jupyterlab-ripple 0.1.0

Ripple: Reactive notebook execution in JupyterLab. Automatically re-executes downstream cells when their dependencies change.

jupyterlab-ripple

Ripple brings reactive execution to JupyterLab notebooks.

When enabled, executing a cell automatically re-executes all downstream cells that depend on its variables — determined via Python AST analysis. No custom kernel required.

How It Works

1. Click the Ripple button in the notebook toolbar to enable reactive mode
2. Edit and run a cell as usual
3. All cells that reference variables defined by that cell automatically re-execute in dependency order

Ripple uses kernel-side ast.parse() to statically analyze each cell's variable definitions and references, building a directed acyclic graph (DAG) of cell dependencies. When a cell executes, its transitive downstream dependents are re-run in topological order.

Features

• Automatic downstream execution — change x = 1 to x = 2, and every cell using x re-runs
• Dependency visualization — colored left borders show each cell's role in the dependency graph:
  • Blue: defines variables used by other cells (upstream)
  • Green: depends on variables from other cells (downstream)
  • Amber: stale — upstream changed but this cell hasn't re-run yet
  • Red: variable conflict (defined in multiple cells) or dependency cycle
• Cycle detection — circular dependencies are detected and flagged (not auto-executed)
• Variable conflict warnings — variables defined in multiple cells are highlighted
• Per-notebook toggle — enable/disable independently for each notebook
• No custom kernel — works with standard ipykernel via silent execution

Architecture

File	Purpose
src/dag.ts	Dependency graph, topological sort (Kahn's), cycle detection (Tarjan's SCC)
src/analyzer.ts	Kernel-side Python AST analysis via silent requestExecute
src/reactiveState.ts	Per-notebook state: analysis cache, graph, stale/conflict tracking
src/reactiveCellExecutor.ts	Custom INotebookCellExecutor wrapping default runCell
src/ui/toggleButton.ts	Toolbar toggle and command registration
src/ui/dependencyIndicators.ts	CSS class management for visual cell indicators

The extension replaces JupyterLab's default INotebookCellExecutor plugin, intercepting cell execution to add reactive propagation.

Limitations

• Side effects not tracked — list.append(x) or obj.attr = val won't trigger downstream re-runs
• Magic commands — % and ! lines are stripped before AST analysis
• Dynamic code — exec(), eval(), and metaprogramming may confuse the analyzer

Requirements

• JupyterLab >= 4.0.0

Install

pip install jupyterlab_ripple

Uninstall

pip uninstall jupyterlab_ripple

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the jupyterlab-ripple directory

# Set up a virtual environment and install package in development mode
python -m venv .venv
source .venv/bin/activate
pip install --editable "."

# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite

# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

Testing the extension

Frontend tests

This extension uses Jest for JavaScript code testing.

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE

License

MIT