  Machine learning algorithms are highly useful for the classification of time
series data in astronomy in this era of peta-scale public survey data releases.
These methods can facilitate the discovery of new unknown events in most
astrophysical areas, as well as improving the analysis of samples of known
phenomena. Machine learning algorithms use features extracted from collected
data as input predictive variables. A public tool called Feature Analysis for
Time Series (FATS) has proved an excellent workhorse for feature extraction,
particularly light curve classification for variable objects. In this study, we
present a major improvement to FATS, which corrects inconvenient design
choices, minor details, and documentation for the re-engineering process. This
improvement comprises a new Python package called "feets", which is important
for future code-refactoring for astronomical software tools.
