As your probe drifted down through this area, it released an assortment of beacons and scanners into the water. It's difficult to navigate in the pitch black open waters of the ocean trench, but if you can build a map of the trench using data from the scanners, you should be able to safely reach the bottom.
The beacons and scanners float motionless in the water; they're designed to maintain the same position for long periods of time. Each scanner is capable of detecting all beacons in a large cube centered on the scanner; beacons that are at most 1000 units away from the scanner in each of the three axes (x
, y
, and z
) have their precise position determined relative to the scanner. However, scanners cannot detect other scanners. The submarine has automatically summarized the relative positions of beacons detected by each scanner (your puzzle input).
For example, if a scanner is at x,y,z
coordinates 500,0,-500
and there are beacons at -500,1000,-1500
and 1501,0,-500
, the scanner could report that the first beacon is at -1000,1000,-1000
(relative to the scanner) but would not detect the second beacon at all.
Unfortunately, while each scanner can report the positions of all detected beacons relative to itself, the scanners do not know their own position. You'll need to determine the positions of the beacons and scanners yourself.
The scanners and beacons map a single contiguous 3d region. This region can be reconstructed by finding pairs of scanners that have overlapping detection regions such that there are at least 12 beacons that both scanners detect within the overlap. By establishing 12 common beacons, you can precisely determine where the scanners are relative to each other, allowing you to reconstruct the beacon map one scanner at a time.
For a moment, consider only two dimensions. Suppose you have the following scanner reports:
--- scanner 0 ---
0,2
4,1
3,3
--- scanner 1 ---
-1,-1
-5,0
-2,1
Drawing x
increasing rightward, y
increasing upward, scanners as S
, and beacons as B
, scanner 0 detects this:
...B.
B....
....B
S....
Scanner 1 detects this:
...B..
B....S
....B.
For this example, assume scanners only need 3 overlapping beacons. Then, the beacons visible to both scanners overlap to produce the following complete map:
...B..
B....S
....B.
S.....
Unfortunately, there's a second problem: the scanners also don't know their rotation or facing direction. Due to magnetic alignment, each scanner is rotated some integer number of 90-degree turns around all of the x
, y
, and z
axes. That is, one scanner might call a direction positive x
, while another scanner might call that direction negative y
. Or, two scanners might agree on which direction is positive x
, but one scanner might be upside-down from the perspective of the other scanner. In total, each scanner could be in any of 24 different orientations: facing positive or negative x
, y
, or z
, and considering any of four directions "up" from that facing.
For example, here is an arrangement of beacons as seen from a scanner in the same position but in different orientations:
--- scanner 0 ---
-1,-1,1
-2,-2,2
-3,-3,3
-2,-3,1
5,6,-4
8,0,7
--- scanner 0 ---
1,-1,1
2,-2,2
3,-3,3
2,-1,3
-5,4,-6
-8,-7,0
--- scanner 0 ---
-1,-1,-1
-2,-2,-2
-3,-3,-3
-1,-3,-2
4,6,5
-7,0,8
--- scanner 0 ---
1,1,-1
2,2,-2
3,3,-3
1,3,-2
-4,-6,5
7,0,8
--- scanner 0 ---
1,1,1
2,2,2
3,3,3
3,1,2
-6,-4,-5
0,7,-8
By finding pairs of scanners that both see at least 12 of the same beacons, you can assemble the entire map. For example, consider the following report:
--- scanner 0 ---
404,-588,-901
528,-643,409
-838,591,734
390,-675,-793
-537,-823,-458
-485,-357,347
-345,-311,381
-661,-816,-575
-876,649,763
-618,-824,-621
553,345,-567
474,580,667
-447,-329,318
-584,868,-557
544,-627,-890
564,392,-477
455,729,728
-892,524,684
-689,845,-530
423,-701,434
7,-33,-71
630,319,-379
443,580,662
-789,900,-551
459,-707,401
--- scanner 1 ---
686,422,578
605,423,415
515,917,-361
