Metadata-Version: 2.1
Name: cdiffer
Version: 0.1.3
Summary: Usefull differ function with Levenshtein distance.
Home-page: https://github.com/kirin123kirin/cdiffer
Author: kirin123kirin
License: GPL2
Description: # Python CExtention 2 Sequence Compare [![Upload pypi.org](https://github.com/kirin123kirin/cdiffer/actions/workflows/pypi.yml/badge.svg?branch=master)](https://github.com/kirin123kirin/cdiffer/actions/workflows/pypi.yml)
        
        **Usefull differ function with Levenshtein distance.**
        
        # How to Install?
        ```shell
        pip install cdiffer
        ```
        
        # Requirement
        * python3.6 or later
        * python2.7
        
        # cdiffer.dist
        Compute absolute Levenshtein distance of two strings.
        
        ## Usage
        dist(sequence, sequence)
        
        ## Examples (it's hard to spell Levenshtein correctly):
        
        ```python
        >>> from cdiffer import dist
        >>>
        >>> dist('coffee', 'cafe')
        3
        >>> dist(list('coffee'), list('cafe'))
        3
        >>> dist(tuple('coffee'), tuple('cafe'))
        3
        >>> dist(iter('coffee'), iter('cafe'))
        3
        >>> dist(range(4), range(5))
        1
        >>> dist('coffee', 'xxxxxx')
        6
        >>> dist('coffee', 'coffee')
        0
        ```
        
        # cdiffer.similar
        
        Compute similarity of two strings.
        
        ## Usage
        similar(sequence, sequence)
        
        The similarity is a number between 0 and 1, it's usually equal or
        somewhat higher than difflib.SequenceMatcher.ratio(), because it's
        based on real minimal edit distance.
        
        ## Examples
        ```python
        >>> from cdiffer import similar
        >>>
        >>> similar('coffee', 'cafe')
        0.6
        >>> similar('hoge', 'bar')
        0.0
        
        ```
        
        # cdiffer.differ
        
        Find sequence of edit operations transforming one string to another.
        
        ## Usage
        differ(source_sequence, destination_sequence, diffonly=False)
        
        ## Examples
        
        ```python
        >>> from cdiffer import differ
        >>>
        >>> for x in differ('coffee', 'cafe'):
        ...     print(x)
        ...
        ['equal',   0, 0,   'c', 'c']
        ['replace', 1, 1,   'o', 'a']
        ['equal',   2, 2,   'f', 'f']
        ['delete',  3, None,'f',None]
        ['delete',  4, None,'e',None]
        ['equal',   5, 3,   'e', 'e']
        >>> for x in differ('coffee', 'cafe', diffonly=True):
        ...     print(x)
        ...
        ['replace', 1, 1,   'o', 'a']
        ['delete',  3, None,'f',None]
        ['delete',  4, None,'e',None]
        ```
        
        ## Performance
        
        
        ```python
        C:\Windows\system>ipython
        Python 3.7.7 (tags/v3.7.7:d7c567b08f, Mar 10 2020, 10:41:24) [MSC v.1900 64 bit (AMD64)]
        Type 'copyright', 'credits' or 'license' for more information
        IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.
        
        In [1]: from cdiffer import *
        
        In [2]: %timeit dist('coffee', 'cafe')
           ...: %timeit dist(list('coffee'), list('cafe'))
           ...: %timeit dist(tuple('coffee'), tuple('cafe'))
           ...: %timeit dist(iter('coffee'), iter('cafe'))
           ...: %timeit dist(range(4), range(5))
           ...: %timeit dist('coffee', 'xxxxxx')
           ...: %timeit dist('coffee', 'coffee')
           ...:
        173 ns ± 0.206 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        741 ns ± 2.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        702 ns ± 2.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        706 ns ± 7.79 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        882 ns ± 7.51 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        210 ns ± 0.335 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        51.8 ns ± 1.18 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        
        In [3]: %timeit similar('coffee', 'cafe')
           ...: %timeit similar(list('coffee'), list('cafe'))
           ...: %timeit similar(tuple('coffee'), tuple('cafe'))
           ...: %timeit similar(iter('coffee'), iter('cafe'))
           ...: %timeit similar(range(4), range(5))
           ...: %timeit similar('coffee', 'xxxxxx')
           ...: %timeit similar('coffee', 'coffee')
           ...:
        186 ns ± 0.476 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        718 ns ± 0.878 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        691 ns ± 1.42 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        706 ns ± 2.01 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        920 ns ± 8.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        223 ns ± 0.938 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        55 ns ± 0.308 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        
        In [4]: %timeit differ('coffee', 'cafe')
           ...: %timeit differ(list('coffee'), list('cafe'))
           ...: %timeit differ(tuple('coffee'), tuple('cafe'))
           ...: %timeit differ(iter('coffee'), iter('cafe'))
           ...: %timeit differ(range(4), range(5))
           ...: %timeit differ('coffee', 'xxxxxx')
           ...: %timeit differ('coffee', 'coffee')
           ...:
        814 ns ± 2.79 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.36 µs ± 2.02 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.33 µs ± 4.19 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.37 µs ± 4.64 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        2.03 µs ± 19.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        865 ns ± 1.89 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        724 ns ± 1.72 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
        In [5]: a = dict(zip('012345', 'coffee'))
           ...: b = dict(zip('0123', 'cafe'))
           ...: %timeit dist(a, b)
           ...: %timeit similar(a, b)
           ...: %timeit differ(a, b)
        320 ns ± 1.26 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        327 ns ± 1.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        983 ns ± 17.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
        
        ```
Keywords: diff,comparison,compare
Platform: Windows
Platform: Linux
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: C
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Description-Content-Type: text/markdown
