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28 July 2014 The LSST metrics analysis framework (MAF)
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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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
R. Lynne Jones, Peter Yoachim, Srinivasan Chandrasekharan, Andrew J. Connolly, Kem H. Cook, Željko Ivezic, K. Simon Krughoff, Catherine Petry, and Stephen T. Ridgway "The LSST metrics analysis framework (MAF)", Proc. SPIE 9149, Observatory Operations: Strategies, Processes, and Systems V, 91490B (28 July 2014);


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