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Copyright © 2002-2004 Joel de Guzman, David Abrahams
+ Distributed under the Boost Software License, Version 1.0. + (See accompanying file LICENSE_1_0.txt or copy at + + http://www.boost.org/LICENSE_1_0.txt + ) + +
Table of Contents
++The Boost Python Library is a framework for interfacing Python and +C++. It allows you to quickly and seamlessly expose C++ classes +functions and objects to Python, and vice-versa, using no special +tools -- just your C++ compiler. It is designed to wrap C++ interfaces +non-intrusively, so that you should not have to change the C++ code at +all in order to wrap it, making Boost.Python ideal for exposing +3rd-party libraries to Python. The library's use of advanced +metaprogramming techniques simplifies its syntax for users, so that +wrapping code takes on the look of a kind of declarative interface +definition language (IDL).
++Following C/C++ tradition, let's start with the "hello, world". A C++ +Function:
+char const* greet() +{ + return "hello, world"; +}+
+can be exposed to Python by writing a Boost.Python wrapper:
+#include <boost/python.hpp> +using namespace boost::python; + +BOOST_PYTHON_MODULE(hello) +{ + def("greet", greet); +}+
+That's it. We're done. We can now build this as a shared library. The +resulting DLL is now visible to Python. Here's a sample Python session:
+>>> import hello +>>> print hello.greet() +hello, world+
Next stop... Building your Hello World module from start to finish...
Last revised: October 12, 2004 at 03:11:11 GMT |
++ |
 |
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++By now you should know how to use Boost.Python to call your C++ code from +Python. However, sometimes you may need to do the reverse: call Python code +from the C++-side. This requires you to embed the Python interpreter +into your C++ program.
+
+Currently, Boost.Python does not directly support everything you'll need
+when embedding. Therefore you'll need to use the
+Python/C API to fill in
+the gaps. However, Boost.Python already makes embedding a lot easier and,
+in a future version, it may become unnecessary to touch the Python/C API at
+all. So stay tuned... 
+To be able to use embedding in your programs, they have to be linked to +both Boost.Python's and Python's static link library.
++Boost.Python's static link library comes in two variants. Both are located +in Boost's /libs/python/build/bin-stage subdirectory. On Windows, the +variants are called boost_python.lib (for release builds) and +boost_python_debug.lib (for debugging). If you can't find the libraries, +you probably haven't built Boost.Python yet. See and Testing on how to do this.
++Python's static link library can be found in the /libs subdirectory of +your Python directory. On Windows it is called pythonXY.lib where X.Y is +your major Python version number.
++Additionally, Python's /include subdirectory has to be added to your +include path.
++In a Jamfile, all the above boils down to:
+projectroot c:\projects\embedded_program ; # location of the program + + # bring in the rules for python + SEARCH on python.jam = $(BOOST_BUILD_PATH) ; + include python.jam ; + + exe embedded_program # name of the executable + : #sources + embedded_program.cpp + : # requirements + <find-library>boost_python <library-path>c:\boost\libs\python + $(PYTHON_PROPERTIES) + <library-path>$(PYTHON_LIB_PATH) + <find-library>$(PYTHON_EMBEDDED_LIBRARY) ; ++
+Being able to build is nice, but there is nothing to build yet. Embedding +the Python interpreter into one of your C++ programs requires these 4 +steps:
++(Of course, there can be other C++ code between all of these steps.)
+Now that we can embed the interpreter in our programs, lets see how to put it to use...
+As you probably already know, objects in Python are reference-counted. +Naturally, the PyObjects of the Python/C API are also reference-counted. +There is a difference however. While the reference-counting is fully +automatic in Python, the Python/C API requires you to do it +by hand. This is +messy and especially hard to get right in the presence of C++ exceptions. +Fortunately Boost.Python provides the handle and +object class templates to automate the process.
++There are two ways in which a function in the Python/C API can return a +PyObject*: as a borrowed reference or as a new reference. Which of +these a function uses, is listed in that function's documentation. The two +require slightely different approaches to reference-counting but both can +be 'handled' by Boost.Python.
++For a function returning a borrowed reference we'll have to tell the +handle that the PyObject* is borrowed with the aptly named +borrowed function. Two functions +returning borrowed references are PyImport_AddModule and PyModule_GetDict. +The former returns a reference to an already imported module, the latter +retrieves a module's namespace dictionary. Let's use them to retrieve the +namespace of the _main_ module:
+object main_module(( + handle<>(borrowed(PyImport_AddModule("__main__"))))); + +object main_namespace = main_module.attr("__dict__");+
+For a function returning a new reference we can just create a handle +out of the raw PyObject* without wrapping it in a call to borrowed. One +such function that returns a new reference is PyRun_String which we'll +discuss in the next section.
+
+ Handle is a class template, so why haven't we been using any template parameters?
+
+handle has a single template parameter specifying the type of the managed object. This type is PyObject 99% of the time, so the parameter was defaulted to PyObject for convenience. Therefore we can use the shorthand handle<> instead of the longer, but equivalent, handle<PyObject>.
+ |
+To run Python code from C++ there is a family of functions in the API +starting with the PyRun prefix. You can find the full list of these +functions here. They +all work similarly so we will look at only one of them, namely:
+PyObject* PyRun_String(char *str, int start, PyObject *globals, PyObject *locals)+
PyRun_String takes the code to execute as a null-terminated (C-style) +string in its str parameter. The function returns a new reference to a +Python object. Which object is returned depends on the start paramater.
++The start parameter is the start symbol from the Python grammar to use +for interpreting the code. The possible values are:
+| Py_eval_input | +for interpreting isolated expressions | +
|---|---|
| Py_file_input | +for interpreting sequences of statements | +
| Py_single_input | +for interpreting a single statement | +
+When using Py_eval_input, the input string must contain a single expression +and its result is returned. When using Py_file_input, the string can +contain an abitrary number of statements and None is returned. +Py_single_input works in the same way as Py_file_input but only accepts a +single statement.
++Lastly, the globals and locals parameters are Python dictionaries +containing the globals and locals of the context in which to run the code. +For most intents and purposes you can use the namespace dictionary of the +_main_ module for both parameters.
++We have already seen how to get the _main_ module's namespace so let's +run some Python code in it:
+object main_module(( + handle<>(borrowed(PyImport_AddModule("__main__"))))); + +object main_namespace = main_module.attr("__dict__"); + +handle<> ignored((PyRun_String( + + "hello = file('hello.txt', 'w')\n" + "hello.write('Hello world!')\n" + "hello.close()" + + , Py_file_input + , main_namespace.ptr() + , main_namespace.ptr()) +));+
+Because the Python/C API doesn't know anything about objects, we used +the object's ptr member function to retrieve the PyObject*.
