Dataflow Notebook Extension for Jupyter/Python

This package is part of the Dataflow Notebooks project and provides the Dataflow Notebook interface for JupyterLab, and is intended to be used with the dfkernel kernel. Dataflow notebooks seek to elevate outputs as memorable waypoints during exploratory computation. To that end,

• Cell identifiers are persistent across sessions and are random UUIDs to signal they do not depend on top-down order.
• As with standard IPython, outputs are designated by being written as expressions or assignments on the last line of a cell.
• Each output is identified by its variable name if one is specified (e.g. a, c,d = 4,5), and the cell identifier if not (e.g. 4 + c)
• Variable names can be reused across cells.
• Cells are executed as closures so only the outputs are accessible from other cells.
• An output can then be referenced in three ways:
  1. unscoped: foo refers to the most recent execution output named foo
  2. persistent: foo$ba012345 refers to output foo from cell ba012345
  3. tagged: foo$bar refers to output foo from the cell tagged as bar
• All output references are transformed to persistent names upon execution.
• Output references implicitly define a dataflow in a directed acyclic graph, and the kernel automatically executes dependencies.

Example Notebook

Requirements

• JupyterLab >= 4.0.0

Install

This extension uses a Jupyter kernel named dfkernel for the backend and a Jupyter extension named dfnotebook for the frontend.

To install the kernel, kernel:

pip install dfkernel

To install the extension, execute:

pip install dfnotebook

Uninstall

To remove the extension, execute:

pip uninstall dfnotebook

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 dfnotebook directory
# Install package in development mode
pip install -e "."
# 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

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

pip uninstall dfnotebook

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named @dfnotebook/dfnotebook-extension within that folder.

Testing the extension

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

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