# User guide¶

Note

In this section, we introduce wrapping problems and how AutoWIG aims at minimize developers effort. Basic concepts and conventions are introduced.

## Problem setting¶

Consider a scientist who has designed multiple C++ libraries for statistical analysis. He would like to distribute his libraries and decide to make them available in Python in order to reach a public of statisticians but also less expert scientists such as biologists. Yet, he is not interested in becoming an expert in C++/Python wrapping, even if it exists classical approaches consisting in writing wrappers with SWIG [Bea03] or Boost.Python [AGK03]. Moreover, he would have serious difficulties to maintain the wrappers, since this semi-automatic process is time consuming and error prone. Instead, he would like to automate the process of generating wrappers in sync with his evolving C++ libraries. That’s what the AutoWIG software aspires to achieve.

## Automating the process¶

Building such a system entails achieving some minimal features:

C++ parsing
In order to automatically expose C++ components in Python, the system requires parsing full legacy code implementing the last C++ standard. It has also to represent C++ constructs in Python, like namespaces, enumerators, enumerations, variables, functions, classes or aliases.
Documentation
The documentation of C++ components has to be associated automatically to their corresponding Python components in order to reduce the redundancy and to keep it up-to-date in only one place.
Pythonic interface
To respect the Python philosophy, C++ language patterns need to be consistently translated into Python. Some syntax or design patterns in C++ code are specific and need to be adapted in order to obtain a functional Python package. Note that this is particularly sensible for C++ operators (e.g. (), <, []) and corresponding Python special functions (e.g. __call__, __lt__, __getitem__, __setitem__) or for object serialization.
Memory management
C++ libraries expose in their interfaces either raw pointers, shared pointers or references, while Python handles memory allocation and garbage collection automatically. The concepts of pointer or references are thus not meaningful in Python. These language differences entail several problems in the memory management of C++ components into Python. A special attention is therefore required for dealing with references (&) and pointers (*) that are highly used in C++.
Error management
C++ exceptions need to be consistently managed in Python. Python doesn’t have the necessary equipment to properly unwind the C++ stack when exception are thrown. It is therefore important to make sure that exceptions thrown by C++ code do not pass into the Python interpreter core. All C++ exceptions thrown by wrappers must therefore be translated into Python errors. This translation must preserve exception names and contents in order to raise informative Python errors.
Dependency management between components
The management of multiple dependencies between C++ libraries with Python bindings is required at run-time from Python. C++ libraries tends to have dependencies. For instance the C++ Standard Template Library containers [PLMS00] are used in many C++ libraries (e.g std::vector, std::set). For such cases, it doesn’t seem relevant that every wrapped C++ library contains wrappers for usual STL containers (e.g. std::vector< double >, std::set< int >). Moreover, loading in the Python interpreter multiple compiled libraries sharing different wrappers from same C++ components could lead to serious side effects. It is therefore required that dependencies across different library bindings can be handled automatically.