++This should create a file called 'hello.txt' in the current directory +containing a phrase that is well-known in programming circles.
+Note that we wrap the return value of PyRun_String in a
+(nameless) handle even though we are not interested in it. If we didn't
+do this, the the returned object would be kept alive unnecessarily. Unless
+you want to be a Dr. Frankenstein, always wrap PyObject*s in handles.
+It's nice that handle manages the reference counting details for us, but +other than that it doesn't do much. Often we'd like to have a more useful +class to manipulate Python objects. But we have already seen such a class +above, and in the previous section: the aptly +named object class and it's derivatives. We've already seen that they +can be constructed from a handle. The following examples should further +illustrate this fact:
+object main_module(( + handle<>(borrowed(PyImport_AddModule("__main__"))))); + +object main_namespace = main_module.attr("__dict__"); + +handle<> ignored((PyRun_String( + + "result = 5 ** 2" + + , Py_file_input + , main_namespace.ptr() + , main_namespace.ptr()) +)); + +int five_squared = extract<int>(main_namespace["result"]);+
+Here we create a dictionary object for the _main_ module's namespace. +Then we assign 5 squared to the result variable and read this variable from +the dictionary. Another way to achieve the same result is to let +PyRun_String return the result directly with Py_eval_input:
+object result((handle<>( + PyRun_String("5 ** 2" + , Py_eval_input + , main_namespace.ptr() + , main_namespace.ptr())) +)); + +int five_squared = extract<int>(result);+
Note that object's member function to return the wrapped
+PyObject* is called ptr instead of get. This makes sense if you
+take into account the different functions that object and handle
+perform.
+If an exception occurs in the execution of some Python code, the PyRun_String function returns a null pointer. Constructing a handle out of this null pointer throws error_already_set, so basically, the Python exception is automatically translated into a C++ exception when using handle:
+try +{ + object result((handle<>(PyRun_String( + "5/0" + , Py_eval_input + , main_namespace.ptr() + , main_namespace.ptr())) + )); + + // execution will never get here: + int five_divided_by_zero = extract<int>(result); +} +catch(error_already_set) +{ + // handle the exception in some way +}+
+The error_already_set exception class doesn't carry any information in itself. To find out more about the Python exception that occurred, you need to use the exception handling functions of the PythonC API in your catch-statement. This can be as simple as calling [@http:/www.python.org/doc/apiexceptionHandling.html#l2h-70 PyErr_Print()] to print the exception's traceback to the console, or comparing the type of the exception with those of the [@http:/www.python.org/doc/api/standardExceptions.html standard exceptions]:
+catch(error_already_set) +{ + if (PyErr_ExceptionMatches(PyExc_ZeroDivisionError)) + { + // handle ZeroDivisionError specially + } + else + { + // print all other errors to stderr + PyErr_Print(); + } +}+
+(To retrieve even more information from the exception you can use some of the other exception handling functions listed here.)
++If you'd rather not have handle throw a C++ exception when it is constructed, you can use the allow_null function in the same way you'd use borrowed:
+handle<> result((allow_null(PyRun_String( + "5/0" + , Py_eval_input + , main_namespace.ptr() + , main_namespace.ptr())))); + +if (!result) + // Python exception occurred +else + // everything went okay, it's safe to use the result ++
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++All C++ exceptions must be caught at the boundary with Python code. This +boundary is the point where C++ meets Python. Boost.Python provides a +default exception handler that translates selected standard exceptions, +then gives up:
+raise RuntimeError, 'unidentifiable C++ Exception'+
+Users may provide custom translation. Here's an example:
+struct PodBayDoorException; +void translator(PodBayDoorException const& x) { + PyErr_SetString(PyExc_UserWarning, "I'm sorry Dave..."); +} +BOOST_PYTHON_MODULE(kubrick) { + register_exception_translator< + PodBayDoorException>(translator); + ...+
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++Now let's expose a C++ class to Python.
++Consider a C++ class/struct that we want to expose to Python:
+struct World +{ + void set(std::string msg) { this->msg = msg; } + std::string greet() { return msg; } + std::string msg; +};+
+We can expose this to Python by writing a corresponding Boost.Python +C++ Wrapper:
+#include <boost/python.hpp> +using namespace boost::python; + +BOOST_PYTHON_MODULE(hello) +{ + class_<World>("World") + .def("greet", &World::greet) + .def("set", &World::set) + ; +}+
+Here, we wrote a C++ class wrapper that exposes the member functions +greet and set. Now, after building our module as a shared library, we +may use our class World in Python. Here's a sample Python session:
+>>> import hello +>>> planet = hello.World() +>>> planet.set('howdy') +>>> planet.greet() +'howdy'+
+Our previous example didn't have any explicit constructors. +Since World is declared as a plain struct, it has an implicit default +constructor. Boost.Python exposes the default constructor by default, +which is why we were able to write
+>>> planet = hello.World()+
+We may wish to wrap a class with a non-default constructor. Let us +build on our previous example:
+struct World +{ + World(std::string msg): msg(msg) {} // added constructor + void set(std::string msg) { this->msg = msg; } + std::string greet() { return msg; } + std::string msg; +};+
+This time World has no default constructor; our previous +wrapping code would fail to compile when the library tried to expose +it. We have to tell class_<World> about the constructor we want to +expose instead.
+#include <boost/python.hpp> +using namespace boost::python; + +BOOST_PYTHON_MODULE(hello) +{ + class_<World>("World", init<std::string>()) + .def("greet", &World::greet) + .def("set", &World::set) + ; +}+
init<std::string>() exposes the constructor taking in a +std::string (in Python, constructors are spelled +""_init_"").
++We can expose additional constructors by passing more init<...>s to +the def() member function. Say for example we have another World +constructor taking in two doubles:
+class_<World>("World", init<std::string>()) + .def(init<double, double>()) + .def("greet", &World::greet) + .def("set", &World::set) +;+
+On the other hand, if we do not wish to expose any constructors at +all, we may use no_init instead:
+class_<Abstract>("Abstract", no_init)+
+This actually adds an _init_ method which always raises a +Python RuntimeError exception.
++Data members may also be exposed to Python so that they can be +accessed as attributes of the corresponding Python class. Each data +member that we wish to be exposed may be regarded as read-only or +read-write. Consider this class Var:
+struct Var +{ + Var(std::string name) : name(name), value() {} + std::string const name; + float value; +};+
+Our C++ Var class and its data members can be exposed to Python:
+class_<Var>("Var", init<std::string>()) + .def_readonly("name", &Var::name) + .def_readwrite("value", &Var::value);+
+Then, in Python, assuming we have placed our Var class inside the namespace +hello as we did before:
+>>> x = hello.Var('pi') +>>> x.value = 3.14 +>>> print x.name, 'is around', x.value +pi is around 3.14+
+Note that name is exposed as read-only while value is exposed +as read-write.
