This package is not maintained any more! Please use https://github.com/serpentine-h2020/SEPpy instead!

soho-loader

Python data loader for SOHO charged particle instruments. At the moment provides released data obtained by SunPy through CDF files from CDAWeb for the following datasets:

• 'SOHO_CELIAS-PM_30S': SOHO CELIAS-PM 30 second data (Info)
• 'SOHO_CELIAS-SEM_15S': SOHO CELIAS-SEM 15 second data (Info)
• 'SOHO_COSTEP-EPHIN_L2-1MIN': SOHO COSTEP-EPHIN Level2 intensity 1 minute data (Info) [1]
• 'SOHO_COSTEP-EPHIN_L3I-1MIN': SOHO COSTEP-EPHIN Level3 intensity 1 minute data (Info)
• 'SOHO_ERNE-LED_L2-1MIN': SOHO ERNE-LED Level2 1 minute data (Info)
• 'SOHO_ERNE-HED_L2-1MIN': SOHO ERNE-HED Level2 1 minute data (Info)

[1]	SOHO COSTEP-EPHIN Level2 intensity 1 minute data is directly obtained through ASCII files from the server of the instrument team, not via SunPy/CDAWeb.

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 instrument descriptions before using the data!

Usage

The standard usecase is to utilize the soho_load function, which returns Pandas dataframe(s) of the measurements.

from soho_loader import soho_load
import datetime as dt

df, meta = soho_load(dataset="SOHO_ERNE-HED_L2-1MIN",
                     startdate=dt.datetime(2021, 4, 16),
                     enddate="2021/04/20",
                     path=None,
                     resample="1min",
                     pos_timestamp=None,
                     max_conn=5)

Input

• dataset: 'SOHO_CELIAS-PM_30S', 'SOHO_CELIAS-SEM_15S', 'SOHO_COSTEP-EPHIN_L2-1MIN', 'SOHO_COSTEP-EPHIN_L3I-1MIN', 'SOHO_ERNE-LED_L2-1MIN', or 'SOHO_ERNE-HED_L2-1MIN'. See above for explanation.
• startdate, enddate: datetime object or "standard" datetime string
• path: String, optional. Local path for storing downloaded data, e.g. path='data/soho/'. By default None. Default setting saves data according to sunpy's Fido standards. The default setting can be changed according to the corresponding sunpy documentation, where the setting that needs to be changed is named download_dir (e.g., one could set it to a shared directory on a multi-user system).
• resample: Pandas frequency (e.g., '1min' or '1h'), or None, optional. Frequency to which the original data is resamepled. By default None.
• pos_timestamp: String, optional. Change the position of the timestamp: 'center' or 'start' of the accumulation interval, by default None.
• max_conn: Integer, optional. The number of parallel download slots used by Fido.fetch, by default 5.

Return

• Pandas dataframe and dictionary of metadata (e.g., energy channels). See info links above for the different datasets for a description of the dataframe columns.

Caveats

From the SOHO COSTEP-EPHIN Level2 intensity 1 minute data ('SOHO_COSTEP-EPHIN_L2-1MIN'), only electron data is returned at the moment. The proton and Helium measurements are not provided, see #3 for more details.

Data folder structure

• COSTEP-EPHIN Level2: All data files are automatically saved in a data subfolder in the current working directory if path is not defined.
• All other instruments: All data files are automatically saved in a sunpy subfolder of the current user home directory if path is not defined.

Combine intensitiy for multiple energy channels (ERNE only)

For ERNE measurements, it's possible to combine the intensities of multiple adjacent energy channels with the function calc_av_en_flux_ERNE. It returns a Pandas Dataframe with the arithmetic mean of all intensities and a string providing the corresponding energy range. The following example demonstrates how to build an average channel of ERNE proton energy channels 2 to 8.

from soho_loader import soho_load, calc_av_en_flux_ERNE

# first, load original data:
df, meta = soho_load(dataset="SOHO_ERNE-HED_L2-1MIN",
                     startdate="2021/04/16",
                     enddate="2021/04/20",
                     path=None,
                     resample="1min",
                     pos_timestamp=None)

# define energy channel range that should be combined:
combine_channels = [2, 8]
erne_avg_int, erne_avg_chstring = calc_av_en_flux_ERNE(df,
                                                       meta['channels_dict_df_p'],
                                                       combine_channels,
                                                       species='p',
                                                       sensor='HET')
print(erne_avg_chstring)

License

This project is Copyright (c) Jan Gieseler and licensed under the terms of the BSD 3-clause license. This package is based upon the Openastronomy packaging guide which is licensed under the BSD 3-clause license. See the licenses folder for more information.

Acknowledgements

The development of this software has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004159 (SERPENTINE).