The cppyy module creates, at run-time, Python-side classes and functions for C++, by querying a C++ reflection system. The default system used is Reflex, which extracts the needed information from C++ header files. Another current backend is based on CINT, and yet another, more important one for the medium- to long-term will be based on cling. The latter sits on top of llvm‘s clang, and will therefore allow the use of C++11. The work on the cling backend has so far been done only for CPython, but bringing it to PyPy is a lot less work than developing it in the first place.
This document describes the version of cppyy that lives in the main branch of PyPy. The development of cppyy happens in the “reflex-support” branch.
To provide bindings to another language in CPython, you program to a generic C-API that exposes many of the interpreter features. With PyPy, however, there is no such generic C-API, because several of the interpreter features (e.g. the memory model) are pluggable and therefore subject to change. Furthermore, a generic API does not allow any assumptions about the calls into another language, forcing the JIT to behave conservatively around these calls and with the objects that cross language boundaries. In contrast, cppyy does not expose an API, but expects one to be implemented by a backend. It makes strong assumptions about the semantics of the API that it uses and that in turn allows the JIT to make equally strong assumptions. This is possible, because the expected API is only for providing C++ language bindings, and does not provide generic programmability.
The cppyy module further offers two features, which result in improved performance as well as better functionality and cross-language integration. First, cppyy itself is written in RPython and therefore open to optimizations by the JIT up until the actual point of call into C++. This means for example, that if variables are already unboxed by the JIT, they can be passed through directly to C++. Second, a backend such as Reflex (and cling far more so) adds dynamic features to C++, thus greatly reducing impedance mismatches between the two languages. For example, Reflex is dynamic enough to allow writing runtime bindings generation in python (as opposed to RPython) and this is used to create very natural “pythonizations” of the bound code. As another example, cling allows automatic instantiations of templates.
See this description of the cppyy architecture for further details.
There are two ways of using cppyy, and the choice depends on how pypy-c was built: the backend can be builtin, or dynamically loadable. The former has the disadvantage of requiring pypy-c to be linked with external C++ libraries (e.g. libReflex.so), but has the advantage of being faster in some cases. That advantage will disappear over time, however, with improvements in the JIT. Therefore, this document assumes that the dynamically loadable backend is chosen (it is, by default). See the backend documentation.
A standalone version of Reflex that also provides the dynamically loadable backend is available for download. That version, as well as any other distribution of Reflex (e.g. the one that comes with ROOT, which may be part of your Linux distribution as part of the selection of scientific software) will also work for a build with the builtin backend.
Besides Reflex, you probably need a version of gccxml installed, which is most easily provided by the packager of your system. If you read up on gccxml, you will probably notice that it is no longer being developed and hence will not provide C++11 support. That’s why the medium term plan is to move to cling. Note that gccxml is only needed to generate reflection libraries. It is not needed to use them.
To install the standalone version of Reflex, after download:
$ tar jxf reflex-2013-08-14.tar.bz2
$ cd reflex-2013-08-14
$ ./build/autogen
$ ./configure <usual set of options such as --prefix>
$ make && make install
The usual rules apply: <prefix>/bin needs to be added to the PATH and <prefix>/lib to the LD_LIBRARY_PATH environment variable. For convenience, this document will assume that there is a REFLEXHOME variable that points to <prefix>. If you downloaded or built the whole of ROOT, REFLEXHOME should be equal to ROOTSYS.
The following is optional, and is only to show how pypy-c can be build from source, for example to get at the main development branch of cppyy. The backend documentation has more details on the backend-specific prerequisites.
Then run the translation to build pypy-c:
$ hg clone https://bitbucket.org/pypy/pypy
$ cd pypy
$ hg up reflex-support # optional
# This example shows python, but using pypy-c is faster and uses less memory
$ python rpython/translator/goal/translate.py --opt=jit pypy/goal/targetpypystandalone --withmod-cppyy
This will build a pypy-c that includes the cppyy module, and through that, Reflex support. Of course, if you already have a pre-built version of the pypy interpreter, you can use that for the translation rather than python. If not, you may want to obtain a binary distribution to speed up the translation step.
