EQ2EMu/EQ2/source/depends/boost_1_72_0/libs/python/doc/faq.qbk

[chapter Frequently Asked Questions (FAQs)
    [quickbook 1.7]
    [id faq]
]

[section How can I wrap a function which takes a function pointer as an argument?]

If what you're trying to do is something like this:
``
  typedef boost::function<void (string s) > funcptr;

  void foo(funcptr fp)
  {
    fp("hello,world!");
  }

  BOOST_PYTHON_MODULE(test)
  {
    def("foo",foo);
  }
``

And then:

``
>>> def hello(s):
...    print s
...
>>> foo(hello)
hello, world!
``
The short answer is: "you can't".  This is not a
Boost.Python limitation so much as a limitation of C++.  The
problem is that a Python function is actually data, and the only
way of associating data with a C++ function pointer is to store it
in a static variable of the function.  The problem with that is
that you can only associate one piece of data with every C++
function, and we have no way of compiling a new C++ function
on-the-fly for every Python function you decide to pass
to `foo`.  In other words, this could work if the C++
function is always going to invoke the /same/ Python
function, but you probably don't want that.

If you have the luxury of changing the C++ code you're
wrapping, pass it an `object` instead and call that;
the overloaded function call operator will invoke the Python
function you pass it behind the `object`.

[endsect]
[section I'm getting the "attempt to return dangling reference" error.
 What am I doing wrong?]

That exception is protecting you from causing a nasty crash. It usually
happens in response to some code like this:
``
  period const &get_floating_frequency() const
  {
    return boost::python::call_method<period const &>( 
      m_self,"get_floating_frequency");
  }
``
And you get:
``
  ReferenceError: Attempt to return dangling reference to object of type:
  class period
``

In this case, the Python method invoked by `call_method`
constructs a new Python object. You're trying to return a reference to a
C++ object (an instance of `class period`) contained within
and owned by that Python object. Because the called method handed back a
brand new object, the only reference to it is held for the duration of
`get_floating_frequency()` above. When the function returns,
the Python object will be destroyed, destroying the instance of
`class period`, and leaving the returned reference dangling.
That's already undefined behavior, and if you try to do anything with
that reference you're likely to cause a crash. Boost.Python detects this
situation at runtime and helpfully throws an exception instead of letting
you do that.

[endsect]
[section Is `return_internal_reference` efficient?]

[*Q:] /I have an object composed of 12 doubles. A `const&` to
this object is returned by a member function of another class. From the
viewpoint of using the returned object in Python I do not care if I get
a copy or a reference to the returned object. In Boost.Python I have the
choice of using `copy_const_reference` or `return_internal_reference`.
Are there considerations that would lead me to prefer one over the other,
such as size of generated code or memory overhead?/

[*A:] `copy_const_reference` will make an instance with storage
for one of your objects, `size = base_size + 12 * sizeof(double)`.
`return_internal_reference` will make an instance with storage for a
pointer to one of your objects, `size = base_size + sizeof(void*)`.
However, it will also create a weak reference object which goes in the
source object's weakreflist and a special callback object to manage the
lifetime of the internally-referenced object. My guess?
`copy_const_reference` is your friend here, resulting in less overall
memory use and less fragmentation, also probably fewer total
cycles.

[endsect]
[section How can I wrap functions which take C++ containers as arguments?]

Ralf W. Grosse-Kunstleve provides these notes:

# Using the regular `class_<>` wrapper:
  ``
    class_<std::vector<double> >("std_vector_double")
      .def(...)
      ...
    ;
  ``
  This can be moved to a template so that several types (`double`, `int`,
  `long`, etc.) can be wrapped with the same code. This technique is used
  in the file `scitbx/include/scitbx/array_family/boost_python/flex_wrapper.h`
  in the "scitbx" package. The file could easily be modified for
  wrapping `std::vector<>` instantiations.
  This type of C++/Python binding is most suitable for containers
  that may contain a large number of elements (>10000).

# Using custom rvalue converters. Boost.Python "rvalue converters"
  match function signatures such as:
  ``
    void foo(std::vector<double> const &array); // pass by const-reference
    void foo(std::vector<double> array); // pass by value
  ``
  Some custom rvalue converters are implemented in the file
  `scitbx/include/scitbx/boost_python/container_conversions.h`
  This code can be used to convert from C++ container types such as
  `std::vector<>` or `std::list<>` to Python tuples and vice
  versa. A few simple examples can be found in the file
  `scitbx/array_family/boost_python/regression_test_module.cpp`
  Automatic C++ container <-> Python tuple conversions are most
  suitable for containers of moderate size. These converters generate
  significantly less object code compared to alternative 1 above.

