GSoC 2014 ideas
A ginga based data explorer / database browser
In IDL, there are two major sources of GUIs for exploring solar data, Solarsoft packages and CRISPEX. Both are using IDL's widget system whose back-end is Motif and make ugly GUIs. Exploring solar data for interesting events and extracting important information is important for being able to do research.
Currently, such a GUI program does not exist for Python. However, with the current capabilities of Ginga, it offers a very good base. It allows us to expand to meet the requirements for solar data. Ginga has already several features such as FITS support and several plugins that allow basic analysis of data. It also supports several back-ends.
Ginga is designed to use a plugin system. This enables SunPy specific plugins to be written that can be installed by any user. We would want to create several plugins that would allow Ginga to be widely spread throughout the solar community.
We would want a plugin that uses SunPy's database explorer module and supporting Solar coordinates using AstroPy's WCS module. Further, we want to expand other Ginga plugins like the slit plugin to work on 3D data to produce x-y plots.
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Requirements: Python, Qt/GTK (depending on back-end choice)
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Difficulty: Medium to Hard
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Mentor: Stuart Mumford, Steven Christe
Coordinates for solar image data are described in Thompson, W. (2009). Currently a subset of these systems and transformation are implemented in sunpy.wcs. Astropy, a core package for astronomy, has a flexible spatial coordinates package in astropy.coordinates. However, this package is currently in the planning phase of a large re-design, which will make it even more applicable for use for solar coordinate data. Astropy Proposal for Enhancement (APE) number 5 APE5 describes the proposed API. It is anticipated that this code will form part of the Astropy 0.4 release scheduled for June/July 2014, and therefore be implemented well before this point.
This project involves using the CoordinateFrame and CoordinateRepresentation framework described in APE5 to implement Helio-Projective and Helio-Centric CoordinateFrames that can be transformed into Cartesian, Spherical and Cylindrical representations and between each other using the bi-directional transformation graph in astropy.coordinates. This project will involve collaboration with the astropy project and potentially pushing patches upstream.
Extensions to this project would be to implement transformations from solar coordinates to other celestial coordinates as supported by astropy. As well as work on integrating astropy.units into the SunPy code base.
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Requirements: Python
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Difficulty: Medium
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Mentor: Stuart Mumford, Steven Christe
Lightcurves are a major datatype in solar physics. A lightcurve is a time-ordered list of scalar measurements. A typical lightcurve consists of observation times, and measurements taken at that time. These measurements could be numeric, such as the amount of soft X-ray flux from the Sun (arising from GOES data), or some kind of string, for example, the Mt. Wilson classification of an active region as a function of time. The current LightCurve object implementation uses the pandas library, since pandas has very many useful capabilities for handling time-ordered data. However, the current implementation needs to be considered in the light of the astropy Time module and the astropy Table module.
As well as the integration of astropy into the lightcurve module, it is important to create a Lightcurve factory class, like Map() see LightCurve Refactor Proposal for details on the proposed factory implementation. There are more challenges faced by the Lightcurve factory than by the Map factory, due to the diverse nature of the Lightcurve data files both for retrieval from online sources and parsing of the CSV files. Having the Lightcurve API follow that of Map is a fundamental step towards uniting the data types in the core library.
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Requirements: Knowledge of, or willingness to learn, the pandas and astropy libraries
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Difficulty: Medium
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Mentor: J. Ireland, A. R. Inglis, Stuart Mumford
A large amount of solar physics data analysis is image processing based. Features in scikit-image such as feature detection and tracking as well as image warping and transformation. There are a few sub-projects under this project idea that could be tackled and expanded by a student.
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Looking into the differences between
sunpy.image.Crotateandskimage.transformspecifically theAffineTransformtype code. The motivation for this is using scikit-image rather than maintaining our own C extension code would make the SunPy code base easier to maintain as well as providing more features. As part of this work a patch to issue #741 could be devised. -
Implement a fine alignment routine to be added to the
MapCubeAPI, to allow the stacking of various images. This is made possible by new features recently added to scikit-image https://github.com/scikit-image/scikit-image/pull/834 -
The outer layers of the Sun do not rotate as a solid body, the equatorial regions rotate faster than the poles. When considering analysis of surface features on the Sun compensating for this rotation is essential. This project would involve finishing work already started on this and understanding the physical processes behind the computational algorithms.
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Requirements: Python
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Difficulty: Medium to Hard
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Mentor: Albert Shih, David Perez-Suarez
SunPy has already the capability to access to the Heliophysics Events Catalogue provided by the HELiophysics Integrated Observatory. This catalogue provides access to different lists of events observed anywhere in the heliosphere. However, HELIO offers a lot more web services of interest for the solar community. Some of the services can provide information of when and where a planet or instrument where located, properties on features detected on the sun, or properties of some heliospheric observations; moreover it also allow the discovery of new data by a propagation model which simulates three different scenarios - Coronal Mass Ejections, High speed solar wind and Solar Energetic Particles events.
This project would consist in the creation of an interface to access to HELIO services in a similar way that other services like HEK or VSO are accessible at the moment. HELIO uses VOTables as the standard to transfer the data, astropy provides support for reading such file format, therefore some understanding of astropy may be needed.
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Requirements: All the services from HELIO are through webservices (SOAP, REST), thus some understanding on that would be beneficial. VOTable parser provided from astropy will be used, so familiarization with such interface will be helpful.
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Difficulty: Medium
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Mentors: David Perez-Suarez, (Marco Soldati, Kevin Benson - HELIO)
Spectroscopy data are usually stored in 3 dimensional arrays (x,y, lambda; or time, y, lambda) with multiple windows (different spectral ranges). The implementation of this data type on SunPy is essential as these datasets are the one which most information contain, like Intensity at different wavelengths, turbulence in the medium, and speed of the plasma. Besides, other information such as temperature or electron density of the plasma can also be obtained. The project will consist in work over the hypermap class and the astropy efforts to be able to handle this dataset, and to make it to interoperate with other data objects like maps (overplot the images), time-series and spectra.
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Requirements: FITS dataformat file understanding, read EIS, CDS data documentation (link?)
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Difficulty: Hard
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Mentors: David Perez-Suarez, Albert Shih
