this post was submitted on 14 Nov 2025
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Advent Of Code

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An unofficial home for the advent of code community on programming.dev! Other challenges are also welcome!

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[โ€“] Pyro@programming.dev 1 points 1 month ago (1 children)

Python

# returns parents of child if found, else None
def get_parents(dnas: list[list[str]], child: int):
    candidates = len(dnas)
    seq_len = len(dnas[0])

    for p1 in range(candidates):
        if p1 == child:
            continue
        for p2 in range(p1 + 1, candidates):
            if p2 == child:
                continue

            for idx in range(seq_len):
                if dnas[child][idx] not in [dnas[p1][idx], dnas[p2][idx]]:
                    # mismatch found
                    break
            else:
                # no-break => all matched
                return p1, p2
    return None

# yields all children with their parents from a collection of dnas
def yield_children(dnas: list[list[str]]):
    for child in range(len(dnas)):
        parents = get_parents(dnas, child)
        if parents is not None:
            yield child, parents


def part1(data: str):
    # parse input data into list of DNA sequences
    dnas = [list(dna.split(":")[1]) for dna in data.splitlines()]
    seq_len = len(dnas[0])

    child, parents = next(yield_children(dnas))
    similarities = [0, 0]
    for idx in range(seq_len):
        for pi in range(2):
            if dnas[child][idx] == dnas[parents[pi]][idx]:
                similarities[pi] += 1

    return similarities[0] * similarities[1]


assert (
    part1("""1:CAAGCGCTAAGTTCGCTGGATGTGTGCCCGCG
2:CTTGAATTGGGCCGTTTACCTGGTTTAACCAT
3:CTAGCGCTGAGCTGGCTGCCTGGTTGACCGCG""")
    == 414
)


def part2(data: str):
    # parse input data into list of DNA sequences
    dnas = [list(dna.split(":")[1]) for dna in data.splitlines()]
    seq_len = len(dnas[0])

    children_gen = yield_children(dnas)

    all_similarity = 0
    for child, parents in children_gen:
        similarities = [0, 0]
        for idx in range(seq_len):
            for pi in range(2):
                if dnas[child][idx] == dnas[parents[pi]][idx]:
                    similarities[pi] += 1
        all_similarity += similarities[0] * similarities[1]
    return all_similarity


assert (
    part2("""1:GCAGGCGAGTATGATACCCGGCTAGCCACCCC
2:TCTCGCGAGGATATTACTGGGCCAGACCCCCC
3:GGTGGAACATTCGAAAGTTGCATAGGGTGGTG
4:GCTCGCGAGTATATTACCGAACCAGCCCCTCA
5:GCAGCTTAGTATGACCGCCAAATCGCGACTCA
6:AGTGGAACCTTGGATAGTCTCATATAGCGGCA
7:GGCGTAATAATCGGATGCTGCAGAGGCTGCTG""")
    == 1245
)


# Disjoint Set Union (Union-Find) data structure
#   with path compression and union by rank
class DSU:
    def __init__(self, size):
        self.parent = list(range(size))
        self.rank = [1] * size

    def find(self, x):
        # path compression
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]

    def union(self, x, y):
        rootX = self.find(x)
        rootY = self.find(y)

        if rootX == rootY:
            return False

        # union by rank
        #   attach smaller rank tree under root of higher rank tree
        if self.rank[rootX] < self.rank[rootY]:
            self.parent[rootX] = rootY
        elif self.rank[rootX] > self.rank[rootY]:
            self.parent[rootY] = rootX
        else:
            # ranks are same, so make one as root and increment its rank by one
            self.parent[rootY] = rootX
            self.rank[rootX] += 1

        return True


def part3(data: str):
    # parse input data into list of DNA sequences
    dnas = [list(dna.split(":")[1]) for dna in data.splitlines()]
    candidates = len(dnas)

    dsu = DSU(candidates)
    children_gen = yield_children(dnas)

    # union children with their parents
    for child, (p1, p2) in children_gen:
        dsu.union(child, p1)
        dsu.union(child, p2)
    
    # record [size, scale_sum] for each group
    groups = {}
    for scale_idx in range(candidates):
        # find the group of the current candidate
        group = dsu.find(scale_idx)
        # update group's size and scale sum
        entry = groups.setdefault(group, [0, 0])
        entry[0] += 1
        entry[1] += scale_idx + 1
    
    # return the maximum scale sum among all groups
    return max(groups.values())[1]


assert (
    part3("""1:GCAGGCGAGTATGATACCCGGCTAGCCACCCC
2:TCTCGCGAGGATATTACTGGGCCAGACCCCCC
3:GGTGGAACATTCGAAAGTTGCATAGGGTGGTG
4:GCTCGCGAGTATATTACCGAACCAGCCCCTCA
5:GCAGCTTAGTATGACCGCCAAATCGCGACTCA
6:AGTGGAACCTTGGATAGTCTCATATAGCGGCA
7:GGCGTAATAATCGGATGCTGCAGAGGCTGCTG""")
) == 12

assert (
    part3("""1:GCAGGCGAGTATGATACCCGGCTAGCCACCCC
2:TCTCGCGAGGATATTACTGGGCCAGACCCCCC
3:GGTGGAACATTCGAAAGTTGCATAGGGTGGTG
4:GCTCGCGAGTATATTACCGAACCAGCCCCTCA
5:GCAGCTTAGTATGACCGCCAAATCGCGACTCA
6:AGTGGAACCTTGGATAGTCTCATATAGCGGCA
7:GGCGTAATAATCGGATGCTGCAGAGGCTGCTG
8:GGCGTAAAGTATGGATGCTGGCTAGGCACCCG""")
) == 36
[โ€“] hades@programming.dev 2 points 1 month ago (1 children)

union find: nice! I was too lazy to use union find, so I brute forced merging of the families, it was fast enough :)

[โ€“] Pyro@programming.dev 1 points 3 weeks ago

Yeah I've got the DSU algorithm ingrained because of the number of the times I had to practice it for coding rounds. I didn't need to do path compression and union by rank either but might as well.