Now test with a trivial example whether all packages are properly installed and functional. First, create a C++ header file with some class in it (note that all functions are made inline for convenience; a real-world example would of course have a corresponding source file):
$ cat MyClass.h
class MyClass {
public:
MyClass(int i = -99) : m_myint(i) {}
int GetMyInt() { return m_myint; }
void SetMyInt(int i) { m_myint = i; }
public:
int m_myint;
};
Then, generate the bindings using genreflex (part of ROOT), and compile the code:
$ genreflex MyClass.h
$ g++ -fPIC -rdynamic -O2 -shared -I$REFLEXHOME/include MyClass_rflx.cpp -o libMyClassDict.so -L$REFLEXHOME/lib -lReflex
Next, make sure that the library can be found through the dynamic lookup path (the LD_LIBRARY_PATH environment variable on Linux, PATH on Windows), for example by adding ”.”. Now you’re ready to use the bindings. Since the bindings are designed to look pythonistic, it should be straightforward:
$ pypy-c
>>>> import cppyy
>>>> cppyy.load_reflection_info("libMyClassDict.so")
<CPPLibrary object at 0xb6fd7c4c>
>>>> myinst = cppyy.gbl.MyClass(42)
>>>> print myinst.GetMyInt()
42
>>>> myinst.SetMyInt(33)
>>>> print myinst.m_myint
33
>>>> myinst.m_myint = 77
>>>> print myinst.GetMyInt()
77
>>>> help(cppyy.gbl.MyClass) # shows that normal python introspection works
That’s all there is to it!
There is one big problem in the code above, that prevents its use in a (large scale) production setting: the explicit loading of the reflection library. Clearly, if explicit load statements such as these show up in code downstream from the MyClass package, then that prevents the MyClass author from repackaging or even simply renaming the dictionary library.
The solution is to make use of an automatic class loader, so that downstream code never has to call load_reflection_info() directly. The class loader makes use of so-called rootmap files, which genreflex can produce. These files contain the list of available C++ classes and specify the library that needs to be loaded for their use (as an aside, this listing allows for a cross-check to see whether reflection info is generated for all classes that you expect). By convention, the rootmap files should be located next to the reflection info libraries, so that they can be found through the normal shared library search path. They can be concatenated together, or consist of a single rootmap file per library. For example:
$ genreflex MyClass.h --rootmap=libMyClassDict.rootmap --rootmap-lib=libMyClassDict.so
$ g++ -fPIC -rdynamic -O2 -shared -I$REFLEXHOME/include MyClass_rflx.cpp -o libMyClassDict.so -L$REFLEXHOME/lib -lReflex
where the first option (--rootmap) specifies the output file name, and the second option (--rootmap-lib) the name of the reflection library where MyClass will live. It is necessary to provide that name explicitly, since it is only in the separate linking step where this name is fixed. If the second option is not given, the library is assumed to be libMyClass.so, a name that is derived from the name of the header file.
With the rootmap file in place, the above example can be rerun without explicit loading of the reflection info library:
$ pypy-c
>>>> import cppyy
>>>> myinst = cppyy.gbl.MyClass(42)
>>>> print myinst.GetMyInt()
42
>>>> # etc. ...
As a caveat, note that the class loader is currently limited to classes only.
The following snippet of C++ is very contrived, to allow showing that such pathological code can be handled and to show how certain features play out in practice:
$ cat MyAdvanced.h
#include <string>
class Base1 {
public:
Base1(int i) : m_i(i) {}
virtual ~Base1() {}
int m_i;
};
class Base2 {
public:
Base2(double d) : m_d(d) {}
virtual ~Base2() {}
double m_d;
};
class C;
class Derived : public virtual Base1, public virtual Base2 {
public:
Derived(const std::string& name, int i, double d) : Base1(i), Base2(d), m_name(name) {}
virtual C* gimeC() { return (C*)0; }
std::string m_name;
};
Base2* BaseFactory(const std::string& name, int i, double d) {
return new Derived(name, i, d);
}
This code is still only in a header file, with all functions inline, for convenience of the example. If the implementations live in a separate source file or shared library, the only change needed is to link those in when building the reflection library.
