.. contents:: Table of Contents
.. section-numbering::


.. _`Boost Consulting`: http://www.boost-consulting.com

==============
 Introduction
==============

Python and C++ are in many ways as different as two languages could
be: while C++ is usually compiled to machine-code, Python is
interpreted.  Python's dynamic type system is often cited as the
foundation of its flexibility, while in C++ static typing is the
cornerstone of its efficiency. C++ has an intricate and difficult
meta-language to support compile-time polymorphism, while Python is
a uniform language with convenient runtime polymorphism.

Yet for many programmers, these very differences mean that Python and
C++ complement one another perfectly.  Performance bottlenecks in
Python programs can be rewritten in C++ for maximal speed, and
authors of powerful C++ libraries choose Python as a middleware
language for its flexible system integration capabilities.
Furthermore, the surface differences mask some strong similarities:

* 'C'-family control structures (if, while, for...)

* Support for object-orientation, functional programming, and generic
  programming (these are both *multi-paradigm* programming languages.)

* Comprehensive operator overloading facilities, recognizing the
  importance of syntactic variability for readability and
  expressivity.

* High-level concepts such as collections and iterators.

* High-level encapsulation facilities (C++: namespaces, Python: modules)
  to support the design of re-usable libraries.

* Exception-handling for effective management of error conditions.

* C++ idioms in common use, such as handle/body classes and
  reference-counted smart pointers mirror Python reference semantics.

Python provides a rich 'C' API for writers of 'C' extension modules.
Unfortunately, using this API directly for exposing C++ type and
function interfaces to Python is much more tedious than it should be.
This is mainly due to the limitations of the 'C' language.  Compared to
C++ and Python, 'C' has only very rudimentary abstraction facilities.
Support for exception-handling is completely missing. One important
undesirable consequence is that 'C' extension module writers are
required to manually manage Python reference counts. Another unpleasant
consequence is a very high degree of repetition of similar code in 'C'
extension modules. Of course highly redundant code does not only cause
frustration for the module writer, but is also very difficult to
maintain.

The limitations of the 'C' API have lead to the development of a
variety of wrapping systems. SWIG_ is probably the most popular package
for the integration of C/C++ and Python. A more recent development is
the SIP_ package, which is specifically designed for interfacing Python
with the Qt_ graphical user interface library. Both SWIG and SIP
introduce a new specialized language for defining the inter-language
bindings. Of course being able to use a specialized language has
advantages, but having to deal with three different languages (Python,
C/C++ and the interface language) also introduces practical and mental
difficulties. The CXX_ package demonstrates an interesting alternative.
It shows that at least some parts of Python's 'C' API can be wrapped
and presented through a much more user-friendly C++ interface. However,
unlike SWIG and SIP, CXX does not include support for wrapping C++
classes as new Python types. CXX is also no longer actively developed.

In some respects Boost.Python combines ideas from SWIG and SIP with
ideas from CXX. Like SWIG and SIP, Boost.Python is a system for
wrapping C++ classes as new Python "built-in" types, and C/C++
functions as Python functions. Like CXX, Boost.Python presents Python's
'C' API through a C++ interface. Boost.Python goes beyond the scope of
other systems with the unique support for C++ virtual functions that
are overrideable in Python, support for organizing extensions as Python
packages with a central registry for inter-language type conversions,
and a convenient mechanism for tying into Python's serialization engine
(pickle). Importantly, all this is achieved without introducing a new
syntax. Boost.Python leverages the power of C++ meta-programming
techniques to introspect about the C++ type system, and presents a
simple, IDL-like C++ interface for exposing C/C++ code in extension
modules. Boost.Python is a pure C++ library, the inter-language
bindings are defined in pure C++, and other than a C++ compiler only
Python itself is required to get started with Boost.Python. Last but
not least, Boost.Python is an unrestricted open source library. There
are no strings attached even for commercial applications.

.. _SWIG: http://www.swig.org/
.. _SIP: http://www.riverbankcomputing.co.uk/sip/index.php
.. _Qt: http://www.trolltech.com/
.. _CXX: http://cxx.sourceforge.net/

===========================
 Boost.Python Design Goals 
===========================

The primary goal of Boost.Python is to allow users to expose C++
classes and functions to Python using nothing more than a C++
compiler.  In broad strokes, the user experience should be one of
directly manipulating C++ objects from Python.

