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day 14 article (#822)
* day 14 article * add browser support * make size more explicit * Specify that tree being solid is assumption * inline -> contiguous
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‎docs/2024/puzzles/day14.md

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import Solver from "../../../../../website/src/components/Solver.js"
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# Day 14: Restroom Redoubt
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by [Bulby](https://github.com/TheDrawingCoder-Gamer)
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## Puzzle description
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https://adventofcode.com/2024/day/14
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## Solution Summary
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1. Parse input into a `List[Robot]`
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2. Make function to advance state by `n` steps
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3. Solve
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* For `part1`, this is advancing the state by 100 then calculating the safety score
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* For `part2`, this is finding the first state where a christmas tree is visible
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## Part 1
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Part 1 shouldn't be too bad. Let's get started with our `Robot` class (and a `Vec2i` class):
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```scala
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case class Vec2i(x: Int, y: Int)
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case class Robot(pos: Vec2i, velocity: Vec2i)
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```
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Now we can parse our input:
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```scala
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def parse(str: String): List[Robot] =
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str.linesIterator.map:
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case s"p=$px,$py v=$vx,$vy" =>
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Robot(Vec2i(px.toInt, py.toInt), Vec2i(vx.toInt, vy.toInt))
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.toList
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```
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Let's define our grid size in a `val`, as it can depend on if we are doing a test input or not:
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```scala
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val size = Vec2i(101, 103)
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```
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The problem text states that when a robot goes off the edge it comes back on the other side, which sounds a lot like modulo.
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Unfortunately, modulo is incorrect in our case for negative numbers. Let's define a remainder instead:
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```scala
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extension (self: Int)
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infix def rem(that: Int): Int =
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val m = math.abs(self) % that
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if self < 0 then
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that - m
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else
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m
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```
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Now we can add a function to `Robot` that advances the state by `n`:
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```scala
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case class Robot(pos: Vec2i, velocity: Vec2i):
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def stepN(n: Int = 1): Robot =
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copy(pos = pos.copy(x = (pos.x + n * velocity.x) rem size.x, y = (pos.y + n * velocity.y) rem size.y))
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```
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Now for the full `List[Robot]`, we can add `stepN` to that too, and also define our safety function:
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```scala
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extension (robots: List[Robot])
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def stepN(n: Int = 1): List[Robot] = robots.map(_.stepN(n))
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def safety: Int =
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val middleX = size.x / 2
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val middleY = size.y / 2
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robots.groupBy: robot =>
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(robot.pos.x.compareTo(middleX), robot.pos.y.compareTo(middleY)) match
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case (0, _) | (_, 0) => -1
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case ( 1, -1) => 0
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case (-1, -1) => 1
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case (-1, 1) => 2
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case ( 1, 1) => 3
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.removed(-1).values.map(_.length).product
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```
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Let's explain this a little. There are 4 quadrants, and as specified there are also lines that aren't in any quadrant.
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We can use `groupBy` to group the robots into quadrants.
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First, we get the midpoints by dividing the size by 2. We then compare the robot to the midpoint, returning `-1` if it's on the line
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(either comparison is equal), and otherwise sort the remaining 4 results into quadrants. We then remove the robots on the line,
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get the length of each of the lists, and multiply them together.
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With this `part1` is easy to implement:
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```scala
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def part1(input: String): Int = parse(input).stepN(100).safety
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```
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## Part 2
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Part 2 wants us to find an image which is really hard. Thankfully, there is one thing I know about Christmas trees: They have
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a lot of lines in a row and are an organized shape.
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We are assuming here that the tree is solid. This assumption is fine in this case, the space to search is finite
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so the worst we can get is a "not found" answer, but in other problems we may want to be more sure about our input.
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The christmas trees I know are really tall, but only in a few columns. So let's test the vertical columns for a few lines that are really long.
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They also have a lot of shorter horizontal lines, so let's also check the rows for a lot of shorter lines.
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Let's also inspect the input more: the grid size is 101 wide and 103 tall. If we've moved vertically 103 times, then we've moved a multiple
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of 103 and are thus back at the start. The same logic applies for horizontal movement, but with 101 instead. This means a robot can, at most, be in
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101 * 103 unique positions, or 10,403, and because all robots are moved at the same time there will only ever be 10,403 unique states. This lets us