If you were to run genreflex like above in the basic example, you will find that not all classes of interest will be reflected, nor will be the global factory function. In particular, std::string will be missing, since it is not defined in this header file, but in a header file that is included. In practical terms, general classes such as std::string should live in a core reflection set, but for the moment assume we want to have it in the reflection library that we are building for this example.
The genreflex script can be steered using a so-called selection file, which is a simple XML file specifying, either explicitly or by using a pattern, which classes, variables, namespaces, etc. to select from the given header file. With the aid of a selection file, a large project can be easily managed: simply #include all relevant headers into a single header file that is handed to genreflex. In fact, if you hand multiple header files to genreflex, then a selection file is almost obligatory: without it, only classes from the last header will be selected. Then, apply a selection file to pick up all the relevant classes. For our purposes, the following rather straightforward selection will do (the name lcgdict for the root is historical, but required):
$ cat MyAdvanced.xml
<lcgdict>
<class pattern="Base?" />
<class name="Derived" />
<class name="std::string" />
<function name="BaseFactory" />
</lcgdict>
Now the reflection info can be generated and compiled:
$ genreflex MyAdvanced.h --selection=MyAdvanced.xml
$ g++ -fPIC -rdynamic -O2 -shared -I$REFLEXHOME/include MyAdvanced_rflx.cpp -o libAdvExDict.so -L$REFLEXHOME/lib -lReflex
and subsequently be used from PyPy:
>>>> import cppyy
>>>> cppyy.load_reflection_info("libAdvExDict.so")
<CPPLibrary object at 0x00007fdb48fc8120>
>>>> d = cppyy.gbl.BaseFactory("name", 42, 3.14)
>>>> type(d)
<class '__main__.Derived'>
>>>> isinstance(d, cppyy.gbl.Base1)
True
>>>> isinstance(d, cppyy.gbl.Base2)
True
>>>> d.m_i, d.m_d
(42, 3.14)
>>>> d.m_name == "name"
True
>>>>
Again, that’s all there is to it!
A couple of things to note, though. If you look back at the C++ definition of the BaseFactory function, you will see that it declares the return type to be a Base2, yet the bindings return an object of the actual type Derived? This choice is made for a couple of reasons. First, it makes method dispatching easier: if bound objects are always their most derived type, then it is easy to calculate any offsets, if necessary. Second, it makes memory management easier: the combination of the type and the memory address uniquely identifies an object. That way, it can be recycled and object identity can be maintained if it is entered as a function argument into C++ and comes back to PyPy as a return value. Last, but not least, casting is decidedly unpythonistic. By always providing the most derived type known, casting becomes unnecessary. For example, the data member of Base2 is simply directly available. Note also that the unreflected gimeC method of Derived does not preclude its use. It is only the gimeC method that is unusable as long as class C is unknown to the system.
