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Hardcopy of pybind11 instead of using git submodules (#552)

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* removed pybind as submodule
* added hardcopy of pybind11 2.10.0
* rename pybind11 folder to avoid conflicts when changing branch

Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch>
This commit is contained in:
Erik Fröjdh
2022-09-09 10:42:43 +02:00
committed by GitHub
parent 236f00c810
commit c0c8c8e21a
218 changed files with 57649 additions and 7 deletions
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PROJECT_NAME = pybind11
INPUT = ../include/pybind11/
RECURSIVE = YES
GENERATE_HTML = NO
GENERATE_LATEX = NO
GENERATE_XML = YES
XML_OUTPUT = .build/doxygenxml
XML_PROGRAMLISTING = YES
MACRO_EXPANSION = YES
EXPAND_ONLY_PREDEF = YES
EXPAND_AS_DEFINED = PYBIND11_RUNTIME_EXCEPTION
ALIASES = "rst=\verbatim embed:rst"
ALIASES += "endrst=\endverbatim"
QUIET = YES
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Chrono
======
When including the additional header file :file:`pybind11/chrono.h` conversions
from C++11 chrono datatypes to python datetime objects are automatically enabled.
This header also enables conversions of python floats (often from sources such
as ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``)
into durations.
An overview of clocks in C++11
------------------------------
A point of confusion when using these conversions is the differences between
clocks provided in C++11. There are three clock types defined by the C++11
standard and users can define their own if needed. Each of these clocks have
different properties and when converting to and from python will give different
results.
The first clock defined by the standard is ``std::chrono::system_clock``. This
clock measures the current date and time. However, this clock changes with to
updates to the operating system time. For example, if your time is synchronised
with a time server this clock will change. This makes this clock a poor choice
for timing purposes but good for measuring the wall time.
The second clock defined in the standard is ``std::chrono::steady_clock``.
This clock ticks at a steady rate and is never adjusted. This makes it excellent
for timing purposes, however the value in this clock does not correspond to the
current date and time. Often this clock will be the amount of time your system
has been on, although it does not have to be. This clock will never be the same
clock as the system clock as the system clock can change but steady clocks
cannot.
The third clock defined in the standard is ``std::chrono::high_resolution_clock``.
This clock is the clock that has the highest resolution out of the clocks in the
system. It is normally a typedef to either the system clock or the steady clock
but can be its own independent clock. This is important as when using these
conversions as the types you get in python for this clock might be different
depending on the system.
If it is a typedef of the system clock, python will get datetime objects, but if
it is a different clock they will be timedelta objects.
Provided conversions
--------------------
.. rubric:: C++ to Python
- ``std::chrono::system_clock::time_point`` → ``datetime.datetime``
System clock times are converted to python datetime instances. They are
in the local timezone, but do not have any timezone information attached
to them (they are naive datetime objects).
- ``std::chrono::duration`` → ``datetime.timedelta``
Durations are converted to timedeltas, any precision in the duration
greater than microseconds is lost by rounding towards zero.
- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta``
Any clock time that is not the system clock is converted to a time delta.
This timedelta measures the time from the clocks epoch to now.
.. rubric:: Python to C++
- ``datetime.datetime`` or ``datetime.date`` or ``datetime.time`` → ``std::chrono::system_clock::time_point``
Date/time objects are converted into system clock timepoints. Any
timezone information is ignored and the type is treated as a naive
object.
- ``datetime.timedelta`` → ``std::chrono::duration``
Time delta are converted into durations with microsecond precision.
- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point``
Time deltas that are converted into clock timepoints are treated as
the amount of time from the start of the clocks epoch.
- ``float`` → ``std::chrono::duration``
Floats that are passed to C++ as durations be interpreted as a number of
seconds. These will be converted to the duration using ``duration_cast``
from the float.
- ``float`` → ``std::chrono::[other_clocks]::time_point``
Floats that are passed to C++ as time points will be interpreted as the
number of seconds from the start of the clocks epoch.

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Custom type casters
===================
In very rare cases, applications may require custom type casters that cannot be
expressed using the abstractions provided by pybind11, thus requiring raw
Python C API calls. This is fairly advanced usage and should only be pursued by
experts who are familiar with the intricacies of Python reference counting.
The following snippets demonstrate how this works for a very simple ``inty``
type that that should be convertible from Python types that provide a
``__int__(self)`` method.
.. code-block:: cpp
struct inty { long long_value; };
void print(inty s) {
std::cout << s.long_value << std::endl;
}
The following Python snippet demonstrates the intended usage from the Python side:
.. code-block:: python
class A:
def __int__(self):
return 123
from example import print
print(A())
To register the necessary conversion routines, it is necessary to add an
instantiation of the ``pybind11::detail::type_caster<T>`` template.
Although this is an implementation detail, adding an instantiation of this
type is explicitly allowed.
.. code-block:: cpp
namespace pybind11 { namespace detail {
template <> struct type_caster<inty> {
public:
/**
* This macro establishes the name 'inty' in
* function signatures and declares a local variable
* 'value' of type inty
*/
PYBIND11_TYPE_CASTER(inty, const_name("inty"));
/**
* Conversion part 1 (Python->C++): convert a PyObject into a inty
* instance or return false upon failure. The second argument
* indicates whether implicit conversions should be applied.
*/
bool load(handle src, bool) {
/* Extract PyObject from handle */
PyObject *source = src.ptr();
/* Try converting into a Python integer value */
PyObject *tmp = PyNumber_Long(source);
if (!tmp)
return false;
/* Now try to convert into a C++ int */
value.long_value = PyLong_AsLong(tmp);
Py_DECREF(tmp);
/* Ensure return code was OK (to avoid out-of-range errors etc) */
return !(value.long_value == -1 && !PyErr_Occurred());
}
/**
* Conversion part 2 (C++ -> Python): convert an inty instance into
* a Python object. The second and third arguments are used to
* indicate the return value policy and parent object (for
* ``return_value_policy::reference_internal``) and are generally
* ignored by implicit casters.
*/
static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) {
return PyLong_FromLong(src.long_value);
}
};
}} // namespace pybind11::detail
.. note::
A ``type_caster<T>`` defined with ``PYBIND11_TYPE_CASTER(T, ...)`` requires
that ``T`` is default-constructible (``value`` is first default constructed
and then ``load()`` assigns to it).
.. warning::
When using custom type casters, it's important to declare them consistently
in every compilation unit of the Python extension module. Otherwise,
undefined behavior can ensue.

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Eigen
#####
`Eigen <http://eigen.tuxfamily.org>`_ is C++ header-based library for dense and
sparse linear algebra. Due to its popularity and widespread adoption, pybind11
provides transparent conversion and limited mapping support between Eigen and
Scientific Python linear algebra data types.
To enable the built-in Eigen support you must include the optional header file
:file:`pybind11/eigen.h`.
Pass-by-value
=============
When binding a function with ordinary Eigen dense object arguments (for
example, ``Eigen::MatrixXd``), pybind11 will accept any input value that is
already (or convertible to) a ``numpy.ndarray`` with dimensions compatible with
the Eigen type, copy its values into a temporary Eigen variable of the
appropriate type, then call the function with this temporary variable.
Sparse matrices are similarly copied to or from
``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` objects.
Pass-by-reference
=================
One major limitation of the above is that every data conversion implicitly
involves a copy, which can be both expensive (for large matrices) and disallows
binding functions that change their (Matrix) arguments. Pybind11 allows you to
work around this by using Eigen's ``Eigen::Ref<MatrixType>`` class much as you
would when writing a function taking a generic type in Eigen itself (subject to
some limitations discussed below).
When calling a bound function accepting a ``Eigen::Ref<const MatrixType>``
type, pybind11 will attempt to avoid copying by using an ``Eigen::Map`` object
that maps into the source ``numpy.ndarray`` data: this requires both that the
data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
``double``); and that the storage is layout compatible. The latter limitation
is discussed in detail in the section below, and requires careful
consideration: by default, numpy matrices and Eigen matrices are *not* storage
compatible.
If the numpy matrix cannot be used as is (either because its types differ, e.g.
passing an array of integers to an Eigen parameter requiring doubles, or
because the storage is incompatible), pybind11 makes a temporary copy and
passes the copy instead.
When a bound function parameter is instead ``Eigen::Ref<MatrixType>`` (note the
lack of ``const``), pybind11 will only allow the function to be called if it
can be mapped *and* if the numpy array is writeable (that is
``a.flags.writeable`` is true). Any access (including modification) made to
the passed variable will be transparently carried out directly on the
``numpy.ndarray``.
This means you can write code such as the following and have it work as
expected:
.. code-block:: cpp
void scale_by_2(Eigen::Ref<Eigen::VectorXd> v) {
v *= 2;
}
Note, however, that you will likely run into limitations due to numpy and
Eigen's difference default storage order for data; see the below section on
:ref:`storage_orders` for details on how to bind code that won't run into such
limitations.
.. note::
Passing by reference is not supported for sparse types.
Returning values to Python
==========================
When returning an ordinary dense Eigen matrix type to numpy (e.g.
``Eigen::MatrixXd`` or ``Eigen::RowVectorXf``) pybind11 keeps the matrix and
returns a numpy array that directly references the Eigen matrix: no copy of the
data is performed. The numpy array will have ``array.flags.owndata`` set to
``False`` to indicate that it does not own the data, and the lifetime of the
stored Eigen matrix will be tied to the returned ``array``.
If you bind a function with a non-reference, ``const`` return type (e.g.
``const Eigen::MatrixXd``), the same thing happens except that pybind11 also
sets the numpy array's ``writeable`` flag to false.
If you return an lvalue reference or pointer, the usual pybind11 rules apply,
as dictated by the binding function's return value policy (see the
documentation on :ref:`return_value_policies` for full details). That means,
without an explicit return value policy, lvalue references will be copied and
pointers will be managed by pybind11. In order to avoid copying, you should
explicitly specify an appropriate return value policy, as in the following
example:
.. code-block:: cpp
class MyClass {
Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000);
public:
Eigen::MatrixXd &getMatrix() { return big_mat; }
const Eigen::MatrixXd &viewMatrix() { return big_mat; }
};
// Later, in binding code:
py::class_<MyClass>(m, "MyClass")
.def(py::init<>())
.def("copy_matrix", &MyClass::getMatrix) // Makes a copy!
.def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal)
.def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal)
;
.. code-block:: python
a = MyClass()
m = a.get_matrix() # flags.writeable = True, flags.owndata = False
v = a.view_matrix() # flags.writeable = False, flags.owndata = False
c = a.copy_matrix() # flags.writeable = True, flags.owndata = True
# m[5,6] and v[5,6] refer to the same element, c[5,6] does not.
Note in this example that ``py::return_value_policy::reference_internal`` is
used to tie the life of the MyClass object to the life of the returned arrays.
You may also return an ``Eigen::Ref``, ``Eigen::Map`` or other map-like Eigen
object (for example, the return value of ``matrix.block()`` and related
methods) that map into a dense Eigen type. When doing so, the default
behaviour of pybind11 is to simply reference the returned data: you must take
care to ensure that this data remains valid! You may ask pybind11 to
explicitly *copy* such a return value by using the
``py::return_value_policy::copy`` policy when binding the function. You may
also use ``py::return_value_policy::reference_internal`` or a
``py::keep_alive`` to ensure the data stays valid as long as the returned numpy
array does.
When returning such a reference of map, pybind11 additionally respects the
readonly-status of the returned value, marking the numpy array as non-writeable
if the reference or map was itself read-only.
.. note::
Sparse types are always copied when returned.
.. _storage_orders:
Storage orders
==============
Passing arguments via ``Eigen::Ref`` has some limitations that you must be
aware of in order to effectively pass matrices by reference. First and
foremost is that the default ``Eigen::Ref<MatrixType>`` class requires
contiguous storage along columns (for column-major types, the default in Eigen)
or rows if ``MatrixType`` is specifically an ``Eigen::RowMajor`` storage type.
The former, Eigen's default, is incompatible with ``numpy``'s default row-major
storage, and so you will not be able to pass numpy arrays to Eigen by reference
without making one of two changes.
(Note that this does not apply to vectors (or column or row matrices): for such
types the "row-major" and "column-major" distinction is meaningless).
The first approach is to change the use of ``Eigen::Ref<MatrixType>`` to the
more general ``Eigen::Ref<MatrixType, 0, Eigen::Stride<Eigen::Dynamic,
Eigen::Dynamic>>`` (or similar type with a fully dynamic stride type in the
third template argument). Since this is a rather cumbersome type, pybind11
provides a ``py::EigenDRef<MatrixType>`` type alias for your convenience (along
with EigenDMap for the equivalent Map, and EigenDStride for just the stride
type).
This type allows Eigen to map into any arbitrary storage order. This is not
the default in Eigen for performance reasons: contiguous storage allows
vectorization that cannot be done when storage is not known to be contiguous at
compile time. The default ``Eigen::Ref`` stride type allows non-contiguous
storage along the outer dimension (that is, the rows of a column-major matrix
or columns of a row-major matrix), but not along the inner dimension.
This type, however, has the added benefit of also being able to map numpy array
slices. For example, the following (contrived) example uses Eigen with a numpy
slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4,
...) and in columns 2, 5, or 8:
.. code-block:: cpp
m.def("scale", [](py::EigenDRef<Eigen::MatrixXd> m, double c) { m *= c; });
.. code-block:: python
# a = np.array(...)
scale_by_2(myarray[0::2, 2:9:3])
The second approach to avoid copying is more intrusive: rearranging the
underlying data types to not run into the non-contiguous storage problem in the
first place. In particular, that means using matrices with ``Eigen::RowMajor``
storage, where appropriate, such as:
.. code-block:: cpp
using RowMatrixXd = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
// Use RowMatrixXd instead of MatrixXd
Now bound functions accepting ``Eigen::Ref<RowMatrixXd>`` arguments will be
callable with numpy's (default) arrays without involving a copying.
You can, alternatively, change the storage order that numpy arrays use by
adding the ``order='F'`` option when creating an array:
.. code-block:: python
myarray = np.array(source, order="F")
Such an object will be passable to a bound function accepting an
``Eigen::Ref<MatrixXd>`` (or similar column-major Eigen type).
One major caveat with this approach, however, is that it is not entirely as
easy as simply flipping all Eigen or numpy usage from one to the other: some
operations may alter the storage order of a numpy array. For example, ``a2 =
array.transpose()`` results in ``a2`` being a view of ``array`` that references
the same data, but in the opposite storage order!
While this approach allows fully optimized vectorized calculations in Eigen, it
cannot be used with array slices, unlike the first approach.
When *returning* a matrix to Python (either a regular matrix, a reference via
``Eigen::Ref<>``, or a map/block into a matrix), no special storage
consideration is required: the created numpy array will have the required
stride that allows numpy to properly interpret the array, whatever its storage
order.
Failing rather than copying
===========================
The default behaviour when binding ``Eigen::Ref<const MatrixType>`` Eigen
references is to copy matrix values when passed a numpy array that does not
conform to the element type of ``MatrixType`` or does not have a compatible
stride layout. If you want to explicitly avoid copying in such a case, you
should bind arguments using the ``py::arg().noconvert()`` annotation (as
described in the :ref:`nonconverting_arguments` documentation).
The following example shows an example of arguments that don't allow data
copying to take place:
.. code-block:: cpp
// The method and function to be bound:
class MyClass {
// ...
double some_method(const Eigen::Ref<const MatrixXd> &matrix) { /* ... */ }
};
float some_function(const Eigen::Ref<const MatrixXf> &big,
const Eigen::Ref<const MatrixXf> &small) {
// ...
}
// The associated binding code:
using namespace pybind11::literals; // for "arg"_a
py::class_<MyClass>(m, "MyClass")
// ... other class definitions
.def("some_method", &MyClass::some_method, py::arg().noconvert());
m.def("some_function", &some_function,
"big"_a.noconvert(), // <- Don't allow copying for this arg
"small"_a // <- This one can be copied if needed
);
With the above binding code, attempting to call the the ``some_method(m)``
method on a ``MyClass`` object, or attempting to call ``some_function(m, m2)``
will raise a ``RuntimeError`` rather than making a temporary copy of the array.
It will, however, allow the ``m2`` argument to be copied into a temporary if
necessary.
Note that explicitly specifying ``.noconvert()`` is not required for *mutable*
Eigen references (e.g. ``Eigen::Ref<MatrixXd>`` without ``const`` on the
``MatrixXd``): mutable references will never be called with a temporary copy.
Vectors versus column/row matrices
==================================
Eigen and numpy have fundamentally different notions of a vector. In Eigen, a
vector is simply a matrix with the number of columns or rows set to 1 at
compile time (for a column vector or row vector, respectively). NumPy, in
contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has
1-dimensional arrays of size N.
When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must
have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy
array to an Eigen value expecting a row vector, or a 1xN numpy array as a
column vector argument.
On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N
as Eigen parameters. If the Eigen type can hold a column vector of length N it
will be passed as such a column vector. If not, but the Eigen type constraints
will accept a row vector, it will be passed as a row vector. (The column
vector takes precedence when both are supported, for example, when passing a
1D numpy array to a MatrixXd argument). Note that the type need not be
explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
When returning an Eigen vector to numpy, the conversion is ambiguous: a row
vector of length 4 could be returned as either a 1D array of length 4, or as a
2D array of size 1x4. When encountering such a situation, pybind11 compromises
by considering the returned Eigen type: if it is a compile-time vector--that
is, the type has either the number of rows or columns set to 1 at compile
time--pybind11 converts to a 1D numpy array when returning the value. For
instances that are a vector only at run-time (e.g. ``MatrixXd``,
``Matrix<float, Dynamic, 4>``), pybind11 returns the vector as a 2D array to
numpy. If this isn't want you want, you can use ``array.reshape(...)`` to get
a view of the same data in the desired dimensions.
.. seealso::
The file :file:`tests/test_eigen.cpp` contains a complete example that
shows how to pass Eigen sparse and dense data types in more detail.

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Functional
##########
The following features must be enabled by including :file:`pybind11/functional.h`.
Callbacks and passing anonymous functions
=========================================
The C++11 standard brought lambda functions and the generic polymorphic
function wrapper ``std::function<>`` to the C++ programming language, which
enable powerful new ways of working with functions. Lambda functions come in
two flavors: stateless lambda function resemble classic function pointers that
link to an anonymous piece of code, while stateful lambda functions
additionally depend on captured variables that are stored in an anonymous
*lambda closure object*.
Here is a simple example of a C++ function that takes an arbitrary function
(stateful or stateless) with signature ``int -> int`` as an argument and runs
it with the value 10.
.. code-block:: cpp
int func_arg(const std::function<int(int)> &f) {
return f(10);
}
The example below is more involved: it takes a function of signature ``int -> int``
and returns another function of the same kind. The return value is a stateful
lambda function, which stores the value ``f`` in the capture object and adds 1 to
its return value upon execution.
.. code-block:: cpp
std::function<int(int)> func_ret(const std::function<int(int)> &f) {
return [f](int i) {
return f(i) + 1;
};
}
This example demonstrates using python named parameters in C++ callbacks which
requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining
methods of classes:
.. code-block:: cpp
py::cpp_function func_cpp() {
return py::cpp_function([](int i) { return i+1; },
py::arg("number"));
}
After including the extra header file :file:`pybind11/functional.h`, it is almost
trivial to generate binding code for all of these functions.
.. code-block:: cpp
#include <pybind11/functional.h>
PYBIND11_MODULE(example, m) {
m.def("func_arg", &func_arg);
m.def("func_ret", &func_ret);
m.def("func_cpp", &func_cpp);
}
The following interactive session shows how to call them from Python.
.. code-block:: pycon
$ python
>>> import example
>>> def square(i):
... return i * i
...
>>> example.func_arg(square)
100L
>>> square_plus_1 = example.func_ret(square)
>>> square_plus_1(4)
17L
>>> plus_1 = func_cpp()
>>> plus_1(number=43)
44L
.. warning::
Keep in mind that passing a function from C++ to Python (or vice versa)
will instantiate a piece of wrapper code that translates function
invocations between the two languages. Naturally, this translation
increases the computational cost of each function call somewhat. A
problematic situation can arise when a function is copied back and forth
between Python and C++ many times in a row, in which case the underlying
wrappers will accumulate correspondingly. The resulting long sequence of
C++ -> Python -> C++ -> ... roundtrips can significantly decrease
performance.
There is one exception: pybind11 detects case where a stateless function
(i.e. a function pointer or a lambda function without captured variables)
is passed as an argument to another C++ function exposed in Python. In this
case, there is no overhead. Pybind11 will extract the underlying C++
function pointer from the wrapped function to sidestep a potential C++ ->
Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`.
.. note::
This functionality is very useful when generating bindings for callbacks in
C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.).
The file :file:`tests/test_callbacks.cpp` contains a complete example
that demonstrates how to work with callbacks and anonymous functions in
more detail.

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.. _type-conversions:
Type conversions
################
Apart from enabling cross-language function calls, a fundamental problem
that a binding tool like pybind11 must address is to provide access to
native Python types in C++ and vice versa. There are three fundamentally
different ways to do this—which approach is preferable for a particular type
depends on the situation at hand.
1. Use a native C++ type everywhere. In this case, the type must be wrapped
using pybind11-generated bindings so that Python can interact with it.
2. Use a native Python type everywhere. It will need to be wrapped so that
C++ functions can interact with it.
3. Use a native C++ type on the C++ side and a native Python type on the
Python side. pybind11 refers to this as a *type conversion*.
Type conversions are the most "natural" option in the sense that native
(non-wrapped) types are used everywhere. The main downside is that a copy
of the data must be made on every Python ↔ C++ transition: this is
needed since the C++ and Python versions of the same type generally won't
have the same memory layout.
pybind11 can perform many kinds of conversions automatically. An overview
is provided in the table ":ref:`conversion_table`".
The following subsections discuss the differences between these options in more
detail. The main focus in this section is on type conversions, which represent
the last case of the above list.
.. toctree::
:maxdepth: 1
overview
strings
stl
functional
chrono
eigen
custom

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Overview
########
.. rubric:: 1. Native type in C++, wrapper in Python
Exposing a custom C++ type using :class:`py::class_` was covered in detail
in the :doc:`/classes` section. There, the underlying data structure is
always the original C++ class while the :class:`py::class_` wrapper provides
a Python interface. Internally, when an object like this is sent from C++ to
Python, pybind11 will just add the outer wrapper layer over the native C++
object. Getting it back from Python is just a matter of peeling off the
wrapper.
.. rubric:: 2. Wrapper in C++, native type in Python
This is the exact opposite situation. Now, we have a type which is native to
Python, like a ``tuple`` or a ``list``. One way to get this data into C++ is
with the :class:`py::object` family of wrappers. These are explained in more
detail in the :doc:`/advanced/pycpp/object` section. We'll just give a quick
example here:
.. code-block:: cpp
void print_list(py::list my_list) {
for (auto item : my_list)
std::cout << item << " ";
}
.. code-block:: pycon
>>> print_list([1, 2, 3])
1 2 3
The Python ``list`` is not converted in any way -- it's just wrapped in a C++
:class:`py::list` class. At its core it's still a Python object. Copying a
:class:`py::list` will do the usual reference-counting like in Python.
Returning the object to Python will just remove the thin wrapper.
.. rubric:: 3. Converting between native C++ and Python types
In the previous two cases we had a native type in one language and a wrapper in
the other. Now, we have native types on both sides and we convert between them.
.. code-block:: cpp
void print_vector(const std::vector<int> &v) {
for (auto item : v)
std::cout << item << "\n";
}
.. code-block:: pycon
>>> print_vector([1, 2, 3])
1 2 3
In this case, pybind11 will construct a new ``std::vector<int>`` and copy each
element from the Python ``list``. The newly constructed object will be passed
to ``print_vector``. The same thing happens in the other direction: a new
``list`` is made to match the value returned from C++.
Lots of these conversions are supported out of the box, as shown in the table
below. They are very convenient, but keep in mind that these conversions are
fundamentally based on copying data. This is perfectly fine for small immutable
types but it may become quite expensive for large data structures. This can be
avoided by overriding the automatic conversion with a custom wrapper (i.e. the
above-mentioned approach 1). This requires some manual effort and more details
are available in the :ref:`opaque` section.
.. _conversion_table:
List of all builtin conversions
-------------------------------
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 binding :ref:`classes`.
+------------------------------------+---------------------------+-----------------------------------+
| Data type | Description | Header file |
+====================================+===========================+===================================+
| ``int8_t``, ``uint8_t`` | 8-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``int16_t``, ``uint16_t`` | 16-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``int32_t``, ``uint32_t`` | 32-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``int64_t``, ``uint64_t`` | 64-bit integers | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``ssize_t``, ``size_t`` | Platform-dependent size | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``float``, ``double`` | Floating point types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``bool`` | Two-state Boolean type | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``char`` | Character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``char16_t`` | UTF-16 character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``char32_t`` | UTF-32 character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``wchar_t`` | Wide character literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``const char *`` | UTF-8 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``const char16_t *`` | UTF-16 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``const char32_t *`` | UTF-32 string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``const wchar_t *`` | Wide string literal | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::string`` | STL dynamic UTF-8 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::u16string`` | STL dynamic UTF-16 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::u32string`` | STL dynamic UTF-32 string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::wstring`` | STL dynamic wide string | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::string_view``, | STL C++17 string views | :file:`pybind11/pybind11.h` |
| ``std::u16string_view``, etc. | | |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::pair<T1, T2>`` | Pair of two custom types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::tuple<...>`` | Arbitrary tuple of types | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::reference_wrapper<...>`` | Reference type wrapper | :file:`pybind11/pybind11.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::complex<T>`` | Complex numbers | :file:`pybind11/complex.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::array<T, Size>`` | STL static array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::vector<T>`` | STL dynamic array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::deque<T>`` | STL double-ended queue | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::valarray<T>`` | STL value array | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::list<T>`` | STL linked list | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::map<T1, T2>`` | STL ordered map | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::unordered_map<T1, T2>`` | STL unordered map | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::set<T>`` | STL ordered set | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::unordered_set<T>`` | STL unordered set | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::optional<T>`` | STL optional type (C++17) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::experimental::optional<T>`` | STL optional type (exp.) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::variant<...>`` | Type-safe union (C++17) | :file:`pybind11/stl.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::filesystem::path<T>`` | STL path (C++17) [#]_ | :file:`pybind11/stl/filesystem.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::function<...>`` | STL polymorphic function | :file:`pybind11/functional.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::chrono::duration<...>`` | STL time duration | :file:`pybind11/chrono.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``std::chrono::time_point<...>`` | STL date/time | :file:`pybind11/chrono.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``Eigen::Matrix<...>`` | Eigen: dense matrix | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``Eigen::Map<...>`` | Eigen: mapped memory | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-----------------------------------+
| ``Eigen::SparseMatrix<...>`` | Eigen: sparse matrix | :file:`pybind11/eigen.h` |
+------------------------------------+---------------------------+-----------------------------------+
.. [#] ``std::filesystem::path`` is converted to ``pathlib.Path`` and
``os.PathLike`` is converted to ``std::filesystem::path``.

