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took a stab at some documentation

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README.md
......@@ -3,6 +3,7 @@
# pybind11 — Seamless operability between C++11 and Python
[![Build Status](https://travis-ci.org/wjakob/pybind11.svg?branch=master)](https://travis-ci.org/wjakob/pybind11)
[![Documentation Status](https://readthedocs.org/projects/pybind11/badge/?version=latest)](http://pybind11.readthedocs.org/en/latest/?badge=latest)
**pybind11** is a lightweight header library that exposes C++ types in Python
and vice versa, mainly to create Python bindings of existing C++ code. Its
......@@ -24,10 +25,15 @@ everything stripped away that isn't relevant for binding generation. The whole
codebase requires less than 3000 lines of code and only depends 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).
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](http://pybind11.readthedocs.org/en/latest).
## Core features
The following core C++ features can be mapped to Python
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
......@@ -45,6 +51,12 @@ The following core C++ features can be mapped to 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.
......@@ -55,199 +67,4 @@ In addition to the core functionality, pybind11 provides some extra goodies:
- Python's slice-based access and assignment operations can be supported with
just a few lines of code.
- 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.
## What does the binding code look like?
Here is a simple example. The directory `example` contains many more.
```C++
#include <pybind/pybind.h>
#include <pybind/operators.h>
namespace py = pybind;
/// Example C++ class which should be bound to Python
class Test {
public:
Test();
Test(int value);
std::string toString();
Test operator+(const Test &e) const;
void print_dict(py::dict dict) {
/* Easily interact with Python types */
for (auto item : dict)
std::cout << "key=" << item.first << ", "
<< "value=" << item.second << std::endl;
}
int value = 0;
};
PYTHON_PLUGIN(example) {
py::module m("example", "pybind example plugin");
py::class_<Test>(m, "Test", "docstring for the Test class")
.def(py::init<>(), "docstring for constructor 1")
.def(py::init<int>(), "docstring for constructor 2")
.def(py::self + py::self, "Addition operator")
.def("__str__", &Test::toString, "Convert to a string representation")
.def("print_dict", &Test::print_dict, "Print a Python dictionary")
.def_readwrite("value", &Test::value, "An instance attribute");
return m.ptr();
}
```
## A collection of specific use cases (mostly buffer-related for now)
For brevity, let's set
```C++
namespace py = pybind;
```
### Exposing buffer views
Python supports an extremely general and convenient approach for exchanging
data between plugin libraries. Types can expose a buffer view which provides
fast direct access to the raw internal representation. Suppose we want to bind
the following simplistic Matrix class:
```C++
class Matrix {
public:
Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
m_data = new float[rows*cols];
}
float *data() { return m_data; }
size_t rows() const { return m_rows; }
size_t cols() const { return m_cols; }
private:
size_t m_rows, m_cols;
float *m_data;
};
```
The following binding code exposes the ``Matrix`` contents as a buffer object,
making it possible to cast Matrixes into NumPy arrays. It is even possible to
completely avoid copy operations with Python expressions like
``np.array(matrix_instance, copy = False)``.
```C++
py::class_<Matrix>(m, "Matrix")
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(float), /* Size of one scalar */
py::format_descriptor<float>::value(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(float) * m.rows(), /* Strides (in bytes) for each index */
sizeof(float) }
);
});
```
The snippet above binds a lambda function, which can create ``py::buffer_info``
description records on demand describing a given matrix. The contents of
``py::buffer_info`` mirror the Python buffer protocol specification.
```C++
struct buffer_info {
void *ptr;
size_t itemsize;
std::string format;
int ndim;
std::vector<size_t> shape;
std::vector<size_t> strides;
};
```
### Taking Python buffer objects as arguments
To create a C++ function that can take a Python buffer object as an argument,
simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
in a great variety of configurations, hence some safety checks are usually
necessary in the function body. Below, you can see an basic example on how to
define a custom constructor for the Eigen double precision matrix
(``Eigen::MatrixXd``) type, which supports initialization from compatible
buffer
objects (e.g. a NumPy matrix).
```C++
py::class_<Eigen::MatrixXd>(m, "MatrixXd")
.def("__init__", [](Eigen::MatrixXd &m, py::buffer b) {
/* Request a buffer descriptor from Python */
py::buffer_info info = b.request();
/* Some sanity checks ... */
if (info.format != py::format_descriptor<double>::value())
throw std::runtime_error("Incompatible format: expected a double array!");
if (info.ndim != 2)
throw std::runtime_error("Incompatible buffer dimension!");
if (info.strides[0] == sizeof(double)) {
/* Buffer has the right layout -- directly copy. */
new (&m) Eigen::MatrixXd(info.shape[0], info.shape[1]);
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
} else {
/* Oops -- the buffer is transposed */
new (&m) Eigen::MatrixXd(info.shape[1], info.shape[0]);
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
m.transposeInPlace();
}
});
```
### Taking NumPy arrays as arguments
By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
restrict the function so that it only accepts NumPy arrays (rather than any
type of Python object satisfying the buffer object protocol).
