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* Use Hamming textdistance library Used Hamming textdistance to sort by closest match. * Global search UI improvements Increased dropdown height to show more results initially (and which can also be scrolled into view). Scrollbars will appear automatically as needed. Remove dropdown when Search box is cleared. * Added textdistance 4.6.2 library
402 lines
18 KiB
Text
402 lines
18 KiB
Text
Metadata-Version: 2.1
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Name: textdistance
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Version: 4.6.2
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Summary: Compute distance between the two texts.
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Home-page: https://github.com/orsinium/textdistance
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Download-URL: https://github.com/orsinium/textdistance/tarball/master
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Author: orsinium
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Author-email: gram@orsinium.dev
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License: MIT
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Keywords: distance between text strings sequences iterators
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Environment :: Plugins
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Programming Language :: Python
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Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
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Requires-Python: >=3.5
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Description-Content-Type: text/markdown
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License-File: LICENSE
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Provides-Extra: dameraulevenshtein
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'dameraulevenshtein'
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Requires-Dist: jellyfish ; extra == 'dameraulevenshtein'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'dameraulevenshtein'
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Provides-Extra: hamming
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Requires-Dist: Levenshtein ; extra == 'hamming'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'hamming'
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Requires-Dist: jellyfish ; extra == 'hamming'
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Requires-Dist: distance ; extra == 'hamming'
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Provides-Extra: jaro
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'jaro'
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Requires-Dist: Levenshtein ; extra == 'jaro'
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Provides-Extra: jarowinkler
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'jarowinkler'
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Requires-Dist: jellyfish ; extra == 'jarowinkler'
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Provides-Extra: levenshtein
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'levenshtein'
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Requires-Dist: Levenshtein ; extra == 'levenshtein'
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Provides-Extra: all
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Requires-Dist: jellyfish ; extra == 'all'
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Requires-Dist: numpy ; extra == 'all'
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Requires-Dist: Levenshtein ; extra == 'all'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'all'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'all'
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Requires-Dist: distance ; extra == 'all'
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Requires-Dist: pylev ; extra == 'all'
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Requires-Dist: py-stringmatching ; extra == 'all'
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Requires-Dist: tabulate ; extra == 'all'
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Provides-Extra: benchmark
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Requires-Dist: jellyfish ; extra == 'benchmark'
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Requires-Dist: numpy ; extra == 'benchmark'
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Requires-Dist: Levenshtein ; extra == 'benchmark'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'benchmark'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'benchmark'
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Requires-Dist: distance ; extra == 'benchmark'
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Requires-Dist: pylev ; extra == 'benchmark'
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Requires-Dist: py-stringmatching ; extra == 'benchmark'
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Requires-Dist: tabulate ; extra == 'benchmark'
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Provides-Extra: benchmarks
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Requires-Dist: jellyfish ; extra == 'benchmarks'
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Requires-Dist: numpy ; extra == 'benchmarks'
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Requires-Dist: Levenshtein ; extra == 'benchmarks'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'benchmarks'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'benchmarks'
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Requires-Dist: distance ; extra == 'benchmarks'
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Requires-Dist: pylev ; extra == 'benchmarks'
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Requires-Dist: py-stringmatching ; extra == 'benchmarks'
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Requires-Dist: tabulate ; extra == 'benchmarks'
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Provides-Extra: common
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Requires-Dist: jellyfish ; extra == 'common'
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Requires-Dist: numpy ; extra == 'common'
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Requires-Dist: Levenshtein ; extra == 'common'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'common'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'common'
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Provides-Extra: extra
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Requires-Dist: jellyfish ; extra == 'extra'
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Requires-Dist: numpy ; extra == 'extra'
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Requires-Dist: Levenshtein ; extra == 'extra'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'extra'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'extra'
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Provides-Extra: extras
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Requires-Dist: jellyfish ; extra == 'extras'
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Requires-Dist: numpy ; extra == 'extras'
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Requires-Dist: Levenshtein ; extra == 'extras'
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Requires-Dist: pyxDamerauLevenshtein ; extra == 'extras'
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Requires-Dist: rapidfuzz >=2.6.0 ; extra == 'extras'
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Provides-Extra: lint
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Requires-Dist: twine ; extra == 'lint'
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Requires-Dist: mypy ; extra == 'lint'