+>>> x.name = 'e' # can't change name + Traceback (most recent call last): + File "<stdin>", line 1, in ? + AttributeError: can't set attribute ++
+In C++, classes with public data members are usually frowned +upon. Well designed classes that take advantage of encapsulation hide +the class' data members. The only way to access the class' data is +through access (getter/setter) functions. Access functions expose class +properties. Here's an example:
+struct Num +{ + Num(); + float get() const; + void set(float value); + ... +};+
+However, in Python attribute access is fine; it doesn't neccessarily break +encapsulation to let users handle attributes directly, because the +attributes can just be a different syntax for a method call. Wrapping our +Num class using Boost.Python:
+class_<Num>("Num") + .add_property("rovalue", &Num::get) + .add_property("value", &Num::get, &Num::set);+
+And at last, in Python:
+>>> x = Num() +>>> x.value = 3.14 +>>> x.value, x.rovalue +(3.14, 3.14) +>>> x.rovalue = 2.17 # error!+
+Take note that the class property rovalue is exposed as read-only +since the rovalue setter member function is not passed in:
+.add_property("rovalue", &Num::get)+
+In the previous examples, we dealt with classes that are not polymorphic. +This is not often the case. Much of the time, we will be wrapping +polymorphic classes and class hierarchies related by inheritance. We will +often have to write Boost.Python wrappers for classes that are derived from +abstract base classes.
++Consider this trivial inheritance structure:
+struct Base { virtual ~Base(); }; +struct Derived : Base {};+
+And a set of C++ functions operating on Base and Derived object +instances:
+void b(Base*); +void d(Derived*); +Base* factory() { return new Derived; }+
+We've seen how we can wrap the base class Base:
+class_<Base>("Base") + /*...*/ + ;+
+Now we can inform Boost.Python of the inheritance relationship between +Derived and its base class Base. Thus:
+class_<Derived, bases<Base> >("Derived") + /*...*/ + ;+
+Doing so, we get some things for free:
++Now, we shall expose the C++ free functions b and d and factory:
+def("b", b); +def("d", d); +def("factory", factory);+
+Note that free function factory is being used to generate new +instances of class Derived. In such cases, we use +return_value_policy<manage_new_object> to instruct Python to adopt +the pointer to Base and hold the instance in a new Python Base +object until the the Python object is destroyed. We shall see more of +Boost.Python call policies later.
+// Tell Python to take ownership of factory's result +def("factory", factory, + return_value_policy<manage_new_object>());+
+In this section, we shall learn how to make functions behave +polymorphically through virtual functions. Continuing our example, let us +add a virtual function to our Base class:
+struct Base +{ + virtual int f() = 0; +};+
+Since f is a pure virtual function, Base is now an abstract +class. Given an instance of our class, the free function call_f +calls some implementation of this virtual function in a concrete +derived class:
+int call_f(Base& b) { return b.f(); }+
+To allow this function to be implemented in a Python derived class, we +need to create a class wrapper:
+struct BaseWrap : Base +{ + BaseWrap(PyObject* self_) + : self(self_) {} + int f() { return call_method<int>(self, "f"); } + PyObject* self; +}; + + +struct BaseWrap : Base +{ + BaseWrap(PyObject* self_) + : self(self_) {} + BaseWrap(PyObject* self_, Base const& copy) + : Base(copy), self(self_) {} + int f() { return call_method<int>(self, "f"); } + int default_f() { return Base::f(); } // <<=== ***ADDED*** + PyObject* self; +};+
|
+member function and methods
+
+ Python, like
+many object oriented languages uses the term methods. Methods
+correspond roughly to C++'s member functions
+ |
+Our class wrapper BaseWrap is derived from Base. Its overridden +virtual member function f in effect calls the corresponding method +of the Python object self, which is a pointer back to the Python +Base object holding our BaseWrap instance.
+|
+Why do we need BaseWrap?
+
+ |
You may ask, "Why do we need the BaseWrap derived class? This could +have been designed so that everything gets done right inside of +Base."
+ + ++One of the goals of Boost.Python is to be minimally intrusive on an +existing C++ design. In principle, it should be possible to expose the +interface for a 3rd party library without changing it. To unintrusively +hook into the virtual functions so that a Python override may be called, we +must use a derived class.
+ + ++Note however that you don't need to do this to get methods overridden +in Python to behave virtually when called fromPython. The only +time you need to do the BaseWrap dance is when you have a virtual +function that's going to be overridden in Python and called +polymorphically fromC++.]
++Wrapping Base and the free function call_f:
+class_<Base, BaseWrap, boost::noncopyable>("Base", no_init) + ; +def("call_f", call_f);+
+Notice that we parameterized the class_ template with BaseWrap as the +second parameter. What is noncopyable? Without it, the library will try +to create code for converting Base return values of wrapped functions to +Python. To do that, it needs Base's copy constructor... which isn't +available, since Base is an abstract class.
++In Python, let us try to instantiate our Base class:
+>>> base = Base() +RuntimeError: This class cannot be instantiated from Python+
+Why is it an error? Base is an abstract class. As such it is advisable +to define the Python wrapper with no_init as we have done above. Doing +so will disallow abstract base classes such as Base to be instantiated.
++Continuing, we can derive from our base class Base in Python and override +the virtual function in Python. Before we can do that, we have to set up +our class_ wrapper as:
+class_<Base, BaseWrap, boost::noncopyable>("Base") + ;+
+Otherwise, we have to suppress the Base class' no_init by adding an +_init_() method to all our derived classes. no_init actually adds +an _init_ method that raises a Python RuntimeError exception.
+>>> class Derived(Base): +... def f(self): +... return 42 +...+
+Cool eh? A Python class deriving from a C++ class!
++Let's now make an instance of our Python class Derived:
+>>> derived = Derived()+
+Calling derived.f():
+>>> derived.f() +42+
+Will yield the expected result. Finally, calling calling the free function +call_f with derived as argument:
+>>> call_f(derived) +42+
+Will also yield the expected result.
