Peter Yoachim ; Srinivasan Chandrasekharan ; Andrew J. Connolly ; Kem H. Cook ; Željko Ivezic ; K. S. Krughoff ; Catherine Petry ; Stephen T. Ridgway
Abstract:
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.
http://adsabs.harvard.edu/abs/2014SPIE.9149E..0BJ
Bibtex reference:
@INPROCEEDINGS{2014SPIE.9149E..0BJ,
author = {{Jones}, R.~L. and {Yoachim}, P. and {Chandrasekharan}, S. and
{Connolly}, A.~J. and {Cook}, K.~H. and {Ivezic}, {\v Z}. and
{Krughoff}, K.~S. and {Petry}, C. and {Ridgway}, S.~T.},
title = "{The LSST metrics analysis framework (MAF)}",
booktitle = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
year = 2014,
series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series},
volume = 9149,
month = jul,
eid = {91490B},
pages = {91490B},
doi = {10.1117/12.2056835},
adsurl = {http://adsabs.harvard.edu/abs/2014SPIE.9149E..0BJ},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
