This repository contains the code used to generate the experimental data, one run of experimental data, and the analysis code used in "Safe Handling Instructions for Missing Data", a paper to be presented at the 2018 meeting of the Python in Science Conferences (SciPy 2018).
- Install the libraries at their pinned versions (found in requirements.txt) into a virtual environment.
- Open the analysis notebook with
jupyter notebook analysis.ipynb - The notebook is pre-populated with the output from the last time it was executed. You are free to modify the code in the notebook and re-run it to see how it changes the output of the analysis.
To re-run the experiment which generated the data, run python make_data.py. Use the -h flag to see experimental parameters that you can set. Be warned that, depending on which options you choose, the total runtime of the experiment may extend beyond several days.