Skip to main content

A Wadler–Lindig ✨pretty-printer✨ for Python

This library is for you if you need:

  • Something like the built-in pprint.pprint, but which consumes less horizontal space. For example in error messages.
  • If you have complicated custom types that you'd like to create pretty well-formatted reprs for. For example nested trees of dataclasses / PyTorch modules / etc.

Main features:

  • Absolutely tiny implementation (96 lines of code for the main Wadler–Lindig algorithm, 342 more for teaching it how to handle all Python types).
  • Simpler than the original algorithm by Wadler & Lindig (removes some dead code).
  • Supports multi-line unbroken text strings.
  • Supports ANSI escape codes and colours.
  • Zero dependencies.

Installation

pip install wadler_lindig

Documentation

Available at https://docs.kidger.site/wadler_lindig.

Example

import dataclasses
import numpy as np
import wadler_lindig as wl

@dataclasses.dataclass
class MyDataclass:
    x: list[str]
    y: np.ndarray

obj = MyDataclass(["lorem", "ipsum", "dolor sit amet"], np.zeros((2, 3)))

wl.pprint(obj, width=30, indent=4)
# MyDataclass(
#     x=[
#         'lorem',
#         'ipsum',
#         'dolor sit amet'
#     ],
#     y=f64[2,3](numpy)
# )

API at a glance

For day-to-day pretty-printing objects: pprint (to stdout), pformat (as a string), pdiff (between two objects).

For creating custom pretty-printed representations:

  • The core Wadler–Lindig document types: AbstractDoc, BreakDoc, ConcatDoc, GroupDoc, NestDoc, TextDoc.
  • pdoc will convert any Python object to a Wadler–Lindig document, with the __pdoc__ method called on custom types if it is available.
  • ansi_format (to add ANSI colour codes), ansi_strip (to remove ANSI codes).
  • Several common helpers for creating Wadler–Lindig documents: array_summary, bracketed, comma, join, named_objs.

FAQ

What is the difference to the built-in `pprint` library?
  1. The main difference is that the Wadler–Lindig algorithm produces output like
MyDataclass(
  x=SomeNestedClass(
    y=[1, 2, 3]
  )
)

In contrast pprint produces output like

MyDataclass(x=SomeNestedClass(y=[1,
                                 2,
                                 3]))

which consumes a lot more horizontal space.

  1. By default we print NumPy arrays / PyTorch tensors / etc. in a concise form e.g. f32[2,3](numpy) to denote a NumPy array with shape (2, 3) and dtype float32. (Set short_arrays=False to disable this.)

  2. We provide support for customising the pretty-printed representations of your custom types. Typically this is done via:

    import wadler_lindig as wl
    
    class MyAmazingClass:
        def __pdoc__(self, **kwargs) -> wl.AbstractDoc:
            ...  # Create your pretty representation here!
    
        def __repr__(self):
            # Calls `__pdoc__` and then formats to a particular width.
            return wl.pformat(self, width=80)
    

    In addition we support a wadler_lindig.pprint(..., custom=...) argument, if you don't own the type and so cannot add a __pdoc__ method.

What is the difference to `black` or `rust format`?

The above are formatters for your source code. This wadler_lindig library is intended as an alternative to the built-in pprint library, which pretty-format Python objects at runtime.

Metadata

Release files for wadler-lindig 0.1.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for wadler-lindig 0.1.8
File Size Uploaded Provenance Actions
wadler_lindig-0.1.8.tar.gz 16.3 kB Without provenance Details for wadler_lindig-0.1.8.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for wadler-lindig 0.1.8
File Interpreter ABI Platform Actions
wadler_lindig-0.1.8-py3-none-any.whl Python 3 none any Details for wadler_lindig-0.1.8-py3-none-any.whl

Total release size: 37.1 kB

Release files / wadler_lindig-0.1.8.tar.gz

Download URL wadler_lindig-0.1.8.tar.gz
Size 16.3 kB
Tags Source
SHA-256 checksum
How to use checksums
1a94d51cd26100dda9e430c64539c5b92b9788c87f10db3e1c87503ab814411a
BLAKE2b-256 checksum
How to use checksums
eabf4caf1aada6c84eb3fadf9341a90367f436a759692456e1a234d444d428ee
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.17

Release files / wadler_lindig-0.1.8-py3-none-any.whl

Download URL wadler_lindig-0.1.8-py3-none-any.whl
Size 20.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8671e8c98449ae3f4e4ba720eccd63a502b3983042f8d112b49a9c6be49e8d92
BLAKE2b-256 checksum
How to use checksums
000bb170089cd44848f25dbc9efda08e5aa4a8402d9a13f0d4d0f3d7808be539
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.17

Security for wadler-lindig 0.1.8

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page