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SciPy Reference Guide, Release 0.8.dev
needs some work). So, this section covers items 1 and 4 from the list. Item 2 is covered later in the chapter covering
the ext_tools module, and distutils is covered by a completely separate document xxx.
Passing Variables in/out of the C/C++ code
Note: Passing variables into the C code is pretty straight forward, but there are subtlties to how variable modifications
in C are returned to Python. see Returning Values for a more thorough discussion of this issue.
Type Conversions
Note: Maybe xxx_converter instead of xxx_specification is a more descriptive name. Might change in
future version?
By default, inline() makes the following type conversions between Python and C++ types.
Table 1.3: Default Data Type Conversions
Python C++
int int
float double
complex std::complex
string py::string
list py::list
dict py::dict
tuple py::tuple
file FILE*
callable py::object
instance py::object
numpy.ndarray PyArrayObject*
wxXXX wxXXX*
The Py:: namespace is defined by the SCXX library which has C++ class equivalents for many Python types. std::
is the namespace of the standard library in C++.
Note:
• I haven’t figured out how to handle long int yet (I think they are currenlty converted to int - - check this).
• Hopefully VTK will be added to the list soon
Python to C++ conversions fill in code in several locations in the generated inline extension function. Below is the
basic template for the function. This is actually the exact code that is generated by calling weave.inline("").
The /* inline code */ section is filled with the code passed to the inline() function call. The
/*argument convserion code*/ and /* cleanup code */ sections are filled with code that handles
conversion from Python to C++ types and code that deallocates memory or manipulates reference counts before the
function returns. The following sections demostrate how these two areas are filled in by the default conversion methods.
* Note: I’m not sure I have reference counting correct on a few of these. The only thing I increase/decrease the
ref count on is NumPy arrays. If you see an issue, please let me know.
NumPy Argument Conversion
Integer, floating point, and complex arguments are handled in a very similar fashion. Consider the following inline
function that has a single integer variable passed in:
1.12. Weave 105