However, it's also important not to translate all interfaces *too*
literally: the idioms of each language must be respected.  For
example, though C++ and Python both have an iterator concept, they are
expressed very differently.  Boost.Python has to be able to bridge the
interface gap.

It must be possible to insulate Python users from crashes resulting
from trivial misuses of C++ interfaces, such as accessing
already-deleted objects.  By the same token the library should
insulate C++ users from low-level Python 'C' API, replacing
error-prone 'C' interfaces like manual reference-count management and
raw ``PyObject`` pointers with more-robust alternatives.

Support for component-based development is crucial, so that C++ types
exposed in one extension module can be passed to functions exposed in
another without loss of crucial information like C++ inheritance
relationships.

Finally, all wrapping must be *non-intrusive*, without modifying or
even seeing the original C++ source code.  Existing C++ libraries have
to be wrappable by third parties who only have access to header files
and binaries.

==========================
 Hello Boost.Python World
==========================

And now for a preview of Boost.Python, and how it improves on the raw
facilities offered by Python. Here's a function we might want to
expose::

    char const* greet(unsigned x)
    {
       static char const* const msgs[] = { "hello", "Boost.Python", "world!" };

       if (x > 2) 
           throw std::range_error("greet: index out of range");

       return msgs[x];
    }

To wrap this function in standard C++ using the Python 'C' API, we'd
need something like this::

    extern "C" // all Python interactions use 'C' linkage and calling convention
    {
        // Wrapper to handle argument/result conversion and checking
        PyObject* greet_wrap(PyObject* args, PyObject * keywords)
        {
             int x;
             if (PyArg_ParseTuple(args, "i", &x))    // extract/check arguments
             {
                 char const* result = greet(x);      // invoke wrapped function
                 return PyString_FromString(result); // convert result to Python
             }
             return 0;                               // error occurred
        }

        // Table of wrapped functions to be exposed by the module
        static PyMethodDef methods[] = {
            { "greet", greet_wrap, METH_VARARGS, "return one of 3 parts of a greeting" }
            , { NULL, NULL, 0, NULL } // sentinel
        };

        // module initialization function
        DL_EXPORT init_hello()
        {
            (void) Py_InitModule("hello", methods); // add the methods to the module
        }
    }

Now here's the wrapping code we'd use to expose it with Boost.Python::

    #include <boost/python.hpp>
    using namespace boost::python;
    BOOST_PYTHON_MODULE(hello)
    {
        def("greet", greet, "return one of 3 parts of a greeting");
    }

and here it is in action::

    >>> import hello
    >>> for x in range(3):
    ...     print hello.greet(x)
    ...
    hello
    Boost.Python
    world!

Aside from the fact that the 'C' API version is much more verbose than
the BPL one, it's worth noting that it doesn't handle a few things
correctly:

* The original function accepts an unsigned integer, and the Python
  'C' API only gives us a way of extracting signed integers. The
  Boost.Python version will raise a Python exception if we try to pass
  a negative number to ``hello.greet``, but the other one will proceed
  to do whatever the C++ implementation does when converting an
  negative integer to unsigned (usually wrapping to some very large
  number), and pass the incorrect translation on to the wrapped
  function.

* That brings us to the second problem: if the C++ ``greet()``
  function is called with a number greater than 2, it will throw an
  exception.  Typically, if a C++ exception propagates across the
  boundary with code generated by a 'C' compiler, it will cause a
  crash.  As you can see in the first version, there's no C++
  scaffolding there to prevent this from happening.  Functions wrapped
  by Boost.Python automatically include an exception-handling layer
  which protects Python users by translating unhandled C++ exceptions
  into a corresponding Python exception.

* A slightly more-subtle limitation is that the argument conversion
  used in the Python 'C' API case can only get that integer ``x`` in
  *one way*.  PyArg_ParseTuple can't convert Python ``long`` objects
  (arbitrary-precision integers) which happen to fit in an ``unsigned
  int`` but not in a ``signed long``, nor will it ever handle a
  wrapped C++ class with a user-defined implicit ``operator unsigned
  int()`` conversion.  The BPL's dynamic type conversion registry
  allows users to add arbitrary conversion methods.