-336,658,858
95,138,22
-476,619,847
-340,-569,-846
567,-361,727
-460,603,-452
669,-402,600
729,430,532
-500,-761,534
-322,571,750
-466,-666,-811
-429,-592,574
-355,545,-477
703,-491,-529
-328,-685,520
413,935,-424
-391,539,-444
586,-435,557
-364,-763,-893
807,-499,-711
755,-354,-619
553,889,-390
--- scanner 2 ---
649,640,665
682,-795,504
-784,533,-524
-644,584,-595
-588,-843,648
-30,6,44
-674,560,763
500,723,-460
609,671,-379
-555,-800,653
-675,-892,-343
697,-426,-610
578,704,681
493,664,-388
-671,-858,530
-667,343,800
571,-461,-707
-138,-166,112
-889,563,-600
646,-828,498
640,759,510
-630,509,768
-681,-892,-333
673,-379,-804
-742,-814,-386
577,-820,562
--- scanner 3 ---
-589,542,597
605,-692,669
-500,565,-823
-660,373,557
-458,-679,-417
-488,449,543
-626,468,-788
338,-750,-386
528,-832,-391
562,-778,733
-938,-730,414
543,643,-506
-524,371,-870
407,773,750
-104,29,83
378,-903,-323
-778,-728,485
426,699,580
-438,-605,-362
-469,-447,-387
509,732,623
647,635,-688
-868,-804,481
614,-800,639
595,780,-596
--- scanner 4 ---
727,592,562
-293,-554,779
441,611,-461
-714,465,-776
-743,427,-804
-660,-479,-426
832,-632,460
927,-485,-438
408,393,-506
466,436,-512
110,16,151
-258,-428,682
-393,719,612
-211,-452,876
808,-476,-593
-575,615,604
-485,667,467
-680,325,-822
-627,-443,-432
872,-547,-609
833,512,582
807,604,487
839,-516,451
891,-625,532
-652,-548,-490
30,-46,-14
Because all coordinates are relative, in this example, all "absolute" positions will be expressed relative to scanner 0 (using the orientation of scanner 0 and as if scanner 0 is at coordinates 0,0,0
).
Scanners 0 and 1 have overlapping detection cubes; the 12 beacons they both detect (relative to scanner 0) are at the following coordinates:
-618,-824,-621
-537,-823,-458
-447,-329,318
404,-588,-901
544,-627,-890
528,-643,409
-661,-816,-575
390,-675,-793
423,-701,434
-345,-311,381
459,-707,401
-485,-357,347
These same 12 beacons (in the same order) but from the perspective of scanner 1 are:
686,422,578
605,423,415
515,917,-361
-336,658,858
-476,619,847
-460,603,-452
729,430,532
-322,571,750
-355,545,-477
413,935,-424
-391,539,-444
553,889,-390
Because of this, scanner 1 must be at 68,-1246,-43
(relative to scanner 0).
Scanner 4 overlaps with scanner 1; the 12 beacons they both detect (relative to scanner 0) are:
459,-707,401
-739,-1745,668
-485,-357,347
432,-2009,850
528,-643,409
423,-701,434
-345,-311,381
408,-1815,803
534,-1912,768
-687,-1600,576
-447,-329,318
-635,-1737,486
So, scanner 4 is at -20,-1133,1061
(relative to scanner 0).
Following this process, scanner 2 must be at 1105,-1205,1229
(relative to scanner 0) and scanner 3 must be at -92,-2380,-20
(relative to scanner 0).
The full list of beacons (relative to scanner 0) is:
-892,524,684
-876,649,763
-838,591,734
-789,900,-551
-739,-1745,668
-706,-3180,-659
-697,-3072,-689
-689,845,-530
-687,-1600,576
-661,-816,-575
-654,-3158,-753
-635,-1737,486
-631,-672,1502
-624,-1620,1868
-620,-3212,371
-618,-824,-621
-612,-1695,1788
-601,-1648,-643
-584,868,-557
-537,-823,-458
-532,-1715,1894
-518,-1681,-600
-499,-1607,-770
-485,-357,347
-470,-3283,303
-456,-621,1527
-447,-329,318
-430,-3130,366
-413,-627,1469
-345,-311,381
-36,-1284,1171
-27,-1108,-65
7,-33,-71
12,-2351,-103
26,-1119,1091
346,-2985,342
366,-3059,397
377,-2827,367
390,-675,-793
396,-1931,-563
404,-588,-901
408,-1815,803
423,-701,434
432,-2009,850
443,580,662
455,729,728
456,-540,1869
459,-707,401
465,-695,1988
474,580,667
496,-1584,1900
497,-1838,-617
527,-524,1933
528,-643,409
534,-1912,768
544,-627,-890
553,345,-567
564,392,-477
568,-2007,-577
605,-1665,1952
612,-1593,1893
630,319,-379
686,-3108,-505
776,-3184,-501
846,-3110,-434
1135,-1161,1235
1243,-1093,1063
1660,-552,429
1693,-557,386
1735,-437,1738
1749,-1800,1813
1772,-405,1572
1776,-675,371
1779,-442,1789
1780,-1548,337
1786,-1538,337
1847,-1591,415
1889,-1729,1762
1994,-1805,1792
In total, there are 79 beacons.
Assemble the full map of beacons. How many beacons are there?
