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SEPpy

Project description

pypi Version python version pytest codecov docs Project Status: Active – The project has reached a stable, usable state and is being actively developed. zenodo doi

This package is in development status! Intended for internal use only, as the syntax is in a floating state and the documentation is incomplete.

A compendium of Python data loaders and analysis tools for in-situ measurements of Solar Energetic Particles (SEP)

So far combines loaders for the following instruments into one PyPI package:

  • BepiColombo: SIXS-P

  • Parker Solar Probe: ISOIS

  • SOHO: CELIAS, COSTEP-EPHIN, ERNE

  • Solar Orbiter: EPD (STEP, EPT, HET)*, MAG

  • STEREO: HET, LET, SEPT, MAG

  • Wind: 3DP

(* Note that solo-epd-loader is a PyPI package itself that just is loaded here for completeness.)

Disclaimer

This software is provided “as is”, with no guarantee. It is no official data source, and not officially endorsed by the corresponding instrument teams. Please always refer to the official data description of each instrument before using the data!

Installation

seppy requires python >= 3.10.

It can be installed from PyPI using:

pip install seppy

Usage

The standard usecase is to utilize the ***_load function, which returns Pandas dataframe(s) of the corresponding measurements and a dictionary containing information on the energy channels. For example the SOHO/ERNE data from Apr 16 to Apr 20, 2021, can be obtained as follows:

from seppy.loader.soho import soho_load

df, meta = soho_load(dataset="SOHO_ERNE-HED_L2-1MIN",
                     startdate="2021/04/16",
                     enddate="2021/04/20")

Note that the syntax is different for each loader! Please refer to this Notebook for more info and examples for the different data sets!

Citation

Please cite the following paper if you use seppy in your publication:

Palmroos, C., Gieseler, J., Dresing, N., Morosan, D.E., Asvestari, E., Yli-Laurila, A., Price, D.J., Valkila, S., Vainio, R. (2022). Solar Energetic Particle Time Series Analysis with Python. Front. Astronomy Space Sci. 9. doi:10.3389/fspas.2022.1073578

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