2024: d14: ex2: add solution

2024/d14/ex1/ex1.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+import dataclasses
+import functools
+import operator
+import sys
+from collections import Counter
+from typing import NamedTuple
+
+
+class Point(NamedTuple):
+    x: int
+    y: int
+
+
+@dataclasses.dataclass
+class Robot:
+    pos: Point
+    vel: Point
+
+    def step(self, dims: Point, delta: int = 1) -> "Robot":
+        x, y = self.pos.x + self.vel.x * delta, self.pos.y + self.vel.y * delta
+        return Robot(
+            Point(x % dims.x, y % dims.y),
+            self.vel,
+        )
+
+
+def solve(input: str) -> int:
+    def parse_robot(input: str) -> Robot:
+        pos, vel = map(lambda s: s.split("=")[1], input.split(" "))
+        return Robot(
+            Point(*map(int, pos.split(","))),
+            Point(*map(int, vel.split(","))),
+        )
+
+    def parse(input: list[str]) -> list[Robot]:
+        return [parse_robot(line) for line in input]
+
+    def compute_safety(robots: list[Robot], dims: Point) -> int:
+        mid_x, mid_y = dims.x // 2, dims.y // 2
+        counts: Counter[tuple[bool, bool]] = Counter()
+        for x, y in map(lambda robot: robot.pos, robots):
+            if x == mid_x or y == mid_y:
+                continue
+            counts[(x < mid_x, y < mid_y)] += 1
+        return functools.reduce(operator.mul, counts.values())
+
+    robots = parse(input.splitlines())
+    dims = Point(101, 103)
+    robots = [robot.step(dims, 100) for robot in robots]
+    return compute_safety(robots, dims)
+
+
+def main() -> None:
+    input = sys.stdin.read()
+    print(solve(input))
+
+
+if __name__ == "__main__":
+    main()

2024/d14/ex1/input

@@ -0,0 +1,500 @@
+p=62,20 v=85,-14
+p=88,18 v=-70,97
+p=51,21 v=35,-22
+p=19,56 v=2,45
+p=93,11 v=-87,26
+p=90,67 v=44,-13
+p=99,34 v=82,57
+p=55,42 v=82,22
+p=61,22 v=-24,-95
+p=100,7 v=18,-29
+p=92,56 v=-56,66
+p=66,15 v=-6,77
+p=62,21 v=68,39
+p=8,80 v=-72,-90
+p=33,40 v=89,-46
+p=28,10 v=-4,-64
+p=4,78 v=-55,1
+p=87,23 v=-32,-10
+p=1,76 v=85,6
+p=15,51 v=-35,-75
+p=58,98 v=-41,80
+p=34,17 v=-25,-47
+p=66,81 v=23,50
+p=48,97 v=-53,88
+p=99,69 v=-97,83
+p=25,23 v=-38,31
+p=16,54 v=91,37
+p=12,65 v=-85,-41
+p=13,62 v=51,64
+p=54,16 v=-91,-30
+p=99,92 v=34,30
+p=44,21 v=-50,-9
+p=0,55 v=15,-46
+p=68,23 v=-53,-51
+p=29,51 v=24,-55
+p=42,10 v=-98,28
+p=76,50 v=-55,-54
+p=83,24 v=65,-70
+p=13,15 v=-85,64
+p=2,53 v=-43,-59
+p=8,88 v=-24,34
+p=100,50 v=33,-20
+p=24,83 v=17,54
+p=18,71 v=52,38
+p=93,21 v=-33,-18
+p=8,78 v=-19,-7
+p=23,31 v=61,-46
+p=76,60 v=47,46
+p=51,26 v=-53,32
+p=32,54 v=-76,33
