Functions

C++ functions are first-class objects in Python and can be used wherever Python functions can be used, including for dynamically constructing classes.

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')
>>>

Function argument type conversions follow the expected rules, with implicit conversions allowed, including between Python builtin types and STL types, but it is rather more efficient to make conversions explicit.

Free functions

All bound C++ code starts off from the global C++ namespace, represented in Python by gbl. This namespace, as any other namespace, is treated as a module after it has been loaded. Thus, we can directly import C++ functions from it and other namespaces that themselves may contain more functions. All lookups on namespaces are done lazily, thus if loading more headers bring in more functions (incl. new overloads), these become available dynamically.

>>> from cppyy.gbl import global_function, Namespace
>>> global_function == Namespace.global_function
False
>>> from cppyy.gbl.Namespace import global_function
>>> global_function == Namespace.global_function
True
>>> from cppyy.gbl import global_function
>>>

Free functions can be bound to a class, following the same rules as apply to Python functions: unless marked as static, they will turn into member functions when bound to an instance, but act as static functions when called through the class. Consider this example:

>>> from cppyy.gbl import Concrete, call_abstract_method
>>> c = Concrete()
>>> Concrete.callit = call_abstract_method
>>> Concrete.callit(c)
called Concrete::abstract_method
>>> c.callit()
called Concrete::abstract_method
>>> Concrete.callit = staticmethod(call_abstract_method)
>>> c.callit()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: void ::call_abstract_method(Abstract* a) =>
    TypeError: takes at least 1 arguments (0 given)
>>> c.callit(c)
called Concrete::abstract_method
>>>

Static methods

Class static functions are treated the same way as free functions, except that they are accessible either through the class or through an instance, just like Python’s staticmethod.

Instance methods

For class methods, see the methods section under the classes heading.

Operators

Globally defined operators are found lazily (ie. can resolve after the class definition by loading the global definition or by defining them interactively) and are mapped onto a Python equivalent when possible. See the operators section under the classes heading for more details.

Templates

Templated functions (and class methods) can either be called using square brackets ([]) to provide the template arguments explicitly, or called directly, through automatic lookup. The template arguments may either be a string of type names (this results in faster code, as it needs no further lookup/verification) or a list of the actual types to use (which tends to be more convenient).

Note: the Python type float maps to the C++ type float, even as Python uses a C double as its internal representation. The motivation is that doing so makes the Python code more readable (and Python may anyway change its internal representation in the future). The same has been true for Python int, which used to be a C long internally.

Examples, using multiply from features.h:

>>> mul = cppyy.gbl.multiply
>>> mul(1, 2)
2
>>> mul(1., 5)
5.0
>>> mul[int](1, 1)
1
>>> mul[int, int](1, 1)
1
>>> mul[int, int, float](1, 1)
1.0
>>> mul[int, int](1, 'a')
 TypeError: Template method resolution failed:
 none of the 6 overloaded methods succeeded. Full details:
 int ::multiply(int a, int b) =>
   TypeError: could not convert argument 2 (int/long conversion expects an integer object)
 ...
 Failed to instantiate "multiply(int,std::string)"
>>> mul['double, double, double'](1., 5)
5.0
>>>

Overloading

C++ supports overloading, whereas Python supports “duck typing”, thus C++ overloads have to be selected dynamically in response to the available “ducks.” This may lead to additional lookups or template instantiations. However, pre-existing methods (incl. auto-instantiated methods) are always preferred over new template instantiations:

>>> global_function(1.)        # selects 'double' overload
2.718281828459045
>>> global_function(1)         # selects 'int' overload
42
>>>

C++ does a static dispatch at compile time based on the argument types. The dispatch is a selection among overloads (incl. templates) visible at the current parse location in the translation unit. Bound C++ in Python does a dynamic dispatch: it considers all overloads visible globally at the time of execution. These two approaches, even if completely in line with the expectations of the respective languages, are fundamentally different and there can thus be discrepancies in overload selection. For example, if overloads live in different header files and are only an implicit conversion apart; or if types that have no direct equivalent in Python, such as e.g. unsigned short, are used.

