Day 11 - UGLY AF
This commit is contained in:
Sebastian Seedorf
6
day11/common.py
Normal file
6
day11/common.py
Normal file
@@ -0,0 +1,6 @@
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def char_to_int(char):
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if char == 'L':
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return -1
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if char == '.':
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return 0
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return 1
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99
day11/input.txt
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99
day11/input.txt
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@@ -0,0 +1,99 @@
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLL.LL.LLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLL.L.LLLLLLLLLL.LLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL..LLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LL.LL.LLLLL.L.LLLLL.LLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLL.LLL.LL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLL.L.LLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLL.LL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLL.LLLLLLLLL.LLLLL.LLLLLLL.LLLL.L.LLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLL
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.L.LL...LLLL.......L....L.LLLLLL.......LL....LL...L..L.LLL...LLL..L.L.L.L..L...............L
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LLLLLLLLLL.LL.LLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLL.LLLL.LLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLL..LLLLLLLLLLLLLLLLLL
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LLLLLLL.LL.LLL.LLLLLLLLLLLLL.L.LLLLLLL.LLLLL.LLLLLLL.LLLLLL..LLLLLL.LLLLL.LLLLLL.LLLLLLLLLLL
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.......L.LLL........LL.L....L.LL...L.....L..LL......L.....L....L.LLLL...L....L..L.L........L
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLL.L.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LL.LLLL.LLLLL.LLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLL.LLLLLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLL
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..L...L.........LL.LLLL...L...LL.L.L..L...L.L...LL..LL...L....L....LL.L...LLL........LLL....
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LLLLLLLL.L.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLL.LLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLL.LL.LLLLLLLLLLL.LLLLLL.LLLLLL.LL.LLLLLLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LL.LLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLL
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L..L...........L....L.LLLL........L..LL....L.L.......L.L.L.....LL......L.....LLL.LL.L.L.LL.L
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLL..LLLLLL.LLLLLLLLLLL.LLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLL.LLL.LLLLL.LLLLLL..LLLLL.LLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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..L.....LLL.L..L...L......LL......L.......LL..L.L.L.L....LLLL....L....L...LL...LLL.L....LLL.
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLL.LLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLL.LL.LL.L.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLLLLLL.LL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLL.L.LLLLLLLLLL..LLLL
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LLLLL.LLLLLLLL.LLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.L.LLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLL..LLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LL.LLL.LLLLLLLLLLL
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.....LL.L...LL....L...L.L.....L...L.L.L.L.L.L.......LL.....L.LL.LL...LLL.L.....L...L......L.
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LL..LLL.LLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLL.L.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLL.LL.LLLLLLLLLLLLL.LLLLLLLLLL.
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLL.L.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.L.LLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLL.L
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.L....LL....L....L.L....L......LL.....L..L.L....LL.......L......L.L..L.......L....L.LLL....L
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLL..LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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.L......L....L.L..L....LL.......L.LL..LL.L.....L..L.L...............LL....L...L....L....L.LL
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LLLLLLLLL..LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL..LLLLLLLLL.LLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLL.LLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL..LLLLLL.LLLLL.LLLLLLLLLLLL.L.LLLLLL..LLLLLLLLLLLL.LLLLLLLLLLL
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.L..L..L.L.L.L.L.L.....L.L...L....LL.L.L....L..L.L.L........L....LL.......LL....L...L.L..LL.
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LLLLLLLLLLLLLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLL..LLLLL.LLLLLL.LLLLL..LLLLLL.LLLLLLLLLLL
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L.LLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL.LL.LLLLLLLLLLLLLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLL.L.LLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLL..LLLLL.LLLLLLLLLLLLL.LLLLL.L.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL..LLLLLLLLLL
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LLLLLLLLLLL.LLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL..LLLLL.LLLLLLLLLL.LLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLL.L.L.LLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLL.LLLLLLLLLLLLL
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LLLLL....L.LL..LLL.L...LL.....L............L...............L..L.LLLLL.L.L.......L..LL.L...L.
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LLLLLLLLLL.LLLLLL.LLLLL..LLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLL.LL.LLLLLL..LLLLL.LLLLLL.L.LLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLL.LLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.L.LLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLL
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.....L...L......L...LL.L.......LL.L...LL..LL.....L.......LL.LL......LL.L...L..L..L...L.LLL..
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL..LLLLLLLLLL
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LLLLLL.LLL.LLLLLL.LLLLLL.LL.LL.LLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLLLLLL.LLLLLLLLL.LLLLLLLLLLL
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LLL.LLL.LL..LLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL.
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LLLLLLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLL.LLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.L.LLLLLLLLLL.LLLL.LL.LLLLL.LLLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLL.L.L.LLLLLLLLL
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LLLLLLLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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.LL.LLL.L.L.L..L....L..L...L......L......L.L.L...L.L..L.L.LLLLL..L.LL.LLL.L...LL.LL...L.L.L.