++Here's what's happening:
++Recall that in the previous section, we +wrapped a class with a pure virtual function that we then implemented in +C++ or Python classes derived from it. Our base class:
+struct Base +{ + virtual int f() = 0; +};+
+had a pure virtual function f. If, however, its member function f was +not declared as pure virtual:
+struct Base +{ + virtual int f() { return 0; } +};+
+and instead had a default implementation that returns 0, as shown above, +we need to add a forwarding function that calls the Base default virtual +function f implementation:
+struct BaseWrap : Base +{ + BaseWrap(PyObject* self_) + : self(self_) {} + int f() { return call_method<int>(self, "f"); } + int default_f() { return Base::f(); } // <<=== ***ADDED*** + PyObject* self; +};+
+Then, Boost.Python needs to keep track of 1) the dispatch function f and +2) the forwarding function to its default implementation default_f. +There's a special def function for this purpose. Here's how it is +applied to our example above:
+class_<Base, BaseWrap, BaseWrap, boost::noncopyable>("Base") + .def("f", &Base::f, &BaseWrap::default_f)+
+Note that we are allowing Base objects to be instantiated this time, +unlike before where we specifically defined the class_<Base> with +no_init.
++In Python, the results would be as expected:
+>>> base = Base() +>>> class Derived(Base): +... def f(self): +... return 42 +... +>>> derived = Derived()+
+Calling base.f():
+>>> base.f() +0+
+Calling derived.f():
+>>> derived.f() +42+
+Calling call_f, passing in a base object:
+>>> call_f(base) +0+
+Calling call_f, passing in a derived object:
+>>> call_f(derived) +42+
+C is well known for the abundance of operators. C++ extends this to the +extremes by allowing operator overloading. Boost.Python takes advantage of +this and makes it easy to wrap C++ operator-powered classes.
++Consider a file position class FilePos and a set of operators that take +on FilePos instances:
+class FilePos { /*...*/ }; + +FilePos operator+(FilePos, int); +FilePos operator+(int, FilePos); +int operator-(FilePos, FilePos); +FilePos operator-(FilePos, int); +FilePos& operator+=(FilePos&, int); +FilePos& operator-=(FilePos&, int); +bool operator<(FilePos, FilePos);+
+The class and the various operators can be mapped to Python rather easily +and intuitively:
+class_<FilePos>("FilePos") + .def(self + int()) // __add__ + .def(int() + self) // __radd__ + .def(self - self) // __sub__ + .def(self - int()) // __sub__ + .def(self += int()) // __iadd__ + .def(self -= other<int>()) + .def(self < self); // __lt__ ++
+The code snippet above is very clear and needs almost no explanation at +all. It is virtually the same as the operators' signatures. Just take +note that self refers to FilePos object. Also, not every class T that +you might need to interact with in an operator expression is (cheaply) +default-constructible. You can use other<T>() in place of an actual +T instance when writing "self expressions".
++Python has a few more Special Methods. Boost.Python supports all of the +standard special method names supported by real Python class instances. A +similar set of intuitive interfaces can also be used to wrap C++ functions +that correspond to these Python special functions. Example:
+class Rational +{ operator double() const; }; + +Rational pow(Rational, Rational); +Rational abs(Rational); +ostream& operator<<(ostream&,Rational); + +class_<Rational>() + .def(float_(self)) // __float__ + .def(pow(self, other<Rational>)) // __pow__ + .def(abs(self)) // __abs__ + .def(str(self)) // __str__ + ;+
+Need we say more?
+|
+ What is the business of operator<<.def(str(self))?
+Well, the method str requires the operator<< to do its work (i.e.
+operator<< is used by the method defined by def(str(self)). |
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++In this chapter, we'll look at Boost.Python powered functions in closer +detail. We shall see some facilities to make exposing C++ functions to +Python safe from potential pifalls such as dangling pointers and +references. We shall also see facilities that will make it even easier for +us to expose C++ functions that take advantage of C++ features such as +overloading and default arguments.
+Read on...
+But before you do, you might want to fire up Python 2.2 or later and type +>>> import this.
+ >>> import this
+ The Zen of Python, by Tim Peters
+ Beautiful is better than ugly.
+ Explicit is better than implicit.
+ Simple is better than complex.
+ Complex is better than complicated.
+ Flat is better than nested.
+ Sparse is better than dense.
+ Readability counts.
+ Special cases aren't special enough to break the rules.
+ Although practicality beats purity.
+ Errors should never pass silently.
+ Unless explicitly silenced.
+ In the face of ambiguity, refuse the temptation to guess.
+ There should be one-- and preferably only one --obvious way to do it
+ Although that way may not be obvious at first unless you're Dutch.
+ Now is better than never.
+ Although never is often better than right now.
+ If the implementation is hard to explain, it's a bad idea.
+ If the implementation is easy to explain, it may be a good idea.
+ Namespaces are one honking great idea -- let's do more of those!
+
++In C++, we often deal with arguments and return types such as pointers +and references. Such primitive types are rather, ummmm, low level and +they really don't tell us much. At the very least, we don't know the +owner of the pointer or the referenced object. No wonder languages +such as Java and Python never deal with such low level entities. In +C++, it's usually considered a good practice to use smart pointers +which exactly describe ownership semantics. Still, even good C++ +interfaces use raw references and pointers sometimes, so Boost.Python +must deal with them. To do this, it may need your help. Consider the +following C++ function:
+X& f(Y& y, Z* z);+
+How should the library wrap this function? A naive approach builds a +Python X object around result reference. This strategy might or might +not work out. Here's an example where it didn't
+>>> x = f(y, z) # x refers to some C++ X +>>> del y +>>> x.some_method() # CRASH!+
+What's the problem?
++Well, what if f() was implemented as shown below:
+X& f(Y& y, Z* z) +{ + y.z = z; + return y.x; +}+
+The problem is that the lifetime of result X& is tied to the lifetime +of y, because the f() returns a reference to a member of the y +object. This idiom is is not uncommon and perfectly acceptable in the +context of C++. However, Python users should not be able to crash the +system just by using our C++ interface. In this case deleting y will +invalidate the reference to X. We have a dangling reference.
++Here's what's happening:
++We could copy result into a new object:
+>>> f(y, z).set(42) # Result disappears +>>> y.x.get() # No crash, but still bad +3.14+
+This is not really our intent of our C++ interface. We've broken our +promise that the Python interface should reflect the C++ interface as +closely as possible.
++Our problems do not end there. Suppose Y is implemented as follows:
+struct Y +{ + X x; Z* z; + int z_value() { return z->value(); } +};+
+Notice that the data member z is held by class Y using a raw +pointer. Now we have a potential dangling pointer problem inside Y:
+>>> x = f(y, z) # y refers to z +>>> del z # Kill the z object +>>> y.z_value() # CRASH!+
+For reference, here's the implementation of f again:
+X& f(Y& y, Z* z) +{ + y.z = z; + return y.x; +}+
+Here's what's happening:
++Call Policies may be used in situations such as the example detailed above. +In our example, return_internal_reference and with_custodian_and_ward +are our friends:
+def("f", f, + return_internal_reference<1, + with_custodian_and_ward<1, 2> >());+
+What are the 1 and 2 parameters, you ask?
