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This repository was archived by the owner on Jul 8, 2025. It is now read-only.
💡 The code base is deprecated and should only be used if necessary.
A new version of Freva is on the way! While it’s still under development,
you can already start using it. If your primary focus is searching and
accessing data, we highly recommend trying out the new Freva client
(https://pypi.org/project/freva-client/)
What is Freva ?
Freva, the free evaluation system framework, is a data search and analysis
platform developed by the atmospheric science community for the atmospheric
science community. With help of Freva researchers can:
quickly and intuitively search for data stored at typical data centers that
host many datasets.
create a common interface for user defined data analysis tools.
apply data analysis tools in a reproducible manner.
Data analysis is realised by user developed data analysis plugins. These plugins
are code agnostic, meaning that users don't have to rewrite the core of their
plugins to make them work with Freva. All that Freva does is providing a user
interface for the plugins.
Currently Freva comes in three different flavours:
a python module that allows the usage of Freva in python environments, like
jupyter notebooks
a command line interface (cli) that allows using Freva from the command
lines and shell scripts.
Deployment is realised via a dedicated repository that holds code to set up
the command line and web user interface as well as all services.
To deploy the system in production
mode consult deployment docs.
How can I set up a local version for development?
To start development with freva clone the repository and its submodules:
A basic local development setup can be created using
Docker and
docker-compose
(Linux users need to install it separately).
This also requires that the .envrc file is sourced.
docker-compose up -d
Dummy data can be injected into a running docker-compose environment with
make dummy-data. This will add some example files into solr and run an
example plugin a few times to add some history data.
When finished, tear down the environment with
docker-compose down
Creating a dedicated anaconda dev environment
We recommend using anaconda to install all packages that are needed for
development. Here we assume that you have a
working anaconda version per-installed on your local computer. To install
the dev environment simply use the following command:
This will automatically set environment variables needed for development.
The freshly installed environment can be activated:
conda activate freva-dev
The conda environment can be deactivated using the following command:
conda deactivate
Note: The conda install command can be slow. If you want to speed up the
installation of the environment we recommend to install the mamba package in
the anaconda base environment and use the mamba command to create the
environment: