def perform(level, box, options):
logging.info("BoundingBox coordinates: ({},{}),({},{}),({},{})".format(
box.miny, box.maxy, box.minx, box.maxx, box.minz, box.maxz))
# ==== PREPARATION =====
(width, height, depth) = utilityFunctions.getBoxSize(box)
logging.info("Selection box dimensions {}, {}, {}".format(
width, height, depth))
world = utilityFunctions.generateMatrix(level, box, width, depth, height)
world_space = utilityFunctions.dotdict({
"y_min": 0,
"y_max": height - 1,
"x_min": 0,
"x_max": width - 1,
"z_min": 0,
"z_max": depth - 1
})
height_map = utilityFunctions.getHeightMap(level, box)
# ==== PARTITIONING OF NEIGHBOURHOODS ====
(center,
neighbourhoods) = generateCenterAndNeighbourhood(world_space, height_map)
all_buildings = []
# ==== GENERATING CITY CENTER ====
minimum_h = 50
minimum_w = 25
mininum_d = 25
iterate = 100
minimum_lots = 6
available_lots = 0
maximum_tries = 50
current_try = 0
threshold = 1
partitioning_list = []
temp_partitioning_list = []
# run the partitioning algorithm for iterate times to get different partitionings of the same area
logging.info(
"Generating {} different partitionings for the the City Centre {}".
format(iterate, center))
while available_lots < minimum_lots and current_try < maximum_tries:
for i in range(iterate):
# generate a partitioning through some algorithm
if RNG.random() < 0.5:
partitioning = binarySpacePartitioning(center[0], center[1],
center[2], center[3],
center[4], center[5],
[])
else:
partitioning = quadtreeSpacePartitioning(
center[0], center[1], center[2], center[3], center[4],
center[5], [])
# remove invalid partitions from the partitioning
valid_partitioning = []
for p in partitioning:
(y_min, y_max, x_min, x_max, z_min, z_max) = (p[0], p[1], p[2],
p[3], p[4], p[5])
failed_conditions = []
cond1 = utilityFunctions.hasValidGroundBlocks(
x_min, x_max, z_min, z_max, height_map)
if cond1 == False: failed_conditions.append(1)
cond2 = utilityFunctions.hasMinimumSize(
y_min, y_max, x_min, x_max, z_min, z_max, minimum_h,
minimum_w, mininum_d)
if cond2 == False: failed_conditions.append(2)
cond3 = utilityFunctions.hasAcceptableSteepness(
x_min, x_max, z_min, z_max, height_map,
utilityFunctions.getScoreArea_type1, threshold)
if cond3 == False: failed_conditions.append(3)
if cond1 and cond2 and cond3:
score = utilityFunctions.getScoreArea_type1(
height_map, x_min, x_max, z_min, z_max)
valid_partitioning.append((score, p))
else:
logging.info(
"Failed Conditions {}".format(failed_conditions))
partitioning_list.extend(valid_partitioning)
logging.info(
"Generated a partition with {} valid lots and {} invalids ones"
.format(len(valid_partitioning),
len(partitioning) - len(valid_partitioning)))
# order partitions by steepness
partitioning_list = sorted(partitioning_list)
final_partitioning = utilityFunctions.getNonIntersectingPartitions(
partitioning_list)
available_lots = len(final_partitioning)
logging.info(
"Current partitioning with most available_lots: {}, current threshold {}"
.format(available_lots, threshold))
threshold += 1
current_try += 1
logging.info("Final lots ({}) for the City Centre {}: ".format(
len(final_partitioning), center))
for p in final_partitioning:
logging.info("\t{}".format(p))
for partition in final_partitioning:
building = generateBuilding(world, partition, height_map)
all_buildings.append(building)
# ==== GENERATING NEIGHBOURHOODS ====
minimum_h = 3
minimum_w = 7
mininum_d = 6
iterate = 100
maximum_tries = 50
current_try = 0
minimum_lots = 20
available_lots = 0
threshold = 1
partitioning_list = []
final_partitioning = []
while available_lots < minimum_lots and current_try < maximum_tries:
partitioning_list = []
for i in range(iterate):
for neigh in neighbourhoods:
logging.info(
"Generating {} different partitionings for the neighbourhood {}"
.format(iterate, neigh))
if RNG.random() < 0.5:
partitioning = binarySpacePartitioning(
neigh[0], neigh[1], neigh[2], neigh[3], neigh[4],
neigh[5], [])
else:
partitioning = quadtreeSpacePartitioning(
neigh[0], neigh[1], neigh[2], neigh[3], neigh[4],
neigh[5], [])
valid_partitioning = []
for p in partitioning:
(y_min, y_max, x_min, x_max, z_min,
z_max) = (p[0], p[1], p[2], p[3], p[4], p[5])
failed_conditions = []
cond1 = utilityFunctions.hasValidGroundBlocks(
x_min, x_max, z_min, z_max, height_map)
if cond1 == False: failed_conditions.append(1)
cond2 = utilityFunctions.hasMinimumSize(
y_min, y_max, x_min, x_max, z_min, z_max, minimum_h,
minimum_w, mininum_d)
if cond2 == False: failed_conditions.append(2)
cond3 = utilityFunctions.hasAcceptableSteepness(
x_min, x_max, z_min, z_max, height_map,
utilityFunctions.getScoreArea_type1, threshold)
if cond3 == False: failed_conditions.append(3)
if cond1 and cond2 and cond3:
score = utilityFunctions.getScoreArea_type1(
height_map, x_min, x_max, z_min, z_max)
valid_partitioning.append((score, p))
logging.info("Passed the 3 conditions!")
