My Advent of Code solutions in Python.
kevinyap.ca/2019/12/going-fast-in-advent-of-code/
advent-of-code
python
Add solution for 2018/22
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+97
2018/day22.py
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2018/day22.py
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import os # NOQA
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import sys # NOQA
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import heapq
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import fileinput
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from collections import Counter, defaultdict, deque, namedtuple # NOQA
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from utils import memoize
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from utils import Point, DIRS, DIRS_4, DIRS_8 # NOQA
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MOD = 20183
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@memoize
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def geo_index(x, y):
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if x == 0 and y == 0:
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return 0
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elif x == TX and y == TY:
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return 0
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elif y == 0:
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return (x * 16807) % MOD
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elif x == 0:
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return (y * 48271) % MOD
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else:
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return (ero_level(x - 1, y) * ero_level(x, y - 1)) % MOD
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@memoize
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def ero_level(x, y):
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return (geo_index(x, y) + DEPTH) % MOD
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@memoize
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def risk_level(x, y):
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return ero_level(x, y) % 3
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lines = fileinput.input()
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DEPTH = int(lines[0].split()[1])
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TX, TY = (int(x) for x in lines[1].split()[1].split(','))
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print "Total risk level for rectangle:", sum(risk_level(x, y) for x in range(TX + 1) for y in range(TY + 1))
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# In rocky regions (0), can use torch (1) or gear (2)
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# In wet regions (1), can use gear (2) or neither (0)
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# In narrow regions (2), can use neither (0) or torch (1)
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#
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# Start at (0, 0) with the torch equipped.
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ROCKY = 0
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WET = 1
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NARROW = 2
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NEITHER = 0
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TORCH = 1
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GEAR = 2
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items = [NEITHER, TORCH, GEAR]
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allowed = {
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ROCKY: [TORCH, GEAR],
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WET: [GEAR, NEITHER],
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NARROW: [NEITHER, TORCH],
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}
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State = namedtuple('State', 'time x y item')
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state = State(0, 0, 0, TORCH)
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horizon = [state]
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seen = {}
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while horizon:
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time, x, y, item = heapq.heappop(horizon)
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if x == TX and y == TY and item == TORCH:
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print "Minutes to reach the target:", time
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break
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# Hand-wavy upper bound on horizontal search limit (technically should be TX + TY)
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if x > TX * 3:
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continue
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if seen.get((x, y, item), 1e10) <= time:
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continue
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next_entry = (time, x, y, item)
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seen[(x, y, item)] = min(seen.get((x, y, item), 1e10), time)
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region = risk_level(x, y)
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for next_item in items:
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if next_item in allowed[risk_level(x, y)] and next_item != item:
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heapq.heappush(horizon, State(time + 7, x, y, next_item))
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for n in Point(x, y).neighbours_4():
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if n.x >= 0 and n.y >= 0 and risk_level(n.x, n.y) != item:
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heapq.heappush(horizon, State(time + 1, n.x, n.y, item))