Code regarding the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network"

This is the Python code used to achieve the results published in the article "Reservoir computing with all-optical non-fading memory in a self-pulsing microresonator network", which was posted as a preprint on arXiv (https://doi.org/10.48550/arXiv.2411.17272) and is currently under review at the journal Advanced Optical Materials. The files whose name starts with "AWGpattern_", were employed to generate the input time series used to drive the AWG and thus the optical modulator. The files whose name starts with "parse_data", were used to parse the raw data acquired with the oscilloscope to extract the machine learning samples. The files whose name starts with "ML_", contains the employed machine learning pipeline for the different cases and tasks described in the article. These are supported by the code in "functions_1.py". The files whose name starts with "ResultsAnalysis_", summarize, elaborate and plot the ML results. Additional plots were generated using the files "Article_waveform_examples.py" and "Article_plots_and_results.py".