Python C Matlab

Creating a Shared Library in MATLAB: A Step-by-Step Guide

As researchers and developers, we often need to share our work with others. Whether it’s a clever simulation model or a fascinating algorithm, being able to package and distribute your MATLAB code efficiently is essential. One effective method for doing this is to create a shared library in MATLAB. This allows others to access your simulation, regardless of the programming language they are using. In this post, we will break down how to create a shared library in MATLAB that can be called from Python, particularly within a Django application.

Understanding the Requirements

Before we dive into the technical details, let’s outline what you’re trying to achieve:

  • Creating a clean set of functions from your MATLAB simulation.
  • Compiling these functions into a C library to increase accessibility.
  • Using SWIG (Simplified Wrapper and Interface Generator) to create a Python wrapper for your C library.
  • Integrating the wrapped library into a Django application.

With these goals in mind, let’s walk through the process.

Step 1: Clean Up Your MATLAB Code

Before proceeding, you should ensure your MATLAB code is optimized and organized:

  • Refactor the Simulation: Break your simulation into smaller, manageable functions. This modularity makes it easier to compile into a library.
  • Testing: Thoroughly test each function to confirm that they work as expected.

Step 2: Create a C Shared Library in MATLAB

MATLAB offers a built-in way to compile functions into a shared library. Here’s how you can do it:

  1. Open MATLAB.
  2. Write your functions: Ensure your MATLAB functions are defined properly as they will be compiled into the library.
  3. Use the codegen command: This command compiles your MATLAB code into C code. Example: 
    codegen myFunction -args {inputArgument1, inputArgument2}
  4. Compile: After generating the C code, compile it into a Dynamic Link Library (DLL) using the provided tools in MATLAB.

Step 3: Utilizing SWIG for Python Wrapper

Now that you have your MATLAB code compiled into a shared library, the next step is to create a wrapper that allows Python to interface with this library.

  1. Install SWIG: If you haven’t done so, make sure that SWIG is installed on your system.
  2. Create an Interface File: Write a SWIG interface file that describes the functions in your library.
  3. Run SWIG: Use SWIG to generate the wrapper code: 
    swig -python -c++ myLibrary.i
  4. Compile the Wrapper: Compile the generated C++ code, linking it with the MATLAB shared library. This step ensures that your Python code can access MATLAB functionalities through the shared library.

Step 4: Integrating with Django

Once you have successfully created the Python wrapper for your MATLAB library, it’s time to integrate this into your Django application.

  1. Install your Library: Ensure your compiled shared library and Python wrapper are accessible in your Django environment.

  2. Import in Django views: You can now import your library in Django views and call the necessary functions:

    from myLibrary import myFunction

  3. Create Views and Templates: Set up your Django views to pass data to the library functions and display results.

Conclusion

By following these steps, you will successfully create a shared library in MATLAB, wrap it for Python using SWIG, and integrate it within a Django application.

Potential Pitfalls

While the plan described above is sound, be wary of the following:

  • Compatibility Issues: Ensure the MATLAB code and its functions are compatible with the C interface.
  • Debugging: Debugging can be more challenging after compilation. Test thoroughly at each stage.
  • Library Path: Make sure that your library paths are correctly set in your environment to prevent runtime errors.

Whether you’re distributing a simulation or integrating complex algorithms into a web application, creating a shared library using MATLAB is a powerful approach. Happy coding!