In many situations, we want to define a function which only accepts a NumPy
array of a certain data type. This is possible via the ``py::array_dtype<T>``
template. For instance, the following function requires the argument to be a
dense array of doubles in C-style ordering.
```C++
void f(py::array_dtype<double> array);
```
When it is invoked with a different type (e.g. an integer), the binding code
will attempt to cast the input into a NumPy array of the requested type.
### Auto-vectorizing a function over NumPy array arguments
Suppose we want to bind a function with the following signature to Python so
that it can process arbitrary NumPy array arguments (vectors, matrices, general
N-D arrays) in addition to its normal arguments:
```C++
double my_func(int x, float y, double z);
```
This is extremely simple to do!
```C++
m.def("vectorized_func", py::vectorize(my_func));
```
Invoking the function like below causes 4 calls to be made to ``my_func`` with
each of the the array elements. The result is returned as a NumPy array of type
``numpy.dtype.float64``.
```Python
>>> x = np.array([[1, 3],[5, 7]])
>>> y = np.array([[2, 4],[6, 8]])
>>> z = 3
>>> result = vectorized_func(x, y, z)
```
The scalar argument ``z`` is transparently replicated 4 times. The input
arrays ``x`` and ``y`` are automatically converted into the right types (they
are of type ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
``numpy.dtype.float32``, respectively)
Sometimes we might want to explitly exclude an argument from the vectorization
because it makes little sense to wrap it in a NumPy array. For instance,
suppose the function signature was
```C++
double my_func(int x, float y, my_custom_type *z);
```
This can be done with a stateful Lambda closure:
```C++
// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
m.def("vectorized_func",
[](py::array_dtype<int> x, py::array_dtype<float> y, my_custom_type *z) {
auto stateful_closure = [z](int x, float y) { return my_func(x, y, z); };
return py::vectorize(stateful_closure)(x, y);
}
);
```
docs/Makefile 0 → 100644
# Makefile for Sphinx documentation
#
# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = .build
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
endif
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
help:
@echo "Please use \`make <target>' where <target> is one of"
@echo " html to make standalone HTML files"
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@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
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clean:
rm -rf $(BUILDDIR)/*
html:
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
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@echo
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@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
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@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/pybind11.qhc"
applehelp:
$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
@echo
@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
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@echo
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@echo "Running LaTeX files through pdflatex..."
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@echo
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docs/advanced.rst 0 → 100644
.. _advanced:
Advanced topics
###############
Operator overloading
====================
Overriding virtual functions in Python
======================================
Passing anonymous functions
===========================
Return value policies
=====================
Functions taking Python objects as arguments
============================================
Callbacks
=========
Buffer protocol
===============
Python supports an extremely general and convenient approach for exchanging
data between plugin libraries. Types can expose a buffer view which provides
fast direct access to the raw internal representation. Suppose we want to bind
the following simplistic Matrix class:
.. code-block:: cpp
class Matrix {
public:
Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) {
m_data = new float[rows*cols];
}
float *data() { return m_data; }
size_t rows() const { return m_rows; }
size_t cols() const { return m_cols; }
private:
size_t m_rows, m_cols;
float *m_data;
};
The following binding code exposes the ``Matrix`` contents as a buffer object,
making it possible to cast Matrixes into NumPy arrays. It is even possible to
completely avoid copy operations with Python expressions like
``np.array(matrix_instance, copy = False)``.
.. code-block:: cpp
py::class_<Matrix>(m, "Matrix")
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(float), /* Size of one scalar */
py::format_descriptor<float>::value(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(float) * m.rows(), /* Strides (in bytes) for each index */
sizeof(float) }
);
});
The snippet above binds a lambda function, which can create ``py::buffer_info``
description records on demand describing a given matrix. The contents of
``py::buffer_info`` mirror the Python buffer protocol specification.
.. code-block:: cpp
struct buffer_info {
void *ptr;
size_t itemsize;
std::string format;
int ndim;
std::vector<size_t> shape;
std::vector<size_t> strides;
};
To create a C++ function that can take a Python buffer object as an argument,
simply use the type ``py::buffer`` as one of its arguments. Buffers can exist
in a great variety of configurations, hence some safety checks are usually
necessary in the function body. Below, you can see an basic example on how to
define a custom constructor for the Eigen double precision matrix
(``Eigen::MatrixXd``) type, which supports initialization from compatible
buffer
objects (e.g. a NumPy matrix).