# Python imports
from itertools import combinations
from pathlib import Path
from typing import Callable, Dict, Generator, Iterable, List, Optional, Set, Tuple
import networkx as nx
import numpy as np
from scipy.spatial.transform import Rotation
# Paths to data
testpath = Path("day19_test.txt")
datapath = Path("day19_data.txt")
We approach this by modelling each scanner as an object, with its own location and a set of beacons with locations given relative to the scanner.
We want to be able to: (i) identify whether one scanner intersects another - i.e. shares at least 12 beacons in common; (ii) reorient a scanner with respect to another scanner, on the basis of the intersecting beacons.
To identify intersection (and intersecting beacons) we calculate the euclidean distance (the norm) between each pair of beacons in each scanner to generate a distance matrix. Then, if two scanners intersect, there must be at least one row of each of their distance matrices for which at least 12 row values match.
To reorient the scanner to a reference scanner, we identify all the intersecting beacons, then iterate through the 24 possible orthogonal rotations in space, calculating the set of vectors of co-ordinate differences each time. When this set of differences matches that for the beacons in the reference scanner, we know we have the appropriate rotation. It then remains to calculate the offset translation that would align the rotated beacons by subtracting any of the rotated beacon co-ordinates from those of their reference counterpart.
class Scanner:
"""Represents a scanner, and its observable beacons"""
def __init__(self, scanner_id: int, beacons: List[List[int]]) -> None:
"""Instantiate Scanner object
:param scanner_id: scanner ID number
:param beacons: list of beacon co-ordinates relative to scanner
Scanner is initiated with location (0, 0, 0)
"""
self.scanner_id = scanner_id
self.beacons = {idx: np.array(_) for idx, _ in enumerate(beacons)}
self.location = np.array([0, 0, 0]).astype(int)
# Calculate matrix of Euclidean distances between beacons
self.__calculate_beacon_distances()
def __calculate_beacon_distances(self) -> None:
"""Calculates euclidean distance array between beacons"""
distarray = []
for idx1 in range(len(self.beacons)):
dists = []
for idx2 in range(len(self.beacons)):
dists.append(np.linalg.norm(self.beacons[idx1] - self.beacons[idx2]))
distarray.append(dists)
self.beacon_distances = np.array(distarray)
def intersects(self, scanner) -> bool:
"""Returns True if this scanner intersects the passed scanner
:param scanner: reference scanner to test for intersection
Intersection is identified as any beacon from this scanner sharing
at least 12 Euclidean distances with any beacon from the passed
scanner.
"""
for bcn1 in self.beacon_distances:
for bcn2 in scanner.beacon_distances:
if len(set(bcn1).intersection(set(bcn2))) >= 12:
return True
return False
def get_beacons(self) -> None:
"""Returns set of beacons"""
return set(tuple(_) for _ in self.beacons.values())
def get_intersecting_beacons(self, scanner) -> List:
"""Returns tuples of beacon co-ordinates that intersect
:param scanner: reference scanner to identify intersecting beacons
"""
beacons = []
for row1, bcn1 in enumerate(self.beacon_distances):
for row2, bcn2 in enumerate(scanner.beacon_distances):
if len(set(bcn1).intersection(set(bcn2))) >= 12:
beacons.append((self.beacons[row1], scanner.beacons[row2]))
return beacons
def match_rotation(self, scanner) -> Optional[Tuple]:
"""Return rotation matrix to align intersecting beacons
:param scanner: reference scanner to test for intersection
"""
mobile, reference = [], []
for bcn1, bcn2 in self.get_intersecting_beacons(scanner):
mobile.append(bcn1)
reference.append(bcn2)
# Shortcut to generate all rotation matrices for the
# orthogonal group
drns = {idx: _ for idx, _ in enumerate(Rotation.create_group("O").as_matrix())}
# Calculate vector of distance between first beacon in reference and all other
# beacons in reference
refdists = [tuple((reference[0] - _).astype(int)) for _ in reference]
# Rotate beacons until distances match
for idx, rot in drns.items():
rotated = [np.matmul(_, rot).astype(int) for _ in mobile]
rotdists = [tuple((rotated[0] - _).astype(int)) for _ in rotated]
if rotdists == refdists:
offset = (reference[0] - rotated[0]).astype(int)
return rot, offset
return None
def reorient(self, scanner) -> None:
"""Update beacon and scanner locations with reference to scanner
:param scanner: reference scanner to align this scanner against
"""
# Get rotation and offset wrt scanner
rot, offset = self.match_rotation(scanner)
# Rotate beacons
self.beacons = {idx: np.matmul(_, rot).astype(int) for idx, _ in self.beacons.items()}
# Offset beacons and scanner
self.beacons = {idx: (_ + offset).astype(int) for idx, _ in self.beacons.items()}
self.location = offset
def load_input(fpath: Path) -> List:
"""Return a collection of Scanner objects
:param fpath: Path to data file
"""
scanners = [] # list of scanners from file
with fpath.open("r") as ifh:
for line in [_.strip() for _ in ifh.readlines()]:
if line.startswith("---"): # new scanner header
scanner_id = line.split()[2]
beacons = [] # holds beacons for this scanner
elif len(line) == 0: # end of scanner info, instantiate
scanners.append(Scanner(scanner_id, beacons))
else: # beacon co-ordinates
beacons.append([int(_) for _ in line.split(",")])
# Catch last scanner
scanners.append(Scanner(scanner_id, beacons))
return scanners
Once we have a set of reoriented scanners, we can identify all of the reference-shifted beacon co-ordinates, and count how many are unique.
