This module performs a fast bitwise hamming distance of two hexadecimal strings.
This looks like:
DEADBEEF = 11011110101011011011111011101111 00000000 = 00000000000000000000000000000000 XOR = 11011110101011011011111011101111 Hamming = number of ones in DEADBEEF ^ 00000000 = 24
This essentially amounts to
>>> import gmpy >>> gmpy.popcount(0xdeadbeef ^ 0x00000000) 24
except with Python strings, so
>>> import gmpy
>>> gmpy.popcount(int("deadbeef", 16) ^ int("00000000", 16))
24
A few assumptions are made and enforced:
- this is a valid hexadecimal string (i.e.,
[a-fA-F0-9]+) - the strings are the same length
- the strings do not begin with
"0x"
There are a lot of fantastic (python) libraries that offer methods to calculate various edit distances, including Hamming distances: Distance, textdistance, scipy, jellyfish, etc.
In this case, I needed a hamming distance library that worked on hexadecimal
strings (i.e., a Python str) and performed blazingly fast.
Furthermore, I often did not care about hex strings greater than 256 bits.
That length constraint is different vs all the other libraries and enabled me
to explore vectorization techniques via numba, numpy, and
SSE/AVX intrinsics.
Lastly, I wanted to minimize dependencies, meaning you do not need to install
numpy, gmpy, cython, pypy, pythran, etc.
Eventually, after playing around with gmpy.popcount, numba.jit,
pythran.run, numpy, I decided to write what I wanted
in essentially raw C. At this point, I'm using raw char* and
int*, so exploring re-writing this in Fortran makes little sense.
To install, ensure you have Python 3.6+. Run
pip install hexhamming
or to install from source
git clone https://github.com/mrecachinas/hexhamming cd hexhamming python setup.py install # or pip install .
If you want to contribute to hexhamming, you should install the dev dependencies
pip install -r requirements-dev.txt
and make sure the tests pass with
python -m pytest -vls .
Using hexhamming is as simple as
>>> from hexhamming import hamming_distance_string
>>> hamming_distance_string("deadbeef", "00000000")
24
New in v2.0.0 : hexhamming now supports byte``s via ``hamming_distance_bytes.
You use it in the exact same way as before, except you pass in a byte string.
>>> from hexhamming import hamming_distance_bytes >>> hamming_distance_bytes(b"\xde\xad\xbe\xef", b"\x00\x00\x00\x00") 24
We also provide a method for a quick boolean check of whether two hexadecimal strings are within a given Hamming distance.
>>> from hexhamming import check_hexstrings_within_dist
>>> check_hexstrings_within_dist("ffff", "fffe", 2)
True
>>> check_hexstrings_within_dist("ffff", "0000", 2)
False
Similarly, hexhamming supports byte arrays via check_bytes_arrays_within_dist, which has
a similar API as check_hexstrings_within_dist, except it expects a byte array. Additionally,
it will check if any element of a byte array is within a specified Hamming Distance of another
byte array.
Below is a benchmark using pytest-benchmark with hexhamming==v1.3.2
my 2020 2.0 GHz quad-core Intel Core i5 16 GB 3733 MHz LPDDR4 macOS Catalina (10.15.5)
with Python 3.7.3 and Apple clang version 11.0.3 (clang-1103.0.32.62).
| Name | Mean (ns) | Std (ns) | Median (ns) | Rounds | Iterations |
|---|---|---|---|---|---|
| test_hamming_distance_bench_3 | 93.8 | 10.5 | 94.3 | 53268 | 200 |
| test_hamming_distance_bench_3_same | 94.2 | 15.2 | 94.9 | 102146 | 100 |
| test_check_hexstrings_within_dist_bench | 231.9 | 104.2 | 216.5 | 195122 | 22 |
| test_hamming_distance_bench_256 | 97.5 | 34.1 | 94.0 | 195122 | 22 |
| test_hamming_distance_bench_1000 | 489.8 | 159.4 | 477.5 | 94411 | 20 |
| test_hamming_distance_bench_1000_same | 497.8 | 87.8 | 496.6 | 18971 | 20 |
| test_hamming_distance_bench_1024 | 509.9 | 299.5 | 506.7 | 18652 | 10 |
| test_hamming_distance_bench_1024_same | 467.4 | 205.9 | 450.4 | 181819 | 10 |