.. code-block:: cpp
py::class_<Eigen::MatrixXd>(m, "MatrixXd")
.def("__init__", [](Eigen::MatrixXd &m, py::buffer b) {
/* Request a buffer descriptor from Python */
py::buffer_info info = b.request();
/* Some sanity checks ... */
if (info.format != py::format_descriptor<double>::value())
throw std::runtime_error("Incompatible format: expected a double array!");
if (info.ndim != 2)
throw std::runtime_error("Incompatible buffer dimension!");
if (info.strides[0] == sizeof(double)) {
/* Buffer has the right layout -- directly copy. */
new (&m) Eigen::MatrixXd(info.shape[0], info.shape[1]);
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
} else {
/* Oops -- the buffer is transposed */
new (&m) Eigen::MatrixXd(info.shape[1], info.shape[0]);
memcpy(m.data(), info.ptr, sizeof(double) * m.size());
m.transposeInPlace();
}
});
NumPy support
=============
By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can
restrict the function so that it only accepts NumPy arrays (rather than any
type of Python object satisfying the buffer object protocol).
In many situations, we want to define a function which only accepts a NumPy
array of a certain data type. This is possible via the ``py::array_dtype<T>``
template. For instance, the following function requires the argument to be a
dense array of doubles in C-style ordering.
.. code-block:: cpp
void f(py::array_dtype<double> array);
When it is invoked with a different type (e.g. an integer), the binding code
will attempt to cast the input into a NumPy array of the requested type.
Note that this feature requires the ``pybind/numpy.h`` header to be included.
Vectorizing functions
=====================
Suppose we want to bind a function with the following signature to Python so
that it can process arbitrary NumPy array arguments (vectors, matrices, general
N-D arrays) in addition to its normal arguments:
.. code-block:: cpp
double my_func(int x, float y, double z);
After including the ``pybind/numpy.h`` header, this is extremely simple:
.. code-block:: cpp
m.def("vectorized_func", py::vectorize(my_func));
Invoking the function like below causes 4 calls to be made to ``my_func`` with
each of the the array elements. The result is returned as a NumPy array of type
``numpy.dtype.float64``.
.. code-block:: python
>>> x = np.array([[1, 3],[5, 7]])
>>> y = np.array([[2, 4],[6, 8]])
>>> z = 3
>>> result = vectorized_func(x, y, z)
The scalar argument ``z`` is transparently replicated 4 times. The input
arrays ``x`` and ``y`` are automatically converted into the right types (they
are of type ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and
``numpy.dtype.float32``, respectively)
Sometimes we might want to explitly exclude an argument from the vectorization
because it makes little sense to wrap it in a NumPy array. For instance,
suppose the function signature was
.. code-block:: cpp
double my_func(int x, float y, my_custom_type *z);
This can be done with a stateful Lambda closure:
.. code-block:: cpp
// Vectorize a lambda function with a capture object (e.g. to exclude some arguments from the vectorization)
m.def("vectorized_func",
[](py::array_dtype<int> x, py::array_dtype<float> y, my_custom_type *z) {
auto stateful_closure = [z](int x, float y) { return my_func(x, y, z); };
return py::vectorize(stateful_closure)(x, y);
}
);
Throwing exceptions
===================
STL data structures
===================
Smart pointers
==============
.. _custom_constructors:
Custom constructors
===================
docs/basics.rst 0 → 100644
.. _basics:
First steps
###########
This sections demonstrates the basic features of pybind11. Before getting
started, make sure that development environment is set up to compile the
included set of examples, which also double as test cases.
Compiling the test cases
========================
Linux/MacOS
-----------
On Linux you'll need to install the **python-dev** or **python3-dev** packages as
well as **cmake**. On Mac OS, the included python version works out of the box,
but **cmake** must still be installed.
After installing the prerequisites, run
.. code-block:: bash
cmake .
make -j 4
followed by
.. code-block:: bash
make test
Windows
-------
On Windows, use the `CMake GUI`_ to create a Visual Studio project. Note that
only the 2015 release and newer versions are supported since pybind11 relies on
various C++11 language features that break older versions of Visual Studio.
After running CMake, open the created :file:`pybind.sln` file and perform a
release build, which will will produce a file named
:file:`Release\\example.pyd`. Copy this file to the :file:`example` directory
and run :file:`example\\run_test.py` using the targeted Python version.
.. _`CMake GUI`: https://cmake.org/runningcmake
.. Note::
When all tests fail, make sure that
1. The Python binary and the testcases are compiled for the same processor
type and bitness (i.e. either **i386** or **x86_64**)
2. The Python binary used to run :file:`example\\run_test.py` matches the
Python version specified in the CMake GUI. This is controlled via
the ``PYTHON_EXECUTABLE`` ``PYTHON_INCLUDE_DIR``, and
``PYTHON_LIBRARY`` variables.
