move knx-linux and knxPyton to examples dir

examples/knxPython/pybind11/docs/Doxyfile

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+INPUT                  = ../include/pybind11/
+RECURSIVE              = YES
+
+GENERATE_HTML          = NO
+GENERATE_LATEX         = NO
+GENERATE_XML           = YES
+XML_OUTPUT             = .build/doxygenxml
+XML_PROGRAMLISTING     = YES
+
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+EXPAND_ONLY_PREDEF     = YES
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+
+ALIASES                = "rst=\verbatim embed:rst"
+ALIASES               += "endrst=\endverbatim"
+
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+WARNINGS               = YES
+WARN_IF_UNDOCUMENTED   = NO

examples/knxPython/pybind11/docs/_static/theme_overrides.css

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+.wy-table-responsive table td,
+.wy-table-responsive table th {
+    white-space: initial !important;
+}
+.rst-content table.docutils td {
+    vertical-align: top !important;
+}
+div[class^='highlight'] pre {
+    white-space: pre;
+    white-space: pre-wrap;
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examples/knxPython/pybind11/docs/advanced/cast/chrono.rst

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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`` → ``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.

examples/knxPython/pybind11/docs/advanced/cast/custom.rst

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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
+a partial overload to the ``pybind11::detail::type_caster<T>`` template.
+Although this is an implementation detail, adding partial overloads to 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, _("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.

examples/knxPython/pybind11/docs/advanced/cast/eigen.rst

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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 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
+expicitly 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 encoutering 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.

examples/knxPython/pybind11/docs/advanced/cast/functional.rst

@@ -0,0 +1,109 @@
+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.

examples/knxPython/pybind11/docs/advanced/cast/index.rst

@@ -0,0 +1,42 @@
+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
+

examples/knxPython/pybind11/docs/advanced/cast/overview.rst

@@ -0,0 +1,163 @@
+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::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::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`      |
++------------------------------------+---------------------------+-------------------------------+

examples/knxPython/pybind11/docs/advanced/cast/stl.rst

@@ -0,0 +1,243 @@
+STL containers
+##############
+
+Automatic conversion
+====================
+
+When including the additional header file :file:`pybind11/stl.h`, conversions
+between ``std::vector<>``/``std::list<>``/``std::array<>``,
+``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.
+
+.. note::
+
+    pybind11 only supports the modern implementation of ``boost::variant``
+    which makes use of variadic templates. This requires Boost 1.56 or newer.
+    Additionally, on Windows, MSVC 2017 is required because ``boost::variant``
+    falls back to the old non-variadic implementation on MSVC 2015.
+
+.. _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 instantiates a partial template overload. 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 */
+        // ....
+
+Please take a look at the :ref:`macro_notes` before using the
+``PYBIND11_MAKE_OPAQUE`` macro.
+
+.. 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.

examples/knxPython/pybind11/docs/advanced/cast/strings.rst

@@ -0,0 +1,305 @@
+Strings, bytes and Unicode conversions
+######################################
+
+.. note::
+
+    This section discusses string handling in terms of Python 3 strings. For
+    Python 2.7, replace all occurrences of ``str`` with ``unicode`` and
+    ``bytes`` with ``str``.  Python 2.7 users may find it best to use ``from
+    __future__ import unicode_literals`` to avoid unintentionally using ``str``
+    instead of ``unicode``.
+
+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:: python
+
+    >>> 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.  On Python 3, 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:: python
+
+    >>> 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:: python
+
+    >>> 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:: python
+
+    >>> 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:: python
+
+    >>> 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;
+        }
+    );
+
+.. warning::
+
+    Wide character strings may not work as described on Python 2.7 or Python
+    3.3 compiled with ``--enable-unicode=ucs2``.
+
+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:: python
+
+    >>> 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:: python
+
+    >>> 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:: python
+
+    >>> 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:: python
+
+    >>> 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>`_

examples/knxPython/pybind11/docs/advanced/embedding.rst

@@ -0,0 +1,261 @@
+.. _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 3.0)
+    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.