The following is not meant to be an exhaustive list, since cppyy is still under active development. Furthermore, the intention is that every feature is as natural as possible on the python side, so if you find something missing in the list below, simply try it out. It is not always possible to provide exact mapping between python and C++ (active memory management is one such case), but by and large, if the use of a feature does not strike you as obvious, it is more likely to simply be a bug. That is a strong statement to make, but also a worthy goal. For the C++ side of the examples, refer to this example code, which was bound using:
$ genreflex example.h --deep --rootmap=libexampleDict.rootmap --rootmap-lib=libexampleDict.so
$ g++ -fPIC -rdynamic -O2 -shared -I$REFLEXHOME/include example_rflx.cpp -o libexampleDict.so -L$REFLEXHOME/lib -lReflex
abstract classes: Are represented as python classes, since they are needed to complete the inheritance hierarchies, but will raise an exception if an attempt is made to instantiate from them. Example:
>>>> from cppyy.gbl import AbstractClass, ConcreteClass
>>>> a = AbstractClass()
Traceback (most recent call last):
File "<console>", line 1, in <module>
TypeError: cannot instantiate abstract class 'AbstractClass'
>>>> issubclass(ConcreteClass, AbstractClass)
True
>>>> c = ConcreteClass()
>>>> isinstance(c, AbstractClass)
True
>>>>
arrays: Supported for builtin data types only, as used from module array. Out-of-bounds checking is limited to those cases where the size is known at compile time (and hence part of the reflection info). Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> from array import array
>>>> c = ConcreteClass()
>>>> c.array_method(array('d', [1., 2., 3., 4.]), 4)
1 2 3 4
>>>>
builtin data types: Map onto the expected equivalent python types, with the caveat that there may be size differences, and thus it is possible that exceptions are raised if an overflow is detected.
casting: Is supposed to be unnecessary. Object pointer returns from functions provide the most derived class known in the hierarchy of the object being returned. This is important to preserve object identity as well as to make casting, a pure C++ feature after all, superfluous. Example:
>>>> from cppyy.gbl import AbstractClass, ConcreteClass
>>>> c = ConcreteClass()
>>>> ConcreteClass.show_autocast.__doc__
'AbstractClass* ConcreteClass::show_autocast()'
>>>> d = c.show_autocast()
>>>> type(d)
<class '__main__.ConcreteClass'>
>>>>
However, if need be, you can perform C++-style reinterpret_casts (i.e. without taking offsets into account), by taking and rebinding the address of an object:
>>>> from cppyy import addressof, bind_object
>>>> e = bind_object(addressof(d), AbstractClass)
>>>> type(e)
<class '__main__.AbstractClass'>
>>>>
classes and structs: Get mapped onto python classes, where they can be instantiated as expected. If classes are inner classes or live in a namespace, their naming and location will reflect that. Example:
>>>> from cppyy.gbl import ConcreteClass, Namespace
>>>> ConcreteClass == Namespace.ConcreteClass
False
>>>> n = Namespace.ConcreteClass.NestedClass()
>>>> type(n)
<class '__main__.Namespace::ConcreteClass::NestedClass'>
>>>>
data members: Public data members are represented as python properties and provide read and write access on instances as expected. Private and protected data members are not accessible. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> c = ConcreteClass()
>>>> c.m_int
42
>>>>
default arguments: C++ default arguments work as expected, but python keywords are not supported. It is technically possible to support keywords, but for the C++ interface, the formal argument names have no meaning and are not considered part of the API, hence it is not a good idea to use keywords. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> c = ConcreteClass() # uses default argument
>>>> c.m_int
42
>>>> c = ConcreteClass(13)
>>>> c.m_int
13
>>>>
doc strings: The doc string of a method or function contains the C++ arguments and return types of all overloads of that name, as applicable. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> print ConcreteClass.array_method.__doc__
void ConcreteClass::array_method(int*, int)
void ConcreteClass::array_method(double*, int)
>>>>
enums: Are translated as ints with no further checking.
functions: Work as expected and live in their appropriate namespace (which can be the global one, cppyy.gbl).
inheritance: All combinations of inheritance on the C++ (single, multiple, virtual) are supported in the binding. However, new python classes can only use single inheritance from a bound C++ class. Multiple inheritance would introduce two “this” pointers in the binding. This is a current, not a fundamental, limitation. The C++ side will not see any overridden methods on the python side, as cross-inheritance is planned but not yet supported. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> help(ConcreteClass)
Help on class ConcreteClass in module __main__:
class ConcreteClass(AbstractClass)
| Method resolution order:
| ConcreteClass
| AbstractClass
| cppyy.CPPObject
| __builtin__.CPPInstance
| __builtin__.object
|
| Methods defined here:
|
| ConcreteClass(self, *args)
| ConcreteClass::ConcreteClass(const ConcreteClass&)
| ConcreteClass::ConcreteClass(int)
| ConcreteClass::ConcreteClass()
|
etc. ....
