Welcome to OptiCommPy’s documentation!

Note

This project is under active development.

OptiCommPy logo

OptiCommPy is a Python-based framework to simulate systems, subsystems, and components of fiber optic communication systems, for educational and research purposes.

Animated density eye diagram of a two-level signal Simulated optical spectrum (power spectral density versus frequency) of a 40G OOK signal centered at 193.1 THz 16-QAM constellations of the received signal: as detected, after dispersion compensation, after adaptive equalization, and after carrier frequency and phase recovery

PyPI package version PyPI monthly downloads Documentation status Zenodo DOI JOSS paper Total PyPI downloads

Available features

  • Several digital modulations available (M-PAM, square M-QAM, M-PSK, OOK) to simulate IM-DD and coherent optical systems.

  • Numerical models to simulate optical transmitters, optical amplification, nonlinear propagation over optical fibers, and optical receivers.

  • CPU and GPU-based implementations of the split-step Fourier Method to simulate polarization multiplexed WDM transmission.

  • Standard digital signal processing (DSP) blocks employed in coherent optical receivers, such as:

    • Signal resampling.

    • Matched filtering.

    • Clock recovery.

    • Electronic chromatic dispersion compensation (EDC).

    • Several NxN MIMO adaptive equalization algorithms.

    • Carrier phase recovery algorithms.

  • Functions to implement forward error correction (FEC) encoders and decoders.

  • For most of the cases, Numba is used to speed up the code.

  • Evaluate transmission performance with metrics such as:

    • Bit-error-rate (BER).

    • Symbol-error-rate (SER).

    • Error vector magnitude (EVM).

    • Mutual information (MI).

    • Generalized mutual information (GMI).

    • Normalized generalized mutual information (NGMI).

  • Visualization of the spectrum of electrical/optical signals, signal constellations, and eyediagrams.

How can I contribute?

  • If you want to contribute to this project:

    • Create a new issue in the GitHub repository to discuss the feature you want to implement.

    • Fork the repository and create a new branch.

    • Make sure you have the latest version of the code.

    • Check the conventions adopted in the code writing (e.g. naming conventions, docstrings, etc.).

    • Remember to write an example of usage for the new feature you are implementing.

    • After the process is finished, send a pull request.

  • If you want to suggest or discuss anything related to OptiCommPy, please get in touch via e-mail (edsonporto88@gmail.com).

Requirements/Dependencies

  • python>=3.10

  • numpy>=1.24.4

  • scipy>=1.13.0

  • matplotlib>=3.7.0

  • numba>=0.54.1

  • tqdm>=4.64.1

  • simple-pid>=1.0.1

  • mpl-scatter-density>=0.8

  • prettytable>=3.16.0

  • cupy-cuda12x>=13.1.0 (optional, in case GPU processing is desired)

Installation

Using pip:

pip install OptiCommPy

Cloning the repository from GitHub:

$ git clone https://github.com/edsonportosilva/OptiCommPy.git
$ cd OptiCommPy
$ pip install .

Documentation

We are continuously making efforts to improve the code documentation. You can find the latest documentation in opticommpy.readthedocs.io.

To build the documentation locally, install OptiCommPy with the documentation dependencies and run Sphinx from the root of the repository:

$ pip install .[docs]
$ sphinx-build -b html docs/source docs/build/html

Citing this repository

Edson Porto da Silva, Adolfo Fernandes Herbster. “OptiCommPy: Open-source Simulation of Fiber Optic Communications with Python”, Journal of Open Source Software, 9(98), 6600, (2024) https://doi.org/10.21105/joss.06600

Examples

Package documentation

Indices and tables