==================
 Library Overview
==================

This section outlines some of the library's major features.  Except as
neccessary to avoid confusion, details of library implementation are
omitted.

-------------------------------------------
 The fundamental type-conversion mechanism
-------------------------------------------

XXX This needs to be rewritten.

Every argument of every wrapped function requires some kind of
extraction code to convert it from Python to C++.  Likewise, the
function return value has to be converted from C++ to Python.
Appropriate Python exceptions must be raised if the conversion fails.
Argument and return types are part of the function's type, and much of
this tedium can be relieved if the wrapping system can extract that
information through  introspection.

Passing a wrapped C++ derived class instance to a C++ function
accepting a pointer or reference to a base class requires knowledge of
the inheritance relationship and how to translate the address of a base
class into that of a derived class.

------------------
 Exposing Classes
------------------

C++ classes and structs are exposed with a similarly-terse interface.
Given::

    struct World
    {
        void set(std::string msg) { this->msg = msg; }
        std::string greet() { return msg; }
        std::string msg;
    };

The following code will expose it in our extension module::
    
    #include <boost/python.hpp>
    BOOST_PYTHON_MODULE(hello)
    {
        class_<World>("World")
            .def("greet", &World::greet)
            .def("set", &World::set)
        ;
    }

Although this code has a certain pythonic familiarity, people
sometimes find the syntax bit confusing because it doesn't look like
most of the C++ code they're used to. All the same, this is just
standard C++.  Because of their flexible syntax and operator
overloading, C++ and Python are great for defining domain-specific
(sub)languages
(DSLs), and that's what we've done in BPL.  To break it down::

    class_<World>("World")

constructs an unnamed object of type ``class_<World>`` and passes
``"World"`` to its constructor.  This creates a new-style Python class
called ``World`` in the extension module, and associates it with the
C++ type ``World`` in the BPL type conversion registry.  We might have
also written::

    class_<World> w("World");

but that would've been more verbose, since we'd have to name ``w``
again to invoke its ``def()`` member function::

        w.def("greet", &World::greet)

There's nothing special about the location of the dot for member
access in the original example: C++ allows any amount of whitespace on
either side of a token, and placing the dot at the beginning of each
line allows us to chain as many successive calls to member functions
as we like with a uniform syntax.  The other key fact that allows
chaining is that ``class_<>`` member functions all return a reference
to ``*this``.

So the example is equivalent to::

    class_<World> w("World");
    w.def("greet", &World::greet);
    w.def("set", &World::set);

It's occasionally useful to be able to break down the components of a
Boost.Python class wrapper in this way, but the rest of this paper
will tend to stick to the terse syntax.

For completeness, here's the wrapped class in use:

>>> import hello
>>> planet = hello.World()
>>> planet.set('howdy')
>>> planet.greet()
'howdy'

Constructors
============

Since our ``World`` class is just a plain ``struct``, it has an
implicit no-argument (nullary) constructor.  Boost.Python exposes the
nullary constructor by default, which is why we were able to write:

>>> planet = hello.World()

However, well-designed classes in any language may require constructor
arguments in order to establish their invariants.  Unlike Python,
where ``__init__`` is just a specially-named method, In C++
constructors cannot be handled like ordinary member functions.  In
particular, we can't take their address: ``&World::World`` is an
error.  The library provides a different interface for specifying
constructors.  Given::

    struct World
    {
        World(std::string msg); // added constructor
        ...

we can modify our wrapping code as follows::

    class_<World>("World", init<std::string>())
        ...

of course, a C++ class may have additional constructors, and we can
expose those as well by passing more instances of ``init<...>`` to
``def()``::

    class_<World>("World", init<std::string>())
        .def(init<double, double>())
        ...

Boost.Python allows wrapped functions, member functions, and
constructors to be overloaded to mirror C++ overloading.

