From 30fcda48fda6c543b09c9b27215e4eb24b421735 Mon Sep 17 00:00:00 2001
From: cupcakearmy <hi@nicco.io>
Date: Wed, 21 Dec 2022 22:39:59 +0100
Subject: [PATCH] 15

---
 2022/15/README.md      |  11 ++++
 2022/15/python/main.py | 119 +++++++++++++++++++++++++++++++++++++++++
 2 files changed, 130 insertions(+)
 create mode 100644 2022/15/README.md
 create mode 100644 2022/15/python/main.py

diff --git a/2022/15/README.md b/2022/15/README.md
new file mode 100644
index 0000000..048660e
--- /dev/null
+++ b/2022/15/README.md
@@ -0,0 +1,11 @@
+# 15
+
+Ugly solution. The second part took me a good minute to compute. Each line is done quite efficiently, it could def. be improved by sorting out what lines we should consider in the first place.
+
+<details>
+  <summary>Solutions</summary>
+  <ol>
+    <li>1</li>
+    <li>13784551204480</li>
+  </ol>
+</details>
diff --git a/2022/15/python/main.py b/2022/15/python/main.py
new file mode 100644
index 0000000..6675f5d
--- /dev/null
+++ b/2022/15/python/main.py
@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+
+import sys
+from dataclasses import dataclass
+from functools import lru_cache
+from os.path import dirname, join
+from typing import Union
+
+# Day 15
+
+# Common
+
+
+def read_input(filename):
+    data = join(dirname(__file__), '..', filename)
+    with open(data) as f:
+        return f.read().strip()
+
+
+test = read_input('test.txt')
+data = read_input('input.txt')
+
+
+@dataclass(unsafe_hash=True)
+class Point:
+    x: int
+    y: int
+
+    @lru_cache(maxsize=None)
+    def manhattan(self, other: 'Point'):
+        return abs(self.x - other.x) + abs(self.y - other.y)
+
+    def tuning_freq(self) -> int:
+        return (self.x * 4000000) + self.y
+
+    @staticmethod
+    def parse(s: str) -> 'Point':
+        return Point(*map(lambda s: int(s[2:]), s.split(', ')))  # Format: x=8, y=7
+
+
+@dataclass
+class Interval:
+    start: int
+    end: int
+
+    def __contains__(self, other: Union[int, 'Interval']) -> bool:
+        if type(other) is int:
+            return self.start <= other and other <= self.end
+        elif isinstance(other, Interval):
+            return other.start in self or other.end in self
+        return False
+
+    def __add__(self, other: 'Interval') -> 'Interval':
+        return Interval(min(self.start, other.start), max(self.end, other.end))
+
+    def __len__(self) -> int:
+        return abs(self.start - self.end) + 1
+
+    @staticmethod
+    def reduce(intervals: list['Interval']):
+        combined: list['Interval'] = []
+        for interval in intervals:
+            for added in combined:
+                if added in interval or interval in added:
+                    combined.remove(added)
+                    combined.append(added + interval)
+                    break
+            else:
+                combined.append(interval)
+        return combined if combined == intervals else Interval.reduce(combined)
+
+
+@dataclass
+class Map:
+    sensors: dict[Point, Point]
+    max_distance: int
+
+    def analyse_line(self, line: int, minimum: int | float, maximum: int | float):
+        intervals: list[Interval] = []
+        for sensor, beacon in self.sensors.items():
+            radius = sensor.manhattan(beacon)  # Radius of the sensor
+            dy = radius - abs(sensor.y - line)  # Check if line is in the radius of the sensor
+            if dy >= 0:
+                # Add interval for scanned line
+                intervals.append(Interval(max(minimum, sensor.x-dy), min(maximum, sensor.x+dy)))
+        intervals = Interval.reduce(intervals)
+        return sum([len(i) for i in intervals]), intervals
+
+    def flag_1(self, line: int):
+        score, _ = self.analyse_line(line, float('-inf'), float('inf'))
+        return score - 1
+
+    def flag_2(self, limit: int) -> int:
+        for line in range(limit):
+            score, intervals = self.analyse_line(line, 0, limit)
+            if score != limit + 1:
+                print(line, intervals)
+                return Point(intervals[0].end + 1, line).tuning_freq()
+        raise Exception("Not found")
+
+    @ staticmethod
+    def parse(data: str) -> 'Map':
+        sensors: dict[Point, Point] = {}
+        max_distance = 0
+        for line in data.splitlines():
+            sensor, beacon = line.split(':')
+            s = Point.parse(sensor[10:])
+            b = Point.parse(beacon[22:])
+            sensors[s] = b
+            max_distance = max(max_distance, s.manhattan(b))
+        return Map(sensors, max_distance)
+
+
+# Running
+
+m = Map.parse(data)
+print(m.flag_1(2000000))
+print(Point(3446137, 3204480).tuning_freq())
+# print(m.flag_2(4_000_000))