Installation

ESMValTool 2.0 requires a Unix(-like) operating system and Python 3.6+.

The ESMValTool supports five different installation methods:

• Installation through Conda package manager (see https://www.continuum.io);

• Installation with Pip and Conda package manager (see https://pypi.org);

• Deployment through a Docker container (see https://www.docker.com);

• Deployment through a Singularity container (see https://sylabs.io/guides/latest/user-guide/);

• From the source code available at https://github.com/ESMValGroup/ESMValTool.

The next sections will detail the procedure to install ESMValTool for each of this methods.

Conda installation

In order to install the Conda package, you will need both Conda and Julia pre-installed, this is because Julia cannot be installed from Conda. For a minimal conda installation (recommended) go to https://conda.io/miniconda.html. It is recommended that you always use the latest version of Conda, as problems have been reported when trying to use older versions. Installation instructions for Julia can be found on the Julia installation instructions page.

Once you have installed the above prerequisites, you can install ESMValTool by running:

conda install esmvaltool -c esmvalgroup -c conda-forge

Here conda is the executable calling the Conda package manager to install esmvaltool and the -c flag specifies the Conda software channels in which the esmvaltool package and its dependencies can be found.

It is also possible to create a new Conda environment and install ESMValTool into it with a single command:

conda create --name esmvaltool -c esmvalgroup -c conda-forge esmvaltool

Don’t forget to activate the newly created environment after the installation:

conda activate esmvaltool

Of course it is also possible to choose a different name than esmvaltool for the environment.

Note

Creating a new Conda environment is often much faster and more reliable than trying to update an existing Conda environment.

Installation of subpackages

The diagnostics bundled in ESMValTool are scripts in four different programming languages: Python, NCL, R, and Julia.

There are four language specific packages available:

• esmvaltool-julia

• esmvaltool-ncl

• esmvaltool-python

• esmvaltool-r

The main esmvaltool package contains all four subpackages listed above.

If you only need to run a recipe with diagnostics in some of these languages, it is possible to install only the dependencies needed to do just that.

• The diagnostic script(s) used in each recipe, are documented in Recipes. The extension of the diagnostic script can be used to see in which language a diagnostic script is written.

• Some of the CMORization scripts are written in Python, while others are written in NCL. Therefore, both esmvaltool-pyhon and esmvaltool-ncl need to be installed in order to be able to run all CMORization scripts.

For example, to only install support for diagnostics written in Python and NCL, run

conda install esmvaltool-python esmvaltool-ncl -c esmvalgroup -c conda-forge

Note that it is only necessary to install Julia prior to the conda installation if you are going to install the esmvaltool-julia package.

Note that the ESMValTool source code is contained in the esmvaltool-python package, so this package will always be installed as a dependency if you install one or more of the packages for other languages.

Pip installation

It is also possible to install ESMValTool from PyPI. However, this requires first installing dependencies that are not available on PyPI in some other way. By far the easiest way to install these dependencies is to use conda. For a minimal conda installation (recommended) go to https://conda.io/miniconda.html.

After installing Conda, download the file with the list of dependencies:

wget https://raw.githubusercontent.com/ESMValGroup/ESMValTool/master/environment.yml

and install these dependencies into a new conda environment with the command

conda env create --name esmvaltool --file environment.yml

Finally, activate the newly created environment

conda activate esmvaltool

and install ESMValTool as well as any remaining Python dependencies with the command:

pip install esmvaltool

If you would like to run Julia diagnostic scripts, you will also need to install Julia and the Julia dependencies:

esmvaltool install Julia

If you would like to run R diagnostic scripts, you will also need to install the R dependencies:

esmvaltool install R

Docker installation

ESMValTool is also provided through DockerHub in the form of docker containers. See https://docs.docker.com for more information about docker containers and how to run them.

You can get the latest release with

docker pull esmvalgroup/esmvaltool:stable

If you want to use the current master branch, use

docker pull esmvalgroup/esmvaltool:latest

To run a container using those images, use:

docker run esmvalgroup/esmvaltool:stable --help

Note that the container does not see the data or environmental variables available in the host by default. You can make data available with -v /path:/path/in/container and environmental variables with -e VARNAME.

For example, the following command would run a recipe

docker run -e HOME -v "$HOME":"$HOME" -v /data:/data esmvalgroup/esmvaltool:stable run examples/recipe_python.yml

with the environmental variable $HOME available inside the container and the data in the directories $HOME and /data, so these can be used to find the configuration file, recipe, and data.

It might be useful to define a bash alias or script to abbreviate the above command, for example

alias esmvaltool="docker run -e HOME -v $HOME:$HOME -v /data:/data esmvalgroup/esmvaltool:stable"

would allow using the esmvaltool command without even noticing that the tool is running inside a Docker container.