+return_internal_reference<1+
+Informs Boost.Python that the first argument, in our case Y& y, is the +owner of the returned reference: X&. The "1" simply specifies the +first argument. In short: "return an internal reference X& owned by the +1st argument Y& y".
+with_custodian_and_ward<1, 2>+
+Informs Boost.Python that the lifetime of the argument indicated by ward +(i.e. the 2nd argument: Z* z) is dependent on the lifetime of the +argument indicated by custodian (i.e. the 1st argument: Y& y).
++It is also important to note that we have defined two policies above. Two +or more policies can be composed by chaining. Here's the general syntax:
+policy1<args..., + policy2<args..., + policy3<args...> > >+
+Here is the list of predefined call policies. A complete reference detailing +these can be found here.
+|
+Remember the Zen, Luke:
+
+
+"Explicit is better than implicit"
+
+"In the face of ambiguity, refuse the temptation to guess"
+ |
+The following illustrates a scheme for manually wrapping an overloaded +member functions. Of course, the same technique can be applied to wrapping +overloaded non-member functions.
++We have here our C++ class:
+struct X +{ + bool f(int a) + { + return true; + } + + bool f(int a, double b) + { + return true; + } + + bool f(int a, double b, char c) + { + return true; + } + + int f(int a, int b, int c) + { + return a + b + c; + }; +};+
+Class X has 4 overloaded functions. We shall start by introducing some +member function pointer variables:
+bool (X::*fx1)(int) = &X::f; +bool (X::*fx2)(int, double) = &X::f; +bool (X::*fx3)(int, double, char)= &X::f; +int (X::*fx4)(int, int, int) = &X::f;+
+With these in hand, we can proceed to define and wrap this for Python:
+.def("f", fx1) +.def("f", fx2) +.def("f", fx3) +.def("f", fx4)+
+Boost.Python wraps (member) function pointers. Unfortunately, C++ function +pointers carry no default argument info. Take a function f with default +arguments:
+int f(int, double = 3.14, char const* = "hello");+
+But the type of a pointer to the function f has no information +about its default arguments:
+int(*g)(int,double,char const*) = f; // defaults lost! ++
+When we pass this function pointer to the def function, there is no way +to retrieve the default arguments:
+def("f", f); // defaults lost! ++
+Because of this, when wrapping C++ code, we had to resort to manual +wrapping as outlined in the previous section, or +writing thin wrappers:
+// write "thin wrappers" +int f1(int x) { f(x); } +int f2(int x, double y) { f(x,y); } + +/*...*/ + + // in module init + def("f", f); // all arguments + def("f", f2); // two arguments + def("f", f1); // one argument ++
+When you want to wrap functions (or member functions) that either:
++Boost.Python now has a way to make it easier. For instance, given a function:
+int foo(int a, char b = 1, unsigned c = 2, double d = 3) +{ + /*...*/ +}+
+The macro invocation:
+BOOST_PYTHON_FUNCTION_OVERLOADS(foo_overloads, foo, 1, 4)+
+will automatically create the thin wrappers for us. This macro will create +a class foo_overloads that can be passed on to def(...). The third +and fourth macro argument are the minimum arguments and maximum arguments, +respectively. In our foo function the minimum number of arguments is 1 +and the maximum number of arguments is 4. The def(...) function will +automatically add all the foo variants for us:
+def("foo", foo, foo_overloads());+
+Objects here, objects there, objects here there everywhere. More frequently +than anything else, we need to expose member functions of our classes to +Python. Then again, we have the same inconveniences as before when default +arguments or overloads with a common sequence of initial arguments come +into play. Another macro is provided to make this a breeze.
++Like BOOST_PYTHON_FUNCTION_OVERLOADS, +BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS may be used to automatically create +the thin wrappers for wrapping member functions. Let's have an example:
+struct george +{ + void + wack_em(int a, int b = 0, char c = 'x') + { + /*...*/ + } +};+
+The macro invocation:
+BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(george_overloads, wack_em, 1, 3)+
+will generate a set of thin wrappers for george's wack_em member function +accepting a minimum of 1 and a maximum of 3 arguments (i.e. the third and +fourth macro argument). The thin wrappers are all enclosed in a class named +george_overloads that can then be used as an argument to def(...):
+.def("wack_em", &george::wack_em, george_overloads());+
+See the overloads reference +for details.
++A similar facility is provided for class constructors, again, with +default arguments or a sequence of overloads. Remember init<...>? For example, +given a class X with a constructor:
+struct X +{ + X(int a, char b = 'D', std::string c = "constructor", double d = 0.0); + /*...*/ +}+
+You can easily add this constructor to Boost.Python in one shot:
+.def(init<int, optional<char, std::string, double> >())+
+Notice the use of init<...> and optional<...> to signify the default +(optional arguments).
++It was mentioned in passing in the previous section that +BOOST_PYTHON_FUNCTION_OVERLOADS and BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS +can also be used for overloaded functions and member functions with a +common sequence of initial arguments. Here is an example:
+void foo() +{ + /*...*/ +} + +void foo(bool a) +{ + /*...*/ +} + +void foo(bool a, int b) +{ + /*...*/ +} + +void foo(bool a, int b, char c) +{ + /*...*/ +}+
+Like in the previous section, we can generate thin wrappers for these +overloaded functions in one-shot:
+BOOST_PYTHON_FUNCTION_OVERLOADS(foo_overloads, foo, 0, 3)+
+Then...
+.def("foo", foo, foo_overloads());+
+Notice though that we have a situation now where we have a minimum of zero +(0) arguments and a maximum of 3 arguments.
++It is important to emphasize however that the overloaded functions must +have a common sequence of initial arguments. Otherwise, our scheme above +will not work. If this is not the case, we have to wrap our functions +manually.
++Actually, we can mix and match manual wrapping of overloaded functions and +automatic wrapping through BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS and +its sister, BOOST_PYTHON_FUNCTION_OVERLOADS. Following up on our example +presented in the section on overloading, since the +first 4 overload functins have a common sequence of initial arguments, we +can use BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS to automatically wrap the +first three of the defs and manually wrap just the last. Here's +how we'll do this:
+BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(xf_overloads, f, 1, 4)+
+Create a member function pointers as above for both X::f overloads:
+bool (X::*fx1)(int, double, char) = &X::f; +int (X::*fx2)(int, int, int) = &X::f;+
+Then...
+.def("f", fx1, xf_overloads()); +.def("f", fx2)+
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++Now the first thing you'd want to do is to build the Hello World module and +try it for yourself in Python. In this section, we shall outline the steps +necessary to achieve that. We shall use the build tool that comes bundled +with every boost distribution: bjam.
