Metadata-Version: 2.1
Name: cdiffer
Version: 0.2.1
Summary: Usefull differ function with Levenshtein distance.
Home-page: https://github.com/kirin123kirin/cdiffer
Author: kirin123kirin
License: GPL2
Description: 
        
        # Python C Extention 2 Sequence Compare
        [![Upload pypi.org](https://github.com/kirin123kirin/cdiffer/actions/workflows/pypi.yml/badge.svg?branch=v0.2.1)](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,
        base on levenshtein edit distance.
        
        ## Examples
        ```python
        >>> from cdiffer import similar
        >>>
        >>> similar('coffee', 'cafe')
        0.7
        >>> 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, rep_rate=60)
        
        ## Examples
        
        ```python
        >>> from cdiffer import differ
        >>>
        >>> for x in differ('coffee', 'cafe'):
        ...     print(x)
        ...
        ['equal', 0, 0, 'c', 'c']
        ['insert', None, 1, None, 'a']
        ['delete', 1, None, 'o', None]
        ['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)
        ...
        ['insert', None, 1, None, 'a']
        ['delete', 1, None, 'o', None]
        ['delete', 3, None, 'f', None]
        ['delete', 4, None, 'e', None]
        >>> # Matching rate option is `rep_rate` (default is 60(%))
        >>> for x in differ('coffee', 'cafe', rep_rate=0):
        ...     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']
        ```
        
        ## 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')
        162 ns ± 0.752 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        709 ns ± 3.33 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        658 ns ± 7.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.08 µs ± 6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.14 µs ± 5.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        199 ns ± 0.38 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        50.3 ns ± 0.103 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')
        161 ns ± 0.079 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        708 ns ± 5.43 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        671 ns ± 2.35 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.11 µs ± 15.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.15 µs ± 7.85 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        196 ns ± 0.242 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
        50.9 ns ± 0.628 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')
        683 ns ± 1.41 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.21 µs ± 9.12 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.16 µs ± 13.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        1.63 µs ± 9.98 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        2.1 µs ± 8.4 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
        1 µs ± 3.05 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        493 ns ± 1.28 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)
        436 ns ± 1.45 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        434 ns ± 2.07 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        932 ns ± 3.91 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
        
        
        ```
        
Keywords: diff,comparison,compare
Platform: Windows
Platform: Linux
Platform: Mac OS-X
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: C
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 :: MacOS
Classifier: Operating System :: POSIX
Description-Content-Type: text/markdown
