viresclient is a Python package which provides access to products (including on-demand processing) from two of ESA's Earth Explorer missions: Swarm and Aeolus. This service is provided for ESA by EOX. For enquiries about the service and problems with accessing your account, please email info@vires.services. For help with usage, please email ashley.smith@ed.ac.uk or raise an issue on GitHub.
There are two VirES services (Virtual environments for Earth Scientists) which viresclient can communicate with:
- VirES for Swarm:
- Interact with the VirES for Swarm graphical interface (web client)
- Browse code recipes: Swarm Notebooks
- JupyterHub: Swarm VRE (Virtual Research Environment)
- Swarm data documentation: Swarm handbook
- Note that this service is not only for Swarm:
- Multi-mission products including magnetometry from CHAMP, CryoSat-2, and more
- INTERMAGNET ground magnetometers via the
AUX_OBScollection - Custom geomagnetic model evaluation
- Read more about the ecosystem: Python tools for ESA's Swarm mission: VirES for Swarm and surrounding ecosystem
- VirES for Aeolus:
- Interact with the VirES for Aeolus graphical interface (web client)
- Browse code recipes: Aeolus Notebooks
- JupterHub: Aeolus VRE
- Aeolus data documentation
Data and models are processed on demand on the VirES server - a combination of measurements from any time interval can be accessed. These are the same data that can be accessed by the VirES GUI. viresclient handles the returned data to allow direct loading as a single pandas.DataFrame, or xarray.Dataset.
from viresclient import SwarmRequest
# Set up connection with server
request = SwarmRequest()
# Set collection to use
# - See https://viresclient.readthedocs.io/en/latest/available_parameters.html
request.set_collection("SW_OPER_MAGA_LR_1B")
# Set mix of products to fetch:
# measurements (variables from the given collection)
# models (magnetic model predictions at spacecraft sampling points)
# auxiliaries (variables available with any collection)
# Optionally set a sampling rate different from the original data
request.set_products(
measurements=["F", "B_NEC"],
models=["CHAOS-Core"],
auxiliaries=["QDLat", "QDLon"],
sampling_step="PT10S"
)
# Fetch data from a given time interval
# - Specify times as ISO-8601 strings or Python datetime
data = request.get_between(
start_time="2014-01-01T00:00",
end_time="2014-01-01T01:00"
)
# Load the data as an xarray.Dataset
ds = data.as_xarray()<xarray.Dataset> Dimensions: (NEC: 3, Timestamp: 360) Coordinates: * Timestamp (Timestamp) datetime64[ns] 2014-01-01 ... 2014-01-01T00:59:50 Dimensions without coordinates: NEC Data variables: Spacecraft (Timestamp) <U1 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' Latitude (Timestamp) float64 -1.229 -1.863 -2.496 ... 48.14 48.77 Longitude (Timestamp) float64 -14.12 -14.13 -14.15 ... 153.6 153.6 Radius (Timestamp) float64 6.878e+06 6.878e+06 ... 6.868e+06 F (Timestamp) float64 2.287e+04 2.281e+04 ... 4.021e+04 F_CHAOS-Core (Timestamp) float64 2.287e+04 2.282e+04 ... 4.02e+04 B_NEC (Timestamp, NEC) float64 2.01e+04 -4.126e+03 ... 3.558e+04 B_NEC_CHAOS-Core (Timestamp, NEC) float64 2.011e+04 ... 3.557e+04 QDLat (Timestamp) float64 -11.99 -12.6 -13.2 ... 41.59 42.25 QDLon (Timestamp) float64 58.02 57.86 57.71 ... -135.9 -136.0 Attributes: Sources: ['SW_OPER_MAGA_LR_1B_20140101T000000_20140101T235959_050... MagneticModels: ["CHAOS-Core = 'CHAOS-Core'(max_degree=20,min_degree=1)"] RangeFilters: []
You can reference viresclient directly using the DOI of our zenodo record. VirES uses data from a number of different sources so please also acknowledge these appropriately.
"We use the Python package, viresclient [1], to access [...] from ESA's VirES for Swarm service [2]"
You can also cite this paper:
Smith A.R.A., Pačes M. and Swarm DISC (2022) Python tools for ESA’s Swarm mission: VirES for Swarm and surrounding ecosystem. Front. Astron. Space Sci. 9:1002697. doi: 10.3389/fspas.2022.1002697