It is implicitly assumed that the Python code is correct as-written and there are no warnings or errors for overloads that C++ would consider ambiguous, but only if every possible overload fails. For example, the following overload would be ambiguous in C++ (the value provided is an integer, but can not be passed through a 4-byte int type), but instead cppyy silently accepts promotion to double:

>>> cppyy.cppdef(r"""\
...   void process_data(double) { std::cerr << "processing double\n"; }
...   void process_data(int32_t) { std::cerr << "processing int\n"; }""")
True
>>> cppyy.gbl.process_data(2**32)  # too large for int32_t type
processing double
>>>

There are two rounds to run-time overload resolution. The first round considers all overloads in sorted order, with promotion but no implicit conversion allowed. The sorting is based on priority scores of each overload. Higher priority is given to overloads with argument types that can be promoted or align better with Python types. E.g. int is preferred over double and double is preferred over float. If argument conversion fails for all overloads during this round and at least one argument converter has indicated that it can do implicit conversion, a second round is tried where implicit conversion, including instantiation of temporaries, is allowed. The implicit creation of temporaries, although convenient, can be costly in terms of run-time performance.

During some template calls, implicit conversion is not allowed, giving preference to new instantiations (as is the case in C++). If, however, a previously instantiated overload is available and would match with promotion, it is preferred over a (costly) new instantiation, unless a template overload is explicitly selected using template arguments. For example:

>>> cppyy.cppdef(r"""\
...   template<typename T>
...   T process_T(T t) { return t; }""")
True
>>> type(cppyy.gbl.process_T(1.0))
<class 'float'>
>>> type(cppyy.gbl.process_T(1))        # selects available "double" overload
<class 'float'>
>>> type(cppyy.gbl.process_T[int](1))   # explicit selection of "int" overload
<class 'int'>
>>>

The template parameters used for instantiation can depend on the argument values. For example, if the type of an argument is Python int, but its value is too large for a 4-byte C++ int, the template may be instantiated with, for example, an int64_t instead (if available on the platform). Since Python does not have unsigned types, the instantiation mechanism strongly prefers signed types. However, if an argument value is too large to fit in a signed integer type, but would fit in an unsigned type, then that will be used.

If it is important that a specific overload is selected, then use the __overload__ method to match a specific function signature. An optional boolean second parameter can be used to restrict the selected method to be const (if True) or non-const (if False). The return value of which is a first-class callable object, that can be stored to by-pass the overload resolution:

>>> gf_double = global_function.__overload__('double')
>>> gf_double(1)        # int implicitly promoted
2.718281828459045
>>>

The __overload__ method only does a lookup; it performs no (implicit) conversions and the types in the signature to match should be the fully resolved ones (no typedefs). To see all overloads available for selection, use help() on the function or look at its __doc__ string:

>>> print(global_function.__doc__)
int ::global_function(int)
double ::global_function(double)
>>>

For convenience, the :any: signature allows matching any overload, for example to reduce a method to its const overload only, use:

MyClass.some_method = MyClass.some_method.__overload__(':any:', True)

Overloads and exceptions

Python error reporting is done using exceptions. Failed argument conversion during overload resolution can lead to different types of exceptions coming from respective attempted overloads. The final error report issued if all overloads fail, is a summary of the individual errors, but by Python language requirements it has to have a single exception type. If all the exception types match, that type is used, but if there is an amalgam of types, the exception type chosen will be TypeError. For example, attempting to pass a too large value through uint8_t will uniquely raise a ValueError

>>> cppyy.cppdef("void somefunc(uint8_t) {}")
True
>>> cppyy.gbl.somefunc(2**16)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: void ::somefunc(uint8_t) =>
    ValueError: could not convert argument 1 (integer to character: value 65536 not in range [0,255])
>>>

But if other overloads are present that fail in a different way, the error report will be a TypeError:

