Python avec Numpy

Bon, j'ai sorti Numpy du coup. C'est modélisé, et assez long en fait.

# Advent of Code 2022, day 8
from __future__ import annotations
from math import prod
from typing import Iterable, Iterator, Set, Tuple, Type
import numpy as np
Coords = Tuple[int, int]
class Grid:
 def __init__(self, matrix: np.ndarray) -> None:
 self.matrix = matrix
 self.ly = matrix.shape[0]
 self.lx = matrix.shape[1]
 def __scans(self) -> Iterator[Iterator[Iterator[Coords]]]:
 yield (((y, x) for x in range(self.lx)) for y in range(self.ly))
 yield (((y, x) for x in range(self.lx - 1, -1, -1))
 for y in range(self.ly))
 yield (((y, x) for y in range(self.ly)) for x in range(self.lx))
 yield (((y, x) for y in range(self.ly - 1, -1, -1))
 for x in range(self.lx))
 def visible_from_outside(self) -> Set[Coords]:
 visible_trees = set() # type: Set[Coords]
 for scan in self.__scans():
 for line in scan:
 max_height = -1
 for coords in line:
 cur_height = self.matrix[coords]
 if cur_height > max_height:
 visible_trees.add(coords)
 max_height = cur_height
 return visible_trees
 def __viewing_distance(self, orig: Coords, line: Iterator[Coords]) -> int:
 height = self.matrix[orig]
 d = 0
 for coords in line:
 d += 1
 if self.matrix[coords] >= height:
 return d
 return d
 def __lines_of_sight(self, coords: Coords):
 y, x = coords
 yield ((y, x_) for x_ in range(x + 1, self.lx))
 yield ((y, x_) for x_ in range(x - 1, -1, -1))
 yield ((y_, x) for y_ in range(y + 1, self.ly))
 yield ((y_, x) for y_ in range(y - 1, -1, -1))
 def scenic_score(self, coords: Coords):
 return prod(self.__viewing_distance(coords, line)
 for line in self.__lines_of_sight(coords))
 @classmethod
 def import_lines(class_: Type[Grid], lines: Iterable[str]) -> Grid:
 matrix = np.genfromtxt(lines, delimiter=1, autostrip=True, dtype=int)
 return class_(matrix)
def solve_both(lines: Iterable[str]) -> Tuple[int, int]:
 """Solve both parts of today's puzzle"""
 # Import
 grid = Grid.import_lines(lines)
 # Part 1
 visible = len(grid.visible_from_outside())
 # Part 2
 max_score = max(grid.scenic_score(coords) for coords
 in np.ndindex(grid.matrix.shape))
 return visible, max_score