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STL containers
##############
Automatic conversion
====================
When including the additional header file :file:`pybind11/stl.h`, conversions
between ``std::vector<>``/``std::deque<>``/``std::list<>``/``std::array<>``/``std::valarray<>``,
``std::set<>``/``std::unordered_set<>``, and
``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and
``dict`` data structures are automatically enabled. The types ``std::pair<>``
and ``std::tuple<>`` are already supported out of the box with just the core
:file:`pybind11/pybind11.h` header.
The major downside of these implicit conversions is that containers must be
converted (i.e. copied) on every Python->C++ and C++->Python transition, which
can have implications on the program semantics and performance. Please read the
next sections for more details and alternative approaches that avoid this.
.. note::
Arbitrary nesting of any of these types is possible.
.. seealso::
The file :file:`tests/test_stl.cpp` contains a complete
example that demonstrates how to pass STL data types in more detail.
.. _cpp17_container_casters:
C++17 library containers
========================
The :file:`pybind11/stl.h` header also includes support for ``std::optional<>``
and ``std::variant<>``. These require a C++17 compiler and standard library.
In C++14 mode, ``std::experimental::optional<>`` is supported if available.
Various versions of these containers also exist for C++11 (e.g. in Boost).
pybind11 provides an easy way to specialize the ``type_caster`` for such
types:
.. code-block:: cpp
// `boost::optional` as an example -- can be any `std::optional`-like container
namespace pybind11 { namespace detail {
template <typename T>
struct type_caster<boost::optional<T>> : optional_caster<boost::optional<T>> {};
}}
The above should be placed in a header file and included in all translation units
where automatic conversion is needed. Similarly, a specialization can be provided
for custom variant types:
.. code-block:: cpp
// `boost::variant` as an example -- can be any `std::variant`-like container
namespace pybind11 { namespace detail {
template <typename... Ts>
struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
// Specifies the function used to visit the variant -- `apply_visitor` instead of `visit`
template <>
struct visit_helper<boost::variant> {
template <typename... Args>
static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
return boost::apply_visitor(args...);
}
};
}} // namespace pybind11::detail
The ``visit_helper`` specialization is not required if your ``name::variant`` provides
a ``name::visit()`` function. For any other function name, the specialization must be
included to tell pybind11 how to visit the variant.
.. warning::
When converting a ``variant`` type, pybind11 follows the same rules as when
determining which function overload to call (:ref:`overload_resolution`), and
so the same caveats hold. In particular, the order in which the ``variant``'s
alternatives are listed is important, since pybind11 will try conversions in
this order. This means that, for example, when converting ``variant<int, bool>``,
the ``bool`` variant will never be selected, as any Python ``bool`` is already
an ``int`` and is convertible to a C++ ``int``. Changing the order of alternatives
(and using ``variant<bool, int>``, in this example) provides a solution.
.. note::
pybind11 only supports the modern implementation of ``boost::variant``
which makes use of variadic templates. This requires Boost 1.56 or newer.
.. _opaque:
Making opaque types
===================
pybind11 heavily relies on a template matching mechanism to convert parameters
and return values that are constructed from STL data types such as vectors,
linked lists, hash tables, etc. This even works in a recursive manner, for
instance to deal with lists of hash maps of pairs of elementary and custom
types, etc.
However, a fundamental limitation of this approach is that internal conversions
between Python and C++ types involve a copy operation that prevents
pass-by-reference semantics. What does this mean?
Suppose we bind the following function
.. code-block:: cpp
void append_1(std::vector<int> &v) {
v.push_back(1);
}
and call it from Python, the following happens:
.. code-block:: pycon
>>> v = [5, 6]
>>> append_1(v)
>>> print(v)
[5, 6]
As you can see, when passing STL data structures by reference, modifications
are not propagated back the Python side. A similar situation arises when
exposing STL data structures using the ``def_readwrite`` or ``def_readonly``
functions:
.. code-block:: cpp
/* ... definition ... */
class MyClass {
std::vector<int> contents;
};
/* ... binding code ... */
py::class_<MyClass>(m, "MyClass")
.def(py::init<>())
.def_readwrite("contents", &MyClass::contents);
In this case, properties can be read and written in their entirety. However, an
``append`` operation involving such a list type has no effect:
.. code-block:: pycon
>>> m = MyClass()
>>> m.contents = [5, 6]
>>> print(m.contents)
[5, 6]
>>> m.contents.append(7)
>>> print(m.contents)
[5, 6]
Finally, the involved copy operations can be costly when dealing with very
large lists. To deal with all of the above situations, pybind11 provides a
macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based
conversion machinery of types, thus rendering them *opaque*. The contents of
opaque objects are never inspected or extracted, hence they *can* be passed by
reference. For instance, to turn ``std::vector<int>`` into an opaque type, add
the declaration
.. code-block:: cpp
PYBIND11_MAKE_OPAQUE(std::vector<int>);
before any binding code (e.g. invocations to ``class_::def()``, etc.). This
macro must be specified at the top level (and outside of any namespaces), since
it adds a template instantiation of ``type_caster``. If your binding code consists of
multiple compilation units, it must be present in every file (typically via a
common header) preceding any usage of ``std::vector<int>``. Opaque types must
also have a corresponding ``class_`` declaration to associate them with a name
in Python, and to define a set of available operations, e.g.:
.. code-block:: cpp
py::class_<std::vector<int>>(m, "IntVector")
.def(py::init<>())
.def("clear", &std::vector<int>::clear)
.def("pop_back", &std::vector<int>::pop_back)
.def("__len__", [](const std::vector<int> &v) { return v.size(); })
.def("__iter__", [](std::vector<int> &v) {
return py::make_iterator(v.begin(), v.end());
}, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */
// ....
.. seealso::
The file :file:`tests/test_opaque_types.cpp` contains a complete
example that demonstrates how to create and expose opaque types using
pybind11 in more detail.
.. _stl_bind:
Binding STL containers
======================
The ability to expose STL containers as native Python objects is a fairly
common request, hence pybind11 also provides an optional header file named
:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try
to match the behavior of their native Python counterparts as much as possible.
The following example showcases usage of :file:`pybind11/stl_bind.h`:
.. code-block:: cpp
// Don't forget this
#include <pybind11/stl_bind.h>
PYBIND11_MAKE_OPAQUE(std::vector<int>);
PYBIND11_MAKE_OPAQUE(std::map<std::string, double>);
// ...
// later in binding code:
py::bind_vector<std::vector<int>>(m, "VectorInt");
py::bind_map<std::map<std::string, double>>(m, "MapStringDouble");
When binding STL containers pybind11 considers the types of the container's
elements to decide whether the container should be confined to the local module
(via the :ref:`module_local` feature). If the container element types are
anything other than already-bound custom types bound without
``py::module_local()`` the container binding will have ``py::module_local()``
applied. This includes converting types such as numeric types, strings, Eigen
types; and types that have not yet been bound at the time of the stl container
binding. This module-local binding is designed to avoid potential conflicts
between module bindings (for example, from two separate modules each attempting
to bind ``std::vector<int>`` as a python type).
It is possible to override this behavior to force a definition to be either
module-local or global. To do so, you can pass the attributes
``py::module_local()`` (to make the binding module-local) or
``py::module_local(false)`` (to make the binding global) into the
``py::bind_vector`` or ``py::bind_map`` arguments:
.. code-block:: cpp
py::bind_vector<std::vector<int>>(m, "VectorInt", py::module_local(false));
Note, however, that such a global binding would make it impossible to load this
module at the same time as any other pybind module that also attempts to bind
the same container type (``std::vector<int>`` in the above example).
See :ref:`module_local` for more details on module-local bindings.
.. seealso::
The file :file:`tests/test_stl_binders.cpp` shows how to use the
convenience STL container wrappers.

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Strings, bytes and Unicode conversions
######################################
Passing Python strings to C++
=============================
When a Python ``str`` is passed from Python to a C++ function that accepts
``std::string`` or ``char *`` as arguments, pybind11 will encode the Python
string to UTF-8. All Python ``str`` can be encoded in UTF-8, so this operation
does not fail.
The C++ language is encoding agnostic. It is the responsibility of the
programmer to track encodings. It's often easiest to simply `use UTF-8
everywhere <http://utf8everywhere.org/>`_.
.. code-block:: c++
m.def("utf8_test",
[](const std::string &s) {
cout << "utf-8 is icing on the cake.\n";
cout << s;
}
);
m.def("utf8_charptr",
[](const char *s) {
cout << "My favorite food is\n";
cout << s;
}
);
.. code-block:: pycon
>>> utf8_test("🎂")
utf-8 is icing on the cake.
🎂
>>> utf8_charptr("🍕")
My favorite food is
🍕
.. note::
Some terminal emulators do not support UTF-8 or emoji fonts and may not
display the example above correctly.
The results are the same whether the C++ function accepts arguments by value or
reference, and whether or not ``const`` is used.
Passing bytes to C++
--------------------
A Python ``bytes`` object will be passed to C++ functions that accept
``std::string`` or ``char*`` *without* conversion. In order to make a function
*only* accept ``bytes`` (and not ``str``), declare it as taking a ``py::bytes``
argument.
Returning C++ strings to Python
===============================
When a C++ function returns a ``std::string`` or ``char*`` to a Python caller,
**pybind11 will assume that the string is valid UTF-8** and will decode it to a
native Python ``str``, using the same API as Python uses to perform
``bytes.decode('utf-8')``. If this implicit conversion fails, pybind11 will
raise a ``UnicodeDecodeError``.
.. code-block:: c++
m.def("std_string_return",
[]() {
return std::string("This string needs to be UTF-8 encoded");
}
);
.. code-block:: pycon
>>> isinstance(example.std_string_return(), str)
True
Because UTF-8 is inclusive of pure ASCII, there is never any issue with
returning a pure ASCII string to Python. If there is any possibility that the
string is not pure ASCII, it is necessary to ensure the encoding is valid
UTF-8.
.. warning::
Implicit conversion assumes that a returned ``char *`` is null-terminated.
If there is no null terminator a buffer overrun will occur.
Explicit conversions
--------------------
If some C++ code constructs a ``std::string`` that is not a UTF-8 string, one
can perform a explicit conversion and return a ``py::str`` object. Explicit
conversion has the same overhead as implicit conversion.
.. code-block:: c++
// This uses the Python C API to convert Latin-1 to Unicode
m.def("str_output",
[]() {
std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1
py::str py_s = PyUnicode_DecodeLatin1(s.data(), s.length());
return py_s;
}
);
.. code-block:: pycon
>>> str_output()
'Send your résumé to Alice in HR'
The `Python C API
<https://docs.python.org/3/c-api/unicode.html#built-in-codecs>`_ provides
several built-in codecs.
One could also use a third party encoding library such as libiconv to transcode
to UTF-8.
Return C++ strings without conversion
-------------------------------------
If the data in a C++ ``std::string`` does not represent text and should be
returned to Python as ``bytes``, then one can return the data as a
``py::bytes`` object.
.. code-block:: c++
m.def("return_bytes",
[]() {
std::string s("\xba\xd0\xba\xd0"); // Not valid UTF-8
return py::bytes(s); // Return the data without transcoding
}
);
.. code-block:: pycon
>>> example.return_bytes()
b'\xba\xd0\xba\xd0'
Note the asymmetry: pybind11 will convert ``bytes`` to ``std::string`` without
encoding, but cannot convert ``std::string`` back to ``bytes`` implicitly.
.. code-block:: c++
m.def("asymmetry",
[](std::string s) { // Accepts str or bytes from Python
return s; // Looks harmless, but implicitly converts to str
}
);
.. code-block:: pycon
>>> isinstance(example.asymmetry(b"have some bytes"), str)
True
>>> example.asymmetry(b"\xba\xd0\xba\xd0") # invalid utf-8 as bytes
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte
Wide character strings
======================
When a Python ``str`` is passed to a C++ function expecting ``std::wstring``,
``wchar_t*``, ``std::u16string`` or ``std::u32string``, the ``str`` will be
encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each
type, in the platform's native endianness. When strings of these types are
returned, they are assumed to contain valid UTF-16 or UTF-32, and will be
decoded to Python ``str``.
.. code-block:: c++
#define UNICODE
#include <windows.h>
m.def("set_window_text",
[](HWND hwnd, std::wstring s) {
// Call SetWindowText with null-terminated UTF-16 string
::SetWindowText(hwnd, s.c_str());
}
);
m.def("get_window_text",
[](HWND hwnd) {
const int buffer_size = ::GetWindowTextLength(hwnd) + 1;
auto buffer = std::make_unique< wchar_t[] >(buffer_size);
::GetWindowText(hwnd, buffer.data(), buffer_size);
std::wstring text(buffer.get());
// wstring will be converted to Python str
return text;
}
);
Strings in multibyte encodings such as Shift-JIS must transcoded to a
UTF-8/16/32 before being returned to Python.
Character literals
==================
C++ functions that accept character literals as input will receive the first
character of a Python ``str`` as their input. If the string is longer than one
Unicode character, trailing characters will be ignored.
When a character literal is returned from C++ (such as a ``char`` or a
``wchar_t``), it will be converted to a ``str`` that represents the single
character.
.. code-block:: c++
m.def("pass_char", [](char c) { return c; });
m.def("pass_wchar", [](wchar_t w) { return w; });
.. code-block:: pycon
>>> example.pass_char("A")
'A'
While C++ will cast integers to character types (``char c = 0x65;``), pybind11
does not convert Python integers to characters implicitly. The Python function
``chr()`` can be used to convert integers to characters.
.. code-block:: pycon
>>> example.pass_char(0x65)
TypeError
>>> example.pass_char(chr(0x65))
'A'
If the desire is to work with an 8-bit integer, use ``int8_t`` or ``uint8_t``
as the argument type.
Grapheme clusters
-----------------
A single grapheme may be represented by two or more Unicode characters. For
example 'é' is usually represented as U+00E9 but can also be expressed as the
combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by
a combining acute accent). The combining character will be lost if the
two-character sequence is passed as an argument, even though it renders as a
single grapheme.
.. code-block:: pycon
>>> example.pass_wchar("é")
'é'
>>> combining_e_acute = "e" + "\u0301"
>>> combining_e_acute
'é'
>>> combining_e_acute == "é"
False
>>> example.pass_wchar(combining_e_acute)
'e'
Normalizing combining characters before passing the character literal to C++
may resolve *some* of these issues:
.. code-block:: pycon
>>> example.pass_wchar(unicodedata.normalize("NFC", combining_e_acute))
'é'
In some languages (Thai for example), there are `graphemes that cannot be
expressed as a single Unicode code point
<http://unicode.org/reports/tr29/#Grapheme_Cluster_Boundaries>`_, so there is
no way to capture them in a C++ character type.
C++17 string views
==================
C++17 string views are automatically supported when compiling in C++17 mode.
They follow the same rules for encoding and decoding as the corresponding STL
string type (for example, a ``std::u16string_view`` argument will be passed
UTF-16-encoded data, and a returned ``std::string_view`` will be decoded as
UTF-8).
References
==========
* `The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) <https://www.joelonsoftware.com/2003/10/08/the-absolute-minimum-every-software-developer-absolutely-positively-must-know-about-unicode-and-character-sets-no-excuses/>`_
* `C++ - Using STL Strings at Win32 API Boundaries <https://msdn.microsoft.com/en-ca/magazine/mt238407.aspx>`_

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.. _embedding:
Embedding the interpreter
#########################
While pybind11 is mainly focused on extending Python using C++, it's also
possible to do the reverse: embed the Python interpreter into a C++ program.
All of the other documentation pages still apply here, so refer to them for
general pybind11 usage. This section will cover a few extra things required
for embedding.
Getting started
===============
A basic executable with an embedded interpreter can be created with just a few
lines of CMake and the ``pybind11::embed`` target, as shown below. For more
information, see :doc:`/compiling`.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.4)
project(example)
find_package(pybind11 REQUIRED) # or `add_subdirectory(pybind11)`
add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)
The essential structure of the ``main.cpp`` file looks like this:
.. code-block:: cpp
#include <pybind11/embed.h> // everything needed for embedding
namespace py = pybind11;
int main() {
py::scoped_interpreter guard{}; // start the interpreter and keep it alive
py::print("Hello, World!"); // use the Python API
}
The interpreter must be initialized before using any Python API, which includes
all the functions and classes in pybind11. The RAII guard class ``scoped_interpreter``
takes care of the interpreter lifetime. After the guard is destroyed, the interpreter
shuts down and clears its memory. No Python functions can be called after this.
Executing Python code
=====================
There are a few different ways to run Python code. One option is to use ``eval``,
``exec`` or ``eval_file``, as explained in :ref:`eval`. Here is a quick example in
the context of an executable with an embedded interpreter:
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
int main() {
py::scoped_interpreter guard{};
py::exec(R"(
kwargs = dict(name="World", number=42)
message = "Hello, {name}! The answer is {number}".format(**kwargs)
print(message)
)");
}
Alternatively, similar results can be achieved using pybind11's API (see
:doc:`/advanced/pycpp/index` for more details).
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
using namespace py::literals;
int main() {
py::scoped_interpreter guard{};
auto kwargs = py::dict("name"_a="World", "number"_a=42);
auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs);
py::print(message);
}
The two approaches can also be combined:
.. code-block:: cpp
#include <pybind11/embed.h>
#include <iostream>
namespace py = pybind11;
using namespace py::literals;
int main() {
py::scoped_interpreter guard{};
auto locals = py::dict("name"_a="World", "number"_a=42);
py::exec(R"(
message = "Hello, {name}! The answer is {number}".format(**locals())
)", py::globals(), locals);
auto message = locals["message"].cast<std::string>();
std::cout << message;
}
Importing modules
=================
Python modules can be imported using ``module_::import()``:
.. code-block:: cpp
py::module_ sys = py::module_::import("sys");
py::print(sys.attr("path"));
For convenience, the current working directory is included in ``sys.path`` when
embedding the interpreter. This makes it easy to import local Python files:
.. code-block:: python
"""calc.py located in the working directory"""
def add(i, j):
return i + j
.. code-block:: cpp
py::module_ calc = py::module_::import("calc");
py::object result = calc.attr("add")(1, 2);
int n = result.cast<int>();
assert(n == 3);
Modules can be reloaded using ``module_::reload()`` if the source is modified e.g.
by an external process. This can be useful in scenarios where the application
imports a user defined data processing script which needs to be updated after
changes by the user. Note that this function does not reload modules recursively.
.. _embedding_modules:
Adding embedded modules
=======================
Embedded binary modules can be added using the ``PYBIND11_EMBEDDED_MODULE`` macro.
Note that the definition must be placed at global scope. They can be imported
like any other module.
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
PYBIND11_EMBEDDED_MODULE(fast_calc, m) {
// `m` is a `py::module_` which is used to bind functions and classes
m.def("add", [](int i, int j) {
return i + j;
});
}
int main() {
py::scoped_interpreter guard{};
auto fast_calc = py::module_::import("fast_calc");
auto result = fast_calc.attr("add")(1, 2).cast<int>();
assert(result == 3);
}
Unlike extension modules where only a single binary module can be created, on
the embedded side an unlimited number of modules can be added using multiple
``PYBIND11_EMBEDDED_MODULE`` definitions (as long as they have unique names).
These modules are added to Python's list of builtins, so they can also be
imported in pure Python files loaded by the interpreter. Everything interacts
naturally:
.. code-block:: python
"""py_module.py located in the working directory"""
import cpp_module
a = cpp_module.a
b = a + 1
.. code-block:: cpp
#include <pybind11/embed.h>
namespace py = pybind11;
PYBIND11_EMBEDDED_MODULE(cpp_module, m) {
m.attr("a") = 1;
}
int main() {
py::scoped_interpreter guard{};
auto py_module = py::module_::import("py_module");
auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__"));
assert(locals["a"].cast<int>() == 1);
assert(locals["b"].cast<int>() == 2);
py::exec(R"(
c = a + b
message = fmt.format(a, b, c)
)", py::globals(), locals);
assert(locals["c"].cast<int>() == 3);
assert(locals["message"].cast<std::string>() == "1 + 2 = 3");
}
Interpreter lifetime
====================
The Python interpreter shuts down when ``scoped_interpreter`` is destroyed. After
this, creating a new instance will restart the interpreter. Alternatively, the
``initialize_interpreter`` / ``finalize_interpreter`` pair of functions can be used
to directly set the state at any time.
Modules created with pybind11 can be safely re-initialized after the interpreter
has been restarted. However, this may not apply to third-party extension modules.
The issue is that Python itself cannot completely unload extension modules and
there are several caveats with regard to interpreter restarting. In short, not
all memory may be freed, either due to Python reference cycles or user-created
global data. All the details can be found in the CPython documentation.
.. warning::
Creating two concurrent ``scoped_interpreter`` guards is a fatal error. So is
calling ``initialize_interpreter`` for a second time after the interpreter
has already been initialized.
Do not use the raw CPython API functions ``Py_Initialize`` and
``Py_Finalize`` as these do not properly handle the lifetime of
pybind11's internal data.
Sub-interpreter support
=======================
Creating multiple copies of ``scoped_interpreter`` is not possible because it
represents the main Python interpreter. Sub-interpreters are something different
and they do permit the existence of multiple interpreters. This is an advanced
feature of the CPython API and should be handled with care. pybind11 does not
currently offer a C++ interface for sub-interpreters, so refer to the CPython
documentation for all the details regarding this feature.
We'll just mention a couple of caveats the sub-interpreters support in pybind11:
1. Sub-interpreters will not receive independent copies of embedded modules.
Instead, these are shared and modifications in one interpreter may be
reflected in another.
2. Managing multiple threads, multiple interpreters and the GIL can be
challenging and there are several caveats here, even within the pure
CPython API (please refer to the Python docs for details). As for
pybind11, keep in mind that ``gil_scoped_release`` and ``gil_scoped_acquire``
do not take sub-interpreters into account.

