Skip to main content

Literate package development with Jupyter

Project description

Literary logo with an orange cursive uppercase L inside black square brackets

Literary

pypi-badge binder-badge

This package is an exploration of the literate programming idea pioneered by Donald Knuth and implemented in the nbdev package. Although nbdev looks to be a very mature and comprehensive tool, it is quite opinionated. This package is an investigation into what a smaller nbdev might look like.

Philosophy

  1. Low mental overhead
    Realistically, most Python programmers that wish to write packages need to have some familiarity with the Python package development model, including the conventional structure of a package. For this reason, I feel that it is important to design literary such that these skills translate directly to designing libraries with notebooks
  2. Minimal downstream impact
    Users of literary packages should not realise that they are consuming notebook-generated code at runtime. This means that a pure-Python package needs to be generated from the notebooks, and it must use the conventional import model. For this reason, literary should only exist as a development dependency of the package.

Differences with nbdev

  • Use of cell tags instead of comments or magics to dictate exports
  • Use of nbconvert machinery to build the pure-Python lib package
  • Use of import hooks to import other notebooks
    • Maintains a similar programming model to conventional module development
    • Reduces the need to modify notebook contents during conversion
  • Minimal runtime overhead
    • Features like patch are removed from the generated module (& imported notebook source) using AST transformations
  • Currently no documentation generation
    • Loosely, the plan is to use existing notebook-book tooling to re-use the existing Jupyter ecosystem

Differences with Knuth

Knuth introduced the tangle and weave programs to produce separate documentation and source code for compilation. Literary differs in treating the notebook as the "ground truth" for documentation + testing, and generating smaller source code for packaging.

Design

The plan for this package is:

  1. Notebooks will be written inside <PACKAGE_NAME>/ in literary project's root directory
  2. Notebooks will respect relative imports and other pure-Python features to minimise the differences between the generated packages and the notebooks
  3. A pure-python generated lib/<PACKAGE_NAME>/ directory will be built before Poetry builds the final project.
    E.g.
    [tool.poetry]
    # ...
    packages = [
      { include = "<PACKAGE_NAME>", from = "lib" },
    ]
    

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

literary-1.7.0.tar.gz (14.8 kB view details)

Uploaded Source

Built Distribution

literary-1.7.0-py3-none-any.whl (17.4 kB view details)

Uploaded Python 3

File details

Details for the file literary-1.7.0.tar.gz.

File metadata

  • Download URL: literary-1.7.0.tar.gz
  • Upload date:
  • Size: 14.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for literary-1.7.0.tar.gz
Algorithm Hash digest
SHA256 ef4ca3a6f7fb34575808439c23f986b1cc4e9889ee7e47a8fe83ecff0da92e8a
MD5 2059eb85f7e00bd086f095fd3fb1f6e9
BLAKE2b-256 73e37f38dc260a2f34c5041419274502a4273fb6935ba1332b4f01c24844f6a1

See more details on using hashes here.

File details

Details for the file literary-1.7.0-py3-none-any.whl.

File metadata

  • Download URL: literary-1.7.0-py3-none-any.whl
  • Upload date:
  • Size: 17.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.9.4

File hashes

Hashes for literary-1.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 e4d4f1928374750b6ce0ff8a8e3b37aed63a8513405a2fabb36c070c4297338d
MD5 01fb8827a7a95a1805cf45e0bcefccca
BLAKE2b-256 69e06f81cf434137cc7d4d2bfa2e0943a3b6c61ff2835ba4288126a361f16b75

See more details on using hashes here.

Supported by

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