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Requires-Dist: isort ; extra == 'lint'
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Requires-Dist: flake8 ; extra == 'lint'
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Requires-Dist: types-tabulate ; extra == 'lint'
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Requires-Dist: flake8-blind-except ; extra == 'lint'
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Requires-Dist: flake8-bugbear ; extra == 'lint'
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Requires-Dist: flake8-commas ; extra == 'lint'
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Requires-Dist: flake8-logging-format ; extra == 'lint'
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Requires-Dist: flake8-mutable ; extra == 'lint'
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Requires-Dist: flake8-pep3101 ; extra == 'lint'
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Requires-Dist: flake8-quotes ; extra == 'lint'
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Requires-Dist: flake8-string-format ; extra == 'lint'
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Requires-Dist: flake8-tidy-imports ; extra == 'lint'
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Requires-Dist: pep8-naming ; extra == 'lint'
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Provides-Extra: test
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Requires-Dist: hypothesis ; extra == 'test'
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Requires-Dist: isort ; extra == 'test'
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Requires-Dist: numpy ; extra == 'test'
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Requires-Dist: pytest ; extra == 'test'
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# TextDistance
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![TextDistance logo](logo.png)
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[![Build Status](https://travis-ci.org/life4/textdistance.svg?branch=master)](https://travis-ci.org/life4/textdistance) [![PyPI version](https://img.shields.io/pypi/v/textdistance.svg)](https://pypi.python.org/pypi/textdistance) [![Status](https://img.shields.io/pypi/status/textdistance.svg)](https://pypi.python.org/pypi/textdistance) [![License](https://img.shields.io/pypi/l/textdistance.svg)](LICENSE)
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**TextDistance** -- python library for comparing distance between two or more sequences by many algorithms.
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Features:
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- 30+ algorithms
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- Pure python implementation
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- Simple usage
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- More than two sequences comparing
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- Some algorithms have more than one implementation in one class.
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- Optional numpy usage for maximum speed.
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## Algorithms
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### Edit based
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| Algorithm | Class | Functions |
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|-------------------------------------------------------------------------------------------|----------------------|------------------------|
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| [Hamming](https://en.wikipedia.org/wiki/Hamming_distance) | `Hamming` | `hamming` |
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| [MLIPNS](http://www.sial.iias.spb.su/files/386-386-1-PB.pdf) | `Mlipns` | `mlipns` |
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| [Levenshtein](https://en.wikipedia.org/wiki/Levenshtein_distance) | `Levenshtein` | `levenshtein` |
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| [Damerau-Levenshtein](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) | `DamerauLevenshtein` | `damerau_levenshtein` |
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| [Jaro-Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) | `JaroWinkler` | `jaro_winkler`, `jaro` |
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| [Strcmp95](http://cpansearch.perl.org/src/SCW/Text-JaroWinkler-0.1/strcmp95.c) | `StrCmp95` | `strcmp95` |
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| [Needleman-Wunsch](https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm) | `NeedlemanWunsch` | `needleman_wunsch` |
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| [Gotoh](http://bioinfo.ict.ac.cn/~dbu/AlgorithmCourses/Lectures/LOA/Lec6-Sequence-Alignment-Affine-Gaps-Gotoh1982.pdf) | `Gotoh` | `gotoh` |
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| [Smith-Waterman](https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm) | `SmithWaterman` | `smith_waterman` |
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### Token based
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| Algorithm | Class | Functions |
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|-------------------------------------------------------------------------------------------|----------------------|---------------|
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| [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) | `Jaccard` | `jaccard` |
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| [Sørensen–Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) | `Sorensen` | `sorensen`, `sorensen_dice`, `dice` |
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| [Tversky index](https://en.wikipedia.org/wiki/Tversky_index) | `Tversky` | `tversky` |
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| [Overlap coefficient](https://en.wikipedia.org/wiki/Overlap_coefficient) | `Overlap` | `overlap` |
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| [Tanimoto distance](https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance) | `Tanimoto` | `tanimoto` |
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| [Cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) | `Cosine` | `cosine` |
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| [Monge-Elkan](https://www.academia.edu/200314/Generalized_Monge-Elkan_Method_for_Approximate_Text_String_Comparison) | `MongeElkan` | `monge_elkan` |
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| [Bag distance](https://github.com/Yomguithereal/talisman/blob/master/src/metrics/bag.js) | `Bag` | `bag` |
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### Sequence based
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| Algorithm | Class | Functions |
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|-----------|-------|-----------|
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| [longest common subsequence similarity](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) | `LCSSeq` | `lcsseq` |
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| [longest common substring similarity](https://docs.python.org/2/library/difflib.html#difflib.SequenceMatcher) | `LCSStr` | `lcsstr` |
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| [Ratcliff-Obershelp similarity](https://en.wikipedia.org/wiki/Gestalt_Pattern_Matching) | `RatcliffObershelp` | `ratcliff_obershelp` |
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### Compression based
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[Normalized compression distance](https://en.wikipedia.org/wiki/Normalized_compression_distance#Normalized_compression_distance) with different compression algorithms.
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Classic compression algorithms:
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| Algorithm | Class | Function |
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|----------------------------------------------------------------------------|-------------|--------------|
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| [Arithmetic coding](https://en.wikipedia.org/wiki/Arithmetic_coding) | `ArithNCD` | `arith_ncd` |
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| [RLE](https://en.wikipedia.org/wiki/Run-length_encoding) | `RLENCD` | `rle_ncd` |
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| [BWT RLE](https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform) | `BWTRLENCD` | `bwtrle_ncd` |
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Normal compression algorithms:
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| Algorithm | Class | Function |
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|----------------------------------------------------------------------------|--------------|---------------|
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| Square Root | `SqrtNCD` | `sqrt_ncd` |
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| [Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) | `EntropyNCD` | `entropy_ncd` |
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Work in progress algorithms that compare two strings as array of bits:
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| Algorithm | Class | Function |
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|--------------------------------------------|-----------|------------|
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| [BZ2](https://en.wikipedia.org/wiki/Bzip2) | `BZ2NCD` | `bz2_ncd` |
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| [LZMA](https://en.wikipedia.org/wiki/LZMA) | `LZMANCD` | `lzma_ncd` |
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| [ZLib](https://en.wikipedia.org/wiki/Zlib) | `ZLIBNCD` | `zlib_ncd` |
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See [blog post](https://articles.life4web.ru/other/ncd/) for more details about NCD.
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### Phonetic
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| Algorithm | Class | Functions |
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|------------------------------------------------------------------------------|----------|-----------|
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| [MRA](https://en.wikipedia.org/wiki/Match_rating_approach) | `MRA` | `mra` |
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| [Editex](https://anhaidgroup.github.io/py_stringmatching/v0.3.x/Editex.html) | `Editex` | `editex` |
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### Simple
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| Algorithm | Class | Functions |
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|---------------------|------------|------------|
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| Prefix similarity | `Prefix` | `prefix` |
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| Postfix similarity | `Postfix` | `postfix` |
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| Length distance | `Length` | `length` |
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| Identity similarity | `Identity` | `identity` |
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| Matrix similarity | `Matrix` | `matrix` |
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## Installation
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### Stable
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Only pure python implementation:
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```bash
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pip install textdistance
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```
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With extra libraries for maximum speed:
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```bash
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pip install "textdistance[extras]"
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```
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With all libraries (required for [benchmarking](#benchmarks) and [testing](#running-tests)):
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```bash
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pip install "textdistance[benchmark]"
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```
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With algorithm specific extras:
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```bash
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pip install "textdistance[Hamming]"
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```
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Algorithms with available extras: `DamerauLevenshtein`, `Hamming`, `Jaro`, `JaroWinkler`, `Levenshtein`.
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### Dev
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Via pip:
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```bash
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pip install -e git+https://github.com/life4/textdistance.git#egg=textdistance
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```
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Or clone repo and install with some extras:
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```bash
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git clone https://github.com/life4/textdistance.git
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pip install -e ".[benchmark]"
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```
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## Usage
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All algorithms have 2 interfaces:
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1. Class with algorithm-specific params for customizing.
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1. Class instance with default params for quick and simple usage.
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All algorithms have some common methods:
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1. `.distance(*sequences)` -- calculate distance between sequences.
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1. `.similarity(*sequences)` -- calculate similarity for sequences.
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1. `.maximum(*sequences)` -- maximum possible value for distance and similarity. For any sequence: `distance + similarity == maximum`.
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1. `.normalized_distance(*sequences)` -- normalized distance between sequences. The return value is a float between 0 and 1, where 0 means equal, and 1 totally different.
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1. `.normalized_similarity(*sequences)` -- normalized similarity for sequences. The return value is a float between 0 and 1, where 0 means totally different, and 1 equal.
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Most common init arguments:
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1. `qval` -- q-value for split sequences into q-grams. Possible values:
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- 1 (default) -- compare sequences by chars.
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- 2 or more -- transform sequences to q-grams.
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- None -- split sequences by words.
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1. `as_set` -- for token-based algorithms:
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- True -- `t` and `ttt` is equal.
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- False (default) -- `t` and `ttt` is different.
