117 lines
3.1 KiB
Python
117 lines
3.1 KiB
Python
import shared
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import matrix
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from dijkstar import Graph, find_path, algorithm
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#def dj_walk(mx, start, end):
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# def next_cost(c, val):
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# if val > c+1:
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# return 9999
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# else:
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# return val
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#
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# start = start[1], start[0]
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# end = end[1], end[0]
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#
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# # Dijkstra from RedBlobGames
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# frontier = PriorityQueue()
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# frontier.put(start, 0)
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# came_from = dict()
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# cost_so_far = dict()
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# came_from[start] = None
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# cost_so_far[start] = 0
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# last = None
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# while not frontier.empty():
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# x, y = frontier.get()
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# cur = (x, y)
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# if cur == end:
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# break
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# for _n in matrix.get_neighbors(mx, x=x, y=y, _dict=True):
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# nxt = (_n["x"], _n["y"])
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# v = _n["value"]
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#
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# new_cost = next_cost(cost_so_far[cur], v)
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# if nxt not in cost_so_far or new_cost < cost_so_far[nxt]:
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# cost_so_far[nxt] = new_cost
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# priority = new_cost
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# frontier.put(nxt, priority)
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# came_from[nxt] = cur
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# last = cur
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# print(len(cost_so_far))
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criteria = lambda _cur, _neighbor: _neighbor - _cur <= 1
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def build_graph(mx):
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graph = Graph()
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for y, row in enumerate(mx):
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for x, _ in enumerate(row):
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neighbors = matrix.valid_neighbors(mx, x=x, y=y, criteria=criteria)
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for neighbor in neighbors:
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graph.add_edge((y, x), (neighbor['y'],neighbor['x']), 1)
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return graph
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#SEEN = []
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#def walk(mx, y, x, seen=[]):
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# print(len(seen))
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# valid_next = matrix.valid_neighbors(mx, y, x, criteria=criteria)
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# print(valid_next)
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# matrix.highlight( mx, red=seen, green=[ *valid_next, ], blue=[ END, ],)
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# for next_yx in valid_next:
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# if next_yx not in seen:
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# seen.append(next_yx)
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# _y, _x = next_yx
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# walk(mx, _y, _x, seen)
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# else:
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# seen.append(next_yx)
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def elev(yx):
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y,x = yx
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return mx[y][x]
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def part1(mx):
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start = matrix.find_in_matrix(mx, "S")
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end = matrix.find_in_matrix(mx, "E")
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mx[start[0]][start[1]] = "a"
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mx[end[0]][end[1]] = "z"
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matrix.apply_to_all(mx, lambda x: ord(x) - ord('a'))
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#walk(mx, cur[0],cur[0], seen=[(cur[0],cur[1])])
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#dj_walk(mx, cur, END)
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graph = build_graph(mx)
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path = find_path(graph, start, end)
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print(len(path.nodes))
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def part2(mx):
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end = matrix.find_in_matrix(mx, "E")
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s = matrix.find_in_matrix(mx, "S")
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mx[s[0]][s[1]] = "a"
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mx[end[0]][end[1]] = "z"
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starts = matrix.find_in_matrix(mx, 'a', one=False)
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matrix.apply_to_all(mx, lambda x: ord(x) - ord('a'))
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graph = build_graph(mx)
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n_counts = []
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for start in starts:
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#walk(mx, cur[0],cur[0], seen=[(cur[0],cur[1])])
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#dj_walk(mx, cur, END)
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try:
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path = find_path(graph, start, end)
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n_counts.append(len(path.nodes)-1)
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except algorithm.NoPathError:
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pass
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print(n_counts)
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print(min(n_counts))
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def main():
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mx = matrix.load_matrix_file(shared.get_fname(12), matrix.split_word_to_chr_list)
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#part1(mx)
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part2(mx)
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if __name__ == "__main__":
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main()
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