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Exceptions
##########
Built-in C++ to Python exception translation
============================================
When Python calls C++ code through pybind11, pybind11 provides a C++ exception handler
that will trap C++ exceptions, translate them to the corresponding Python exception,
and raise them so that Python code can handle them.
pybind11 defines translations for ``std::exception`` and its standard
subclasses, and several special exception classes that translate to specific
Python exceptions. Note that these are not actually Python exceptions, so they
cannot be examined using the Python C API. Instead, they are pure C++ objects
that pybind11 will translate the corresponding Python exception when they arrive
at its exception handler.
.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+--------------------------------------+--------------------------------------+
| Exception thrown by C++ | Translated to Python exception type |
+======================================+======================================+
| :class:`std::exception` | ``RuntimeError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::bad_alloc` | ``MemoryError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::domain_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::invalid_argument` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::length_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::out_of_range` | ``IndexError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::range_error` | ``ValueError`` |
+--------------------------------------+--------------------------------------+
| :class:`std::overflow_error` | ``OverflowError`` |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::stop_iteration` | ``StopIteration`` (used to implement |
| | custom iterators) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::index_error` | ``IndexError`` (used to indicate out |
| | of bounds access in ``__getitem__``, |
| | ``__setitem__``, etc.) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::key_error` | ``KeyError`` (used to indicate out |
| | of bounds access in ``__getitem__``, |
| | ``__setitem__`` in dict-like |
| | objects, etc.) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::value_error` | ``ValueError`` (used to indicate |
| | wrong value passed in |
| | ``container.remove(...)``) |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::type_error` | ``TypeError`` |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::buffer_error` | ``BufferError`` |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::import_error` | ``ImportError`` |
+--------------------------------------+--------------------------------------+
| :class:`pybind11::attribute_error` | ``AttributeError`` |
+--------------------------------------+--------------------------------------+
| Any other exception | ``RuntimeError`` |
+--------------------------------------+--------------------------------------+
Exception translation is not bidirectional. That is, *catching* the C++
exceptions defined above will not trap exceptions that originate from
Python. For that, catch :class:`pybind11::error_already_set`. See :ref:`below
<handling_python_exceptions_cpp>` for further details.
There is also a special exception :class:`cast_error` that is thrown by
:func:`handle::call` when the input arguments cannot be converted to Python
objects.
Registering custom translators
==============================
If the default exception conversion policy described above is insufficient,
pybind11 also provides support for registering custom exception translators.
Similar to pybind11 classes, exception translators can be local to the module
they are defined in or global to the entire python session. To register a simple
exception conversion that translates a C++ exception into a new Python exception
using the C++ exception's ``what()`` method, a helper function is available:
.. code-block:: cpp
py::register_exception<CppExp>(module, "PyExp");
This call creates a Python exception class with the name ``PyExp`` in the given
module and automatically converts any encountered exceptions of type ``CppExp``
into Python exceptions of type ``PyExp``.
A matching function is available for registering a local exception translator:
.. code-block:: cpp
py::register_local_exception<CppExp>(module, "PyExp");
It is possible to specify base class for the exception using the third
parameter, a ``handle``:
.. code-block:: cpp
py::register_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);
py::register_local_exception<CppExp>(module, "PyExp", PyExc_RuntimeError);
Then ``PyExp`` can be caught both as ``PyExp`` and ``RuntimeError``.
The class objects of the built-in Python exceptions are listed in the Python
documentation on `Standard Exceptions <https://docs.python.org/3/c-api/exceptions.html#standard-exceptions>`_.
The default base class is ``PyExc_Exception``.
When more advanced exception translation is needed, the functions
``py::register_exception_translator(translator)`` and
``py::register_local_exception_translator(translator)`` can be used to register
functions that can translate arbitrary exception types (and which may include
additional logic to do so). The functions takes a stateless callable (e.g. a
function pointer or a lambda function without captured variables) with the call
signature ``void(std::exception_ptr)``.
When a C++ exception is thrown, the registered exception translators are tried
in reverse order of registration (i.e. the last registered translator gets the
first shot at handling the exception). All local translators will be tried
before a global translator is tried.
Inside the translator, ``std::rethrow_exception`` should be used within
a try block to re-throw the exception. One or more catch clauses to catch
the appropriate exceptions should then be used with each clause using
``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set
the python exception to a custom exception type (see below).
To declare a custom Python exception type, declare a ``py::exception`` variable
and use this in the associated exception translator (note: it is often useful
to make this a static declaration when using it inside a lambda expression
without requiring capturing).
The following example demonstrates this for a hypothetical exception classes
``MyCustomException`` and ``OtherException``: the first is translated to a
custom python exception ``MyCustomError``, while the second is translated to a
standard python RuntimeError:
.. code-block:: cpp
static py::exception<MyCustomException> exc(m, "MyCustomError");
py::register_exception_translator([](std::exception_ptr p) {
try {
if (p) std::rethrow_exception(p);
} catch (const MyCustomException &e) {
exc(e.what());
} catch (const OtherException &e) {
PyErr_SetString(PyExc_RuntimeError, e.what());
}
});
Multiple exceptions can be handled by a single translator, as shown in the
example above. If the exception is not caught by the current translator, the
previously registered one gets a chance.
If none of the registered exception translators is able to handle the
exception, it is handled by the default converter as described in the previous
section.
.. seealso::
The file :file:`tests/test_exceptions.cpp` contains examples
of various custom exception translators and custom exception types.
.. note::
Call either ``PyErr_SetString`` or a custom exception's call
operator (``exc(string)``) for every exception caught in a custom exception
translator. Failure to do so will cause Python to crash with ``SystemError:
error return without exception set``.
Exceptions that you do not plan to handle should simply not be caught, or
may be explicitly (re-)thrown to delegate it to the other,
previously-declared existing exception translators.
Note that ``libc++`` and ``libstdc++`` `behave differently <https://stackoverflow.com/questions/19496643/using-clang-fvisibility-hidden-and-typeinfo-and-type-erasure/28827430>`_
with ``-fvisibility=hidden``. Therefore exceptions that are used across ABI boundaries need to be explicitly exported, as exercised in ``tests/test_exceptions.h``.
See also: "Problems with C++ exceptions" under `GCC Wiki <https://gcc.gnu.org/wiki/Visibility>`_.
Local vs Global Exception Translators
=====================================
When a global exception translator is registered, it will be applied across all
modules in the reverse order of registration. This can create behavior where the
order of module import influences how exceptions are translated.
If module1 has the following translator:
.. code-block:: cpp
py::register_exception_translator([](std::exception_ptr p) {
try {
if (p) std::rethrow_exception(p);
} catch (const std::invalid_argument &e) {
PyErr_SetString("module1 handled this")
}
}
and module2 has the following similar translator:
.. code-block:: cpp
py::register_exception_translator([](std::exception_ptr p) {
try {
if (p) std::rethrow_exception(p);
} catch (const std::invalid_argument &e) {
PyErr_SetString("module2 handled this")
}
}
then which translator handles the invalid_argument will be determined by the
order that module1 and module2 are imported. Since exception translators are
applied in the reverse order of registration, which ever module was imported
last will "win" and that translator will be applied.
If there are multiple pybind11 modules that share exception types (either
standard built-in or custom) loaded into a single python instance and
consistent error handling behavior is needed, then local translators should be
used.
Changing the previous example to use ``register_local_exception_translator``
would mean that when invalid_argument is thrown in the module2 code, the
module2 translator will always handle it, while in module1, the module1
translator will do the same.
.. _handling_python_exceptions_cpp:
Handling exceptions from Python in C++
======================================
When C++ calls Python functions, such as in a callback function or when
manipulating Python objects, and Python raises an ``Exception``, pybind11
converts the Python exception into a C++ exception of type
:class:`pybind11::error_already_set` whose payload contains a C++ string textual
summary and the actual Python exception. ``error_already_set`` is used to
propagate Python exception back to Python (or possibly, handle them in C++).
.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+--------------------------------------+--------------------------------------+
| Exception raised in Python | Thrown as C++ exception type |
+======================================+======================================+
| Any Python ``Exception`` | :class:`pybind11::error_already_set` |
+--------------------------------------+--------------------------------------+
For example:
.. code-block:: cpp
try {
// open("missing.txt", "r")
auto file = py::module_::import("io").attr("open")("missing.txt", "r");
auto text = file.attr("read")();
file.attr("close")();
} catch (py::error_already_set &e) {
if (e.matches(PyExc_FileNotFoundError)) {
py::print("missing.txt not found");
} else if (e.matches(PyExc_PermissionError)) {
py::print("missing.txt found but not accessible");
} else {
throw;
}
}
Note that C++ to Python exception translation does not apply here, since that is
a method for translating C++ exceptions to Python, not vice versa. The error raised
from Python is always ``error_already_set``.
This example illustrates this behavior:
.. code-block:: cpp
try {
py::eval("raise ValueError('The Ring')");
} catch (py::value_error &boromir) {
// Boromir never gets the ring
assert(false);
} catch (py::error_already_set &frodo) {
// Frodo gets the ring
py::print("I will take the ring");
}
try {
// py::value_error is a request for pybind11 to raise a Python exception
throw py::value_error("The ball");
} catch (py::error_already_set &cat) {
// cat won't catch the ball since
// py::value_error is not a Python exception
assert(false);
} catch (py::value_error &dog) {
// dog will catch the ball
py::print("Run Spot run");
throw; // Throw it again (pybind11 will raise ValueError)
}
Handling errors from the Python C API
=====================================
Where possible, use :ref:`pybind11 wrappers <wrappers>` instead of calling
the Python C API directly. When calling the Python C API directly, in
addition to manually managing reference counts, one must follow the pybind11
error protocol, which is outlined here.
After calling the Python C API, if Python returns an error,
``throw py::error_already_set();``, which allows pybind11 to deal with the
exception and pass it back to the Python interpreter. This includes calls to
the error setting functions such as ``PyErr_SetString``.
.. code-block:: cpp
PyErr_SetString(PyExc_TypeError, "C API type error demo");
throw py::error_already_set();
// But it would be easier to simply...
throw py::type_error("pybind11 wrapper type error");
Alternately, to ignore the error, call `PyErr_Clear
<https://docs.python.org/3/c-api/exceptions.html#c.PyErr_Clear>`_.
Any Python error must be thrown or cleared, or Python/pybind11 will be left in
an invalid state.
Chaining exceptions ('raise from')
==================================
Python has a mechanism for indicating that exceptions were caused by other
exceptions:
.. code-block:: py
try:
print(1 / 0)
except Exception as exc:
raise RuntimeError("could not divide by zero") from exc
To do a similar thing in pybind11, you can use the ``py::raise_from`` function. It
sets the current python error indicator, so to continue propagating the exception
you should ``throw py::error_already_set()``.
.. code-block:: cpp
try {
py::eval("print(1 / 0"));
} catch (py::error_already_set &e) {
py::raise_from(e, PyExc_RuntimeError, "could not divide by zero");
throw py::error_already_set();
}
.. versionadded:: 2.8
.. _unraisable_exceptions:
Handling unraisable exceptions
==============================
If a Python function invoked from a C++ destructor or any function marked
``noexcept(true)`` (collectively, "noexcept functions") throws an exception, there
is no way to propagate the exception, as such functions may not throw.
Should they throw or fail to catch any exceptions in their call graph,
the C++ runtime calls ``std::terminate()`` to abort immediately.
Similarly, Python exceptions raised in a class's ``__del__`` method do not
propagate, but are logged by Python as an unraisable error. In Python 3.8+, a
`system hook is triggered
<https://docs.python.org/3/library/sys.html#sys.unraisablehook>`_
and an auditing event is logged.
Any noexcept function should have a try-catch block that traps
class:`error_already_set` (or any other exception that can occur). Note that
pybind11 wrappers around Python exceptions such as
:class:`pybind11::value_error` are *not* Python exceptions; they are C++
exceptions that pybind11 catches and converts to Python exceptions. Noexcept
functions cannot propagate these exceptions either. A useful approach is to
convert them to Python exceptions and then ``discard_as_unraisable`` as shown
below.
.. code-block:: cpp
void nonthrowing_func() noexcept(true) {
try {
// ...
} catch (py::error_already_set &eas) {
// Discard the Python error using Python APIs, using the C++ magic
// variable __func__. Python already knows the type and value and of the
// exception object.
eas.discard_as_unraisable(__func__);
} catch (const std::exception &e) {
// Log and discard C++ exceptions.
third_party::log(e);
}
}
.. versionadded:: 2.6

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Functions
#########
Before proceeding with this section, make sure that you are already familiar
with the basics of binding functions and classes, as explained in :doc:`/basics`
and :doc:`/classes`. The following guide is applicable to both free and member
functions, i.e. *methods* in Python.
.. _return_value_policies:
Return value policies
=====================
Python and C++ use fundamentally different ways of managing the memory and
lifetime of objects managed by them. This can lead to issues when creating
bindings for functions that return a non-trivial type. Just by looking at the
type information, it is not clear whether Python should take charge of the
returned value and eventually free its resources, or if this is handled on the
C++ side. For this reason, pybind11 provides a several *return value policy*
annotations that can be passed to the :func:`module_::def` and
:func:`class_::def` functions. The default policy is
:enum:`return_value_policy::automatic`.
Return value policies are tricky, and it's very important to get them right.
Just to illustrate what can go wrong, consider the following simple example:
.. code-block:: cpp
/* Function declaration */
Data *get_data() { return _data; /* (pointer to a static data structure) */ }
...
/* Binding code */
m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python
What's going on here? When ``get_data()`` is called from Python, the return
value (a native C++ type) must be wrapped to turn it into a usable Python type.
In this case, the default return value policy (:enum:`return_value_policy::automatic`)
causes pybind11 to assume ownership of the static ``_data`` instance.
When Python's garbage collector eventually deletes the Python
wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator
delete()``) due to the implied ownership. At this point, the entire application
will come crashing down, though errors could also be more subtle and involve
silent data corruption.
In the above example, the policy :enum:`return_value_policy::reference` should have
been specified so that the global data instance is only *referenced* without any
implied transfer of ownership, i.e.:
.. code-block:: cpp
m.def("get_data", &get_data, py::return_value_policy::reference);
On the other hand, this is not the right policy for many other situations,
where ignoring ownership could lead to resource leaks.
As a developer using pybind11, it's important to be familiar with the different
return value policies, including which situation calls for which one of them.
The following table provides an overview of available policies:
.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+--------------------------------------------------+----------------------------------------------------------------------------+
| Return value policy | Description |
+==================================================+============================================================================+
| :enum:`return_value_policy::take_ownership` | Reference an existing object (i.e. do not create a new copy) and take |
| | ownership. Python will call the destructor and delete operator when the |
| | object's reference count reaches zero. Undefined behavior ensues when the |
| | C++ side does the same, or when the data was not dynamically allocated. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::copy` | Create a new copy of the returned object, which will be owned by Python. |
| | This policy is comparably safe because the lifetimes of the two instances |
| | are decoupled. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::move` | Use ``std::move`` to move the return value contents into a new instance |
| | that will be owned by Python. This policy is comparably safe because the |
| | lifetimes of the two instances (move source and destination) are decoupled.|
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference` | Reference an existing object, but do not take ownership. The C++ side is |
| | responsible for managing the object's lifetime and deallocating it when |
| | it is no longer used. Warning: undefined behavior will ensue when the C++ |
| | side deletes an object that is still referenced and used by Python. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::reference_internal` | Indicates that the lifetime of the return value is tied to the lifetime |
| | of a parent object, namely the implicit ``this``, or ``self`` argument of |
| | the called method or property. Internally, this policy works just like |
| | :enum:`return_value_policy::reference` but additionally applies a |
| | ``keep_alive<0, 1>`` *call policy* (described in the next section) that |
| | prevents the parent object from being garbage collected as long as the |
| | return value is referenced by Python. This is the default policy for |
| | property getters created via ``def_property``, ``def_readwrite``, etc. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::automatic` | This policy falls back to the policy |
| | :enum:`return_value_policy::take_ownership` when the return value is a |
| | pointer. Otherwise, it uses :enum:`return_value_policy::move` or |
| | :enum:`return_value_policy::copy` for rvalue and lvalue references, |
| | respectively. See above for a description of what all of these different |
| | policies do. This is the default policy for ``py::class_``-wrapped types. |
+--------------------------------------------------+----------------------------------------------------------------------------+
| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the |
| | return value is a pointer. This is the default conversion policy for |
| | function arguments when calling Python functions manually from C++ code |
| | (i.e. via ``handle::operator()``) and the casters in ``pybind11/stl.h``. |
| | You probably won't need to use this explicitly. |
+--------------------------------------------------+----------------------------------------------------------------------------+
Return value policies can also be applied to properties:
.. code-block:: cpp
class_<MyClass>(m, "MyClass")
.def_property("data", &MyClass::getData, &MyClass::setData,
py::return_value_policy::copy);
Technically, the code above applies the policy to both the getter and the
setter function, however, the setter doesn't really care about *return*
value policies which makes this a convenient terse syntax. Alternatively,
targeted arguments can be passed through the :class:`cpp_function` constructor:
.. code-block:: cpp
class_<MyClass>(m, "MyClass")
.def_property("data",
py::cpp_function(&MyClass::getData, py::return_value_policy::copy),
py::cpp_function(&MyClass::setData)
);
.. warning::
Code with invalid return value policies might access uninitialized memory or
free data structures multiple times, which can lead to hard-to-debug
non-determinism and segmentation faults, hence it is worth spending the
time to understand all the different options in the table above.
.. note::
One important aspect of the above policies is that they only apply to
instances which pybind11 has *not* seen before, in which case the policy
clarifies essential questions about the return value's lifetime and
ownership. When pybind11 knows the instance already (as identified by its
type and address in memory), it will return the existing Python object
wrapper rather than creating a new copy.
.. note::
The next section on :ref:`call_policies` discusses *call policies* that can be
specified *in addition* to a return value policy from the list above. Call
policies indicate reference relationships that can involve both return values
and parameters of functions.
.. note::
As an alternative to elaborate call policies and lifetime management logic,
consider using smart pointers (see the section on :ref:`smart_pointers` for
details). Smart pointers can tell whether an object is still referenced from
C++ or Python, which generally eliminates the kinds of inconsistencies that
can lead to crashes or undefined behavior. For functions returning smart
pointers, it is not necessary to specify a return value policy.
.. _call_policies:
Additional call policies
========================
In addition to the above return value policies, further *call policies* can be
specified to indicate dependencies between parameters or ensure a certain state
for the function call.
Keep alive
----------
In general, this policy is required when the C++ object is any kind of container
and another object is being added to the container. ``keep_alive<Nurse, Patient>``
indicates that the argument with index ``Patient`` should be kept alive at least
until the argument with index ``Nurse`` is freed by the garbage collector. Argument
indices start at one, while zero refers to the return value. For methods, index
``1`` refers to the implicit ``this`` pointer, while regular arguments begin at
index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse``
with value ``None`` is detected at runtime, the call policy does nothing.
When the nurse is not a pybind11-registered type, the implementation internally
relies on the ability to create a *weak reference* to the nurse object. When
the nurse object is not a pybind11-registered type and does not support weak
references, an exception will be thrown.
If you use an incorrect argument index, you will get a ``RuntimeError`` saying
``Could not activate keep_alive!``. You should review the indices you're using.
Consider the following example: here, the binding code for a list append
operation ties the lifetime of the newly added element to the underlying
container:
.. code-block:: cpp
py::class_<List>(m, "List")
.def("append", &List::append, py::keep_alive<1, 2>());
For consistency, the argument indexing is identical for constructors. Index
``1`` still refers to the implicit ``this`` pointer, i.e. the object which is
being constructed. Index ``0`` refers to the return type which is presumed to
be ``void`` when a constructor is viewed like a function. The following example
ties the lifetime of the constructor element to the constructed object:
.. code-block:: cpp
py::class_<Nurse>(m, "Nurse")
.def(py::init<Patient &>(), py::keep_alive<1, 2>());
.. note::
``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse,
Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient ==
0) policies from Boost.Python.
Call guard
----------
The ``call_guard<T>`` policy allows any scope guard type ``T`` to be placed
around the function call. For example, this definition:
.. code-block:: cpp
m.def("foo", foo, py::call_guard<T>());
is equivalent to the following pseudocode:
.. code-block:: cpp
m.def("foo", [](args...) {
T scope_guard;
return foo(args...); // forwarded arguments
});
The only requirement is that ``T`` is default-constructible, but otherwise any
scope guard will work. This is very useful in combination with ``gil_scoped_release``.
See :ref:`gil`.
Multiple guards can also be specified as ``py::call_guard<T1, T2, T3...>``. The
constructor order is left to right and destruction happens in reverse.
.. seealso::
The file :file:`tests/test_call_policies.cpp` contains a complete example
that demonstrates using `keep_alive` and `call_guard` in more detail.
.. _python_objects_as_args:
Python objects as arguments
===========================
pybind11 exposes all major Python types using thin C++ wrapper classes. These
wrapper classes can also be used as parameters of functions in bindings, which
makes it possible to directly work with native Python types on the C++ side.
For instance, the following statement iterates over a Python ``dict``:
.. code-block:: cpp
void print_dict(const py::dict& dict) {
/* Easily interact with Python types */
for (auto item : dict)
std::cout << "key=" << std::string(py::str(item.first)) << ", "
<< "value=" << std::string(py::str(item.second)) << std::endl;
}
It can be exported:
.. code-block:: cpp
m.def("print_dict", &print_dict);
And used in Python as usual:
.. code-block:: pycon
>>> print_dict({"foo": 123, "bar": "hello"})
key=foo, value=123
key=bar, value=hello
For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`.
Accepting \*args and \*\*kwargs
===============================
Python provides a useful mechanism to define functions that accept arbitrary
numbers of arguments and keyword arguments:
.. code-block:: python
def generic(*args, **kwargs):
... # do something with args and kwargs
Such functions can also be created using pybind11:
.. code-block:: cpp
void generic(py::args args, const py::kwargs& kwargs) {
/// .. do something with args
if (kwargs)
/// .. do something with kwargs
}
/// Binding code
m.def("generic", &generic);
The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives
from ``py::dict``.
You may also use just one or the other, and may combine these with other
arguments. Note, however, that ``py::kwargs`` must always be the last argument
of the function, and ``py::args`` implies that any further arguments are
keyword-only (see :ref:`keyword_only_arguments`).
Please refer to the other examples for details on how to iterate over these,
and on how to cast their entries into C++ objects. A demonstration is also
available in ``tests/test_kwargs_and_defaults.cpp``.
.. note::
When combining \*args or \*\*kwargs with :ref:`keyword_args` you should
*not* include ``py::arg`` tags for the ``py::args`` and ``py::kwargs``
arguments.
Default arguments revisited
===========================
The section on :ref:`default_args` previously discussed basic usage of default
arguments using pybind11. One noteworthy aspect of their implementation is that
default arguments are converted to Python objects right at declaration time.
Consider the following example:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg("arg") = SomeType(123));
In this case, pybind11 must already be set up to deal with values of the type
``SomeType`` (via a prior instantiation of ``py::class_<SomeType>``), or an
exception will be thrown.
Another aspect worth highlighting is that the "preview" of the default argument
in the function signature is generated using the object's ``__repr__`` method.
If not available, the signature may not be very helpful, e.g.:
.. code-block:: pycon
FUNCTIONS
...
| myFunction(...)
| Signature : (MyClass, arg : SomeType = <SomeType object at 0x101b7b080>) -> NoneType
...
The first way of addressing this is by defining ``SomeType.__repr__``.
Alternatively, it is possible to specify the human-readable preview of the
default argument manually using the ``arg_v`` notation:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)"));
Sometimes it may be necessary to pass a null pointer value as a default
argument. In this case, remember to cast it to the underlying type in question,
like so:
.. code-block:: cpp
py::class_<MyClass>("MyClass")
.def("myFunction", py::arg("arg") = static_cast<SomeType *>(nullptr));
.. _keyword_only_arguments:
Keyword-only arguments
======================
Python implements keyword-only arguments by specifying an unnamed ``*``
argument in a function definition:
.. code-block:: python
def f(a, *, b): # a can be positional or via keyword; b must be via keyword
pass
f(a=1, b=2) # good
f(b=2, a=1) # good
f(1, b=2) # good
f(1, 2) # TypeError: f() takes 1 positional argument but 2 were given
Pybind11 provides a ``py::kw_only`` object that allows you to implement
the same behaviour by specifying the object between positional and keyword-only
argument annotations when registering the function:
.. code-block:: cpp
m.def("f", [](int a, int b) { /* ... */ },
py::arg("a"), py::kw_only(), py::arg("b"));
.. versionadded:: 2.6
A ``py::args`` argument implies that any following arguments are keyword-only,
as if ``py::kw_only()`` had been specified in the same relative location of the
argument list as the ``py::args`` argument. The ``py::kw_only()`` may be
included to be explicit about this, but is not required.
.. versionchanged:: 2.9
This can now be combined with ``py::args``. Before, ``py::args`` could only
occur at the end of the argument list, or immediately before a ``py::kwargs``
argument at the end.
Positional-only arguments
=========================
Python 3.8 introduced a new positional-only argument syntax, using ``/`` in the
function definition (note that this has been a convention for CPython
positional arguments, such as in ``pow()``, since Python 2). You can
do the same thing in any version of Python using ``py::pos_only()``:
.. code-block:: cpp
m.def("f", [](int a, int b) { /* ... */ },
py::arg("a"), py::pos_only(), py::arg("b"));
You now cannot give argument ``a`` by keyword. This can be combined with
keyword-only arguments, as well.
.. versionadded:: 2.6
.. _nonconverting_arguments:
Non-converting arguments
========================
Certain argument types may support conversion from one type to another. Some
examples of conversions are:
* :ref:`implicit_conversions` declared using ``py::implicitly_convertible<A,B>()``
* Calling a method accepting a double with an integer argument
* Calling a ``std::complex<float>`` argument with a non-complex python type
(for example, with a float). (Requires the optional ``pybind11/complex.h``
header).
* Calling a function taking an Eigen matrix reference with a numpy array of the
wrong type or of an incompatible data layout. (Requires the optional
``pybind11/eigen.h`` header).
This behaviour is sometimes undesirable: the binding code may prefer to raise
an error rather than convert the argument. This behaviour can be obtained
through ``py::arg`` by calling the ``.noconvert()`` method of the ``py::arg``
object, such as:
.. code-block:: cpp
m.def("floats_only", [](double f) { return 0.5 * f; }, py::arg("f").noconvert());
m.def("floats_preferred", [](double f) { return 0.5 * f; }, py::arg("f"));
Attempting the call the second function (the one without ``.noconvert()``) with
an integer will succeed, but attempting to call the ``.noconvert()`` version
will fail with a ``TypeError``:
.. code-block:: pycon
>>> floats_preferred(4)
2.0
>>> floats_only(4)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: floats_only(): incompatible function arguments. The following argument types are supported:
1. (f: float) -> float
Invoked with: 4
You may, of course, combine this with the :var:`_a` shorthand notation (see
:ref:`keyword_args`) and/or :ref:`default_args`. It is also permitted to omit
the argument name by using the ``py::arg()`` constructor without an argument
name, i.e. by specifying ``py::arg().noconvert()``.
.. note::
When specifying ``py::arg`` options it is necessary to provide the same
number of options as the bound function has arguments. Thus if you want to
enable no-convert behaviour for just one of several arguments, you will
need to specify a ``py::arg()`` annotation for each argument with the
no-convert argument modified to ``py::arg().noconvert()``.
.. _none_arguments:
Allow/Prohibiting None arguments
================================
When a C++ type registered with :class:`py::class_` is passed as an argument to
a function taking the instance as pointer or shared holder (e.g. ``shared_ptr``
or a custom, copyable holder as described in :ref:`smart_pointers`), pybind
allows ``None`` to be passed from Python which results in calling the C++
function with ``nullptr`` (or an empty holder) for the argument.
To explicitly enable or disable this behaviour, using the
``.none`` method of the :class:`py::arg` object:
.. code-block:: cpp
py::class_<Dog>(m, "Dog").def(py::init<>());
py::class_<Cat>(m, "Cat").def(py::init<>());
m.def("bark", [](Dog *dog) -> std::string {
if (dog) return "woof!"; /* Called with a Dog instance */
else return "(no dog)"; /* Called with None, dog == nullptr */
}, py::arg("dog").none(true));
m.def("meow", [](Cat *cat) -> std::string {
// Can't be called with None argument
return "meow";
}, py::arg("cat").none(false));
With the above, the Python call ``bark(None)`` will return the string ``"(no
dog)"``, while attempting to call ``meow(None)`` will raise a ``TypeError``:
.. code-block:: pycon
>>> from animals import Dog, Cat, bark, meow
>>> bark(Dog())
'woof!'
>>> meow(Cat())
'meow'
>>> bark(None)
'(no dog)'
>>> meow(None)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: meow(): incompatible function arguments. The following argument types are supported:
1. (cat: animals.Cat) -> str
Invoked with: None
The default behaviour when the tag is unspecified is to allow ``None``.
.. note::
Even when ``.none(true)`` is specified for an argument, ``None`` will be converted to a
``nullptr`` *only* for custom and :ref:`opaque <opaque>` types. Pointers to built-in types
(``double *``, ``int *``, ...) and STL types (``std::vector<T> *``, ...; if ``pybind11/stl.h``
is included) are copied when converted to C++ (see :doc:`/advanced/cast/overview`) and will
not allow ``None`` as argument. To pass optional argument of these copied types consider
using ``std::optional<T>``
.. _overload_resolution:
Overload resolution order
=========================
When a function or method with multiple overloads is called from Python,
pybind11 determines which overload to call in two passes. The first pass
attempts to call each overload without allowing argument conversion (as if
every argument had been specified as ``py::arg().noconvert()`` as described
above).
If no overload succeeds in the no-conversion first pass, a second pass is
attempted in which argument conversion is allowed (except where prohibited via
an explicit ``py::arg().noconvert()`` attribute in the function definition).
If the second pass also fails a ``TypeError`` is raised.
Within each pass, overloads are tried in the order they were registered with
pybind11. If the ``py::prepend()`` tag is added to the definition, a function
can be placed at the beginning of the overload sequence instead, allowing user
overloads to proceed built in functions.
What this means in practice is that pybind11 will prefer any overload that does
not require conversion of arguments to an overload that does, but otherwise
prefers earlier-defined overloads to later-defined ones.
.. note::
pybind11 does *not* further prioritize based on the number/pattern of
overloaded arguments. That is, pybind11 does not prioritize a function
requiring one conversion over one requiring three, but only prioritizes
overloads requiring no conversion at all to overloads that require
conversion of at least one argument.
.. versionadded:: 2.6
The ``py::prepend()`` tag.
Binding functions with template parameters
==========================================
You can bind functions that have template parameters. Here's a function:
.. code-block:: cpp
template <typename T>
void set(T t);
C++ templates cannot be instantiated at runtime, so you cannot bind the
non-instantiated function:
.. code-block:: cpp
// BROKEN (this will not compile)
m.def("set", &set);
You must bind each instantiated function template separately. You may bind
each instantiation with the same name, which will be treated the same as
an overloaded function:
.. code-block:: cpp
m.def("set", &set<int>);
m.def("set", &set<std::string>);
Sometimes it's more clear to bind them with separate names, which is also
an option:
.. code-block:: cpp
m.def("setInt", &set<int>);
m.def("setString", &set<std::string>);

