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
solo_epd_loader requires python >= 3.6, and it depends on cdflib and heliopy (which will be automatically installed). It can be installed from PyPI using:
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, orstep(string)viewing:sun,asun,north, orsouth(string); not needed forsensor = steplevel:llorl2(string)startdate,enddate: YYYYMMDD, e.g., 20210415 (integer) (if noenddateis provided,enddate = startdatewill be used)path: directory in which Solar Orbiter data is/should be organized; e.g./home/userxyz/solo/data/(string)autodownload: ifTruewill try to download missing data files from SOAR (bolean)
Return
- For
sensor=eptorhet:- Pandas dataframe with proton fluxes and errors (for EPT also alpha particles) in ‘particles / (s cm^2 sr MeV)’
- Pandas dataframe with electron fluxes and errors in ‘particles / (s cm^2 sr MeV)’
- 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:- Pandas dataframe with fluxes and errors in ‘particles / (s cm^2 sr MeV)’
- 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! 
Example 4 - reproducing EPT data from Fig. 2 in Wimmer-Schweingruber et al. 2021 [2]
from solo_epd_loader import epd_load
import datetime
# 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! 
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.
6 May 18, 2022
2 Oct 18, 2021
93 Dec 21, 2022
11 Nov 12, 2022
2 Dec 04, 2021
2 Mar 01, 2022
2 Feb 14, 2022
3 Sep 19, 2021
1 Jul 13, 2022
4.7k Jan 02, 2023
205 Jan 06, 2023
2 Sep 02, 2022
8 Sep 23, 2022
1 Jul 17, 2022
192 Dec 28, 2022
261 Jan 03, 2023
42 Oct 11, 2022
23 Dec 17, 2022
1 Feb 10, 2022