Commit e9f7347a authored by Jeroen F.J. Laros's avatar Jeroen F.J. Laros

Structured documentation.

parent 3a6d8bc8
Trie implementation using nested dictionaries
=============================================
This library provides a trie_ implementation using nested dictionaries. Apart
from the basic operations, a number of functions for *approximate matching* are
implemented.
Please see ReadTheDocs_ for the latest documentation.
.. _trie: https://en.wikipedia.org/wiki/Trie
.. _ReadTheDocs: http://dict-trie.readthedocs.io/en/latest/index.html
Contributors
============
- Jeroen F.J. Laros <J.F.J.Laros@lumc.nl> (Original author, maintainer)
Find out who contributed:
::
git shortlog -s -e
.. doc_test documentation.
.. include:: ../README.rst
.. toctree::
:maxdepth: 2
:caption: Contents:
installation
usage
credits
Installation
============
The software is distributed via PyPI_, it can be installed with ``pip``:
::
pip install dict-trie
From source
-----------
The source is hosted on GitHub_, to install the latest development version, use
the following commands.
::
git clone https://github.com/jfjlaros/dict-trie.git
cd dict-trie
pip install .
.. _PyPI: https://pypi.org/project/dict-trie
.. _GitHub: https://github.com/jfjlaros/dict-trie.git
Usage
=====
The library provides the ``Trie`` class.
Basic operations
----------------
Initialisation of the trie is done via the constructor by providing a list of
words.
.. code:: python
>>> from dict_trie import Trie
>>>
>>> trie = Trie(['abc', 'te', 'test'])
Alternatively, an empty trie can be made to which words can be added with the
``add`` function.
.. code:: python
>>> trie = Trie()
>>> trie.add('abc')
>>> trie.add('te')
>>> trie.add('test')
Membership can be tested with the ``in`` statement.
.. code:: python
>>> 'abc' in trie
True
Test whether a prefix is present by using the ``has_prefix`` function.
.. code:: python
>>> trie.has_prefix('ab')
True
Remove a word from the trie with the ``remove`` function. This function returns
``False`` if the word was not in the trie.
.. code:: python
>>> trie.remove('abc')
True
>>> 'abc' in trie
False
>>> trie.remove('abc')
False
Iterate over all words in a trie.
.. code:: python
>>> list(trie)
['abc', 'te', 'test']
Approximate matching
--------------------
A trie can be used to efficiently find a word that is similar to a query word.
This is implemented via a number of functions that search for a word, allowing
a given number of mismatches. These functions are divided in two families, one
using the Hamming distance which only allows substitutions, the other using the
Levenshtein distance which allows substitutions, insertions and deletions.
To find a word that has at most Hamming distance 2 to the word 'abe', the
``hamming`` function is used.
.. code:: python
>>> trie = Trie(['abc', 'aaa', 'ccc'])
>>> trie.hamming('abe', 2)
'aaa'
To get all words that have at most Hamming distance 2 to the word 'abe', the
``all_hamming`` function is used. This function returns a generator.
.. code:: python
>>> list(trie.all_hamming('abe', 2))
['aaa', 'abc']
In order to find a word that is closest to the query word, the ``best_hamming``
function is used. In this case a word with distance 1 is returned.
.. code:: python
>>> trie.best_hamming('abe', 2)
'abc'
The functions ``levenshtein``, ``all_levenshtein`` and ``best_levenshtein`` are
used in a similar way.
Other functionalities
---------------------
A trie can be populated with all words of a fixed length over an alphabet by
using the ``fill`` function.
.. code:: python
>>> trie = Trie()
>>> trie.fill(('a', 'b'), 2)
>>> list(trie)
['aa', 'ab', 'ba', 'bb']
The trie data structure can be accessed via the ``root`` member variable.
.. code:: python
>>> trie.root
{'a': {'a': {'': 1}, 'b': {'': 1}}, 'b': {'a': {'': 1}, 'b': {'': 1}}}
>>> trie.root.keys()
['a', 'b']
The distance functions ``all_hamming`` and ``all_levenshtein`` also have
counterparts that give the developer more information by returning a list of
tuples containing not only the matched word, but also its distance to the query
string and a CIGAR_-like string.
The following encoding is used in the CIGAR-like string:
+-------------+---------------+
| character | description |
+-------------+---------------+
| = | match |
+-------------+---------------+
| X | mismatch |
+-------------+---------------+
| I | insertion |
+-------------+---------------+
| D | deletion |
+-------------+---------------+
In the following example, we search for all words with Hamming distance 1 to
the word 'acc'. In the results we see a match with the word 'abc' having
distance 1 and a mismatch at position 2.
.. code:: python
>>> trie = Trie(['abc'])
>>> list(trie.all_hamming_('acc', 1))
[('abc', 1, '=X=')]
Similarly, we can search for all words having Levenshtein distance 2 to the
word 'acb'. The word 'abc' matches three times, once by deleting the 'b' on
position 2 and inserting a 'b' after position 3, once by inserting a 'c' after
position 1 and deleting the last character and once by introducing two
mismatches.
.. code:: python
>>> list(trie.all_levenshtein_('acb', 2))
[('abc', 2, '=D=I'), ('abc', 2, '=XX'), ('abc', 2, '=I=D')]
.. _CIGAR: https://samtools.github.io/hts-specs/SAMv1.pdf
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