sciyoshi

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sciyoshi

import numpy as np
from scipy.ndimage import convolve

lines = INPUT.splitlines()
alg = [i for i, c in enumerate(lines[0]) if c == "#"]
img = np.pad(np.array([[int(c == "#") for c in l] for l in lines[2:]]), ((50, 50), (50, 50)))
rot = np.array([[1, 2, 4], [8, 16, 32], [64, 128, 256]])

for i in range(50):
    img = np.isin(convolve(img, rot, mode="constant", cval=i % 2), alg).astype(int)

sciyoshi

def convert(l, d=0):
    for i, e in enumerate(l):
        if isinstance(e, list):
            yield from convert(e, d + 1)
        else:
            yield (e, d)


def reduce(l):
    c = list(l)

sciyoshi

from utils import Pt, Grd
import math

grd = Grd()
for i, l in enumerate(LINES):
    for j, c in enumerate(l):
        grd[Pt(i, j)] = int(c)


def expand(pt):

sciyoshi

import { promises } from "fs";
import crypto from "crypto";
import path from "path";
import { print, parse } from "graphql";

const plugin = {
	name: "relay",
	setup: build => {
		build.onLoad({ filter: /\.tsx$/, namespace: "" }, async args => {
			let contents = await promises.readFile(args.path, "utf8");

sciyoshi

import collections

all_ingredients = collections.Counter()
possible_ingredients = collections.defaultdict(set)

for line in open("inputs/day21").read().splitlines():
    ingredients, allergens = line.rstrip(")").split(" (contains ")
    ingredients = ingredients.split()
    allergens = allergens.split(", ")
    all_ingredients.update(ingredients)

sciyoshi

import functools
import operator
import itertools
import networkx
import numpy as np

tiles = {}

for tiledata in open("inputs/day20").read().strip().split("\n\n"):
    a, *tilelines = tiledata.splitlines()

sciyoshi

class ParseError(Exception):
    pass

def parse_rule(alts):
    def parse(inp):
        for rule in alts:
            s = inp
            for el in rule:
                try:
                    s = rules[el](s)

sciyoshi

class a(int):
    def __mul__(self, b):
        return a(int(self) + b)
    def __add__(self, b):
        return a(int(self) + b)
    def __sub__(self, b):
        return a(int(self) * b)

def ev(expr, pt2=False):
    expr = re.sub(r"(\d+)", r"a(\1)", expr)

sciyoshi

import numpy as np
from scipy.ndimage import convolve

LINES = open("inputs/day17").read().splitlines()

dims = 3  # 4 for part 2
steps = 6
front = 2 * steps + 1
filt = np.full((3,) * dims, 1)
filt[(1,) * dims] = 100