A disadvantage of using alternative 2 is that operators such as
arithmetic +,-,*,/,% are not available. It would be useful to have custom
rvalue converters that convert to a "math_array" type instead of tuples.
This is currently not implemented but is possible within the framework of
Boost.Python V2 as it will be released in the next couple of weeks. [ed.:
this was posted on 2002/03/10]

It would also be useful to also have "custom lvalue converters" such
as `std::vector<>` <-> Python list. These converters would
support the modification of the Python list from C++. For example:

C++:
``
  void foo(std::vector<double> &array)
  {
    for(std::size_t i=0;i&lt;array.size();i++) {
      array[i] *= 2;
    }
  }
``
Python: [python]
``
  >>> l = [1, 2, 3]
  >>> foo(l)
  >>> print l
  [2, 4, 6]
``
Custom lvalue converters require changes to the Boost.Python core library
and are currently not available.

P.S.:

The "scitbx" files referenced above are available via anonymous
CVS:
``
  cvs -d:pserver:anonymous@cvs.cctbx.sourceforge.net:/cvsroot/cctbx login
  cvs -d:pserver:anonymous@cvs.cctbx.sourceforge.net:/cvsroot/cctbx co scitbx
``

[endsect]
[section fatal error C1204:Compiler limit:internal structure overflow]

[*Q:] /I get this error message when compiling a large source file. What can I do?/

[*A:] You have two choices:

# Upgrade your compiler (preferred)

# Break your source file up into multiple translation units.

  `my_module.cpp`: [c++]

  ``
    ...
    void more_of_my_module();
    BOOST_PYTHON_MODULE(my_module)
    {
      def("foo", foo);
      def("bar", bar);
      ...
      more_of_my_module();
    }
  ``
  `more_of_my_module.cpp`:
  ``
    void more_of_my_module()
    {
      def("baz", baz);
      ...
    }
  ``
  If you find that a `class_<...>` declaration
  can't fit in a single source file without triggering the error, you
  can always pass a reference to the `class_` object to a
  function in another source file, and call some of its member
  functions (e.g. `.def(...)`) in the auxilliary source
  file:

  `more_of_my_class.cpp`:
  ``
    void more_of_my_class(class&lt;my_class&gt;&amp; x)
    {
      x
       .def("baz", baz)
       .add_property("xx", &my_class::get_xx, &my_class::set_xx)
       ;
      ...
    }
  ``

[endsect]
[section How do I debug my Python extensions?]

Greg Burley gives the following answer for Unix GCC users:

[:Once you have created a boost python extension for your c++ library or
  class, you may need to debug the code. Afterall this is one of the
  reasons for wrapping the library in python. An expected side-effect or
  benefit of using BPL is that debugging should be isolated to the c++
  library that is under test, given that python code is minimal and
  boost::python either works or it doesn't. (ie. While errors can occur
  when the wrapping method is invalid, most errors are caught by the
  compiler ;-).

  The basic steps required to initiate a gdb session to debug a c++
  library via python are shown here. Note, however that you should start
  the gdb session in the directory that contains your BPL my_ext.so
  module.

  ``
    (gdb) target exec python
    (gdb) run
    >>> from my_ext import *
    >>> [C-c]
    (gdb) break MyClass::MyBuggyFunction
    (gdb) cont
    >>> pyobj = MyClass()
    >>> pyobj.MyBuggyFunction()
    Breakpoint 1, MyClass::MyBuggyFunction ...
    Current language:  auto; currently c++
    (gdb) do debugging stuff
  ``
]

Greg's approach works even better using Emacs' "gdb"
command, since it will show you each line of source as you step through it.

On *Windows*, my favorite debugging solution is the debugger that
comes with Microsoft Visual C++ 7. This debugger seems to work with code
generated by all versions of Microsoft and Metrowerks toolsets; it's rock
solid and "just works" without requiring any special tricks from the
user.

Raoul Gough has provided the following for gdb on Windows:

[:gdb support for Windows DLLs has improved lately, so it is
  now possible to debug Python extensions using a few
  tricks. Firstly, you will need an up-to-date gdb with support
  for minimal symbol extraction from a DLL. Any gdb from version 6
  onwards, or Cygwin gdb-20030214-1 and onwards should do. A
  suitable release will have a section in the gdb.info file under
  Configuration - Native - Cygwin Native -
  Non-debug DLL symbols. Refer to that info section for more
  details of the procedures outlined here.

