Utilities¶

This section provides information on tools that are useful when developing ESMValTool. Tools that are specific to ESMValTool live in the esmvaltool/utils directory, while others can be installed using the usual package managers.

Pre-commit¶

pre-commit is a handy tool that can run many tools for checking code quality with a single command. Usually it is used just before committing, to avoid accidentally committing mistakes. It knows knows which tool to run for each filetype, and therefore provides a convenient way to check your code!

To run pre-commit on your code, go to the ESMValTool directory (cd ESMValTool) and run

pre-commit run

By default, pre-commit will only run on the files that have been changed, meaning those that have been staged in git (i.e. after git add your_script.py).

To make it only check some specific files, use

pre-commit run --files your_script.py

or

pre-commit run --files your_script.R

Alternatively, you can configure pre-commit to run on the staged files before every commit (i.e. git commit), by installing it as a git hook using

pre-commit install

Pre-commit hooks are used to inspect the code that is about to be committed. The commit will be aborted if files are changed or if any issues are found that cannot be fixed automatically. Some issues cannot be fixed (easily), so to bypass the check, run

git commit --no-verify

or

git commit -n

or uninstall the pre-commit hook

pre-commit uninstall

Note that the configuration of pre-commit lives in .pre-commit-config.yaml.

nclcodestyle¶

A tool for checking the style of NCL code, based on pycodestyle. Install ESMValTool in development mode (pip install -e '.[develop]') to make it available. To use it, run

nclcodestyle /path/to/file.ncl

Colormap samples¶

Tool to generate colormap samples for ESMValTool’s default Python and NCL colormaps.

Run

esmvaltool colortables python

or

esmvaltool colortables ncl

to generate the samples.

Testing recipes¶

Tools for testing recipes.

Test recipe settings¶

A tool for generating recipes with various diagnostic settings, to test of those work. Install ESMValTool in development mode (pip install -e '.[develop]') to make it available. To use it, run

test_recipe --help

draft_release_notes.py¶

draft_release_notes.py is a script for drafting release notes based on the titles and labels of the GitHub pull requests that have been merged since the previous release.

To use it, install the package pygithub:

pip install pygithub

Create a GitHub access token (leave all boxes for additional permissions unchecked) and store it in the file ~/.github_api_key.

Edit the script and update the date and time of the previous release and run the script:

python esmvaltool/utils/draft_release_notes.py ${REPOSITORY}

REPOSITORY can be either esmvalcore or esmvaltool depending on the release notes you want to create.

Review the resulting output (in .rst format) and if anything needs changing, change it on GitHub and re-run the script until the changelog looks acceptable. In particular, make sure that pull requests have the correct label, so they are listed in the correct category. Finally, copy and paste the generated content at the top of the changelog.

Converting Version 1 Namelists to Version 2 Recipes¶

The xml2yml converter can turn the old xml namelists into new-style yml recipes. It is implemented as a xslt stylesheet that needs a processor that is xslt 2.0 capable. With this, you simply process your old namelist with the stylesheet xml2yml.xsl to produce a new yml recipe.

After the conversion you need to manually check the mip information in the variables! Also, check the caveats below!

Howto¶

One freely available processor is the Java based saxon. You can download the free he edition here. Unpack the zip file into a new directory. Then, provided you have Java installed, you can convert your namelist simply with:

java -jar $SAXONDIR/saxon9he.jar -xsl:xml2yml.xsl -s:namelist.xml -o:recipe.yml

Caveats/Known Limitations¶

At the moment, not all model schemes (OBS, CMIP5, CMIP5_ETHZ…) are supported. They are, however, relatively easy to add, so if you need help adding a new one, please let me know!
The documentation section (namelist_summary in the old file) is not automatically converted.
In version 1, one could name an exclude, similar to the reference model. This is no longer possible and the way to do it is to include the models with another additional_models tag in the variable section. That conversion is not performed by this tool.

Authored by Klaus Zimmermann, direct questions and comments to klaus.zimmermann@smhi.se

Recipe filler¶

If you need to fill in a blank recipe with additional datasets, you can do that with the command recipe_filler. This runs a tool to obtain a set of additional datasets when given a blank recipe, and you can give an arbitrary number of data parameters. The blank recipe should contain, to the very least, a list of diagnostics, each with their variable(s). Example of running the tool:

recipe_filler recipe.yml

where recipe.yml is the recipe that needs to be filled with additional datasets; a minimal example of this recipe could be:

diagnostics:
  diagnostic:
    variables:
      ta:
        mip: Amon  # required
        start_year: 1850  # required
        end_year: 1900  # required

Key features¶

you can add as many variable parameters as are needed; if not added, the tool will use the "*" wildcard and find all available combinations;
you can restrict the number of datasets to be looked for with the dataset: key for each variable, pass a list of datasets as value, e.g. dataset: [MPI-ESM1-2-LR, MPI-ESM-LR];
you can specify a pair of experiments, e.g. exp: [historical, rcp85] for each variable; this will look for each available dataset per experiment and assemble an aggregated data stretch from each experiment to complete for the total data length specified by start_year and end_year; equivalent to ESMValTool’s syntax on multiple experiments; this option needs an ensemble to be declared explicitly; it will return no entry if there are gaps in data;
start_year and end_year are required and are used to filter out the datasets that don’t have data in the interval; as noted above, the tool will not return datasets with partial coverage from start_year to end_year; if you want all possible years hence no filtering on years just use "*" for start and end years;
config-user: rootpath: CMIPX may be a list, rootpath lists are supported;
all major DRS paths (including default, BADC, ETHZ etc) are supported;
speedup is achieved through CMIP mip tables lookup, so mip is required in recipe;

Caveats¶

the tool doesn’t yet work with derived variables; it will not return any available datasets;
operation restricted to CMIP data only, OBS lookup is not available yet.