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LLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLL.LLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.LLLLLLL.LLLLLL.LLLLL..LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLLLLLLLL.LLLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLL
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LLLL.LLLLL.LLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLL.LLLLLLL.LLLLLL.LLLLL.LLLLLLLLLLLLLLLLLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LL.LLLLLLLLLLLLLLLL.LLLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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..L.LLLL.L...LL.L....L..LL..L.....L....L.L...LL.......L.L..LL..LL............L...LL.....L...
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLL.LLLLLLLL.LLLL.LLLLLLLLLLL
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LLLLLLLLLLLLL.LLL.LLLLLL..LLLL.LLLLLLLLLLLLL.LLLLLLLLLLLLLL.LLLLLL.LLLLLL.LLLLLLLLLL..LLLLL.
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LLLLLLLLLL.LLLLLL..LLLLL.LLLLL.LLLLLLL..LLLL.LLLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL.L.LLLLL.LLLLLLLLLLLLLL.LLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLL.LLLLLL.LLLLLL.LLLLL.LLLLL.L.LLLLLLLLLLLLL.LL.LLLLL.LLLL.LLLLLL.LLLLLLLLLL.LLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLL..LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLL.LLLLLLLLLLL
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LLLLLLLLLL.LLL.LLLLLLLLL.LLLLL.LLLLLLLLLLLL..LLLLLLL.LLLLL..LLLLLL.LLLLLL.LLLLLLLLLLLLLLLLLL
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LLLLLLLLLLLLLLLLL.LLLLLL.LLLLL.LLLLLLL.LLLLLLLLLLLLL.LLLLLL.LLLLLL.LL.LLLLLLLLLL.LLLLLLLLLLL
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52
day11/part1.py
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52
day11/part1.py
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@@ -0,0 +1,52 @@
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import numpy as np
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from numpy.lib.stride_tricks import as_strided
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from day11.common import char_to_int
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def get_filters(arr):
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KERNEL_SHAPE = (3, 3)
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return as_strided(
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arr,
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shape=(
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arr.shape[0] - KERNEL_SHAPE[0] + 1, # The feature map is a few pixels smaller than the input
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arr.shape[1] - KERNEL_SHAPE[1] + 1,
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KERNEL_SHAPE[0],
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KERNEL_SHAPE[1],
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),
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strides=(
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arr.strides[0],
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arr.strides[1],
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arr.strides[0], # When we move one step in the 3rd dimension, we should move one step in the original data too
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arr.strides[1],
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),
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writeable=False, # totally use this to avoid writing to memory in weird places
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)
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def parse(arr):
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if arr[1, 1] == 0:
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return 0
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if np.count_nonzero(arr == 1) == 0:
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return 1
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if arr[1, 1] == 1 and np.count_nonzero(arr == 1) >= 5:
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return -1
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return arr[1, 1]
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lines = np.array([[char_to_int(x) for x in line.strip()] for line in open("input.txt")], dtype=np.byte)
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lines = np.pad(lines, 1, 'constant', constant_values=0)
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shape = np.array(lines.shape)
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last = -1
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while True:
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curr = np.count_nonzero(lines == 1)
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if curr == last:
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print(last)
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break
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last = curr
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expanded_input = get_filters(lines)
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new_input = np.zeros(shape, dtype=np.byte)
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for idx in np.ndindex(*shape-2):
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target = tuple(np.array(idx) + 1)
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new_input[target] = parse(expanded_input[idx])
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lines = new_input
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50
day11/part2.py
Normal file
50
day11/part2.py
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@@ -0,0 +1,50 @@
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import numpy as np
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from day11.common import char_to_int
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lines = np.array([[char_to_int(x) for x in line.strip()] for line in open("input.txt")], dtype=np.byte)
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shape = lines.shape
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RAYS = [(-1, -1), (0, -1), (1, -1), (-1, 0), (1, 0), (-1, 1), (0, 1), (1, 1)]
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last = -1
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while True:
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curr = np.count_nonzero(lines == 1)
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if curr == last:
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print(last)
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break
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last = curr
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new = np.zeros_like(lines, dtype=np.byte)
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for idx, val in np.ndenumerate(lines):
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if val == 0:
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continue
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if val == -1:
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found = False
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for ray in RAYS:
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ray = np.array(ray)
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pos = np.array(idx) + ray
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while 0 <= pos[0] < shape[0] and 0 <= pos[1] < shape[1]:
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if lines[tuple(pos)] == 1:
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found = True
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break
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elif lines[tuple(pos)] == -1:
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break
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pos = pos + ray
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if found:
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break
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new[idx] = 1 if not found else -1
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continue
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if val == 1:
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found = 0
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for ray in RAYS:
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ray = np.array(ray)
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pos = np.array(idx) - ray
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while 0 <= pos[0] < shape[0] and 0 <= pos[1] < shape[1]:
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if lines[tuple(pos)] == 1:
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found += 1
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break
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elif lines[tuple(pos)] == -1:
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break
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pos = pos - ray
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new[idx] = -1 if found >= 5 else 1
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lines = new
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Reference in New Issue
Block a user