.. seealso::
Advanced users who are already familiar with Boost.Python may want to skip
the tutorial and look at the test cases in the :file:`example` directory,
which exercise all features of pybind11.
Creating bindings for a simple function
=======================================
Let's start by creating Python bindings for an extremely simple function, which
adds two numbers and returns their result:
.. code-block:: cpp
int add(int i, int j) {
return i + j;
}
For simplicity [#f1]_, we'll put both this function and the binding code into
a file named :file:`example.cpp` with the following contents:
.. code-block:: cpp
#include <pybind/pybind.h>
int add(int i, int j) {
return i + j;
}
namespace py = pybind;
PYTHON_PLUGIN(example) {
py::module m("example", "pybind example plugin");
m.def("add", &add, "A function which adds two numbers");
return m.ptr();
}
The :func:`PYTHON_PLUGIN` macro creates a function that will be called when an
``import`` statement is issued from within Python. The next line creates a
module named ``example`` (with the supplied docstring). The method
:func:`module::def` generates binding code that exposes the
``add()`` function to Python. The last line returns the internal Python object
associated with ``m`` to the Python interpreter.
.. note::
Notice how little code was needed to expose our function to Python: all
details regarding the function's parameters and return value were
automatically inferred using template metaprogramming. This overall
approach and the used syntax are borrowed from Boost.Python, though the
underlying implementation is very different.
pybind11 is a header-only-library, hence it is not necessary to link against
any special libraries (other than Python itself). On Windows, use the CMake
build file discussed in section :ref:`cmake`. On Linux and Mac OS, the above
example can be compiled using the following command
.. code-block:: bash
$ c++ -O3 -shared -std=c++11 -I <path-to-pybind>/include `python-config --cflags --libs` example.cpp -o example.so
In general, it is advisable to include several additional build parameters
that can considerably reduce the size of the created binary. Refer to section
:ref:`cmake` for a detailed example of a suitable cross-platform CMake-based
build system.
Assuming that the created file :file:`example.so` (:file:`example.pyd` on Windows)
is located in the current directory, the following interactive Python session
shows how to load and execute the example.
.. code-block:: python
% python
Python 2.7.10 (default, Aug 22 2015, 20:33:39)
[GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import example
>>> example.add(1, 2)
3L
.. _keyword_args:
Keyword arguments
=================
With a simple modification code, it is possible to inform Python about the
names of the arguments ("i" and "j" in this case).
.. code-block:: cpp
m.def("add", &add, "A function which adds two numbers",
py::arg("i"), py::arg("j"));
:class:`arg` is one of several special tag classes which can be used to pass
metadata into :func:`module::def`. With this modified binding code, we can now
call the function using keyword arguments, which is a more readable alternative
particularly for functions taking many parameters:
.. code-block:: python
>>> import example
>>> example.add(i=1, j=2)
3L
The keyword names also appear in the function signatures within the documentation.
.. code-block:: python
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int32_t, j: int32_t) -> int32_t
A function which adds two numbers
.. _default_args:
Default arguments
=================
Suppose now that the function to be bound has default arguments, e.g.:
.. code-block:: cpp
int add(int i = 1, int j = 2) {
return i + j;
}
Unfortunately, pybind11 cannot automatically extract these parameters, since they
are not part of the function's type information. However, they are simple to specify
using an extension of :class:`arg`:
.. code-block:: cpp
m.def("add", &add, "A function which adds two numbers",
py::arg("i") = 1, py::arg("j") = 2);
The default values also appear within the documentation.
.. code-block:: python
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int32_t = 1L, j: int32_t = 2L) -> int32_t
A function which adds two numbers
.. _supported_types:
Supported data types
====================
The following basic data types are supported out of the box (some may require
an additional extension header to be included). To pass other data structures
as arguments and return values, refer to the section on :ref:`classes`.
+------------------------+--------------------------+---------------------+
| Data type | Description | Header file |
+========================+==========================+=====================+
| int8_t, uint8_t | 8-bit integers | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| int16_t, uint16_t | 16-bit integers | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| int32_t, uint32_t | 32-bit integers | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| int64_t, uint64_t | 64-bit integers | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| ssize_t, size_t | Platform-dependent size | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| float, double | Floating point types | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| bool | Two-state Boolean type | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| char | Character literal | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| const char * | UTF-8 string literal | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| std::string | STL dynamic UTF-8 string | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| std::pair<T1, T2> | Pair of two custom types | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| std::tuple<....> | Arbitrary tuple of types | pybind/pybind.h |
+------------------------+--------------------------+---------------------+
| std::complex<T> | Complex numbers | pybind/complex.h |
+------------------------+--------------------------+---------------------+
| std::vector<T> | STL dynamic array | pybind/stl.h |
+------------------------+--------------------------+---------------------+
| std::map<T1, T2> | STL dynamic maps | pybind/stl.h |
+------------------------+--------------------------+---------------------+
| std::function<...> | STL polymorphic function | pybind/functional.h |
+------------------------+--------------------------+---------------------+
.. [#f1] In practice, implementation and binding code will generally be located
in separate files.
