id,page,ref,title,content,breadcrumbs,references
contributing:contributing-upgrading-codemirror,contributing,contributing-upgrading-codemirror,Upgrading CodeMirror,"Datasette bundles  CodeMirror  for the SQL editing interface, e.g. on  this page . Here are the steps for upgrading to a new version of CodeMirror: 
             
                 
                     Install the packages with: 
                     npm i codemirror @codemirror/lang-sql 
                 
                 
                     Build the bundle using the version number from package.json with: 
                     node_modules/.bin/rollup datasette/static/cm-editor-6.0.1.js \
  -f iife \
  -n cm \
  -o datasette/static/cm-editor-6.0.1.bundle.js \
  -p @rollup/plugin-node-resolve \
  -p @rollup/plugin-terser 
                 
                 
                     Update the version reference in the  codemirror.html  template.","[""Contributing""]","[{""href"": ""https://codemirror.net/"", ""label"": ""CodeMirror""}, {""href"": ""https://latest.datasette.io/fixtures"", ""label"": ""this page""}]"
contributing:contributing-using-fixtures,contributing,contributing-using-fixtures,Using fixtures,"To run Datasette itself, type  datasette . 
             You're going to need at least one SQLite database. A quick way to get started is to use the fixtures database that Datasette uses for its own tests. 
             You can create a copy of that database by running this command: 
             python tests/fixtures.py fixtures.db 
             Now you can run Datasette against the new fixtures database like so: 
             datasette fixtures.db 
             This will start a server at  http://127.0.0.1:8001/ . 
             Any changes you make in the  datasette/templates  or  datasette/static  folder will be picked up immediately (though you may need to do a force-refresh in your browser to see changes to CSS or JavaScript). 
             If you want to change Datasette's Python code you can use the  --reload  option to cause Datasette to automatically reload any time the underlying code changes: 
             datasette --reload fixtures.db 
             You can also use the  fixtures.py  script to recreate the testing version of  metadata.json  used by the unit tests. To do that: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json 
             Or to output the plugins used by the tests, run this: 
             python tests/fixtures.py fixtures.db fixtures-metadata.json fixtures-plugins
Test tables written to fixtures.db
- metadata written to fixtures-metadata.json
Wrote plugin: fixtures-plugins/register_output_renderer.py
Wrote plugin: fixtures-plugins/view_name.py
Wrote plugin: fixtures-plugins/my_plugin.py
Wrote plugin: fixtures-plugins/messages_output_renderer.py
Wrote plugin: fixtures-plugins/my_plugin_2.py 
             Then run Datasette like this: 
             datasette fixtures.db -m fixtures-metadata.json --plugins-dir=fixtures-plugins/","[""Contributing""]",[]
contributing:devenvironment,contributing,devenvironment,Setting up a development environment,"If you have Python 3.8 or higher installed on your computer (on OS X the quickest way to do this  is using homebrew ) you can install an editable copy of Datasette using the following steps. 
             If you want to use GitHub to publish your changes, first  create a fork of datasette  under your own GitHub account. 
             Now clone that repository somewhere on your computer: 
             git clone git@github.com:YOURNAME/datasette 
             If you want to get started without creating your own fork, you can do this instead: 
             git clone git@github.com:simonw/datasette 
             The next step is to create a virtual environment for your project and use it to install Datasette's dependencies: 
             cd datasette
# Create a virtual environment in ./venv
python3 -m venv ./venv
# Now activate the virtual environment, so pip can install into it
source venv/bin/activate
# Install Datasette and its testing dependencies
python3 -m pip install -e '.[test]' 
             That last line does most of the work:  pip install -e  means ""install this package in a way that allows me to edit the source code in place"". The  .[test]  option means ""use the setup.py in this directory and install the optional testing dependencies as well"".","[""Contributing""]","[{""href"": ""https://docs.python-guide.org/starting/install3/osx/"", ""label"": ""is using homebrew""}, {""href"": ""https://github.com/simonw/datasette/fork"", ""label"": ""create a fork of datasette""}]"
contributing:general-guidelines,contributing,general-guidelines,General guidelines,"main should always be releasable . Incomplete features should live in branches. This ensures that any small bug fixes can be quickly released. 
                 
                 
                     The ideal commit  should bundle together the implementation, unit tests and associated documentation updates. The commit message should link to an associated issue. 
                 
                 
                     New plugin hooks  should only be shipped if accompanied by a separate release of a non-demo plugin that uses them.","[""Contributing""]",[]
contributing:id1,contributing,id1,Contributing,"Datasette is an open source project. We welcome contributions! 
         This document describes how to contribute to Datasette core. You can also contribute to the wider Datasette ecosystem by creating new  Plugins .",[],[]