def best_destination(positions: list[int],
cost_fn: CostFunction = cost_linear):
# TODO: optimize
# convex function -> stop after it starts to grow
# can be likely estimated with a quadratic function (for both cost_fns?)
# part 1 -> probably median works?
return min(range(*minmax(positions)),
key=lambda d: alignment_cost(positions, d, cost_fn))
def draw_plan(self):
def tile_at(p: Point) -> Tile:
if p == self.pos:
return Tile.ROBOT
elif p == Point(0, 0):
return Tile.ORIGIN
elif p in self.plan:
return self.plan[p]
else:
return Tile.UNKNOWN
def char_at(cx: int, cy: int) -> str:
return tile_at(Point(cx, cy)).char
min_x, max_x = minmax(p.x for p in self.plan.keys())
min_y, max_y = minmax(p.y for p in self.plan.keys())
for y in range(min_y, max_y + 1):
print(''.join(char_at(x, y) for x in range(min_x, max_x + 1)))
def draw(self):
if not self.bugs:
print("all dead :(")
return
def c(x: int, y: int, z: int) -> str:
pos = (x, y, z)
if (x, y) == self.center:
assert pos not in self.bugs
return '?'
elif pos in self.bugs:
return '#'
else:
return '.'
min_z, max_z = minmax(z for x, y, z in self.bugs)
for z in range(min_z, max_z + 1):
print(f"Depth {z}:")
for y in range(self.height):
print(''.join(c(x, y, z) for x in range(self.width)))
print()
def draw_grid(grid: Grid):
min_x, max_x = minmax(pos.x for pos in grid.keys())
min_y, max_y = minmax(pos.y for pos in grid.keys())
c = {Color.BLACK: '.', Color.WHITE: '#', Color.NONE: ' '}
for y in range(min_y, max_y + 1):
print(''.join(c[grid[Point(x, y)]] for x in range(min_x, max_x + 1)))
def _xrange(self, margin: int = 3) -> range:
xmin, xmax = minmax(self.alive)
return range(xmin - margin, xmax + margin + 1)