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fuzzysearch
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:alt: License
fuzzysearch is a Python library for fuzzy substring searches. It implements efficient
ad-hoc searching for approximate sub-sequences. Matching is done using a generalized
Levenshtein Distance metric, with configurable parameters.
* Free software: `MIT license <LICENSE>`_
* Documentation: http://fuzzysearch.rtfd.org.
Installation
------------
Just install using pip::
$ pip install fuzzysearch
Features
--------
* Fuzzy sub-sequence search: Find parts of a sequence which match a given
sub-sequence.
* Easy to use: A single function to call which returns a list of matches.
* Set a maximum Levenshtein Distance for matches, including individual limits
for the number of substitutions, insertions and/or deletions allowed for
near-matches.
* Includes optimized implementations for specific use-cases, e.g. allowing
only substitutions.
Simple Examples
---------------
Just call `find_near_matches()` with the sequence to search, the sub-sequence
you're looking for, and the matching parameters:
.. code:: python
>>> from fuzzysearch import find_near_matches
# search for 'PATTERN' with a maximum Levenshtein Distance of 1
>>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1)
[Match(start=3, end=9, dist=1)]
.. code:: python
>>> sequence = '''\
GACTAGCACTGTAGGGATAACAATTTCACACAGGTGGACAATTACATTGAAAATCACAGATTGGTCACACACACA
TTGGACATACATAGAAACACACACACATACATTAGATACGAACATAGAAACACACATTAGACGCGTACATAGACA
CAAACACATTGACAGGCAGTTCAGATGATGACGCCCGACTGATACTCGCGTAGTCGTGGGAGGCAAGGCACACAG
GGGATAGG'''
>>> subsequence = 'TGCACTGTAGGGATAACAAT' # distance = 1
>>> find_near_matches(subsequence, sequence, max_l_dist=2)
[Match(start=3, end=24, dist=1)]
Advanced Example
----------------
The search function supports four possible match criteria, which may be supplied in any combination:
* maximum Levenshtein distance
* maximum # of subsitutions
* maximum # of deletions (elements appearing in the pattern search for, which are skipped in the matching sub-sequence)
* maximum # of insertions (elements added in the matching sub-sequence which don't appear in the pattern search for)
Not supplying a criterion means that there is no limit for it. For this reason, one must always supply `max_l_dist` and/or all of the other three criteria.
.. code:: python
>>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1)
[Match(start=3, end=9, dist=1)]
# this will not match since max-deletions is set to zero
>>> find_near_matches('PATTERN', '---PATERN---', max_l_dist=1, max_deletions=0)
[]
# note that a deletion + insertion may be combined to match a substution
>>> find_near_matches('PATTERN', '---PAT-ERN---', max_deletions=1, max_insertions=1, max_substitutions=0)
[Match(start=3, end=10, dist=1)] # the Levenshtein distance is still 1
# ... but deletion + insertion may also match other, non-substitution differences
>>> find_near_matches('PATTERN', '---PATERRN---', max_deletions=1, max_insertions=1, max_substitutions=0)
[Match(start=3, end=10, dist=2)]
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