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https://github.com/microsoft/gather
Spit shine for Jupyter notebooks 🧽✨
https://github.com/microsoft/gather
jupyterlab-extension
Last synced: 9 days ago
JSON representation
Spit shine for Jupyter notebooks 🧽✨
- Host: GitHub
- URL: https://github.com/microsoft/gather
- Owner: microsoft
- License: mit
- Archived: true
- Created: 2018-10-08T23:32:10.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-10-03T23:29:38.000Z (9 months ago)
- Last Synced: 2024-06-18T04:43:30.556Z (10 days ago)
- Topics: jupyterlab-extension
- Language: Jupyter Notebook
- Homepage: https://microsoft.github.io/gather
- Size: 10.4 MB
- Stars: 530
- Watchers: 13
- Forks: 38
- Open Issues: 38
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Lists
- awesome-stars - gather
- awesome-jupyter-resources - GitHub - 40% open · ⏱️ 14.02.2020): (JupyterLab扩展)
README
# nbgather: 🧽✨ Spit shine for Jupyter notebooks
Tools for cleaning code, recovering lost code, and comparing
versions of code in Jupyter Lab.
Download the alpha extension with the following command:
```bash
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.](https://gke.mybinder.org/v2/gh/microsoft/gather/master?urlpath=lab/tree/binder%2FTry%20out%20nbgather.ipynb)
**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.
## Usage Tips
**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](https://github.com/andrewhead/python-program-analysis/tree/master/src/specs).
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](https://github.com/andrewhead/python-program-analysis#api-specs)
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 `[]`.
## Contributing
To run the development version of nbgather, run:
```bash
git clone # 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, ) 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:
### Formatting the code
Before submitting a pull request with changed code, format the code
files by running `npm run format:all`.
### Testing the code
To run the tests from the command line, call:
```bash
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.
### Troubleshooting
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.