crispy: A Python Package for Using Imaging Spectropolatimetric Data in Solar Physics

Author: John A. Armstrong, Univeristy of Glasgow

The following repository contains the source code for the crispy Python package, aptly named due to my working with the CRisp Imagining SpectroPolarimeter (CRISP) instrument mounted at the Swedish 1-m Solar Telescope (SST) in La Palma, Spain. (However, the code with work any imaging spectropolarimetric data regardless of instrument so long as it follows normal FITS standards or a custom zarr standard I have grown accustomed to using, see the docs for more details).

Why does this exist?

crispy started its life as the base code for data viewing/augmentation/analysis for my PhD as the tools for data exploration and exploitation of this kind of solar physics data did not previously exist.

What is imaging spectropolarimetric data?

The type of data that crispy is built to deal with is optical imaging spectropolarimetry data. This kind of data consists of measurements of a number of Stokes profiles at specific narrowband wavelength points over an extended field-of-view over a given length of time. What this means is that we have five-dimensional data structures ordered (t, stokes, λ, y, x). This kind of data is very powerful for exploring the lower solar atmopshere due to having relatively high spectral, polarimetric and spatial resolutions. The time resolution can be not shabby too.

How do I get this moderately cool Python package?

crispy can be found on PyPI using:

pip install sst-crispy

or can be installed from source using:

git clone https://github.com/bionic-toucan/crispy
cd crispy
python setup.py install --user

Acknowledgements

I would like to thank Chris Osborne (https://github.com/Goobley) for the improved rotation algorithm used for data rotated in the images plane to coincide with the helioprojective plane and also for listening to me complaining all the time and telling me about the wonders of properties.