{"id": "contributing:contributing-bug-fix-branch", "page": "contributing", "ref": "contributing-bug-fix-branch", "title": "Releasing bug fixes from a branch", "content": "If it's necessary to publish a bug fix release without shipping new features that have landed on  main  a release branch can be used. \n             Create it from the relevant last tagged release like so: \n             git branch 0.52.x 0.52.4\ngit checkout 0.52.x \n             Next cherry-pick the commits containing the bug fixes: \n             git cherry-pick COMMIT \n             Write the release notes in the branch, and update the version number in  version.py . Then push the branch: \n             git push -u origin 0.52.x \n             Once the tests have completed, publish the release from that branch target using the GitHub  Draft a new release  form. \n             Finally, cherry-pick the commit with the release notes and version number bump across to  main : \n             git checkout main\ngit cherry-pick COMMIT\ngit push", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://github.com/simonw/datasette/releases/new\", \"label\": \"Draft a new release\"}]"}
{"id": "contributing:contributing-continuous-deployment", "page": "contributing", "ref": "contributing-continuous-deployment", "title": "Continuously deployed demo instances", "content": "The demo instance at  latest.datasette.io  is re-deployed automatically to Google Cloud Run for every push to  main  that passes the test suite. This is implemented by the GitHub Actions workflow at  .github/workflows/deploy-latest.yml . \n             Specific branches can also be set to automatically deploy by adding them to the  on: push: branches  block at the top of the workflow YAML file. Branches configured in this way will be deployed to a new Cloud Run service whether or not their tests pass. \n             The Cloud Run URL for a branch demo can be found in the GitHub Actions logs.", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://latest.datasette.io/\", \"label\": \"latest.datasette.io\"}, {\"href\": \"https://github.com/simonw/datasette/blob/main/.github/workflows/deploy-latest.yml\", \"label\": \".github/workflows/deploy-latest.yml\"}]"}
{"id": "contributing:contributing-formatting-prettier", "page": "contributing", "ref": "contributing-formatting-prettier", "title": "Prettier", "content": "To install Prettier,  install Node.js  and then run the following in the root of your  datasette  repository checkout: \n                 npm install \n                 This will install Prettier in a  node_modules  directory. You can then check that your code matches the coding style like so: \n                 npm run prettier -- --check \n                 > prettier\n> prettier 'datasette/static/*[!.min].js' \"--check\"\n\nChecking formatting...\n[warn] datasette/static/plugins.js\n[warn] Code style issues found in the above file(s). Forgot to run Prettier? \n                 You can fix any problems by running: \n                 npm run fix", "breadcrumbs": "[\"Contributing\", \"Code formatting\"]", "references": "[{\"href\": \"https://nodejs.org/en/download/package-manager/\", \"label\": \"install Node.js\"}]"}
{"id": "contributing:contributing-formatting-blacken-docs", "page": "contributing", "ref": "contributing-formatting-blacken-docs", "title": "blacken-docs", "content": "The  blacken-docs  command applies Black formatting rules to code examples in the documentation. Run it like this: \n                 blacken-docs -l 60 docs/*.rst", "breadcrumbs": "[\"Contributing\", \"Code formatting\"]", "references": "[{\"href\": \"https://pypi.org/project/blacken-docs/\", \"label\": \"blacken-docs\"}]"}
{"id": "contributing:contributing-documentation", "page": "contributing", "ref": "contributing-documentation", "title": "Editing and building the documentation", "content": "Datasette's documentation lives in the  docs/  directory and is deployed automatically using  Read The Docs . \n             The documentation is written using reStructuredText. You may find this article on  The subset of reStructuredText worth committing to memory  useful. \n             You can build it locally by installing  sphinx  and  sphinx_rtd_theme  in your Datasette development environment and then running  make html  directly in the  docs/  directory: \n             # You may first need to activate your virtual environment:\nsource venv/bin/activate\n\n# Install the dependencies needed to build the docs\npip install -e .[docs]\n\n# Now build the docs\ncd docs/\nmake html \n             This will create the HTML version of the documentation in  docs/_build/html . You can open it in your browser like so: \n             open _build/html/index.html \n             Any time you make changes to a  .rst  file you can re-run  make html  to update the built documents, then refresh them in your browser. \n             For added productivity, you can use use  sphinx-autobuild  to run Sphinx in auto-build mode. This will run a local webserver serving the docs that automatically rebuilds them and refreshes the page any time you hit save in your editor. \n             sphinx-autobuild  will have been installed when you ran  pip install -e .[docs] . In your  docs/  directory you can start the server by running the following: \n             make livehtml \n             Now browse to  http://localhost:8000/  to view the documentation. Any edits you make should be instantly reflected in your browser.", "breadcrumbs": "[\"Contributing\"]", "references": "[{\"href\": \"https://readthedocs.org/\", \"label\": \"Read The Docs\"}, {\"href\": \"https://simonwillison.net/2018/Aug/25/restructuredtext/\", \"label\": \"The subset of reStructuredText worth committing to memory\"}, {\"href\": \"https://pypi.org/project/sphinx-autobuild/\", \"label\": \"sphinx-autobuild\"}]"}