def find_shortest_path(grid, start_node, end_node):
    marked_grid = [[None for i in range(len(j))] for j in grid]
    step = 0
    current_wave = [start_node]
    manhattan_distance = ((-1, 0), (1, 0), (0, -1), (0, 1))

    def generate_new_wave(current, new_wave):
        possible_steps = list(filter(lambda x: -1 not in x and len(marked_grid) not in x
                                                           and marked_grid[x[0]][x[1]] is None
                                                           and x not in new_wave,
                                     [tuple(map(lambda x, y: x + y, current, n)) for n in manhattan_distance]))
        for step in possible_steps:
            yield step

    def build_new_wave():
        new_wave = []
        for current in current_wave:
            marked_grid[current[0]][current[1]] = step

            for drop in generate_new_wave(current, new_wave):
                new_wave.append(drop)

        return new_wave

    while marked_grid[end_node[0]][end_node[1]] is None:
        if len(current_wave) == 0:
            step += 1
            return False

        current_wave = build_new_wave()
        step += 1

    recovered_path = []
    step -= 1

    while step != 0:
        recovered_path.append(end_node)
        for n in manhattan_distance:
            candidate = tuple(map(lambda x, y: x + y, end_node, n))
            try:
                if marked_grid[candidate[0]][candidate[1]] == step - 1:
                    end_node = candidate
                    step -= 1
                    break
            except IndexError:
                pass
            
    return list(reversed(recovered_path + [start_node]))


grid = [[(0,0), (0,1), (0,2), (0,3), (0,4)],
        [(1,0), (1,1), (1,2), (1,3), (1,4)],
        [(2,0), (2,1), (2,2), (2,3), (2,4)],
        [(3,0), (3,1), (3,2), (3,3), (3,4)],
        [(4,0), (4,1), (4,2), (4,3), (4,4)]
        ]

start_node = (0, 0)
end_node = (4, 4)

print(find_shortest_path(grid, start_node, end_node))

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