==============
Super Collator
==============
.. |py39| image:: docs/_images/badge-py39.svg
.. |py310| image:: docs/_images/badge-py310.svg
.. |py311| image:: docs/_images/badge-py311.svg
.. |py312| image:: docs/_images/badge-py312.svg
.. |pypy39| image:: docs/_images/badge-pypy39.svg
.. |coverage| image:: docs/_images/badge-coverage.svg
|py39| |py310| |py311| |py312| |pypy39| |coverage|
Collates textual sources with relaxed spelling. Uses Gotoh's variant of the
Needleman-Wunsch sequence alignment algorithm.
.. code-block:: shell
$ pip install super-collator
.. code-block:: python
>>> from super_collator.aligner import Aligner
>>> from super_collator.ngrams import NGrams
>>> from super_collator.super_collator import to_table
>>> aligner = Aligner(-0.5, -0.5, -0.5)
>>> a = "Lorem ipsum dollar amat adipiscing elit"
>>> b = "qui dolorem ipsum quia dolor sit amet consectetur adipisci velit"
>>>
>>> a = [NGrams(s).load(s, 3) for s in a.split()]
>>> b = [NGrams(s).load(s, 3) for s in b.split()]
>>>
>>> a, b, score = aligner.align(a, b, NGrams.similarity, lambda: NGrams("-"))
>>> print(to_table(list(map(str, a)), list(map(str, b)))) # doctest: +NORMALIZE_WHITESPACE
- Lorem ipsum - dollar - amat - adipiscing elit
qui dolorem ipsum quia dolor sit amet consectetur adipisci velit
Documentation: https://cceh.github.io/super-collator/
PyPi: https://pypi.org/project/super-collator/
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"description": "==============\nSuper Collator\n==============\n\n.. |py39| image:: docs/_images/badge-py39.svg\n\n.. |py310| image:: docs/_images/badge-py310.svg\n\n.. |py311| image:: docs/_images/badge-py311.svg\n\n.. |py312| image:: docs/_images/badge-py312.svg\n\n.. |pypy39| image:: docs/_images/badge-pypy39.svg\n\n.. |coverage| image:: docs/_images/badge-coverage.svg\n\n|py39| |py310| |py311| |py312| |pypy39| |coverage|\n\nCollates textual sources with relaxed spelling. Uses Gotoh's variant of the\nNeedleman-Wunsch sequence alignment algorithm.\n\n.. code-block:: shell\n\n $ pip install super-collator\n\n.. code-block:: python\n\n >>> from super_collator.aligner import Aligner\n >>> from super_collator.ngrams import NGrams\n >>> from super_collator.super_collator import to_table\n\n >>> aligner = Aligner(-0.5, -0.5, -0.5)\n >>> a = \"Lorem ipsum dollar amat adipiscing elit\"\n >>> b = \"qui dolorem ipsum quia dolor sit amet consectetur adipisci velit\"\n >>>\n >>> a = [NGrams(s).load(s, 3) for s in a.split()]\n >>> b = [NGrams(s).load(s, 3) for s in b.split()]\n >>>\n >>> a, b, score = aligner.align(a, b, NGrams.similarity, lambda: NGrams(\"-\"))\n >>> print(to_table(list(map(str, a)), list(map(str, b)))) # doctest: +NORMALIZE_WHITESPACE\n - Lorem ipsum - dollar - amat - adipiscing elit\n qui dolorem ipsum quia dolor sit amet consectetur adipisci velit\n\nDocumentation: https://cceh.github.io/super-collator/\n\nPyPi: https://pypi.org/project/super-collator/\n",
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