entrainment
is a rule-based model created on Python to test and to
demonstrate the 24h light/dark cycle entrainment
phenomenon.
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).
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.
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.
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@Unpublished{,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},
}
We welcome contributions, including bug reports.
Take a moment to review our Guidelines for Contributing.
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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supporter!
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message.