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Python implementation of an arousal dynamics model for prediction alertness based on light exposure

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Light-Driven Arousal Dynamics Model

This repository aims at implementing the arousal dynamics model as described in:

[Postnova et al. 2016] Postnova, S., Lockley, S. W., & Robinson, P. A. (2016). Sleep Propensity under Forced Desynchrony in a Model of Arousal State Dynamics. Journal of Biological Rhythms, 31(5), 498–508. https://doi.org/10.1177/0748730416658806

[Abeysuriya et al. 2018] Abeysuriya RG, Lockley SW, Robinson PA, Postnova S. A unified model of melatonin, 6-sulfatoxymelatonin, and sleep dynamics. Journal of Pineal Research, 64(4), e12474. https://doi.org/10.1111/jpi.12474

[Postnova et al. 2018] Postnova, S., Lockley, S. W., & Robinson, P. A. (2018). Prediction of Cognitive Performance and Subjective Sleepiness Using a Model of Arousal Dynamics. Journal of Biological Rhythms, 33(2), 203–218. https://doi.org/10.1177/0748730418758454

[Tekieh et al. 2020] Tekieh, T., Lockley, S. W., Robinson, P. A., McCloskey, S., Zobaer, M. S., & Postnova, S. (2020). Modeling melanopsin‐mediated effects of light on circadian phase, melatonin suppression, and subjective sleepiness. Journal of Pineal Research, 69(3), e12681. https://doi.org/10.1111/jpi.12681

Parameters and equations are taken from the papers.

Project led by Clotilde Pierson. Implementation by Alexander Ulbrich.

Setup

We recommend working in a conda environment using miniconda:

conda create --name arousal --file requirements.txt

Once the environment is setup, activate:

conda activate arousal

To Do

  • optimize for S_b and S_c constants for daytime
  • add debug flags where relevant
  • clean up old notebooks (won't run on current models.py)
  • add more unit tests, including comparison with original data if possible
  • have a notebook to run on scenarios and compare outputs (aMT6s, rho_b, KSS)
  • improve plots with better names for axes
  • re-organize constants in particular for the melatonin concentrations
  • add missing docstring and fix linting where approriate

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Python implementation of an arousal dynamics model for prediction alertness based on light exposure

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