  Secondly, it seems necessary to set a breakpoint in the
  Python interpreter, rather than using ^C to break execution. A
  good place to set this breakpoint is PyOS_Readline, which will
  stop execution immediately before reading each interactive
  Python command. You have to let Python start once under the
  debugger, so that it loads its own DLL, before you can set the
  breakpoint:

  ``
    $ gdb python
    GNU gdb 2003-09-02-cvs (cygwin-special)
    [...]

    (gdb) run
    Starting program: /cygdrive/c/Python22/python.exe
    Python 2.2.2 (#37, Oct 14 2002, 17:02:34) [MSC 32 bit (Intel)] on win32
    Type "help", "copyright", "credits" or "license" for more information.
    >>> ^Z


    Program exited normally.
    (gdb) break *&PyOS_Readline
    Breakpoint 1 at 0x1e04eff0
    (gdb) run
    Starting program: /cygdrive/c/Python22/python.exe
    Python 2.2.2 (#37, Oct 14 2002, 17:02:34) [MSC 32 bit (Intel)] on win32
    Type "help", "copyright", "credits" or "license" for more information.

    Breakpoint 1, 0x1e04eff0 in python22!PyOS_Readline ()
       from /cygdrive/c/WINNT/system32/python22.dll
    (gdb) cont
    Continuing.
    >>> from my_ext import *

    Breakpoint 1, 0x1e04eff0 in python22!PyOS_Readline ()
       from /cygdrive/c/WINNT/system32/python22.dll
    (gdb) # my_ext now loaded (with any debugging symbols it contains)
  ``
]

[h2 Debugging extensions through Boost.Build]

If you are launching your extension module tests with _bb_ using the
`boost-python-runtest` rule, you can ask it to launch your
debugger for you by adding "--debugger=/debugger/" to your bjam
command-line:
``
  bjam -sTOOLS=vc7.1 "--debugger=devenv /debugexe" test
  bjam -sTOOLS=gcc -sPYTHON_LAUNCH=gdb test
``
It can also be extremely useful to add the `-d+2` option when
you run your test, because Boost.Build will then show you the exact
commands it uses to invoke it. This will invariably involve setting up
PYTHONPATH and other important environment variables such as
LD_LIBRARY_PATH which may be needed by your debugger in order to get
things to work right.

[endsect]
[section Why doesn't my `*=` operator work?]

[*Q:] ['I have exported my class to python, with many overloaded
operators. it works fine for me except the `*=`
operator. It always tells me "can't multiply sequence with non int
type". If I use `p1.__imul__(p2)` instead of
`p1 *= p2`, it successfully executes my code. What's
wrong with me?]

[*A:] There's nothing wrong with you. This is a bug in Python
      2.2. You can see the same effect in Pure Python (you can learn a lot
      about what's happening in Boost.Python by playing with new-style
      classes in Pure Python).
``
>>> class X(object):
...     def __imul__(self, x):
...         print 'imul'
...
>>> x = X()
>>> x *= 1
``
To cure this problem, all you need to do is upgrade your Python to
version 2.2.1 or later.

[endsect]
[section Does Boost.Python work with Mac OS X?]

It is known to work under 10.2.8 and 10.3 using
Apple's gcc 3.3 compiler:
``gcc (GCC) 3.3 20030304 (Apple Computer, Inc. build 1493)``
Under 10.2.8 get the August 2003 gcc update (free at [@http://connect.apple.com]).
Under 10.3 get the Xcode Tools v1.0 (also free).

Python 2.3 is required. The Python that ships with 10.3 is
fine. Under 10.2.8 use these commands to install Python
as a framework:
``./configure --enable-framework
make
make frameworkinstall``

The last command requires root privileges because the target
directory is `/Library/Frameworks/Python.framework/Versions/2.3`.
However, the installation does not interfere with the Python
version that ships with 10.2.8.

It is also crucial to increase the `stacksize` before
starting compilations, e.g.:
``limit stacksize 8192k``
If the `stacksize` is too small the build might crash with
internal compiler errors.

Sometimes Apple's compiler exhibits a bug by printing an error
like the following while compiling a
`boost::python::class_<your_type>`
template instantiation:
``
  .../inheritance.hpp:44: error: cannot
    dynamic_cast `p' (of type `struct cctbx::boost_python::<unnamed>::add_pair*
    ') to type `void*' (source type is not polymorphic)
``

We do not know a general workaround, but if the definition of
`your_type` can be modified the following was found
to work in all cases encountered so far:
``
  struct your_type
  {
    // before defining any member data
  #if defined(__MACH__) &amp;&amp; defined(__APPLE_CC__) &amp;&amp; __APPLE_CC__ == 1493
    bool dummy_;
  #endif
    // now your member data, e.g.
    double x;
    int j;
    // etc.
  };
``
[endsect]
[section How can I find the existing PyObject that holds a C++ object?]

