Objective

This assignment builds on Homework 02, integrating new topics like:

• Python and built-in data types.
• Object-oriented programming in Python.
• Python modules and packages.
• Use of Python ecosystem for scientific computing (NumPy, SciPy, Matplotlib, seaborn, pandas, ...)
• C++ and Python integration using pybind11.

Your task is to enhance the C++ scientific computing toolbox developed in Homework 02 with Python bindings and additional functionalities.

Build your Homework 03 on module A) and one module among B), C), D) from Homework 02, of your choice.

Tasks (1/2)

1. Python bindings using pybind11:

   • Create Python bindings for the C++ modules using pybind11.
   • Ensure Python users can seamlessly use the functionalities of these modules.

2. Advanced Python-C++ integration:

   • Demonstrate integration where Python and C++ interact more complexly, like C++ callbacks being used in Python or vice versa.
   • Explore efficiency gains from this hybrid approach in data-intensive tasks.

Tasks (2/2)

3. Object-oriented Python extensions:

   • Design Python classes that complement your C++ modules, showcasing OOP principles.
   • Implement features that leverage Python’s flexibility, like dynamic typing, decorators, magic methods, and context management.

4. Data analysis and visualization in Python:

   • Add functionality to visualize results using a Python plotting library (e.g., Matplotlib, seaborn).
   • Integrate NumPy for data manipulation, SciPy for advanced computations, and/or pandas for data analysis.

Requirements

• Compatibility: Ensure Python bindings are compatible with the C++ codebase from Homework 02.
• Documentation: Update the README with instructions on using the Python interface, including installation of the toolbox and any dependencies.
• Examples and testing: Provide Python scripts or notebooks demonstrating the use of the toolbox in real-world scenarios. Include tests verifying that the Python interface correctly interacts with the C++ modules.
• Third-party libraries: The integration of third-party C++ libraries or Python packages is highly encouraged.

Submission

1. Include a README file that:
   • Clearly states which module(s) you implemented.
   • Lists all group members and their individual contribution to the project.
   • Provides a concise discussion of the obtained results, with a focus on design choices and considerations about the performance balance between C++ and Python.
2. Use CMake as a build system to detect pybind11 and exploit its functionalities. Clearly specify the commands needed to compile the C++ library and and to run the Python code successfully.
3. Submit a single compressed file (named Homework_03_Surname1_Surname2.ext) containing all source code (possibly organizing in proper subfolders the C++ and Python parts), the README, and any other relevant files or third-party libraries (please comply to their licences).

Evaluation grid

1. Functionality (up to 2 points): C++ code and Python bindings work correctly with all modules.
2. Integration (up to 2 points): Seamless integration between Python and C++ components.
3. Code organization, code quality and documentation (up to 0.5 points): Clean, well-organized code and clear, instructive documentation.
4. Examples and testing (up to 0.5 points): Practical examples with adequate tests.
5. Bonus points (up to 1 points):
   • Profile the code to detect computational bottlenecks and optimize the performance.
   • Implement automated testing frameworks to ensure the robustness of the code.
   • Create a Python package of the toolbox using setuptools, possibly making the package easily installable via pip.