Stuart Mumford
The University of Sheffield

David Pérez-Suárez
Finnish Meteorological Institute

Steven Christe
NASA Goddard Space Flight Center

Florian Mayer
Vienna University of Technology

Russell J. Hewett
Massachusetts Institute of Technology

Abstract

SunPy is a data analysis toolkit which provides the necessary software for analyzing solar and heliospheric datasets in Python. SunPy aims to provide a free and open-source alternative to the current standard, an IDL-based solar data analysis environment known as SolarSoft (SSW). We present the latest release of SunPy, version 0.3. Though still in active development, SunPy already provides important functionality for solar data analysis. SunPy provides data structures for representing the most common solar data types: images, lightcurves, and spectra. To enable the acquisition of scientific data, SunPy provides integration with the Virtual Solar Observatory (VSO), a single source for accessing most solar data sets, and integration with the Heliophysics Event Knowledgebase (HEK), a database of transient solar events such as solar flares or coronal mass ejections. SunPy utilizes many packages from the greater scientific Python community, including NumPy and SciPy for core data types and analysis routines, PyFITS for opening image files, in FITS format, from major solar missions (e.g., SDO/AIA, SOHO/EIT, SOHO/LASCO, and STEREO) into WCS-aware map objects, and pandas for advanced time-series analysis tools for data from missions such as GOES, SDO/EVE, and Proba2/LYRA, as well as support for radio spectra (e.g., e-Callisto). Future releases will build upon and integrate with current work in the Astropy project and the rest of the scientific python community, to bring greater functionality to SunPy users.

Keywords

Python, Solar Physics, Scientific Python

DOI

10.25080/Majora-8b375195-00c

Bibtex entry

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Copyright © 2013 Stuart Mumford et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.