+p=66,65 v=-42,62
+p=19,3 v=78,-23
+p=96,46 v=88,-76
+p=53,44 v=12,-13
+p=64,56 v=-30,64
+p=41,52 v=35,-24
+p=49,9 v=-28,35
+p=77,18 v=9,-43
+p=57,3 v=97,-97
+p=81,57 v=-23,27
+p=13,73 v=-22,-90
+p=97,76 v=-46,-49
+p=63,43 v=-80,99
+p=90,96 v=-37,-92
+p=89,33 v=82,-42
+p=79,64 v=-19,-58
+p=64,65 v=59,-90
+p=84,54 v=17,24
+p=95,42 v=18,-13
+p=90,43 v=-36,-62
+p=83,2 v=32,-97
+p=34,69 v=-38,-78
+p=91,17 v=18,31
+p=42,82 v=74,-94
+p=1,33 v=-72,-42
+p=97,82 v=-20,-94
+p=83,83 v=-23,40
+p=56,22 v=85,-22
+p=50,42 v=25,-70
+p=32,1 v=88,-60
+p=63,71 v=-3,-33
+p=49,85 v=-67,59
+p=18,8 v=-23,39
+p=24,60 v=-76,-87
+p=34,100 v=-88,-85
+p=84,84 v=44,79
+p=16,101 v=53,-64
+p=44,33 v=88,-25
+p=52,22 v=-43,-35
+p=30,63 v=-23,-86
+p=14,40 v=65,-50
+p=56,98 v=-41,88
+p=42,11 v=-14,-72
+p=72,9 v=-4,-72
+p=26,71 v=-47,-62
+p=92,18 v=-71,15
+p=81,40 v=59,7
+p=79,69 v=16,64
+p=4,40 v=24,-83
+p=17,78 v=65,-32
+p=87,85 v=81,-36
+p=98,27 v=42,42
+p=100,55 v=72,95
+p=94,12 v=-80,61
+p=73,34 v=96,-38
+p=32,30 v=-63,-38
+p=45,37 v=-27,-75
+p=6,0 v=-72,-68
+p=24,58 v=39,41
+p=25,74 v=-26,-15
+p=99,82 v=80,64
+p=88,54 v=-94,-21
+p=100,81 v=29,-28
+p=84,87 v=56,6
+p=71,17 v=-95,-96
+p=37,89 v=-9,-18
+p=42,30 v=-78,-59
+p=92,17 v=-68,14
+p=31,55 v=88,66
+p=28,10 v=-13,-62
+p=30,30 v=29,60
+p=89,80 v=8,-42
+p=23,75 v=41,-44
+p=65,78 v=-55,42
+p=1,80 v=-9,19
+p=54,78 v=36,83
+p=10,40 v=66,-95
+p=67,71 v=8,58
+p=23,17 v=12,69
+p=36,70 v=76,21
+p=7,63 v=-60,-65
+p=39,96 v=87,41
+p=8,56 v=-34,-90
+p=64,66 v=9,79
+p=2,61 v=4,17
+p=64,51 v=28,-6
+p=91,25 v=-41,69
+p=92,3 v=-85,47
+p=61,41 v=-90,57
+p=86,61 v=-69,70
+p=82,45 v=7,-13
+p=23,39 v=-78,-40
+p=58,3 v=-30,15
+p=87,30 v=87,-25
+p=21,84 v=39,34
+p=36,26 v=89,-59
+p=90,51 v=-83,-91
+p=5,39 v=79,-87
+p=5,63 v=66,-74
+p=71,45 v=21,-21
+p=49,40 v=-68,-66
+p=68,93 v=96,96
+p=67,96 v=46,51
+p=72,44 v=-32,82
+p=15,90 v=15,2
+p=26,78 v=28,-44
+p=7,3 v=-87,-39
+p=74,101 v=94,-76
+p=90,20 v=-6,-46
+p=22,17 v=-68,-63
+p=12,81 v=-37,14
+p=37,84 v=-15,22
+p=75,31 v=83,-63
+p=48,2 v=-90,-27
+p=44,94 v=-63,31
+p=73,71 v=-50,22
+p=22,101 v=39,51
+p=49,94 v=-90,26
+p=93,67 v=5,-12
+p=57,83 v=-68,80
+p=86,82 v=44,-32
+p=3,64 v=87,-62
+p=43,87 v=-20,-57
+p=40,18 v=-8,-56
+p=80,46 v=-89,57
+p=79,26 v=-18,27
+p=3,27 v=-98,77
+p=99,44 v=42,-99
+p=25,27 v=-72,23
+p=98,24 v=43,85
+p=54,93 v=-28,-60
+p=29,4 v=86,80
+p=42,96 v=-75,14
+p=75,77 v=54,81
+p=96,63 v=-46,-33
+p=51,94 v=88,-69
+p=31,88 v=51,39
+p=75,15 v=-43,-84
+p=8,18 v=93,63
+p=20,65 v=99,30
+p=71,5 v=72,-98
+p=31,95 v=63,-19
+p=87,70 v=-68,71
+p=11,55 v=15,37
+p=63,36 v=10,36
+p=41,98 v=37,10
+p=27,78 v=26,-36
