GitLab

In C++11 mode, `boost::apply_visitor` requires an explicit `result_type`.
This also adds optional tests for `boost::variant` in C++11/14, if boost
is available. In C++17 mode, `std::variant` is tested instead.

* Ensure :ref: for virtual_and_inheritance is parsed.

* Add quick blurb about __init__ with inherited types.

[skip ci]

boost::apply_visitor accepts its arguments by non-const lvalue
reference, which fails to bind to an rvalue reference.  Change the
example to remove the argument forwarding.

Attempting to mix py::module_local and non-module_local classes results
in some unexpected/undesirable behaviour:

- if a class is registered non-local by some other module, a later
  attempt to register it locally fails.  It doesn't need to: it is
  perfectly acceptable for the local registration to simply override
  the external global registration.
- going the other way (i.e. module `A` registers a type `T` locally,
  then `B` registers the same type `T` globally) causes a more serious
  issue: `A.T`'s constructors no longer work because the `self` argument
  gets converted to a `B.T`, which then fails to resolve.

Changing the cast precedence to prefer local over global fixes this and
makes it work more consistently, regardless of module load order.

This commit adds a `py::module_local` attribute that lets you confine a
registered type to the module (more technically, the shared object) in
which it is defined, by registering it with:

    py::class_<C>(m, "C", py::module_local())

This will allow the same C++ class `C` to be registered in different
modules with independent sets of class definitions.  On the Python side,
two such types will be completely distinct; on the C++ side, the C++
type resolves to a different Python type in each module.

This applies `py::module_local` automatically to `stl_bind.h` bindings
when the container value type looks like something global: i.e. when it
is a converting type (for example, when binding a `std::vector<int>`),
or when it is a registered type itself bound with `py::module_local`.
This should help resolve potential future conflicts (e.g. if two
completely unrelated modules both try to bind a `std::vector<int>`.
Users can override the automatic selection by adding a
`py::module_local()` or `py::module_local(false)`.

Note that this does mildly break backwards compatibility: bound stl
containers of basic types like `std::vector<int>` cannot be bound in one
module and returned in a different module.  (This can be re-enabled with
`py::module_local(false)` as described above, but with the potential for
eventual load conflicts).

Resolves #645.

[skip ci]

Wrapped long lines and removed a few trailing spaces.

This commit allows multiple inheritance of pybind11 classes from
Python, e.g.

    class MyType(Base1, Base2):
        def __init__(self):
            Base1.__init__(self)
            Base2.__init__(self)

where Base1 and Base2 are pybind11-exported classes.

This requires collapsing the various builtin base objects
(pybind11_object_56, ...) introduced in 2.1 into a single
pybind11_object of a fixed size; this fixed size object allocates enough
space to contain either a simple object (one base class & small* holder
instance), or a pointer to a new allocation that can contain an
arbitrary number of base classes and holders, with holder size
unrestricted.

* "small" here means having a sizeof() of at most 2 pointers, which is
enough to fit unique_ptr (sizeof is 1 ptr) and shared_ptr (sizeof is 2
ptrs).

To minimize the performance impact, this repurposes
`internals::registered_types_py` to store a vector of pybind-registered
base types.  For direct-use pybind types (e.g. the `PyA` for a C++ `A`)
this is simply storing the same thing as before, but now in a vector;
for Python-side inherited types, the map lets us avoid having to do a
base class traversal as long as we've seen the class before.  The
change to vector is needed for multiple inheritance: Python types
inheriting from multiple registered bases have one entry per base.

Fix spelling

[skip ci]

This commit also adds `doc()` to `object_api` as a shortcut for the
`attr("__doc__")` accessor.

The module macro changes from:
```c++
PYBIND11_PLUGIN(example) {
    pybind11::module m("example", "pybind11 example plugin");
    m.def("add", [](int a, int b) { return a + b; });
    return m.ptr();
}
```

to:

```c++
PYBIND11_MODULE(example, m) {
    m.doc() = "pybind11 example plugin";
    m.def("add", [](int a, int b) { return a + b; });
}
```

Using the old macro results in a deprecation warning. The warning
actually points to the `pybind11_init` function (since attributes
don't bind to macros), but the message should be quite clear:
"PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE".

