bpl_mods.txt

C++的一个好库。。。现在很流行

    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
   necessary 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