This is a collection of a few more features listed that do not have a proper place yet in the rest of the documentation.

The C++ code used for the examples below can be found here, and it is assumed that that code is loaded at the start of any session. Download it, save it under the name features.h, and load it:

>>> import cppyy
>>> cppyy.include('features.h')

Special variables

There are several conventional “special variables” that control behavior of functions or provide (internal) information. Often, these can be set/used in pythonizations to handle memory management or Global Interpreter Lock (GIL) release.

  • __python_owns__: a flag that every bound instance carries and determines whether Python or C++ owns the C++ instance (and associated memory). If Python owns the instance, it will be destructed when the last Python reference to the proxy disappears. You can check/change the ownership with the __python_owns__ flag that every bound instance carries. Example:

    >>> from cppyy.gbl import Concrete
    >>> c = Concrete()
    >>> c.__python_owns__         # True: object created in Python
  • __creates__: a flag that every C++ overload carries and determines whether the return value is owned by C++ or Python: if True, Python owns the return value, otherwise C++.

  • __set_lifeline__: a flag that every C++ overload carries and determines whether the return value should place a back-reference on self, to prevent the latter from going out of scope before the return value does. The default is False, but will be automatically set at run-time if a return value’s address is a C++ object pointing into the memory of this, or if self is a by-value return.

  • __release_gil__: a flag that every C++ overload carries and determines whether the Global Interpreter Lock (GIL) should be released during the C++ call to allow multi-threading. The default is False.

  • __useffi__: a flag that every C++ overload carries and determines whether generated wrappers or direct foreign functions should be used. This is for PyPy only; the flag has no effect on CPython.

  • __sig2exc__: a flag that every C++ overload carries and determines whether C++ signals (such as SIGABRT) should be converted into Python exceptions.

  • __cpp_name__: a string that every C++ bound class carries and contains the actual C++ name (as opposed to __name__ which has the Python name). This can be useful for template instantiations, documentation, etc.

STL algorithms

It is usually easier to use a Python equivalent or code up the effect of an STL algorithm directly, but when operating on a large container, calling an STL algorithm may offer better performance. It is important to note that all STL algorithms are templates and need the correct types to be properly instantiated. STL containers offer typedefs to obtain those exact types and these should be used rather than relying on the usual implicit conversions of Python types to C++ ones. For example, as there is no char type in Python, the std::remove call below can not be instantiated using a Python string, but the std::string::value_type must be used instead:

>>> cppstr = cppyy.gbl.std.string
>>> n = cppstr('this is a C++ string')
>>> print(n)
this is a C++ string
>>> n.erase(cppyy.gbl.std.remove(n.begin(), n.end(), cppstr.value_type(' ')))
<cppyy.gbl.__wrap_iter<char*> object at 0x7fba35d1af50>
>>> print(n)

Reduced typing

Note: from cppyy.interactive import * is no longer supported for CPython 3.11 and later because the dict object features it relies on have been removed.

Typing cppyy.gbl all the time gets old rather quickly, but the dynamic nature of cppyy makes something like from cppyy.gbl import * impossible. For example, classes can be defined dynamically after that statement and then they would be missed by the import. In scripts, it is easy enough to rebind names to achieve a good amount of reduction in typing (and a modest performance improvement to boot, because of fewer dictionary lookups), e.g.:

import cppyy
std = cppyy.gbl.std
v = std.vector[int](range(10))

But even such rebinding becomes annoying for (brief) interactive sessions.

For CPython only (and not with tools such as IPython or in IDEs that replace the interactive prompt), there is a fix, using from cppyy.interactive import *. This makes lookups in the global dictionary of the current frame also consider everything under cppyy.gbl. This feature comes with a performance penalty and is not meant for production code. Example usage:

>>> from cppyy.interactive import *
>>> v = std.vector[int](range(10))
>>> print(list(v))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> cppdef("struct SomeStruct {};")
>>> s = SomeStruct()          # <- dynamically made available
>>> s
<cppyy.gbl.SomeStruct object at 0x7fa9b8624320>

For PyPy, IPython, etc. cppyy.gbl is simply rebound as g and cppyy.gbl.std is made available as std. Not as convenient as full lookup, and missing any other namespaces that may be available, but still saves some typing in may cases.

Odds and ends

  • 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 and functions available for binding, even if these may not have been created yet. Once created, namespaces are registered as modules, to allow importing from them. The global namespace is cppyy.gbl.
  • NULL: Is represented as cppyy.nullptr. Starting C++11, the keyword nullptr is used to represent NULL. For clarity of intent, it is recommended to use this instead of None (or the integer 0, which can serve in some cases), as None is better understood as void in C++.