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Miscellaneous
#############
.. _macro_notes:
General notes regarding convenience macros
==========================================
pybind11 provides a few convenience macros such as
:func:`PYBIND11_DECLARE_HOLDER_TYPE` and ``PYBIND11_OVERRIDE_*``. Since these
are "just" macros that are evaluated in the preprocessor (which has no concept
of types), they *will* get confused by commas in a template argument; for
example, consider:
.. code-block:: cpp
PYBIND11_OVERRIDE(MyReturnType<T1, T2>, Class<T3, T4>, func)
The limitation of the C preprocessor interprets this as five arguments (with new
arguments beginning after each comma) rather than three. To get around this,
there are two alternatives: you can use a type alias, or you can wrap the type
using the ``PYBIND11_TYPE`` macro:
.. code-block:: cpp
// Version 1: using a type alias
using ReturnType = MyReturnType<T1, T2>;
using ClassType = Class<T3, T4>;
PYBIND11_OVERRIDE(ReturnType, ClassType, func);
// Version 2: using the PYBIND11_TYPE macro:
PYBIND11_OVERRIDE(PYBIND11_TYPE(MyReturnType<T1, T2>),
PYBIND11_TYPE(Class<T3, T4>), func)
The ``PYBIND11_MAKE_OPAQUE`` macro does *not* require the above workarounds.
.. _gil:
Global Interpreter Lock (GIL)
=============================
When calling a C++ function from Python, the GIL is always held.
The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be
used to acquire and release the global interpreter lock in the body of a C++
function call. In this way, long-running C++ code can be parallelized using
multiple Python threads. Taking :ref:`overriding_virtuals` as an example, this
could be realized as follows (important changes highlighted):
.. code-block:: cpp
:emphasize-lines: 8,9,31,32
class PyAnimal : public Animal {
public:
/* Inherit the constructors */
using Animal::Animal;
/* Trampoline (need one for each virtual function) */
std::string go(int n_times) {
/* Acquire GIL before calling Python code */
py::gil_scoped_acquire acquire;
PYBIND11_OVERRIDE_PURE(
std::string, /* Return type */
Animal, /* Parent class */
go, /* Name of function */
n_times /* Argument(s) */
);
}
};
PYBIND11_MODULE(example, m) {
py::class_<Animal, PyAnimal> animal(m, "Animal");
animal
.def(py::init<>())
.def("go", &Animal::go);
py::class_<Dog>(m, "Dog", animal)
.def(py::init<>());
m.def("call_go", [](Animal *animal) -> std::string {
/* Release GIL before calling into (potentially long-running) C++ code */
py::gil_scoped_release release;
return call_go(animal);
});
}
The ``call_go`` wrapper can also be simplified using the ``call_guard`` policy
(see :ref:`call_policies`) which yields the same result:
.. code-block:: cpp
m.def("call_go", &call_go, py::call_guard<py::gil_scoped_release>());
Binding sequence data types, iterators, the slicing protocol, etc.
==================================================================
Please refer to the supplemental example for details.
.. seealso::
The file :file:`tests/test_sequences_and_iterators.cpp` contains a
complete example that shows how to bind a sequence data type, including
length queries (``__len__``), iterators (``__iter__``), the slicing
protocol and other kinds of useful operations.
Partitioning code over multiple extension modules
=================================================
It's straightforward to split binding code over multiple extension modules,
while referencing types that are declared elsewhere. Everything "just" works
without any special precautions. One exception to this rule occurs when
extending a type declared in another extension module. Recall the basic example
from Section :ref:`inheritance`.
.. 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);
Suppose now that ``Pet`` bindings are defined in a module named ``basic``,
whereas the ``Dog`` bindings are defined somewhere else. The challenge is of
course that the variable ``pet`` is not available anymore though it is needed
to indicate the inheritance relationship to the constructor of ``class_<Dog>``.
However, it can be acquired as follows:
.. code-block:: cpp
py::object pet = (py::object) py::module_::import("basic").attr("Pet");
py::class_<Dog>(m, "Dog", pet)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Alternatively, you can specify the base class as a template parameter option to
``class_``, which performs an automated lookup of the corresponding Python
type. Like the above code, however, this also requires invoking the ``import``
function once to ensure that the pybind11 binding code of the module ``basic``
has been executed:
.. code-block:: cpp
py::module_::import("basic");
py::class_<Dog, Pet>(m, "Dog")
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Naturally, both methods will fail when there are cyclic dependencies.
Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g.
via the command line flag ``-fvisibility=hidden`` on GCC/Clang), which is
required for proper pybind11 functionality, can interfere with the ability to
access types defined in another extension module. Working around this requires
manually exporting types that are accessed by multiple extension modules;
pybind11 provides a macro to do just this:
.. code-block:: cpp
class PYBIND11_EXPORT Dog : public Animal {
...
};
Note also that it is possible (although would rarely be required) to share arbitrary
C++ objects between extension modules at runtime. Internal library data is shared
between modules using capsule machinery [#f6]_ which can be also utilized for
storing, modifying and accessing user-defined data. Note that an extension module
will "see" other extensions' data if and only if they were built with the same
pybind11 version. Consider the following example:
.. code-block:: cpp
auto data = reinterpret_cast<MyData *>(py::get_shared_data("mydata"));
if (!data)
data = static_cast<MyData *>(py::set_shared_data("mydata", new MyData(42)));
If the above snippet was used in several separately compiled extension modules,
the first one to be imported would create a ``MyData`` instance and associate
a ``"mydata"`` key with a pointer to it. Extensions that are imported later
would be then able to access the data behind the same pointer.
.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules
Module Destructors
==================
pybind11 does not provide an explicit mechanism to invoke cleanup code at
module destruction time. In rare cases where such functionality is required, it
is possible to emulate it using Python capsules or weak references with a
destruction callback.
.. code-block:: cpp
auto cleanup_callback = []() {
// perform cleanup here -- this function is called with the GIL held
};
m.add_object("_cleanup", py::capsule(cleanup_callback));
This approach has the potential downside that instances of classes exposed
within the module may still be alive when the cleanup callback is invoked
(whether this is acceptable will generally depend on the application).
Alternatively, the capsule may also be stashed within a type object, which
ensures that it not called before all instances of that type have been
collected:
.. code-block:: cpp
auto cleanup_callback = []() { /* ... */ };
m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback);
Both approaches also expose a potentially dangerous ``_cleanup`` attribute in
Python, which may be undesirable from an API standpoint (a premature explicit
call from Python might lead to undefined behavior). Yet another approach that
avoids this issue involves weak reference with a cleanup callback:
.. code-block:: cpp
// Register a callback function that is invoked when the BaseClass object is collected
py::cpp_function cleanup_callback(
[](py::handle weakref) {
// perform cleanup here -- this function is called with the GIL held
weakref.dec_ref(); // release weak reference
}
);
// Create a weak reference with a cleanup callback and initially leak it
(void) py::weakref(m.attr("BaseClass"), cleanup_callback).release();
.. note::
PyPy does not garbage collect objects when the interpreter exits. An alternative
approach (which also works on CPython) is to use the :py:mod:`atexit` module [#f7]_,
for example:
.. code-block:: cpp
auto atexit = py::module_::import("atexit");
atexit.attr("register")(py::cpp_function([]() {
// perform cleanup here -- this function is called with the GIL held
}));
.. [#f7] https://docs.python.org/3/library/atexit.html
Generating documentation using Sphinx
=====================================
Sphinx [#f4]_ has the ability to inspect the signatures and documentation
strings in pybind11-based extension modules to automatically generate beautiful
documentation in a variety formats. The python_example repository [#f5]_ contains a
simple example repository which uses this approach.
There are two potential gotchas when using this approach: first, make sure that
the resulting strings do not contain any :kbd:`TAB` characters, which break the
docstring parsing routines. You may want to use C++11 raw string literals,
which are convenient for multi-line comments. Conveniently, any excess
indentation will be automatically be removed by Sphinx. However, for this to
work, it is important that all lines are indented consistently, i.e.:
.. code-block:: cpp
// ok
m.def("foo", &foo, R"mydelimiter(
The foo function
Parameters
----------
)mydelimiter");
// *not ok*
m.def("foo", &foo, R"mydelimiter(The foo function
Parameters
----------
)mydelimiter");
By default, pybind11 automatically generates and prepends a signature to the docstring of a function
registered with ``module_::def()`` and ``class_::def()``. Sometimes this
behavior is not desirable, because you want to provide your own signature or remove
the docstring completely to exclude the function from the Sphinx documentation.
The class ``options`` allows you to selectively suppress auto-generated signatures:
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
py::options options;
options.disable_function_signatures();
m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers");
}
Note that changes to the settings affect only function bindings created during the
lifetime of the ``options`` instance. When it goes out of scope at the end of the module's init function,
the default settings are restored to prevent unwanted side effects.
.. [#f4] http://www.sphinx-doc.org
.. [#f5] http://github.com/pybind/python_example
.. _avoiding-cpp-types-in-docstrings:
Avoiding C++ types in docstrings
================================
Docstrings are generated at the time of the declaration, e.g. when ``.def(...)`` is called.
At this point parameter and return types should be known to pybind11.
If a custom type is not exposed yet through a ``py::class_`` constructor or a custom type caster,
its C++ type name will be used instead to generate the signature in the docstring:
.. code-block:: text
| __init__(...)
| __init__(self: example.Foo, arg0: ns::Bar) -> None
^^^^^^^
This limitation can be circumvented by ensuring that C++ classes are registered with pybind11
before they are used as a parameter or return type of a function:
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
auto pyFoo = py::class_<ns::Foo>(m, "Foo");
auto pyBar = py::class_<ns::Bar>(m, "Bar");
pyFoo.def(py::init<const ns::Bar&>());
pyBar.def(py::init<const ns::Foo&>());
}

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Python C++ interface
####################
pybind11 exposes Python types and functions using thin C++ wrappers, which
makes it possible to conveniently call Python code from C++ without resorting
to Python's C API.
.. toctree::
:maxdepth: 2
object
numpy
utilities

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.. _numpy:
NumPy
#####
Buffer protocol
===============
Python supports an extremely general and convenient approach for exchanging
data between plugin libraries. Types can expose a buffer view [#f2]_, which
provides fast direct access to the raw internal data 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 Matrices 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", py::buffer_protocol())
.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>::format(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(float) * m.cols(), /* Strides (in bytes) for each index */
sizeof(float) }
);
});
Supporting the buffer protocol in a new type involves specifying the special
``py::buffer_protocol()`` tag in the ``py::class_`` constructor and calling the
``def_buffer()`` method with a lambda function that creates a
``py::buffer_info`` description record on demand describing a given matrix
instance. The contents of ``py::buffer_info`` mirror the Python buffer protocol
specification.
.. code-block:: cpp
struct buffer_info {
void *ptr;
py::ssize_t itemsize;
std::string format;
py::ssize_t ndim;
std::vector<py::ssize_t> shape;
std::vector<py::ssize_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 a 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
/* Bind MatrixXd (or some other Eigen type) to Python */
typedef Eigen::MatrixXd Matrix;
typedef Matrix::Scalar Scalar;
constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit;
py::class_<Matrix>(m, "Matrix", py::buffer_protocol())
.def(py::init([](py::buffer b) {
typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
/* Request a buffer descriptor from Python */
py::buffer_info info = b.request();
/* Some basic validation checks ... */
if (info.format != py::format_descriptor<Scalar>::format())
throw std::runtime_error("Incompatible format: expected a double array!");
if (info.ndim != 2)
throw std::runtime_error("Incompatible buffer dimension!");
auto strides = Strides(
info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar),
info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar));
auto map = Eigen::Map<Matrix, 0, Strides>(
static_cast<Scalar *>(info.ptr), info.shape[0], info.shape[1], strides);
return Matrix(map);
}));
For reference, the ``def_buffer()`` call for this Eigen data type should look
as follows:
.. code-block:: cpp
.def_buffer([](Matrix &m) -> py::buffer_info {
return py::buffer_info(
m.data(), /* Pointer to buffer */
sizeof(Scalar), /* Size of one scalar */
py::format_descriptor<Scalar>::format(), /* Python struct-style format descriptor */
2, /* Number of dimensions */
{ m.rows(), m.cols() }, /* Buffer dimensions */
{ sizeof(Scalar) * (rowMajor ? m.cols() : 1),
sizeof(Scalar) * (rowMajor ? 1 : m.rows()) }
/* Strides (in bytes) for each index */
);
})
For a much easier approach of binding Eigen types (although with some
limitations), refer to the section on :doc:`/advanced/cast/eigen`.
.. seealso::
The file :file:`tests/test_buffers.cpp` contains a complete example
that demonstrates using the buffer protocol with pybind11 in more detail.
.. [#f2] http://docs.python.org/3/c-api/buffer.html
Arrays
======
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 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_t<T>``
template. For instance, the following function requires the argument to be a
NumPy array containing double precision values.
.. code-block:: cpp
void f(py::array_t<double> array);
When it is invoked with a different type (e.g. an integer or a list of
integers), the binding code will attempt to cast the input into a NumPy array
of the requested type. This feature requires the :file:`pybind11/numpy.h`
header to be included. Note that :file:`pybind11/numpy.h` does not depend on
the NumPy headers, and thus can be used without declaring a build-time
dependency on NumPy; NumPy>=1.7.0 is a runtime dependency.
Data in NumPy arrays is not guaranteed to packed in a dense manner;
furthermore, entries can be separated by arbitrary column and row strides.
Sometimes, it can be useful to require a function to only accept dense arrays
using either the C (row-major) or Fortran (column-major) ordering. This can be
accomplished via a second template argument with values ``py::array::c_style``
or ``py::array::f_style``.
.. code-block:: cpp
void f(py::array_t<double, py::array::c_style | py::array::forcecast> array);
The ``py::array::forcecast`` argument is the default value of the second
template parameter, and it ensures that non-conforming arguments are converted
into an array satisfying the specified requirements instead of trying the next
function overload.
There are several methods on arrays; the methods listed below under references
work, as well as the following functions based on the NumPy API:
- ``.dtype()`` returns the type of the contained values.
- ``.strides()`` returns a pointer to the strides of the array (optionally pass
an integer axis to get a number).
- ``.flags()`` returns the flag settings. ``.writable()`` and ``.owndata()``
are directly available.
- ``.offset_at()`` returns the offset (optionally pass indices).
- ``.squeeze()`` returns a view with length-1 axes removed.
- ``.view(dtype)`` returns a view of the array with a different dtype.
- ``.reshape({i, j, ...})`` returns a view of the array with a different shape.
``.resize({...})`` is also available.
- ``.index_at(i, j, ...)`` gets the count from the beginning to a given index.
There are also several methods for getting references (described below).
Structured types
================
In order for ``py::array_t`` to work with structured (record) types, we first
need to register the memory layout of the type. This can be done via
``PYBIND11_NUMPY_DTYPE`` macro, called in the plugin definition code, which
expects the type followed by field names:
.. code-block:: cpp
struct A {
int x;
double y;
};
struct B {
int z;
A a;
};
// ...
PYBIND11_MODULE(test, m) {
// ...
PYBIND11_NUMPY_DTYPE(A, x, y);
PYBIND11_NUMPY_DTYPE(B, z, a);
/* now both A and B can be used as template arguments to py::array_t */
}
The structure should consist of fundamental arithmetic types, ``std::complex``,
previously registered substructures, and arrays of any of the above. Both C++
arrays and ``std::array`` are supported. While there is a static assertion to
prevent many types of unsupported structures, it is still the user's
responsibility to use only "plain" structures that can be safely manipulated as
raw memory without violating invariants.
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 ``pybind11/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 array elements. The significant advantage of this compared to
solutions like ``numpy.vectorize()`` is that the loop over the elements runs
entirely on the C++ side and can be crunched down into a tight, optimized loop
by the compiler. The result is returned as a NumPy array of type
``numpy.dtype.float64``.
.. code-block:: pycon
>>> 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).
.. note::
Only arithmetic, complex, and POD types passed by value or by ``const &``
reference are vectorized; all other arguments are passed through as-is.
Functions taking rvalue reference arguments cannot be vectorized.
In cases where the computation is too complicated to be reduced to
``vectorize``, it will be necessary to create and access the buffer contents
manually. The following snippet contains a complete example that shows how this
works (the code is somewhat contrived, since it could have been done more
simply using ``vectorize``).
.. code-block:: cpp
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
namespace py = pybind11;
py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) {
py::buffer_info buf1 = input1.request(), buf2 = input2.request();
if (buf1.ndim != 1 || buf2.ndim != 1)
throw std::runtime_error("Number of dimensions must be one");
if (buf1.size != buf2.size)
throw std::runtime_error("Input shapes must match");
/* No pointer is passed, so NumPy will allocate the buffer */
auto result = py::array_t<double>(buf1.size);
py::buffer_info buf3 = result.request();
double *ptr1 = static_cast<double *>(buf1.ptr);
double *ptr2 = static_cast<double *>(buf2.ptr);
double *ptr3 = static_cast<double *>(buf3.ptr);
for (size_t idx = 0; idx < buf1.shape[0]; idx++)
ptr3[idx] = ptr1[idx] + ptr2[idx];
return result;
}
PYBIND11_MODULE(test, m) {
m.def("add_arrays", &add_arrays, "Add two NumPy arrays");
}
.. seealso::
The file :file:`tests/test_numpy_vectorize.cpp` contains a complete
example that demonstrates using :func:`vectorize` in more detail.
Direct access
=============
For performance reasons, particularly when dealing with very large arrays, it
is often desirable to directly access array elements without internal checking
of dimensions and bounds on every access when indices are known to be already
valid. To avoid such checks, the ``array`` class and ``array_t<T>`` template
class offer an unchecked proxy object that can be used for this unchecked
access through the ``unchecked<N>`` and ``mutable_unchecked<N>`` methods,
where ``N`` gives the required dimensionality of the array:
.. code-block:: cpp
m.def("sum_3d", [](py::array_t<double> x) {
auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable
double sum = 0;
for (py::ssize_t i = 0; i < r.shape(0); i++)
for (py::ssize_t j = 0; j < r.shape(1); j++)
for (py::ssize_t k = 0; k < r.shape(2); k++)
sum += r(i, j, k);
return sum;
});
m.def("increment_3d", [](py::array_t<double> x) {
auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false
for (py::ssize_t i = 0; i < r.shape(0); i++)
for (py::ssize_t j = 0; j < r.shape(1); j++)
for (py::ssize_t k = 0; k < r.shape(2); k++)
r(i, j, k) += 1.0;
}, py::arg().noconvert());
To obtain the proxy from an ``array`` object, you must specify both the data
type and number of dimensions as template arguments, such as ``auto r =
myarray.mutable_unchecked<float, 2>()``.
If the number of dimensions is not known at compile time, you can omit the
dimensions template parameter (i.e. calling ``arr_t.unchecked()`` or
``arr.unchecked<T>()``. This will give you a proxy object that works in the
same way, but results in less optimizable code and thus a small efficiency
loss in tight loops.
Note that the returned proxy object directly references the array's data, and
only reads its shape, strides, and writeable flag when constructed. You must
take care to ensure that the referenced array is not destroyed or reshaped for
the duration of the returned object, typically by limiting the scope of the
returned instance.
The returned proxy object supports some of the same methods as ``py::array`` so
that it can be used as a drop-in replacement for some existing, index-checked
uses of ``py::array``:
- ``.ndim()`` returns the number of dimensions
- ``.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to
the ``const T`` or ``T`` data, respectively, at the given indices. The
latter is only available to proxies obtained via ``a.mutable_unchecked()``.
- ``.itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``.
- ``.ndim()`` returns the number of dimensions.
- ``.shape(n)`` returns the size of dimension ``n``
- ``.size()`` returns the total number of elements (i.e. the product of the shapes).
- ``.nbytes()`` returns the number of bytes used by the referenced elements
(i.e. ``itemsize()`` times ``size()``).
.. seealso::
The file :file:`tests/test_numpy_array.cpp` contains additional examples
demonstrating the use of this feature.
Ellipsis
========
Python provides a convenient ``...`` ellipsis notation that is often used to
slice multidimensional arrays. For instance, the following snippet extracts the
middle dimensions of a tensor with the first and last index set to zero.
.. code-block:: python
a = ... # a NumPy array
b = a[0, ..., 0]
The function ``py::ellipsis()`` function can be used to perform the same
operation on the C++ side:
.. code-block:: cpp
py::array a = /* A NumPy array */;
py::array b = a[py::make_tuple(0, py::ellipsis(), 0)];
Memory view
===========
For a case when we simply want to provide a direct accessor to C/C++ buffer
without a concrete class object, we can return a ``memoryview`` object. Suppose
we wish to expose a ``memoryview`` for 2x4 uint8_t array, we can do the
following:
.. code-block:: cpp
const uint8_t buffer[] = {
0, 1, 2, 3,
4, 5, 6, 7
};
m.def("get_memoryview2d", []() {
return py::memoryview::from_buffer(
buffer, // buffer pointer
{ 2, 4 }, // shape (rows, cols)
{ sizeof(uint8_t) * 4, sizeof(uint8_t) } // strides in bytes
);
})
This approach is meant for providing a ``memoryview`` for a C/C++ buffer not
managed by Python. The user is responsible for managing the lifetime of the
buffer. Using a ``memoryview`` created in this way after deleting the buffer in
C++ side results in undefined behavior.
We can also use ``memoryview::from_memory`` for a simple 1D contiguous buffer:
.. code-block:: cpp
m.def("get_memoryview1d", []() {
return py::memoryview::from_memory(
buffer, // buffer pointer
sizeof(uint8_t) * 8 // buffer size
);
})
.. versionchanged:: 2.6
``memoryview::from_memory`` added.

