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xcube-dev / xcube-cds

An xcube plugin to generate data cubes from the Climate Data Store (CDS) API
MIT License
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xcube-cds

An xcube plugin which can generate data cubes from the Copernicus Climate Data Store (CDS) API.

Description

xcube-cds uses the ECMWF's cdsapi library, along with user-supplied CDS account details, to fetch data from the Copernicus CDS.

Currently supported datasets:

Setup

Configuring access to the CDS API

In order to use the CDS API via the xcube_cds plugin, you need to obtain a Personal Access Token and write it to a configuration file. Additionally, you need to use the CDS website to agree in advance to the terms of use for any datasets you want to acccess.

Obtain a CDS Personal Access Token

You can obtain a CDS Personal Access Token as follows:

  1. Create a user account on the CDS Website.
  2. Log in to the website with your username and password.
  3. Navigate to your user page, where you can find your Personal Access Token.

Configure CDS API access

Your CDS Personal Access Token must be made available to the CDS API library. You can do this by creating a file named .cdsapirc in your home directory, with the following format:

url: https://cds-beta.climate.copernicus.eu/api
key: <PERSONAL-ACCESS-TOKEN>

Replace <PERSONAL-ACCESS-TOKEN> with your Personal Access Token.

Agree to the terms of use for the datasets you require

The datasets available through CDS have associated terms of use. Before accessing a dataset via the API, you must agree to its terms of use, which can only be done via the CDS website, as follows:

  1. Log in to the CDS website, and go to 'Datasets' to find the dataset you require.
  2. On the dataset's web page, select the ‘Download’ tab.
  3. Scroll to the bottom of the page, and you will see a section titled ‘Terms of use’, which will contain either an ‘Accept terms’ button to allow you to accept the terms, or a confirmation that you have already accepted the terms.

Once you have accepted the terms on the website, the dataset will also be made available to you through the API.

Installing the xcube-cds plugin

This section describes three alternative methods you can use to install the xcube-cds plugin.

For installation of conda packages, we recommend mamba. It is also possible to use conda, but note that installation may be significantly slower with conda than with mamba. If using conda rather than mamba, replace the mamba command with conda in the installation commands given below.

Installation into a new environment with mamba

This method creates a new conda environment and installs the latest conda-forge release of xcube-cds, along with all its required dependencies, into the newly created environment.

xcube-cds and all necessary dependencies (including xcube itself) are available on conda-forge, and can be installed using the conda package manager. The conda package manager itself can be obtained in the miniconda distribution. Once conda is installed, xcube-cds can be installed like this:

mamba create --name xcube-cds-environment --channel conda-forge xcube-cds
mamba activate xcube-cds-environment

The name of the environment may be freely chosen.

Installation into an existing environment with mamba

This method assumes that you have an existing conda environment and you want to install xcube-cds into it.

xcube-cds can also be installed into an existing conda environment. With the existing environment activated, execute this command:

mamba install --channel conda-forge xcube-cds

Once again, xcube and any other necessary dependencies will be installed automatically if they are not already installed.

Installation into an existing environment from the repository

If you want to install xcube-cds directly from the git repository (for example if order to use an unreleased version or to modify the code), you can do so as follows:

mamba create --name xcube-cds-environment --channel conda-forge --only-deps xcube-cds
mamba activate xcube-cds-environment
git clone https://github.com/dcs4cop/xcube-cds.git
python -m pip install --no-deps --editable xcube-cds/

This installs all the dependencies of xcube-cds into a fresh conda environment, then installs xcube-cds into this environment from the repository.

Testing

You can run the unit tests for xcube-cds by executing

pytest

in the xcube-cds repository. Note that, in order to successfully run the tests using the current repository version of xcube-cds, you may also need to install the repository version of xcube rather than its latest conda-forge release.

To create a test coverage report, you can use

coverage run --include='xcube_cds/**' --module pytest
coverage html

This will write a coverage report to htmlcov/index.html.

Use

Jupyter notebooks demonstrating the use of the xcube-cds plugin can be found in the examples/notebooks/ subdirectory of the repository.

Preparing a release

This section is intended for developers preparing a new release of xcube-cds.

Pre-release tasks

Making a GitHub release

Create a release tag on GitHub.

Creating the release will automatically produce a source code archive as an associated asset, which will be needed to create the conda package.

Updating the conda package

These instructions are based on the documentation at https://conda-forge.org/docs/maintainer/updating_pkgs.html .

Conda-forge packages are produced from a github feedstock repository belonging to the conda-forge organization. The feedstock for xcube-cds is at https://github.com/conda-forge/xcube-cds-feedstock . The package is updated by forking this repository (to a personal GitHub account, not to an organization), creating a new branch for the changes, and creating a pull request to merge this branch into conda-forge's feedstock repository.

The instructions below describe the manual procedure of creating a branch and pull request yourself. conda-forge also has a bot called regro-cf-autotick-bot which should automatically create a branch and pull request for each new GitHub release, within about an hour or two of the release appearing on GitHub. The bot also automatically updates the version number and release hash in the recipe, but further manual modifications (in the form of commits pushed to the PR) may be required, for example in order to update dependency lists. The manual procedure may be necessary or desirable in some cases, for example:

If you are basing the release on the automatically created pull request, skip the "create a new branch" and "create a pull request" steps below, and instead make the necessary changes (if any) on the branch created by the bot. If a release is made using a manually created branch, the bot will remove its own branch and pull request. (Sometimes it's necessary to apply the bot-rerun label to the pull request to make this happen.)

In detail, the steps are as follows.

  1. Fork the feedstock repository to a personal GitHub account (not an organization).

  2. Clone the repository locally and create a new branch. The name of the branch is not strictly prescribed, but it's sensible to choose an informative name like update_0_5_3.

  3. Rerender the feedstock using conda-smithy. This updates common conda-forge feedstock files. It's probably easiest to install conda-smithy in a fresh environment for this.

    mamba create -c conda-forge -n smithy conda-smithy
mamba activate smithy
conda-smithy rerender -c auto

    It's also possible to have the rendering done by a bot as part of the pull request, but this may fail if it doesn't have the necessary permissions.

  4. Update recipe/meta.yaml for the new version. Mainly this will involve the following steps:

    1. Update the value of the version variable (or, if the version number has not changed, increment the build number).

    2. If the version number has changed, ensure that the build number is set to 0.

    3. Update the sha256 hash of the source archive prepared by GitHub.

    4. If the dependencies have changed, update the list of dependencies in the -run subsection to match those in the environment.yml file.

    5. Make sure that the list of recipe maintainers (in the extra.recipe-maintainers section of the meta.yaml file) is up to date.

    6. Make sure that the list of code owners (in .github/CODEOWNERS) is up to date.

  5. Commit the changes and push them to GitHub, then create a pull request from your branch for a merge of your update branch into the main branch at conda-forge.

  6. Once conda-forge's automated checks have passed and the reviewers (if any) have approved the changes, merge the pull request.

Once the pull request has been merged, the updated package should become available from conda-forge within a couple of hours. Usually the updated package is visible on https://anaconda.org/conda-forge/xcube-cds some time before it becomes accessible to mamba search and mamba install.

Post-release tasks