memory: C++ instances created by calling their constructor from python are owned by python. You can check/change the ownership with the _python_owns flag that every bound instance carries. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> c = ConcreteClass()
>>>> c._python_owns # True: object created in Python
True
>>>>
methods: Are represented as python methods and work as expected. They are first class objects and can be bound to an instance. Virtual C++ methods work as expected. To select a specific virtual method, do like with normal python classes that override methods: select it from the class that you need, rather than calling the method on the instance. To select a specific overload, use the __dispatch__ special function, which takes the name of the desired method and its signature (which can be obtained from the doc string) as arguments.
namespaces: Are represented as python classes. Namespaces are more open-ended than classes, so sometimes initial access may result in updates as data and functions are looked up and constructed lazily. Thus the result of dir() on a namespace shows the classes available, even if they may not have been created yet. It does not show classes that could potentially be loaded by the class loader. Once created, namespaces are registered as modules, to allow importing from them. Namespace currently do not work with the class loader. Fixing these bootstrap problems is on the TODO list. The global namespace is cppyy.gbl.
operator conversions: If defined in the C++ class and a python equivalent exists (i.e. all builtin integer and floating point types, as well as bool), it will map onto that python conversion. Note that char* is mapped onto __str__. Example:
>>>> from cppyy.gbl import ConcreteClass
>>>> print ConcreteClass()
Hello operator const char*!
>>>>
operator overloads: If defined in the C++ class and if a python equivalent is available (not always the case, think e.g. of operator||), then they work as expected. Special care needs to be taken for global operator overloads in C++: first, make sure that they are actually reflected, especially for the global overloads for operator== and operator!= of STL vector iterators in the case of gcc (note that they are not needed to iterator over a vector). Second, make sure that reflection info is loaded in the proper order. I.e. that these global overloads are available before use.
pointers: For builtin data types, see arrays. For objects, a pointer to an object and an object looks the same, unless the pointer is a data member. In that case, assigning to the data member will cause a copy of the pointer and care should be taken about the object’s life time. If a pointer is a global variable, the C++ side can replace the underlying object and the python side will immediately reflect that.
PyObject*: Arguments and return types of PyObject* can be used, and passed on to CPython API calls. Since these CPython-like objects need to be created and tracked (this all happens through cpyext) this interface is not particularly fast.
static data members: Are represented as python property objects on the class and the meta-class. Both read and write access is as expected.
static methods: Are represented as python’s staticmethod objects and can be called both from the class as well as from instances.
strings: The std::string class is considered a builtin C++ type and mixes quite well with python’s str. Python’s str can be passed where a const char* is expected, and an str will be returned if the return type is const char*.
templated classes: Are represented in a meta-class style in python. This may look a little bit confusing, but conceptually is rather natural. For example, given the class std::vector<int>, the meta-class part would be std.vector. Then, to get the instantiation on int, do std.vector(int) and to create an instance of that class, do std.vector(int)():
>>>> import cppyy
>>>> cppyy.load_reflection_info('libexampleDict.so')
>>>> cppyy.gbl.std.vector # template metatype
<cppyy.CppyyTemplateType object at 0x00007fcdd330f1a0>
>>>> cppyy.gbl.std.vector(int) # instantiates template -> class
<class '__main__.std::vector<int>'>
>>>> cppyy.gbl.std.vector(int)() # instantiates class -> object
<__main__.std::vector<int> object at 0x00007fe480ba4bc0>
>>>>
Note that templates can be build up by handing actual types to the class instantiation (as done in this vector example), or by passing in the list of template arguments as a string. The former is a lot easier to work with if you have template instantiations using classes that themselves are templates in the arguments (think e.g a vector of vectors). All template classes must already exist in the loaded reflection info, they do not work (yet) with the class loader.