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Python types
############
.. _wrappers:
Available wrappers
==================
All major Python types are available as thin C++ wrapper classes. These
can also be used as function parameters -- see :ref:`python_objects_as_args`.
Available types include :class:`handle`, :class:`object`, :class:`bool_`,
:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`,
:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`,
:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`,
:class:`array`, and :class:`array_t`.
.. warning::
Be sure to review the :ref:`pytypes_gotchas` before using this heavily in
your C++ API.
.. _instantiating_compound_types:
Instantiating compound Python types from C++
============================================
Dictionaries can be initialized in the :class:`dict` constructor:
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
py::dict d("spam"_a=py::none(), "eggs"_a=42);
A tuple of python objects can be instantiated using :func:`py::make_tuple`:
.. code-block:: cpp
py::tuple tup = py::make_tuple(42, py::none(), "spam");
Each element is converted to a supported Python type.
A `simple namespace`_ can be instantiated using
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace");
py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42);
Attributes on a namespace can be modified with the :func:`py::delattr`,
:func:`py::getattr`, and :func:`py::setattr` functions. Simple namespaces can
be useful as lightweight stand-ins for class instances.
.. _simple namespace: https://docs.python.org/3/library/types.html#types.SimpleNamespace
.. _casting_back_and_forth:
Casting back and forth
======================
In this kind of mixed code, it is often necessary to convert arbitrary C++
types to Python, which can be done using :func:`py::cast`:
.. code-block:: cpp
MyClass *cls = ...;
py::object obj = py::cast(cls);
The reverse direction uses the following syntax:
.. code-block:: cpp
py::object obj = ...;
MyClass *cls = obj.cast<MyClass *>();
When conversion fails, both directions throw the exception :class:`cast_error`.
.. _python_libs:
Accessing Python libraries from C++
===================================
It is also possible to import objects defined in the Python standard
library or available in the current Python environment (``sys.path``) and work
with these in C++.
This example obtains a reference to the Python ``Decimal`` class.
.. code-block:: cpp
// Equivalent to "from decimal import Decimal"
py::object Decimal = py::module_::import("decimal").attr("Decimal");
.. code-block:: cpp
// Try to import scipy
py::object scipy = py::module_::import("scipy");
return scipy.attr("__version__");
.. _calling_python_functions:
Calling Python functions
========================
It is also possible to call Python classes, functions and methods
via ``operator()``.
.. code-block:: cpp
// Construct a Python object of class Decimal
py::object pi = Decimal("3.14159");
.. code-block:: cpp
// Use Python to make our directories
py::object os = py::module_::import("os");
py::object makedirs = os.attr("makedirs");
makedirs("/tmp/path/to/somewhere");
One can convert the result obtained from Python to a pure C++ version
if a ``py::class_`` or type conversion is defined.
.. code-block:: cpp
py::function f = <...>;
py::object result_py = f(1234, "hello", some_instance);
MyClass &result = result_py.cast<MyClass>();
.. _calling_python_methods:
Calling Python methods
========================
To call an object's method, one can again use ``.attr`` to obtain access to the
Python method.
.. code-block:: cpp
// Calculate e^π in decimal
py::object exp_pi = pi.attr("exp")();
py::print(py::str(exp_pi));
In the example above ``pi.attr("exp")`` is a *bound method*: it will always call
the method for that same instance of the class. Alternately one can create an
*unbound method* via the Python class (instead of instance) and pass the ``self``
object explicitly, followed by other arguments.
.. code-block:: cpp
py::object decimal_exp = Decimal.attr("exp");
// Compute the e^n for n=0..4
for (int n = 0; n < 5; n++) {
py::print(decimal_exp(Decimal(n));
}
Keyword arguments
=================
Keyword arguments are also supported. In Python, there is the usual call syntax:
.. code-block:: python
def f(number, say, to):
... # function code
f(1234, say="hello", to=some_instance) # keyword call in Python
In C++, the same call can be made using:
.. code-block:: cpp
using namespace pybind11::literals; // to bring in the `_a` literal
f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++
Unpacking arguments
===================
Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with
other arguments:
.. code-block:: cpp
// * unpacking
py::tuple args = py::make_tuple(1234, "hello", some_instance);
f(*args);
// ** unpacking
py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance);
f(**kwargs);
// mixed keywords, * and ** unpacking
py::tuple args = py::make_tuple(1234);
py::dict kwargs = py::dict("to"_a=some_instance);
f(*args, "say"_a="hello", **kwargs);
Generalized unpacking according to PEP448_ is also supported:
.. code-block:: cpp
py::dict kwargs1 = py::dict("number"_a=1234);
py::dict kwargs2 = py::dict("to"_a=some_instance);
f(**kwargs1, "say"_a="hello", **kwargs2);
.. seealso::
The file :file:`tests/test_pytypes.cpp` contains a complete
example that demonstrates passing native Python types in more detail. The
file :file:`tests/test_callbacks.cpp` presents a few examples of calling
Python functions from C++, including keywords arguments and unpacking.
.. _PEP448: https://www.python.org/dev/peps/pep-0448/
.. _implicit_casting:
Implicit casting
================
When using the C++ interface for Python types, or calling Python functions,
objects of type :class:`object` are returned. It is possible to invoke implicit
conversions to subclasses like :class:`dict`. The same holds for the proxy objects
returned by ``operator[]`` or ``obj.attr()``.
Casting to subtypes improves code readability and allows values to be passed to
C++ functions that require a specific subtype rather than a generic :class:`object`.
.. code-block:: cpp
#include <pybind11/numpy.h>
using namespace pybind11::literals;
py::module_ os = py::module_::import("os");
py::module_ path = py::module_::import("os.path"); // like 'import os.path as path'
py::module_ np = py::module_::import("numpy"); // like 'import numpy as np'
py::str curdir_abs = path.attr("abspath")(path.attr("curdir"));
py::print(py::str("Current directory: ") + curdir_abs);
py::dict environ = os.attr("environ");
py::print(environ["HOME"]);
py::array_t<float> arr = np.attr("ones")(3, "dtype"_a="float32");
py::print(py::repr(arr + py::int_(1)));
These implicit conversions are available for subclasses of :class:`object`; there
is no need to call ``obj.cast()`` explicitly as for custom classes, see
:ref:`casting_back_and_forth`.
.. note::
If a trivial conversion via move constructor is not possible, both implicit and
explicit casting (calling ``obj.cast()``) will attempt a "rich" conversion.
For instance, ``py::list env = os.attr("environ");`` will succeed and is
equivalent to the Python code ``env = list(os.environ)`` that produces a
list of the dict keys.
.. TODO: Adapt text once PR #2349 has landed
Handling exceptions
===================
Python exceptions from wrapper classes will be thrown as a ``py::error_already_set``.
See :ref:`Handling exceptions from Python in C++
<handling_python_exceptions_cpp>` for more information on handling exceptions
raised when calling C++ wrapper classes.
.. _pytypes_gotchas:
Gotchas
=======
Default-Constructed Wrappers
----------------------------
When a wrapper type is default-constructed, it is **not** a valid Python object (i.e. it is not ``py::none()``). It is simply the same as
``PyObject*`` null pointer. To check for this, use
``static_cast<bool>(my_wrapper)``.
Assigning py::none() to wrappers
--------------------------------
You may be tempted to use types like ``py::str`` and ``py::dict`` in C++
signatures (either pure C++, or in bound signatures), and assign them default
values of ``py::none()``. However, in a best case scenario, it will fail fast
because ``None`` is not convertible to that type (e.g. ``py::dict``), or in a
worse case scenario, it will silently work but corrupt the types you want to
work with (e.g. ``py::str(py::none())`` will yield ``"None"`` in Python).

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Utilities
#########
Using Python's print function in C++
====================================
The usual way to write output in C++ is using ``std::cout`` while in Python one
would use ``print``. Since these methods use different buffers, mixing them can
lead to output order issues. To resolve this, pybind11 modules can use the
:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency.
Python's ``print`` function is replicated in the C++ API including optional
keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as
expected in Python:
.. code-block:: cpp
py::print(1, 2.0, "three"); // 1 2.0 three
py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three
auto args = py::make_tuple("unpacked", true);
py::print("->", *args, "end"_a="<-"); // -> unpacked True <-
.. _ostream_redirect:
Capturing standard output from ostream
======================================
Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print,
but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr``
redirection. Replacing a library's printing with ``py::print <print>`` may not
be feasible. This can be fixed using a guard around the library function that
redirects output to the corresponding Python streams:
.. code-block:: cpp
#include <pybind11/iostream.h>
...
// Add a scoped redirect for your noisy code
m.def("noisy_func", []() {
py::scoped_ostream_redirect stream(
std::cout, // std::ostream&
py::module_::import("sys").attr("stdout") // Python output
);
call_noisy_func();
});
.. warning::
The implementation in ``pybind11/iostream.h`` is NOT thread safe. Multiple
threads writing to a redirected ostream concurrently cause data races
and potentially buffer overflows. Therefore it is currently a requirement
that all (possibly) concurrent redirected ostream writes are protected by
a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more
background see the discussions under
`PR #2982 <https://github.com/pybind/pybind11/pull/2982>`_ and
`PR #2995 <https://github.com/pybind/pybind11/pull/2995>`_.
This method respects flushes on the output streams and will flush if needed
when the scoped guard is destroyed. This allows the output to be redirected in
real time, such as to a Jupyter notebook. The two arguments, the C++ stream and
the Python output, are optional, and default to standard output if not given. An
extra type, ``py::scoped_estream_redirect <scoped_estream_redirect>``, is identical
except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with
``py::call_guard``, which allows multiple items, but uses the default constructor:
.. code-block:: cpp
// Alternative: Call single function using call guard
m.def("noisy_func", &call_noisy_function,
py::call_guard<py::scoped_ostream_redirect,
py::scoped_estream_redirect>());
The redirection can also be done in Python with the addition of a context
manager, using the ``py::add_ostream_redirect() <add_ostream_redirect>`` function:
.. code-block:: cpp
py::add_ostream_redirect(m, "ostream_redirect");
The name in Python defaults to ``ostream_redirect`` if no name is passed. This
creates the following context manager in Python:
.. code-block:: python
with ostream_redirect(stdout=True, stderr=True):
noisy_function()
It defaults to redirecting both streams, though you can use the keyword
arguments to disable one of the streams if needed.
.. note::
The above methods will not redirect C-level output to file descriptors, such
as ``fprintf``. For those cases, you'll need to redirect the file
descriptors either directly in C or with Python's ``os.dup2`` function
in an operating-system dependent way.
.. _eval:
Evaluating Python expressions from strings and files
====================================================
pybind11 provides the ``eval``, ``exec`` and ``eval_file`` functions to evaluate
Python expressions and statements. The following example illustrates how they
can be used.
.. code-block:: cpp
// At beginning of file
#include <pybind11/eval.h>
...
// Evaluate in scope of main module
py::object scope = py::module_::import("__main__").attr("__dict__");
// Evaluate an isolated expression
int result = py::eval("my_variable + 10", scope).cast<int>();
// Evaluate a sequence of statements
py::exec(
"print('Hello')\n"
"print('world!');",
scope);
// Evaluate the statements in an separate Python file on disk
py::eval_file("script.py", scope);
C++11 raw string literals are also supported and quite handy for this purpose.
The only requirement is that the first statement must be on a new line following
the raw string delimiter ``R"(``, ensuring all lines have common leading indent:
.. code-block:: cpp
py::exec(R"(
x = get_answer()
if x == 42:
print('Hello World!')
else:
print('Bye!')
)", scope
);
.. note::
`eval` and `eval_file` accept a template parameter that describes how the
string/file should be interpreted. Possible choices include ``eval_expr``
(isolated expression), ``eval_single_statement`` (a single statement, return
value is always ``none``), and ``eval_statements`` (sequence of statements,
return value is always ``none``). `eval` defaults to ``eval_expr``,
`eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut
for ``eval<eval_statements>``.

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Smart pointers
##############
std::unique_ptr
===============
Given a class ``Example`` with Python bindings, it's possible to return
instances wrapped in C++11 unique pointers, like so
.. code-block:: cpp
std::unique_ptr<Example> create_example() { return std::unique_ptr<Example>(new Example()); }
.. code-block:: cpp
m.def("create_example", &create_example);
In other words, there is nothing special that needs to be done. While returning
unique pointers in this way is allowed, it is *illegal* to use them as function
arguments. For instance, the following function signature cannot be processed
by pybind11.
.. code-block:: cpp
void do_something_with_example(std::unique_ptr<Example> ex) { ... }
The above signature would imply that Python needs to give up ownership of an
object that is passed to this function, which is generally not possible (for
instance, the object might be referenced elsewhere).
std::shared_ptr
===============
The binding generator for classes, :class:`class_`, can be passed a template
type that denotes a special *holder* type that is used to manage references to
the object. If no such holder type template argument is given, the default for
a type named ``Type`` is ``std::unique_ptr<Type>``, which means that the object
is deallocated when Python's reference count goes to zero.
It is possible to switch to other types of reference counting wrappers or smart
pointers, which is useful in codebases that rely on them. For instance, the
following snippet causes ``std::shared_ptr`` to be used instead.
.. code-block:: cpp
py::class_<Example, std::shared_ptr<Example> /* <- holder type */> obj(m, "Example");
Note that any particular class can only be associated with a single holder type.
One potential stumbling block when using holder types is that they need to be
applied consistently. Can you guess what's broken about the following binding
code?
.. code-block:: cpp
class Child { };
class Parent {
public:
Parent() : child(std::make_shared<Child>()) { }
Child *get_child() { return child.get(); } /* Hint: ** DON'T DO THIS ** */
private:
std::shared_ptr<Child> child;
};
PYBIND11_MODULE(example, m) {
py::class_<Child, std::shared_ptr<Child>>(m, "Child");
py::class_<Parent, std::shared_ptr<Parent>>(m, "Parent")
.def(py::init<>())
.def("get_child", &Parent::get_child);
}
The following Python code will cause undefined behavior (and likely a
segmentation fault).
.. code-block:: python
from example import Parent
print(Parent().get_child())
The problem is that ``Parent::get_child()`` returns a pointer to an instance of
``Child``, but the fact that this instance is already managed by
``std::shared_ptr<...>`` is lost when passing raw pointers. In this case,
pybind11 will create a second independent ``std::shared_ptr<...>`` that also
claims ownership of the pointer. In the end, the object will be freed **twice**
since these shared pointers have no way of knowing about each other.
There are two ways to resolve this issue:
1. For types that are managed by a smart pointer class, never use raw pointers
in function arguments or return values. In other words: always consistently
wrap pointers into their designated holder types (such as
``std::shared_ptr<...>``). In this case, the signature of ``get_child()``
should be modified as follows:
.. code-block:: cpp
std::shared_ptr<Child> get_child() { return child; }
2. Adjust the definition of ``Child`` by specifying
``std::enable_shared_from_this<T>`` (see cppreference_ for details) as a
base class. This adds a small bit of information to ``Child`` that allows
pybind11 to realize that there is already an existing
``std::shared_ptr<...>`` and communicate with it. In this case, the
declaration of ``Child`` should look as follows:
.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this
.. code-block:: cpp
class Child : public std::enable_shared_from_this<Child> { };
.. _smart_pointers:
Custom smart pointers
=====================
pybind11 supports ``std::unique_ptr`` and ``std::shared_ptr`` right out of the
box. For any other custom smart pointer, transparent conversions can be enabled
using a macro invocation similar to the following. It must be declared at the
top namespace level before any binding code:
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a
placeholder name that is used as a template parameter of the second argument.
Thus, feel free to use any identifier, but use it consistently on both sides;
also, don't use the name of a type that already exists in your codebase.
The macro also accepts a third optional boolean parameter that is set to false
by default. Specify
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>, true);
if ``SmartPtr<T>`` can always be initialized from a ``T*`` pointer without the
risk of inconsistencies (such as multiple independent ``SmartPtr`` instances
believing that they are the sole owner of the ``T*`` pointer). A common
situation where ``true`` should be passed is when the ``T`` instances use
*intrusive* reference counting.
Please take a look at the :ref:`macro_notes` before using this feature.
By default, pybind11 assumes that your custom smart pointer has a standard
interface, i.e. provides a ``.get()`` member function to access the underlying
raw pointer. If this is not the case, pybind11's ``holder_helper`` must be
specialized:
.. code-block:: cpp
// Always needed for custom holder types
PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr<T>);
// Only needed if the type's `.get()` goes by another name
namespace pybind11 { namespace detail {
template <typename T>
struct holder_helper<SmartPtr<T>> { // <-- specialization
static const T *get(const SmartPtr<T> &p) { return p.getPointer(); }
};
}}
The above specialization informs pybind11 that the custom ``SmartPtr`` class
provides ``.get()`` functionality via ``.getPointer()``.
.. seealso::
The file :file:`tests/test_smart_ptr.cpp` contains a complete example
that demonstrates how to work with custom reference-counting holder types
in more detail.

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.. _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 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 macOS, the included python version works out of the box,
but **cmake** must still be installed.
After installing the prerequisites, run
.. code-block:: bash
mkdir build
cd build
cmake ..
make check -j 4
The last line will both compile and run the tests.
Windows
-------
On Windows, only **Visual Studio 2017** and newer are supported.
.. Note::
To use the C++17 in Visual Studio 2017 (MSVC 14.1), pybind11 requires the flag
``/permissive-`` to be passed to the compiler `to enforce standard conformance`_. When
building with Visual Studio 2019, this is not strictly necessary, but still advised.
.. _`to enforce standard conformance`: https://docs.microsoft.com/en-us/cpp/build/reference/permissive-standards-conformance?view=vs-2017
To compile and run the tests:
.. code-block:: batch
mkdir build
cd build
cmake ..
cmake --build . --config Release --target check
This will create a Visual Studio project, compile and run the target, all from the
command line.
.. Note::
If all tests fail, make sure that the Python binary and the testcases are compiled
for the same processor type and bitness (i.e. either **i386** or **x86_64**). You
can specify **x86_64** as the target architecture for the generated Visual Studio
project using ``cmake -A x64 ..``.
.. 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:`tests` directory,
which exercise all features of pybind11.
Header and namespace conventions
================================
For brevity, all code examples assume that the following two lines are present:
.. code-block:: cpp
#include <pybind11/pybind11.h>
namespace py = pybind11;
Some features may require additional headers, but those will be specified as needed.
.. _simple_example:
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 <pybind11/pybind11.h>
int add(int i, int j) {
return i + j;
}
PYBIND11_MODULE(example, m) {
m.doc() = "pybind11 example plugin"; // optional module docstring
m.def("add", &add, "A function that adds two numbers");
}
.. [#f1] In practice, implementation and binding code will generally be located
in separate files.
The :func:`PYBIND11_MODULE` macro creates a function that will be called when an
``import`` statement is issued from within Python. The module name (``example``)
is given as the first macro argument (it should not be in quotes). The second
argument (``m``) defines a variable of type :class:`py::module_ <module>` which
is the main interface for creating bindings. The method :func:`module_::def`
generates binding code that exposes the ``add()`` function to Python.
.. 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 and there are no intermediate (magic) translation steps.
On Linux, the above example can be compiled using the following command:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
.. note::
If you used :ref:`include_as_a_submodule` to get the pybind11 source, then
use ``$(python3-config --includes) -Iextern/pybind11/include`` instead of
``$(python3 -m pybind11 --includes)`` in the above compilation, as
explained in :ref:`building_manually`.
For more details on the required compiler flags on Linux and macOS, see
:ref:`building_manually`. For complete cross-platform compilation instructions,
refer to the :ref:`compiling` page.
The `python_example`_ and `cmake_example`_ repositories are also a good place
to start. They are both complete project examples with cross-platform build
systems. The only difference between the two is that `python_example`_ uses
Python's ``setuptools`` to build the module, while `cmake_example`_ uses CMake
(which may be preferable for existing C++ projects).
.. _python_example: https://github.com/pybind/python_example
.. _cmake_example: https://github.com/pybind/cmake_example
Building the above C++ code will produce a binary module file that can be
imported to Python. Assuming that the compiled module is located in the
current directory, the following interactive Python session shows how to
load and execute the example:
.. code-block:: pycon
$ python
Python 3.9.10 (main, Jan 15 2022, 11:48:04)
[Clang 13.0.0 (clang-1300.0.29.3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import example
>>> example.add(1, 2)
3
>>>
.. _keyword_args:
Keyword arguments
=================
With a simple code modification, 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:: pycon
>>> import example
>>> example.add(i=1, j=2)
3L
The keyword names also appear in the function signatures within the documentation.
.. code-block:: pycon
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int, j: int) -> int
A function which adds two numbers
A shorter notation for named arguments is also available:
.. code-block:: cpp
// regular notation
m.def("add1", &add, py::arg("i"), py::arg("j"));
// shorthand
using namespace pybind11::literals;
m.def("add2", &add, "i"_a, "j"_a);
The :var:`_a` suffix forms a C++11 literal which is equivalent to :class:`arg`.
Note that the literal operator must first be made visible with the directive
``using namespace pybind11::literals``. This does not bring in anything else
from the ``pybind11`` namespace except for literals.
.. _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:: pycon
>>> help(example)
....
FUNCTIONS
add(...)
Signature : (i: int = 1, j: int = 2) -> int
A function which adds two numbers
The shorthand notation is also available for default arguments:
.. code-block:: cpp
// regular notation
m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2);
// shorthand
m.def("add2", &add, "i"_a=1, "j"_a=2);
Exporting variables
===================
To expose a value from C++, use the ``attr`` function to register it in a
module as shown below. Built-in types and general objects (more on that later)
are automatically converted when assigned as attributes, and can be explicitly
converted using the function ``py::cast``.
.. code-block:: cpp
PYBIND11_MODULE(example, m) {
m.attr("the_answer") = 42;
py::object world = py::cast("World");
m.attr("what") = world;
}
These are then accessible from Python:
.. code-block:: pycon
>>> import example
>>> example.the_answer
42
>>> example.what
'World'
.. _supported_types:
Supported data types
====================
A large number of data types are supported out of the box and can be used
seamlessly as functions arguments, return values or with ``py::cast`` in general.
For a full overview, see the :doc:`advanced/cast/index` section.

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import datetime as dt
import os
import random
nfns = 4 # Functions per class
nargs = 4 # Arguments per function
def generate_dummy_code_pybind11(nclasses=10):
decl = ""
bindings = ""
for cl in range(nclasses):
decl += f"class cl{cl:03};\n"
decl += "\n"
for cl in range(nclasses):
decl += f"class {cl:03} {{\n"
decl += "public:\n"
bindings += f' py::class_<cl{cl:03}>(m, "cl{cl:03}")\n'
for fn in range(nfns):
ret = random.randint(0, nclasses - 1)
params = [random.randint(0, nclasses - 1) for i in range(nargs)]
decl += f" cl{ret:03} *fn_{fn:03}("
decl += ", ".join(f"cl{p:03} *" for p in params)
decl += ");\n"
bindings += f' .def("fn_{fn:03}", &cl{cl:03}::fn_{fn:03})\n'
decl += "};\n\n"
bindings += " ;\n"
result = "#include <pybind11/pybind11.h>\n\n"
result += "namespace py = pybind11;\n\n"
result += decl + "\n"
result += "PYBIND11_MODULE(example, m) {\n"
result += bindings
result += "}"
return result
def generate_dummy_code_boost(nclasses=10):
decl = ""
bindings = ""
for cl in range(nclasses):
decl += f"class cl{cl:03};\n"
decl += "\n"
for cl in range(nclasses):
decl += "class cl%03i {\n" % cl
decl += "public:\n"
bindings += f' py::class_<cl{cl:03}>("cl{cl:03}")\n'
for fn in range(nfns):
ret = random.randint(0, nclasses - 1)
params = [random.randint(0, nclasses - 1) for i in range(nargs)]
decl += f" cl{ret:03} *fn_{fn:03}("
decl += ", ".join(f"cl{p:03} *" for p in params)
decl += ");\n"
bindings += f' .def("fn_{fn:03}", &cl{cl:03}::fn_{fn:03}, py::return_value_policy<py::manage_new_object>())\n'
decl += "};\n\n"
bindings += " ;\n"
result = "#include <boost/python.hpp>\n\n"
result += "namespace py = boost::python;\n\n"
result += decl + "\n"
result += "BOOST_PYTHON_MODULE(example) {\n"
result += bindings
result += "}"
return result
for codegen in [generate_dummy_code_pybind11, generate_dummy_code_boost]:
print("{")
for i in range(0, 10):
nclasses = 2**i
with open("test.cpp", "w") as f:
f.write(codegen(nclasses))
n1 = dt.datetime.now()
os.system(
"g++ -Os -shared -rdynamic -undefined dynamic_lookup "
"-fvisibility=hidden -std=c++14 test.cpp -I include "
"-I /System/Library/Frameworks/Python.framework/Headers -o test.so"
)
n2 = dt.datetime.now()
elapsed = (n2 - n1).total_seconds()
size = os.stat("test.so").st_size
print(" {%i, %f, %i}," % (nclasses * nfns, elapsed, size))
print("}")

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Benchmark
=========
The following is the result of a synthetic benchmark comparing both compilation
time and module size of pybind11 against Boost.Python. A detailed report about a
Boost.Python to pybind11 conversion of a real project is available here: [#f1]_.
.. [#f1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
Setup
-----
A python script (see the ``docs/benchmark.py`` file) was used to generate a set
of files with dummy classes whose count increases for each successive benchmark
(between 1 and 2048 classes in powers of two). Each class has four methods with
a randomly generated signature with a return value and four arguments. (There
was no particular reason for this setup other than the desire to generate many
unique function signatures whose count could be controlled in a simple way.)
Here is an example of the binding code for one class:
.. code-block:: cpp
...
class cl034 {
public:
cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *);
cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *);
cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *);
cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *);
};
...
PYBIND11_MODULE(example, m) {
...
py::class_<cl034>(m, "cl034")
.def("fn_000", &cl034::fn_000)
.def("fn_001", &cl034::fn_001)
.def("fn_002", &cl034::fn_002)
.def("fn_003", &cl034::fn_003)
...
}
The Boost.Python version looks almost identical except that a return value
policy had to be specified as an argument to ``def()``. For both libraries,
compilation was done with
.. code-block:: bash
Apple LLVM version 7.0.2 (clang-700.1.81)
and the following compilation flags
.. code-block:: bash
g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14
Compilation time
----------------
The following log-log plot shows how the compilation time grows for an
increasing number of class and function declarations. pybind11 includes many
fewer headers, which initially leads to shorter compilation times, but the
performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for
the largest largest file with 2048 classes and a total of 8192 methods -- a
modest **1.2x** speedup relative to Boost.Python, which required 116.35
seconds).
.. only:: not latex
.. image:: pybind11_vs_boost_python1.svg
.. only:: latex
.. image:: pybind11_vs_boost_python1.png
Module size
-----------
Differences between the two libraries become much more pronounced when
considering the file size of the generated Python plugin: for the largest file,
the binary generated by Boost.Python required 16.8 MiB, which was **2.17
times** / **9.1 megabytes** larger than the output generated by pybind11. For
very small inputs, Boost.Python has an edge in the plot below -- however, note
that it stores many definitions in an external library, whose size was not
included here, hence the comparison is slightly shifted in Boost.Python's
favor.
.. only:: not latex
.. image:: pybind11_vs_boost_python2.svg
.. only:: latex
.. image:: pybind11_vs_boost_python2.png