+|
+Building without bjam
+
+
+ Besides bjam, there are of course other ways to get your module built.
+ What's written here should not be taken as "the one and only way".
+ There are of course other build tools apart from bjam.
+
+
+ Take note however that the preferred build tool for Boost.Python is bjam.
+ There are so many ways to set up the build incorrectly. Experience shows
+ that 90% of the "I can't build Boost.Python" problems come from people
+ who had to use a different tool.
+ |
+We shall skip over the details. Our objective will be to simply create the +hello world module and run it in Python. For a complete reference to +building Boost.Python, check out: building.html. +After this brief bjam tutorial, we should have built two DLLs:
++if you are on Windows, and
++if you are on Unix.
++The tutorial example can be found in the directory: +libs/python/example/tutorial. There, you can find:
++The hello.cpp file is our C++ hello world example. The Jamfile is a +minimalist bjam script that builds the DLLs for us.
++Before anything else, you should have the bjam executable in your boost +directory or somewhere in your path such that bjam can be executed in +the command line. Pre-built Boost.Jam executables are available for most +platforms. The complete list of Bjam executables can be found +here.
+
+Here is our minimalist Jamfile:
+subproject libs/python/example/tutorial ; + + SEARCH on python.jam = $(BOOST_BUILD_PATH) ; + include python.jam ; + + extension hello # Declare a Python extension called hello + : hello.cpp # source + <dll>../../build/boost_python # dependencies + ; ++
+First, we need to specify our location in the boost project hierarchy. +It so happens that the tutorial example is located in /libs/python/example/tutorial. +Thus:
+subproject libs/python/example/tutorial ; ++
+Then we will include the definitions needed by Python modules:
+SEARCH on python.jam = $(BOOST_BUILD_PATH) ; + include python.jam ; ++
+Finally we declare our hello extension:
+extension hello # Declare a Python extension called hello + : hello.cpp # source + <dll>../../build/boost_python # dependencies + ; ++
bjam is run using your operating system's command line interpreter.
+Start it up.
+Make sure that the environment is set so that we can invoke the C++ +compiler. With MSVC, that would mean running the Vcvars32.bat batch +file. For instance:
+C:\Program Files\Microsoft Visual Studio\VC98\bin\Vcvars32.bat+
+Some environment variables will have to be setup for proper building of our +Python modules. Example:
+set PYTHON_ROOT=c:/dev/tools/python +set PYTHON_VERSION=2.2+
+The above assumes that the Python installation is in c:/dev/tools/python +and that we are using Python version 2.2. You'll have to tweak this path +appropriately.
+
+ Be sure not to include a third number, e.g. not "2.2.1",
+even if that's the version you have. |
+Now we are ready... Be sure to cd to libs/python/example/tutorial +where the tutorial "hello.cpp" and the "Jamfile" is situated.
++Finally:
+bjam -sTOOLS=msvc+
+We are again assuming that we are using Microsoft Visual C++ version 6. If +not, then you will have to specify the appropriate tool. See +Building Boost Libraries for +further details.
++It should be building now:
+cd C:\dev\boost\libs\python\example\tutorial + bjam -sTOOLS=msvc + ...patience... + ...found 1703 targets... + ...updating 40 targets... ++
+And so on... Finally:
+vc-C++ ........\libs\python\example\tutorial\bin\hello.pyd\msvc\debug\ + runtime-link-dynamic\hello.obj + hello.cpp + vc-Link ........\libs\python\example\tutorial\bin\hello.pyd\msvc\debug\ + runtime-link-dynamic\hello.pyd ........\libs\python\example\tutorial\bin\ + hello.pyd\msvc\debug\runtime-link-dynamic\hello.lib + Creating library ........\libs\python\example\tutorial\bin\hello.pyd\ + msvc\debug\runtime-link-dynamic\hello.lib and object ........\libs\python\ + example\tutorial\bin\hello.pyd\msvc\debug\runtime-link-dynamic\hello.exp + ...updated 40 targets... ++
+If all is well, you should now have:
++if you are on Windows, and
++if you are on Unix.
+boost_python.dll can be found somewhere in libs\python\build\bin +while hello.pyd can be found somewhere in +libs\python\example\tutorial\bin. After a successful build, you can just +link in these DLLs with the Python interpreter. In Windows for example, you +can simply put these libraries inside the directory where the Python +executable is.
++You may now fire up Python and run our hello module:
+>>> import hello +>>> print hello.greet() +hello, world+
There you go... Have fun!
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++In C++, and STL in particular, we see iterators everywhere. Python also has +iterators, but these are two very different beasts.
+C++ iterators:
+Python Iterators:
++The typical Python iteration protocol: for y in x... is as follows:
+iter = x.__iter__() # get iterator +try: + while 1: + y = iter.next() # get each item + ... # process y +except StopIteration: pass # iterator exhausted+
+Boost.Python provides some mechanisms to make C++ iterators play along +nicely as Python iterators. What we need to do is to produce +appropriate _iter_ function from C++ iterators that is compatible +with the Python iteration protocol. For example:
+object get_iterator = iterator<vector<int> >(); +object iter = get_iterator(v); +object first = iter.next();+
+Or for use in class_<>:
+.def("__iter__", iterator<vector<int> >())+
range
++We can create a Python savvy iterator using the range function:
++Here, start/finish may be one of:
+iterator
++Given a container T, iterator is a shortcut that simply calls range +with &T::begin, &T::end.
++Let's put this into action... Here's an example from some hypothetical +bogon Particle accelerator code:
+f = Field() +for x in f.pions: + smash(x) +for y in f.bogons: + count(y)+
+Now, our C++ Wrapper:
+class_<F>("Field") + .property("pions", range(&F::p_begin, &F::p_end)) + .property("bogons", range(&F::b_begin, &F::b_end));+
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++Python is dynamically typed, unlike C++ which is statically typed. Python +variables may hold an integer, a float, list, dict, tuple, str, long etc., +among other things. In the viewpoint of Boost.Python and C++, these +Pythonic variables are just instances of class object. We shall see in +this chapter how to deal with Python objects.
++As mentioned, one of the goals of Boost.Python is to provide a +bidirectional mapping between C++ and Python while maintaining the Python +feel. Boost.Python C++ objects are as close as possible to Python. This +should minimize the learning curve significantly.
+
+Class object wraps PyObject*. All the intricacies of dealing with +PyObjects such as managing reference counting are handled by the +object class. C++ object interoperability is seamless. Boost.Python C++ +objects can in fact be explicitly constructed from any C++ object.
