MAVIS: The preliminary data reduction pipeline

We outline the two workflows used for the reduction of science data from the MCAO Assisted Visible Imager and Spectrograph (MAVIS), and describe the inputs, outputs, and static calibration files required for each process of the workflows. Ronchi masks and pinhole masks are used in combination to determine the geometry of the spectrograph slices, and wavelength calibrations will be enhanced with Etalons. The precision required for the Imager astrometry is obtained by the mid-spatial frequency distortion calibrations. To prototype these complex methods and to test the efficacy of pixel tables and error handling we are using the new ESO PyCPL and PyHDRL libraries, which provide an interface to ESO’s classic Common Pipeline Library (CPL) in the Python ecosystem.