Skip to main content

Fearless interactivity for Jupyter notebooks.

Project description

-----------------------------------------------------

➤ nbsafety

Checked with mypy License: BSD3 Binder

About

nbsafety adds a layer of protection to computational notebooks by solving the stale dependency problem when executing cells out-of-order. Here's an example in action:

nbsafety example

When the first cell is rerun, the second cell now contains a reference to an updated f and is suggested for re-execution with a turquoise highlight. The third cell contains a reference to a stale y -- y is stale due to its dependency on an old value of f. As such, the third cell is marked as unsafe for re-execution with a red highlight. Once the second cell is rerun, it is now suggested to re-execute the third cell in order to refresh its stale output.

nbsafety accomplishes its magic using a combination of a runtime tracer (to build the implicit dependency graph) and a static checker (to provide warnings before running a cell), both of which are deeply aware of Python's data model. In particular, nbsafety requires minimal to no changes in user behavior, opting to get out of the way unless absolutely necessary and letting you use notebooks the way you prefer.

Install

pip install nbsafety

Interface

The kernel ships with an extension that highlights cells with live references to stale symbols using red UI elements. It furthermore uses turquoise highlights for cells with live references to updated symbols, as well as for cells that resolve staleness.

Running

To run an nbsafety kernel in Jupyter, select "Python 3 (nbsafety)" from the list of notebook types in Jupyter's "New" dropdown dialogue. For JupyterLab, similarly select "Python 3 (nbsafety)" from the list of available kernels in the Launcher tab.

Jupyter Notebook Entrypoint: Jupyter Lab Entrypoint:

Uninstall

pip uninstall nbsafety

License

Code in this project licensed under the BSD-3-Clause License.

-----------------------------------------------------

➤ Contributors

Stephen Macke Ray Gong Shreya Shankar
Stephen Macke Ray Gong Shreya Shankar

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nbplus-0.0.80.tar.gz (150.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nbplus-0.0.80-py2.py3-none-any.whl (164.3 kB view details)

Uploaded Python 2Python 3

File details

Details for the file nbplus-0.0.80.tar.gz.

File metadata

  • Download URL: nbplus-0.0.80.tar.gz
  • Upload date:
  • Size: 150.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.7

File hashes

Hashes for nbplus-0.0.80.tar.gz
Algorithm Hash digest
SHA256 f598f35aaabc4ba9bc6548ef0b6563205c9ff4bb366230c6ec5ecf72c065beac
MD5 18f034368d31f5181edc7bdaedbb646c
BLAKE2b-256 97226a5a25069da4922d12f00bb64638d9b3ea03f32a6008988bd6a3e987c3bb

See more details on using hashes here.

File details

Details for the file nbplus-0.0.80-py2.py3-none-any.whl.

File metadata

  • Download URL: nbplus-0.0.80-py2.py3-none-any.whl
  • Upload date:
  • Size: 164.3 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.7

File hashes

Hashes for nbplus-0.0.80-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 b50b73b81879961139c50837aeef856c49b9172297ec96497e1b3a88e1cd8cf9
MD5 66e9204f6151cebf07d2f9d93f38a594
BLAKE2b-256 c8f2d89e0d10eab10c1005b5e1b40f4821d44b497469df9841f1f7519dd73be4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page