def timeToDeliver(self, path_str):
start, end, graph = self.create_graph(path_str)
path = shortest_path(graph, start, end)
dist = shortest_distance(graph, start, end)
instructions = self.get_min_moves(path)
cost = self.get_cost(instructions)
return path, dist, instructions, cost
def timeToDeliver(self, path_str):
start, end, graph = self.create_graph(path_str)
path = shortest_path(graph, start, end)
dist = shortest_distance(graph, start, end)
instructions = self.get_min_moves(path)
cost = self.get_cost(instructions)
return path, dist, instructions, cost
def lowest(self, harmful_areas, deadly_areas):
matrix = list()
for row in range(0, size):
row = list()
matrix.append(row)
for col in range(0, size):
row.append(safe)
for h in harmful_areas:
x1, y1, x2, y2 = [int(n) for n in h.split()]
for i in range(min(x1, x2), max(x1, x2) + 1):
for j in range(min(y1, y2), max(y1, y2) + 1):
matrix[i][j] = harmful
for d in deadly_areas:
x1, y1, x2, y2 = [int(n) for n in d.split()]
for i in range(min(x1, x2), max(x1, x2) + 1):
for j in range(min(y1, y2), max(y1, y2) + 1):
matrix[i][j] = deadly
graph = {}
for i in range(0, size):
for j in range(0, size):
state = matrix[i][j]
if (state == deadly and not (i == 0 and j == 0)):
continue
node = (i, j)
neighbours = list()
if i > 0 and matrix[i - 1][j] != deadly :
neighbours.append(((i - 1, j), matrix[i - 1][j]))
if i < size - 1 and matrix[i + 1][j] != deadly :
neighbours.append(((i + 1, j), matrix[i + 1][j]))
if j > 0 and matrix[i][j - 1] != deadly :
neighbours.append(((i, j - 1), matrix[i][j - 1]))
if j < size - 1 and matrix[i][j + 1] != deadly :
neighbours.append(((i, j + 1), matrix[i][j + 1]))
graph[node] = neighbours
matrix = None
min_path = shortest_path(graph, (0, 0), (size - 1, size - 1))
min_dist = shortest_distance(graph, (0, 0), (size - 1, size - 1))
return min_dist
