The format strings that are known at compile time are now accessible
via both ::value and ::format(), and format strings for everything
else is accessible via ::format(). This makes it backwards compatible.
This allows exposing a dict-like interface to python code, allowing
iteration over keys via:
for k in custommapping:
...
while still allowing iteration over pairs, so that you can also
implement 'dict.items()' functionality which returns a pair iterator,
allowing:
for k, v in custommapping.items():
...
example-sequences-and-iterators is updated with a custom class providing
both types of iteration.
This commit rewrites the examples that look for constructor/destructor
calls to do so via static variable tracking rather than output parsing.
The added ConstructorStats class provides methods to keep track of
constructors and destructors, number of default/copy/move constructors,
and number of copy/move assignments. It also provides a mechanism for
storing values (e.g. for value construction), and then allows all of
this to be checked at the end of a test by getting the statistics for a
C++ (or python mapping) class.
By not relying on the precise pattern of constructions/destructions,
but rather simply ensuring that every construction is matched with a
destruction on the same object, we ensure that everything that gets
created also gets destroyed as expected.
This replaces all of the various "std::cout << whatever" code in
constructors/destructors with
`print_created(this)`/`print_destroyed(this)`/etc. functions which
provide similar output, but now has a unified format across the
different examples, including a new ### prefix that makes mixed example
output and lifecycle events easier to distinguish.
With this change, relaxed mode is no longer needed, which enables
testing for proper destruction under MSVC, and under any other compiler
that generates code calling extra constructors, or optimizes away any
constructors. GCC/clang are used as the baseline for move
constructors; the tests are adapted to allow more move constructors to
be evoked (but other types are constructors much have matching counts).
This commit also disables output buffering of tests, as the buffering
sometimes results in C++ output ending up in the middle of python
output (or vice versa), depending on the OS/python version.
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.
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.
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 renames example files from `exampleN` to `example-description`.
Specifically, the following renaming is applied:
example1 -> example-methods-and-attributes
example2 -> example-python-types
example3 -> example-operator-overloading
example4 -> example-constants-and-functions
example5 -> example-callbacks (*)
example6 -> example-sequence-and-iterators
example7 -> example-buffers
example8 -> example-custom-ref-counting
example9 -> example-modules
example10 -> example-numpy-vectorize
example11 -> example-arg-keywords-and-defaults
example12 -> example-virtual-functions
example13 -> example-keep-alive
example14 -> example-opaque-types
example15 -> example-pickling
example16 -> example-inheritance
example17 -> example-stl-binders
example18 -> example-eval
example19 -> example-custom-exceptions
* the inheritance parts of example5 are moved into example-inheritance
(previously example16), and the remainder is left as example-callbacks.
This commit also renames the internal variables ("Example1",
"Example2", "Example4", etc.) into non-numeric names ("ExampleMandA",
"ExamplePythonTypes", "ExampleWithEnum", etc.) to correspond to the
file renaming.
The order of tests is preserved, but this can easily be changed if
there is some more natural ordering by updating the list in
examples/CMakeLists.txt.
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.
This allows (and changes the current examples) to exit with status 99 to
skip a test instead of outputting a special string ("NumPy missing").
This also fixes the eigen test, which currently fails when eigen
headers are available but NumPy is not, to skip instead of failing when
NumPy isn't available.
Add and declare to Python functions
double_mat_cm() --- compute 2* a column-major matrix
double_mat_rm() --- compute 2* a row-major matrix
to 'eigen.cpp' tests / example.
Passing a non-contiguous one-dimensional numpy array gives incorrect
results, so three of these tests fail. The only one passing is the
simple case where the numpy array is contiguous and we are building a
column-major vector. Subsequent commit will fix the three failing
cases.
