solo-epd-loader

|pypi Version| |conda version| |python version| |pytest| |codecov| |license| |zenodo doi|

.. |pypi Version| image:: https://img.shields.io/pypi/v/solo-epd-loader?style=flat&logo=pypi :target: https://pypi.org/project/solo-epd-loader/ .. |conda version| image:: https://img.shields.io/conda/vn/conda-forge/solo-epd-loader?style=flat&logo=anaconda :target: https://anaconda.org/conda-forge/solo-epd-loader/ .. |license| image:: https://img.shields.io/conda/l/conda-forge/solo-epd-loader?style=flat :target: https://github.com/jgieseler/solo-epd-loader/blob/main/LICENSE.rst .. |python version| image:: https://img.shields.io/pypi/pyversions/solo-epd-loader?style=flat&logo=python .. |zenodo doi| image:: https://zenodo.org/badge/446889843.svg :target: https://zenodo.org/badge/latestdoi/446889843 .. |pytest| image:: https://github.com/jgieseler/solo-epd-loader/workflows/pytest/badge.svg .. |codecov| image:: https://codecov.io/gh/jgieseler/solo-epd-loader/branch/main/graph/badge.svg?token=Z8dueEWqKS :target: https://codecov.io/gh/jgieseler/solo-epd-loader

Python data loader for Solar Orbiter's (SolO) Energetic Particle Detector (EPD) <http://espada.uah.es/epd/>. At the moment provides level 2 (l2) and low latency (ll) data (more details on data levels here <http://espada.uah.es/epd/EPD_data_overview.php>) obtained through CDF files from ESA's Solar Orbiter Archive (SOAR) <http://soar.esac.esa.int/soar>_ for the following sensors:

• Electron Proton Telescope (EPT)
• High Energy Telescope (HET)
• SupraThermal Electrons and Protons (STEP)

Current caveats:

• Only the standard rates data products are supported (i.e., no burst or high cadence data).
• For EPT and HET, only electrons, protons and alpha particles are processed (i.e., for HET He3, He4, C, N, O, Fe are omitted at the moment).
• For STEP, electron data needs to be calculated manually.
• The Suprathermal Ion Spectrograph (SIS) is not yet included.

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 EPD data description <http://espada.uah.es/epd/EPD_data.php>_ before using the data!

Installation

solo_epd_loader requires python >= 3.6.

It can be installed either from PyPI <https://pypi.org/project/solo-epd-loader/>_ using:

.. code:: bash

pip install solo-epd-loader

or from Anaconda <https://anaconda.org/conda-forge/solo-epd-loader/>_ using:

.. code:: bash

conda install -c conda-forge solo-epd-loader

Usage

The standard usecase is to utilize the epd_load function, which returns Pandas dataframe(s) of the EPD measurements and a dictionary containing information on the energy channels.

.. code:: python

from solo_epd_loader import epd_load

df_1, df_2, energies = epd_load(sensor, startdate, enddate=None, level='l2', viewing=None, path=None, autodownload=False, only_averages=False)

Input

-  ``sensor``: ``'ept'``, ``'het'``, or ``'step'`` (string)
-  ``startdate``, ``enddate``: Datetime object (e.g., ``dt.date(2021,12,31)`` or ``dt.datetime(2021,4,15)``) or integer of the form yyyymmdd with empty positions filled with zeros, e.g. ``20210415`` (if no ``enddate`` is provided, ``enddate = startdate`` will be used)
-  ``level``: ``'l2'`` or ``'ll'`` (string); defines level of data product: level 2 (``'l2'``) or low-latency (``'ll'``). By default ``'l2'``.
-  ``viewing``: ``'sun'``, ``'asun'``, ``'north'``, ``'south'``, ``'omni'`` (string) or ``None``; not
   needed for ``sensor = 'step'``. ``'omni'`` is just calculated as the average of the other four viewing directions: ``('sun'+'asun'+'north'+'south')/4``
-  ``path``: directory in which Solar Orbiter data is/should be
   organized; e.g. ``'/home/userxyz/solo/data/'`` (string). See `Data folder structure`_ for more details.
-  ``autodownload``: if ``True``, will try to download missing data files
   from SOAR (bolean)
- ``only_averages``: If ``True``, will for STEP only return the averaged fluxes, and not the data of each of the 15 Pixels. This will reduce the memory consumption. By default ``False``.