++To illustrate, this Python code snippet:
+def f(x, y): + if (y == 'foo'): + x[3:7] = 'bar' + else: + x.items += y(3, x) + return x + +def getfunc(): + return f;+
+Can be rewritten in C++ using Boost.Python facilities this way:
+object f(object x, object y) { + if (y == "foo") + x.slice(3,7) = "bar"; + else + x.attr("items") += y(3, x); + return x; +} +object getfunc() { + return object(f); +}+
+Apart from cosmetic differences due to the fact that we are writing the +code in C++, the look and feel should be immediately apparent to the Python +coder.
++Boost.Python comes with a set of derived object types corresponding to +that of Python's:
++These derived object types act like real Python types. For instance:
+str(1) ==> "1"+
+Wherever appropriate, a particular derived object has corresponding +Python type's methods. For instance, dict has a keys() method:
+d.keys()+
make_tuple is provided for declaring tuple literals. Example:
+make_tuple(123, 'D', "Hello, World", 0.0);+
+In C++, when Boost.Python objects are used as arguments to functions, +subtype matching is required. For example, when a function f, as +declared below, is wrapped, it will only accept instances of Python's +str type and subtypes.
+void f(str name) +{ + object n2 = name.attr("upper")(); // NAME = name.upper() + str NAME = name.upper(); // better + object msg = "%s is bigger than %s" % make_tuple(NAME,name); +}+
+In finer detail:
+str NAME = name.upper();+
+Illustrates that we provide versions of the str type's methods as C++ +member functions.
+object msg = "%s is bigger than %s" % make_tuple(NAME,name);+
+Demonstrates that you can write the C++ equivalent of "format" % x,y,z +in Python, which is useful since there's no easy way to do that in std C++.
+
Beware the common pitfall of forgetting that the constructors
+of most of Python's mutable types make copies, just as in Python.
+Python:
+>>> d = dict(x.__dict__) # copies x.__dict__ +>>> d['whatever'] # modifies the copy+
+C++:
+dict d(x.attr("__dict__")); # copies x.__dict__ +d['whatever'] = 3; # modifies the copy+
+Due to the dynamic nature of Boost.Python objects, any class_<T> may +also be one of these types! The following code snippet wraps the class +(type) object.
++We can use this to create wrapped instances. Example:
+object vec345 = ( + class_<Vec2>("Vec2", init<double, double>()) + .def_readonly("length", &Point::length) + .def_readonly("angle", &Point::angle) + )(3.0, 4.0); + +assert(vec345.attr("length") == 5.0);+
+At some point, we will need to get C++ values out of object instances. This +can be achieved with the extract<T> function. Consider the following:
+double x = o.attr("length"); // compile error ++
+In the code above, we got a compiler error because Boost.Python +object can't be implicitly converted to doubles. Instead, what +we wanted to do above can be achieved by writing:
+double l = extract<double>(o.attr("length")); +Vec2& v = extract<Vec2&>(o); +assert(l == v.length());+
+The first line attempts to extract the "length" attribute of the +Boost.Python objecto. The second line attempts to extract the +Vec2 object from held by the Boost.Python objecto.
++Take note that we said "attempt to" above. What if the Boost.Python +objecto does not really hold a Vec2 type? This is certainly +a possibility considering the dynamic nature of Python objects. To +be on the safe side, if the C++ type can't be extracted, an +appropriate exception is thrown. To avoid an exception, we need to +test for extractibility:
+extract<Vec2&> x(o); +if (x.check()) { + Vec2& v = x(); ...+
The astute reader might have noticed that the extract<T>
+facility in fact solves the mutable copying problem:
dict d = extract<dict>(x.attr("__dict__")); +d['whatever'] = 3; # modifies x.__dict__ !+
+Boost.Python has a nifty facility to capture and wrap C++ enums. While +Python has no enum type, we'll often want to expose our C++ enums to +Python as an int. Boost.Python's enum facility makes this easy while +taking care of the proper conversions from Python's dynamic typing to C++'s +strong static typing (in C++, ints cannot be implicitly converted to +enums). To illustrate, given a C++ enum:
+enum choice { red, blue };+
+the construct:
+enum_<choice>("choice") + .value("red", red) + .value("blue", blue) + ;+
+can be used to expose to Python. The new enum type is created in the +current scope(), which is usually the current module. The snippet above +creates a Python class derived from Python's int type which is +associated with the C++ type passed as its first parameter.
+|
+what is a scope?
+
+ The scope is a class that has an
+associated global Python object which controls the Python namespace in
+which new extension classes and wrapped functions will be defined as
+attributes. Details can be found here. |
+You can access those values in Python as
+>>> my_module.choice.red +my_module.choice.red+
+where my_module is the module where the enum is declared. You can also +create a new scope around a class:
+scope in_X = class_<X>("X") + .def( ... ) + .def( ... ) + ; + +// Expose X::nested as X.nested +enum_<X::nested>("nested") + .value("red", red) + .value("blue", blue) + ;+
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+ |
+Home | +Libraries | +People | +FAQ | +More | +
++Here are presented some useful techniques that you can use while wrapping code with Boost.Python.
++A Python package is a collection of modules that provide to the user a certain +functionality. If you're not familiar on how to create packages, a good +introduction to them is provided in the +Python Tutorial.
++But we are wrapping C++ code, using Boost.Python. How can we provide a nice +package interface to our users? To better explain some concepts, let's work +with an example.
++We have a C++ library that works with sounds: reading and writing various +formats, applying filters to the sound data, etc. It is named (conveniently) +sounds. Our library already has a neat C++ namespace hierarchy, like so:
+sounds::core +sounds::io +sounds::filters+
+We would like to present this same hierarchy to the Python user, allowing him +to write code like this:
+import sounds.filters +sounds.filters.echo(...) # echo is a C++ function+
+The first step is to write the wrapping code. We have to export each module +separately with Boost.Python, like this:
+/* file core.cpp */ +BOOST_PYTHON_MODULE(core) +{ + /* export everything in the sounds::core namespace */ + ... +} + +/* file io.cpp */ +BOOST_PYTHON_MODULE(io) +{ + /* export everything in the sounds::io namespace */ + ... +} + +/* file filters.cpp */ +BOOST_PYTHON_MODULE(filters) +{ + /* export everything in the sounds::filters namespace */ + ... +}+
+Compiling these files will generate the following Python extensions: +core.pyd, io.pyd and filters.pyd.
+|
+ The extension .pyd is used for python extension modules, which
+are just shared libraries. Using the default for your system, like .so for
+Unix and .dll for Windows, works just as well. |
+Now, we create this directory structure for our Python package:
+ sounds/
+ _init_.py
+ core.pyd
+ filters.pyd
+ io.pyd
+
++The file _init_.py is what tells Python that the directory sounds/ is +actually a Python package. It can be a empty file, but can also perform some +magic, that will be shown later.
