OptiCommPy 0.9.0

Optical Communications Algorithms with Python

.. image:: ../../figures/logo_OptiCommPy.jpg :width: 75%
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OptiCommPy is a Python-based framework to simulate systems, subsystems, and components of fiber optic communication systems, for educational and research purposes.

.. image:: ../../figures/eyeDisp.gif :width: 45%
.. image:: ../../figures/40GOOK_spectrum.jpg :width: 45%

.. image:: ../../figures/DSP.jpg :width: 600px
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|PyPI| |PyPI - Downloads| |Documentation Status| |DOI|

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 <https://en.wikipedia.org/wiki/Split-step_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.

• For most of the cases, Numba <https://numba.pydata.org/>__ is used to speed up the core DSP functions.

• 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, implement the feature you want and send me a pull request. If you want to suggest new features or discuss anything related to OptiCommPy, please get in touch with me ([e-mail]).

Requirements/Dependencies

• python>=3.2
• numpy>=1.24.4
• scipy>=1.13.0
• matplotlib>=3.7.0
• numba>=0.54.1,<=0.57.0
• tqdm>=4.64.1
• simple-pid>=1.0.1
• mpl-scatter-density>=0.7.0
• sphinx-rtd-theme>=1.2.2
• nbsphinx>=0.9.3
• nbsphinx-link>=1.3.0
• 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 <https://opticommpy.readthedocs.io/en/latest/index.html>__.

Citing this repository

Edson Porto da Silva, Adolfo Herbster, Carlos Daniel Fontes da Silva, & Joaquin Matres. (2023). edsonportosilva/OptiCommPy: v0.7.0-alpha (v0.7.0-alpha). Zenodo. https://doi.org/10.5281/zenodo.10304945

.. |PyPI| image:: https://img.shields.io/pypi/v/OptiCommPy?label=pypi%20package .. |PyPI - Downloads| image:: https://img.shields.io/pypi/dm/OptiCommPy .. |Documentation Status| image:: https://readthedocs.org/projects/opticommpy/badge/?version=latest :target: https://opticommpy.readthedocs.io/en/latest/?badge=latest .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10304945.svg :target: https://doi.org/10.5281/zenodo.10304945