[: "I am wrapping a function that always returns a pointer to an
      already-held C++ object."]

One way to do that is to hijack the mechanisms used for wrapping a class
with virtual functions. If you make a wrapper class with an initial
PyObject* constructor argument and store that PyObject* as "self", you
can get back to it by casting down to that wrapper type in a thin wrapper
function. For example:
``
  class X { X(int); virtual ~X(); ... };
  X* f();  // known to return Xs that are managed by Python objects


  // wrapping code

  struct X_wrap : X
  {
    X_wrap(PyObject* self, int v) : self(self), X(v) {}
    PyObject* self;
  };

  handle<> f_wrap()
  {
    X_wrap* xw = dynamic_cast<X_wrap*>(f());
    assert(xw != 0);
    return handle<>(borrowed(xw->self));
  }

  ...

  def("f", f_wrap());
  class_<X,X_wrap,boost::noncopyable>("X", init<int>())
   ...
   ;
``

Of course, if X has no virtual functions you'll have to use
`static_cast` instead of `dynamic_cast` with no
runtime check that it's valid. This approach also only works if the
`X` object was constructed from Python, because
`X`\ s constructed from C++ are of course never
`X_wrap` objects.

Another approach to this requires you to change your C++ code a bit;
if that's an option for you it might be a better way to go. work we've
been meaning to get to anyway. When a `shared_ptr<X>` is
converted from Python, the shared_ptr actually manages a reference to the
containing Python object. When a shared_ptr<X> is converted back to
Python, the library checks to see if it's one of those "Python object
managers" and if so just returns the original Python object. So you could
just write `object(p)` to get the Python object back. To
exploit this you'd have to be able to change the C++ code you're wrapping
so that it deals with shared_ptr instead of raw pointers.

There are other approaches too. The functions that receive the Python
object that you eventually want to return could be wrapped with a thin
wrapper that records the correspondence between the object address and
its containing Python object, and you could have your f_wrap function
look in that mapping to get the Python object out.

[endsect]
[section How can I wrap a function which needs to take ownership of a raw pointer?]

[*Q:] Part of an API that I'm wrapping goes something like this:

``
  struct A {}; struct B { void add( A* ); }
  where B::add() takes ownership of the pointer passed to it.
``

However:

``
  a = mod.A()
  b = mod.B()
  b.add( a )
  del a
  del b
  # python interpreter crashes
  # later due to memory corruption.
``

Even binding the lifetime of a to b via `with_custodian_and_ward` doesn't prevent
the python object a from ultimately trying to delete the object it's pointing to.
Is there a way to accomplish a 'transfer-of-ownership' of a wrapped C++ object?

--Bruce Lowery

Yes: Make sure the C++ object is held by auto_ptr:
``
  class_<A, std::auto_ptr<A> >("A")
    ...
    ;
``
Then make a thin wrapper function which takes an auto_ptr parameter:
``
  void b_insert(B &b, std::auto_ptr<A> a)
  {
    b.insert(a.get());
    a.release();
  }
``
Wrap that as B.add. Note that pointers returned via `manage_new_object`
will also be held by `auto_ptr`, so this transfer-of-ownership
will also work correctly.

[endsect]
[section Compilation takes too much time and eats too much memory!
 What can I do to make it faster?]

Please refer to the `Reducing Compiling Time` section in the _tutorial_.

[endsect]
[section How do I create sub-packages using Boost.Python?]

Please refer to the `Creating Packages` section in the _tutorial_.

[endsect]
[section error C2064: term does not evaluate to a function taking 2 arguments]

/Niall Douglas provides these notes:/

If you see Microsoft Visual C++ 7.1 (MS Visual Studio .NET 2003) issue
an error message like the following it is most likely due to a bug
in the compiler:
``
  boost\boost\python\detail\invoke.hpp(76):
  error C2064: term does not evaluate to a function taking 2 arguments"
``
This message is triggered by code like the following:
``
  #include <boost/python.hpp>

  using namespace boost::python;

  class FXThread
  {
  public:
    bool setAutoDelete(bool doso) throw();
  };

  void Export_FXThread()
  {
    class_< FXThread >("FXThread")
        .def("setAutoDelete", &amp;FXThread::setAutoDelete)
    ;
  }
``
The bug is related to the `throw()` modifier.
As a workaround cast off the modifier. E.g.:
``
   .def("setAutoDelete", (bool (FXThread::*)(bool)) &FXThread::setAutoDelete)
``
(The bug has been reported to Microsoft.)

