  Nulling interferometry is a powerful observing technique to reach exoplanets
and circumstellar dust at separations too small for direct imaging with
single-dish telescopes and too large for indirect methods. With near-future
instrumentation, it bears the potential to detect young, hot planets near the
snow lines of their host stars. A future space mission could detect and
characterize a large number of rocky, habitable-zone planets around nearby
stars at thermal-infrared wavelengths. The null self-calibration is a method
aiming at modelling the statistical distribution of the nulled signal. It has
proven to be more sensitive and accurate than average-based data reduction
methods in nulling interferometry. This statistical approach opens the
possibility of designing a GPU-based Python package to reduce the data from any
of these instruments, by simply providing the data and a simulator of the
instrument. GRIP is a toolbox to reduce nulling and interferometric data based
on the statistical self-calibration method. In this article, we present the
main features of GRIP as well as applications on real data.
