Abstract: Corral generates astronomical pipelines. Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. Written in Python, Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling custom data models, processing stages, and communication alerts. It also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities.
Credit: Cabral, Juan; Sanchez, Bruno; Beroiz, Martin; Dominguez, Mariano; Lares, Marcelo; Gurovich, Sebastian; Granitto, Pablo
Preferred citation method: https://ui.adsabs.harvard.edu/#abs/2017A&C....20..140C