examples/knxPython/pybind11/docs/advanced/exceptions.rst

@@ -0,0 +1,142 @@
+Exceptions
+##########
+
+Built-in exception translation
+==============================
+
+When C++ code invoked from Python throws an ``std::exception``, it is
+automatically converted into a Python ``Exception``. pybind11 defines multiple
+special exception classes that will map to different types of Python
+exceptions:
+
+.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}|
+
++--------------------------------------+------------------------------+
+|  C++ exception type                  |  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`           | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`std::range_error`            | ``ValueError``               |
++--------------------------------------+------------------------------+
+| :class:`pybind11::stop_iteration`    | ``StopIteration`` (used to   |
+|                                      | implement custom iterators)  |
++--------------------------------------+------------------------------+
+| :class:`pybind11::index_error`       | ``IndexError`` (used to      |
+|                                      | indicate out of bounds       |
+|                                      | accesses in ``__getitem__``, |
+|                                      | ``__setitem__``, etc.)       |
++--------------------------------------+------------------------------+
+| :class:`pybind11::value_error`       | ``ValueError`` (used to      |
+|                                      | indicate wrong value passed  |
+|                                      | in ``container.remove(...)`` |
++--------------------------------------+------------------------------+
+| :class:`pybind11::key_error`         | ``KeyError`` (used to        |
+|                                      | indicate out of bounds       |
+|                                      | accesses in ``__getitem__``, |
+|                                      | ``__setitem__`` in dict-like |
+|                                      | objects, etc.)               |
++--------------------------------------+------------------------------+
+| :class:`pybind11::error_already_set` | Indicates that the Python    |
+|                                      | exception flag has already   |
+|                                      | been initialized             |
++--------------------------------------+------------------------------+
+
+When a Python function invoked from C++ throws an exception, it is converted
+into a C++ exception of type :class:`error_already_set` whose string payload
+contains a textual summary.
+
+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.
+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``.
+
+When more advanced exception translation is needed, the function
+``py::register_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 function 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).
+
+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::
+
+    You must 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.

examples/knxPython/pybind11/docs/advanced/functions.rst

@@ -0,0 +1,498 @@
+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, 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`           | **Default policy.** 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.                                                               |
++--------------------------------------------------+----------------------------------------------------------------------------+
+| :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()). You probably won't need to use this.        |
++--------------------------------------------------+----------------------------------------------------------------------------+
+
+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.
+
+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(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, 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 as long as the ``py::args`` and ``py::kwargs`` arguments are the last
+arguments accepted by the function.
+
+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") = (SomeType *) nullptr);
+
+.. _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, d == 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``.
+
+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.
+
+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.

examples/knxPython/pybind11/docs/advanced/misc.rst

@@ -0,0 +1,272 @@
+Miscellaneous
+#############
+
+.. _macro_notes:
+
+General notes regarding convenience macros
+==========================================
+
+pybind11 provides a few convenience macros such as
+:func:`PYBIND11_MAKE_OPAQUE` and :func:`PYBIND11_DECLARE_HOLDER_TYPE`, and
+``PYBIND11_OVERLOAD_*``. 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 such as ``PYBIND11_OVERLOAD(MyReturnValue<T1,
+T2>, myFunc)``. In this case, the preprocessor assumes that the comma indicates
+the beginning of the next parameter. Use a ``typedef`` to bind the template to
+another name and use it in the macro to avoid this problem.
+
+.. _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_OVERLOAD_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 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 = (MyData *) py::get_shared_data("mydata");
+    if (!data)
+        data = (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 colelcted
+    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();
+
+
+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

examples/knxPython/pybind11/docs/advanced/pycpp/index.rst

@@ -0,0 +1,13 @@
+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

examples/knxPython/pybind11/docs/advanced/pycpp/numpy.rst

@@ -0,0 +1,366 @@
+.. _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.rows(),             /* 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;
+        ssize_t itemsize;
+        std::string format;
+        ssize_t ndim;
+        std::vector<ssize_t> shape;
+        std::vector<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 an basic example on how to
+define a custom constructor for the Eigen double precision matrix
+(``Eigen::MatrixXd``) type, which supports initialization from compatible
+buffer objects (e.g. a NumPy matrix).
+
+.. code-block:: cpp
+
+    /* 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("__init__", [](Matrix &m, py::buffer b) {
+            typedef Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic> Strides;
+
+            /* Request a buffer descriptor from Python */
+            py::buffer_info info = b.request();
+
+            /* Some sanity 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);
+
+            new (&m) 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. Note that this feature requires the
+:file:`pybind11/numpy.h` header to be included.
+
+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.
+
+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 = (double *) buf1.ptr,
+               *ptr2 = (double *) buf2.ptr,
+               *ptr3 = (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 (ssize_t i = 0; i < r.shape(0); i++)
+            for (ssize_t j = 0; j < r.shape(1); j++)
+                for (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 (ssize_t i = 0; i < r.shape(0); i++)
+            for (ssize_t j = 0; j < r.shape(1); j++)
+                for (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``:
+
+- ``r.ndim()`` returns the number of dimensions
+
+- ``r.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.

