Simulation data for replicating the results and figures of the study, "A general metric for the similarity of both stochastic and deterministic system dynamics"
This dataset allows for the replication of all results and figures appearing in the paper "A general metric for the similarity of both stochastic and deterministic system dynamics" forthcoming in the journal Entropy. The scripts for running all experiments and for generating figures can be found at https://github.com/jantzen/distance_metric, and code for the methods tested in these experiments is attainable through the PyPI package "eugene" (e.g., via "pip install eugene") or at https://github.com/jantzen/eugene. However, by themselves these scripts will not exactly reproduce the data we reported. This is because most of our numerical experiments involve a stochastic component such that one cannot generate exactly the same data twice. We have therefore preserved in this dataset the specific results of the experimental runs reported in the published study.
To replicate a figure from the paper, extract the files in the relevant zip archive into the same directory as the figure generation script, and rename the extracted data file folder "data". So, for example, to run the generate_figures.py script, download the generate_data.zip file and extract in the same directory as the figure script. Change the name of the resulting "generate_data" folder to "data", and then run "python3 generate_figures.py".
The codes at the GitHub repository links provided above, available on 13 September 2021, are included in this dataset as zip files: eugene-master.zip and distance_metric-main.zip
National Science Foundation Grant No.1454190
National Science Foundation Grant No.1708622
PublisherUniversity Libraries, Virginia Tech