Singularity installation

Docker is usually forbidden in clusters due to security reasons. However, there is a more secure alternative to run containers that is usually available on them: Singularity.

Singularity can use docker containers directly from DockerHub with the following command

singularity run docker://esmvalgroup/esmvaltool:stable run examples/recipe_python.yml

Note that the container does not see the data available in the host by default. You can make host data available with -B /path:/path/in/container.

It might be useful to define a bash alias or script to abbreviate the above command, for example

alias esmvaltool="singularity run -B $HOME:$HOME -B /data:/data docker://esmvalgroup/esmvaltool:stable"

would allow using the esmvaltool command without even noticing that the tool is running inside a Singularity container.

Some clusters may not allow to connect to external services, in those cases you can first create a singularity image locally:

singularity build esmvaltool.sif docker://esmvalgroup/esmvaltool:stable

and then upload the image file esmvaltool.sif to the cluster. To run the container using the image file esmvaltool.sif use:

singularity run esmvaltool.sif run examples/recipe_python.yml

Install from source

Obtaining the source code

The ESMValTool source code is available on a public GitHub repository: https://github.com/ESMValGroup/ESMValTool

The easiest way to obtain it is to clone the repository using git (see https://git-scm.com/). To clone the public repository:

git clone https://github.com/ESMValGroup/ESMValTool.git

It is also possible to work in one of the ESMValTool private repositories, e.g.:

git clone https://github.com/ESMValGroup/ESMValTool-private.git

By default, this command will create a folder called ESMValTool containing the source code of the tool.

GitHub also allows one to download the source code in as a tar.gz or zip file. If you choose to use this option, download the compressed file and extract its contents at the desired location.

Prerequisites

It is recommended to use conda to manage ESMValTool dependencies. For a minimal conda installation go to https://conda.io/miniconda.html. To simplify the installation process, an environment definition file is provided in the repository (environment.yml in the root folder).

Attention

Some systems provide a preinstalled version of conda (e.g., via the module environment). However, several users reported problems when installing NCL with such versions. It is therefore preferable to use a local, fully user-controlled conda installation. Using an older version of conda can also be a source of problems, so if you have conda installed already, make sure it is up to date by running conda update -n base conda.

To enable the conda command, please source the appropriate configuration file from your ~/.bashrc file:

source <prefix>/etc/profile.d/conda.sh

or ~/.cshrc/~/.tcshrc file:

source <prefix>/etc/profile.d/conda.csh

where <prefix> is the install location of your anaconda or miniconda (e.g. /home/$USER/anaconda3 or /home/$USER/miniconda3).

Note

Note that during the installation, conda will ask you if you want the installation to be automatically sourced from your .bashrc or .bash-profile files; if you answered yes, then conda will write bash directives to those files and every time you get to your shell, you will automatically be inside conda’s (base) environment. To deactivate this feature, look for the # >>> conda initialize >>> code block in your .bashrc or .bash-profile and comment the whole block out.

The ESMValTool conda environment file can also be used as a requirements list for those cases in which a conda installation is not possible or advisable. From now on, we will assume that the installation is going to be done through conda.

Ideally, you should create a conda environment for ESMValTool, so it is independent from any other Python tools present in the system.

Note that it is advisable to update conda to the latest version before installing ESMValTool, using the command (as mentioned above)

conda update --name base conda

To create an environment, go to the directory containing the ESMValTool source code (called ESMValTool if you did not choose a different name) and run

conda env create --name esmvaltool --file environment.yml

This installs the ESMValCore package from conda as a dependency.

The environment is called esmvaltool by default, but it is possible to use the option --name SOME_ENVIRONMENT_NAME to define a custom name. You should then activate the environment using the command:

conda activate esmvaltool

It is also possible to update an existing environment from the environment file. This may be useful when updating an older installation of ESMValTool:

conda env update --name esmvaltool --file environment.yml

but if you run into trouble, please try creating a new environment.

Attention

From now on, we assume that the conda environment for ESMValTool is activated.

Software installation

Once all prerequisites are fulfilled, ESMValTool can be installed by running the following commands in the directory containing the ESMValTool source code (called ESMValTool if you did not choose a different name):

pip install -e '.[develop]'

If you would like to run Julia diagnostic scripts, you will also need to install Julia and the Julia dependencies:

esmvaltool install Julia

If you would like to run R diagnostic scripts, you will also need to install the R dependencies. Install the R dependency packages:

esmvaltool install R

The next step is to check that the installation works properly. To do this, run the tool with:

esmvaltool --help

If everything was installed properly, ESMValTool should have printed a help message to the console.