How To Wrap A Python Object In Cc++

Python is an interpreted, object-oriented language that provides powerful features like dynamic typing, reflection, and high-level data types out of the box. One of the key strengths is Python's rich and capable object model that enables rapid application development and clean, readable code.

However, for CPU or memory-intensive sections of an application, Python's dynamic nature incurs performance overheads compared to statically compiled languages like C/C++. So, there are clear benefits in combining Python's programming ease for high-level logic with C/C++'s speed and efficiency for performance-critical routines.

An effective technique to get the "best of both worlds" is to wrap Python objects so they are directly usable from C or C++ code. It allows keeping key application logic, classes, and data structures in high-productivity Python while tapping into this from low-level C/C++ code. Seamless interoperability opens up many interesting possibilities.

This article explores different approaches for wrapping Python objects to be consumed in C/C++. We will cover concepts like:

• ✅ Accessing the Python C API directly
• ✅ Using helper libraries like Boost.Python or pybind11
• ✅ Creating and manipulating Python objects from C++
• ✅ Calling Python object methods from C++
• ✅ Managing references and memory correctly

Some experience in both Python and C/C++ is assumed. By the end, you should understand the techniques required to leverage Python's object-oriented features in your own high-performance C/C++ applications. Let's get started!

The goal is to provide background on why accessing Python objects from C/C++ is useful in many domains and introduce the core topics covered in the rest of the article. Please let me know if you want me to modify or expand the introduction further.

Steps to Access Python Objects

1. Accessing the Python C API directly

• ✅ Include Python.h header.
• ✅ Call Py_Initialize to start the Python interpreter.
• ✅ Use functions like PyListNew, PyDictSetItem to create and manipulate Python objects.
• ✅ Use PyMethodNew and PyEvalCallObject to call object methods.
• ✅ Manage reference counts manually with every create/copy/delete.

1. Using helper libraries like Boost.Python or pybind11

• ✅ Include library headers instead of direct Python headers.
• ✅ Dramatically simplified object creation/destruction.
• ✅ Wrapper functions are provided instead of directly calling Python API.
• ✅ Automatic reference counting for memory management.
• ✅ Exception translation and handling.

1. Creating and manipulating Python objects from C++

• ✅ PyList_New to make lists, dicts, etc.
• ✅ PyTuple_SetItem to create and fill tuples.
• ✅ Custom class wrapping via PyType_Ready.
• ✅ Create objects through wrapper layers rather than Python API.

1. Calling Python object methods from C++

• ✅ PyInstanceMethod_New to get callable bound to an instance.
• ✅ PyEval_CallObject executes method call.
• ✅ Boost/pybind11 wrappers again simplify.

1. Managing references and memory correctly

• ✅ Manual reference counting by incrementing/decrementing every object via Py_XINCREF/DECREF.
• ✅ Skipping can lead to crashes due to stale pointers.
• ✅ Helper libraries provide automatic reference counting.

So, in summary, integrating C++/Python via the Python C API is possible but lower-level and error-prone. Helper libraries like pybind11 or Boost. Considerably simplify wrapping and interacting with Python objects from C++ code. They handle tedious low-level memory management while exposing simple, native-feeling interfaces.

Code Implementation

1. Accessing Python C API directly
Example
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// Create a Python list
PyObject *list = PyList_New(3); 

// Print string representation 
PyObject *str = PyObject_Str(list);
puts(PyUnicode_AsUTF8(str));
Try it Yourself

Output:

Explanation:

• ✅ 'PyObject' is the base structure used to represent all Python objects in C/C++.
• ✅ 'PyList_New' is the C API function to create a new empty Python list with an initial allocated size of 3.
• ✅ 'PyObject_Str' will return the Python string representation of the given object.
• ✅ We convert that 'PyObject' string back into a C string using 'PyUnicode_AsUTF8' .
• ✅ Finally, we print the UTF-8 encoded string using the C 'puts' function.