docs/classes.rst 0 → 100644
.. _classes:
Object-oriented code
####################
Creating bindings for a custom type
===================================
Let's now look at a more complex example where we'll create bindings for a
custom C++ data structure named ``Pet``. Its definition is given below:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name) : name(name) { }
void setName(const std::string &name_) { name = name_; }
const std::string &getName() const { return name; }
std::string name;
};
The binding code for ``Pet`` looks as follows:
.. code-block:: cpp
#include <pybind/pybind.h>
namespace py = pybind;
PYTHON_PLUGIN(example) {
py::module m("example", "pybind example plugin");
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def("setName", &Pet::setName)
.def("getName", &Pet::getName);
return m.ptr();
}
:class:`class_` creates bindings for a C++ `class` or `struct`-style data
structure. :func:`init` is a convenience function that takes the types of a
constructor's parameters as template arguments and wraps the corresponding
constructor (see the :ref:`custom_constructors` section for details). An
interactive Python session demonstrating this example is shown below:
.. code-block:: python
% python
>>> import example
>>> p = example.Pet('Molly')
>>> print(p)
<example.Pet object at 0x10cd98060>
>>> p.getName()
u'Molly'
>>> p.setName('Charly')
>>> p.getName()
u'Charly'
Keyword and default arguments
=============================
It is possible to specify keyword and default arguments using the syntax
discussed in the previous chapter. Refer to the sections :ref:`keyword_args`
and :ref:`default_args` for details.
Binding lambda functions
========================
Note how ``print(p)`` produced a rather useless summary of our data structure in the example above:
.. code-block:: python
>>> print(p)
<example.Pet object at 0x10cd98060>
To address this, we could bind an utility function that returns a human-readable
summary to the special method slot named ``__repr__``. Unfortunately, there is no
suitable functionality in the ``Pet`` data structure, and it would be nice if
we did not have to change it. This can easily be accomplished by binding a
Lambda function instead:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def("setName", &Pet::setName)
.def("getName", &Pet::getName)
.def("__repr__",
[](const Pet &a) {
return "<example.Pet named '" + a.name + "'>";
}
);
Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
With the above change, the same Python code now produces the following output:
.. code-block:: python
>>> print(p)
<example.Pet named 'Molly'>
Instance and static fields
==========================
We can also directly expose the ``name`` field using the
:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly`
method also exists for ``const`` fields.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name)
// ... remainder ...
This makes it possible to write
.. code-block:: python
>>> p = example.Pet('Molly')
>>> p.name
u'Molly'
>>> p.name = 'Charly'
>>> p.name
u'Charly'
Now suppose that ``Pet::name`` was a private internal variable
that can only be accessed via setters and getters.
.. code-block:: cpp
class Pet {
public:
Pet(const std::string &name) : name(name) { }
void setName(const std::string &name_) { name = name_; }
const std::string &getName() const { return name; }
private:
std::string name;
};
In this case, the method :func:`class_::def_property`
(:func:`class_::def_property_readonly` for read-only data) can be used to
provide an interface that is indistinguishable from within Python:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_property("name", &Pet::getName, &Pet::setName)
// ... remainder ...
.. seealso::
Similar functions :func:`class_::def_readwrite_static`,
:func:`class_::def_readonly_static` :func:`class_::def_property_static`,
and :func:`class_::def_property_readonly_static` are provided for binding
static variables and properties.
Inheritance
===========
Suppose now that the example consists of two data structures with an
inheritance relationship:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name) : name(name) { }
std::string name;
};
struct Dog : Pet {
Dog(const std::string &name) : Pet(name) { }
std::string bark() const { return "woof!"; }
};
To capture the hierarchical relationship in pybind11, we must assign a name to
the ``Pet`` :class:`class_` instance and reference it when binding the ``Dog``
class.
.. code-block:: cpp
py::class_<Pet> pet(m, "Pet");
pet.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name);
py::class_<Dog>(m, "Dog", pet /* <- specify parent */)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Instances then expose fields and methods of both types:
.. code-block:: python
>>> p = example.Dog('Molly')
>>> p.name
u'Molly'
>>> p.bark()
u'woof!'