+p=53,93 v=30,18
+p=78,25 v=-58,-80
+p=21,100 v=-73,66
+p=36,33 v=25,11
+p=65,3 v=-92,-68
+p=46,22 v=-64,48
+p=49,87 v=-91,38
+p=82,37 v=6,16
+p=24,94 v=-30,-3
+p=14,94 v=94,37
+p=28,32 v=39,-1
+p=35,19 v=-68,-25
+p=3,51 v=-47,70
+p=12,6 v=-29,-28
+p=28,19 v=15,2
+p=75,45 v=95,-67
+p=76,4 v=7,-93
+p=45,29 v=-63,-37
+p=41,22 v=-64,93
+p=67,45 v=10,-36
+p=51,1 v=21,-74
+p=25,84 v=1,-81
+p=15,36 v=-47,-29
+p=44,98 v=24,-81
+p=79,30 v=45,94
+p=45,57 v=12,-57
+p=12,23 v=65,78
+p=65,76 v=-68,17
+p=100,100 v=79,23
+p=75,22 v=58,-84
+p=82,65 v=69,-74
+p=76,6 v=-61,-61
+p=61,38 v=27,53
+p=68,94 v=86,37
+p=64,71 v=6,-88
+p=21,93 v=-53,-65
+p=32,86 v=64,-98
+p=11,11 v=-95,-95
+p=36,32 v=-38,65
+p=23,95 v=91,-65
+p=31,33 v=12,-34
+p=12,5 v=-99,-72
+p=50,3 v=-91,-93
+p=74,66 v=33,13
+p=22,7 v=-12,-97
+p=11,6 v=41,-43
+p=14,16 v=-21,60
+p=95,76 v=81,-61
+p=3,55 v=79,99
+p=43,97 v=-1,-60
+p=73,66 v=31,-33
+p=60,33 v=73,-57
+p=63,57 v=71,-95
+p=86,11 v=-20,-80
+p=50,28 v=86,-34
+p=66,5 v=-80,45
+p=96,87 v=67,6
+p=52,16 v=-53,40
+p=31,31 v=38,32
+p=96,23 v=-34,-72
+p=46,64 v=-91,45
+p=14,19 v=-73,60
+p=31,85 v=12,-32
+p=19,36 v=-36,-46
+p=39,21 v=-52,15
+p=98,34 v=-10,81
+p=96,65 v=-9,1
+p=97,75 v=-21,34
+p=22,0 v=64,2
+p=8,15 v=-24,36
+p=67,1 v=61,-89
+p=64,76 v=70,34
+p=22,29 v=-74,-54
+p=73,48 v=-41,-75
+p=83,7 v=-82,-72
+p=57,6 v=-66,-93
+p=31,21 v=-50,81
+p=90,21 v=-56,-35
+p=79,73 v=54,77
+p=62,64 v=60,29
+p=51,70 v=-96,-6
+p=35,71 v=75,-33
+p=9,56 v=53,-58
+p=63,48 v=-94,49
+p=61,72 v=82,55
+p=51,79 v=-66,5
+p=2,62 v=46,-46
+p=56,2 v=10,-64
+p=42,74 v=-64,75
+p=0,83 v=17,-61
+p=3,59 v=-97,33
+p=40,82 v=-15,47
+p=89,8 v=94,19
+p=76,26 v=-95,-23
+p=4,86 v=79,76
+p=4,32 v=-77,31
+p=35,38 v=-9,-59
+p=78,87 v=72,-57
+p=72,72 v=-72,67
+p=94,33 v=-2,-93
+p=59,96 v=37,-19
+p=90,24 v=-83,-1
+p=88,61 v=69,50
+p=67,28 v=35,-66
+p=40,63 v=75,29
+p=84,27 v=-42,-91
+p=40,21 v=-31,-30
+p=1,50 v=-34,-95
+p=87,59 v=96,-94
+p=36,19 v=-78,-68
+p=5,43 v=-78,91
+p=27,49 v=91,-99
+p=61,85 v=11,14
+p=89,61 v=-83,-97
+p=31,78 v=-86,81
+p=76,69 v=33,-82
+p=74,28 v=-43,-71
+p=23,27 v=-35,86
+p=80,74 v=-33,-86
+p=99,10 v=43,-10
+p=59,55 v=-41,-95
+p=89,94 v=58,43
+p=72,87 v=7,67
+p=68,73 v=-29,-57
+p=1,46 v=80,41
+p=45,33 v=36,-38
+p=24,4 v=46,21
+p=18,94 v=-35,5
+p=82,45 v=53,74
+p=93,75 v=-83,-86
+p=14,3 v=51,-80
+p=74,30 v=-30,-96
+p=70,38 v=-29,-34
+p=55,76 v=-94,-13
+p=11,82 v=-10,38
+p=11,55 v=-35,-4
+p=93,76 v=6,-57
+p=17,57 v=-80,-42
+p=47,82 v=18,94
+p=84,70 v=-84,1
+p=48,94 v=-90,6
+p=95,41 v=-51,57
+p=18,53 v=55,-84
+p=100,20 v=-20,-56
+p=49,26 v=-17,34
+p=31,62 v=92,-86
+p=69,2 v=-81,82