else:
logging.info(
"Failed Conditions {}".format(failed_conditions))
partitioning_list.extend(valid_partitioning)
logging.info(
"Generated a partition with {} valid lots and {} invalids ones"
.format(len(valid_partitioning),
len(partitioning) - len(valid_partitioning)))
temp_partitioning_list.extend(partitioning_list)
# order partitions by steepness
temp_partitioning_list = sorted(temp_partitioning_list)
final_partitioning = utilityFunctions.getNonIntersectingPartitions(
temp_partitioning_list)
available_lots = len(final_partitioning)
logging.info(
"Current neighbourhood partitioning with most available_lots: {}, current threshold {}"
.format(available_lots, threshold))
threshold += 1
current_try += 1
logging.info("Final lots ({})for the neighbourhood {}: ".format(
len(final_partitioning), neigh))
for p in final_partitioning:
logging.info("\t{}".format(p))
for partition in final_partitioning:
house = generateHouse(world, partition, height_map)
all_buildings.append(house)
# ==== GENERATE PATH MAP ====
# generate a path map that gives the cost of moving to each neighbouring cell
pathMap = utilityFunctions.getPathMap(height_map, width, depth)
# ==== CONNECTING BUILDINGS WITH ROADS ====
logging.info("Calling MST on {} buildings".format(len(all_buildings)))
MST = utilityFunctions.getMST_Manhattan(all_buildings, pathMap, height_map)
pavementBlockID = 4
pavementBlockSubtype = 0
for m in MST:
p1 = m[1]
p2 = m[2]
logging.info("Pavement with distance {} between {} and {}".format(
m[0], p1.entranceLot, p2.entranceLot))
path = utilityFunctions.aStar(p1.entranceLot, p2.entranceLot, pathMap,
height_map)
if path != None:
logging.info(
"Found path between {} and {}. Generating road...".format(
p1.entranceLot, p2.entranceLot))
GeneratePath.generatPath(world, path, height_map,
(pavementBlockID, pavementBlockSubtype))
else:
logging.info(
"Couldnt find path between {} and {}. Generating a straight road between them..."
