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