+p=76,43 v=-71,97
+p=23,15 v=-62,97
+p=26,23 v=-87,40
+p=67,48 v=34,16
+p=64,11 v=-54,85
+p=85,58 v=8,-49
+p=80,25 v=57,-6
+p=45,35 v=49,-9
+p=96,65 v=-20,-25
+p=91,49 v=68,16
+p=8,62 v=29,33
+p=97,50 v=69,-85
+p=52,53 v=-2,70
+p=70,68 v=44,83
+p=65,7 v=-17,39
+p=45,14 v=-87,-34
+p=94,47 v=-93,-46
+p=29,84 v=70,11
+p=70,73 v=-23,-6
+p=100,51 v=45,18
+p=21,2 v=-13,-19
+p=67,32 v=-95,-82
+p=61,2 v=-54,64
+p=17,12 v=65,35
+p=67,54 v=-74,-89
+p=2,6 v=5,-92
+p=26,77 v=-12,-28
+p=39,77 v=12,-86
+p=70,57 v=84,-83
+p=8,88 v=-60,13
+p=19,37 v=-38,-89
+p=0,14 v=6,-2
+p=24,59 v=-24,-82
+p=5,13 v=-11,-47
+p=21,27 v=88,23
+p=89,75 v=-20,83
+p=68,66 v=19,1
+p=34,72 v=-74,25
+p=27,70 v=14,-90
+p=89,9 v=-44,2
+p=5,67 v=-61,-36
+p=52,13 v=-53,-84
+p=50,21 v=-90,-9
+p=21,16 v=2,64
+p=69,21 v=-18,-88
+p=64,85 v=-80,34
+p=31,70 v=50,95
+p=15,8 v=-23,52
+p=97,52 v=28,41
+p=85,6 v=8,-15
+p=9,33 v=-74,52
+p=46,59 v=90,96
+p=71,50 v=-93,98
+p=15,74 v=-24,-82
+p=54,87 v=-15,-36
+p=37,25 v=-15,7
+p=92,13 v=86,78
+p=90,14 v=20,-68
+p=68,37 v=-5,4
+p=58,56 v=48,-99
+p=27,16 v=-36,-32
+p=4,49 v=-58,65
+p=5,50 v=-60,98
+p=13,31 v=-48,-91
+p=58,75 v=46,8
+p=66,88 v=-6,-98
+p=95,16 v=32,23
+p=86,21 v=-57,-55
+p=19,65 v=-32,-98
+p=83,57 v=-46,-95
+p=37,10 v=38,64
+p=87,33 v=46,8
+p=45,54 v=-53,-58
+p=35,95 v=-39,-7
+p=89,65 v=58,-45
+p=82,80 v=-82,-65
+p=35,7 v=25,76
+p=66,92 v=-16,-44
+p=75,89 v=-92,66
+p=48,84 v=98,42
+p=70,84 v=-30,-44
+p=49,51 v=-92,28
+p=27,85 v=62,-36
+p=31,21 v=3,-64
+p=61,101 v=-15,88
+p=56,44 v=90,68
+p=83,49 v=-95,-83
+p=69,38 v=8,-92
+p=34,13 v=-32,43
+p=0,51 v=-75,25
+p=39,36 v=76,69
+p=66,59 v=70,-96
+p=78,42 v=-31,-62
+p=5,5 v=-33,-22
+p=54,74 v=-12,44
+p=25,26 v=25,85
+p=74,88 v=-39,56
+p=65,83 v=33,-28
+p=87,90 v=95,30
+p=71,20 v=-29,93
+p=66,9 v=83,-96
+p=38,51 v=50,41
+p=66,87 v=44,46
+p=30,31 v=-49,-14
+p=70,32 v=73,28
+p=0,94 v=95,26
+p=87,80 v=-83,46
+p=92,70 v=-58,99
+p=48,67 v=-1,41
+p=43,74 v=49,34
+p=87,51 v=43,8
+p=23,63 v=39,-86
+p=2,77 v=48,7
+p=32,60 v=-63,-37
+p=99,3 v=-21,31
+p=82,55 v=57,8
+p=5,68 v=-46,-41
+p=32,77 v=-38,88
+p=84,0 v=6,-64
+p=20,95 v=52,26
+p=29,98 v=-16,83
+p=44,81 v=-27,-73
+p=43,40 v=-41,-42
+p=24,73 v=-88,-49
+p=99,45 v=-97,8
+p=37,9 v=-31,-57
+p=70,11 v=8,-39
+p=0,11 v=96,57
+p=83,70 v=70,75
+p=65,71 v=-40,-65
+p=82,66 v=73,-80
+p=31,41 v=52,-96
+p=19,72 v=65,75
+p=24,4 v=-86,54
+p=75,85 v=85,30
+p=2,83 v=17,-7
+p=9,87 v=-22,59
+p=24,3 v=-12,21
+p=5,74 v=-34,46
+p=22,90 v=-37,-11
+p=20,84 v=4,95
+p=95,10 v=-96,-55
+p=52,9 v=23,-14
+p=20,51 v=-63,-75
+p=64,27 v=24,48
+p=50,2 v=-90,-68
+p=54,47 v=23,70
+p=26,22 v=51,-51
+p=4,49 v=-72,-54
