Code for ``Unifying pairwise interactions in complex dynamics''

This repository illustrates how the figures in the paper, ``Unifying pairwise interactions in complex dynamics'', were created.

We provide both precomputed CSV files, with which to recreate the figures, as well as scripts to generate these CSVs from scratch.

Download pyspi and create an environment

First, download pyspi and create a conda environment to install the package as per the documentation. In linux, this involves the following steps from a terminal (in your desired directory):

git clone [email protected]:olivercliff/pyspi.git
cd pyspi
conda create -n pyspi python=3.6.7
conda activate pyspi

NOTE: Python version 3.6.7 should only be used for the legacy pynats branch. If using the latest pyspi, please create a conda environment with python=3.9.

You will also likely need to download and install octave; follow the instructions here.

Switch to the pynats branch and install

The pynats branch was retroactively added to the pyspi repository as legacy code that was used to generate the results from the main paper. If you would like to replicate the results from the paper as closely as possible, I would recommend checking out the pynats branch, which is also available as a release. However, given this work was not computed via a container, such as docker, the results may vary slightly from those originally reported.

In the pyspi folder and environment that were created above, checkout and install the pynats branch (this will take a while):

git checkout pynats
pip install .

Generating the figures

This repository regenerates Figs. 2 and 3 of the main paper, and a number of supporting figures from the appendix. Because of the large amount of processing required to generate these figures from scratch, we have provided pre-computed CSV files (in the data directory) from which you can easily regenerate the figures using the generate_figures.ipynb notebook.

Re-generating results for figures

In order to re-compute the CSV files from the raw MTS data, we have provided three scripts:

• process_mts_database.py, which computes all SPIs for each of the 1053 MTS datasets in the database;
• process_uea_data.py, which computes all SPIs for each of the 40 MTS datasets in the UEA BasicMotions dataset for the classification study;
• classify.py, which uses the precomputed SPIs for the UEA dataset (i.e., the output of process_uea_data.py) for classifying the time series.

Once each of these scripts have been executed, re-run the generate_figure.ipynb notebook, pointing to the location of the new CSV files to regenerate the figures.

NOTE: The script process_mts_database.py was computed on a cluster, where each dataset was evaluated separately. If running this script locally, it will likely take several months to complete (and so I would advise using the pyspi distribute script to recompute all SPIs on all 1053 datasets, if necessary).