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feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).
This code is inspired on Zackay & Ofek 2017 series of papers "How to coadd images?". It can perform the statistical proper-coadd of several images using a spatially variant PSF estimation, and also difference image analysis by several strategies based on the Zackay, Ofek and Gal-Yam methodology on "Proper image subtraction - optimal transient detection, photometry and hypothesis testing". It is Open Source, and is written in Python Programming Language, is object oriented and extendable.
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.