This is a simple Python 3 implementation of the base122 binary-to-text encoding made for JS/HTML here by kevinAlbs. Base122 isn't 8-bit clean, so it can only be used on ascii characters.
base122.py contains an encode method and a decode method. It does not have the encode/decode file feature of kevinAlbs' version.
from base122 import encode, decode
example_str = 'hello world!'
print(encode(example_str)) # bytearray(b'4\x19-Fc<@w7\\MF!\x04')
print(decode(encode(example_str)) # 'hello world!'
print(decode(example_str) # TypeError: You can only decode an encoded string!Do not use anything from original_base122.py unless you're comparing, as that's the version I transliterated that stuck almost exactly to the original NodeJS version but in Python. You should use the current base122.py, as that's quite a bit faster due to some optimizations I made for Python.
The pybase122.pyx is much faster when compiled with Cython 3.0a6 vs the normal script. It runs about 6x faster than the other script on my test string of 10000 runs of 25 words/180 bytes of Lorem Ipsum text on a 3-core, 3.8GHz AMD Ryzen 9 3900X VPS. I first set the string as the text encoded/decoded with ascii, then ran decode(encode(TEST_STR)), and timed 10000 runs of that.
180 bytes
Python 3.8.3 | Cython 3.0a6 | base64 3.8.3 | base85 3.8.3
Encode: .204ms per run | .028ms | .0014ms | .025ms
Decode: .138ms per run | .014ms | .0012ms | .042ms
Total: .342ms per run | .052ms | .0026ms | .067ms
Results: Tentatively, base64 > Cython 3.0a6 > base85 > Python 3.8.3. I haven't tested memory usage or CPU usage though, so this could be a flawed benchmark. In addition, one very interesting thing I noticed was that the base64 encoding/decoding speed stayed basically the same whether 180 bytes or 20, while base85 scaled slower than Cython, and Python scaled quickly. I'll make a desmos graph of my results soon and link it here.
I learned to code Python (horribly) by playing with the internals of a friend's Discord bot, and it just so happened that they decided to store their data in a database with jsonpickle-encoded strings. While I migrated my fork of the bot to not use jsonpickle and make the db atomic, I have another friend who still uses jsonpickle on their fork, and their database is huge. I figured that by adapting base122 to Python, they'd switch over from using base64 encoding for the internal steps in jsonpickle to base122, because a potential switch to base85 wasn't that big of a deal for them (33% bloat to 25% for base85, compared to 14% for base122). While my rationale for making this may end up being pointless if they stop using jsonpickle, it's still fun to adapt something new to Python that hasn't been done before. Plus, maybe it'll help someone stumbling upon this someday!
Please state the Python version you're using, provide a description of the issue, and give the shortest/simplest example you can with the results. Example: "Hi, I'm using Python 3.9 and the text comes out garbled when I decode an encoded string:"
from base122 import encode, decode
example_str = 'мир привет!' # 'hello world!'
print(encode(example_str)) # bytearray(b'h/\x1a\x0bFF')
print(decode(encode(example_str)) # миÑMy response: "Hello, base122 doesn't work with utf-8 strings because it isn't 8-bit clean. Please use only ascii with this. Thanks for trying it out though!"
All contributions are welcome! If you want to add something or speed something up*, feel free to submit a pull request, I'll try to check it as soon as I can!
*If you want to speed something up, please include timing for affected lines from line_profiler, which can be found here. If the new code significantly impacts readability, please include it as a new file.