:exc: isn't valid.

This extends py::vectorize to automatically pass through
non-vectorizable arguments.  This removes the need for the documented
"explicitly exclude an argument" workaround.

Vectorization now applies to arithmetic, std::complex, and POD types,
passed as plain value or by const lvalue reference (previously only
pass-by-value types were supported).  Non-const lvalue references and
any other types are passed through as-is.

Functions with rvalue reference arguments (whether vectorizable or not)
are explicitly prohibited: an rvalue reference is inherently not
something that can be passed multiple times and is thus unsuitable to
being in a vectorized function.

The vectorize returned value is also now more sensitive to inputs:
previously it would return by value when all inputs are of size 1; this
is now amended to having all inputs of size 1 *and* 0 dimensions.  Thus
if you pass in, for example, [[1]], you get back a 1x1, 2D array, while
previously you got back just the resulting single value.

Vectorization of member function specializations is now also supported
via `py::vectorize(&Class::method)`; this required passthrough support
for the initial object pointer on the wrapping function pointer.

This attribute lets you disable (or explicitly enable) passing None to
an argument that otherwise would allow it by accepting
a value by raw pointer or shared_ptr.

This exposed a few underlying issues:

1. is_pod_struct was too strict to allow this. I've relaxed it to
require only trivially copyable and standard layout, rather than POD
(which additionally requires a trivial constructor, which std::complex
violates).

2. format_descriptor<std::complex<T>>::format() returned numpy format
strings instead of PEP3118 format strings, but register_dtype
feeds format codes of its fields to _dtype_from_pep3118. I've changed it
to return PEP3118 format codes. format_descriptor is a public type, so
this may be considered an incompatible change.

3. register_structured_dtype tried to be smart about whether to mark
fields as unaligned (with ^). However, it's examining the C++ alignment,
rather than what numpy (or possibly PEP3118) thinks the alignment should
be. For complex values those are different. I've made it mark all fields
as ^ unconditionally, which should always be safe even if they are
aligned, because we explicitly mark the padding.

Resolves #800.

Both C++ arrays and std::array are supported, including mixtures like
std::array<int, 2>[4]. In a multi-dimensional array of char, the last
dimension is used to construct a numpy string type.

Under MSVC we were ignoring PYBIND11_CPP_STANDARD and simply not
passing any standard (which makes MSVC default to its C++14 mode).

MSVC 2015u3 added the `/std:c++14` and `/std:c++latest` flags; the
latter, under MSVC 2017, enables some C++17 features (such as
`std::optional` and `std::variant`), so it is something we need to
start supporting under MSVC.

This makes the PYBIND11_CPP_STANDARD cmake variable work under MSVC,
defaulting it to /std:c++14 (matching the default -std=c++14 for
non-MSVC).

It also adds a new appveyor test running under MSVC 2017 with
/std:c++latest, which runs (and passes) the
`std::optional`/`std::variant` tests.

Also updated the documentation to clarify the c++ flags and add show
MSVC flag examples.

We're current copy by creating an Eigen::Map into the input numpy
array, then assigning that to the basic eigen type, effectively having
Eigen do the copy.  That doesn't work for negative strides, though:
Eigen doesn't allow them.

This commit makes numpy do the copying instead by allocating the eigen
type, then having numpy copy from the input array into a numpy reference
into the eigen object's data.  This also saves a copy when type
conversion is required: numpy can do the conversion on-the-fly as part
of the copy.

Finally this commit also makes non-reference parameters respect the
convert flag, declining the load when called in a noconvert pass with a
convertible, but non-array input or an array with the wrong dtype.

```c++
m.def("foo", foo, py::call_guard<T>());
```

is equivalent to:

```c++
m.def("foo", [](args...) {
    T scope_guard;
    return foo(args...); // forwarded arguments
});
```

* Support raw string literals as input for py::eval
* Dedent only when needed

* nicer py::capsule destructor mechanism
* added destructor-only version of capsule & tests
* added documentation for module destructors (fixes #733)

The extends the previous unchecked support with the ability to
determine the dimensions at runtime.  This incurs a small performance
hit when used (versus the compile-time fixed alternative), but is still considerably
faster than the full checks on every call that happen with
`.at()`/`.mutable_at()`.