>>> cppyy.cppdef(r"""
...   void somefunc(uint8_t) {}
...   void somefunc(std::string) {}""")
True
>>> cppyy.gbl.somefunc(2**16)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: none of the 2 overloaded methods succeeded. Full details:
  void ::somefunc(std::string) =>
    TypeError: could not convert argument 1
  void ::somefunc(uint8_t) =>
    ValueError: could not convert argument 1 (integer to character: value 65536 not in range [0,255])
>>>

Since C++ exceptions are converted to Python ones, there is an interplay possible between the two as part of overload resolution and cppyy allows C++ exceptions as such, enabling detailed type disambiguation and input validation. (The original use case was for filling database fields, requiring an exact field label and data type match.)

If, however, all methods fail and there is only one C++ exception (the other exceptions originating from argument conversion, never succeeding to call into C++), this C++ exception will be preferentially reported and will have the original C++ type.

Return values

Most return types are readily amenable to automatic memory management: builtin returns, by-value returns, (const-)reference returns to internal data, smart pointers, etc. The important exception is pointer returns.

A function that returns a pointer to an object over which Python should claim ownership, should have its __creates__ flag set through its pythonization. Well-written APIs will have clear clues in their naming convention about the ownership rules. For example, functions called New..., Clone..., etc. can be expected to return freshly allocated objects. A basic name-matching in the pythonization then makes it simple to mark all these functions as creators.

The return values are auto-casted.

*args and **kwds

C++ default arguments work as expected. Keywords, however, are a Python language feature that does not exist in C++. Many C++ function declarations do have formal arguments, but these are not part of the C++ interface (the argument names are repeated in the definition, making the names in the declaration irrelevant: they do not even need to be provided). Thus, although cppyy will map keyword argument names to formal argument names from the C++ declaration, use of this feature is not recommended unless you have a guarantee that the names in C++ the interface are maintained. Example:

>>> from cppyy.gbl import Concrete
>>> c = Concrete()       # uses default argument
>>> c.m_int
42
>>> c = Concrete(13)     # uses provided argument
>>> c.m_int
13
>>> args = (27,)
>>> c = Concrete(*args)  # argument pack
>>> c.m_int
27
>>> c = Concrete(n=17)
>>> c.m_int
17
>>> kwds = {'n' : 18}
>>> c = Concrete(**kwds)
>>> c.m_int
18
>>>

Callbacks

Python callables (functions/lambdas/instances) can be passed to C++ through function pointers and/or std::function. This involves creation of a temporary wrapper, which has the same life time as the Python callable it wraps, so the callable needs to be kept alive on the Python side if the C++ side stores the callback. Example:

>>> from cppyy.gbl import call_int_int
>>> print(call_int_int.__doc__)
int ::call_int_int(int(*)(int,int) f, int i1, int i2)
>>> def add(a, b):
...    return a+b
...
>>> call_int_int(add, 3, 7)
7
>>> call_int_int(lambda x, y: x*y, 3, 7)
21
>>>

Python functions can be used to instantiate C++ templates, assuming the type information of the arguments and return types can be inferred. If this can not be done directly from the template arguments, then it can be provided through Python annotations, by explicitly adding the __annotations__ special data member (e.g. for older versions of Python that do not support annotations), or by the function having been bound by cppyy in the first place. For example:

>>> import cppyy
>>> cppyy.cppdef("""\
... template<typename R, typename... U, typename... A>
... R callT(R(*f)(U...), A&&... a) {
...    return f(a...);
... }""")
True
>>> def f(a: 'int') -> 'double':
...     return 3.1415*a
...
>>> cppyy.gbl.callT(f, 2)
6.283
>>> def f(a: 'int', b: 'int') -> 'int':
...     return 3*a*b
...
>>> cppyy.gbl.callT(f, 6, 7)
126
>>>

extern “C”

Functions with C linkage are supported and are simply represented as overloads of a single function. Such functions are allowed both globally as well as in namespaces.