Optimize search even further

world.py

@@ -83,6 +83,13 @@ class World:
 	_diag: int
 	"Real world distance represented by a diagonal movement, in millimeters"
 
+	_lon_scale2: int
+	"Longitudinal scale squared"
+	_lat_scale2: int
+	"Latitudinal scale squared"
+	_diag2: int
+	"Diagonal scale squared"
+
 	def __init__(self,
 		  tiles: Sequence[Tuple[Terrain, int]],
 		  width: int = 395,
@@ -93,7 +100,11 @@ class World:
 		self.width = width
 		self.lon_scale = lon_scale
 		self.lat_scale = lat_scale
-		self._diag = isqrt(lon_scale * lon_scale + lat_scale * lat_scale)
+
+		self._lon_scale2 = lon_scale * lon_scale
+		self._lat_scale2 = lat_scale * lat_scale
+		self._diag2 = self._lon_scale2 + self._lat_scale2
+		self._diag = isqrt(self._diag2)
 
 	def _adjacency(self, p: Point) -> List[Tuple[Point, int]]:
 		"""
@@ -101,31 +112,31 @@ class World:
 
 		In addition to each point, a number is returned representing the distance as the
 		crow flies between the center of the original point and the center of the adjacent
-		point, in millimeters.
+		point, in millimeters **AND SQUARED**.
 
 		This does not take into account the presence, value, or elevation of tiles
 		represented on these points.
 
-		>>> world = World([], lon_scale=10_290, lat_scale=7_550)
+		>>> world = World([], lon_scale=3, lat_scale=4)
 		>>> world._adjacency(Point(13, 12)) #doctest: +NORMALIZE_WHITESPACE
-		[((12, 11), 12762),
+		[((12, 11), 25),
-		 ((13, 11),  7550),
+		 ((13, 11), 16),
-		 ((14, 11), 12762),
+		 ((14, 11), 25),
-		 ((12, 12), 10290),
+		 ((12, 12),  9),
-		 ((14, 12), 10290),
+		 ((14, 12),  9),
-		 ((12, 13), 12762),
+		 ((12, 13), 25),
-		 ((13, 13),  7550),
+		 ((13, 13), 16),
-		 ((14, 13), 12762)]
+		 ((14, 13), 25)]
 		"""
 		return [
-			(Point(p.x - 1, p.y - 1), self._diag    ),
+			(Point(p.x - 1, p.y - 1), self._diag2     ),
-			(Point(p.x    , p.y - 1), self.lat_scale),
+			(Point(p.x    , p.y - 1), self._lat_scale2),
-			(Point(p.x + 1, p.y - 1), self._diag    ),
+			(Point(p.x + 1, p.y - 1), self._diag2     ),
-			(Point(p.x - 1, p.y    ), self.lon_scale),
+			(Point(p.x - 1, p.y    ), self._lon_scale2),
-			(Point(p.x + 1, p.y    ), self.lon_scale),
+			(Point(p.x + 1, p.y    ), self._lon_scale2),
-			(Point(p.x - 1, p.y + 1), self._diag    ),
+			(Point(p.x - 1, p.y + 1), self._diag2     ),
-			(Point(p.x    , p.y + 1), self.lat_scale),
+			(Point(p.x    , p.y + 1), self._lat_scale2),
-			(Point(p.x + 1, p.y + 1), self._diag    )
+			(Point(p.x + 1, p.y + 1), self._diag2     )
 		]
 
 	def __getitem__(self, loc: Point) -> Tuple[Terrain, int]:
@@ -228,33 +239,36 @@ class World:
 
 			And there it is!  The travel time returned by the `.neighbors` function!
 		"""
+		loc_terrain, loc_elevation = self[loc]
 		return [
 			(
 				adj_point,
 				(
 					# 2 * Movement speed (seconds / km = microseconds / millimeter)
 					(
-						self[loc][0] +
+						loc_terrain +
-						self[adj_point][0]
+						adj_terrain
 					)
 
 					# * Distance travelled (millimeters) = 2 * Time cost (microseconds)
 					* isqrt(
 						# Crow Distance Squared
-						(crow_distance * crow_distance)
+						crow_distance2
 						# + Elevation Change Squared = 3D distance squared
-						+ self.elevation_difference(loc, adj_point) ** 2
+						+ (loc_elevation - adj_elevation) ** 2
 					)
 
 					# / 2 = Time cost (microseconds)
 					// 2
 				)
 			)
-			for (adj_point, crow_distance) in self._adjacency(loc)
+			for (adj_point, crow_distance2) in self._adjacency(loc)
+			for (adj_terrain, adj_elevation) in (self[adj_point],)
 			if adj_point.x >= 0
 				and  adj_point.y >= 0
 				and adj_point.x < self.width
 				and adj_point.y < len(self.tiles) // self.width
+				and adj_terrain < 4294967296
 		]
 
 	def heuristic(self, a: Point, b: Point) -> int: