updated readme

README.md

@@ -1 +1,51 @@
-# turboshtein
+# turboshtein
+
+[![gh-actions-ci](https://img.shields.io/github/workflow/status/nathanrooy/turboshtein/ci?style=flat-square)](https://github.com/nathanrooy/turboshtein/actions?query=workflow%3Aci)
+[![GitHub license](https://img.shields.io/github/license/nathanrooy/turboshtein?style=flat-square)](https://github.com/nathanrooy/turboshtein/blob/master/LICENSE)
+[![PyPI pyversions](https://img.shields.io/pypi/pyversions/turboshtein.svg?style=flat-square)](https://pypi.org/pypi/turboshtein/)
+[![PyPi Version](https://img.shields.io/pypi/v/turboshtein.svg?style=flat-square)](https://pypi.org/project/turboshtein)
+
+## Background
+I didn't study computer science so bitwise operations have always been a bit of a gray area for me. I finally gave in and decided to spend some time reading up on the subject and ended up implementing the Myers bit-parallel algorithm for computing Levenshtein edit distance. I was fairly happy with the results so I wrapped the original C code in Python and uploaded it to PyPI. Coincidentally, it's also pretty fast.
+
+## Usage
+Install via PyPI:
+
+```sh
+pip install turboshtein
+```
+
+Or not
+```
+python setup.py install
+```
+
+Computing levenshtein edit distance between two strings:
+```sh
+>>> From turboshtein import levenshtein
+>>> levenshtein("saturday", "sunday")
+3
+```
+
+Note that there are several limitations, mainly:
+- Max string length must be less than 64.
+- ASCII characters only.
+
+## Performance
+Values represent the number of string pairs processed per second.
+
+|                                 library |     m=n=8 |    m=n=16 |    m=n=24 |    m=n=32 |    m=n=40 |    m=n=48 |    m=n=56 |
+|----------------------------------------:|----------:|----------:|----------:|----------:|----------:|----------:|----------:|
+|                 turboshtein.levenshtein | 7,307,254 | 5,841,998 | 4,898,786 | 4,381,383 | 3,787,679 | 3,460,706 | 3,098,978 |
+| rapidfuzz.distance.Levenshtein.distance | 6,115,119 | 5,000,410 | 4,430,285 | 3,895,723 | 3,313,308 | 3,132,127 | 2,732,176 |
+|          jellyfish.levenshtein_distance | 5,726,999 | 4,930,084 | 4,329,111 | 3,814,256 | 3,119,216 | 3,090,797 | 2,727,032 |
+|                    Levenshtein.distance | 3,725,694 | 1,389,628 |   710,292 |   389,632 |   249,694 |   180,106 |   131,543 |
+|                textdistance.levenshtein |   407,845 |   393,642 |   371,919 |   375,995 |   374,140 |   374,889 |   359,589 |
+
+Test setup:
+- XPS 15 9570
+- Intel i7-8750H
+- Fedora Linux 36 (Workstation Edition)
+
+## References
+- Gene Myers. 1999. A fast bit-vector algorithm for approximate string matching based on dynamic programming. J. ACM 46, 3 (May 1999), 395-415. https://doi.org/10.1145/316542.316550