JUICE on ESA DataLabs

DataLabs workshop 2022

B. Seignovert, G. Tobie, C. Vallat, N. Altobelli, I. Belgacem & M. Costa

Science planning opportunities and coverage analysis

JUICE

  • ESA L-1 mission
  • Selected in 2012
  • Launch in April 2023
  • Arrived at Jupiter in 2031
  • Orbit around Ganymede in 2035

Mission planning

1 - Timelines and filtering

2 - Tracks and footprints

3 - Regions of interest intersections

SPICE

🌶 🤔

SPICE

  • Developed by NASA / JPL
  • Started in 1982
  • 1st public release ~1995
  • Used in many space missions
  • Fully tested
  • Very reliable
  • C / Fortran / IDL / Matlab / Java

pacecraft

lanet

nstrument

amera

vents

SPICE

  • Expert users
  • Mission / instrument specific
  • Complex setup
  • Very large kernels dataset

pacecraft

lanet

nstrument

amera

vents

See Alfredo Escalante talk at 3 PM

-coverage

moon

  • Python 3.8+ object oriented library

  • Open-source BSD licence

  • Use only numpy, matplotlib and spiceypy

  • Developed at the LPG/Nantes Université
  • Founded by ESA JUICE SOC

moon-coverage.univ-nantes.fr

pip install moon-coverage

How to install it?

Python

Coming soon…

Conda-Forge

import matplotlib.pyplot as plt
from moon_coverage import TourConfig, CALLISTO, CallistoROIs

tour = TourConfig(mk=5.0, spacecraft='JUICE', instrument='MAJIS_IR', target='Callisto')

fig = plt.figure()
ax = fig.add_subplot(projection=CALLISTO)

ax.add_collection(CallistoROIs(edgecolor='white', linestyle='--'))

for flyby in tour.flybys:
    traj = flyby.where((flyby.alt < 750) & (flyby.inc < 90))

    ax.add_collection(traj.fovs('inc', vmin=0, vmax=90))

    if traj in CallistoROIs:
        ax.plot(traj & CallistoROIs, color='red')
        ax.add_collection((CallistoROIs & traj)(edgecolor='red'))
        
ax.colorbar(vmin=0, vmax=90, label='inc', extend='max')
✍️ 20 lines from scratch to plot! 📈

Callisto flybys planning

Demo time ! 👀

https://datalabs.esa.int/datalab-launch-wizard/jl-juice
https://juigitlab.esac.esa.int/datalab/moon-coverage-notebooks

DataLabs advantages

  • No setup for the user
  • Access to all ESA SPICE kernels (no download required)
  • Re-use of existing notebooks (git plugin)

Current limitations

  • Limited number of users
  • Volume manual mounting
  • No team shared workspaces
  • Tools upgrade (👉 SciApps)
  • Real time collaboration
  • Open in ESA DataLabs

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This presentation:
The tool:
Contact us:

slid.es/seignovert/datalabs-2022

moon-coverage.univ-nantes.fr

moon-coverage@univ-nantes.fr

JupyterLab Real Time Collaboration on Binder