entrainment

Overview

entrainment is a rule-based model created on Python to test and to demonstrate the 24h light/dark cycle entrainment phenomenon.

Prerequisites

You need to have some familiarity with the Python programming language 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 and the Coursera course from Google Crash Course on Python (free for audit students).

Installation

You can install entrainment from GitHub with:

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

We don’t intend to publish this package on PyPI.

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() documentation to learn more.

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.

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@Unpublished{,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.

Acknowledgments

The initial development of entrainment was supported by a scholarship provided by the University of Sao Paulo (USP) (❤️).

This model was initially created for the SCX5002 - Complex System I class of the Graduate Program in Modeling Complex Systems (PPG-SCX) of the University of Sao Paulo (USP), under the guidance of Prof. Dr. Camilo Rodrigues Neto.

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