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SunPy - Python for Solar Physics | | SunPy Community
; Stuart J Mumford
; Steven Christe
; David Pérez-Suárez
; Jack Ireland
; Albert Y Shih
; Andrew R Inglis
; Simon Liedtke
; Russell J Hewett
; Florian Mayer
; Keith Hughitt
; Nabil Freij
; Tomas Meszaros
; Samuel M Bennett
; Michael Malocha
; John Evans
; Ankit Agrawal
; Andrew J Leonard
; Thomas P Robitaille
; Benjamin Mampaey
; Jose Iván Campos-Rozo
; Michael S Kirk
; | | Date: |
11 May 2015 | | Abstract: | This paper presents SunPy (version 0.5), a community-developed Python package
for solar physics. Python, a free, cross-platform, general-purpose, high-level
programming language, has seen widespread adoption among the scientific
community, resulting in the availability of a large number of software
packages, from numerical computation (NumPy, SciPy) and machine learning
(scikit-learn) to visualisation and plotting (matplotlib). SunPy is a
data-analysis environment specialising in providing the software necessary to
analyse solar and heliospheric data in Python. SunPy is open-source software
(BSD licence) and has an open and transparent development workflow that anyone
can contribute to. SunPy provides access to solar data through integration with
the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase
(HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It
currently supports image data from major solar missions (e.g., SDO, SOHO,
STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and
PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe
SunPy’s functionality, provide examples of solar data analysis in SunPy, and
show how Python-based solar data-analysis can leverage the many existing tools
already available in Python. We discuss the future goals of the project and
encourage interested users to become involved in the planning and development
of SunPy. | | Source: | arXiv, 1505.2563 | | Services: | Forum | Review | PDF | Favorites | |
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