2ac5044a05
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)"));
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|---|---|---|
| docs | ||
| example | ||
| include/pybind11 | ||
| tools | ||
| .appveyor.yml | ||
| .gitignore | ||
| .gitmodules | ||
| .travis.yml | ||
| CMakeLists.txt | ||
| LICENSE | ||
| logo.png | ||
| MANIFEST.in | ||
| README.md | ||
| setup.cfg | ||
| setup.py | ||
pybind11 — Seamless operability between C++11 and Python
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. The core header files only require ~2K lines of code and depend on Python (2.7 or 3.x) and the C++ standard library. This compact implementation was possible thanks to some of the new C++11 language features (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/latest.
Core features
pybind11 can map the following core C++ features to Python
- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
- Exceptions
- Enumerations
- Callbacks
- Custom operators
- STL data structures
- Smart pointers with reference counting like
std::shared_ptr - Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
Goodies
In addition to the core functionality, pybind11 provides some extra goodies:
-
pybind11 uses C++11 move constructors and move assignment operators whenever possible to efficiently transfer custom data types.
-
It is possible to bind C++11 lambda functions with captured variables. The lambda capture data is stored inside the resulting Python function object.
-
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.
License
pybind11 is provided under a BSD-style license that can be found in the
LICENSE.txt file. By using, distributing, or contributing to this project,
you agree to the terms and conditions of this license.