pip install viresclient
conda install -c conda-forge viresclient
viresclient is a Python package which connects to a VirES server, of which there are two: VirES for Swarm (https://vires.services) and VirES for Aeolus (https://aeolus.services), through the WPS interface. This package handles product requests and downloads, enabling easy access to data and models from 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 firstname.lastname@example.org. For help with usage, please email email@example.com (for Swarm data) or raise an issue on GitHub.
For code recipes and more, see Swarm Notebooks & Aeolus Notebooks. To start experimenting right away, viresclient is installed on the “Virtual Research Environment” (VRE), which is a managed Jupyter-based system provided for ESA by EOX. The service is free and open to all, accessible through your VirES account - check the notebooks to read more and get started.
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: 
How to acknowledge VirES#
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.