amyreese / diff-match-patch

Diff Match Patch is a high-performance library in multiple languages that manipulates plain text.

diff-match-patch

Google's Diff Match and Patch library, packaged for modern Python.

Install

diff-match-patch is supported on Python 2.7 or Python 3.4 or newer. You can install it from PyPI:

python -m pip install diff-match-patch

Usage

Generating a patchset (analogous to unified diff) between two texts:

from diff_match_patch import diff_match_patch

dmp = diff_match_patch()
patches = dmp.patch_make(text1, text2)
diff = dmp.patch_toText(patches)

Applying a patchset to a text can then be done with:

from diff_match_patch import diff_match_patch

dmp = diff_match_patch()
patches = dmp.patch_fromText(diff)
new_text, _ = dmp.patch_apply(patches, text)

Original README

The Diff Match and Patch libraries offer robust algorithms to perform the operations required for synchronizing plain text.

1. Diff:
   • Compare two blocks of plain text and efficiently return a list of differences.
   • Diff Demo
2. Match:
   • Given a search string, find its best fuzzy match in a block of plain text. Weighted for both accuracy and location.
   • Match Demo
3. Patch:
   • Apply a list of patches onto plain text. Use best-effort to apply patch even when the underlying text doesn't match.
   • Patch Demo

Originally built in 2006 to power Google Docs, this library is now available in C++, C#, Dart, Java, JavaScript, Lua, Objective C, and Python.

Reference

• API - Common API across all languages.
• Line or Word Diffs - Less detailed diffs.
• Plain Text vs. Structured Content - How to deal with data like XML.
• Unidiff - The patch serialization format.
• Support - Newsgroup for developers.

Languages

Although each language port of Diff Match Patch uses the same API, there are some language-specific notes.

• C++
• C#
• Dart
• Java
• JavaScript
• Lua
• Objective-C
• Python

A standardized speed test tracks the relative performance of diffs in each language.

Algorithms

This library implements Myer's diff algorithm which is generally considered to be the best general-purpose diff. A layer of pre-diff speedups and post-diff cleanups surround the diff algorithm, improving both performance and output quality.

This library also implements a Bitap matching algorithm at the heart of a flexible matching and patching strategy.