sesdiff 0.3.2

Generates a shortest edit script (Myers' diff algorithm) to indicate how to get from the strings in column A to the strings in column B. Also provides the edit distance (levenshtein). This is the Python binding.

Sesdiff python binding

Description

This is a python library that reads relates two strings and computes the shortest edit script (Myers' diff algorithm) to go from the string in column A to the string in column B. It also computes the edit distance (aka levenshtein distance).

There is also a command line version available.

Installation

pip install sesdiff

Usage

The shortest_edit_script function returns an EditScript instance containing the instructions needed to get from string A to string B. Instructions are represented as two-tuples with the first string being a character representing the edit instruction type (+ for insertion, - for deletion, = for identity) and the second the actual textual content.

from sesdiff import shortest_edit_script, Mode

#normal mode
result = shortest_edit_script("hablaron","hablar")
assert result.distance() == 2
assert len(result) == 2 
assert result[0] == ('=',"hablar")
assert result[1] == ('-',"on")

#print all instructions
for instruction in result:
    print(instruction)

#suffix mode
result = shortest_edit_script("hablaron","hablar", Mode.SUFFIX)
assert result.distance() == 2
assert len(result) == 1
assert result[0] == ('-',"on")

#works fine with unicode:
result = shortest_edit_script("говорим","говорить")
assert result.distance() == 3
assert len(result) == 3
assert result[0] == ('=',"говори")
assert result[1] == ('-',"м")
assert result[2] == ('+',"ть")

Limitations

The apply functionality from the main library/CLI tool is not implemented yet.

Do not use this library if you're merely interested in computing levenshtein distance, it comes with performance overhead to return the actual edit scripts.