Basic class wrapping

21.2 Basic class wrapping

As a very basic example of wrapping a class, let’s consider a minimal wrapper for SessionReader:

Listing 21.2: Basic class wrapping with pybind11

1void export_SessionReader(py::module &m) 
2{ 
3  py::class_<SessionReader, std::shared_ptr<SessionReader>>( 
4      m, "SessionReader") 
5 
6    .def_static("CreateInstance", SessionReader_CreateInstance) 
7 
8    .def("GetSessionName", &SessionReader::GetSessionName, 
9         py::return_value_policy::copy) 
10 
11    .def("InitSession", &SessionReader::InitSession, 
12         py::arg("filenames") = py::list()) 
13 
14    .def("Finalise", &SessionReader::Finalise) 
15    ; 
16}

21.2.1 `py::class_<>`

This pybind11 object defines a Python class in C++. It is templated, and in this case we have the following template arguments:

SessionReader is the class that will be wrapped;
std::shared_ptr<SessionReader> indicates that this object should be stored inside a shared (or smart) pointer, which we frequently use throughout the library, as can be seen by the frequent use of SessionReaderSharedPtr.

We then have two arguments:

m is the module we are going to define the class within.
"SessionReader" is the name of the class in Python.

21.2.2 Wrapping member functions

We then call the .def function on the class_<>, which allows us to define member functions on our class. This is equivalent to def-ing a function in Python. .def has two required parameters, and one optional parameter:

The function name as a string, e.g. "GetSessionName"
A function pointer that defines the C++ function that will be called
An optional return policy, which we need to define when the C++ function returns a reference.

pybind11 is very smart and can convert many Python objects to their equivalent C++ function arguments, and C++ return types of the function to their respective Python object. Many times therefore, one only needs to define the .def() call.

However, there are some instances where we need to do some additional conversion, mask some C++ complexity from the Python interface, or deal with functions that return references. We describe ways to deal with this below.

Thin wrappers

Instead of defining a function pointer to a member of the C++ class, we can define a function pointer to a separate function that defines some extra functionality. This is called a thin wrapper.

As an example, consider the CreateInstance function. In C++ we pass this function the command line arguments in the usual argc, argv format. In Python, command line arguments are defined as a list of strings inside sys.argv. However, pybind11 does not know how to convert this list to argc, argv, so we need some additional code.

Listing 21.3: Thin wrapper example

1SessionReaderSharedPtr SessionReader_CreateInstance(py::list &ns) 
2{ 
3    // ... some code here that converts a Python list to the standard 
4    // c/c++ (int argc, char **argv) format for command line arguments. 
5    // Then use this to construct a SessionReader and return it. 
6    SessionReaderSharedPtr sr = SessionReader::CreateInstance(argc, argv); 
7    return sr; 
8}

In Python, we can then simply call session = SessionReader.CreateInstance(sys.argv).

Dealing with references

When dealing with functions in C++ that return references, e.g. const NekDouble &GetFactor() we need to supply an additional argument to .def(), since Python immutable types such as strings and integers cannot be passed by reference. There are also lifetime implications; i.e. if the C++ object being referenced is destroyed whilst still being used in Python, this would cause the program to abort.

To address this, we can define a return value policy which allows pybind11 to take various actions based on the intent of the function. For a full list of options, consult the pybind11] guide on return value policies. However in this case we opt to use copy as highlighted above, which will create a copy of the const reference and return this to Python, thereby avoiding any lifetime issues.

Dealing with `Array<OneD, >`

The LibUtilities/Python/BasicUtils/SharedArray.cpp file contains a number of functions that allow for the automatic conversion of Nektar++ Array<OneD, > storage to and from NumPy ndarray objects. This means that you can wrap functions that take these as parameters and return arrays very easily. However bear in mind the following caveats:

Any NumPy ndarray created from an Array<OneD, > (and vice versa) will share their memory. Although this avoids expensive memory copies, it means that changing the C++ array changes the contents of the NumPy array (and vice versa).
Many functions in Nektar++ return Arrays through argument parameters. In Python this is a very unnatural way to write functions. For example:
```
1# This is good 
2x, y, z = exp.GetCoords() 
3# This is bad 
4x, y, z = np.zeros(10), np.zeros(10), np.zeros(10) 
5exp.GetCoords(x,y,z) 
6# After this point x, y and z are updated as a result of GetCoords().
```
Use thin wrappers to overcome this problem. For examples of how to do this, particularly in returning tuples, consult the StdRegions/StdExpansion.cpp wrapper which contains numerous examples.
TwoD and ThreeD arrays are not supported.

More information on the memory management and how the memory is shared can be found in Section 23.

21.2.3 Inheritance

Nektar++ makes heavy use of inheritance, which can be translated to Python quite easily using pybind11. For a good example of how to do this, you can examine the StdRegions wrapper for StdExpansion and its elements such as StdQuadExp. In a cut-down form, these look like the following:

Listing 21.4: Inheritance with pybind11

1void export_StdExpansion(py::module &m) 
2{ 
3    py::class_<StdExpansion, std::shared_ptr<StdExpansion>>(m, "StdExpansion"); 
4} 
5void export_StdQuadExp(py::module &m) 
6{ 
7    py::class_<StdQuadExp, StdExpansion, std::shared_ptr<StdQuadExp>>( 
8        m, "StdQuadExp", py::multiple_inheritance()) 
9        .def(py::init<const LibUtilities::BasisKey &, 
10                      const LibUtilities::BasisKey &>()); 
11}

Note the following:

StdExpansion is an abstract class, so it has no initialiser and is non-copyable.
We write the parent classes of StdQuadExp in the call to class_. Note that this does not necessarily need to include the full hierarchy of C++ inheritance: in StdRegions the inheritance graph for StdQuadExp looks like StdExpansion -> StdExpansion2D -> StdQuadExp. In the above wrapper, we omit the StdExpansion2D reference entirely. However, in order to do skip classes in the inheritance tree, we need to tell pybind11 about this by passing the py::multiple_inheritance argument to the object.
py::init<> is used to show how to wrap a C++ constructor. This can accept any arguments for which you have either written explicit wrappers or pybind11 already knows how to convert.

21.2.4 Wrapping enums

Most Nektar++ enumerators come in the form:

1enum MyEnum { 
2    eItemOne, 
3    eItemTwo, 
4    SIZE_MyEnum 
5}; 
6static const char *MyEnumMap[] = { 
7    "ItemOne" 
8    "ItemTwo" 
9    "ItemThree" 
10};

To wrap this, you can use the NEKPY_WRAP_ENUM macro defined in NekPyConfig.hpp, which in this case can be used as NEKPY_WRAP_ENUM(m, MyEnum, MyEnumMap). Note that if instead of const char * the map is defined as a const std::string, you can use the NEKPY_WRAP_ENUM_STRING macro.

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