/sospice

Python data analysis tools for the SPICE extreme-UV spectrometer on Solar Orbiter

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

sospice: Python data analysis tools for Solar Orbiter/SPICE

Latest version on PyPI Documentation Status PyPI - Python Version CI

SPICE is an extreme-UV imaging spectrometer on board the Solar Orbiter mission. A generic SPICE data analysis user’s manual (including Python and IDL tips) is available on the IAS wiki.

sospice is intended to be a simple way of accessing all necessary instrument-specific functionalities required for day-to-day SPICE data analysis, in complement to more generic Python packages such as sunpy and sunraster.

This package is in its early stages of development. Please see the issues to see how you can contribute.

Documentation for this package is available on Read the Docs.

sospice functionalities

  • Calibration: calibrate

    • spice_error: Computation of uncertainties on data, coming from different noise components.

  • Catalog: catalog

    • Catalog: access and read catalog, find files in catalog.
    • Release: find and access releases.
    • FileMetadata: file metadata and download.

  • Instrument modelling: instrument_modelling

    • Spice: instrument calibration parameters, effective area, quantum efficiency...
    • Study: study parameters.
    • Observation: a SPICE observation with some study (including low-level functions used to compute the uncertainties on the data).

  • Other utilities: util

    • sigma_clipping: sigma clipping (for cosmic rays removal).
    • fov: plot SPICE field-of-views on a background map.

Package philosophy

We want sospice to be:

  • Convenient to install. It is installable by pip and it is published on PyPI
  • Useful, providing a single package for all SPICE-specific steps of your data analysis.
  • Easy to use, with simple interface functions to operations performed by lower-level functions.
  • Well documented. We use sphinx to build documentation from the Python docstrings.
  • Thoroughly tested. We use pytest and aim at a high test coverage ratio. Tests are run automatically with Github actions.
  • Well integrated in the SunPy ecosystem. In the long term, we aim at getting the SunPy affiliated package status.

Contributions from the community are welcome, in particular as issues or pull requests.

Citation

See the citation file.