Data Members and Properties
===========================

Any publicly-accessible data members in a C++ class can be easily
exposed as either ``readonly`` or ``readwrite`` attributes::

    class_<World>("World", init<std::string>())
        .def_readonly("msg", &World::msg)
        ...

and can be used directly in Python:

>>> planet = hello.World('howdy')
>>> planet.msg
'howdy'

This does *not* result in adding attributes to the ``World`` instance
``__dict__``, which can result in substantial memory savings when
wrapping large data structures.  In fact, no instance ``__dict__``
will be created at all unless attributes are explicitly added from
Python.  BPL owes this capability to the new Python 2.2 type system,
in particular the descriptor interface and ``property`` type.

In C++, publicly-accessible data members are considered a sign of poor
design because they break encapsulation, and style guides usually
dictate the use of "getter" and "setter" functions instead.  In
Python, however, ``__getattr__``, ``__setattr__``, and since 2.2,
``property`` mean that attribute access is just one more
well-encapsulated syntactic tool at the programmer's disposal.  BPL
bridges this idiomatic gap by making Python ``property`` creation
directly available to users.  So if ``msg`` were private, we could
still expose it as attribute in Python as follows::

    class_<World>("World", init<std::string>())
        .add_property("msg", &World::greet, &World::set)
        ...

The example above mirrors the familiar usage of properties in Python
2.2+:

>>> class World(object):
...     __init__(self, msg):
...         self.__msg = msg
...     def greet(self):
...         return self.__msg
...     def set(self, msg):
...         self.__msg = msg
...     msg = property(greet, set)

Operators and Special Functions
===============================

The ability to write arithmetic operators for user-defined types that
C++ and Python both allow the definition of has been a major factor in
the popularity of both languages for scientific computing.  The
success of packages like NumPy attests to the power of exposing
operators in extension modules.  In this example we'll wrap a class
representing a position in a large file::

    class FilePos { /*...*/ };

    // Linear offset
    FilePos     operator+(FilePos, int);
    FilePos     operator+(int, FilePos);
    FilePos     operator-(FilePos, int);
    
    // Distance between two FilePos objects
    int         operator-(FilePos, FilePos);

    // Offset with assignment
    FilePos&    operator+=(FilePos&, int);
    FilePos&    operator-=(FilePos&, int);

    // Comparison
    bool        operator<(FilePos, FilePos);

The wrapping code looks like this::

    class_<FilePos>("FilePos")
        .def(self + int())     // __add__
        .def(int() + self)     // __radd__
        .def(self - int())     // __sub__

        .def(self - self)      // __sub__

        .def(self += int())    // __iadd__
        .def(self -= int())    // __isub__

        .def(self < self);     // __lt__
        ;

The magic is performed using a simplified application of "expression
templates" [VELD1995]_, a technique originally developed by for
optimization of high-performance matrix algebra expressions.  The
essence is that instead of performing the computation immediately,
operators are overloaded to construct a type *representing* the
computation.  In matrix algebra, dramatic optimizations are often
available when the structure of an entire expression can be taken into
account, rather than processing each operation "greedily".
Boost.Python uses the same technique to build an appropriate Python
callable object based on an expression involving ``self``, which is
then added to the class.

Inheritance
===========

C++ inheritance relationships can be represented to Boost.Python by adding
an optional ``bases<...>`` argument to the ``class_<...>`` template
parameter list as follows::

     class_<Derived, bases<Base1,Base2> >("Derived")
          ...

This has two effects:  

1. When the ``class_<...>`` is created, Python type objects
   corresponding to ``Base1`` and ``Base2`` are looked up in the BPL
   registry, and are used as bases for the new Python ``Derived`` type
   object [#mi]_, so methods exposed for the Python ``Base1`` and
   ``Base2`` types are automatically members of the ``Derived`` type.
   Because the registry is global, this works correctly even if
   ``Derived`` is exposed in a different module from either of its
   bases.

2. C++ conversions from ``Derived`` to its bases are added to the
   Boost.Python registry.  Thus wrapped C++ methods expecting (a
   pointer or reference to) an object of either base type can be
   called with an object wrapping a ``Derived`` instance.  Wrapped
   member functions of class ``T`` are treated as though they have an
   implicit first argument of ``T&``, so these conversions are
   neccessary to allow the base class methods to be called for derived
   objects.