For compatibility with other bindings generators, use of square brackets instead of parenthesis to instantiate templates is supported as well.
templated functions: Automatically participate in overloading and are used in the same way as other global functions.
templated methods: For now, require an explicit selection of the template parameters. This will be changed to allow them to participate in overloads as expected.
typedefs: Are simple python references to the actual classes to which they refer.
unary operators: Are supported if a python equivalent exists, and if the operator is defined in the C++ class.
You can always find more detailed examples and see the full of supported features by looking at the tests in pypy/module/cppyy/test.
If a feature or reflection info is missing, this is supposed to be handled gracefully. In fact, there are unit tests explicitly for this purpose (even as their use becomes less interesting over time, as the number of missing features decreases). Only when a missing feature is used, should there be an exception. For example, if no reflection info is available for a return type, then a class that has a method with that return type can still be used. Only that one specific method can not be used.
A bit of special care needs to be taken for the use of templates. For a templated class to be completely available, it must be guaranteed that said class is fully instantiated, and hence all executable C++ code is generated and compiled in. The easiest way to fulfill that guarantee is by explicit instantiation in the header file that is handed to genreflex. The following example should make that clear:
$ cat MyTemplate.h
#include <vector>
class MyClass {
public:
MyClass(int i = -99) : m_i(i) {}
MyClass(const MyClass& s) : m_i(s.m_i) {}
MyClass& operator=(const MyClass& s) { m_i = s.m_i; return *this; }
~MyClass() {}
int m_i;
};
#ifdef __GCCXML__
template class std::vector<MyClass>; // explicit instantiation
#endif
If you know for certain that all symbols will be linked in from other sources, you can also declare the explicit template instantiation extern. An alternative is to add an object to an unnamed namespace:
namespace {
std::vector<MyClass> vmc;
} // unnamed namespace
Unfortunately, this is not always enough for gcc. The iterators of vectors, if they are going to be used, need to be instantiated as well, as do the comparison operators on those iterators, as these live in an internal namespace, rather than in the iterator classes. Note that you do NOT need this iterators to iterator over a vector. You only need them if you plan to explicitly call e.g. begin and end methods, and do comparisons of iterators. One way to handle this, is to deal with this once in a macro, then reuse that macro for all vector classes. Thus, the header above needs this (again protected with #ifdef __GCCXML__), instead of just the explicit instantiation of the vector<MyClass>:
#define STLTYPES_EXPLICIT_INSTANTIATION_DECL(STLTYPE, TTYPE) \
template class std::STLTYPE< TTYPE >; \
template class __gnu_cxx::__normal_iterator<TTYPE*, std::STLTYPE< TTYPE > >; \
template class __gnu_cxx::__normal_iterator<const TTYPE*, std::STLTYPE< TTYPE > >;\
namespace __gnu_cxx { \
template bool operator==(const std::STLTYPE< TTYPE >::iterator&, \
const std::STLTYPE< TTYPE >::iterator&); \
template bool operator!=(const std::STLTYPE< TTYPE >::iterator&, \
const std::STLTYPE< TTYPE >::iterator&); \
}
STLTYPES_EXPLICIT_INSTANTIATION_DECL(vector, MyClass)
Then, still for gcc, the selection file needs to contain the full hierarchy as well as the global overloads for comparisons for the iterators:
$ cat MyTemplate.xml
<lcgdict>
<class pattern="std::vector<*>" />
<class pattern="std::vector<*>::iterator" />
<function name="__gnu_cxx::operator=="/>
<function name="__gnu_cxx::operator!="/>
<class name="MyClass" />
</lcgdict>
Run the normal genreflex and compilation steps:
$ genreflex MyTemplate.h --selection=MyTemplate.xml
$ g++ -fPIC -rdynamic -O2 -shared -I$REFLEXHOME/include MyTemplate_rflx.cpp -o libTemplateDict.so -L$REFLEXHOME/lib -lReflex
Note: this is a dirty corner that clearly could do with some automation, even if the macro already helps. Such automation is planned. In fact, in the Cling world, the backend can perform the template instantations and generate the reflection info on the fly, and none of the above will any longer be necessary.
