advent-of-code VS advent_of_code

My solution in Common Lisp.

Dot file generated by my code and then run through fdp from Graphviz.

Just a simple relief-shaded image like you might find on a map. Principally generated with GDAL. The particular commands used are at the bottom of my solution code for the day. I also made a more flat version, which shows the elevation colors better, but really doesn't feel right in terms of elevation.

The code is written in Common Lisp using Cairo and ffmpeg. https://gitlab.com/asciiphil/advent-of-code/-/blob/master/2022/09.lisp

So I spend more time analyzing the input calculation. I wrote up that analysis and committed it to my repository, too.

This was a bit difficult. My original part one solution worked completely differently to what I had to do for part two.

My solution in Common Lisp, 2335/2041.

No. It's just here, as part of my Advent of Code repository.

The code is at the bottom of my day 9 source file, in Common Lisp. (The AdvMAME3x code was something I originally wrote for my 2020 day 20 visualization.)

As a side note, I took inspiration from a post the other day about adding AoC badges to your repository README file. I use GitLab, which lets you define badges as properties of your repository. So I added badges to my repository's header area, driven by a JSON file in the repository. The JSON file is updated manually by a script. (I might add some automated updates at some point, but manual works okay for now.)

Here's the relevant extract from my (recursive) solution. Full code on GitHub.

Like others, I used lcm. Here's an extract of the solution, omitting parsing and main(). Full solution on GitHub.

Solutions to parts 1 and 2 nearly identical and pretty much worked first time. Nothing clever here. This was far simpler than I thought it would be. I probably spent most time trying to think of a mathematical way of getting all points between p1 and p2. In the end, I just constructed two ranges. Full code here.

Full code on github.

The core of my solution below using numpy (because I'm learning it!). See github for full code.

Python 3 solution using numpy. I got held up because I assume (but know better) that numpy arrays are [x,y] instead of [row, col], which is [y,x].

Below is the new solution, which works for part 1 and 2. The full code is on GitHub.

These are the key functions. The whole code is on GitHub

Part 2 (extract shown below) was a matter of starting with the low points found in part 1 then recursively looking around for relevant points. Full code on GitHub

Code is on GitHub.