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|
// Part 2 could be further optimised by taking into account which sensors touch at manhattan distance + 1
// But the main trick was recognising traversing the circumference as a way to reduce the search space.
package main
import (
"bufio"
"fmt"
"log"
"math"
"os"
"regexp"
"strconv"
)
type Coord struct {
x, y int
}
type Sensor struct {
loc Coord
closestBeacon Coord
}
type visiter []byte
func manhattanDistance(c1, c2 Coord) int {
return int(math.Abs(float64(c2.x-c1.x)) + math.Abs(float64(c2.y-c1.y)))
}
func points(s Sensor) map[Coord]struct{} {
d := manhattanDistance(s.loc, s.closestBeacon)
points := []Coord{{s.loc.x + d + 1, s.loc.y}}
circ := make(map[Coord]struct{})
for len(points) > 0 {
p := points[0]
points = points[1:]
for _, a := range []Coord{
{p.x - 1, p.y + 1},
{p.x + 1, p.y - 1},
{p.x - 1, p.y - 1},
{p.x + 1, p.y + 1},
} {
if manhattanDistance(s.loc, a) == d+1 {
_, ok := circ[a]
if !ok {
circ[a] = struct{}{}
points = append(points, a)
}
}
}
}
return circ
}
func findAtRow(distances []int, sensors []Sensor, row, begin, end int) (int, []Coord) {
covered := 0
notCovered := []Coord{}
for i := begin; i < end; i++ {
coord := Coord{i, row}
cov := false
for i, c := range sensors {
if d := manhattanDistance(c.loc, coord); d <= distances[i] {
cov = true
if coord != c.loc && coord != c.closestBeacon {
covered++
break
}
}
}
if !cov {
notCovered = append(notCovered, coord)
}
}
return covered, notCovered
}
func main() {
fi, err := os.Open("day15.txt")
if err != nil {
log.Fatal(err)
}
bs := bufio.NewScanner(fi)
inputPattern := regexp.MustCompile(
`Sensor at x=(-?\d+), y=(-?\d+): closest beacon is at x=(-?\d+), y=(-?\d+)`,
)
sensors := []Sensor(nil)
for bs.Scan() {
line := bs.Text()
coordstrings := inputPattern.FindAllStringSubmatch(line, 3)[0][1:]
coords := make([]int, len(coordstrings))
for i, c := range coordstrings {
n, err := strconv.Atoi(c)
if err != nil {
log.Fatal(err)
}
coords[i] = n
}
c := []Coord{{coords[0], coords[1]}, {coords[2], coords[3]}}
sensors = append(sensors, Sensor{c[0], c[1]})
}
xmax, xmin := 0, 0
ymax, ymin := 0, 0
for _, c := range sensors {
if c.loc.x > xmax {
xmax = c.loc.x
}
if c.loc.x+c.closestBeacon.x > xmax {
xmax = c.loc.x + c.closestBeacon.x
}
if c.loc.x < xmin {
xmin = c.loc.x
}
if c.loc.x-c.closestBeacon.x < xmin {
xmin = c.loc.x - c.closestBeacon.x
}
if c.loc.y > ymax {
ymax = c.loc.y
}
if c.loc.y+c.closestBeacon.y > ymax {
ymax = c.loc.y + c.closestBeacon.y
}
if c.loc.y < ymin {
ymin = c.loc.y
}
if c.loc.y-c.closestBeacon.y < ymin {
ymin = c.loc.y - c.closestBeacon.y
}
}
distances := []int(nil)
for _, c := range sensors {
distances = append(distances, manhattanDistance(c.loc, c.closestBeacon))
}
fmt.Println(xmin, xmax, ymin, ymax)
row := 2_000_000
count, _ := findAtRow(distances, sensors, row, xmin, xmax)
fmt.Println(count)
lower, upper := 0, 4_000_000
target := Coord{}
t := false
for _, s := range sensors {
p := points(s)
for c := range p {
notCovered := true
if c.x < lower || c.x > upper {
continue
}
if c.y < lower || c.y > upper {
continue
}
for _, other := range sensors {
if other != s {
d := manhattanDistance(other.loc, other.closestBeacon)
if d >= manhattanDistance(other.loc, c) {
notCovered = false
}
}
}
if notCovered {
target = c
t = true
break
}
}
if t {
break
}
}
fmt.Println(target)
}
|
