commit | abc29cad020bb035f698fdc3e5d33c5c551ed772 | [log] [tgz] |
---|---|---|
author | Jason Rhinelander <jason@imaginary.ca> | Mon Jan 23 03:50:00 2017 -0500 |
committer | Jason Rhinelander <jason@imaginary.ca> | Fri Feb 03 20:18:15 2017 -0500 |
tree | b54940c23e473f794e2677e6a4294b90f97df55a | |
parent | 709675a7aaa3e8f60ab6b9a03e93daa14f3b2372 [diff] |
Add support for non-converting arguments This adds support for controlling the `convert` flag of arguments through the py::arg annotation. This then allows arguments to be flagged as non-converting, which the type_caster is able to use to request different behaviour. Currently, AFAICS `convert` is only used for type converters of regular pybind11-registered types; all of the other core type_casters ignore it. We can, however, repurpose it to control internal conversion of converters like Eigen and `array`: most usefully to give callers a way to disable the conversion that would otherwise occur when a `Eigen::Ref<const Eigen::Matrix>` argument is passed a numpy array that requires conversion (either because it has an incompatible stride or the wrong dtype). Specifying a noconvert looks like one of these: m.def("f1", &f, "a"_a.noconvert() = "default"); // Named, default, noconvert m.def("f2", &f, "a"_a.noconvert()); // Named, no default, no converting m.def("f3", &f, py::arg().noconvert()); // Unnamed, no default, no converting (The last part--being able to declare a py::arg without a name--is new: previous py::arg() only accepted named keyword arguments). Such an non-convert argument is then passed `convert = false` by the type caster when loading the argument. Whether this has an effect is up to the type caster itself, but as mentioned above, this would be extremely helpful for the Eigen support to give a nicer way to specify a "no-copy" mode than the custom wrapper in the current PR, and moreover isn't an Eigen-specific hack.
pybind11 is a lightweight header-only library that exposes C++ types in Python and vice versa, mainly to create Python bindings of existing C++ code. Its goals and syntax are similar to the excellent Boost.Python library by David Abrahams: to minimize boilerplate code in traditional extension modules by inferring type information using compile-time introspection.
The main issue with Boost.Python—and the reason for creating such a similar project—is Boost. Boost is an enormously large and complex suite of utility libraries that works with almost every C++ compiler in existence. This compatibility has its cost: arcane template tricks and workarounds are necessary to support the oldest and buggiest of compiler specimens. Now that C++11-compatible compilers are widely available, this heavy machinery has become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with everything stripped away that isn't relevant for binding generation. Without comments, the core header files only require ~4K lines of code and depend on Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This compact implementation was possible thanks to some of the new C++11 language features (specifically: tuples, lambda functions and variadic templates). Since its creation, this library has grown beyond Boost.Python in many ways, leading to dramatically simpler binding code in many common situations.
Tutorial and reference documentation is provided at http://pybind11.readthedocs.org/en/master. A PDF version of the manual is available here.
pybind11 can map the following core C++ features to Python
std::shared_ptr
In addition to the core functionality, pybind11 provides some extra goodies:
Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an implementation-agnostic interface.
It is possible to bind C++11 lambda functions with captured variables. The lambda capture data is stored inside the resulting Python function object.
pybind11 uses C++11 move constructors and move assignment operators whenever possible to efficiently transfer custom data types.
It's easy to expose the internal storage of custom data types through Pythons' buffer protocols. This is handy e.g. for fast conversion between C++ matrix classes like Eigen and NumPy without expensive copy operations.
pybind11 can automatically vectorize functions so that they are transparently applied to all entries of one or more NumPy array arguments.
Python's slice-based access and assignment operations can be supported with just a few lines of code.
Everything is contained in just a few header files; there is no need to link against any additional libraries.
Binaries are generally smaller by a factor of at least 2 compared to equivalent bindings generated by Boost.Python. A recent pybind11 conversion of PyRosetta, an enormous Boost.Python binding project, reported a binary size reduction of 5.4x and compile time reduction by 5.8x.
When supported by the compiler, two new C++14 features (relaxed constexpr and return value deduction) are used to precompute function signatures at compile time, leading to smaller binaries.
With little extra effort, C++ types can be pickled and unpickled similar to regular Python objects.
This project was created by Wenzel Jakob. Significant features and/or improvements to the code were contributed by Jonas Adler, Sylvain Corlay, Trent Houliston, Axel Huebl, @hulucc, Sergey Lyskov Johan Mabille, Tomasz Miąsko, Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim Schellart, and Ivan Smirnov.
pybind11 is provided under a BSD-style license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.