To reorient the scanners, we take the first scanner (scanner 0) as the reference point, centred at (0, 0, 0) and make it the only member of the oriented set of scanners. We iterate over each remaining scanner in turn until we find one that intersects. We reorient that scanner and add it to the oriented set. Then we continue iterating over unoriented scanners, testing them against oriented scanners for intersection, reorienting them, and continuing, until all scanners are placed in space.
Then we identify the unique beacons in that set of scanners.
def reorient_scanners(scanners: List[Scanner]) -> List[Scanner]:
"""Returns all scanners, oriented with respect to first scanner in list
:param scanners: collection of Scanner objects
"""
oriented = [] # scanners that have been placed
# Take scanner 0 as the reference scanner
oriented.append(scanners.pop(0))
# Iterate over scanners until all scanners can be placed
while len(scanners):
scn2 = scanners.pop(0)
for scn1 in oriented:
if scn2.intersects(scn1):
rot, offset = scn2.match_rotation(scn1)
scn2.reorient(scn1)
oriented.append(scn2)
break
if scn2 not in oriented:
scanners.append(scn2)
return oriented[:]
def get_unique_beacons(scanners):
"""Returns the set of unique beacon locations in the list of scanners
:param scanners: collection of oriented Scanner objects
"""
# Identify all unique beacons
beacons = set()
for scn in scanners:
beacons = beacons.union(scn.get_beacons())
return beacons
We apply these first to the test data:
scanners = load_input(testpath)
print(f"Loaded {len(scanners)=} scanners")
scanners = reorient_scanners(scanners)
beacons = get_unique_beacons(scanners)
print(f"Number of unique beacons: {len(beacons)}")
Loaded len(scanners)=5 scanners Number of unique beacons: 79
Then to the puzzle data
scanners = load_input(datapath)
print(f"Loaded {len(scanners)=} scanners")
scanners = reorient_scanners(scanners)
beacons = get_unique_beacons(scanners)
print(f"Number of unique beacons: {len(beacons)}")
Loaded len(scanners)=39 scanners Number of unique beacons: 442
Sometimes, it's a good idea to appreciate just how big the ocean is. Using the Manhattan distance, how far apart do the scanners get?
In the above example, scanners 2 (1105,-1205,1229)
and 3 (-92,-2380,-20)
are the largest Manhattan distance apart. In total, they are 1197 + 1175 + 1249 = 3621
units apart.
What is the largest Manhattan distance between any two scanners?
The Manhattan distance is straightforward to calculate using numpy
:
def get_max_manhattan_distance(scanners: List[Scanner]) -> int:
"""Returns maximum distance between locations of any pair of scanners
:param scanners: collection of Scanner objects
"""
maxdist = 0
while len(scanners):
scn1 = scanners.pop(0)
for scn2 in scanners:
maxdist = max(maxdist, np.absolute(scn1.location-scn2.location).sum())
return maxdist
With the test data (the first solution is rerun to guarantee correct orientation/location of scanners):
scanners = load_input(testpath)
print(f"Loaded {len(scanners)=} scanners")
scanners = reorient_scanners(scanners)
beacons = get_unique_beacons(scanners)
print(f"Number of unique beacons: {len(beacons)}")
print(f"Largest distance between scanners: {get_max_manhattan_distance(scanners)}")
Loaded len(scanners)=5 scanners Number of unique beacons: 79 Largest distance between scanners: 3621
And with the puzzle data:
scanners = load_input(datapath)
print(f"Loaded {len(scanners)=} scanners")
scanners = reorient_scanners(scanners)
beacons = get_unique_beacons(scanners)
print(f"Number of unique beacons: {len(beacons)}")
print(f"Largest distance between scanners: {get_max_manhattan_distance(scanners)}")
Loaded len(scanners)=39 scanners Number of unique beacons: 442 Largest distance between scanners: 11079