.format(p1.entranceLot, p2.entranceLot))
GeneratePath.generatPath_StraightLine(
world, p1.entranceLot[1], p1.entranceLot[2], p2.entranceLot[1],
p2.entranceLot[2], height_map,
(pavementBlockID, pavementBlockSubtype))
# ==== UPDATE WORLD ====
world.updateWorld()
def perform(level, box, options):
logging.info("BoundingBox coordinates: ({},{}),({},{}),({},{})".format(
box.miny, box.maxy, box.minx, box.maxx, box.minz, box.maxz))
# ==== PREPARATION =====
(width, height, depth) = utilityFunctions.getBoxSize(box)
logging.info("Selection box dimensions {}, {}, {}".format(
width, height, depth))
world = utilityFunctions.generateMatrix(level, box, width, depth, height)
world_space = utilityFunctions.dotdict({
"y_min": 0,
"y_max": height - 1,
"x_min": 0,
"x_max": width - 1,
"z_min": 0,
"z_max": depth - 1
})
height_map = utilityFunctions.getHeightMap(level, box, None, False)
# === Wood quantity and Biome analyzer === #
air_like = [
0, 4, 5, 6, 20, 23, 29, 30, 35, 37, 38, 39, 40, 44, 46, 47, 50, 59, 66,
83, 85, 86, 95, 102, 104, 105, 107, 126, 141, 142, 160, 175
]
wood_height_map = utilityFunctions.getHeightMap(level, box, air_like, True)
(usable_wood, biome) = EnvironmentAnalyzer.determinate_usable_wood(
level, wood_height_map, box.minx, box.maxx, box.minz, box.maxz)
# === City stats === #
biome_with_well = ['Desert', 'Badlands']
APARTMENT_SIZE = 2
HOUSE_SIZE = 4
GREENHOUSE_CAPACITY = 15
inhabitants = 0
greenhouse_count = 0
well_count = 0
# ==== PARTITIONING OF THE SELECTION IN AREA IN ORDER TO FLATTEN ====
# Work in progress do not use
#earthwork_height_map = utilityFunctions.getHeightMap(level,box, None, True)
#area_partitioning_and_flattening(world_space, earthwork_height_map, world, biome)
# ==== PARTITIONING OF NEIGHBOURHOODS ====
(center,
neighbourhoods) = generateCenterAndNeighbourhood(world_space, height_map)
all_buildings = []
# ==== GENERATING CITY CENTER ====
minimum_h = 50
minimum_w = 25
mininum_d = 25
iterate = 100
minimum_lots = 6
available_lots = 0
maximum_tries = 50
current_try = 0
threshold = 1
partitioning_list = []
temp_partitioning_list = []
# run the partitioning algorithm for iterate times to get different partitionings of the same area
logging.info(
"Generating {} different partitionings for the the City Centre {}".
format(iterate, center))
while available_lots < minimum_lots and current_try < maximum_tries:
for i in range(iterate):
# generate a partitioning through some algorithm
if RNG.random() < 0.5:
partitioning = binarySpacePartitioning(center[0], center[1],
center[2], center[3],
center[4], center[5],
[])
else:
partitioning = quadtreeSpacePartitioning(
center[0], center[1], center[2], center[3], center[4],
center[5], [])
# remove invalid partitions from the partitioning
valid_partitioning = []
for p in partitioning:
(y_min, y_max, x_min, x_max, z_min, z_max) = (p[0], p[1], p[2],
p[3], p[4], p[5])
failed_conditions = []
cond1 = utilityFunctions.hasValidGroundBlocks(
x_min, x_max, z_min, z_max, height_map)
if cond1 == False: failed_conditions.append(1)
cond2 = utilityFunctions.hasMinimumSize(
y_min, y_max, x_min, x_max, z_min, z_max, minimum_h,
minimum_w, mininum_d)
if cond2 == False: failed_conditions.append(2)
cond3 = utilityFunctions.hasAcceptableSteepness(
x_min, x_max, z_min, z_max, height_map,
utilityFunctions.getScoreArea_type1, threshold)
if cond3 == False: failed_conditions.append(3)
if cond1 and cond2 and cond3:
score = utilityFunctions.getScoreArea_type1(
height_map, x_min, x_max, z_min, z_max)
valid_partitioning.append((score, p))
else:
logging.info(
"Failed Conditions {}".format(failed_conditions))
partitioning_list.extend(valid_partitioning)
logging.info(
"Generated a partition with {} valid lots and {} invalids ones"
.format(len(valid_partitioning),
len(partitioning) - len(valid_partitioning)))
# order partitions by steepness
partitioning_list = sorted(partitioning_list)
final_partitioning = utilityFunctions.getNonIntersectingPartitions(
partitioning_list)
available_lots = len(final_partitioning)
logging.info(
"Current partitioning with most available_lots: {}, current threshold {}"
.format(available_lots, threshold))
threshold += 1
current_try += 1
logging.info("Final lots ({}) for the City Centre {}: ".format(
len(final_partitioning), center))
for p in final_partitioning:
logging.info("\t{}".format(p))
for partition in final_partitioning:
building, apartments_inhabitants = generateBuilding(
world, partition, height_map, usable_wood, biome)
inhabitants += apartments_inhabitants
all_buildings.append(building)
# ==== GENERATING NEIGHBOURHOODS ====
minimum_h = 10
minimum_w = 16
mininum_d = 16
iterate = 100
maximum_tries = 50
current_try = 0
minimum_lots = 20
available_lots = 0
threshold = 1
partitioning_list = []
final_partitioning = []
while available_lots < minimum_lots and current_try < maximum_tries:
partitioning_list = []
for i in range(iterate):
for neigh in neighbourhoods:
logging.info(
"Generating {} different partitionings for the neighbourhood {}"
.format(iterate, neigh))
if RNG.random() < 0.5:
partitioning = binarySpacePartitioning(
neigh[0], neigh[1], neigh[2], neigh[3], neigh[4],
neigh[5], [])
else:
partitioning = quadtreeSpacePartitioning(
neigh[0], neigh[1], neigh[2], neigh[3], neigh[4],
neigh[5], [])
valid_partitioning = []
for p in partitioning:
(y_min, y_max, x_min, x_max, z_min,
z_max) = (p[0], p[1], p[2], p[3], p[4], p[5])
failed_conditions = []
cond1 = utilityFunctions.hasValidGroundBlocks(
x_min, x_max, z_min, z_max, height_map)
if cond1 == False: failed_conditions.append(1)
cond2 = utilityFunctions.hasMinimumSize(
y_min, y_max, x_min, x_max, z_min, z_max, minimum_h,
minimum_w, mininum_d)
if cond2 == False: failed_conditions.append(2)
cond3 = utilityFunctions.hasAcceptableSteepness(
x_min, x_max, z_min, z_max, height_map,
utilityFunctions.getScoreArea_type1, threshold)
if cond3 == False: failed_conditions.append(3)
if cond1 and cond2 and cond3:
score = utilityFunctions.getScoreArea_type1(
height_map, x_min, x_max, z_min, z_max)
valid_partitioning.append((score, p))
logging.info("Passed the 3 conditions!")
else:
logging.info(
"Failed Conditions {}".format(failed_conditions))
partitioning_list.extend(valid_partitioning)
logging.info(
"Generated a partition with {} valid lots and {} invalids ones"
.format(len(valid_partitioning),
len(partitioning) - len(valid_partitioning)))
temp_partitioning_list.extend(partitioning_list)
# order partitions by steepness
temp_partitioning_list = sorted(temp_partitioning_list)
final_partitioning = utilityFunctions.getNonIntersectingPartitions(
temp_partitioning_list)
available_lots = len(final_partitioning)
logging.info(
"Current neighbourhood partitioning with most available_lots: {}, current threshold {}"
.format(available_lots, threshold))
threshold += 1
current_try += 1
logging.info("Final lots ({})for the neighbourhood {}: ".format(
len(final_partitioning), neigh))
for p in final_partitioning:
logging.info("\t{}".format(p))
for partition in final_partitioning:
if well_count < 1 and biome in biome_with_well:
well = generateWell(world, partition, height_map, biome)
well_count += 1
all_buildings.append(well)
elif greenhouse_count * GREENHOUSE_CAPACITY < inhabitants:
greenhouse = generateGreenhouse(world, partition, height_map,
usable_wood, biome)
greenhouse_count += 1
all_buildings.append(greenhouse)
else:
house = generateHouse(world, partition, height_map, usable_wood,
biome)
inhabitants += RNG.randint(1, HOUSE_SIZE)
all_buildings.append(house)
# ==== GENERATE PATH MAP ====
# generate a path map that gives the cost of moving to each neighbouring cell
pathMap = utilityFunctions.getPathMap(height_map, width, depth)
# ==== CONNECTING BUILDINGS WITH ROADS ====
logging.info("Calling MST on {} buildings".format(len(all_buildings)))
MST = utilityFunctions.getMST_Manhattan(all_buildings, pathMap, height_map)
pavementBlockID = BlocksInfo.getPavmentId(biome)
for m in MST:
p1 = m[1]
p2 = m[2]
logging.info("Pavement with distance {} between {} and {}".format(
m[0], p1.entranceLot, p2.entranceLot))
path = utilityFunctions.aStar(p1.entranceLot, p2.entranceLot, pathMap,
height_map)
if path != None:
logging.info(
"Found path between {} and {}. Generating road...".format(
p1.entranceLot, p2.entranceLot))
GeneratePath.generatPath(world, path, height_map, pavementBlockID)
else:
logging.info(
"Couldnt find path between {} and {}. Generating a straight road between them..."
.format(p1.entranceLot, p2.entranceLot))
GeneratePath.generatPath_StraightLine(world, p1.entranceLot[1],
p1.entranceLot[2],
p2.entranceLot[1],
p2.entranceLot[2],
height_map, pavementBlockID)
# ==== UPDATE WORLD ====
world.updateWorld()