Of course it's possible to derive new Python classes from wrapped C++
class instances.  Because Boost.Python uses the new-style class
system, that works very much as for the Python built-in types.  There
is one significant detail in which it differs: the built-in types
generally establish their invariants in their ``__new__`` function, so
that derived classes do not need to call ``__init__`` on the base
class before invoking its methods :

>>> class L(list):
...      def __init__(self):
...          pass
...
>>> L().reverse()
>>> 

Because C++ object construction is a one-step operation, C++ instance
data cannot be constructed until the arguments are available, in the
``__init__`` function:

>>> class D(SomeBPLClass):
...      def __init__(self):
...          pass
...
>>> D().some_bpl_method()
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
TypeError: bad argument type for built-in operation

This happened because Boost.Python couldn't find instance data of type
``SomeBPLClass`` within the ``D`` instance; ``D``'s ``__init__``
function masked construction of the base class.  It could be corrected
by either removing ``D``'s ``__init__`` function or having it call
``SomeBPLClass.__init__(...)`` explicitly.

Virtual Functions
=================

Deriving new types in Python from extension classes is not very
interesting unless they can be used polymorphically from C++.  In
other words, Python method implementations should appear to override
the implementation of C++ virtual functions when called *through base
class pointers/references from C++*.  Since the only way to alter the
behavior of a virtual function is to override it in a derived class,
the user must build a special derived class to dispatch a polymorphic
class' virtual functions::

    //
    // interface to wrap:
    //
    class Base
    {
     public:
        virtual int f(std::string x) { return 42; }
        virtual ~Base();
    };

    int calls_f(Base const& b, std::string x) { return b.f(x); }

    //
    // Wrapping Code
    //

    // Dispatcher class
    struct BaseWrap : Base
    {
        // Store a pointer to the Python object
        BaseWrap(PyObject* self_) : self(self_) {}
        PyObject* self;

        // Default implementation, for when f is not overridden
        int f_default(std::string x) { return this->Base::f(x); }
        // Dispatch implementation
        int f(std::string x) { return call_method<int>(self, "f", x); }
    };

    ...
        def("calls_f", calls_f);
        class_<Base, BaseWrap>("Base")
            .def("f", &Base::f, &BaseWrap::f_default)
            ;

Now here's some Python code which demonstrates:

>>> class Derived(Base):
...     def f(self, s):
...          return len(s)
...
>>> calls_f(Base(), 'foo')
42
>>> calls_f(Derived(), 'forty-two')
9

Things to notice about the dispatcher class:

* The key element which allows overriding in Python is the
  ``call_method`` invocation, which uses the same global type
  conversion registry as the C++ function wrapping does to convert its
  arguments from C++ to Python and its return type from Python to C++.

* Any constructor signatures you wish to wrap must be replicated with
  an initial ``PyObject*`` argument

* The dispatcher must store this argument so that it can be used to
  invoke ``call_method``

* The ``f_default`` member function is needed when the function being
  exposed is not pure virtual; there's no other way ``Base::f`` can be
  called on an object of type ``BaseWrap``, since it overrides ``f``.

Admittedly, this formula is tedious to repeat, especially on a project
with many polymorphic classes; that it is neccessary reflects
limitations in C++'s compile-time reflection capabilities.  Several
efforts are underway to write front-ends for Boost.Python which can
generate these dispatchers (and other wrapping code) automatically.
If these are successful it will mark a move away from wrapping
everything directly in pure C++ for many of our users.

