Installation and First Usage#


For VRE users (it’s free! read more: Swarm, Aeolus), viresclient is already installed and configured so skip these steps

1. Installation#

Python ≥ 3.6 is required. Testing is primarily on Linux, but macOS and Windows should also work. Available through both pip and conda (conda-forge).

pip install viresclient

2. First usage / Configuration#


For Jupyter notebook users, just try:

from viresclient import SwarmRequest
request = SwarmRequest()

and you will automatically be prompted to set a token.

A first usage guide is provided as a Jupyter notebook (view). To run the notebook on your computer running Jupyter locally, right click here to download, or use git to get the whole example repository:

git clone

Access to the service is through the same user account as on the web interface ( and is enabled through an access token (essentially a password). To get a token, log in to the website and click on your name on the top right to access the settings (or follow this link). From here, click on “Manage access tokens” and follow the instructions to create a new token.

To set your token in the client, use either the Python interface:

from viresclient import set_token
# (you will now be prompted to enter the token)

or the command line tool:

$ viresclient set_token
Enter access token: r-8-mlkP_RBx4mDv0di5Bzt3UZ52NGg-

$ viresclient set_default_server

See also: see Configuration Details and Access Token Management

3. Example use#


A brief introduction is given here. For more possibilities, see Introduction to notebooks, and VirES capabilities.

See also Swarm access through VirES

Choose which collection to access (see Available parameters for Swarm for more options):

import datetime as dt
from viresclient import SwarmRequest

request = SwarmRequest()

Next, use .set_products() to choose a combination of variables to retrieve, specified by keywords.

  • measurements are measured by the satellite and members of the specified collection

  • models are evaluated on the server at the positions of the satellite

  • auxiliaries are additional parameters not unique to the collection

  • if residuals is set to True then only data-model residuals are returned

  • optionally use sampling_step to specify a resampling of the original time series (an ISO-8601 duration).

  measurements=["F", "B_NEC"],
  models=["MCO_SHA_2C", "MMA_SHA_2C-Primary", "MMA_SHA_2C-Secondary"],
  auxiliaries=["QDLat", "QDLon", "MLT", "OrbitNumber", "SunZenithAngle"],

Set a parameter range filter to apply. You can add multiple filters in sequence.

request.set_range_filter(parameter="Latitude", minimum=0, maximum=90)
request.set_range_filter("Longitude", 0, 90)

Specify the time range from which to retrieve data, make the request to the server:

data = request.get_between(

Transfer your data to a pandas.DataFrame, or a xarray.Dataset, or just save it as is:

df = data.as_dataframe()
ds = data.as_xarray()
data.to_file('outfile.cdf', overwrite=False)

The returned data has columns for:

  • Spacecraft, Timestamp, Latitude, Longitude, Radius

  • those specified by measurements and auxiliaries

… and model values and residuals, named as:

  • F_<model_id> – scalar field

  • B_NEC_<model_id> – vector field

  • F_res_<model_id> – scalar field residual (F - F_<model_id>)

  • B_NEC_res_<model_id> – vector field residual (B_NEC - B_NEC_<model_id>)