Return

• For sensor = 'ept' or 'het':

  1. Pandas dataframe with proton fluxes and errors (for EPT also alpha particles) in ‘particles / (s cm^2 sr MeV)’

  2. Pandas dataframe with electron fluxes and errors in ‘particles / (s cm^2 sr MeV)’

  3. Dictionary with energy information for all particles:

     • String with energy channel info
     • Value of lower energy bin edge in MeV
     • Value of energy bin width in MeV

• For sensor = 'step':

  1. Pandas dataframe with fluxes and errors in ‘particles / (s cm^2 sr MeV)’

  2. Dictionary with energy information for all particles:

     • String with energy channel info
     • Value of lower energy bin edge in MeV
     • Value of energy bin width in MeV

SupraThermal Electron Proton (STEP) sensor electron measurements

Please note that the STEP electron measurements are not directly provided in the publically released data, but need to be calculated from them. This process is not straightforward, and the resulting data is prone to uncertainties (like contamination). Thus it should only be used scientifically with caution! Please refer to the official EPD data description <http://espada.uah.es/epd/EPD_data.php>_ before using the data!

Data folder structure

The path variable provided to the module should be the base directory where the corresponding cdf data files should be placed in subdirectories. First subfolder defines the data product level (l2 or low_latency at the moment), the next one the instrument (so far only epd), and finally the sensor (ept, het or step).

For example, the folder structure could look like this: /home/userxyz/solo/data/l2/epd/het. In this case, you should call the loader with path='/home/userxyz/solo/data'; i.e., the base directory for the data.

You can use the (automatic) download function described in the following section to let the subfolders be created initially automatically. NB: It might be that you need to run the code with sudo or admin privileges in order to be able to create new folders on your system.

Data download within Python

While using epd_load() to obtain the data, one can choose to automatically download missing data files from SOAR <http://soar.esac.esa.int/soar>_ directly from within python. They are saved in the folder provided by the path argument (see above). For that, just add autodownload=True to the function call:

.. code:: python

from solo_epd_loader import epd_load

df_protons, df_electrons, energies =
epd_load(sensor='het', level='l2', startdate=20200820, enddate=20200821, viewing='sun', path='/home/userxyz/solo/data/', autodownload=True)

plot protons and alphas

ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

plot electrons

ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

Note: The code will always download the latest version of the file available at SOAR. So in case a file V01.cdf is already locally present, V02.cdf will be downloaded nonetheless.

Example 1 - low latency data

Example code that loads low latency (ll) electron and proton (+alphas) fluxes (and errors) for EPT NORTH telescope from Apr 15 2021 to Apr 16 2021 into two Pandas dataframes (one for protons & alphas, one for electrons). In general available are ‘sun’, ‘asun’, ‘north’, ‘south’, and ‘omni’ viewing directions for ‘ept’ and ‘het’ telescopes of SolO/EPD.

.. code:: python

from matplotlib import pyplot as plt from solo_epd_loader import epd_load

df_protons, df_electrons, energies =
epd_load(sensor='ept', level='ll', startdate=20210415, enddate=20210416, viewing='north', path='/home/userxyz/solo/data/')

plot protons and alphas

ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

plot electrons

ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

Example 2 - level 2 data

Example code that loads level 2 (l2) electron and proton (+alphas) fluxes (and errors) for HET SUN telescope from Aug 20 2020 to Aug 20 2020 into two Pandas dataframes (one for protons & alphas, one for electrons).

.. code:: python

from matplotlib import pyplot as plt from solo_epd_loader import epd_load

df_protons, df_electrons, energies =
epd_load(sensor='het', level='l2', startdate=20200820, enddate=20200821, viewing='sun', path='/home/userxyz/solo/data/')

plot protons and alphas

ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

plot electrons

ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60)) plt.show()

Example 3 - partly reproducing Fig. 2 <https://www.aanda.org/articles/aa/full_html/2021/12/aa39883-20/F2.html>_ from Gómez-Herrero et al. 2021 [#]_

.. code:: python

from matplotlib import pyplot as plt from solo_epd_loader import epd_load import numpy as np

set your local path here

lpath = '/home/userxyz/solo/data'

load ept sun viewing data

df_protons_ept, df_electrons_ept, energies_ept =
epd_load(sensor='ept', level='l2', startdate=20200708, enddate=20200724, viewing='sun', path=lpath, autodownload=True)

load step data

df_step, energies_step =
epd_load(sensor='step', level='l2', startdate=20200708, enddate=20200724, path=lpath, autodownload=True)

change time resolution to get smoother curve (resample with mean)

resample = '60min'