---------------
 Serialization
---------------

*Serialization* is the process of converting objects in memory to a
form that can be stored on disk or sent over a network connection. The
serialized object (most often a plain string) can be retrieved and
converted back to the original object. A good serialization system will
automatically convert entire object hierarchies. Python's standard
``pickle`` module is such a system.  It leverages the language's strong
runtime introspection facilities for serializing practically arbitrary
user-defined objects. With a few simple and unintrusive provisions this
powerful machinery can be extended to also work for wrapped C++ objects.
Here is an example::

    #include <string>

    struct World
    {
        World(std::string a_msg) : msg(a_msg) {}
        std::string greet() const { return msg; }
        std::string msg;
    };

    #include <boost/python.hpp>
    using namespace boost::python;

    struct World_picklers : pickle_suite
    {
      static tuple
      getinitargs(World const& w) { return make_tuple(w.greet()); }
    };

    BOOST_PYTHON_MODULE(hello)
    {
        class_<World>("World", init<std::string>())
            .def("greet", &World::greet)
            .def_pickle(World_picklers())
        ;
    }

Now let's create a ``World`` object and put it to rest on disk::

    >>> import hello
    >>> import pickle
    >>> a_world = hello.World("howdy")
    >>> pickle.dump(a_world, open("my_world", "w"))

In a potentially *different script* on a potentially *different
computer* with a potentially *different operating system*::

    >>> import pickle
    >>> resurrected_world = pickle.load(open("my_world", "r"))
    >>> resurrected_world.greet()
    'howdy'

Of course the ``cPickle`` module can also be used for faster
processing.

Boost.Python's ``pickle_suite`` fully supports the ``pickle`` protocol
defined in the standard Python documentation. There is a one-to-one
correspondence between the standard pickling methods (``__getinitargs__``,
``__getstate__``, ``__setstate__``) and the functions defined by the
user in the class derived from ``pickle_suite`` (``getinitargs``,
``getstate``, ``setstate``). The ``class_::def_pickle()`` member function
is used to establish the Python bindings for all user-defined functions
simultaneously. Correct signatures for these functions are enforced at
compile time. Non-sensical combinations of the three pickle functions
are also rejected at compile time. These measures are designed to
help the user in avoiding obvious errors.

Enabling serialization of more complex C++ objects requires a little
more work than is shown in the example above. Fortunately the
``object`` interface (see next section) greatly helps in keeping the
code manageable.

------------------
 Object interface
------------------

Experienced extension module authors will be familiar with the 'C' view
of Python objects, the ubiquitous ``PyObject*``. Most if not all Python
'C' API functions involve ``PyObject*`` as arguments or return type.  A
major complication is the raw reference counting interface presented to
the 'C' programmer. E.g. some API functions return *new references* and
others return *borrowed references*. It is up to the extension module
writer to properly increment and decrement reference counts.  This
quickly becomes cumbersome and error prone, especially if there are
multiple execution paths.

Boost.Python provides a type ``object`` which is essentially a high
level wrapper around ``PyObject*``. ``object`` automates reference
counting as much as possible. It also provides the facilities for
converting arbitrary C++ types to Python objects and vice versa.
This significantly reduces the learning effort for prospective
extension module writers.

Creating an ``object`` from any other type is extremely simple::

    object o(3);

``object`` has templated interactions with all other types, with
automatic to-python conversions. It happens so naturally that it's
easily overlooked.

The ``extract<T>`` class template can be used to convert Python objects
to C++ types::

    double x = extract<double>(o);

All registered user-defined conversions are automatically accessible
through the ``object`` interface. With reference to the ``World`` class
defined in previous examples::

    object as_python_object(World("howdy"));
    World back_as_c_plus_plus_object = extract<World>(as_python_object);

If a C++ type cannot be converted to a Python object an appropriate
exception is thrown at runtime.  Similarly, an appropriate exception is
thrown if a C++ type cannot be extracted from a Python object.
``extract<T>`` provides facilities for avoiding exceptions if this is
desired.

The ``object::attr()`` member function is available for accessing
and manipulating attributes of Python objects. For example::

    object planet(World());
    planet.attr("set")("howdy");

``planet.attr("set")`` returns a callable ``object``.  ``"howdy"`` is
converted to a Python string object which is then passed as an argument
to the ``set`` method.

The ``object`` type is accompanied by a set of derived types
that mirror the Python built-in types such as ``list``, ``dict``,
``tuple``, etc. as much as possible. This enables convenient
manipulation of these high-level types from C++::

    dict d;
    d["some"] = "thing";
    d["lucky_number"] = 13;
    list l = d.keys();

This almost looks and works like regular Python code, but it is pure C++.