++Now our package is ready. All the user has to do is put sounds into his +PYTHONPATH and fire up the interpreter:
+>>> import sounds.io +>>> import sounds.filters +>>> sound = sounds.io.open('file.mp3') +>>> new_sound = sounds.filters.echo(sound, 1.0)+
+Nice heh?
++This is the simplest way to create hierarchies of packages, but it is not very +flexible. What if we want to add a pure Python function to the filters +package, for instance, one that applies 3 filters in a sound object at once? +Sure, you can do this in C++ and export it, but why not do so in Python? You +don't have to recompile the extension modules, plus it will be easier to write +it.
++If we want this flexibility, we will have to complicate our package hierarchy a +little. First, we will have to change the name of the extension modules:
+/* file core.cpp */ +BOOST_PYTHON_MODULE(_core) +{ + ... + /* export everything in the sounds::core namespace */ +}+
+Note that we added an underscore to the module name. The filename will have to +be changed to _core.pyd as well, and we do the same to the other extension modules. +Now, we change our package hierarchy like so:
+sounds/ + _init_.py + core/ + _init_.py + _core.pyd + filters/ + _init_.py + _filters.pyd + io/ + _init_.py + _io.pyd ++
+Note that we created a directory for each extension module, and added a +_init_.py to each one. But if we leave it that way, the user will have to +access the functions in the core module with this syntax:
+>>> import sounds.core._core +>>> sounds.core._core.foo(...)+
+which is not what we want. But here enters the _init_.py magic: everything +that is brought to the _init_.py namespace can be accessed directly by the +user. So, all we have to do is bring the entire namespace from _core.pyd +to core/_init.py]. So add this line of code to [^sounds/core/init_.py:
+from _core import *+
+We do the same for the other packages. Now the user accesses the functions and +classes in the extension modules like before:
+>>> import sounds.filters +>>> sounds.filters.echo(...)+
+with the additional benefit that we can easily add pure Python functions to +any module, in a way that the user can't tell the difference between a C++ +function and a Python function. Let's add a pure Python function, +echo_noise, to the filters package. This function applies both the +echo and noise filters in sequence in the given sound object. We +create a file named sounds/filters/echo_noise.py and code our function:
+import _filters +def echo_noise(sound): + s = _filters.echo(sound) + s = _filters.noise(sound) + return s+
+Next, we add this line to soundsfilters_init_.py:
+from echo_noise import echo_noise+
+And that's it. The user now accesses this function like any other function +from the filters package:
+>>> import sounds.filters +>>> sounds.filters.echo_noise(...)+
+Thanks to Python's flexibility, you can easily add new methods to a class, +even after it was already created:
+>>> class C(object): pass +>>> +>>> # a regular function +>>> def C_str(self): return 'A C instance!' +>>> +>>> # now we turn it in a member function +>>> C.__str__ = C_str +>>> +>>> c = C() +>>> print c +A C instance! +>>> C_str(c) +A C instance!+
+Yes, Python rox.
+We can do the same with classes that were wrapped with Boost.Python. Suppose +we have a class point in C++:
+class point {...}; + +BOOST_PYTHON_MODULE(_geom) +{ + class_<point>("point")...; +}+
+If we are using the technique from the previous session, +Creating Packages, we can code directly into geom/_init_.py:
+from _geom import * + +# a regular function +def point_str(self): + return str((self.x, self.y)) + +# now we turn it into a member function +point.__str__ = point_str+
All point instances created from C++ will also have this member function! +This technique has several advantages:
++You can even add a little syntactic sugar with the use of metaclasses. Let's +create a special metaclass that "injects" methods in other classes.
++# The one Boost.Python uses for all wrapped classes. +# You can use here any class exported by Boost instead of "point" +BoostPythonMetaclass = point.__class__ + +class injector(object): + class __metaclass__(BoostPythonMetaclass): + def __init__(self, name, bases, dict): + for b in bases: + if type(b) not in (self, type): + for k,v in dict.items(): + setattr(b,k,v) + return type.__init__(self, name, bases, dict) + +# inject some methods in the point foo +class more_point(injector, point): + def __repr__(self): + return 'Point(x=%s, y=%s)' % (self.x, self.y) + def foo(self): + print 'foo!'+
+Now let's see how it got:
+>>> print point() +Point(x=10, y=10) +>>> point().foo() +foo!+
+Another useful idea is to replace constructors with factory functions:
+_point = point + +def point(x=0, y=0): + return _point(x, y)+
+In this simple case there is not much gained, but for constructurs with +many overloads and/or arguments this is often a great simplification, again +with virtually zero memory footprint and zero compile-time overhead for +the keyword support.
++If you have ever exported a lot of classes, you know that it takes quite a good +time to compile the Boost.Python wrappers. Plus the memory consumption can +easily become too high. If this is causing you problems, you can split the +class_ definitions in multiple files:
+/* file point.cpp */ +#include <point.h> +#include <boost/python.hpp> + +void export_point() +{ + class_<point>("point")...; +} + +/* file triangle.cpp */ +#include <triangle.h> +#include <boost/python.hpp> + +void export_triangle() +{ + class_<triangle>("triangle")...; +}+
+Now you create a file main.cpp, which contains the BOOST_PYTHON_MODULE +macro, and call the various export functions inside it.
+void export_point(); +void export_triangle(); + +BOOST_PYTHON_MODULE(_geom) +{ + export_point(); + export_triangle(); +}+
+Compiling and linking together all this files produces the same result as the +usual approach:
+#include <boost/python.hpp> +#include <point.h> +#include <triangle.h> + +BOOST_PYTHON_MODULE(_geom) +{ + class_<point>("point")...; + class_<triangle>("triangle")...; +}+
+but the memory is kept under control.
++This method is recommended too if you are developing the C++ library and +exporting it to Python at the same time: changes in a class will only demand +the compilation of a single cpp, instead of the entire wrapper code.
+|
+ If you're exporting your classes with Pyste,
+take a look at the --multiple option, that generates the wrappers in
+various files as demonstrated here. |
|
+ This method is useful too if you are getting the error message
+"fatal error C1204:Compiler limit:internal structure overflow" when compiling
+a large source file, as explained in the FAQ. |
| + | Copyright © 2002-2004 Joel de Guzman, David Abrahams | +
+Next stop... Building your Hello World module from start to finish...
Start it up.
There you go... Have fun!
Read on...
-Now that we can embed the interpreter in our programs, lets see how to put it to use...
Next stop... Building your Hello World module from start to finish...
Start it up.
There you go... Have fun!
Read on...
+Now that we can embed the interpreter in our programs, lets see how to put it to use...