fig, axs = plt.subplots(2, sharex=True, figsize=(8, 10), dpi=200) axs[0].set_prop_cycle('color', plt.cm.Oranges_r(np.linspace(0,1,7))) axs[1].set_prop_cycle('color', plt.cm.winter(np.linspace(0,1,7)))

plot selection of ept electron channels

for channel in [0, 8, 16, 26]: df_electrons_ept['Electron_Flux'][f'Electron_Flux_{channel}'].resample(resample).mean().plot( ax = axs[0], logy=True, label='EPT '+energies_ept["Electron_Bins_Text"][channel][0])

plot selection of step ion channels

for channel in [8, 17, 33]: df_step[f'Magnet_Avg_Flux_{channel}'].resample(resample).mean().plot( ax = axs[1], logy=True, label='STEP '+energies_step["Bins_Text"][channel][0])

plot selection of ept ion channels

for channel in [6, 22, 32, 48]: df_protons_ept['Ion_Flux'][f'Ion_Flux_{channel}'].resample(resample).mean().plot( ax = axs[1], logy=True, label='EPT '+energies_ept["Ion_Bins_Text"][channel][0])

axs[0].set_ylim([0.3, 4e6]) axs[1].set_ylim([0.01, 5e8])

axs[0].set_ylabel("Electron flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$") axs[1].set_ylabel("Ion flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$") axs[0].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) axs[1].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.subplots_adjust(hspace=0) fig.savefig("gh2021_fig_2.png", bbox_inches = "tight") plt.close('all')

NB: This is just an approximate reproduction with different energy channels, different time resolution, and different viewing direction! Note also that the STEP data can not be used straightforwardly. |Figure|

Example 4 - partly reproducing Fig. 2e <https://www.aanda.org/articles/aa/full_html/2021/12/aa40940-21/F2.html>_ from Wimmer-Schweingruber et al. 2021 [#]_

.. code:: python

from matplotlib import pyplot as plt from solo_epd_loader import epd_load import datetime import pandas as pd

set your local path here

lpath = '/home/userxyz/solo/data'

load data

df_protons_sun, df_electrons_sun, energies =
epd_load(sensor='ept', level='l2', startdate=20201210, enddate=20201211, viewing='sun', path=lpath, autodownload=True) df_protons_asun, df_electrons_asun, energies =
epd_load(sensor='ept', level='l2', startdate=20201210, enddate=20201211, viewing='asun', path=lpath, autodownload=True) df_protons_south, df_electrons_south, energies =
epd_load(sensor='ept', level='l2', startdate=20201210, enddate=20201211, viewing='south', path=lpath, autodownload=True) df_protons_north, df_electrons_north, energies =
epd_load(sensor='ept', level='l2', startdate=20201210, enddate=20201211, viewing='north', path=lpath, autodownload=True)

plot mean intensities of two energy channels; 'channel' defines the lower one

channel = 6 ax = pd.concat([df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel}'], df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel+1}']], axis=1).mean(axis=1).plot(logy=True, label='sun', color='#d62728') ax = pd.concat([df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel}'], df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel+1}']], axis=1).mean(axis=1).plot(logy=True, label='asun', color='#ff7f0e') ax = pd.concat([df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel}'], df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel+1}']], axis=1).mean(axis=1).plot(logy=True, label='north', color='#1f77b4') ax = pd.concat([df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel}'], df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel+1}']], axis=1).mean(axis=1).plot(logy=True, label='south', color='#2ca02c')

plt.xlim([datetime.datetime(2020, 12, 10, 23, 0), datetime.datetime(2020, 12, 11, 12, 0)])

ax.set_ylabel("Electron flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$") plt.title('EPT electrons ('+str(energies['Electron_Bins_Low_Energy'][channel]) + '-' + str(energies['Electron_Bins_Low_Energy'][channel+2])+' MeV)') plt.legend() plt.show()

NB: This is just an approximate reproduction; e.g., the channel combination is a over-simplified approximation! |image1|

References

.. [#] First near-relativistic solar electron events observed by EPD onboard Solar Orbiter, Gómez-Herrero et al., A&A, 656 (2021) L3, https://doi.org/10.1051/0004-6361/202039883

.. [#] First year of energetic particle measurements in the inner heliosphere with Solar Orbiter’s Energetic Particle Detector, Wimmer-Schweingruber et al., A&A, 656 (2021) A22, https://doi.org/10.1051/0004-6361/202140940

.. |Figure| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/gh2021_fig_2.png .. |image1| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/ws2021_fig_2d.png

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 <https://github.com/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).