examples/knxPython/pybind11/docs/advanced/pycpp/object.rst

@@ -0,0 +1,170 @@
+Python types
+############
+
+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`.
+
+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/

examples/knxPython/pybind11/docs/advanced/pycpp/utilities.rst

@@ -0,0 +1,144 @@
+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();
+    });
+
+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:: py
+
+    // 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>``.

examples/knxPython/pybind11/docs/advanced/smart_ptrs.rst

@@ -0,0 +1,173 @@
+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.

examples/knxPython/pybind11/docs/basics.rst

@@ -0,0 +1,293 @@
+.. _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 Mac OS, the included python version works out of the box,
+but **cmake** must still be installed.
+
+After installing the prerequisites, run
+
+.. code-block:: bash
+
+   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 2015** and newer are supported since pybind11 relies
+on various C++11 language features that break older versions of Visual Studio.
+
+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 which 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`
+
+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 2.7.10 (default, Aug 22 2015, 20:33:39)
+    [GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.1)] on darwin
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import example
+    >>> example.add(1, 2)
+    3L
+    >>>
+
+.. _keyword_args:
+
+Keyword arguments
+=================
+
+With a simple modification code, it is possible to inform Python about the
+names of the arguments ("i" and "j" in this case).
+
+.. code-block:: cpp
+
+    m.def("add", &add, "A function which adds two numbers",
+          py::arg("i"), py::arg("j"));
+
+:class:`arg` is one of several special tag classes which can be used to pass
+metadata into :func:`module::def`. With this modified binding code, we can now
+call the function using keyword arguments, which is a more readable alternative
+particularly for functions taking many parameters:
+
+.. code-block:: 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.

examples/knxPython/pybind11/docs/benchmark.py

@@ -0,0 +1,88 @@
+import random
+import os
+import time
+import datetime as dt
+
+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 += "class cl%03i;\n" % cl
+    decl += '\n'
+
+    for cl in range(nclasses):
+        decl += "class cl%03i {\n" % cl
+        decl += "public:\n"
+        bindings += '    py::class_<cl%03i>(m, "cl%03i")\n' % (cl, cl)
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params  = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += "    cl%03i *fn_%03i(" % (ret, fn)
+            decl += ", ".join("cl%03i *" % p for p in params)
+            decl += ");\n"
+            bindings += '        .def("fn_%03i", &cl%03i::fn_%03i)\n' % \
+                (fn, cl, fn)
+        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 += "class cl%03i;\n" % cl
+    decl += '\n'
+
+    for cl in range(nclasses):
+        decl += "class cl%03i {\n" % cl
+        decl += "public:\n"
+        bindings += '    py::class_<cl%03i>("cl%03i")\n' % (cl, cl)
+        for fn in range(nfns):
+            ret = random.randint(0, nclasses - 1)
+            params  = [random.randint(0, nclasses - 1) for i in range(nargs)]
+            decl += "    cl%03i *fn_%03i(" % (ret, fn)
+            decl += ", ".join("cl%03i *" % p for p in params)
+            decl += ");\n"
+            bindings += '        .def("fn_%03i", &cl%03i::fn_%03i, py::return_value_policy<py::manage_new_object>())\n' % \
+                (fn, cl, fn)
+        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 ("}")

