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A Jupyter notebook that simulates a Lotka-Volterra predator-prey system, investigating finite observational principles by calculating the weight per process and classifying it as relevant or irrelevant based on the user's observational window.

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Transient Dynamics Through Observational Principle Normalization

WHAT

transient-dynamics.ipynb is a Jupyter notebook that generates a simulation of a Lotka-Volterra predator-prey system, investigating finite observational principles by calculating the weight per process and classifying it as relevant or irrelevant based upon the inherent observational window set by the user. The inputs for the notebook involve the characteristics of the phage and bacteria population, the observer's observational time, and the ability to perceive change (observer's tolerance). The outputs of the notebook include population, weight, and relative error figures, alongside an Excel file output for all array-like variables produced during analysis and a text file for the float variables produced in calculations.

The quickest way to use transient-dynamics.ipynb is by running it in a Google Colab notebook in the cloud, requiring only a Google account. It can simulate scenarios and can also be run within Conda environments or on Visual Studio Code to simulate the scenarios.

The methodology and application of transient-dynamics.ipynb are available in a published pre-print on the bioRxiv pre-print server:

conceptual_figure

Sergio Cobo-López, Matthew Witt, Lucas J. Carbajal, Forest L. Rohwer, Antoni Luque. "Emerging dynamic regimes and tipping points from finite empirical principles." bioRxiv 2023.12.27.573307; doi: https://doi.org/10.1101/2023.12.27.573307

Below, we provide a basic description of the notebook and repository. We recommend visiting transient-dynamics.ipynb's online documentation for a more detailed guide to using the notebook.

License: transient-dynamics.ipynb is under an MIT license. If you use transient-dynamics.ipynb on any scientific or scholarly work, please cite the above reference to support this academic and open-source initiative from the Luque lab.

CONTRIBUTORS

This tool was produced by @luquelab, inspired by the original manuscript's repository (needle-finder) by Sergio Cobo-López. The main contributors are Lucas J. Carbajal and Antoni Luque, with additional assistance and guidance provided by the rest of the members in the Luque Lab.

WHEN

This is an evolving repository Started: 2024-09-11

End: Ongoing

FILES & FOLDERS

FOLDER: /notebooks --> This folder contains the Jupyter notebook and former python script to run simulations on Lotka-Volterra phage-bacteria model.

FOLDER: /data --> This folder contains the raw data associated with the project and the potential references.

FOLDER: /docs --> This folder contains the manuscript, digital copies of the cited references, a tutorial file, and other associated files for publication.

FOLDER: /results --> This folder contains the results, compiled and post-production edited figures of the project.

The syntax of markdown files (.md) is CommonMark unless specified otherwise (https://commonmark.org/help/)

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A Jupyter notebook that simulates a Lotka-Volterra predator-prey system, investigating finite observational principles by calculating the weight per process and classifying it as relevant or irrelevant based on the user's observational window.

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