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.. _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 <pybind11/pybind11.h>
namespace py = pybind11;
PYBIND11_MODULE(example, m) {
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def("setName", &Pet::setName)
.def("getName", &Pet::getName);
}
: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:: pycon
% python
>>> import example
>>> p = example.Pet("Molly")
>>> print(p)
<example.Pet object at 0x10cd98060>
>>> p.getName()
'Molly'
>>> p.setName("Charly")
>>> p.getName()
'Charly'
.. seealso::
Static member functions can be bound in the same way using
:func:`class_::def_static`.
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:: pycon
>>> print(p)
<example.Pet object at 0x10cd98060>
To address this, we could bind a 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:: pycon
>>> print(p)
<example.Pet named 'Molly'>
.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object.
.. _properties:
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:: pycon
>>> p = example.Pet("Molly")
>>> p.name
'Molly'
>>> p.name = "Charly"
>>> p.name
'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 a field-like interface within Python that will transparently call
the setter and getter functions:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_property("name", &Pet::getName, &Pet::setName)
// ... remainder ...
Write only properties can be defined by passing ``nullptr`` as the
input for the read function.
.. 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. Please also see the section on
:ref:`static_properties` in the advanced part of the documentation.
Dynamic attributes
==================
Native Python classes can pick up new attributes dynamically:
.. code-block:: pycon
>>> class Pet:
... name = "Molly"
...
>>> p = Pet()
>>> p.name = "Charly" # overwrite existing
>>> p.age = 2 # dynamically add a new attribute
By default, classes exported from C++ do not support this and the only writable
attributes are the ones explicitly defined using :func:`class_::def_readwrite`
or :func:`class_::def_property`.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<>())
.def_readwrite("name", &Pet::name);
Trying to set any other attribute results in an error:
.. code-block:: pycon
>>> p = example.Pet()
>>> p.name = "Charly" # OK, attribute defined in C++
>>> p.age = 2 # fail
AttributeError: 'Pet' object has no attribute 'age'
To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag
must be added to the :class:`py::class_` constructor:
.. code-block:: cpp
py::class_<Pet>(m, "Pet", py::dynamic_attr())
.def(py::init<>())
.def_readwrite("name", &Pet::name);
Now everything works as expected:
.. code-block:: pycon
>>> p = example.Pet()
>>> p.name = "Charly" # OK, overwrite value in C++
>>> p.age = 2 # OK, dynamically add a new attribute
>>> p.__dict__ # just like a native Python class
{'age': 2}
Note that there is a small runtime cost for a class with dynamic attributes.
Not only because of the addition of a ``__dict__``, but also because of more
expensive garbage collection tracking which must be activated to resolve
possible circular references. Native Python classes incur this same cost by
default, so this is not anything to worry about. By default, pybind11 classes
are more efficient than native Python classes. Enabling dynamic attributes
just brings them on par.
.. _inheritance:
Inheritance and automatic downcasting
=====================================
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!"; }
};
There are two different ways of indicating a hierarchical relationship to
pybind11: the first specifies the C++ base class as an extra template
parameter of the :class:`class_`:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &>())
.def_readwrite("name", &Pet::name);
// Method 1: template parameter:
py::class_<Dog, Pet /* <- specify C++ parent type */>(m, "Dog")
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Alternatively, we can also assign a name to the previously bound ``Pet``
:class:`class_` object 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);
// Method 2: pass parent class_ object:
py::class_<Dog>(m, "Dog", pet /* <- specify Python parent type */)
.def(py::init<const std::string &>())
.def("bark", &Dog::bark);
Functionality-wise, both approaches are equivalent. Afterwards, instances will
expose fields and methods of both types:
.. code-block:: pycon
>>> p = example.Dog("Molly")
>>> p.name
'Molly'
>>> p.bark()
'woof!'
The C++ classes defined above are regular non-polymorphic types with an
inheritance relationship. This is reflected in Python:
.. code-block:: cpp
// Return a base pointer to a derived instance
m.def("pet_store", []() { return std::unique_ptr<Pet>(new Dog("Molly")); });
.. code-block:: pycon
>>> p = example.pet_store()
>>> type(p) # `Dog` instance behind `Pet` pointer
Pet # no pointer downcasting for regular non-polymorphic types
>>> p.bark()
AttributeError: 'Pet' object has no attribute 'bark'
The function returned a ``Dog`` instance, but because it's a non-polymorphic
type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only
considered polymorphic if it has at least one virtual function and pybind11
will automatically recognize this:
.. code-block:: cpp
struct PolymorphicPet {
virtual ~PolymorphicPet() = default;
};
struct PolymorphicDog : PolymorphicPet {
std::string bark() const { return "woof!"; }
};
// Same binding code
py::class_<PolymorphicPet>(m, "PolymorphicPet");
py::class_<PolymorphicDog, PolymorphicPet>(m, "PolymorphicDog")
.def(py::init<>())
.def("bark", &PolymorphicDog::bark);
// Again, return a base pointer to a derived instance
m.def("pet_store2", []() { return std::unique_ptr<PolymorphicPet>(new PolymorphicDog); });
.. code-block:: pycon
>>> p = example.pet_store2()
>>> type(p)
PolymorphicDog # automatically downcast
>>> p.bark()
'woof!'
Given a pointer to a polymorphic base, pybind11 performs automatic downcasting
to the actual derived type. Note that this goes beyond the usual situation in
C++: we don't just get access to the virtual functions of the base, we get the
concrete derived type including functions and attributes that the base type may
not even be aware of.
.. seealso::
For more information about polymorphic behavior see :ref:`overriding_virtuals`.
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 function overloads that will be tried in
sequence.
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def(py::init<const std::string &, int>())
.def("set", static_cast<void (Pet::*)(int)>(&Pet::set), "Set the pet's age")
.def("set", static_cast<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:: pycon
>>> help(example.Pet)
class Pet(__builtin__.object)
| Methods defined here:
|
| __init__(...)
| Signature : (Pet, str, int) -> NoneType
|
| set(...)
| 1. Signature : (Pet, int) -> NoneType
|
| Set the pet's age
|
| 2. Signature : (Pet, str) -> NoneType
|
| Set the pet's name
If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative
syntax to cast the overloaded function:
.. code-block:: cpp
py::class_<Pet>(m, "Pet")
.def("set", py::overload_cast<int>(&Pet::set), "Set the pet's age")
.def("set", py::overload_cast<const std::string &>(&Pet::set), "Set the pet's name");
Here, ``py::overload_cast`` only requires the parameter types to be specified.
The return type and class are deduced. This avoids the additional noise of
``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based
on constness, the ``py::const_`` tag should be used:
.. code-block:: cpp
struct Widget {
int foo(int x, float y);
int foo(int x, float y) const;
};
py::class_<Widget>(m, "Widget")
.def("foo_mutable", py::overload_cast<int, float>(&Widget::foo))
.def("foo_const", py::overload_cast<int, float>(&Widget::foo, py::const_));
If you prefer the ``py::overload_cast`` syntax but have a C++11 compatible compiler only,
you can use ``py::detail::overload_cast_impl`` with an additional set of parentheses:
.. code-block:: cpp
template <typename... Args>
using overload_cast_ = pybind11::detail::overload_cast_impl<Args...>;
py::class_<Pet>(m, "Pet")
.def("set", overload_cast_<int>()(&Pet::set), "Set the pet's age")
.def("set", overload_cast_<const std::string &>()(&Pet::set), "Set the pet's name");
.. [#cpp14] A compiler which supports the ``-std=c++14`` flag.
.. note::
To define multiple overloaded constructors, simply declare one after the
other using the ``.def(py::init<...>())`` syntax. The existing machinery
for specifying keyword and default arguments also works.
Enumerations and internal types
===============================
Let's now suppose that the example class contains internal types like enumerations, e.g.:
.. code-block:: cpp
struct Pet {
enum Kind {
Dog = 0,
Cat
};
struct Attributes {
float age = 0;
};
Pet(const std::string &name, Kind type) : name(name), type(type) { }
std::string name;
Kind type;
Attributes attr;
};
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)
.def_readwrite("attr", &Pet::attr);
py::enum_<Pet::Kind>(pet, "Kind")
.value("Dog", Pet::Kind::Dog)
.value("Cat", Pet::Kind::Cat)
.export_values();
py::class_<Pet::Attributes>(pet, "Attributes")
.def(py::init<>())
.def_readwrite("age", &Pet::Attributes::age);
To ensure that the nested types ``Kind`` and ``Attributes`` are created within the scope of ``Pet``, the
``pet`` :class:`class_` instance must be supplied to the :class:`enum_` and :class:`class_`
constructor. The :func:`enum_::export_values` function exports the enum entries
into the parent scope, which should be skipped for newer C++11-style strongly
typed enums.
.. code-block:: pycon
>>> p = Pet("Lucy", Pet.Cat)
>>> p.type
Kind.Cat
>>> int(p.type)
1L
The entries defined by the enumeration type are exposed in the ``__members__`` property:
.. code-block:: pycon
>>> Pet.Kind.__members__
{'Dog': Kind.Dog, 'Cat': Kind.Cat}
The ``name`` property returns the name of the enum value as a unicode string.
.. note::
It is also possible to use ``str(enum)``, however these accomplish different
goals. The following shows how these two approaches differ.
.. code-block:: pycon
>>> p = Pet("Lucy", Pet.Cat)
>>> pet_type = p.type
>>> pet_type
Pet.Cat
>>> str(pet_type)
'Pet.Cat'
>>> pet_type.name
'Cat'
.. note::
When the special tag ``py::arithmetic()`` is specified to the ``enum_``
constructor, pybind11 creates an enumeration that also supports rudimentary
arithmetic and bit-level operations like comparisons, and, or, xor, negation,
etc.
.. code-block:: cpp
py::enum_<Pet::Kind>(pet, "Kind", py::arithmetic())
...
By default, these are omitted to conserve space.

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CMake helpers
-------------
Pybind11 can be used with ``add_subdirectory(extern/pybind11)``, or from an
install with ``find_package(pybind11 CONFIG)``. The interface provided in
either case is functionally identical.
.. cmake-module:: ../../tools/pybind11Config.cmake.in

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.. _compiling:
Build systems
#############
.. _build-setuptools:
Building with setuptools
========================
For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay
has kindly provided an example project which shows how to set up everything,
including automatic generation of documentation using Sphinx. Please refer to
the [python_example]_ repository.
.. [python_example] https://github.com/pybind/python_example
A helper file is provided with pybind11 that can simplify usage with setuptools.
To use pybind11 inside your ``setup.py``, you have to have some system to
ensure that ``pybind11`` is installed when you build your package. There are
four possible ways to do this, and pybind11 supports all four: You can ask all
users to install pybind11 beforehand (bad), you can use
:ref:`setup_helpers-pep518` (good, but very new and requires Pip 10),
:ref:`setup_helpers-setup_requires` (discouraged by Python packagers now that
PEP 518 is available, but it still works everywhere), or you can
:ref:`setup_helpers-copy-manually` (always works but you have to manually sync
your copy to get updates).
An example of a ``setup.py`` using pybind11's helpers:
.. code-block:: python
from glob import glob
from setuptools import setup
from pybind11.setup_helpers import Pybind11Extension
ext_modules = [
Pybind11Extension(
"python_example",
sorted(glob("src/*.cpp")), # Sort source files for reproducibility
),
]
setup(..., ext_modules=ext_modules)
If you want to do an automatic search for the highest supported C++ standard,
that is supported via a ``build_ext`` command override; it will only affect
``Pybind11Extensions``:
.. code-block:: python
from glob import glob
from setuptools import setup
from pybind11.setup_helpers import Pybind11Extension, build_ext
ext_modules = [
Pybind11Extension(
"python_example",
sorted(glob("src/*.cpp")),
),
]
setup(..., cmdclass={"build_ext": build_ext}, ext_modules=ext_modules)
If you have single-file extension modules that are directly stored in the
Python source tree (``foo.cpp`` in the same directory as where a ``foo.py``
would be located), you can also generate ``Pybind11Extensions`` using
``setup_helpers.intree_extensions``: ``intree_extensions(["path/to/foo.cpp",
...])`` returns a list of ``Pybind11Extensions`` which can be passed to
``ext_modules``, possibly after further customizing their attributes
(``libraries``, ``include_dirs``, etc.). By doing so, a ``foo.*.so`` extension
module will be generated and made available upon installation.
``intree_extension`` will automatically detect if you are using a ``src``-style
layout (as long as no namespace packages are involved), but you can also
explicitly pass ``package_dir`` to it (as in ``setuptools.setup``).
Since pybind11 does not require NumPy when building, a light-weight replacement
for NumPy's parallel compilation distutils tool is included. Use it like this:
.. code-block:: python
from pybind11.setup_helpers import ParallelCompile
# Optional multithreaded build
ParallelCompile("NPY_NUM_BUILD_JOBS").install()
setup(...)
The argument is the name of an environment variable to control the number of
threads, such as ``NPY_NUM_BUILD_JOBS`` (as used by NumPy), though you can set
something different if you want; ``CMAKE_BUILD_PARALLEL_LEVEL`` is another choice
a user might expect. You can also pass ``default=N`` to set the default number
of threads (0 will take the number of threads available) and ``max=N``, the
maximum number of threads; if you have a large extension you may want set this
to a memory dependent number.
If you are developing rapidly and have a lot of C++ files, you may want to
avoid rebuilding files that have not changed. For simple cases were you are
using ``pip install -e .`` and do not have local headers, you can skip the
rebuild if an object file is newer than its source (headers are not checked!)
with the following:
.. code-block:: python
from pybind11.setup_helpers import ParallelCompile, naive_recompile
ParallelCompile("NPY_NUM_BUILD_JOBS", needs_recompile=naive_recompile).install()
If you have a more complex build, you can implement a smarter function and pass
it to ``needs_recompile``, or you can use [Ccache]_ instead. ``CXX="cache g++"
pip install -e .`` would be the way to use it with GCC, for example. Unlike the
simple solution, this even works even when not compiling in editable mode, but
it does require Ccache to be installed.
Keep in mind that Pip will not even attempt to rebuild if it thinks it has
already built a copy of your code, which it deduces from the version number.
One way to avoid this is to use [setuptools_scm]_, which will generate a
version number that includes the number of commits since your last tag and a
hash for a dirty directory. Another way to force a rebuild is purge your cache
or use Pip's ``--no-cache-dir`` option.
.. [Ccache] https://ccache.dev
.. [setuptools_scm] https://github.com/pypa/setuptools_scm
.. _setup_helpers-pep518:
PEP 518 requirements (Pip 10+ required)
---------------------------------------
If you use `PEP 518's <https://www.python.org/dev/peps/pep-0518/>`_
``pyproject.toml`` file, you can ensure that ``pybind11`` is available during
the compilation of your project. When this file exists, Pip will make a new
virtual environment, download just the packages listed here in ``requires=``,
and build a wheel (binary Python package). It will then throw away the
environment, and install your wheel.
Your ``pyproject.toml`` file will likely look something like this:
.. code-block:: toml
[build-system]
requires = ["setuptools>=42", "wheel", "pybind11~=2.6.1"]
build-backend = "setuptools.build_meta"
.. note::
The main drawback to this method is that a `PEP 517`_ compliant build tool,
such as Pip 10+, is required for this approach to work; older versions of
Pip completely ignore this file. If you distribute binaries (called wheels
in Python) using something like `cibuildwheel`_, remember that ``setup.py``
and ``pyproject.toml`` are not even contained in the wheel, so this high
Pip requirement is only for source builds, and will not affect users of
your binary wheels. If you are building SDists and wheels, then
`pypa-build`_ is the recommended official tool.
.. _PEP 517: https://www.python.org/dev/peps/pep-0517/
.. _cibuildwheel: https://cibuildwheel.readthedocs.io
.. _pypa-build: https://pypa-build.readthedocs.io/en/latest/
.. _setup_helpers-setup_requires:
Classic ``setup_requires``
--------------------------
If you want to support old versions of Pip with the classic
``setup_requires=["pybind11"]`` keyword argument to setup, which triggers a
two-phase ``setup.py`` run, then you will need to use something like this to
ensure the first pass works (which has not yet installed the ``setup_requires``
packages, since it can't install something it does not know about):
.. code-block:: python
try:
from pybind11.setup_helpers import Pybind11Extension
except ImportError:
from setuptools import Extension as Pybind11Extension
It doesn't matter that the Extension class is not the enhanced subclass for the
first pass run; and the second pass will have the ``setup_requires``
requirements.
This is obviously more of a hack than the PEP 518 method, but it supports
ancient versions of Pip.
.. _setup_helpers-copy-manually:
Copy manually
-------------
You can also copy ``setup_helpers.py`` directly to your project; it was
designed to be usable standalone, like the old example ``setup.py``. You can
set ``include_pybind11=False`` to skip including the pybind11 package headers,
so you can use it with git submodules and a specific git version. If you use
this, you will need to import from a local file in ``setup.py`` and ensure the
helper file is part of your MANIFEST.
Closely related, if you include pybind11 as a subproject, you can run the
``setup_helpers.py`` inplace. If loaded correctly, this should even pick up
the correct include for pybind11, though you can turn it off as shown above if
you want to input it manually.
Suggested usage if you have pybind11 as a submodule in ``extern/pybind11``:
.. code-block:: python
DIR = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(DIR, "extern", "pybind11"))
from pybind11.setup_helpers import Pybind11Extension # noqa: E402
del sys.path[-1]
.. versionchanged:: 2.6
Added ``setup_helpers`` file.
Building with cppimport
========================
[cppimport]_ is a small Python import hook that determines whether there is a C++
source file whose name matches the requested module. If there is, the file is
compiled as a Python extension using pybind11 and placed in the same folder as
the C++ source file. Python is then able to find the module and load it.
.. [cppimport] https://github.com/tbenthompson/cppimport
.. _cmake:
Building with CMake
===================
For C++ codebases that have an existing CMake-based build system, a Python
extension module can be created with just a few lines of code:
.. code-block:: cmake
cmake_minimum_required(VERSION 3.4...3.18)
project(example LANGUAGES CXX)
add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)
This assumes that the pybind11 repository is located in a subdirectory named
:file:`pybind11` and that the code is located in a file named :file:`example.cpp`.
The CMake command ``add_subdirectory`` will import the pybind11 project which
provides the ``pybind11_add_module`` function. It will take care of all the
details needed to build a Python extension module on any platform.
A working sample project, including a way to invoke CMake from :file:`setup.py` for
PyPI integration, can be found in the [cmake_example]_ repository.
.. [cmake_example] https://github.com/pybind/cmake_example
.. versionchanged:: 2.6
CMake 3.4+ is required.
Further information can be found at :doc:`cmake/index`.
pybind11_add_module
-------------------
To ease the creation of Python extension modules, pybind11 provides a CMake
function with the following signature:
.. code-block:: cmake
pybind11_add_module(<name> [MODULE | SHARED] [EXCLUDE_FROM_ALL]
[NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...])
This function behaves very much like CMake's builtin ``add_library`` (in fact,
it's a wrapper function around that command). It will add a library target
called ``<name>`` to be built from the listed source files. In addition, it
will take care of all the Python-specific compiler and linker flags as well
as the OS- and Python-version-specific file extension. The produced target
``<name>`` can be further manipulated with regular CMake commands.
``MODULE`` or ``SHARED`` may be given to specify the type of library. If no
type is given, ``MODULE`` is used by default which ensures the creation of a
Python-exclusive module. Specifying ``SHARED`` will create a more traditional
dynamic library which can also be linked from elsewhere. ``EXCLUDE_FROM_ALL``
removes this target from the default build (see CMake docs for details).
Since pybind11 is a template library, ``pybind11_add_module`` adds compiler
flags to ensure high quality code generation without bloat arising from long
symbol names and duplication of code in different translation units. It
sets default visibility to *hidden*, which is required for some pybind11
features and functionality when attempting to load multiple pybind11 modules
compiled under different pybind11 versions. It also adds additional flags
enabling LTO (Link Time Optimization) and strip unneeded symbols. See the
:ref:`FAQ entry <faq:symhidden>` for a more detailed explanation. These
latter optimizations are never applied in ``Debug`` mode. If ``NO_EXTRAS`` is
given, they will always be disabled, even in ``Release`` mode. However, this
will result in code bloat and is generally not recommended.
As stated above, LTO is enabled by default. Some newer compilers also support
different flavors of LTO such as `ThinLTO`_. Setting ``THIN_LTO`` will cause
the function to prefer this flavor if available. The function falls back to
regular LTO if ``-flto=thin`` is not available. If
``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` is set (either ``ON`` or ``OFF``), then
that will be respected instead of the built-in flag search.
.. note::
If you want to set the property form on targets or the
``CMAKE_INTERPROCEDURAL_OPTIMIZATION_<CONFIG>`` versions of this, you should
still use ``set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF)`` (otherwise a
no-op) to disable pybind11's ipo flags.
The ``OPT_SIZE`` flag enables size-based optimization equivalent to the
standard ``/Os`` or ``-Os`` compiler flags and the ``MinSizeRel`` build type,
which avoid optimizations that that can substantially increase the size of the
resulting binary. This flag is particularly useful in projects that are split
into performance-critical parts and associated bindings. In this case, we can
compile the project in release mode (and hence, optimize performance globally),
and specify ``OPT_SIZE`` for the binding target, where size might be the main
concern as performance is often less critical here. A ~25% size reduction has
been observed in practice. This flag only changes the optimization behavior at
a per-target level and takes precedence over the global CMake build type
(``Release``, ``RelWithDebInfo``) except for ``Debug`` builds, where
optimizations remain disabled.
.. _ThinLTO: http://clang.llvm.org/docs/ThinLTO.html
Configuration variables
-----------------------
By default, pybind11 will compile modules with the compiler default or the
minimum standard required by pybind11, whichever is higher. You can set the
standard explicitly with
`CMAKE_CXX_STANDARD <https://cmake.org/cmake/help/latest/variable/CMAKE_CXX_STANDARD.html>`_:
.. code-block:: cmake
set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ version selection") # or 11, 14, 17, 20
set(CMAKE_CXX_STANDARD_REQUIRED ON) # optional, ensure standard is supported
set(CMAKE_CXX_EXTENSIONS OFF) # optional, keep compiler extensions off
The variables can also be set when calling CMake from the command line using
the ``-D<variable>=<value>`` flag. You can also manually set ``CXX_STANDARD``
on a target or use ``target_compile_features`` on your targets - anything that
CMake supports.
Classic Python support: The target Python version can be selected by setting
``PYBIND11_PYTHON_VERSION`` or an exact Python installation can be specified
with ``PYTHON_EXECUTABLE``. For example:
.. code-block:: bash
cmake -DPYBIND11_PYTHON_VERSION=3.6 ..
# Another method:
cmake -DPYTHON_EXECUTABLE=/path/to/python ..
# This often is a good way to get the current Python, works in environments:
cmake -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") ..
find_package vs. add_subdirectory
---------------------------------
For CMake-based projects that don't include the pybind11 repository internally,
an external installation can be detected through ``find_package(pybind11)``.
See the `Config file`_ docstring for details of relevant CMake variables.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.4...3.18)
project(example LANGUAGES CXX)
find_package(pybind11 REQUIRED)
pybind11_add_module(example example.cpp)
Note that ``find_package(pybind11)`` will only work correctly if pybind11
has been correctly installed on the system, e. g. after downloading or cloning
the pybind11 repository :
.. code-block:: bash
# Classic CMake
cd pybind11
mkdir build
cd build
cmake ..
make install
# CMake 3.15+
cd pybind11
cmake -S . -B build
cmake --build build -j 2 # Build on 2 cores
cmake --install build
Once detected, the aforementioned ``pybind11_add_module`` can be employed as
before. The function usage and configuration variables are identical no matter
if pybind11 is added as a subdirectory or found as an installed package. You
can refer to the same [cmake_example]_ repository for a full sample project
-- just swap out ``add_subdirectory`` for ``find_package``.
.. _Config file: https://github.com/pybind/pybind11/blob/master/tools/pybind11Config.cmake.in
.. _find-python-mode:
FindPython mode
---------------
CMake 3.12+ (3.15+ recommended, 3.18.2+ ideal) added a new module called
FindPython that had a highly improved search algorithm and modern targets
and tools. If you use FindPython, pybind11 will detect this and use the
existing targets instead:
.. code-block:: cmake
cmake_minimum_required(VERSION 3.15...3.22)
project(example LANGUAGES CXX)
find_package(Python 3.6 COMPONENTS Interpreter Development REQUIRED)
find_package(pybind11 CONFIG REQUIRED)
# or add_subdirectory(pybind11)
pybind11_add_module(example example.cpp)
You can also use the targets (as listed below) with FindPython. If you define
``PYBIND11_FINDPYTHON``, pybind11 will perform the FindPython step for you
(mostly useful when building pybind11's own tests, or as a way to change search
algorithms from the CMake invocation, with ``-DPYBIND11_FINDPYTHON=ON``.
.. warning::
If you use FindPython to multi-target Python versions, use the individual
targets listed below, and avoid targets that directly include Python parts.
There are `many ways to hint or force a discovery of a specific Python
installation <https://cmake.org/cmake/help/latest/module/FindPython.html>`_),
setting ``Python_ROOT_DIR`` may be the most common one (though with
virtualenv/venv support, and Conda support, this tends to find the correct
Python version more often than the old system did).
.. warning::
When the Python libraries (i.e. ``libpythonXX.a`` and ``libpythonXX.so``
on Unix) are not available, as is the case on a manylinux image, the
``Development`` component will not be resolved by ``FindPython``. When not
using the embedding functionality, CMake 3.18+ allows you to specify
``Development.Module`` instead of ``Development`` to resolve this issue.
.. versionadded:: 2.6
Advanced: interface library targets
-----------------------------------
Pybind11 supports modern CMake usage patterns with a set of interface targets,
available in all modes. The targets provided are:
``pybind11::headers``
Just the pybind11 headers and minimum compile requirements
``pybind11::pybind11``
Python headers + ``pybind11::headers``
``pybind11::python_link_helper``
Just the "linking" part of pybind11:module
``pybind11::module``
Everything for extension modules - ``pybind11::pybind11`` + ``Python::Module`` (FindPython CMake 3.15+) or ``pybind11::python_link_helper``
``pybind11::embed``
Everything for embedding the Python interpreter - ``pybind11::pybind11`` + ``Python::Python`` (FindPython) or Python libs
``pybind11::lto`` / ``pybind11::thin_lto``
An alternative to `INTERPROCEDURAL_OPTIMIZATION` for adding link-time optimization.
``pybind11::windows_extras``
``/bigobj`` and ``/mp`` for MSVC.
``pybind11::opt_size``
``/Os`` for MSVC, ``-Os`` for other compilers. Does nothing for debug builds.
Two helper functions are also provided:
``pybind11_strip(target)``
Strips a target (uses ``CMAKE_STRIP`` after the target is built)
``pybind11_extension(target)``
Sets the correct extension (with SOABI) for a target.
You can use these targets to build complex applications. For example, the
``add_python_module`` function is identical to:
.. code-block:: cmake
cmake_minimum_required(VERSION 3.4)
project(example LANGUAGES CXX)
find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11)
add_library(example MODULE main.cpp)
target_link_libraries(example PRIVATE pybind11::module pybind11::lto pybind11::windows_extras)
pybind11_extension(example)
if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
# Strip unnecessary sections of the binary on Linux/macOS
pybind11_strip(example)
endif()
set_target_properties(example PROPERTIES CXX_VISIBILITY_PRESET "hidden"
CUDA_VISIBILITY_PRESET "hidden")
Instead of setting properties, you can set ``CMAKE_*`` variables to initialize these correctly.
.. warning::
Since pybind11 is a metatemplate library, it is crucial that certain
compiler flags are provided to ensure high quality code generation. In
contrast to the ``pybind11_add_module()`` command, the CMake interface
provides a *composable* set of targets to ensure that you retain flexibility.
It can be especially important to provide or set these properties; the
:ref:`FAQ <faq:symhidden>` contains an explanation on why these are needed.
.. versionadded:: 2.6
.. _nopython-mode:
Advanced: NOPYTHON mode
-----------------------
If you want complete control, you can set ``PYBIND11_NOPYTHON`` to completely
disable Python integration (this also happens if you run ``FindPython2`` and
``FindPython3`` without running ``FindPython``). This gives you complete
freedom to integrate into an existing system (like `Scikit-Build's
<https://scikit-build.readthedocs.io>`_ ``PythonExtensions``).
``pybind11_add_module`` and ``pybind11_extension`` will be unavailable, and the
targets will be missing any Python specific behavior.
.. versionadded:: 2.6
Embedding the Python interpreter
--------------------------------
In addition to extension modules, pybind11 also supports embedding Python into
a C++ executable or library. In CMake, simply link with the ``pybind11::embed``
target. It provides everything needed to get the interpreter running. The Python
headers and libraries are attached to the target. Unlike ``pybind11::module``,
there is no need to manually set any additional properties here. For more
information about usage in C++, see :doc:`/advanced/embedding`.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.4...3.18)
project(example LANGUAGES CXX)
find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11)
add_executable(example main.cpp)
target_link_libraries(example PRIVATE pybind11::embed)
.. _building_manually:
Building manually
=================
pybind11 is a header-only library, hence it is not necessary to link against
any special libraries and there are no intermediate (magic) translation steps.
On Linux, you can compile an example such as the one given in
:ref:`simple_example` using the following command:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
The ``python3 -m pybind11 --includes`` command fetches the include paths for
both pybind11 and Python headers. This assumes that pybind11 has been installed
using ``pip`` or ``conda``. If it hasn't, you can also manually specify
``-I <path-to-pybind11>/include`` together with the Python includes path
``python3-config --includes``.
On macOS: the build command is almost the same but it also requires passing
the ``-undefined dynamic_lookup`` flag so as to ignore missing symbols when
building the module:
.. code-block:: bash
$ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix)
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 that works on all platforms including Windows.
.. note::
On Linux and macOS, it's better to (intentionally) not link against
``libpython``. The symbols will be resolved when the extension library
is loaded into a Python binary. This is preferable because you might
have several different installations of a given Python version (e.g. the
system-provided Python, and one that ships with a piece of commercial
software). In this way, the plugin will work with both versions, instead
of possibly importing a second Python library into a process that already
contains one (which will lead to a segfault).
Building with Bazel
===================
You can build with the Bazel build system using the `pybind11_bazel
<https://github.com/pybind/pybind11_bazel>`_ repository.
Generating binding code automatically
=====================================
The ``Binder`` project is a tool for automatic generation of pybind11 binding
code by introspecting existing C++ codebases using LLVM/Clang. See the
[binder]_ documentation for details.
.. [binder] http://cppbinder.readthedocs.io/en/latest/about.html
[AutoWIG]_ is a Python library that wraps automatically compiled libraries into
high-level languages. It parses C++ code using LLVM/Clang technologies and
generates the wrappers using the Mako templating engine. The approach is automatic,
extensible, and applies to very complex C++ libraries, composed of thousands of
classes or incorporating modern meta-programming constructs.
.. [AutoWIG] https://github.com/StatisKit/AutoWIG
[robotpy-build]_ is a is a pure python, cross platform build tool that aims to
simplify creation of python wheels for pybind11 projects, and provide
cross-project dependency management. Additionally, it is able to autogenerate
customizable pybind11-based wrappers by parsing C++ header files.
.. [robotpy-build] https://robotpy-build.readthedocs.io