examples/knxPython/pybind11/docs/benchmark.rst

@@ -0,0 +1,97 @@
+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
+
+

examples/knxPython/pybind11/docs/classes.rst

@@ -0,0 +1,500 @@
+.. _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()
+    u'Molly'
+    >>> p.setName('Charly')
+    >>> p.getName()
+    u'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 an utility function that returns a human-readable
+summary to the special method slot named ``__repr__``. Unfortunately, there is no
+suitable functionality in the ``Pet`` data structure, and it would be nice if
+we did not have to change it. This can easily be accomplished by binding a
+Lambda function instead:
+
+.. code-block:: cpp
+
+        py::class_<Pet>(m, "Pet")
+            .def(py::init<const std::string &>())
+            .def("setName", &Pet::setName)
+            .def("getName", &Pet::getName)
+            .def("__repr__",
+                [](const Pet &a) {
+                    return "<example.Pet named '" + a.name + "'>";
+                }
+            );
+
+Both stateless [#f1]_ and stateful lambda closures are supported by pybind11.
+With the above change, the same Python code now produces the following output:
+
+.. code-block:: 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
+    u'Molly'
+    >>> p.name = 'Charly'
+    >>> p.name
+    u'Charly'
+
+Now suppose that ``Pet::name`` was a private internal variable
+that can only be accessed via setters and getters.
+
+.. code-block:: cpp
+
+    class Pet {
+    public:
+        Pet(const std::string &name) : name(name) { }
+        void setName(const std::string &name_) { name = name_; }
+        const std::string &getName() const { return name; }
+    private:
+        std::string name;
+    };
+
+In this case, the method :func:`class_::def_property`
+(:func:`class_::def_property_readonly` for read-only data) can be used to
+provide 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 ...
+
+.. 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 upcasting
+===================================
+
+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
+    u'Molly'
+    >>> p.bark()
+    u'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 upcasting 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 upcast
+    >>> p.bark()
+    u'woof!'
+
+Given a pointer to a polymorphic base, pybind11 performs automatic upcasting
+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", (void (Pet::*)(int)) &Pet::set, "Set the pet's age")
+       .def("set", (void (Pet::*)(const std::string &)) &Pet::set, "Set the pet's name");
+
+The overload signatures are also visible in the method's docstring:
+
+.. code-block:: 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_));
+
+
+.. [#cpp14] A compiler which supports the ``-std=c++14`` flag
+            or Visual Studio 2015 Update 2 and newer.
+
+.. 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 an internal enumeration type,
+e.g.:
+
+.. code-block:: cpp
+
+    struct Pet {
+        enum Kind {
+            Dog = 0,
+            Cat
+        };
+
+        Pet(const std::string &name, Kind type) : name(name), type(type) { }
+
+        std::string name;
+        Kind type;
+    };
+
+The binding code for this example looks as follows:
+
+.. code-block:: cpp
+
+    py::class_<Pet> pet(m, "Pet");
+
+    pet.def(py::init<const std::string &, Pet::Kind>())
+        .def_readwrite("name", &Pet::name)
+        .def_readwrite("type", &Pet::type);
+
+    py::enum_<Pet::Kind>(pet, "Kind")
+        .value("Dog", Pet::Kind::Dog)
+        .value("Cat", Pet::Kind::Cat)
+        .export_values();
+
+To ensure that the ``Kind`` type is created within the scope of ``Pet``, the
+``pet`` :class:`class_` instance must be supplied to the :class:`enum_`.
+constructor. The :func:`enum_::export_values` function 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}
+
+.. 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.

