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python/todo.txt
Dave Abrahams 349da66a52 *** empty log message ***
[SVN r8078]
2000-10-31 21:19:11 +00:00

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Better python and C++ exception handling/error reporting.
long long support
use Python generic numeric coercion in from_python() for C++ numeric types
Consider renaming PyPtr to Reference.
Report Cygwin linker memory issues
pickling support
Make abstract classes non-instantiable (?)
Support for Python LONG types in Objects.h
Concept checking for to_python<T>() template function (Ullrich did this)
Throw TypeError after asserting when objects from objects.cpp detect a type mismatch.
Add callback-through-function ability to callback.h
Fix repr() for extension classes to print
<extension_class /module-name/./class-name/ at /address/>
Testing
Python 2.0
object revival in __del__
More thorough tests of objects.h/cpp classes
Optimizations
Reference-counting for UniquePodSet?
Remove one level of indirection on type objects (no vtbl?).
Specializations of Caller<> for commmon combinations of argument types (?)
Collect common code into a nice shared library.
Documentation:
differences between Python classes and ExtensionClasses
additional capabilities of ExtensionClasses
slice adjustment
Why special attributes other than __doc__ and __name__ are immutable.
An example of the problems with the built-in Python classes.
>>> class A:
... def __getattr__(self, name):
... return 'A.__getattr__'
...
>>> class B(A): pass
...
>>> class C(B): pass
...
>>> C().x
'A.__getattr__'
>>> B.__bases__ = ()
>>> C().x
'A.__getattr__'
Multiple inheritance
Smart pointers
exception handling
Advanced Topics:
Advanced Type Conversion
adding conversions for fundamental types
generic conversions for template types (with partial spec).
Interacting with built-in Python objects and types from C++
dealing with non-const reference/pointer parameters
Private virtual functions with default implementations
extending multiple-argument support using gen_all.py
Calling back into Python:
// caveat: UNTESTED!
#include <py_cpp/pyptr.h>
#include <py_cpp/callback.h>
#include <py_cpp/py.h>
#include <Python.h>
int main()
{
try {
py::Ptr module(PyImport_ImportModule("weapons"));
const int strength = 10;
const char* manufacturer = "Vordon Empire";
py::Ptr a_blaster(py::Callback<py::Ptr>::call_method(
module.get(), "Blaster", strength, manufacturer));
py::Callback<void>::call_method(a_blaster.get(), "Fire");
int old_strength = py::Callback<int>::call_method(a_blaster.get(), "get_strength");
py::Callback<void>::call_method(a_blaster.get(), "set_strength", 5);
}
catch(...)
{
}
}
Fancy wrapping tricks
templates
Yes. If you look at the examples in extclass_demo.cpp you'll see that I have
exposed several template instantiations (e.g. std::pair<int,int>) in Python.
Keep in mind, however, that you can only expose a template instantiation,
not a template. In other words, MyTemplate<Foo> can be exposed. MyTemplate
itself cannot.
Well, that's not strictly true. Wow, this is more complicated to explain
than I thought.
You can't make an ExtensionClass<MyTemplate>, since after all MyTemplate is
not a type. You can only expose a concrete type to Python.
What you *can* do (if your compiler supports partial ordering of function
templates - MSVC is broken and does not) is to write appropriate
from_python() and to_python() functions for converting a whole class of
template instantiations to/from Python. That won't let you create an
instance of MyTemplate<SomePythonType> from Python, but it will let you
pass/return arbitrary MyTemplate<SomeCplusplusType> instances to/from your
wrapped C++ functions.
template <class T>
MyTemplate<T> from_python(PyObject* x, py::Type<MyTemplate<T> >)
{
// code to convert x into a MyTemplate<T>... that part is up to you
}
template <class T>
PyObject* from_python(const MyTemplate<T>&)
{
// code to convert MyTemplate<T> into a PyObject*... that part is up to
you
}
For example, you could use this to convert Python lists to/from
std::vector<T> automatically.
