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Day 12: Garden Groups

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[-] VegOwOtenks@lemmy.world 2 points 1 week ago* (last edited 1 week ago)

Haskell

Detecting regions is a floodfill. For Part 2, I select all adjacent tiles that are not part of a region and group them by the direction relative to the closest region tile, then group adjacent tiles with the same direction again and count.

Edit:

Takes 0.06s

Reveal Code

import Control.Arrow

import Data.Array.Unboxed (UArray)
import Data.Set (Set)
import Data.Map (Map)

import qualified Data.List as List
import qualified Data.Set as Set
import qualified Data.Map as Map
import qualified Data.Array.Unboxed as UArray

parse :: String -> UArray (Int, Int) Char
parse s = UArray.listArray ((1, 1), (n, m)) . filter (/= '\n') $ s
        where
                n = takeWhile (/= '\n') >>> length $ s
                m = filter (== '\n') >>> length >>> pred $ s

neighborCoordinates (p1, p2) = [(p1-1, p2), (p1, p2-1), (p1, p2+1), (p1+1, p2)]

allNeighbors p a = neighborCoordinates
        >>> filter (UArray.inRange (UArray.bounds a))
        $ p

regionNeighbors p a = allNeighbors p
        >>> filter ((a UArray.!) >>> (== pTile))
        $ a
        where
                pTile = a UArray.! p

floodArea :: Set (Int, Int) -> Set (Int, Int) -> UArray (Int, Int) Char -> Set (Int, Int)
floodArea e o a
        | Set.null o = e
        | otherwise  = floodArea e' o' a
        where
                e' = Set.union e o
                o' = Set.fold (Set.union . Set.fromDistinctAscList .  (filter (`Set.notMember` e')) . (flip regionNeighbors a)) Set.empty o

findRegions garden = findRegions' (Set.fromList . UArray.indices $ garden) garden

findRegions' remainingIndices garden
        | Set.null remainingIndices = []
        | otherwise = removedIndices : findRegions' remainingIndices' garden
        where
                removedIndices = floodArea Set.empty (Set.singleton . Set.findMin $ remainingIndices) garden
                remainingIndices' = Set.difference remainingIndices removedIndices

perimeter region = Set.fold ((+) . length . filter (`Set.notMember` region) . neighborCoordinates) 0 region

part1 rs = map (Set.size &&& perimeter)
        >>> map (uncurry (*))
        >>> sum
        $ rs

turnLeft ( 0, 1) = (-1, 0) -- right
turnLeft ( 0,-1) = ( 1, 0) -- left
turnLeft ( 1, 0) = ( 0, 1) -- down
turnLeft (-1, 0) = ( 0,-1) -- up

turnRight = turnLeft . turnLeft . turnLeft

move (py, px) (dy, dx) = (py + dy, px + dx)

tupleDelta (y1, x1) (y2, x2) = (y1-y2, x1-x2)

isRegionInner region p = all (`Set.member` region) (neighborCoordinates p)

groupEdges d ps
        | Set.null ps = []
        | otherwise   = collectedEdge : groupEdges d ps'
        where
                ps' = Set.difference ps collectedEdge
                collectedEdge = Set.union leftPoints rightPoints
                leftPoints = iterate (move dl)
                        >>> takeWhile (`Set.member` ps)
                        >>> Set.fromList
                        $ currentPoint
                rightPoints = iterate (move dr)
                        >>> takeWhile (`Set.member` ps)
                        >>> Set.fromList
                        $ currentPoint
                currentPoint = Set.findMin ps
                dr = turnRight d
                dl = turnLeft  d

linearPerimeter region = Map.foldr ((+) . length) 0 $ groupedEdges
        where 
                edgeTiles = Set.filter (not . isRegionInner region) region
                regionNeighbors = List.concatMap (\ p -> map (p,). filter (`Set.notMember` region) . neighborCoordinates $ p) . Set.toList $ region
                groupedNeighbors = List.map (uncurry tupleDelta &&& Set.singleton . snd)
                        >>> Map.fromListWith (Set.union)
                        $ regionNeighbors
                groupedEdges = Map.mapWithKey groupEdges
                        $ groupedNeighbors

part2 rs = map (Set.size &&& linearPerimeter)
        >>> map (uncurry (*))
        >>> sum
        $ rs

main = getContents
        >>= print
        . (part1 &&& part2)
        . findRegions
        . parse

this post was submitted on 12 Dec 2024
17 points (94.7% liked)

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