Subsequent use should be as expected. Note the meta-class style of “instantiating” the template:
>>>> import cppyy
>>>> cppyy.load_reflection_info("libTemplateDict.so")
>>>> std = cppyy.gbl.std
>>>> MyClass = cppyy.gbl.MyClass
>>>> v = std.vector(MyClass)()
>>>> v += [MyClass(1), MyClass(2), MyClass(3)]
>>>> for m in v:
.... print m.m_i,
....
1 2 3
>>>>
Other templates work similarly, but are typically simpler, as there are no similar issues with iterators for e.g. std::list. The arguments to the template instantiation can either be a string with the full list of arguments, or the explicit classes. The latter makes for easier code writing if the classes passed to the instantiation are themselves templates.
The following is an experimental feature of cppyy. It mostly works, but there are some known issues (e.g. with return-by-value). Soon it should be the default mode, however.
With a slight modification of Reflex, it can provide function pointers for C++ methods, and hence allow PyPy to call those pointers directly, rather than calling C++ through a Reflex stub.
The standalone version of Reflex provided has been patched, but if you get Reflex from another source (most likely with a ROOT distribution), locate the file genreflex-methptrgetter.patch in pypy/module/cppyy and apply it to the genreflex python scripts found in $ROOTSYS/lib:
$ cd $ROOTSYS/lib
$ patch -p2 < genreflex-methptrgetter.patch
With this patch, genreflex will have grown the --with-methptrgetter option. Use this option when running genreflex, and add the -Wno-pmf-conversions option to g++ when compiling. The rest works the same way: the fast path will be used transparently (which also means that you can’t actually find out whether it is in use, other than by running a micro-benchmark or a JIT test).
Most of the ideas in cppyy come originally from the PyROOT project. Although PyROOT does not support Reflex directly, it has an alter ego called “PyCintex” that, in a somewhat roundabout way, does. If you installed ROOT, rather than just Reflex, PyCintex should be available immediately if you add $ROOTSYS/lib to the PYTHONPATH environment variable.
There are a couple of minor differences between PyCintex and cppyy, most to do with naming. The one that you will run into directly, is that PyCintex uses a function called loadDictionary rather than load_reflection_info (it has the same rootmap-based class loader functionality, though, making this point somewhat moot). The reason for this is that Reflex calls the shared libraries that contain reflection info “dictionaries.” However, in python, the name dictionary already has a well-defined meaning, so a more descriptive name was chosen for cppyy. In addition, PyCintex requires that the names of shared libraries so loaded start with “lib” in their name. The basic example above, rewritten for PyCintex thus goes like this:
$ python
>>> import PyCintex
>>> PyCintex.loadDictionary("libMyClassDict.so")
>>> myinst = PyCintex.gbl.MyClass(42)
>>> print myinst.GetMyInt()
42
>>> myinst.SetMyInt(33)
>>> print myinst.m_myint
33
>>> myinst.m_myint = 77
>>> print myinst.GetMyInt()
77
>>> help(PyCintex.gbl.MyClass) # shows that normal python introspection works
Other naming differences are such things as taking an address of an object. In PyCintex, this is done with AddressOf whereas in cppyy the choice was made to follow the naming as in ctypes and hence use addressof (PyROOT/PyCintex predate ctypes by several years, and the ROOT project follows camel-case, hence the differences).
Of course, this is python, so if any of the naming is not to your liking, all you have to do is provide a wrapper script that you import instead of importing the cppyy or PyCintex modules directly. In that wrapper script you can rename methods exactly the way you need it.
In the cling world, all these differences will be resolved.
To change versions of CPython (to Python3, another version of Python, or later to the Py3k version of PyPy), the only part that requires recompilation is the bindings module, be it cppyy or libPyROOT.so (in PyCintex). Although genreflex is indeed a Python tool, the generated reflection information is completely independent of Python.