Types that are already wrapped by other libraries
It's not documented yet, but you should be able to use a raw PyObject* or a
py::Ptr as one parameter to your C++ function. Then you can manipulate it as
any other generic Python object.
Alternatively, If the NTL gives you a C/C++ interface, you can also write
your own converter function:
some_ntl_type& from_python(PyObject* p, py::Type<some_NTL_type&>)
{
// an Example implementation. Basically, you need
// to extract the NTL type from the PyObject*.
if (p->ob_type != NTL_long_type) {
PyErr_SetString(PyExc_TypeErr, "NTL long required");
throw py::ArgumentError();
}
return *static_cast<some_NTL_type*>(p);
}
then the C++ functions you're wrapping can take a some_NTL_type& parameter
directly.
enums
To handle this case, you need to decide how you want the enum to show up in
Python (since Python doesn't have enums). If you are satisfied with a Python
int as a way to get your enum value, you can write a simple from_python()
function in namespace py::
#ifndef PY_NO_INLINE_FRIENDS_IN_NAMESPACE
namespace py {
#endif
mynamespace::SomeMeasure from_python(PyObject* x,
py::Type<mynamespace::SomeMeasure>)
{
return static_cast<mynamespace::SomeMeasure>(
from_python(x, py::Type<unsigned long>()));
}
#ifndef PY_NO_INLINE_FRIENDS_IN_NAMESPACE
}
#endif
You may also want to add a bunch of lines like this to your module
initialization:
mymodule.add(PyInt_FromLong(case1), "case1");
mymodule.add(PyInt_FromLong(case2), "case2");
...
raw C++ arrays
You could expose a function like this one to get the desired effect:
#include <py_cpp/objects.h>
void set_len(UnitCell& x, py::Tuple tuple)
{
double len[3];
for (std::size_t i =0; i < 3; ++i)
len[i] = py::from_python(tuple[i].get(), py::Type<double>());
x.set_len(len);
}
"Thin converting wrappers" for constructors
hijack some of the functionality
described in the section on Overridable Virtual Functions (even though you
don't have any virtual functions). I suggest this workaround:
struct UnitCellWrapper : UnitCell
{
UnitCellWrapper(PyObject* self, py::Tuple x, py::Tuple y)
: UnitCell(from_python(x[1], py::Type<double>()),
from_python(x[2], py::Type<double>()),
from_python(x[3], py::Type<double>()),
from_python(y[1], py::Type<double>()),
from_python(y[2], py::Type<double>()),
from_python(y[3], py::Type<double>()))
{}
}
py::ClassWrapper<UnitCell, UnitCellWrapper> unit_cell_class;
unit_cell_class.def(py::Constructor<py::Tuple, py::Tuple>());
...
returning references to wrapped objects
the importance of declaration order of ClassWrappers/ExtensionInstances
out parameters and non-const pointers
Miscellaneous
About the vc6 project and the debug build
About doctest.py
Boost remarks:
> > One of us is completely nuts ;->. How can I move the test
> > (is_prefix(enablers[i].name + 2, name + 2)) outside the loop if it
depends
> > on the loop index, i?
> >
> name += 2;
> for()
> {
> if (is_prefix(enablers[i].name + 2, name))
> }
I see now. I guess I should stop pussyfooting and either go for optimization
or clarity here, eh?
------
> Re: Dict
> Why abbreviate this? Code is read 5 or 6 times for every time its
> written. The few extra characters don't affect compile time or program
> speed. It's part of my personal goal of write what you mean, name them
what
> they are.
I completely agree. Abbrevs rub me the wrong way, 2 ;->
-------
Later:
keyword and varargs?
Put explicit Type<> arguments at the beginnings of overloads, to make them look more like template instance specifications.
Known bugs
can't handle 'const void' return values
Who returns 'const void'? I did it once, by mistake ;)