"""
Classic A-star algorithm for path finding.
"""
from __future__ import annotations
import math
from typing import (
cast,
List,
Optional,
Set,
Tuple,
Union,
Dict
)
from arcade import (
Sprite, SpriteList,
check_for_collision_with_list,
get_sprites_at_point
)
from arcade.math import get_distance, lerp_2d
from arcade.types import Point
__all__ = [
"AStarBarrierList",
"astar_calculate_path",
"has_line_of_sight"
]
def _spot_is_blocked(position: Point,
moving_sprite: Sprite,
blocking_sprites: SpriteList) -> bool:
"""
Return if position is blocked
:param position: position to put moving_sprite at
:param moving_sprite: Sprite to use
:param blocking_sprites: List of Sprites to check against
:return: If the Sprite would hit anything in blocking_sprites at the position
"""
original_pos = moving_sprite.position
moving_sprite.position = position
hit_list = check_for_collision_with_list(moving_sprite, blocking_sprites)
moving_sprite.position = original_pos
return len(hit_list) > 0
def _heuristic(start: Point, goal: Point) -> float:
"""
Returns a heuristic value for the passed points.
:param start: The 1st point to compare
:param goal: The 2nd point to compare
:return: The heuristic of the 2 points
"""
# Use Chebyshev distance heuristic if we can move one square either
# adjacent or diagonal
d = 1
d2 = 1
dx = abs(start[0] - goal[0])
dy = abs(start[1] - goal[1])
return d * (dx + dy) + (d2 - 2 * d) * min(dx, dy)
class _AStarGraph(object):
"""
A grid which tracks 2 barriers and a moving sprite.
:param Union[List, Tuple, Set] barriers: Is turned into a set, and then used for _AStarSearch
:param left: Far left side x value
:param right: Far right side x value
:param bottom: Far bottom side y value
:param top: Far top side y value
"""
def __init__(self, barriers: Union[List, Tuple, Set],
left: int,
right: int,
bottom: int,
top: int,
diagonal_movement: bool):
self.barriers = barriers if barriers is set else set(barriers)
self.left = left
self.right = right
self.top = top
self.bottom = bottom
if diagonal_movement:
self.movement_directions = ( # type: ignore
(1, 0), (-1, 0),
(0, 1), (0, -1),
(1, 1), (-1, 1),
(1, -1), (-1, -1)
)
else:
self.movement_directions = (1, 0), (-1, 0), (0, 1), (0, -1) # type: ignore
def get_vertex_neighbours(self, pos: Point) -> List[Tuple[float, float]]:
"""
Return neighbors for this point according to ``self.movement_directions``
These are not guaranteed to be reachable or valid points.
:param pos: Which position to search around
:return: Returns vertexes around the point
"""
n = []
# Moves allow link a chess king
for dx, dy in self.movement_directions:
x2 = pos[0] + dx
y2 = pos[1] + dy
if x2 < self.left or x2 > self.right or y2 < self.bottom or y2 > self.top:
continue
n.append((x2, y2))
return n
def move_cost(self, a: Point, b: Point) -> float:
"""
Returns a float of the cost to move
Moving diagonally costs more than to the side.
A barrier's cost is float("inf) so that that
the Algorithm will never go on it
:param a: The 1st point to compare
:param b: The 2nd point to compare
:return: The move cost of moving between of the 2 points
"""
if b in self.barriers:
return float('inf') # Infitely high cost to enter barrier squares
elif a[0] == b[0] or a[1] == b[1]:
return 1 # Normal movement cost
else:
return 1.42
def _AStarSearch(start: Point, end: Point, graph: _AStarGraph) -> Optional[List[Point]]:
"""
Returns a path from start to end using the AStarSearch Algorithm
Graph is used to check for barriers.
:param start: point to start at
:param end: point to end at
:return: The path from start to end. Returns None if is path is not found
"""
G: Dict[Point, float] = {} # Actual movement cost to each position from the start position
F: Dict[Point, float] = {} # Estimated movement cost of start to end going via this position
# Initialize starting values
G[start] = 0
F[start] = _heuristic(start, end)
closed_vertices = set()
open_vertices = {start}
came_from = {} # type: ignore
count = 0
while len(open_vertices) > 0:
count += 1
if count > 500:
break
# Get the vertex in the open list with the lowest F score
current = None
current_fscore = math.inf
for pos in sorted(open_vertices):
if current is None or F[pos] < current_fscore:
current_fscore = F[pos]
current = pos
# Check if we have reached the goal
if current == end and current is not None:
# Retrace our route backward
path = [current]
while current in came_from:
current = came_from[current]
path.append(current) # type: ignore
path.reverse()
if F[end] >= 10000:
return None
else:
return path
# return path, F[end] # Done!
# Mark the current vertex as closed
open_vertices.remove(current) # type: ignore
closed_vertices.add(current) # type: ignore
# Update scores for vertices near the current position
for neighbour in sorted(graph.get_vertex_neighbours(current)): # type: ignore
if neighbour in closed_vertices:
continue # We have already processed this node exhaustively
candidate_g = G[current] + graph.move_cost(current, neighbour) # type: ignore
if neighbour not in open_vertices:
open_vertices.add(neighbour) # Discovered a new vertex
elif candidate_g >= G[neighbour]:
continue # This G score is worse than previously found
# Adopt this G score
came_from[neighbour] = current
G[neighbour] = candidate_g
h = _heuristic(neighbour, end)
F[neighbour] = G[neighbour] + h
# Out-of-bounds
return None
def _collapse(pos: Point, grid_size: float):
"""Makes Point pos smaller by grid_size"""
return int(pos[0] // grid_size), int(pos[1] // grid_size)
def _expand(pos: Point, grid_size: float):
"""Makes Point pos larger by grid_size"""
return int(pos[0] * grid_size), int(pos[1] * grid_size)
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class AStarBarrierList:
"""
Class that manages a list of barriers that can be encountered during
A* path finding.
