It was already pretty badly intrusive, but it also appears to make MSVC
segfault. Rather than investigating and fixing it, it's easier to just
remove it.
As discussed in #320.
The adds a documentation block that mentions that the trampoline classes
must provide overrides for both the classes' own virtual methods *and*
any inherited virtual methods. It also provides a templated solution to
avoiding method duplication.
The example includes a third method (only mentioned in the "see also"
section of the documentation addition), using multiple inheritance.
While this approach works, and avoids code generation in deep
hierarchies, it is intrusive by requiring that the wrapped classes use
virtual inheritance, which itself is more instrusive if any of the
virtual base classes need anything other than default constructors. As
per the discussion in #320, it is kept as an example, but not suggested
in the documentation.
Functions returning specialized Eigen matrices like Eigen::DiagonalMatrix and
Eigen::SelfAdjointView--which inherit from EigenBase but not
DenseBase--isn't currently allowed; such classes are explicitly copyable
into a Matrix (by definition), and so we can support functions that
return them by copying the value into a Matrix then casting that
resulting dense Matrix into a numpy.ndarray. This commit does exactly
that.
Sergey Lyskov pointed out that the trampoline mechanism used to override
virtual methods from within Python caused unnecessary overheads when
instantiating the original (i.e. non-extended) class.
This commit removes this inefficiency, but some syntax changes were
needed to achieve this. Projects using this features will need to make a
few changes:
In particular, the example below shows the old syntax to instantiate a
class with a trampoline:
class_<TrampolineClass>("MyClass")
.alias<MyClass>()
....
This is what should be used now:
class_<MyClass, std::unique_ptr<MyClass, TrampolineClass>("MyClass")
....
Importantly, the trampoline class is now specified as the *third*
argument to the class_ template, and the alias<..>() call is gone. The
second argument with the unique pointer is simply the default holder
type used by pybind11.
This somewhat heavyweight solution will avoid size_t/long long/long/int
mismatches on various platforms once and for all. The previous template
overloads could e.g. not handle size_t on Darwin.
One gotcha: the 'format_descriptor<T>::value()' syntax changed to just
'format_descriptor<T>::value'
- new pybind11::base<> attribute to indicate a subclass relationship
- unified infrastructure for parsing variadic arguments in class_ and cpp_function
- use 'handle' and 'object' more consistently everywhere
Previously, pybind11 required classes using std::shared_ptr<> to derive
from std::enable_shared_from_this<> (or compilation failures would ensue).
Everything now also works for classes that don't do this, assuming that
some basic rules are followed (e.g. never passing "raw" pointers of
instances manged by shared pointers). The safer
std::enable_shared_from_this<> approach continues to be supported.
This modification taps into some newer C++14 features (if present) to
generate function signatures considerably more efficiently at compile
time rather than at run time.
With this change, pybind11 binaries are now *2.1 times* smaller compared
to the Boost.Python baseline in the benchmark. Compilation times get a
nice improvement as well.
Visual Studio 2015 unfortunately doesn't implement 'constexpr' well
enough yet to support this change and uses a runtime fallback.
The cpp_function class accepts a variadic argument, which was formerly
processed twice -- once at registration time, and once in the dispatch
lambda function. This is not only unnecessarily slow but also leads to
code bloat since it adds to the object code generated for every bound
function. This change removes the second pass at dispatch time.
One noteworthy change of this commit is that default arguments are now
constructed (and converted to Python objects) right at declaration time.
Consider the following example:
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg("arg") = SomeType(123));
In this case, the change means that pybind11 must already be set up to
deal with values of the type 'SomeType', or an exception will be thrown.
Another change is that the "preview" of the default argument in the
function signature is generated using the __repr__ special method. If
it is not available in this type, the signature may not be very helpful,
i.e.:
| myFunction(...)
| Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> None
One workaround (other than defining SomeType.__repr__) is to specify the
human-readable preview of the default argument manually using the more
cumbersome arg_t notation:
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg_t<SomeType>("arg", SomeType(123), "SomeType(123)"));
Using object class to hold converted object automatically deallocates
object if an exception is thrown or scope is left before constructing
complete Python object.
Additionally added method object::release() that allows to release
ownership of python object without decreasing its reference count.
