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https://github.com/danielvartan/entrainment

☀️⏳ Rule-based model of the 24h light/dark cycle entrainment phenomenon
https://github.com/danielvartan/entrainment

chronobiology chronotype complex-systems entrainment model python

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☀️⏳ Rule-based model of the 24h light/dark cycle entrainment phenomenon

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README 

        
---

output: github_document

---



```{r}

#| label = "setup",

#| include = FALSE



knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "docs/source/_static/readme_",

  out.width = "100%"

)

```



# entrainment



[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)

[![Documentation Status](https://readthedocs.org/projects/entrainment/badge/?version=latest)](https://entrainment.readthedocs.io/en/latest/?badge=latest)

[![License: MIT](https://img.shields.io/badge/license-MIT-green)](https://choosealicense.com/licenses/mit/)

[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](https://www.contributor-covenant.org/version/2/1/code_of_conduct/)



## Overview



`entrainment` is a rule-based model created on Python to test and to demonstrate the 24h light/dark cycle [entrainment phenomenon](https://en.wikipedia.org/wiki/Entrainment_(chronobiology)).



## Prerequisites



You need to have some familiarity with the [Python programming language](https://www.python.org/) to use `entrainment` main functions.



In case you don't feel comfortable with Python, we strongly recommend checking Jake VanderPlas free and online book [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) and the Coursera course from Google [Crash Course on Python](https://www.coursera.org/learn/python-crash-course) (free for audit students).



## Installation



You can install `entrainment` from GitHub with:



```bash

pip install git+https://github.com/giperbio/entrainment.git#egg=entrainment

```



We don't intend to publish this package on [PyPI](https://pypi.org/).



## Usage



The following example illustrates how to run the model.



Please note that in this example all of the model arguments are assigned. You don't need to assign values to all arguments, you can just use the default values. Check [`run_model()`](https://entrainment.readthedocs.io/en/latest/reference.html#entrainment.run_model) documentation to learn more.



```{python}

#| label = "setup-python",

#| include = FALSE



import numpy as np



np.random.seed(1000)

```



```{python}

#| label = "usage-example",

#| results = FALSE,

#| fig.alt = "24h light/dark cycle entrainment of a population located at the south of Brazil by season"



import entrainment



model = entrainment.run_model(

    n = 10**3, # Number of subjects/turtles to create

    tau_range = (23.5, 24.6), # Limits for assigning 'Tau' values

    tau_mean = 24.15, # Mean value for the 'Tau' distribution

    tau_sd = 0.2, # Standard deviation value for the 'Tau' distribution

    k_range = (0.001, 0.01), # Limits for assigning the 'k' values

    k_mean = 0.001, # Mean value for the 'k' distribution

    k_sd = 0.005, # Standard deviation value for the 'k' distribution

    lam_c = 3750, # Critical 'lambda' value

    labren_id = 1, # LABREN id of the global horizontal irradiation means

    by = "season", # Series resolution (choices: "month", "season", "year")

    n_cycles = 3, # Number of cycles to run

    start_at = 0, # Index number indicating the start of the series

    repetitions = 10**2, # Number of repetitions

    plot = True, # Activate or deactivate the plot output

    show_progress = True # Activate or deactivate verbose mode

    )

```



You can learn more about the available functions going to the [package documentation website](https://entrainment.readthedocs.io).



## Citation



If you use `entrainment` in your research, please consider citing it. We put a lot of work to build and maintain a free and open-source Python package. You can find the citation below.



``` 

Vartanian, D. (2023). {entrainment}: rule-based model of the 24h light/dark cycle entrainment phenomenon. Python package version 0.0.0.9000. https://github.com/giperbio/entrainment

```



A BibTeX entry for LaTeX users is



```

@Unpublished{,

    title = {{entrainment}: rule-based model of the 24h light/dark cycle entrainment phenomenon},

    author = {Daniel Vartanian},

    year = {2023},

    url = {https://github.com/giperbio/entrainment},

    note = {Python package version 0.0.0.9000},

}

```



## Contributing



We welcome contributions, including bug reports.



Take a moment to review our [Guidelines for Contributing](https://entrainment.readthedocs.io/en/latest/contributing.html).



## Acknowledgments



The initial development of `entrainment` was supported by a scholarship provided by the [University of Sao Paulo (USP)](http://usp.br/) (❤️).



This model was initially created for the [SCX5002 - Complex System I](https://uspdigital.usp.br/janus/Disciplina?tipo=D&sgldis=SCX5002&nomdis=&origem=C) class of the [Graduate Program in Modeling Complex Systems (PPG-SCX)](https://www.prpg.usp.br/pt-br/faca-pos-na-usp/programas-de-pos-graduacao/621-modelagem-de-sistemas-complexos) of the [University of Sao Paulo (USP)](https://www5.usp.br/), under the guidance of [Prof. Dr. Camilo Rodrigues Neto](https://orcid.org/0000-0001-6783-6695).







Become an `entrainment` supporter!



Click [here](https://github.com/sponsors/danielvartan) to make a donation. Please indicate the `entrainment` package in your donation message.