# PyDaddy (Python Data-Driven Dynamics)

PyDaddy is a comprehensive and easy to use python package to discover data-derived stochastic differential equations from time series data. PyDaddy takes the time series of state variable \(x\), scalar or 2-dimensional vector, as input and discovers an SDE of the form:

where \(\eta(t)\) is uncorrelated white noise. The function \(f\) is called the *drift*, and governs the deterministic part of the dynamics. \(g^2\) is called the *diffusion* and governs the stochastic part of the dynamics.

PyDaddy also provides a range of functionality such as equation-learning for the drift and diffusion functions using sparse regresssion and a suite of diagnostic functions.

## Get Started

To take PyDaddy for a walk, see the tutorial notebooks. The notebooks can be executed online on Google Colab; no installation is necessary!

To install PyDaddy on your system, see installation instructions.

See the usage guide and advanced usage tips for detailed instructions and tips on how to use PyDaddy.

## Citation

If you are using this package in your research, please cite the associated paper as follows:

Nabeel, A., Karichannavar, A., Palathingal, S., Jhawar, J., Brückner, D. B., Danny Raj, M., & Guttal, V., “Discovering stochastic dynamical equations from ecological time series data”, arXiv preprint arXiv:2205.02645, to appear in *The American Naturalist*.