Code transferred from MATLAB to Python by Mykhailo Isak and Mykola Voloshyn.

Setup Instructions

1. Download all project files from GitHub.

2. Create a Python virtual environment:

   • Windows (PowerShell):
     python -m venv venv
.\venv\Scripts\Activate.ps1

   • Windows (cmd):
     python -m venv venv
.\venv\Scripts\activate.bat

   • Linux / macOS:
     python3 -m venv venv
source venv/bin/activate

3. Install dependencies:
   pip install -r requirements.txt

4. Run the project:
   python main.py

Optional Settings

For higher accuracy (with increased runtime) or faster execution (with potentially lower accuracy), you can adjust the following parameters:

• popsize - Population size. Increasing this improves accuracy but also increases runtime.

• maxit - Maximum number of generations. More iterations yield better results but take longer to compute.

• tolerance and abs_tolerance - The algorithm stops if the variation in population energies (standard deviation) is small enough, specifically when it’s less than or equal to $abs_tolerance + tolerance * |mean\ energy|$

• workers - how many threads are being used by optimizer (-1 for all threads)

You can also add your own data to the Data folder if needed. To do this:

1. Create a JSON file similar to the existing ones. You can add or remove layers by editing the thickness and refractive index values as needed.
2. Include two time-domain data files (as .txt files):
   • One for the reference measurement
   • One for the sample measurement These files should contain the time-dependent experimental data and must also be placed in the Data folder.