advent-of-code VS advent_of_code

Pretty happy with this one ``` def solve() -> List[int]: seen = [] won = [] scores = [] for n in NUMS: seen.append(n) for board in BOARDS: transpose = list(zip(*board)) for i, line in enumerate(board): if (all(num in seen for num in line) or all(num in seen for num in transpose[i])) and board not in won: won.append(board) scores.append(sum(sum(num for num in line if num not in seen) for line in board) * seen[-1]) return scores

day02 of doing only one-liners and I already failed for the second part, it would have been a huge mess. The first part is pretty messy as well so if anyone can improve on it I would be very grateful ! https://github.com/masmeert/advent-of-code/blob/master/2021/day02/main.py

I actually did the same as you when I first solved it But I saw other people use the l[3] > l[n-3] trick, it didn't make much sense at first but now I get it and it's quite clever. Basically take [1 2 1 3], the windows would be w1=[1 2 1] and w2=[2 1 3], as 2 and 1 appear in both windows what really matters is w1[0] and w2[2] which are unique to each: 1+(2+1) < (2+1)+3 == 1 < 3.

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.