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#!/usr/bin/env python3
#
# 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 os
import re
import subprocess
import sys
from pathlib import Path
DIR = Path(__file__).parent.resolve()
# 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 = [
"breathe",
"sphinx_copybutton",
"sphinxcontrib.rsvgconverter",
"sphinxcontrib.moderncmakedomain",
]
breathe_projects = {"pybind11": ".build/doxygenxml/"}
breathe_default_project = "pybind11"
breathe_domain_by_extension = {"h": "cpp"}
# 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 = "2017, 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.
# Read the listed version
with open("../pybind11/_version.py") as f:
code = compile(f.read(), "../pybind11/_version.py", "exec")
loc = {}
exec(code, loc)
# The full version, including alpha/beta/rc tags.
version = loc["__version__"]
# 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", "release.rst"]
# The reST default role (used for this markup: `text`) to use for all
# documents.
default_role = "any"
# 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.
html_theme = "furo"
# 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<version> 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"]
html_css_files = [
"css/custom.css",
]
# 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_engine = "pdflatex"
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.
# remove blank pages (between the title page and the TOC, etc.)
"classoptions": ",openany,oneside",
"preamble": r"""
\usepackage{fontawesome}
\usepackage{textgreek}
\DeclareUnicodeCharacter{00A0}{}
\DeclareUnicodeCharacter{2194}{\faArrowsH}
\DeclareUnicodeCharacter{1F382}{\faBirthdayCake}
\DeclareUnicodeCharacter{1F355}{\faAdjust}
\DeclareUnicodeCharacter{0301}{'}
\DeclareUnicodeCharacter{03C0}{\textpi}
""",
# 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 = 'pybind11-logo.png'
# 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",
author,
"pybind11",
"One line description of project.",
"Miscellaneous",
),
]
# Documents to append as an appendix to all manuals.
# texinfo_appendices = []
# If false, no module index is generated.
# texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
# texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
# texinfo_no_detailmenu = False
primary_domain = "cpp"
highlight_language = "cpp"
def generate_doxygen_xml(app):
build_dir = os.path.join(app.confdir, ".build")
if not os.path.exists(build_dir):
os.mkdir(build_dir)
try:
subprocess.call(["doxygen", "--version"])
retcode = subprocess.call(["doxygen"], cwd=app.confdir)
if retcode < 0:
sys.stderr.write(f"doxygen error code: {-retcode}\n")
except OSError as e:
sys.stderr.write(f"doxygen execution failed: {e}\n")
def prepare(app):
with open(DIR.parent / "README.rst") as f:
contents = f.read()
if app.builder.name == "latex":
# Remove badges and stuff from start
contents = contents[contents.find(r".. start") :]
# Filter out section titles for index.rst for LaTeX
contents = re.sub(r"^(.*)\n[-~]{3,}$", r"**\1**", contents, flags=re.MULTILINE)
with open(DIR / "readme.rst", "w") as f:
f.write(contents)
def clean_up(app, exception):
(DIR / "readme.rst").unlink()
def setup(app):
# Add hook for building doxygen xml when needed
app.connect("builder-inited", generate_doxygen_xml)
# Copy the readme in
app.connect("builder-inited", prepare)
# Clean up the generated readme
app.connect("build-finished", clean_up)

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Frequently asked questions
##########################
"ImportError: dynamic module does not define init function"
===========================================================
1. Make sure that the name specified in PYBIND11_MODULE is identical to the
filename of the extension library (without suffixes such as ``.so``).
2. If the above did not fix the issue, you are likely using an incompatible
version of Python that does not match what you compiled with.
"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``"
========================================================================
See the first answer.
"SystemError: dynamic module not initialized properly"
======================================================
See the first answer.
The Python interpreter immediately crashes when importing my module
===================================================================
See the first answer.
.. _faq_reference_arguments:
Limitations involving reference arguments
=========================================
In C++, it's fairly common to pass arguments using mutable references or
mutable pointers, which allows both read and write access to the value
supplied by the caller. This is sometimes done for efficiency reasons, or to
realize functions that have multiple return values. Here are two very basic
examples:
.. code-block:: cpp
void increment(int &i) { i++; }
void increment_ptr(int *i) { (*i)++; }
In Python, all arguments are passed by reference, so there is no general
issue in binding such code from Python.
However, certain basic Python types (like ``str``, ``int``, ``bool``,
``float``, etc.) are **immutable**. This means that the following attempt
to port the function to Python doesn't have the same effect on the value
provided by the caller -- in fact, it does nothing at all.
.. code-block:: python
def increment(i):
i += 1 # nope..
pybind11 is also affected by such language-level conventions, which means that
binding ``increment`` or ``increment_ptr`` will also create Python functions
that don't modify their arguments.
Although inconvenient, one workaround is to encapsulate the immutable types in
a custom type that does allow modifications.
An other alternative involves binding a small wrapper lambda function that
returns a tuple with all output arguments (see the remainder of the
documentation for examples on binding lambda functions). An example:
.. code-block:: cpp
int foo(int &i) { i++; return 123; }
and the binding code
.. code-block:: cpp
m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); });
How can I reduce the build time?
================================
It's good practice to split binding code over multiple files, as in the
following example:
:file:`example.cpp`:
.. code-block:: cpp
void init_ex1(py::module_ &);
void init_ex2(py::module_ &);
/* ... */
PYBIND11_MODULE(example, m) {
init_ex1(m);
init_ex2(m);
/* ... */
}
:file:`ex1.cpp`:
.. code-block:: cpp
void init_ex1(py::module_ &m) {
m.def("add", [](int a, int b) { return a + b; });
}
:file:`ex2.cpp`:
.. code-block:: cpp
void init_ex2(py::module_ &m) {
m.def("sub", [](int a, int b) { return a - b; });
}
:command:`python`:
.. code-block:: pycon
>>> import example
>>> example.add(1, 2)
3
>>> example.sub(1, 1)
0
As shown above, the various ``init_ex`` functions should be contained in
separate files that can be compiled independently from one another, and then
linked together into the same final shared object. Following this approach
will:
1. reduce memory requirements per compilation unit.
2. enable parallel builds (if desired).
3. allow for faster incremental builds. For instance, when a single class
definition is changed, only a subset of the binding code will generally need
to be recompiled.
"recursive template instantiation exceeded maximum depth of 256"
================================================================
If you receive an error about excessive recursive template evaluation, try
specifying a larger value, e.g. ``-ftemplate-depth=1024`` on GCC/Clang. The
culprit is generally the generation of function signatures at compile time
using C++14 template metaprogramming.
.. _`faq:hidden_visibility`:
"'SomeClass' declared with greater visibility than the type of its field 'SomeClass::member' [-Wattributes]"
============================================================================================================
This error typically indicates that you are compiling without the required
``-fvisibility`` flag. pybind11 code internally forces hidden visibility on
all internal code, but if non-hidden (and thus *exported*) code attempts to
include a pybind type (for example, ``py::object`` or ``py::list``) you can run
into this warning.
To avoid it, make sure you are specifying ``-fvisibility=hidden`` when
compiling pybind code.
As to why ``-fvisibility=hidden`` is necessary, because pybind modules could
have been compiled under different versions of pybind itself, it is also
important that the symbols defined in one module do not clash with the
potentially-incompatible symbols defined in another. While Python extension
modules are usually loaded with localized symbols (under POSIX systems
typically using ``dlopen`` with the ``RTLD_LOCAL`` flag), this Python default
can be changed, but even if it isn't it is not always enough to guarantee
complete independence of the symbols involved when not using
``-fvisibility=hidden``.
Additionally, ``-fvisibility=hidden`` can deliver considerably binary size
savings. (See the following section for more details.)
.. _`faq:symhidden`:
How can I create smaller binaries?
==================================
To do its job, pybind11 extensively relies on a programming technique known as
*template metaprogramming*, which is a way of performing computation at compile
time using type information. Template metaprogramming usually instantiates code
involving significant numbers of deeply nested types that are either completely
removed or reduced to just a few instructions during the compiler's optimization
phase. However, due to the nested nature of these types, the resulting symbol
names in the compiled extension library can be extremely long. For instance,
the included test suite contains the following symbol:
.. only:: html
.. code-block:: none
_​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_
.. only:: not html
.. code-block:: cpp
__ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_
which is the mangled form of the following function type:
.. code-block:: cpp
pybind11::cpp_function::cpp_function<void, Example2, std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> >, std::__1::allocator<std::__1::basic_string<wchar_t, std::__1::char_traits<wchar_t>, std::__1::allocator<wchar_t> > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28])
The memory needed to store just the mangled name of this function (196 bytes)
is larger than the actual piece of code (111 bytes) it represents! On the other
hand, it's silly to even give this function a name -- after all, it's just a
tiny cog in a bigger piece of machinery that is not exposed to the outside
world. So we'll generally only want to export symbols for those functions which
are actually called from the outside.
This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC
and Clang, which sets the default symbol visibility to *hidden*, which has a
tremendous impact on the final binary size of the resulting extension library.
(On Visual Studio, symbols are already hidden by default, so nothing needs to
be done there.)
In addition to decreasing binary size, ``-fvisibility=hidden`` also avoids
potential serious issues when loading multiple modules and is required for
proper pybind operation. See the previous FAQ entry for more details.
How can I properly handle Ctrl-C in long-running functions?
===========================================================
Ctrl-C is received by the Python interpreter, and holds it until the GIL
is released, so a long-running function won't be interrupted.
To interrupt from inside your function, you can use the ``PyErr_CheckSignals()``
function, that will tell if a signal has been raised on the Python side. This
function merely checks a flag, so its impact is negligible. When a signal has
been received, you must either explicitly interrupt execution by throwing
``py::error_already_set`` (which will propagate the existing
``KeyboardInterrupt``), or clear the error (which you usually will not want):
.. code-block:: cpp
PYBIND11_MODULE(example, m)
{
m.def("long running_func", []()
{
for (;;) {
if (PyErr_CheckSignals() != 0)
throw py::error_already_set();
// Long running iteration
}
});
}
CMake doesn't detect the right Python version
=============================================
The CMake-based build system will try to automatically detect the installed
version of Python and link against that. When this fails, or when there are
multiple versions of Python and it finds the wrong one, delete
``CMakeCache.txt`` and then add ``-DPYTHON_EXECUTABLE=$(which python)`` to your
CMake configure line. (Replace ``$(which python)`` with a path to python if
your prefer.)
You can alternatively try ``-DPYBIND11_FINDPYTHON=ON``, which will activate the
new CMake FindPython support instead of pybind11's custom search. Requires
CMake 3.12+, and 3.15+ or 3.18.2+ are even better. You can set this in your
``CMakeLists.txt`` before adding or finding pybind11, as well.
Inconsistent detection of Python version in CMake and pybind11
==============================================================
The functions ``find_package(PythonInterp)`` and ``find_package(PythonLibs)``
provided by CMake for Python version detection are modified by pybind11 due to
unreliability and limitations that make them unsuitable for pybind11's needs.
Instead pybind11 provides its own, more reliable Python detection CMake code.
Conflicts can arise, however, when using pybind11 in a project that *also* uses
the CMake Python detection in a system with several Python versions installed.
This difference may cause inconsistencies and errors if *both* mechanisms are
used in the same project.
There are three possible solutions:
1. Avoid using ``find_package(PythonInterp)`` and ``find_package(PythonLibs)``
from CMake and rely on pybind11 in detecting Python version. If this is not
possible, the CMake machinery should be called *before* including pybind11.
2. Set ``PYBIND11_FINDPYTHON`` to ``True`` or use ``find_package(Python
COMPONENTS Interpreter Development)`` on modern CMake (3.12+, 3.15+ better,
3.18.2+ best). Pybind11 in these cases uses the new CMake FindPython instead
of the old, deprecated search tools, and these modules are much better at
finding the correct Python.
3. Set ``PYBIND11_NOPYTHON`` to ``TRUE``. Pybind11 will not search for Python.
However, you will have to use the target-based system, and do more setup
yourself, because it does not know about or include things that depend on
Python, like ``pybind11_add_module``. This might be ideal for integrating
into an existing system, like scikit-build's Python helpers.
How to cite this project?
=========================
We suggest the following BibTeX template to cite pybind11 in scientific
discourse:
.. code-block:: bash
@misc{pybind11,
author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
year = {2017},
note = {https://github.com/pybind/pybind11},
title = {pybind11 -- Seamless operability between C++11 and Python}
}

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.. only:: latex
Intro
=====
.. include:: readme.rst
.. only:: not latex
Contents:
.. toctree::
:maxdepth: 1
changelog
upgrade
.. toctree::
:caption: The Basics
:maxdepth: 2
installing
basics
classes
compiling
.. toctree::
:caption: Advanced Topics
:maxdepth: 2
advanced/functions
advanced/classes
advanced/exceptions
advanced/smart_ptrs
advanced/cast/index
advanced/pycpp/index
advanced/embedding
advanced/misc
.. toctree::
:caption: Extra Information
:maxdepth: 1
faq
benchmark
limitations
reference
cmake/index

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.. _installing:
Installing the library
######################
There are several ways to get the pybind11 source, which lives at
`pybind/pybind11 on GitHub <https://github.com/pybind/pybind11>`_. The pybind11
developers recommend one of the first three ways listed here, submodule, PyPI,
or conda-forge, for obtaining pybind11.
.. _include_as_a_submodule:
Include as a submodule
======================
When you are working on a project in Git, you can use the pybind11 repository
as a submodule. From your git repository, use:
.. code-block:: bash
git submodule add -b stable ../../pybind/pybind11 extern/pybind11
git submodule update --init
This assumes you are placing your dependencies in ``extern/``, and that you are
using GitHub; if you are not using GitHub, use the full https or ssh URL
instead of the relative URL ``../../pybind/pybind11`` above. Some other servers
also require the ``.git`` extension (GitHub does not).
From here, you can now include ``extern/pybind11/include``, or you can use
the various integration tools (see :ref:`compiling`) pybind11 provides directly
from the local folder.
Include with PyPI
=================
You can download the sources and CMake files as a Python package from PyPI
using Pip. Just use:
.. code-block:: bash
pip install pybind11
This will provide pybind11 in a standard Python package format. If you want
pybind11 available directly in your environment root, you can use:
.. code-block:: bash
pip install "pybind11[global]"
This is not recommended if you are installing with your system Python, as it
will add files to ``/usr/local/include/pybind11`` and
``/usr/local/share/cmake/pybind11``, so unless that is what you want, it is
recommended only for use in virtual environments or your ``pyproject.toml``
file (see :ref:`compiling`).
Include with conda-forge
========================
You can use pybind11 with conda packaging via `conda-forge
<https://github.com/conda-forge/pybind11-feedstock>`_:
.. code-block:: bash
conda install -c conda-forge pybind11
Include with vcpkg
==================
You can download and install pybind11 using the Microsoft `vcpkg
<https://github.com/Microsoft/vcpkg/>`_ dependency manager:
.. code-block:: bash
git clone https://github.com/Microsoft/vcpkg.git
cd vcpkg
./bootstrap-vcpkg.sh
./vcpkg integrate install
vcpkg install pybind11
The pybind11 port in vcpkg is kept up to date by Microsoft team members and
community contributors. If the version is out of date, please `create an issue
or pull request <https://github.com/Microsoft/vcpkg/>`_ on the vcpkg
repository.
Global install with brew
========================
The brew package manager (Homebrew on macOS, or Linuxbrew on Linux) has a
`pybind11 package
<https://github.com/Homebrew/homebrew-core/blob/master/Formula/pybind11.rb>`_.
To install:
.. code-block:: bash
brew install pybind11
.. We should list Conan, and possibly a few other C++ package managers (hunter,
.. perhaps). Conan has a very clean CMake integration that would be good to show.
Other options
=============
Other locations you can find pybind11 are `listed here
<https://repology.org/project/python:pybind11/versions>`_; these are maintained
by various packagers and the community.

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Limitations
###########
Design choices
^^^^^^^^^^^^^^
pybind11 strives to be a general solution to binding generation, but it also has
certain limitations:
- pybind11 casts away ``const``-ness in function arguments and return values.
This is in line with the Python language, which has no concept of ``const``
values. This means that some additional care is needed to avoid bugs that
would be caught by the type checker in a traditional C++ program.
- The NumPy interface ``pybind11::array`` greatly simplifies accessing
numerical data from C++ (and vice versa), but it's not a full-blown array
class like ``Eigen::Array`` or ``boost.multi_array``. ``Eigen`` objects are
directly supported, however, with ``pybind11/eigen.h``.
Large but useful features could be implemented in pybind11 but would lead to a
significant increase in complexity. Pybind11 strives to be simple and compact.
Users who require large new features are encouraged to write an extension to
pybind11; see `pybind11_json <https://github.com/pybind/pybind11_json>`_ for an
example.
Known bugs
^^^^^^^^^^
These are issues that hopefully will one day be fixed, but currently are
unsolved. If you know how to help with one of these issues, contributions
are welcome!
- Intel 20.2 is currently having an issue with the test suite.
`#2573 <https://github.com/pybind/pybind11/pull/2573>`_
- Debug mode Python does not support 1-5 tests in the test suite currently.
`#2422 <https://github.com/pybind/pybind11/pull/2422>`_
- PyPy3 7.3.1 and 7.3.2 have issues with several tests on 32-bit Windows.
Known limitations
^^^^^^^^^^^^^^^^^
These are issues that are probably solvable, but have not been fixed yet. A
clean, well written patch would likely be accepted to solve them.
- Type casters are not kept alive recursively.
`#2527 <https://github.com/pybind/pybind11/issues/2527>`_
One consequence is that containers of ``char *`` are currently not supported.
`#2245 <https://github.com/pybind/pybind11/issues/2245>`_
- The ``cpptest`` does not run on Windows with Python 3.8 or newer, due to DLL
loader changes. User code that is correctly installed should not be affected.
`#2560 <https://github.com/pybind/pybind11/issue/2560>`_
Python 3.9.0 warning
^^^^^^^^^^^^^^^^^^^^
Combining older versions of pybind11 (< 2.6.0) with Python on exactly 3.9.0
will trigger undefined behavior that typically manifests as crashes during
interpreter shutdown (but could also destroy your data. **You have been
warned**).
This issue was `fixed in Python <https://github.com/python/cpython/pull/22670>`_.
As a mitigation for this bug, pybind11 2.6.0 or newer includes a workaround
specifically when Python 3.9.0 is detected at runtime, leaking about 50 bytes
of memory when a callback function is garbage collected. For reference, the
pybind11 test suite has about 2,000 such callbacks, but only 49 are garbage
collected before the end-of-process. Wheels (even if built with Python 3.9.0)
will correctly avoid the leak when run in Python 3.9.1, and this does not
affect other 3.X versions.

