Tools for cleaning code, recovering lost code, and comparing versions of code in Jupyter Lab.

Download the alpha extension with the following command:

jupyter labextension install nbgather

Then you can clean and compare versions of your code like so:

Want to try it out first? Play around with nbgather on an example notebook on BinderHub.

Did the install fail? Make sure Jupyter Lab is up-to-date, and that you are running Jupyter Lab from Python 3.

This project is in alpha: The code this collects will sometimes be more than you want. It saves your a history of all code you've executed and the outputs it produces to the notebook's metadata. The user interface has a few quirks.

Help us make this a real, practical, and really useful tool. We welcome any and all feedback and contributions. We are particularly in need of the opinions and efforts of those with a penchant for hacking code analysis.

Can it extract more precise slices of code? Yes. First submit a pull request telling us the desired extraction behavior, so we can incorporate this behavior into the tool.

Meanwhile, you can help the backend make more precise slices by telling the tool which functions don't modify their arguments. By default, the tool assumes that functions change all arguments they're called with, and the objects they're called on, with exceptions for some common APIs. To edit the slicing rules, open the Advanced Settings Editor in the Jupyter Lab Settings menu and choose the "nbgather" tab. In your user-defined settings, override moduleMap, following this format to specify which functions don't modify their arguments.

How do I clear the notebook's history? Open up your .ipynb file in a text editor, find the history key in the top-level metadata object, and set history to [].

To run the development version of nbgather, run:

git clone <this-repository-url>  # clone the repository
npm install                      # download dependencies
jupyter labextension link .      # install this package in Jupyter Lab
npm run watch                    # automatically recompile source code
jupyter lab --watch              # launch Jupyter Lab, automatically re-load extension

This requires npm version 4 or later, and was tested most recently with Node v9.5.0.

Submit all change as a pull request. Feel free to author the the lead contributor (Andrew Head, andrewhead@berkeley.edu) if you have any questions about getting started with the code or about features or updates you'd like to contribute.

Also, make sure to format the code and test it before submitting a pull request, as described below:

Before submitting a pull request with changed code, format the code files by running npm run format:all.

To run the tests from the command line, call:

npm run test

The first time you run tests, they will take about a minute to finish. The second time, and all subsequent times, the tests will take only a few seconds. The first test run takes longer because the Jest test runner transpiles dependencies like the '@jupyterlab' libraries into a dialect of JavaScript it expects before running the tests.

Here are some tips for dealing with build errors we've encountered while developing code gathering tools:

• Errors about missing semicolons in React types files: upgrade the typescript and ts-node packages
• Conflicting dependencies: upgrade either the Python Jupyter Lab (may require Python upgrade to Python 3 to get the most recent version of Jupyter Lab) or the Jupyter Lab npm pacakges
• Other build issues: we've found some issues can be solved by just deleting your node_modules/ directory and reinstalling it.