:param moving_sprite: Sprite that will be moving
:param blocking_sprites: Sprites that can block movement
:param grid_size: Size of the grid, in pixels
:param left: Left border of playing field
:param right: Right border of playing field
:param bottom: Bottom of playing field
:param top: Top of playing field
:param barrier_list: SpriteList of barriers to use in _AStarSearch, None if not recalculated
"""
def __init__(self,
moving_sprite: Sprite,
blocking_sprites: SpriteList,
grid_size: int,
left: int,
right: int,
bottom: int,
top: int):
self.grid_size = grid_size
self.bottom = int(bottom // grid_size)
self.top = int(top // grid_size)
self.left = int(left // grid_size)
self.right = int(right // grid_size)
self.moving_sprite = moving_sprite
self.blocking_sprites = blocking_sprites
self.barrier_list = None
self.recalculate()
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def recalculate(self):
"""
Recalculate blocking sprites.
"""
# --- Iterate through the blocking sprites and find where we are blocked
# Save original location
original_pos = self.moving_sprite.position
# Create a set of barriers
self.barrier_list = set()
# Loop through the grid
for cx in range(self.left, self.right + 1):
for cy in range(self.bottom, self.top + 1):
# Grid location
cpos = cx, cy
# Pixel location
pos = _expand(cpos, self.grid_size)
# See if we'll have a collision if our sprite is at this location
self.moving_sprite.position = pos
if len(check_for_collision_with_list(self.moving_sprite, self.blocking_sprites)) > 0:
self.barrier_list.add(cpos)
# Restore original location
self.moving_sprite.position = original_pos
self.barrier_list = sorted(self.barrier_list)
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def astar_calculate_path(start_point: Point,
end_point: Point,
astar_barrier_list: AStarBarrierList,
diagonal_movement: bool = True) -> Optional[List[Point]]:
"""
Calculates the path using AStarSearch Algorithm and returns the path
:param start_point: Where it starts
:param end_point: Where it ends
:param astar_barrier_list: AStarBarrierList with the boundries to use in the AStarSearch Algorithm
:param diagonal_movement: Whether of not to use diagonals in the AStarSearch Algorithm
:return: List of points(the path), or None if no path is found
"""
grid_size = astar_barrier_list.grid_size
mod_start = _collapse(start_point, grid_size)
mod_end = _collapse(end_point, grid_size)
left = astar_barrier_list.left
right = astar_barrier_list.right
bottom = astar_barrier_list.bottom
top = astar_barrier_list.top
barrier_list = astar_barrier_list.barrier_list
graph = _AStarGraph(barrier_list, left, right, bottom, top, diagonal_movement) # type: ignore
result = _AStarSearch(mod_start, mod_end, graph)
if result is None:
return None
# Currently 'result' is in grid locations. We need to convert them to pixel
# locations.
revised_result = [_expand(p, grid_size) for p in result]
return cast(List[Point], revised_result)
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def has_line_of_sight(observer: Point,
target: Point,
walls: SpriteList,
max_distance: float = float("inf"),
check_resolution: int = 2) -> bool:
"""
Determine if we have line of sight between two points.
:param observer: Start position
:param target: End position position
:param walls: List of all blocking sprites
:param max_distance: Max distance point 1 can see
:param check_resolution: Check every x pixels for a sprite. Trade-off between accuracy and speed.
.. warning:: Try to make sure spatial hashing is enabled on ``walls``!
If spatial hashing is not enabled, this function may run
very slowly!
:return: Whether or not oberver to target is blocked by any wall in walls
"""
if max_distance <= 0:
raise ValueError("max_distance must be greater than zero")
if check_resolution <= 0:
raise ValueError("check_resolution must be greater than zero")
distance = get_distance(observer[0], observer[1],
target[0], target[1])
if distance == 0:
return True
steps = int(distance // check_resolution)
for step in range(steps + 1):
step_distance = step * check_resolution
u = step_distance / distance
midpoint = lerp_2d(observer, target, u)
if step_distance > max_distance:
return False
sprite_list = get_sprites_at_point(midpoint, walls)
if len(sprite_list) > 0:
return False
return True
# NOTE: Rewrite this
# def dda_step(start: Point, end: Point):
# """
# Bresenham's line algorithm
# :param start:
# :param end:
# :return: List of points
# """
# x1, y1 = start
# x2, y2 = end
# dx = x2 - x1
# dy = y2 - y1
# steps = max(abs(dx), abs(dy))
# x_inc = dx / steps
# y_inc = dy / steps
# x = x1
# y = y1
# points = []
# for _ in range(steps):
# points.append((int(x), int(y)))
# x += x_inc
# y += y_inc
# return points