The mid-IR instrument, CanariCam, is one of the first light instruments of the GTC. To achieve a rapid scientific payback in imaging and spectroscopy, and to support our planned AGN observations using CanariCam, we have developed a data reduction pipeline called “RedCan” to quickly reduce and analyze large amounts of data. Our primary goal is to produce a pipeline able to take a list of observations and reduce them with the minimum amount of user inputs. Therefore, little or no previous knowledge of mid-IR spectroscopic data reduction process are needed by the user. RedCan is able to perform:

(1) the identification of files (e.g. acquisition, standard or target) and automatic identification of the associated standard to the target according to proximity on sky and time of observation;
(2) flat-field correction;
(3) stacking of data, including the exclusion of bad chop-nods and registering;
(4) flux calibration of images and calculation the total flux of the brightest source for each stacked image;
(5) computation of slit losses when acquisition images are available;
(6) wavelength calibration using the sky spectra;
(7) trace determination of standard star and spectrum;
(8) extraction of spectra with the desired extraction aperture; and
(9) computation of the merged spectrum when more than one spectrum is available for the same source.

The input of the pipeline is essentially a text listing of fits files. Some inputs, such as the extraction method, aperture, or offset, can be provided by the user. RedCan is able to produce several extractions simultaneously with several apertures or offsets. RedCan is written by merging Python,IDL and C-Shell routines, and uses the astronomical IDL libraries and the Gemini IRAF routines under pyraf. It is easy to install and a manual is nearing completion. It has been tested with T-ReCs data and it is undergoing testing with CanariCam commissioning data. This presentation will explain how to install and use it, showing examples of the final products we can obtain.