Prepare Fitting App for Publishing

[ahmadomira]

1. Implement Fitting Layer
   • Implement a dedicated layer in the application responsible for managing the fitting process.
   • Fitting layer should implement multithreading options, allowing users to specify the number of threads to optimize performance.
   • Ensure the fitting layer can be easily extended or modified for future enhancements.

Be aware of multithreading/-processing difference across Windows, Linux, and MacOS.

2. Backend Enhancements

   • Backend inputs:
     • Number of threads
     • Parameter boundaries
     • Fitting model (e.g., dye-alone, IDA, DBA, or GDA)
     • Supplementary information (e.g., dye, experiment protocol, etc.)
   • Backend outputs:
     • Trajectories: Return the computed trajectories as part of the results.
     • Optimal parameter set: Provide the best-fit parameters as a list for further analysis or use.

3. Loss Function Refactoring

   • Refactor all loss functions to accept parameters strictly as a list for consistency.
   • Package any additional information required by the loss functions into a dedicated class, for clean and maintainable code.
   • Document the new structure and usage patterns for developers.

4. Documentation and Testing

   • Add documentation to the fitting layer and backend.
   • Ensure that all features are covered by unit tests, including edge cases and error handling.
   • Create a comprehensive test suite to validate the fitting process across different scenarios.

5. Shipping the App

   • Consider 'compiling' the app using Nuitka for better performance. If it doesn't work, try cs_freeze. Last resort, stick to PyInstaller.
   • Write a README file with a quick start guide.