Overloaded methods
==================
Sometimes there are several overloaded C++ methods with the same name taking
different kinds of input arguments:
.. code-block:: cpp
struct Pet {
Pet(const std::string &name, int age) : name(name), age(age) { }
void set(int age) { age = age; }
void set(const std::string &name) { name = name; }
std::string name;
int age;
};
Attempting to bind ``Pet::set`` will cause an error since the compiler does not
know which method the user intended to select. We can disambiguate by casting
them to function pointers. Binding multiple functions to the same Python name
automatically creates a chain of fucnction overloads that will be tried in
sequence.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &, int>())
.def("set", (void (Pet::*)(int)) &Pet::set, "Set the pet's age")
.def("set", (void (Pet::*)(const std::string &)) &Pet::set, "Set the pet's name");
The overload signatures are also visible in the method's docstring:
.. code-block:: python
>>> help(example.Pet)
class Pet(__builtin__.object)
| Methods defined here:
|
| __init__(...)
| Signature : (Pet, str, int32_t) -> None
|
| set(...)
| 1. Signature : (Pet, int32_t) -> None
|
| Set the pet's age
|
| 2. Signature : (Pet, str) -> None
|
| Set the pet's name
|
Enumerations and internal types
===============================
Let's now suppose that the example class also contains an internal enumeration
type.
.. code-block:: cpp
struct Pet {
enum Kind {
Dog = 0,
Cat
};
Pet(const std::string &name, Kind type) : name(name), type(type) { }
std::string name;
Kind type;
};
The binding code for this example looks as follows:
.. code-block:: cpp
py::class_<Pet> pet(m, "Pet");
pet.def(py::init<const std::string &, Pet::Kind>())
.def_readwrite("name", &Pet::name)
.def_readwrite("type", &Pet::type);
py::enum_<Pet::Kind>(pet, "Kind")
.value("Dog", Pet::Kind::Dog)
.value("Cat", Pet::Kind::Cat)
.export_values();
To ensure that the ``Kind`` type is created within the scope of ``Pet``, the
``pet`` :class:`class_` instance must be supplied to the :class:`enum_`.
constructor. The :func:`enum_::export_values` function ensures that the enum
entries are exported into the parent scope; skip this call for new C++11-style
strongly typed enums.
.. code-block:: python
>>> p = Pet('Lucy', Pet.Cat)
>>> p.type
Kind.Cat
>>> int(p.type)
1L
.. [#f1] (those with an empty pair of brackets ``[]`` as the capture object)
docs/cmake.rst 0 → 100644
.. _cmake:
Building with CMake
===================
The following snippet should be a good starting point to create bindings across
platforms. It assumes that the code is located in a file named :file:`example.cpp`,
and that the pybind11 repository is located in a subdirectory named :file:`pybind11`.
.. code-block:: cmake
cmake_minimum_required(VERSION 2.8)
project(example)
# Add a CMake parameter for choosing a desired Python version
set(EXAMPLE_PYTHON_VERSION "" CACHE STRING "Python version to use for compiling the example library")
# Set a default build configuration if none is specified. 'MinSizeRel' produces the smallest binaries
if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
message(STATUS "Setting build type to 'MinSizeRel' as none was specified.")
set(CMAKE_BUILD_TYPE MinSizeRel CACHE STRING "Choose the type of build." FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release"
"MinSizeRel" "RelWithDebInfo")
endif()
string(TOUPPER "${CMAKE_BUILD_TYPE}" U_CMAKE_BUILD_TYPE)
# Try to autodetect Python (can be overridden manually if needed)
set(Python_ADDITIONAL_VERSIONS 3.4 3.5 3.6)
find_package(PythonLibs ${EXAMPLE_PYTHON_VERSION} REQUIRED)
if (UNIX)
# Enable C++11 mode
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
# Enable link time optimization and set the default symbol
# visibility to hidden (very important to obtain small binaries)
if (NOT ${U_CMAKE_BUILD_TYPE} MATCHES DEBUG)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden -flto")
endif()
endif()
# Include path for Python header files
include_directories(${PYTHON_INCLUDE_DIR})
# Include path for pybind11 header files -- this may need to be changed depending on your setup
include_directories(${PROJECT_SOURCE_DIR}/pybind11/include)
# Create the binding library
add_library(example SHARED
example.cpp
# ... extra files go here ...
)
# Don't add a 'lib' prefix to the shared library
set_target_properties(example PROPERTIES PREFIX "")
if (WIN32)
if (MSVC)
# Enforce size-based optimization and link time code generation
# on MSVC (~30% smaller binaries in experiments). /bigobj is needed
# for bigger binding projects due to the limit to 64k addressable sections
# /MP enables multithreaded builds (relevant when there are many files).
set_target_properties(example PROPERTIES COMPILE_FLAGS "/Os /GL /MP /bigobj")
set_target_properties(example PROPERTIES LINK_FLAGS "/LTCG")
endif()
# .PYD file extension on Windows
set_target_properties(example PROPERTIES SUFFIX ".pyd")
# Link against the Python shared library
target_link_libraries(example ${PYTHON_LIBRARY})
elseif (UNIX)