+p=53,47 v=2,-86
+p=21,29 v=-37,13
+p=37,70 v=38,-40
+p=6,41 v=43,-20
+p=88,23 v=-38,-68
+p=67,20 v=79,-71
+p=37,7 v=24,25

2024/d14/ex2/ex2.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+
+import dataclasses
+import functools
+import itertools
+import sys
+from typing import Literal, NamedTuple
+
+
+class Point(NamedTuple):
+    x: int
+    y: int
+
+
+@dataclasses.dataclass
+class Robot:
+    pos: Point
+    vel: Point
+
+    def step(self, dims: Point, delta: int = 1) -> "Robot":
+        x, y = self.pos.x + self.vel.x * delta, self.pos.y + self.vel.y * delta
+        return Robot(
+            Point(x % dims.x, y % dims.y),
+            self.vel,
+        )
+
+
+def solve(input: str) -> int:
+    def parse_robot(input: str) -> Robot:
+        pos, vel = map(lambda s: s.split("=")[1], input.split(" "))
+        return Robot(
+            Point(*map(int, pos.split(","))),
+            Point(*map(int, vel.split(","))),
+        )
+
+    def parse(input: list[str]) -> list[Robot]:
+        return [parse_robot(line) for line in input]
+
+    def find_tree(robots: list[Robot], dims: Point) -> int:
+        def compute_positions(step: int) -> list[Point]:
+            return [robot.step(dims, step).pos for robot in robots]
+
+        def compute_variance(values: list[int]) -> float:
+            avg = sum(values) / len(values)
+            variance = sum((n - avg) ** 2 for n in values) / len(values)
+            return variance
+
+        def cluster_variance(step: int, dimension: Literal["x", "y"]) -> float:
+            return compute_variance(
+                [getattr(p, dimension) for p in compute_positions(step)]
+            )
+
+        # The tree should have robots clustered together in X and Y
+        cluster_x = min(
+            range(dims.x),
+            key=functools.partial(cluster_variance, dimension="x"),
+        )
+        cluster_y = min(
+            range(dims.y),
+            key=functools.partial(cluster_variance, dimension="y"),
+        )
+
+        # And those clusers should repeat modulo each dimension
+        for i in itertools.count(cluster_x, step=dims.x):
+            if i % dims.y == cluster_y:
+                return i
+        assert False  # Sanity check
+
+    robots = parse(input.splitlines())
+    dims = Point(101, 103)
+    return find_tree(robots, dims)
+
+
+def main() -> None:
+    input = sys.stdin.read()
+    print(solve(input))
+
+
+if __name__ == "__main__":
+    main()

2024/d14/ex2/input

@@ -0,0 +1,500 @@
+p=62,20 v=85,-14
+p=88,18 v=-70,97
+p=51,21 v=35,-22
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