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## Examples
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For example, [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance):
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```python
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import textdistance
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textdistance.hamming('test', 'text')
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# 1
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textdistance.hamming.distance('test', 'text')
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# 1
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textdistance.hamming.similarity('test', 'text')
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# 3
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textdistance.hamming.normalized_distance('test', 'text')
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# 0.25
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textdistance.hamming.normalized_similarity('test', 'text')
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# 0.75
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textdistance.Hamming(qval=2).distance('test', 'text')
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# 2
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```
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Any other algorithms have same interface.
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## Articles
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A few articles with examples how to use textdistance in the real world:
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- [Guide to Fuzzy Matching with Python](http://theautomatic.net/2019/11/13/guide-to-fuzzy-matching-with-python/)
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- [String similarity — the basic know your algorithms guide!](https://itnext.io/string-similarity-the-basic-know-your-algorithms-guide-3de3d7346227)
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- [Normalized compression distance](https://articles.life4web.ru/other/ncd/)
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## Extra libraries
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For main algorithms textdistance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). [Install](#installation) textdistance with extras for this feature.
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You can disable this by passing `external=False` argument on init:
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```python3
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import textdistance
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hamming = textdistance.Hamming(external=False)
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hamming('text', 'testit')
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# 3
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```
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Supported libraries:
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1. [Distance](https://github.com/doukremt/distance)
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1. [jellyfish](https://github.com/jamesturk/jellyfish)
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1. [py_stringmatching](https://github.com/anhaidgroup/py_stringmatching)
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1. [pylev](https://github.com/toastdriven/pylev)
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1. [Levenshtein](https://github.com/maxbachmann/Levenshtein)
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1. [pyxDamerauLevenshtein](https://github.com/gfairchild/pyxDamerauLevenshtein)
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Algorithms:
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1. DamerauLevenshtein
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1. Hamming
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1. Jaro
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1. JaroWinkler
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1. Levenshtein
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## Benchmarks
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Without extras installation:
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| algorithm | library | time |
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|--------------------|-----------------------|---------|
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| DamerauLevenshtein | rapidfuzz | 0.00312 |
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| DamerauLevenshtein | jellyfish | 0.00591 |
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| DamerauLevenshtein | pyxdameraulevenshtein | 0.03335 |
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| DamerauLevenshtein | **textdistance** | 0.83524 |
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| Hamming | Levenshtein | 0.00038 |
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| Hamming | rapidfuzz | 0.00044 |
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| Hamming | jellyfish | 0.00091 |
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| Hamming | distance | 0.00812 |
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| Hamming | **textdistance** | 0.03531 |
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| Jaro | rapidfuzz | 0.00092 |
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| Jaro | jellyfish | 0.00191 |
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| Jaro | **textdistance** | 0.07365 |
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| JaroWinkler | rapidfuzz | 0.00094 |
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| JaroWinkler | jellyfish | 0.00195 |
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| JaroWinkler | **textdistance** | 0.07501 |
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| Levenshtein | rapidfuzz | 0.00099 |
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| Levenshtein | Levenshtein | 0.00122 |
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| Levenshtein | jellyfish | 0.00254 |
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| Levenshtein | pylev | 0.15688 |
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| Levenshtein | distance | 0.28669 |
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| Levenshtein | **textdistance** | 0.53902 |
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Total: 24 libs.
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Yeah, so slow. Use TextDistance on production only with extras.
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Textdistance use benchmark's results for algorithm's optimization and try to call fastest external lib first (if possible).
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You can run benchmark manually on your system:
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```bash
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pip install textdistance[benchmark]
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python3 -m textdistance.benchmark
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```
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TextDistance show benchmarks results table for your system and save libraries priorities into `libraries.json` file in TextDistance's folder. This file will be used by textdistance for calling fastest algorithm implementation. Default [libraries.json](textdistance/libraries.json) already included in package.
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## Running tests
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All you need is [task](https://taskfile.dev/). See [Taskfile.yml](./Taskfile.yml) for the list of available commands. For example, to run tests including third-party libraries usage, execute `task pytest-external:run`.
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## Contributing
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PRs are welcome!
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- Found a bug? Fix it!
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- Want to add more algorithms? Sure! Just make it with the same interface as other algorithms in the lib and add some tests.
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- Can make something faster? Great! Just avoid external dependencies and remember that everything should work not only with strings.
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- Something else that do you think is good? Do it! Just make sure that CI passes and everything from the README is still applicable (interface, features, and so on).
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- Have no time to code? Tell your friends and subscribers about `textdistance`. More users, more contributions, more amazing features.
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Thank you :heart:
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