examples/knxPython/pybind11/docs/compiling.rst

@@ -0,0 +1,273 @@
+.. _compiling:
+
+Build systems
+#############
+
+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
+
+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 2.8.12)
+    project(example)
+
+    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
+
+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] 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.
+
+.. _ThinLTO: http://clang.llvm.org/docs/ThinLTO.html
+
+Configuration variables
+-----------------------
+
+By default, pybind11 will compile modules with the C++14 standard, if available
+on the target compiler, falling back to C++11 if C++14 support is not
+available.  Note, however, that this default is subject to change: future
+pybind11 releases are expected to migrate to newer C++ standards as they become
+available.  To override this, the standard flag can be given explicitly in
+``PYBIND11_CPP_STANDARD``:
+
+.. code-block:: cmake
+
+    # Use just one of these:
+    # GCC/clang:
+    set(PYBIND11_CPP_STANDARD -std=c++11)
+    set(PYBIND11_CPP_STANDARD -std=c++14)
+    set(PYBIND11_CPP_STANDARD -std=c++1z) # Experimental C++17 support
+    # MSVC:
+    set(PYBIND11_CPP_STANDARD /std:c++14)
+    set(PYBIND11_CPP_STANDARD /std:c++latest) # Enables some MSVC C++17 features
+
+    add_subdirectory(pybind11)  # or find_package(pybind11)
+
+Note that this and all other configuration variables must be set **before** the
+call to ``add_subdirectory`` or ``find_package``. The variables can also be set
+when calling CMake from the command line using the ``-D<variable>=<value>`` flag.
+
+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 ..
+    # or
+    cmake -DPYTHON_EXECUTABLE=path/to/python ..
+
+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 2.8.12)
+    project(example)
+
+    find_package(pybind11 REQUIRED)
+    pybind11_add_module(example example.cpp)
+
+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
+
+Advanced: interface library target
+----------------------------------
+
+When using a version of CMake greater than 3.0, pybind11 can additionally
+be used as a special *interface library* . The target ``pybind11::module``
+is available with pybind11 headers, Python headers and libraries as needed,
+and C++ compile definitions attached. This target is suitable for linking
+to an independently constructed (through ``add_library``, not
+``pybind11_add_module``) target in the consuming project.
+
+.. code-block:: cmake
+
+    cmake_minimum_required(VERSION 3.0)
+    project(example)
+
+    find_package(pybind11 REQUIRED)  # or add_subdirectory(pybind11)
+
+    add_library(example MODULE main.cpp)
+    target_link_libraries(example PRIVATE pybind11::module)
+    set_target_properties(example PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}"
+                                             SUFFIX "${PYTHON_MODULE_EXTENSION}")
+
+.. 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
+    library only provides the *minimal* set of parameters to ensure that the
+    code using pybind11 compiles, but it does **not** pass these extra compiler
+    flags (i.e. this is up to you).
+
+    These include Link Time Optimization (``-flto`` on GCC/Clang/ICPC, ``/GL``
+    and ``/LTCG`` on Visual Studio) and .OBJ files with many sections on Visual
+    Studio (``/bigobj``).  The :ref:`FAQ <faq:symhidden>` contains an
+    explanation on why these are needed.
+
+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 3.0)
+    project(example)
+
+    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 flags given here assume that you're using Python 3. For Python 2, just
+change the executable appropriately (to ``python`` or ``python2``).
+
+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``.
+
+Note that Python 2.7 modules don't use a special suffix, so you should simply
+use ``example.so`` instead of ``example`python3-config --extension-suffix```.
+Besides, the ``--extension-suffix`` option may or may not be available, depending
+on the distribution; in the latter case, the module extension can be manually
+set to ``.so``.
+
+On Mac OS: 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).
+
+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

examples/knxPython/pybind11/docs/conf.py

@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# pybind11 documentation build configuration file, created by
+# sphinx-quickstart on Sun Oct 11 19:23:48 2015.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import shlex
+import subprocess
+
+# 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']
+
+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.
+#
+# The short X.Y version.
+version = '2.2'
+# The full version, including alpha/beta/rc tags.
+release = '2.2.3'
+
+# 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.
+
+on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
+
+if not on_rtd:  # only import and set the theme if we're building docs locally
+    import sphinx_rtd_theme
+    html_theme = 'sphinx_rtd_theme'
+    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+    html_context = {
+        'css_files': [
+            '_static/theme_overrides.css'
+        ]
+    }
+else:
+    html_context = {
+        'css_files': [
+            '//media.readthedocs.org/css/sphinx_rtd_theme.css',            
+            '//media.readthedocs.org/css/readthedocs-doc-embed.css',    
+            '_static/theme_overrides.css'
+        ]
+    }
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+#   'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
+#   'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr'
+#html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+#html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+#html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'pybind11doc'
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+'preamble': '\DeclareUnicodeCharacter{00A0}{}',
+
+# 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("doxygen error code: {}\n".format(-retcode))
+    except OSError as e:
+        sys.stderr.write("doxygen execution failed: {}\n".format(e))
+
+
+def setup(app):
+    """Add hook for building doxygen xml when needed"""
+    app.connect("builder-inited", generate_doxygen_xml)

