reference_internal requires an `instance` field to track the returned
reference's parent, but that's just a duplication of what
keep_alive<0,1> does, so use a keep alive to do this to eliminate the
duplication.
The pointer to the first member of a class instance is the same as the
pointer to instance itself; pybind11 has some workarounds for this to
not track registered instances that have a registered parent with the
same address. This doesn't work everywhere, however: issue #328 is a
failure of this for a mutator operator which resolves its argument to
the parent rather than the child, as is needed in #328.
This commit resolves the issue (and restores tracking of same-address
instances) by changing registered_instances from an unordered_map to an
unordered_multimap that allows duplicate instances for the same pointer
to be recorded, then resolves these differences by checking the type of
each matched instance when looking up an instance. (A
unordered_multimap seems cleaner for this than a unordered_map<list> or
similar because, the vast majority of the time, the instance will be
unique).
Currently pybind11 always translates values returned by Python functions
invoked from C++ code by copying, even when moving is feasible--and,
more importantly, even when moving is required.
The first, and relatively minor, concern is that moving may be
considerably more efficient for some types. The second problem,
however, is more serious: there's currently no way python code can
return a non-copyable type to C++ code.
I ran into this while trying to add a PYBIND11_OVERLOAD of a virtual
method that returns just such a type: it simply fails to compile because
this:
overload = ...
overload(args).template cast<ret_type>();
involves a copy: overload(args) returns an object instance, and the
invoked object::cast() loads the returned value, then returns a copy of
the loaded value.
We can, however, safely move that returned value *if* the object has the
only reference to it (i.e. if ref_count() == 1) and the object is
itself temporary (i.e. if it's an rvalue).
This commit does that by adding an rvalue-qualified object::cast()
method that allows the returned value to be move-constructed out of the
stored instance when feasible.
This basically comes down to three cases:
- For objects that are movable but not copyable, we always try the move,
with a runtime exception raised if this would involve moving a value
with multiple references.
- When the type is both movable and non-trivially copyable, the move
happens only if the invoked object has a ref_count of 1, otherwise the
object is copied. (Trivially copyable types are excluded from this
case because they are typically just collections of primitive types,
which can be copied just as easily as they can be moved.)
- Non-movable and trivially copy constructible objects are simply
copied.
This also adds examples to example-virtual-functions that shows both a
non-copyable object and a movable/copyable object in action: the former
raises an exception if returned while holding a reference, the latter
invokes a move constructor if unreferenced, or a copy constructor if
referenced.
Basically this allows code such as:
class MyClass(Pybind11Class):
def somemethod(self, whatever):
mt = MovableType(whatever)
# ...
return mt
which allows the MovableType instance to be returned to the C++ code
via its move constructor.
Of course if you attempt to violate this by doing something like:
self.value = MovableType(whatever)
return self.value
you get an exception--but right now, the pybind11-side of that code
won't compile at all.
Example signatures (old => new):
foo(int) => foo(arg0: int)
bar(Object, int) => bar(self: Object, arg0: int)
The change makes the signatures uniform for named and unnamed arguments
and it helps static analysis tools reconstruct function signatures from
docstrings.
This also tweaks the signature whitespace style to better conform to
PEP 8 for annotations and default arguments:
" : " => ": "
" = " => "="
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.
Some Eigen objects, such as those returned by matrix.diagonal() and
matrix.block() have non-standard stride values because they are
basically just maps onto the underlying matrix without copying it (for
example, the primary diagonal of a 3x3 matrix is a vector-like object
with .src equal to the full matrix data, but with stride 4). Returning
such an object from a pybind11 method breaks, however, because pybind11
assumes vectors have stride 1, and that matrices have strides equal to
the number of rows/columns or 1 (depending on whether the matrix is
stored column-major or row-major).
This commit fixes the issue by making pybind11 use Eigen's stride
methods when copying the data.
PR #309 broke scoped enums, which failed to compile because the added:
value == value2
comparison isn't valid for a scoped enum (they aren't implicitly
convertible to the underlying type). This commit fixes it by
explicitly converting the enum value to its underlying type before
doing the comparison.
It also adds a scoped enum example to the constants-and-functions
example that triggers the problem fixed in this commit.
Eigen::Ref is a common way to pass eigen dense types without needing a
template, e.g. the single definition `void
func(Eigen::Ref<Eigen::MatrixXd> x)` can be called with any double
matrix-like object.
The current pybind11 eigen support fails with internal errors if
attempting to bind a function with an Eigen::Ref<...> argument because
Eigen::Ref<...> satisfies the "is_eigen_dense" requirement, but can't
compile if actually used: Eigen::Ref<...> itself is not default
constructible, and so the argument std::tuple containing an
Eigen::Ref<...> isn't constructible, which results in compilation
failure.
This commit adds support for Eigen::Ref<...> by giving it its own
type_caster implementation which consists of an internal type_caster of
the referenced type, load/cast methods that dispatch to the internal
type_caster, and a unique_ptr to an Eigen::Ref<> instance that gets
set during load().
There is, of course, no performance advantage for pybind11-using code of
using Eigen::Ref<...>--we are allocating a matrix of the derived type
when loading it--but this has the advantage of allowing pybind11 to bind
transparently to C++ methods taking Eigen::Refs.
This changes the exception error message of a bad-arguments error to
suppress the constructor argument when the failure is a constructor.
This changes both the "Invoked with: " output to omit the object
instances, and rewrites the constructor signature to make it look
like a constructor (changing the first argument to the object name, and
removing the ' -> NoneType' return type.
GCC-6 adds a -Wplacement-new warning that warns for placement-new into a
space that is too small, which is sometimes being triggered here (e.g.
example5 always generates the warning under g++-6). It's a false
warning, however: the line immediately before just checked the size, and
so this line is never going to actually be reached in the cases where
the GCC warning is being triggered.
This localizes the warning disabling just to this one spot as there are
other placement-new uses in pybind11 where this warning could warn about
legitimate future problems.
This commit adds an additional _ template function for compile-time
selection between two description strings. This in turn allows the
elimination of needing two name() methods in type_caster<arithmetic
types> and type_caster<eigen types>, which allows them to start using
PYBIND11_TYPE_CASTER instead, simplifying their code by eliminating all
the code that they are duplicating from the macro.
In eigen.h, type_caster<Type>::load(): For the 'ndim == 1' case, use
the 'InnerStride' type because there is only an inner stride for a
vector. Choose between (n_elts x 1) or (1 x n_elts) according to
whether we're constructing a Vector or a RowVector.