# It's quite common to have multiple copies of the same Python version
# installed on one's system. E.g.: one copy from the OS and another copy
# that's statically linked into an application like Blender or Maya.
# If we link our plugin library against the OS Python here and import it
# into Blender or Maya later on, this will cause segfaults when multiple
# conflicting Python instances are active at the same time.
# Windows is not affected by this issue since it handles DLL imports
# differently. The solution for Linux and Mac OS is simple: we just don't
# link against the Python library. The resulting shared library will have
# missing symbols, but that's perfectly fine -- they will be resolved at
# import time.
# .SO file extension on Linux/Mac OS
set_target_properties(example PROPERTIES SUFFIX ".so")
# Strip unnecessary sections of the binary on Linux/Mac OS
if(APPLE)
set_target_properties(example PROPERTIES MACOSX_RPATH ".")
set_target_properties(example PROPERTIES LINK_FLAGS "-undefined dynamic_lookup -dead_strip")
if (NOT ${U_CMAKE_BUILD_TYPE} MATCHES DEBUG)
add_custom_command(TARGET example POST_BUILD COMMAND strip -u -r ${PROJECT_BINARY_DIR}/example.so)
endif()
else()
if (NOT ${U_CMAKE_BUILD_TYPE} MATCHES DEBUG)
add_custom_command(TARGET example POST_BUILD COMMAND strip ${PROJECT_BINARY_DIR}/example.so)
endif()
endif()
endif()
docs/conf.py 0 → 100644
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# pybind11 documentation build configuration file, created by
# sphinx-quickstart on Sun Oct 11 19:23:48 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
import shlex
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = []
# Add any paths that contain templates here, relative to this directory.
templates_path = ['.templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = 'pybind11'
copyright = '2015, Wenzel Jakob'
author = 'Wenzel Jakob'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '1.0'
# The full version, including alpha/beta/rc tags.
release = '1.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['.build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
#pygments_style = 'monokai'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
#import alabaster
#html_theme_path = [alabaster.get_path()]
#extensions = ['alabaster']
#html_theme = 'alabaster'
#html_sidebars = {
#'**': [
#'about.html',
#'navigation.html',
#'relations.html',
#'searchbox.html'
#]
#}
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['.static']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
#html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# Now only 'ja' uses this config value
#html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = 'pybind11doc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Latex figure (float) alignment
#'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'pybind11.tex', 'pybind11 Documentation',
'Wenzel Jakob', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pybind11', 'pybind11 Documentation',
[author], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'pybind11', 'pybind11 Documentation',
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docs/index.rst 0 → 100644
Welcome to pybind11's documentation!
====================================
Contents:
.. toctree::
:maxdepth: 2
intro
basics
classes
advanced
cmake
reference
docs/intro.rst 0 → 100644
About this project
==================
**pybind11** is a lightweight header 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.
.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
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 whole
codebase requires less than 3000 lines of code and only depends 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).
Core features
*************
The following core C++ features can be mapped 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:
- 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.
docs/reference.rst 0 → 100644
.. _reference:
Reference
#########
Macros
======
.. function:: PYTHON_PLUGIN(const char *name)
This macro creates the entry point that will be invoked when the Python
interpreter imports a plugin library. Please create a
:class:`module` in the function body and return the pointer to its
underlying Python object at the end.
.. code-block:: cpp
PYTHON_PLUGIN(example) {
pybind::module m("example", "pybind example plugin");
/// Set up bindings here
return m.ptr();
}
.. _core_types:
Convenience classes for arbitrary Python types
==============================================
Without reference counting
--------------------------
.. class:: handle
The :class:`handle` class is a thin wrapper around an arbitrary Python
object (i.e. a ``PyObject *`` in Python's C API). It does not perform any
automatic reference counting and merely provides a basic C++ interface to
various Python API functions.
.. seealso::
The :class:`object` class inherits from :class:`handle` and adds automatic
reference counting features.
.. function:: handle::handle()
The default constructor creates a handle with a ``nullptr``-valued pointer.
.. function:: handle::handle(const handle&)
Copy constructor
.. function:: handle::handle(PyObject *)
Creates a :class:`handle` from the given raw Python object pointer.
.. function:: PyObject * handle::ptr()
Return the ``PyObject *`` underlying a :class:`handle`.
.. function:: void handle::inc_ref()
Manually increase the reference count of the Python object. Usually, it is
preferable to use the :class:`object` class which derives from
:class:`handle` and calls this function automatically.