[endsect]
[section How can I automatically convert my custom string type to and from a Python string?]

/Ralf W. Grosse-Kunstleve provides these notes:/

Below is a small, self-contained demo extension module that shows
how to do this. Here is the corresponding trivial test:
``
  import custom_string
  assert custom_string.hello() == "Hello world."
  assert custom_string.size("california") == 10
``
If you look at the code you will find:

* A custom `to_python` converter (easy):
        `custom_string_to_python_str`

*A custom lvalue converter (needs more code):
        `custom_string_from_python_str`

The custom converters are registered in the global Boost.Python
registry near the top of the module initialization function. Once
flow control has passed through the registration code the automatic
conversions from and to Python strings will work in any module
imported in the same process.

``
  #include <boost/python/module.hpp>
  #include <boost/python/def.hpp>
  #include <boost/python/to_python_converter.hpp>

  namespace sandbox { namespace {

  class custom_string
  {
    public:
      custom_string() {}
      custom_string(std::string const &value) : value_(value) {}
      std::string const &value() const { return value_; }
    private:
      std::string value_;
  };

  struct custom_string_to_python_str
  {
    static PyObject* convert(custom_string const &s)
    {
      return boost::python::incref(boost::python::object(s.value()).ptr());
    }
  };

  struct custom_string_from_python_str
  {
    custom_string_from_python_str()
    {
      boost::python::converter::registry::push_back(
        &convertible,
        &construct,
        boost::python::type_id<custom_string>());
    }

    static void* convertible(PyObject* obj_ptr)
    {
      if (!PyString_Check(obj_ptr)) return 0;
      return obj_ptr;
    }

    static void construct(
      PyObject* obj_ptr,
      boost::python::converter::rvalue_from_python_stage1_data* data)
    {
      const char* value = PyString_AsString(obj_ptr);
      if (value == 0) boost::python::throw_error_already_set();
      void* storage = (
        (boost::python::converter::rvalue_from_python_storage<custom_string>*)
          data)->storage.bytes;
      new (storage) custom_string(value);
      data->convertible = storage;
    }
  };

  custom_string hello() { return custom_string("Hello world."); }

  std::size_t size(custom_string const &s) { return s.value().size(); }

  void init_module()
  {
    using namespace boost::python;

    boost::python::to_python_converter<
      custom_string,
      custom_string_to_python_str>();

    custom_string_from_python_str();

    def("hello", hello);
    def("size", size);
  }

  }} // namespace sandbox::<anonymous>

  BOOST_PYTHON_MODULE(custom_string)
  {
    sandbox::init_module();
  }
``
[endsect]
[section Why is my automatic to-python conversion not being found?]

/Niall Douglas provides these notes:/

If you define custom converters similar to the ones
shown above the `def_readonly()` and `def_readwrite()`
member functions provided by `boost::python::class_` for
direct access to your member data will not work as expected.
This is because `def_readonly("bar",&foo::bar)` is
equivalent to:

``
  .add_property("bar", make_getter(&foo::bar, return_internal_reference()))
``
Similarly, `def_readwrite("bar",&foo::bar)` is
equivalent to:

``
  .add_property("bar", make_getter(&foo::bar, return_internal_reference()),
                     make_setter(&foo::bar, return_internal_reference())
``
In order to define return value policies compatible with the
custom conversions replace `def_readonly()` and
`def_readwrite()` by `add_property()`. E.g.:

``
  .add_property("bar", make_getter(&foo::bar, return_value_policy<return_by_value>()),
                     make_setter(&foo::bar, return_value_policy<return_by_value>()))
``

[endsect]
[section Is Boost.Python thread-aware/compatible with multiple interpreters?]

/Niall Douglas provides these notes:/

The quick answer to this is: no.

The longer answer is that it can be patched to be so, but it's
complex. You will need to add custom lock/unlock wrapping of every
time your code enters Boost.Python (particularly every virtual
function override) plus heavily modify
`boost/python/detail/invoke.hpp` with custom unlock/lock
wrapping of every time Boost.Python enters your code. You must
furthermore take care to /not/ unlock/lock when Boost.Python
is invoking iterator changes via `invoke.hpp`.

There is a patched `invoke.hpp` posted on the C++-SIG
mailing list archives and you can find a real implementation of all
the machinery necessary to fully implement this in the TnFOX
project at [@http://sourceforge.net/projects/tnfox/ this]
SourceForge project location.

[endsect]