SolO EPD Loader

Python data loader for Solar Orbiter’s (SolO) Energetic Particle Detector
(EPD). Provides level 2 (l2) and low latency (ll)
data obtained through CDF files from ESA’s
Solar Orbiter Archive (SOAR) for the following
sensors:

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

Installation

pip install 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.

from solo_epd_loader import epd_load

df_1, df_2, energies = \
    epd_load(sensor, viewing, level, startdate, enddate, path, autodownload)

Input

  • sensor: ept, het, or step (string)
  • viewing: sun, asun, north, or south (string); not
    needed for sensor = step
  • level: ll or l2 (string)
  • startdate, enddate: YYYYMMDD, e.g., 20210415 (integer) (if no
    enddate is provided, enddate = startdate will be used)
  • path: directory in which Solar Orbiter data is/should be
    organized; e.g. /home/userxyz/solo/data/ (string)
  • autodownload: if True will try to download missing data files
    from SOAR (bolean)

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

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
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:

from solo_epd_loader import epd_load

df_protons, df_electrons, energies = \
    epd_load(sensor='het', viewing='sun', level='l2',
             startdate=20200820, enddate=20200821, \
             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’, and ‘south’
viewing directions for ‘ept’ and ‘het’ telescopes of SolO/EPD.

from solo_epd_loader import *

df_protons, df_electrons, energies = \
    epd_load(sensor='ept', viewing='north', level='ll',
             startdate=20210415, enddate=20210416, \
             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).

from solo_epd_loader import epd_load

df_protons, df_electrons, energies = \
    epd_load(sensor='het', viewing='sun', level='l2',
             startdate=20200820, enddate=20200821, \
             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 – reproducing EPT data from Fig. 2 in Gómez-Herrero et al. 2021 [1]

from solo_epd_loader import epd_load

# set your local path here
lpath = '/home/userxyz/solo/data'

# load data
df_protons, df_electrons, energies = \
    epd_load(sensor='ept', viewing='sun', 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)
fig.suptitle('EPT Sun')

# plot selection of channels
for channel in [0, 8, 16, 26]:
    df_electrons['Electron_Flux'][f'Electron_Flux_{channel}']\
        .resample(resample).mean().plot(ax = axs[0], logy=True,
        label=energies["Electron_Bins_Text"][channel][0])
for channel in [6, 22, 32, 48]:
    df_protons['Ion_Flux'][f'Ion_Flux_{channel}']\
        .resample(resample).mean().plot(ax = axs[1], logy=True,
        label=energies["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()
axs[1].legend()
plt.subplots_adjust(hspace=0)
plt.show()

NB: This is just an approximate reproduction with different energy
channels (smaller, not combined) and different time resolution!
Figure

Example 4 – reproducing EPT data from Fig. 2 in Wimmer-Schweingruber et al. 2021 [2]

from solo_epd_loader import epd_load

# set your local path here
lpath = '/home/userxyz/solo/data'

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

[1] 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
[2] 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

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 licence. See the licenses folder for
more information.

GitHub

View Github