We describe the Metrics Analysis Framework (MAF), an open-source python framework developed to provide a user-friendly, customizable, easily-extensible set of tools for analyzing data sets. MAF is part of the Large Synoptic Survey Telescope (LSST) Simulations effort. Its initial goal is to provide a tool to evaluate LSST Operations Simulation (OpSim) simulated surveys to help understand the effects of telescope scheduling on survey performance, however MAF can be applied to a much wider range of datasets. The building blocks of the framework are Metrics (algorithms to analyze a given quantity of data), Slicers (subdividing the overall data set into smaller data slices as relevant for each Metric), and Database classes (to access the dataset and read data into memory). We describe how these building blocks work together, and provide an example of using MAF to evaluate different dithering strategies. We also outline how users can write their own custom Metrics and use these within the framework.

Date Published: 28 July 2014
PDF: 18 pages
Proc. SPIE 9149, Observatory Operations: Strategies, Processes, and Systems V, 91490B (28 July 2014); doi: 10.1117/12.2056835

Published in SPIE Proceedings Vol. 9149:
Observatory Operations: Strategies, Processes, and Systems V
Alison B. Peck; Chris R. Benn; Robert L. Seaman, Editor(s)