.. function:: void handle::dec_ref()
Manually decrease the reference count of the Python object. Usually, it is
preferable to use the :class:`object` class which derives from
:class:`handle` and calls this function automatically.
.. function:: void handle::ref_count()
Return the object's current reference count
.. function:: handle handle::get_type()
Return a handle to the Python type object underlying the instance
.. function detail::accessor handle::operator[](handle key)
Return an internal functor to invoke the object's sequence protocol.
Casting the returned ``detail::accessor`` instance to a :class:`handle` or
:class:`object` subclass causes a corresponding call to ``__getitem__``.
Assigning a :class:`handle` or :class:`object` subclass causes a call to
``__setitem__``.
.. function detail::accessor handle::operator[](const char *key)
See the above function (the only difference is that they key is provided as
a string literal).
.. function detail::accessor handle::attr(handle key)
Return an internal functor to access the object's attributes.
Casting the returned ``detail::accessor`` instance to a :class:`handle` or
:class:`object` subclass causes a corresponding call to ``__getattr``.
Assigning a :class:`handle` or :class:`object` subclass causes a call to
``__setattr``.
.. function detail::accessor handle::attr(const char *key)
See the above function (the only difference is that they key is provided as
a string literal).
.. function operator handle::bool() const
Return ``true`` when the :class:`handle` wraps a valid Python object.
.. function str handle::str() const
Return a string representation of the object. This is analogous to
the ``str()`` function in Python.
.. function:: template <typename T> T handle::cast()
Attempt to cast the Python object into the given C++ type. A
:class:`cast_error` will be throw upon failure.
.. function:: template <typename ... Args> object handle::call(Args&&... args)
Assuming the Python object is a function or implements the ``__call__``
protocol, ``call()`` invokes the underlying function, passing an arbitrary
set of parameters. The result is returned as a :class:`object` and may need
to be converted back into a Python object using :func:`template <typename T> handle::cast`.
When some of the arguments cannot be converted to Python objects, the
function will throw a :class:`cast_error` exception. When the Python
function call fails, a :class:`error_already_set` exception is thrown.
With reference counting
-----------------------
.. class:: object : public handle
Like :class:`handle`, the object class is a thin wrapper around an
arbitrary Python object (i.e. a ``PyObject *`` in Python's C API). In
contrast to :class:`handle`, it optionally increases the object's reference
count upon construction, and it *always* decreases the reference count when
the :class:`object` instance goes out of scope and is destructed. When
using :class:`object` instances consistently, it is much easier to get
reference counting right at the first attempt.
.. function:: object::object(const object &o)
Copy constructor; always increases the reference count
.. function:: object::object(const handle &h, bool borrowed)
Creates a :class:`object` from the given :class:`handle`. The reference
count is only increased if the ``borrowed`` parameter is set to ``true``.
.. function:: object::object(PyObject *ptr, bool borrowed)
Creates a :class:`object` from the given raw Python object pointer. The
reference count is only increased if the ``borrowed`` parameter is set to
``true``.
.. function:: object::object(object &&other)
Move constructor; steals the object from ``other`` and preserves its
reference count.
.. function:: object::~object()
Constructor, which automatically calls :func:`handle::dec_ref()`.
Convenience classes for specific Python types
=============================================
.. class:: module : public object
.. function:: module::module(const char *name, const char *doc = nullptr)
Create a new top-level Python module with the given name and docstring
.. function:: module module::def_submodule(const char *name, const char *doc = nullptr)
Create and return a new Python submodule with the given name and docstring.
This also works recursively, i.e.
.. code-block:: cpp
pybind::module m("example", "pybind example plugin");
pybind::module m2 = m.def_submodule("sub", "A submodule of 'example'");
pybind::module m3 = m2.def_submodule("subsub", "A submodule of 'example.sub'");
.. cpp:function:: template <typename Func, typename ... Extra> module& module::def(const char *name, Func && f, Extra && ... extra)
Create Python binding for a new function within the module scope. ``Func``
can be a plain C++ function, a function pointer, or a lambda function. For
details on the ``Extra&& ... extra`` argument, see section :ref:`extras`.
.. _extras:
Passing extra arguments to the def function
===========================================
.. class:: arg
.. function:: arg::arg(const char *name)
.. function:: template <typename T> arg_t<T> arg::operator=(const T &value)
.. class:: template <typename T> arg_t<T> : public arg
Represents a named argument with a default value
.. class:: sibling
Used to specify a handle to an existing sibling function; used internally
to implement function overloading in :func:`module::def` and
:func:`class_::def`.
.. function:: sibling::sibling(handle handle)
.. class doc
This is class is internally used by pybind11.
.. function:: doc::doc(const char *value)
Create a new docstring with the specified value
.. class name
This is class is internally used by pybind11.
.. function:: name::name(const char *value)
Used to specify the function name
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