Tutorials (no installation required)

The following tutorial notebooks will familiarize you with the various functionalities of PyDaddy. These notebooks can be executed on Google Colab by clicking the ‘Open in Colab’ buttons (no installation required).


While executing the tutorial notebooks on Colab, do not forget to execute the cell saying:

%pip install git+https://github.com/tee-lab/PyDaddy.git

This sets up PyDaddy on your Colab environment.

You can also download these notebooks from the GitHub repo.. To download the notebooks, click on the github buttons to open the notebook. Right-click on the ‘Raw’ button to and click ‘Save Linked File…’ to save the file to your computer.

Getting started with PyDaddy

colab-getting-started github-getting-started

This notebook introduces the basic functionalities of PyDaddy, using a 1-dimensional dataset. The notebook explores how to visually inspect drift and diffusion functions, how to fit analytical expressions to them, and how to use the various diagnostic tools provided.

Getting started with vector data

colab-vector github-vector

PyDaddy also works with 2-dimensional vector data. This notebook demonstrates PyDaddy operation with a vector dataset.

Recovering SDEs from synthetic time series

colab-synthetic github-synthetic

This notebook generates a simulated time series from a user-specified SDE, and uses PyDaddy to recover the drift and diffusion functions from the simulated time series. Use this notebook to play around with the PyDaddy fitting procedure and gain insights.

Advanced function fitting

colab-advanced-fitting github-advanced-fitting

PyDaddy can discover analytical expressions for the drift and diffusion functions. This notebook describes how to customize the fitting procedure to obtain best results.

Exporting data

colab-exporting github-exporting

This notebook demonstrates how to export the recovered drift and diffusion data as CSV files or Pandas data-frames.

Fitting non-polynomial functions (Experimental)

colab-nonpoly-fitting github-nonpoly-fitting

By default, PyDaddy fits polynomials for the drift and diffusion functions. However, this behaviour can be customized by providing a custom library of functions for the sparse regression procedure, this notebook demonstrates how to do this. (This is an experimental feature and not all functionality will work with non-polynomial functions).

Example analysis: Fish schooling

colab-fish github-fish

An example analysis of a fish schooling dataset (Jhawar et. al., Nature Physics, 2020) using PyDaddy.

Example analysis: Cell hopping

colab-cell github-cell

An example analysis of a confined cell migration dataset (Brückner et. al., Nature Physics, 2019) using PyDaddy.