personal_code VS Advent-of-Code

I finally switched off plug the other day and it was actually super easy, I’d been putting it off. here’s the diff if it’s helpful at all

pretty basic config, but messy - half of it is accumulated vim config from two decades.

hah, I did the same thing so I won't bother posting it. here's my source

here's my part 2, which runs in 2 seconds.

Pypy with a sparse dict of dicts: 176 ms (pypy)

source is here, I mostly wanted to learn a little curses.

my dijkstra in python ran in 12 seconds; adding the heuristic to make it A* brought it down to .2 seconds.

Python 3, 6/13, code here

Python 3 (to process the input) and C++ (to actually solve the problem), 81/143, view code here

Python 3, 27/22: Here is my Part 1 and Part 2 code.

Python 3, 28th on star 1 and 29th on star 2, both parts are <=20 lines of code. Main thing which is notable is that I used "\u2588" instead of # symbols and spaces instead of periods, made the capital letters much easier to read. I also have my solutions in this GitHub repo.

I also used coordinate compression, but had a slightly different take: Keep a 3D boolean array representing whether each points in our compressed space of points is turned on or off. And then for each instruction, just go through all the points affected by that instruction and set their state to true if the instruction is "on" or false if the instruction is "off". This approach runs in about 2 seconds in Scala, and honestly, I'm not sure that the complex cube intersection approach runs faster than this coordinate compression approach, although it would be nice to see a cube intersection approach written in Scala so we can compare apples to apples.

Python 3.8.10 82/68

Not sure if you are interested in solutions from past years, but I did Haskell for AoC 2019, although I'm not the best Haskell programmer so some of it is not that well designed. If you're interested, feel free to take a look at my solutions.

I've posted my solution to GitHub here. (It's written in Scala because I am trying to use Advent of Code to learn Scala, but it's probably not idiomatic Scala so if you have any tips/suggestions for improvement, let me know!) When I tested this on the sample, Part 1 took about 20 seconds, part 2 took about 27 seconds, and the extension took about 135 seconds. So this isn't a very good solution, but going from 40 steps to 1000000000 steps gave us only a 5-times factor speed-up, which is evidence that the time complexity of the algorithm is not growing linearly in the number of steps.

If you'd like to see my full Scala solution, you can find it on my GitHub here. Have fun, and thanks for reading!