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fail fast while writing our code in case we messed something up.
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This is arbitrary and only really possible by print debugging your code, but here's my final code:
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```scala
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extension (robots: List[Robot])
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def findEasterEgg: Int =
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(0 to (size.x * size.y)).find: i =>
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val newRobots = robots.stepN(i)
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newRobots.groupBy(_.pos.y).count(_._2.length >= 10) > 15 && newRobots.groupBy(_.pos.x).count(_._2.length >= 15) >= 3
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.getOrElse(-1)
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```
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We don't even need to check if the lines are contiguous - our test is strict enough with counting lines that it works regardless. Results may vary
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on your input. Here I test if there are more than 15 horizontal lines with a length of 10 or more, and that there are 3 or more vertical lines
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with length 15 or greater.
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Then let's hook it up to `part2`:
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```scala
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def part2(input: String): Int = parse(input).findEasterEgg
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```
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My Christmas tree looks like this:
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```
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```
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With this information of how this looks we could make a smarter `findEasterEgg` with the knowledge of the border. The border
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makes it much easier as we only have to check for 2 contiguous lines in each dimension.
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## Final Code
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```scala
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case class Vec2i(x: Int, y: Int)
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val size = Vec2i(101, 103)
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extension (self: Int)
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infix def rem(that: Int): Int =
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val m = math.abs(self) % that
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if self < 0 then
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that - m
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else
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m
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case class Robot(pos: Vec2i, velocity: Vec2i)
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def stepN(n: Int = 1): Robot =
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copy(pos = pos.copy(x = (pos.x + n * velocity.x) rem size.x, y = (pos.y + n * velocity.y) rem size.y))
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def parse(str: String): List[Robot] =
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str.linesIterator.map:
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case s"p=$px,$py v=$vx,$vy" =>
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Robot(Vec2i(px.toInt, py.toInt), Vec2i(vx.toInt, vy.toInt))
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.toList
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extension (robots: List[Robot])
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def stepN(n: Int = 1): List[Robot] = robots.map(_.stepN(n))
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def safety: Int =
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val middleX = size.x / 2
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val middleY = size.y / 2
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robots.groupBy: robot =>
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(robot.pos.x.compareTo(middleX), robot.pos.y.compareTo(middleY)) match
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case (0, _) | (_, 0) => -1
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case ( 1, -1) => 0
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case (-1, -1) => 1
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case (-1, 1) => 2
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case ( 1, 1) => 3
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.removed(-1).values.map(_.length).product
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def findEasterEgg: Int =
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(0 to 10403).find: i =>
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val newRobots = robots.stepN(i)
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newRobots.groupBy(_.pos.y).count(_._2.length >= 10) > 15 && newRobots.groupBy(_.pos.x).count(_._2.length >= 15) >= 3
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.getOrElse(-1)
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def part1(input: String): Int = parse(input).stepN(100).safety
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def part2(input: String): Int = parse(input).findEasterEgg
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```
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## Run it in the browser
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### Part 1
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<Solver puzzle="day14-part1" year="2024"/>
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### Part 2
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<Solver puzzle="day14-part2" year="2024"/>
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## Solutions from the community
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- [Solution](https://github.com/nikiforo/aoc24/blob/main/src/main/scala/io/github/nikiforo/aoc24/D14T2.scala) by [Artem Nikiforov](https://github.com/nikiforo)
@@ -16,7 +320,6 @@ https://adventofcode.com/2024/day/14
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- [Solution](https://github.com/spamegg1/aoc/blob/master/2024/14/14.scala#L165) by [Spamegg](https://github.com/spamegg1)
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- [Solution](https://github.com/jnclt/adventofcode2024/blob/main/day14/restroom-redoubt.sc) by [jnclt](https://github.com/jnclt)
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- [Solution](https://github.com/Philippus/adventofcode/blob/main/src/main/scala/adventofcode2024/Day14.scala) by [Philippus Baalman](https://github.com/philippus)
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- [Writeup](https://thedrawingcoder-gamer.github.io/aoc-writeups/2024/day14.html) by [Bulby](https://github.com/TheDrawingCoder-Gamer)
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- [Solution](https://github.com/jportway/advent2024/blob/master/src/main/scala/Day14.scala) by [Joshua Portway](https://github.com/jportway)
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- [Solution](https://github.com/AvaPL/Advent-of-Code-2024/tree/main/src/main/scala/day14) by [Paweł Cembaluk](https://github.com/AvaPL)
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‎solver/src/main/scala/adventofcode/Solver.scala

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Map(
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"day13-part1" -> day13.part1,
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"day13-part2" -> day13.part2,
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"day14-part1" -> day14.part1,
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"day14-part2" -> day14.part2,
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"day21-part1" -> day21.part1,
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"day21-part2" -> day21.part2,
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"day22-part1" -> day22.part1,

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