examples/knxPython/pybind11/docs/faq.rst

@@ -0,0 +1,289 @@
+Frequently asked questions
+##########################
+
+"ImportError: dynamic module does not define init function"
+===========================================================
+
+You are likely using an incompatible version of Python (for instance, the
+extension library was compiled against Python 2, while the interpreter is
+running on top of some version of Python 3, or vice versa).
+
+"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.
+
+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 invoke CMake as follows:
+
+.. code-block:: bash
+
+    cmake -DPYTHON_EXECUTABLE:FILEPATH=<path-to-python-executable> .
+
+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, ``-fvisiblity=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 metaprogamming 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.
+
+Another aspect that can require a fair bit of code are function signature
+descriptions. pybind11 automatically generates human-readable function
+signatures for docstrings, e.g.:
+
+.. code-block:: none
+
+     |  __init__(...)
+     |      __init__(*args, **kwargs)
+     |      Overloaded function.
+     |
+     |      1. __init__(example.Example1) -> NoneType
+     |
+     |      Docstring for overload #1 goes here
+     |
+     |      2. __init__(example.Example1, int) -> NoneType
+     |
+     |      Docstring for overload #2 goes here
+     |
+     |      3. __init__(example.Example1, example.Example1) -> NoneType
+     |
+     |      Docstring for overload #3 goes here
+
+
+In C++11 mode, these are generated at run time using string concatenation,
+which can amount to 10-20% of the size of the resulting binary. If you can,
+enable C++14 language features (using ``-std=c++14`` for GCC/Clang), in which
+case signatures are efficiently pre-generated at compile time. Unfortunately,
+Visual Studio's C++14 support (``constexpr``) is not good enough as of April
+2016, so it always uses the more expensive run-time approach.
+
+Working with ancient Visual Studio 2009 builds on Windows
+=========================================================
+
+The official Windows distributions of Python are compiled using truly
+ancient versions of Visual Studio that lack good C++11 support. Some users
+implicitly assume that it would be impossible to load a plugin built with
+Visual Studio 2015 into a Python distribution that was compiled using Visual
+Studio 2009. However, no such issue exists: it's perfectly legitimate to
+interface DLLs that are built with different compilers and/or C libraries.
+Common gotchas to watch out for involve not ``free()``-ing memory region
+that that were ``malloc()``-ed in another shared library, using data
+structures with incompatible ABIs, and so on. pybind11 is very careful not
+to make these types of mistakes.
+
+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}
+    }
+

examples/knxPython/pybind11/docs/index.rst

@@ -0,0 +1,47 @@
+.. only: not latex
+
+    .. image:: pybind11-logo.png
+
+pybind11 --- Seamless operability between C++11 and Python
+==========================================================
+
+.. only: not latex
+
+    Contents:
+
+.. toctree::
+   :maxdepth: 1
+
+   intro
+   changelog
+   upgrade
+
+.. toctree::
+   :caption: The Basics
+   :maxdepth: 2
+
+   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

examples/knxPython/pybind11/docs/intro.rst

@@ -0,0 +1,95 @@
+.. image:: pybind11-logo.png
+
+About this project
+==================
+**pybind11** is a lightweight header-only library that exposes C++ types in Python
+and vice versa, mainly to create Python bindings of existing C++ code. Its
+goals and syntax are similar to the excellent `Boost.Python`_ library by David
+Abrahams: to minimize boilerplate code in traditional extension modules by
+inferring type information using compile-time introspection.
+
+.. _Boost.Python: http://www.boost.org/doc/libs/release/libs/python/doc/index.html
+
+The main issue with Boost.Python—and the reason for creating such a similar
+project—is Boost. Boost is an enormously large and complex suite of utility
+libraries that works with almost every C++ compiler in existence. This
+compatibility has its cost: arcane template tricks and workarounds are
+necessary to support the oldest and buggiest of compiler specimens. Now that
+C++11-compatible compilers are widely available, this heavy machinery has
+become an excessively large and unnecessary dependency.
+Think of this library as a tiny self-contained version of Boost.Python with
+everything stripped away that isn't relevant for binding generation. Without
+comments, the core header files only require ~4K lines of code and depend on
+Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
+compact implementation was possible thanks to some of the new C++11 language
+features (specifically: tuples, lambda functions and variadic templates). Since
+its creation, this library has grown beyond Boost.Python in many ways, leading
+to dramatically simpler binding code in many common situations.
+
+Core features
+*************
+The following core C++ features can be mapped to Python
+
+- Functions accepting and returning custom data structures per value, reference, or pointer
+- Instance methods and static methods
+- Overloaded functions
+- Instance attributes and static attributes
+- Arbitrary exception types
+- Enumerations
+- Callbacks
+- Iterators and ranges
+- Custom operators
+- Single and multiple inheritance
+- STL data structures
+- Iterators and ranges
+- Smart pointers with reference counting like ``std::shared_ptr``
+- Internal references with correct reference counting
+- C++ classes with virtual (and pure virtual) methods can be extended in Python
+
+Goodies
+*******
+In addition to the core functionality, pybind11 provides some extra goodies:
+
+- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
+  implementation-agnostic interface.
+
+- It is possible to bind C++11 lambda functions with captured variables. The
+  lambda capture data is stored inside the resulting Python function object.
+
+- pybind11 uses C++11 move constructors and move assignment operators whenever
+  possible to efficiently transfer custom data types.
+
+- It's easy to expose the internal storage of custom data types through
+  Pythons' buffer protocols. This is handy e.g. for fast conversion between
+  C++ matrix classes like Eigen and NumPy without expensive copy operations.
+
+- pybind11 can automatically vectorize functions so that they are transparently
+  applied to all entries of one or more NumPy array arguments.
+
+- Python's slice-based access and assignment operations can be supported with
+  just a few lines of code.
+
+- Everything is contained in just a few header files; there is no need to link
+  against any additional libraries.
+
+- Binaries are generally smaller by a factor of at least 2 compared to
+  equivalent bindings generated by Boost.Python. A recent pybind11 conversion
+  of `PyRosetta`_, an enormous Boost.Python binding project, reported a binary
+  size reduction of **5.4x** and compile time reduction by **5.8x**.
+
+- When supported by the compiler, two new C++14 features (relaxed constexpr and
+  return value deduction) are used to precompute function signatures at compile
+  time, leading to smaller binaries.
+
+- With little extra effort, C++ types can be pickled and unpickled similar to
+  regular Python objects.
+
+.. _PyRosetta: http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf
+
+Supported compilers
+*******************
+
+1. Clang/LLVM (any non-ancient version with C++11 support)
+2. GCC 4.8 or newer
+3. Microsoft Visual Studio 2015 or newer
+4. Intel C++ compiler v17 or newer (v16 with pybind11 v2.0 and v15 with pybind11 v2.0 and a `workaround <https://github.com/pybind/pybind11/issues/276>`_ )