1. Using the pybind11 helper library
Example
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#include <pybind11/pybind11.h>
namespace py = pybind11;

PYBIND11_MODULE(example, m) {
  m.attr("py_list") = py::list(); 
}

// Usage
import example
print(example.py_list)
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Output:

1. Creating and manipulating Python objects from C
Example
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// Dictionary manipulation	
PyObject *dict = PyDict_New();
PyDict_SetItemString(dict, "key", PyLong_FromLong(10)); 
PyDict_SetItemString(dict, "other", PyUnicode_FromString("value"));

// Print string representation
PyObject *str = PyObject_Str(dict);    
puts(PyUnicode_AsUTF8(str));
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Output:

{'key': 10, 'other': 'value'}

1. Calling Python object methods from C++
Example
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PyObject *str = PyObject_CallMethod(dict, "get", "s", "key");

puts(PyUnicode_AsUTF8(str));
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Output:

1. Managing memory correctly
Example
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int refCount = dict->ob_refcnt;
Py_DECREF(dict);  

std::cout << "Ref count: " << refCount - 1;
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Output:

Ref count: 1

The Python C API

The foundation for wrapping Python objects in C/C++ is the Python/C API. This standard set of functions and interfaces exposed by the Python interpreter allows other code to interact with Python objects. It is what Python itself is built on top of.

Some key capabilities offered by the Python/C API include:

• ✅ Creating new Python objects from C/C++
• ✅ Calling Python object methods from C/C++
• ✅ Getting and setting attributes on Python objects
• ✅ Properly handling reference counting and memory management
• ✅ Catching and handling Python exceptions
• ✅ Loading Python modules and calling their functions

So, by leveraging the dozens of available API calls, you can fully control and manipulate Python objects and environments from straight C/C++ code.

Basic Object Wrapping

The steps to wrap a basic Python object like an integer from C++ are:

• ✅ Include the Python header files
• ✅ Call API functions to create a new Python integer object
• ✅ Use API functions to call supported methods on the object
• ✅ Access attributes through the API
• ✅ Handle reference counting for the object

For example:

1. Include headers
Example
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#include <Python.h>
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2. Create integer
Example
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PyObject *pInt = PyLong_FromLong(10);
Try it Yourself
3. Manipulate object
Example
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PyNumber_Add(pInt, pInt);
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4. Attribute access
Example
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Py_DECREF(pInt);
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5. Reference handling
Example
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Py_DECREF(pInt);
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Class Wrapping:

To wrap a custom Python class, you need to:

• ✅ Recreate the class using C structs and functions.
• ✅ Expose Python method calls to linked C functions.
• ✅ Register the wrapper class with the interpreter.

It takes more effort but provides full control of a Python class from C++.

Simplifying with Helper Libraries:

Manually working with the Python C API can be cumbersome. So libraries like Boost.Python and pybind11 simplify the process significantly through high-level wrappers and automatic reference counting.

For example, with pybind11:

#include <pybind11/pybind11.h>

PYBIND11_MODULE(example, m) {
    py::class_<MyWrappedClass>(m, "MyWrappedClass")
        .def(py::init<>()) 
        .def("wrapped_method", &MyWrappedClass::wrapped_method); 
}

These libraries make wrapping Python objects in C++ clean and native feeling.

There are more details around lifetime management, exception handling, and advanced wrapping techniques. But this overview covers the major steps for exposing Python objects in C/C++ code through the Python C API, directly or via helper libraries.

Conclusion:

When you combine Python's capabilities with C/C++'s performance, you open up new possibilities for applications that need both productivity and speed. By making Python objects accessible from C/C++, code developers can take advantage of each language's strengths where they're most suitable.

The Python/C API lies at the heart of this process, offering a range of functions and interfaces to interact with the Python interpreter and object model from C/C++ code. Using this low-level API, C/C++ programs can easily create Python objects, call their methods, access attributes, and manage memory efficiently. However, manually wrapping Python with the C API involves a lot of boilerplate code and handling reference counting.

Libraries like Boost.Python and pybind11 offer users high-level wrappers around the Python API to streamline this integration process. With a few lines of C++ code, these libraries can automatically generate bindings to expose Python classes, modules and objects for use in C++. The helper libraries take care of reference counting, exception handling and other intricate details at a level.

This results in an experience when incorporating Python into C++. Sure, there are challenges, remember, such as handling how objects are managed across languages and the potential performance impacts of frequently switching between Python and compiled C/C++ code. Like with any solution involving languages, it's important to design it to make the most of each language's strengths. Python objects likely shouldn't be wrapped for every minor routine, but focusing on just the core performance-critical sections can yield substantial gains.

Combining the latest C++ standards with Python's mature object model can be a powerful union when leveraged appropriately. C++ developers can tap into Python's rich data science libraries and frameworks. Python developers can inject high-performance kernels without sacrificing coding productivity. No matter the use case, wrapping Python objects in C/C++ provides a viable solution for the challenging quest to unite simplicity and speed in modern software development.