=================
 Thinking hybrid
=================

For many applications runtime performance considerations are very
important. This is particularly true for most scientific applications.
Often the performance considerations dictate the use of a compiled
language for the core algorithms. Traditionally the decision to use a
particular programming language is an exclusive one. Because of the
practical and mental difficulties of combining different languages many
systems are written in just one language. This is quite unfortunate
because the price payed for runtime performance is typically a
significant overhead due to static typing. For example, our experience
shows that developing maintainable C++ code is typically much more
time-consuming and requires much more hard-earned working experience
than developing useful Python code. A related observation is that many
compiled packages are augmented by some type of rudimentary scripting
layer. These ad hoc solutions clearly show that many times a compiled
language alone does not get the job done. On the other hand it is also
clear that a pure Python implementation is too slow for numerically
intensive production code.

Boost.Python enables us to *think hybrid* when developing new
applications. Python can be used for rapidly prototyping a
new application. Python's ease of use and the large pool of standard
libraries give us a head start on the way to a first working system. If
necessary, the working procedure can be used to discover the
rate-limiting algorithms. To maximize performance these can be
reimplemented in C++, together with the Boost.Python bindings needed to
tie them back into the existing higher-level procedure.

Of course, this *top-down* approach is less attractive if it is clear
from the start that many algorithms will eventually have to be
implemented in a compiled language. Fortunately Boost.Python also
enables us to pursue a *bottom-up* approach. We have used this approach
very successfully in the development of a toolbox for scientific
applications (scitbx) that we will describe elsewhere. The toolbox
started out mainly as a library of C++ classes with Boost.Python
bindings, and for a while the growth was mainly concentrated on the C++
parts. However, as the toolbox is becoming more complete, more and more
newly added functionality can be implemented in Python. We expect this
trend to continue, as illustrated qualitatively in this figure:

.. image:: python_cpp_mix.png

This figure shows the ratio of newly added C++ and Python code over
time as new algorithms are implemented. We expect this ratio to level
out near 70% Python. The increasing ability to solve new problems
mostly with the easy-to-use Python language rather than a necessarily
more arcane statically typed language is the return on the investment
of learning how to use Boost.Python. The ability to solve some problems
entirely using only Python will enable a larger group of people to
participate in the rapid development of new applications.

=============
 Conclusions
=============

The examples in this paper illustrate that Boost.Python enables
seamless interoperability between C++ and Python. Importantly, this is
achieved without introducing a third syntax: the Python/C++ interface
definitions are written in pure C++. This avoids any problems with
parsing the C++ code to be interfaced to Python, yet the interface
definitions are concise and maintainable. Freed from most of the
development-time penalties of crossing a language boundary, software
designers can take full advantage of two rich and complimentary
language environments. In practice it turns out that some things are
very difficult to do with pure Python/C (e.g. an efficient array
library with an intuitive interface in the compiled language) and
others are very difficult to do with pure C++ (e.g. serialization).
If one has the luxury of being able to design a software system as a
hybrid system from the ground up there are many new ways of avoiding
road blocks in one language or the other.

.. I'm not ready to give up on all of this quite yet

.. Perhaps one day we'll have a language with the simplicity and
   expressive power of Python and the compile-time muscle of C++.  Being
   able to take advantage of all of these facilities without paying the
   mental and development-time penalties of crossing a language barrier
   would bring enormous benefits.  Until then, interoperability tools
   like Boost.Python can help lower the barrier and make the benefits of
   both languages more accessible to both communities.

===========
 Footnotes
===========

.. [#mi] For hard-core new-style class/extension module writers it is
   worth noting that the normal requirement that all extension classes
   with data form a layout-compatible single-inheritance chain is
   lifted for Boost.Python extension classes.  Clearly, either
   ``Base1`` or ``Base2`` has to occupy a different offset in the
   ``Derived`` class instance.  This is possible because the wrapped
   part of BPL extension class instances is never assumed to have a
   fixed offset within the wrapper.

===========
 Citations
===========

.. [VELD1995] T. Veldhuizen, "Expression Templates," C++ Report,
   Vol. 7 No. 5 June 1995, pp. 26-31.
   http://osl.iu.edu/~tveldhui/papers/Expression-Templates/exprtmpl.html
