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An experimental diff library for generating operation deltas that represent the difference between two sequences of comparable items.

Project description


An open licensed (MIT) library for performing generating deltas (A.K.A sequences of operations) representing the difference between two sequences of comparable tokens.

This library is intended to be used to make experimental difference detection strategies more easily available. There are currently two strategies available:

deltas.sequence_matcher.diff(a, b):
A shameless wrapper around difflib.SequenceMatcher to get it to work within the structure of deltas.

deltas.segment_matcher.diff(a, b, segmenter=None):
A generalized difference detector that is designed to detect block moves and copies based on the use of a Segmenter.


from deltas import segment_matcher, text_split
a = text_split.tokenize("This is some text. This is some other text.")`|
b = text_split.tokenize("This is some other text. This is some text.")
operations = segment_matcher.diff(a, b)

for op in operations:
 print(, repr(''.join(a[op.a1:op.a2])),

equal 'This is some other text.' 'This is some other text.'
insert ' ' ' '
equal 'This is some text.' 'This is some text.'
delete ' ' ''


By default Deltas performs tokenization by regexp text splitting. We included CJK tokenization functionality. If text consists of at least 1/4 (default value) Japanse or Korean symbols it is tokenized by language specific Tokenizer. Else, Chinese Tokenizer is used.

  • Chinese Tokenizer - Jieba
  • Japanese Tokenizer - Sudachi
  • Korean Tokenizer - KoNLPy(Okt)

Tokenization example:

import mwapi
import deltas
import deltas.tokenizers

# example title ["China", "Haiku", "Kimchi"]: "中国" - Chinese(zh), "俳句" - Japanese(ja), "김치" - Korean(ko)
session = mwapi.Session("")
doc = session.get(action="query", prop="revisions", titles="中国", rvprop="content", rvslots="main",formatversion=2)
text = doc['query']['pages'][0]['revisions'][0]['slots']['main']['content']

# text processed only by regexp tokenizer
tokenized_text = deltas.tokenizers.wikitext_split.tokenize(text)
# text processed regexp tokenizer with cjk post processing
tokenized_text_cjk = deltas.tokenizers.wikitext_split_w_cjk.tokenize(text)


pip install sudachidict_full
# and link sudachi to dict
sudachipy link -t full

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