gnlse-python is a Python set of scripts for solving Generalized Nonlinear Schrodringer Equation. It is one of the WUST-FOG students projects developed by Fiber Optics Group, WUST.

1. Create a virtual environment with python -m venv gnlse or using conda.
2. Activate it with . gnlse/bin/activate.
3. Clone this repository git clone https://github.com/WUST-FOG/gnlse-python.git
4. Install the requirements in this directory pip install -r requirements.txt.
5. Install gnlse package pip install . or set PYTHONPATH enviroment variable

python -m venv gnlse
. gnlse/bin/activate
git clone https://github.com/WUST-FOG/gnlse-python.git
cd gnlse-python
pip install -r requirements
pip install .

We provided some examples in examples subdirectory. They can be run by typing name of the script without any arguments.

Example:

cd gnlse-python/examples

python test_Dudley.py

And you expect to visualise supercontinuum generation process in use of 3 types of pulses (simulation similar to Fig.3 of Dudley et. al, RMP 78 1135 (2006)):

supercontinuum_generation

• Modular Design

  Main core of gnlse module is derived from the RK4IP matlab script written by J.C.Travers, H. Frosz and J.M. Dudley that is provided in "Supercontinuum Generation in Optical Fibers", edited by J. M. Dudley and J. R. Taylor (Cambridge 2010). The toolbox prepares integration using SCIPYs ode solvers (adaptive step size). We decompose the solver framework into different components and one can easily construct a customized simulations by accounting different physical phenomena, ie. self stepening, Raman response.

• Raman response models

  We implement three different raman response functions:

  • 'blowwood': Blow and D. Wood, IEEE J. of Quant. Elec., vol. 25, no. 12, pp. 2665–2673, Dec. 1989,
  • 'linagrawal': Lin and Agrawal, Opt. Lett., vol. 31, no. 21, pp. 3086–3088, Nov. 2006,
  • 'hollenbeck': Hollenbeck and Cantrell J. Opt. Soc. Am. B / Vol. 19, No. 12 / December 2002.

• Dispersion operator

  We implement two version of dispersion operator:

  • dispersion calculated from Taylor expansion,
  • dispersion calculated from effective refractive indicies.

• Available demos

  We prepare few examples in examples subdirectory:

  • plot_input_pulse.py: plots envelope of different impulse shapes,
  • plot_Raman_response.py: plots different Raman in temporal domain,
  • test_3rd_order_soliton.py: evolution of the spectral and temporal characteristics of the 3rd order soliton,
  • test_dispersion.py: example of supercontinuum generation using different dispersion operators,
  • test_Dudley.py: example of supercontinuum generation with three types of input pulse,
  • test_gvd.py: example of impuls broadening due to group velocity dispersion,
  • test_import_export.py: example of saving file with .mat extension,
  • test_raman.py: example of soliton fision for diffrent raman response functions,
  • test_spm.py: example of self phase modulation,
  • test_spm+gvd.py: example of generation of 1st order soliton.

v1.0.0 was released in 13/8/2020. The master branch works with python 3.7.

• 1.0.0 -> Aug 13th, 2020
  • The first proper release
  • CHANGE: Complete documentation and code

• Adam Pawłowski
• Paweł Redman
• Daniel Szulc
• Magda Zatorska
• Sylwia Majchrowska
• Karol Tarnowski

gnlse-python is an open source project that is contributed by researchers, engineers, and students from Wroclaw University of Science and Technology as a part of Fiber Optics Group's nonlinear simulations projects. The python code based on MATLAB code published in 'Supercontinuum Generation in Optical Fibers' by J. M. Dudley and J. R. Taylor, available at http://scgbook.info/.

@misc{WUST-FOG2020,
  author = {Paw\lowski, A., Redman, P., Szulc, D., Zatorska, M., 
            Majchrowska, S., Tarnowski, K.
           },
  title = {gnlse-python},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/WUST-FOG/gnlse-python}},
}

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update example tests as appropriate.

MIT