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.. _reference:
.. warning::
Please be advised that the reference documentation discussing pybind11
internals is currently incomplete. Please refer to the previous sections
and the pybind11 header files for the nitty gritty details.
Reference
#########
.. _macros:
Macros
======
.. doxygendefine:: PYBIND11_MODULE
.. _core_types:
Convenience classes for arbitrary Python types
==============================================
Common member functions
-----------------------
.. doxygenclass:: object_api
:members:
Without reference counting
--------------------------
.. doxygenclass:: handle
:members:
With reference counting
-----------------------
.. doxygenclass:: object
:members:
.. doxygenfunction:: reinterpret_borrow
.. doxygenfunction:: reinterpret_steal
Convenience classes for specific Python types
=============================================
.. doxygenclass:: module_
:members:
.. doxygengroup:: pytypes
:members:
Convenience functions converting to Python types
================================================
.. doxygenfunction:: make_tuple(Args&&...)
.. doxygenfunction:: make_iterator(Iterator, Sentinel, Extra &&...)
.. doxygenfunction:: make_iterator(Type &, Extra&&...)
.. doxygenfunction:: make_key_iterator(Iterator, Sentinel, Extra &&...)
.. doxygenfunction:: make_key_iterator(Type &, Extra&&...)
.. doxygenfunction:: make_value_iterator(Iterator, Sentinel, Extra &&...)
.. doxygenfunction:: make_value_iterator(Type &, Extra&&...)
.. _extras:
Passing extra arguments to ``def`` or ``class_``
================================================
.. doxygengroup:: annotations
:members:
Embedding the interpreter
=========================
.. doxygendefine:: PYBIND11_EMBEDDED_MODULE
.. doxygenfunction:: initialize_interpreter
.. doxygenfunction:: finalize_interpreter
.. doxygenclass:: scoped_interpreter
Redirecting C++ streams
=======================
.. doxygenclass:: scoped_ostream_redirect
.. doxygenclass:: scoped_estream_redirect
.. doxygenfunction:: add_ostream_redirect
Python built-in functions
=========================
.. doxygengroup:: python_builtins
:members:
Inheritance
===========
See :doc:`/classes` and :doc:`/advanced/classes` for more detail.
.. doxygendefine:: PYBIND11_OVERRIDE
.. doxygendefine:: PYBIND11_OVERRIDE_PURE
.. doxygendefine:: PYBIND11_OVERRIDE_NAME
.. doxygendefine:: PYBIND11_OVERRIDE_PURE_NAME
.. doxygenfunction:: get_override
Exceptions
==========
.. doxygenclass:: error_already_set
:members:
.. doxygenclass:: builtin_exception
:members:
Literals
========
.. doxygennamespace:: literals

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On version numbers
^^^^^^^^^^^^^^^^^^
The two version numbers (C++ and Python) must match when combined (checked when
you build the PyPI package), and must be a valid `PEP 440
<https://www.python.org/dev/peps/pep-0440>`_ version when combined.
For example:
.. code-block:: C++
#define PYBIND11_VERSION_MAJOR X
#define PYBIND11_VERSION_MINOR Y
#define PYBIND11_VERSION_PATCH Z.dev1
For beta, ``PYBIND11_VERSION_PATCH`` should be ``Z.b1``. RC's can be ``Z.rc1``.
Always include the dot (even though PEP 440 allows it to be dropped). For a
final release, this must be a simple integer. There is also a HEX version of
the version just below.
To release a new version of pybind11:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you don't have nox, you should either use ``pipx run nox`` instead, or use
``pipx install nox`` or ``brew install nox`` (Unix).
- Update the version number
- Update ``PYBIND11_VERSION_MAJOR`` etc. in
``include/pybind11/detail/common.h``. PATCH should be a simple integer.
- Update the version HEX just below, as well.
- Update ``pybind11/_version.py`` (match above)
- Run ``nox -s tests_packaging`` to ensure this was done correctly.
- Ensure that all the information in ``setup.cfg`` is up-to-date, like
supported Python versions.
- Add release date in ``docs/changelog.rst``.
- Check to make sure
`needs-changelog <https://github.com/pybind/pybind11/pulls?q=is%3Apr+is%3Aclosed+label%3A%22needs+changelog%22>`_
issues are entered in the changelog (clear the label when done).
- ``git add`` and ``git commit``, ``git push``. **Ensure CI passes**. (If it
fails due to a known flake issue, either ignore or restart CI.)
- Add a release branch if this is a new minor version, or update the existing release branch if it is a patch version
- New branch: ``git checkout -b vX.Y``, ``git push -u origin vX.Y``
- Update branch: ``git checkout vX.Y``, ``git merge <release branch>``, ``git push``
- Update tags (optional; if you skip this, the GitHub release makes a
non-annotated tag for you)
- ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``.
- ``git push --tags``.
- Update stable
- ``git checkout stable``
- ``git merge master``
- ``git push``
- Make a GitHub release (this shows up in the UI, sends new release
notifications to users watching releases, and also uploads PyPI packages).
(Note: if you do not use an existing tag, this creates a new lightweight tag
for you, so you could skip the above step.)
- GUI method: Under `releases <https://github.com/pybind/pybind11/releases>`_
click "Draft a new release" on the far right, fill in the tag name
(if you didn't tag above, it will be made here), fill in a release name
like "Version X.Y.Z", and copy-and-paste the markdown-formatted (!) changelog
into the description (usually ``cat docs/changelog.rst | pandoc -f rst -t gfm``).
Check "pre-release" if this is a beta/RC.
- CLI method: with ``gh`` installed, run ``gh release create vX.Y.Z -t "Version X.Y.Z"``
If this is a pre-release, add ``-p``.
- Get back to work
- Make sure you are on master, not somewhere else: ``git checkout master``
- Update version macros in ``include/pybind11/detail/common.h`` (set PATCH to
``0.dev1`` and increment MINOR).
- Update ``_version.py`` to match
- Run ``nox -s tests_packaging`` to ensure this was done correctly.
- Add a spot for in-development updates in ``docs/changelog.rst``.
- ``git add``, ``git commit``, ``git push``
If a version branch is updated, remember to set PATCH to ``1.dev1``.
If you'd like to bump homebrew, run:
.. code-block:: console
brew bump-formula-pr --url https://github.com/pybind/pybind11/archive/vX.Y.Z.tar.gz
Conda-forge should automatically make a PR in a few hours, and automatically
merge it if there are no issues.
Manual packaging
^^^^^^^^^^^^^^^^
If you need to manually upload releases, you can download the releases from the job artifacts and upload them with twine. You can also make the files locally (not recommended in general, as your local directory is more likely to be "dirty" and SDists love picking up random unrelated/hidden files); this is the procedure:
.. code-block:: bash
nox -s build
twine upload dist/*
This makes SDists and wheels, and the final line uploads them.

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breathe==4.34.0
furo==2022.6.21
sphinx==5.0.2
sphinx-copybutton==0.5.0
sphinxcontrib-moderncmakedomain==3.21.4
sphinxcontrib-svg2pdfconverter==1.2.0

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Upgrade guide
#############
This is a companion guide to the :doc:`changelog`. While the changelog briefly
lists all of the new features, improvements and bug fixes, this upgrade guide
focuses only the subset which directly impacts your experience when upgrading
to a new version. But it goes into more detail. This includes things like
deprecated APIs and their replacements, build system changes, general code
modernization and other useful information.
.. _upgrade-guide-2.9:
v2.9
====
* Any usage of the recently added ``py::make_simple_namespace`` should be
converted to using ``py::module_::import("types").attr("SimpleNamespace")``
instead.
* The use of ``_`` in custom type casters can now be replaced with the more
readable ``const_name`` instead. The old ``_`` shortcut has been retained
unless it is being used as a macro (like for gettext).
.. _upgrade-guide-2.7:
v2.7
====
*Before* v2.7, ``py::str`` can hold ``PyUnicodeObject`` or ``PyBytesObject``,
and ``py::isinstance<str>()`` is ``true`` for both ``py::str`` and
``py::bytes``. Starting with v2.7, ``py::str`` exclusively holds
``PyUnicodeObject`` (`#2409 <https://github.com/pybind/pybind11/pull/2409>`_),
and ``py::isinstance<str>()`` is ``true`` only for ``py::str``. To help in
the transition of user code, the ``PYBIND11_STR_LEGACY_PERMISSIVE`` macro
is provided as an escape hatch to go back to the legacy behavior. This macro
will be removed in future releases. Two types of required fixes are expected
to be common:
* Accidental use of ``py::str`` instead of ``py::bytes``, masked by the legacy
behavior. These are probably very easy to fix, by changing from
``py::str`` to ``py::bytes``.
* Reliance on py::isinstance<str>(obj) being ``true`` for
``py::bytes``. This is likely to be easy to fix in most cases by adding
``|| py::isinstance<bytes>(obj)``, but a fix may be more involved, e.g. if
``py::isinstance<T>`` appears in a template. Such situations will require
careful review and custom fixes.
.. _upgrade-guide-2.6:
v2.6
====
Usage of the ``PYBIND11_OVERLOAD*`` macros and ``get_overload`` function should
be replaced by ``PYBIND11_OVERRIDE*`` and ``get_override``. In the future, the
old macros may be deprecated and removed.
``py::module`` has been renamed ``py::module_``, but a backward compatible
typedef has been included. This change was to avoid a language change in C++20
that requires unqualified ``module`` not be placed at the start of a logical
line. Qualified usage is unaffected and the typedef will remain unless the
C++ language rules change again.
The public constructors of ``py::module_`` have been deprecated. Use
``PYBIND11_MODULE`` or ``module_::create_extension_module`` instead.
An error is now thrown when ``__init__`` is forgotten on subclasses. This was
incorrect before, but was not checked. Add a call to ``__init__`` if it is
missing.
A ``py::type_error`` is now thrown when casting to a subclass (like
``py::bytes`` from ``py::object``) if the conversion is not valid. Make a valid
conversion instead.
The undocumented ``h.get_type()`` method has been deprecated and replaced by
``py::type::of(h)``.
Enums now have a ``__str__`` method pre-defined; if you want to override it,
the simplest fix is to add the new ``py::prepend()`` tag when defining
``"__str__"``.
If ``__eq__`` defined but not ``__hash__``, ``__hash__`` is now set to
``None``, as in normal CPython. You should add ``__hash__`` if you intended the
class to be hashable, possibly using the new ``py::hash`` shortcut.
The constructors for ``py::array`` now always take signed integers for size,
for consistency. This may lead to compiler warnings on some systems. Cast to
``py::ssize_t`` instead of ``std::size_t``.
The ``tools/clang`` submodule and ``tools/mkdoc.py`` have been moved to a
standalone package, `pybind11-mkdoc`_. If you were using those tools, please
use them via a pip install from the new location.
The ``pybind11`` package on PyPI no longer fills the wheel "headers" slot - if
you were using the headers from this slot, they are available by requesting the
``global`` extra, that is, ``pip install "pybind11[global]"``. (Most users will
be unaffected, as the ``pybind11/include`` location is reported by ``python -m
pybind11 --includes`` and ``pybind11.get_include()`` is still correct and has
not changed since 2.5).
.. _pybind11-mkdoc: https://github.com/pybind/pybind11-mkdoc
CMake support:
--------------
The minimum required version of CMake is now 3.4. Several details of the CMake
support have been deprecated; warnings will be shown if you need to change
something. The changes are:
* ``PYBIND11_CPP_STANDARD=<platform-flag>`` is deprecated, please use
``CMAKE_CXX_STANDARD=<number>`` instead, or any other valid CMake CXX or CUDA
standard selection method, like ``target_compile_features``.
* If you do not request a standard, pybind11 targets will compile with the
compiler default, but not less than C++11, instead of forcing C++14 always.
If you depend on the old behavior, please use ``set(CMAKE_CXX_STANDARD 14 CACHE STRING "")``
instead.
* Direct ``pybind11::module`` usage should always be accompanied by at least
``set(CMAKE_CXX_VISIBILITY_PRESET hidden)`` or similar - it used to try to
manually force this compiler flag (but not correctly on all compilers or with
CUDA).
* ``pybind11_add_module``'s ``SYSTEM`` argument is deprecated and does nothing;
linking now behaves like other imported libraries consistently in both
config and submodule mode, and behaves like a ``SYSTEM`` library by
default.
* If ``PYTHON_EXECUTABLE`` is not set, virtual environments (``venv``,
``virtualenv``, and ``conda``) are prioritized over the standard search
(similar to the new FindPython mode).
In addition, the following changes may be of interest:
* ``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` will be respected by
``pybind11_add_module`` if set instead of linking to ``pybind11::lto`` or
``pybind11::thin_lto``.
* Using ``find_package(Python COMPONENTS Interpreter Development)`` before
pybind11 will cause pybind11 to use the new Python mechanisms instead of its
own custom search, based on a patched version of classic ``FindPythonInterp``
/ ``FindPythonLibs``. In the future, this may become the default. A recent
(3.15+ or 3.18.2+) version of CMake is recommended.
v2.5
====
The Python package now includes the headers as data in the package itself, as
well as in the "headers" wheel slot. ``pybind11 --includes`` and
``pybind11.get_include()`` report the new location, which is always correct
regardless of how pybind11 was installed, making the old ``user=`` argument
meaningless. If you are not using the function to get the location already, you
are encouraged to switch to the package location.
v2.2
====
Deprecation of the ``PYBIND11_PLUGIN`` macro
--------------------------------------------
``PYBIND11_MODULE`` is now the preferred way to create module entry points.
The old macro emits a compile-time deprecation warning.
.. code-block:: cpp
// old
PYBIND11_PLUGIN(example) {
py::module m("example", "documentation string");
m.def("add", [](int a, int b) { return a + b; });
return m.ptr();
}
// new
PYBIND11_MODULE(example, m) {
m.doc() = "documentation string"; // optional
m.def("add", [](int a, int b) { return a + b; });
}
New API for defining custom constructors and pickling functions
---------------------------------------------------------------
The old placement-new custom constructors have been deprecated. The new approach
uses ``py::init()`` and factory functions to greatly improve type safety.
Placement-new can be called accidentally with an incompatible type (without any
compiler errors or warnings), or it can initialize the same object multiple times
if not careful with the Python-side ``__init__`` calls. The new-style custom
constructors prevent such mistakes. See :ref:`custom_constructors` for details.
.. code-block:: cpp
// old -- deprecated (runtime warning shown only in debug mode)
py::class<Foo>(m, "Foo")
.def("__init__", [](Foo &self, ...) {
new (&self) Foo(...); // uses placement-new
});
// new
py::class<Foo>(m, "Foo")
.def(py::init([](...) { // Note: no `self` argument
return new Foo(...); // return by raw pointer
// or: return std::make_unique<Foo>(...); // return by holder
// or: return Foo(...); // return by value (move constructor)
}));
Mirroring the custom constructor changes, ``py::pickle()`` is now the preferred
way to get and set object state. See :ref:`pickling` for details.
.. code-block:: cpp
// old -- deprecated (runtime warning shown only in debug mode)
py::class<Foo>(m, "Foo")
...
.def("__getstate__", [](const Foo &self) {
return py::make_tuple(self.value1(), self.value2(), ...);
})
.def("__setstate__", [](Foo &self, py::tuple t) {
new (&self) Foo(t[0].cast<std::string>(), ...);
});
// new
py::class<Foo>(m, "Foo")
...
.def(py::pickle(
[](const Foo &self) { // __getstate__
return py::make_tuple(self.value1(), self.value2(), ...); // unchanged
},
[](py::tuple t) { // __setstate__, note: no `self` argument
return new Foo(t[0].cast<std::string>(), ...);
// or: return std::make_unique<Foo>(...); // return by holder
// or: return Foo(...); // return by value (move constructor)
}
));
For both the constructors and pickling, warnings are shown at module
initialization time (on import, not when the functions are called).
They're only visible when compiled in debug mode. Sample warning:
.. code-block:: none
pybind11-bound class 'mymodule.Foo' is using an old-style placement-new '__init__'
which has been deprecated. See the upgrade guide in pybind11's docs.
Stricter enforcement of hidden symbol visibility for pybind11 modules
---------------------------------------------------------------------
pybind11 now tries to actively enforce hidden symbol visibility for modules.
If you're using either one of pybind11's :doc:`CMake or Python build systems
<compiling>` (the two example repositories) and you haven't been exporting any
symbols, there's nothing to be concerned about. All the changes have been done
transparently in the background. If you were building manually or relied on
specific default visibility, read on.
Setting default symbol visibility to *hidden* has always been recommended for
pybind11 (see :ref:`faq:symhidden`). On Linux and macOS, hidden symbol
visibility (in conjunction with the ``strip`` utility) yields much smaller
module binaries. `CPython's extension docs`_ also recommend hiding symbols
by default, with the goal of avoiding symbol name clashes between modules.
Starting with v2.2, pybind11 enforces this more strictly: (1) by declaring
all symbols inside the ``pybind11`` namespace as hidden and (2) by including
the ``-fvisibility=hidden`` flag on Linux and macOS (only for extension
modules, not for embedding the interpreter).
.. _CPython's extension docs: https://docs.python.org/3/extending/extending.html#providing-a-c-api-for-an-extension-module
The namespace-scope hidden visibility is done automatically in pybind11's
headers and it's generally transparent to users. It ensures that:
* Modules compiled with different pybind11 versions don't clash with each other.
* Some new features, like ``py::module_local`` bindings, can work as intended.
The ``-fvisibility=hidden`` flag applies the same visibility to user bindings
outside of the ``pybind11`` namespace. It's now set automatic by pybind11's
CMake and Python build systems, but this needs to be done manually by users
of other build systems. Adding this flag:
* Minimizes the chances of symbol conflicts between modules. E.g. if two
unrelated modules were statically linked to different (ABI-incompatible)
versions of the same third-party library, a symbol clash would be likely
(and would end with unpredictable results).
* Produces smaller binaries on Linux and macOS, as pointed out previously.
Within pybind11's CMake build system, ``pybind11_add_module`` has always been
setting the ``-fvisibility=hidden`` flag in release mode. From now on, it's
being applied unconditionally, even in debug mode and it can no longer be opted
out of with the ``NO_EXTRAS`` option. The ``pybind11::module`` target now also
adds this flag to its interface. The ``pybind11::embed`` target is unchanged.
The most significant change here is for the ``pybind11::module`` target. If you
were previously relying on default visibility, i.e. if your Python module was
doubling as a shared library with dependents, you'll need to either export
symbols manually (recommended for cross-platform libraries) or factor out the
shared library (and have the Python module link to it like the other
dependents). As a temporary workaround, you can also restore default visibility
using the CMake code below, but this is not recommended in the long run:
.. code-block:: cmake
target_link_libraries(mymodule PRIVATE pybind11::module)
add_library(restore_default_visibility INTERFACE)
target_compile_options(restore_default_visibility INTERFACE -fvisibility=default)
target_link_libraries(mymodule PRIVATE restore_default_visibility)
Local STL container bindings
----------------------------
Previous pybind11 versions could only bind types globally -- all pybind11
modules, even unrelated ones, would have access to the same exported types.
However, this would also result in a conflict if two modules exported the
same C++ type, which is especially problematic for very common types, e.g.
``std::vector<int>``. :ref:`module_local` were added to resolve this (see
that section for a complete usage guide).
``py::class_`` still defaults to global bindings (because these types are
usually unique across modules), however in order to avoid clashes of opaque
types, ``py::bind_vector`` and ``py::bind_map`` will now bind STL containers
as ``py::module_local`` if their elements are: builtins (``int``, ``float``,
etc.), not bound using ``py::class_``, or bound as ``py::module_local``. For
example, this change allows multiple modules to bind ``std::vector<int>``
without causing conflicts. See :ref:`stl_bind` for more details.
When upgrading to this version, if you have multiple modules which depend on
a single global binding of an STL container, note that all modules can still
accept foreign ``py::module_local`` types in the direction of Python-to-C++.
The locality only affects the C++-to-Python direction. If this is needed in
multiple modules, you'll need to either:
* Add a copy of the same STL binding to all of the modules which need it.
* Restore the global status of that single binding by marking it
``py::module_local(false)``.
The latter is an easy workaround, but in the long run it would be best to
localize all common type bindings in order to avoid conflicts with
third-party modules.
Negative strides for Python buffer objects and numpy arrays
-----------------------------------------------------------
Support for negative strides required changing the integer type from unsigned
to signed in the interfaces of ``py::buffer_info`` and ``py::array``. If you
have compiler warnings enabled, you may notice some new conversion warnings
after upgrading. These can be resolved using ``static_cast``.
Deprecation of some ``py::object`` APIs
---------------------------------------
To compare ``py::object`` instances by pointer, you should now use
``obj1.is(obj2)`` which is equivalent to ``obj1 is obj2`` in Python.
Previously, pybind11 used ``operator==`` for this (``obj1 == obj2``), but
that could be confusing and is now deprecated (so that it can eventually
be replaced with proper rich object comparison in a future release).
For classes which inherit from ``py::object``, ``borrowed`` and ``stolen``
were previously available as protected constructor tags. Now the types
should be used directly instead: ``borrowed_t{}`` and ``stolen_t{}``
(`#771 <https://github.com/pybind/pybind11/pull/771>`_).
Stricter compile-time error checking
------------------------------------
Some error checks have been moved from run time to compile time. Notably,
automatic conversion of ``std::shared_ptr<T>`` is not possible when ``T`` is
not directly registered with ``py::class_<T>`` (e.g. ``std::shared_ptr<int>``
or ``std::shared_ptr<std::vector<T>>`` are not automatically convertible).
Attempting to bind a function with such arguments now results in a compile-time
error instead of waiting to fail at run time.
``py::init<...>()`` constructor definitions are also stricter and now prevent
bindings which could cause unexpected behavior:
.. code-block:: cpp
struct Example {
Example(int &);
};
py::class_<Example>(m, "Example")
.def(py::init<int &>()); // OK, exact match
// .def(py::init<int>()); // compile-time error, mismatch
A non-``const`` lvalue reference is not allowed to bind to an rvalue. However,
note that a constructor taking ``const T &`` can still be registered using
``py::init<T>()`` because a ``const`` lvalue reference can bind to an rvalue.
v2.1
====
Minimum compiler versions are enforced at compile time
------------------------------------------------------
The minimums also apply to v2.0 but the check is now explicit and a compile-time
error is raised if the compiler does not meet the requirements:
* GCC >= 4.8
* clang >= 3.3 (appleclang >= 5.0)
* MSVC >= 2015u3
* Intel C++ >= 15.0
The ``py::metaclass`` attribute is not required for static properties
---------------------------------------------------------------------
Binding classes with static properties is now possible by default. The
zero-parameter version of ``py::metaclass()`` is deprecated. However, a new
one-parameter ``py::metaclass(python_type)`` version was added for rare
cases when a custom metaclass is needed to override pybind11's default.
.. code-block:: cpp
// old -- emits a deprecation warning
py::class_<Foo>(m, "Foo", py::metaclass())
.def_property_readonly_static("foo", ...);
// new -- static properties work without the attribute
py::class_<Foo>(m, "Foo")
.def_property_readonly_static("foo", ...);
// new -- advanced feature, override pybind11's default metaclass
py::class_<Bar>(m, "Bar", py::metaclass(custom_python_type))
...
v2.0
====
Breaking changes in ``py::class_``
----------------------------------
These changes were necessary to make type definitions in pybind11
future-proof, to support PyPy via its ``cpyext`` mechanism (`#527
<https://github.com/pybind/pybind11/pull/527>`_), and to improve efficiency
(`rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_).
1. Declarations of types that provide access via the buffer protocol must
now include the ``py::buffer_protocol()`` annotation as an argument to
the ``py::class_`` constructor.
.. code-block:: cpp
py::class_<Matrix>("Matrix", py::buffer_protocol())
.def(py::init<...>())
.def_buffer(...);
2. Classes which include static properties (e.g. ``def_readwrite_static()``)
must now include the ``py::metaclass()`` attribute. Note: this requirement
has since been removed in v2.1. If you're upgrading from 1.x, it's
recommended to skip directly to v2.1 or newer.
3. This version of pybind11 uses a redesigned mechanism for instantiating
trampoline classes that are used to override virtual methods from within
Python. This led to the following user-visible syntax change:
.. code-block:: cpp
// old v1.x syntax
py::class_<TrampolineClass>("MyClass")
.alias<MyClass>()
...
// new v2.x syntax
py::class_<MyClass, TrampolineClass>("MyClass")
...
Importantly, both the original and the trampoline class are now specified
as arguments to the ``py::class_`` template, and the ``alias<..>()`` call
is gone. The new scheme has zero overhead in cases when Python doesn't
override any functions of the underlying C++ class.
`rev. 86d825 <https://github.com/pybind/pybind11/commit/86d825>`_.
The class type must be the first template argument given to ``py::class_``
while the trampoline can be mixed in arbitrary order with other arguments
(see the following section).
Deprecation of the ``py::base<T>()`` attribute
----------------------------------------------
``py::base<T>()`` was deprecated in favor of specifying ``T`` as a template
argument to ``py::class_``. This new syntax also supports multiple inheritance.
Note that, while the type being exported must be the first argument in the
``py::class_<Class, ...>`` template, the order of the following types (bases,
holder and/or trampoline) is not important.
.. code-block:: cpp
// old v1.x
py::class_<Derived>("Derived", py::base<Base>());
// new v2.x
py::class_<Derived, Base>("Derived");
// new -- multiple inheritance
py::class_<Derived, Base1, Base2>("Derived");
// new -- apart from `Derived` the argument order can be arbitrary
py::class_<Derived, Base1, Holder, Base2, Trampoline>("Derived");
Out-of-the-box support for ``std::shared_ptr``
----------------------------------------------
The relevant type caster is now built in, so it's no longer necessary to
include a declaration of the form:
.. code-block:: cpp
PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr<T>)
Continuing to do so won't cause an error or even a deprecation warning,
but it's completely redundant.
Deprecation of a few ``py::object`` APIs
----------------------------------------
All of the old-style calls emit deprecation warnings.
+---------------------------------------+---------------------------------------------+
| Old syntax | New syntax |
+=======================================+=============================================+
| ``obj.call(args...)`` | ``obj(args...)`` |
+---------------------------------------+---------------------------------------------+
| ``obj.str()`` | ``py::str(obj)`` |
+---------------------------------------+---------------------------------------------+
| ``auto l = py::list(obj); l.check()`` | ``py::isinstance<py::list>(obj)`` |
+---------------------------------------+---------------------------------------------+
| ``py::object(ptr, true)`` | ``py::reinterpret_borrow<py::object>(ptr)`` |
+---------------------------------------+---------------------------------------------+
| ``py::object(ptr, false)`` | ``py::reinterpret_steal<py::object>(ptr)`` |
+---------------------------------------+---------------------------------------------+
| ``if (obj.attr("foo"))`` | ``if (py::hasattr(obj, "foo"))`` |
+---------------------------------------+---------------------------------------------+
| ``if (obj["bar"])`` | ``if (obj.contains("bar"))`` |
+---------------------------------------+---------------------------------------------+