examples/knxPython/pybind11/docs/limitations.rst

@@ -0,0 +1,20 @@
+Limitations
+###########
+
+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``.
+
+These features could be implemented but would lead to a significant increase in
+complexity. I've decided to draw the line here to keep this project simple and
+compact. Users who absolutely require these features are encouraged to fork
+pybind11.
+

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examples/knxPython/pybind11/docs/reference.rst

@@ -0,0 +1,102 @@
+.. _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:
+
+.. _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:
+
+Exceptions
+==========
+
+.. doxygenclass:: error_already_set
+    :members:
+
+.. doxygenclass:: builtin_exception
+    :members:
+
+
+Literals
+========
+
+.. doxygennamespace:: literals

examples/knxPython/pybind11/docs/release.rst

@@ -0,0 +1,25 @@
+To release a new version of pybind11:
+
+- Update the version number and push to pypi
+    - Update ``pybind11/_version.py`` (set release version, remove 'dev').
+    - Update ``PYBIND11_VERSION_MAJOR`` etc. in ``include/pybind11/detail/common.h``.
+    - Ensure that all the information in ``setup.py`` is up-to-date.
+    - Update version in ``docs/conf.py``.
+    - Tag release date in ``docs/changelog.rst``.
+    - ``git add`` and ``git commit``.
+    - if new minor version: ``git checkout -b vX.Y``, ``git push -u origin vX.Y``
+    - ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``.
+    - ``git push``
+    - ``git push --tags``.
+    - ``python setup.py sdist upload``.
+    - ``python setup.py bdist_wheel upload``.
+- Update conda-forge (https://github.com/conda-forge/pybind11-feedstock) via PR
+    - download release package from Github: ``wget https://github.com/pybind/pybind11/archive/vX.Y.Z.tar.gz``
+    - compute checksum: ``shasum -a 256  vX.Y.Z.tar.gz``
+    - change version number and checksum in ``recipe/meta.yml``
+- Get back to work
+    - Update ``_version.py`` (add 'dev' and increment minor).
+    - Update version in ``docs/conf.py``
+    - Update version macros in ``include/pybind11/common.h``
+    - ``git add`` and ``git commit``.
+      ``git push``

examples/knxPython/pybind11/docs/requirements.txt

@@ -0,0 +1 @@
+breathe == 4.5.0

examples/knxPython/pybind11/docs/upgrade.rst

@@ -0,0 +1,404 @@
+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.
+
+
+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(f.value1(), f.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 it's 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"))``                |
++---------------------------------------+---------------------------------------------+