Overview

We use Django and Django Rest Framework for build our backend API.

The goodman-pipeline package processes are very granulated as it should be. But for a web application such as ours we found out that the best option was to group many of the processes in blocks as described below. But in an observing run there are many types of files. We have calibration and science files.

File Types

Calibration files:

Calibration files are BIAS and FLAT files. They need to be combined into master BIAS and master FLATs. Each proposal has also associated a dedicated data reduction settings and there can be defined, among other things, what are the minimum number of calibration files required to trigger a combination of a master BIAS or FLAT.

Science files:

Science files are EXPOSE SPECTRUM and ARC though one can argue that ARC are calibration files, in a general sense they are treated as science files in this case.

Process Blocks

Reduce

Identifies suitable master flats and master bias and applies the basic data reduction process. For spectroscopic data it also trims the non-illuminated areas which produce artifacts. In general the process looks like this:

An EXPOSE files, which is the type given to imaging data can have the following process.

Image Trim → Bias Subtract → Flatfielding → Cosmic Ray Removal

An SPECTRUM file will have the following:

Image Trim → Slit Trim → Bias Subtract → Flatfielding → Cosmic Ray Removal

Extract

Identifies targets in a SPECTRUM file and finds a suitable and compatible ARC lamps and performs fractional pixel extractions.

Identify → Trace → Extract

Wavelength Calibration

Using the extracted lamps it performs the wavelength calibration process using a template lamp from a library and series of cross correlations, then fits a mathematical model, resamples the spectrum to a linear dispersion axis and saves the file.

Flux Calibration

This is not implemented yet but it will.

Astrometric Solution

This functionality is under development.