Decision landscapes are visually rich representations of decision making data. Any experimental data on mouse/hand tracking can potentially benefit from decision landscape visualisations (DLV).
PyDLV is currently developed in Python 3.6 using the numpy/scipy framework, pandas is used for data manipulation. Another key dependency is matplotlib.
To install, clone the repository and run
python setup.py install
To see DLV's in action, first download the data of O'Hora et al (2013) here. Then follow the tutorial notebook. Note that tutorial and other demos are not installed along with the module, so you have to download them separately. Place the tutorial notebook somewhere along the downloaded data, and open the notebook. Adjust the path to the data file, and have fun!
If you're not familiar with Jupyter Notebooks, here is a good starting guide. Also, nbopen is much recommended!
When you're done with tutorial, you can use the scripts in the demos directory as examples. You may also find useful the notebook with the figures from the paper.
The project is currently in active development, stay tuned for updates!
The paper reporting the method is publised in Royal Society Open Science: http://rsos.royalsocietypublishing.org/content/4/11/170482
- "Decision landscapes: visualizing mouse-tracking data" A. Zgonnikov, A. Aleni, P. T. Piiroinen, D. O'Hora, M. di Bernardo, R. Soc. open sci. 2017 4 170482; DOI: 10.1098/rsos.170482, 8 November 2017