def __init__(self, level, clone=False):
if clone:
self.level = level
self.verbose = False
else:
self.iterations = 0
self.level = level
self.verbose = False
self.positions = []
self.corridor_graph = corridormap.build_graph(level.blockHeights, level.width, level.height)
self.kdtree = scipy.spatial.cKDTree([self.corridor_graph.node[n]["position"] for n in self.corridor_graph.nodes()])
self.lookup = [[None for y in range(level.height)] for x in range(level.width)]
node_count = len(self.corridor_graph.nodes())
for x, y in itertools.product(range(level.width), range(level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
self.explored = [False for x in range(len(self.corridor_graph.nodes()))]
# Initialise the seen grids to false.
self.seen = [ [False for y in range(level.height)] for x in range(level.width)]
# Initialise the visibility to false.
self.visibility = [ [False for y in range(level.height)] for x in range(level.width)]
def initialize(self):
"""
Called at the start of the game. Any heavy computation should be carried out here to save time later on.
Here we construct the corridor map.
"""
# Set parameters for visualisation
self.window = visualizer.VisualizerWindow(self)
self.window.drawBots = True
self.game_state_updated = True
# Generate our corridor map, capturing useful information out of the map
self.corridor_graph = corridormap.build_graph(self.level.blockHeights, self.level.width, self.level.height)
# We use scipy to perform a nearest neighbour look-up.
# This allows use to work out which node the bots are closest to.
self.kdtree = scipy.spatial.cKDTree([self.corridor_graph.node[n]["position"]
for n in self.corridor_graph.nodes()])
# Using the nearest neighbour tree. For each grid block, which is 1m x 1m, what is the closest node.
self.lookup = [[None for y in range(self.level.height)] for x in range(self.level.width)]
node_count = len(self.corridor_graph.nodes())
for x, y in itertools.product(range(self.level.width), range(self.level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
# Initialise all nodes to not having been visited.
self.explored = [False for x in range(len(self.corridor_graph.nodes()))]
self.initialise = True
def initialize(self):
layer = self.visualizer.anonymousLayer(True)
self.visualizer.addLabel("Initializing...", "centered-info-box", Color(222, 222, 222, 255), None)
self.visualizer.endLayer()
self.bestBotShapeId = -1
self.selectedBot = None
self.selectedBotShapeId = -1
self.lastClickTime = time.time()
self.botWaypoints = {}
self.graph = corridormap.build_graph(self.level.blockHeights, self.level.width, self.level.height)
self.kdtree = scipy.spatial.cKDTree([self.graph.node[node]["position"] for node in self.graph.nodes()])
self.lookup = [[None for y in range(self.level.height)] for x in range(self.level.width)]
node_count = len(self.graph.nodes())
for x, y in itertools.product(range(self.level.width), range(self.level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
for node in self.graph.nodes():
self.graph.node[node]["distances"] = nx.single_source_dijkstra_path_length(self.graph, node, weight="weight")
self.graph.node[node]["explored"] = False
self.initGraphVisualization()
self.visualizer.hideLayer(layer)
def initialize(self):
# Generate our corridor map, capturing useful information out of the map
self.corridor_graph = corridormap.build_graph(self.level.blockHeights, self.level.width, self.level.height)
# We use scipy to perform a nearest neighbour look-up.
# This allows use to work out which node the bots are closest to.
self.kdtree = scipy.spatial.cKDTree([self.corridor_graph.node[n]["position"]
for n in self.corridor_graph.nodes()])
# Using the nearest neighbour tree. For each grid block, which is 1m x 1m, what is the closest node.
self.lookup = [[None for y in range(self.level.height)] for x in range(self.level.width)]
node_count = len(self.corridor_graph.nodes())
for x, y in itertools.product(range(self.level.width), range(self.level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
self.terminal_nodes = []
for node in self.corridor_graph.nodes():
# If only one neighbor then it's a terminal node.
if len(self.corridor_graph.neighbors(node)) == 1:
self.terminal_nodes.append(node)
# Initialise all nodes to not having been visited.
self.explored = [False for x in range(len(self.corridor_graph.nodes()))]
self.initialise = True
def initialize(self):
layer = self.visualizer.anonymousLayer(True)
self.visualizer.addLabel("Initializing...", "centered-info-box", Color(222, 222, 222, 255), None)
self.visualizer.endLayer()
self.graph = corridormap.build_graph(self.level.blockHeights, self.level.width, self.level.height)
self.kdtree = scipy.spatial.cKDTree([self.graph.node[n]["position"] for n in self.graph.nodes()])
self.botPath = {}
self.botPathColors = {}
self.mouseLeftClicked = False
self.debug = 0
for n in self.graph.nodes():
self.graph.node[n]["explored"] = False
self.graph.node[n]["cell_count"] = 0
self.graph.node[n]["visible_count"] = 0
self.graph.node[n]["seen_count"] = 0
self.lookup = [ [None for y in range(self.level.height)] for x in range(self.level.width)]
node_count = len(self.graph.nodes())
for x, y in itertools.product(range(self.level.width), range(self.level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
if self.level.blockHeights[x][y] == 0:
self.graph.node[i]["cell_count"] += 1
closest = d
self.seen = [ [-1 for y in range(self.level.height)] for x in range(self.level.width)]
self.seen_dirty = []
self.visibility = [ [False for y in range(self.level.height)] for x in range(self.level.width)]
self.visibility_previous = None
self.initVisualization()
self.exploreTasks = { bot : None for bot in self.game.team.members }
#set the colors for the graph path
for bot in self.game.team.members:
if bot.name == "Blue0":
self.botPathColors[bot] = Color(200, 100, 50)
if bot.name == "Blue3":
self.botPathColors[bot] = Color(80, 10, 180)
if bot.name == "Blue2":
self.botPathColors[bot] = Color(0, 0, 255)
if bot.name == "Blue1":
self.botPathColors[bot] = Color(0, 0, 0)
self.visualizer.hideLayer(layer)
def initialize(self):
self.graph = corridormap.build_graph(self.level.blockHeights, self.level.width, self.level.height)
self.kdtree = scipy.spatial.cKDTree([self.graph.node[n]["position"] for n in self.graph.nodes()])
self.lookup = [ [None for y in range(self.level.height)] for x in range(self.level.width)]
node_count = len(self.graph.nodes())
for x, y in itertools.product(range(self.level.width), range(self.level.height)):
closest = None
for d, i in zip(*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
for n in self.graph.nodes():
self.graph.node[n]["explored"] = False
self.initGraphVisualization()
def initialize(self):
"""
Called at the start of the game. Any heavy computation should be carried out here to save time later on.
Here we construct the corridor map.
"""
# Set parameters for visualisation
self.window = visualizer.VisualizerWindow(self)
self.window.drawBots = True
self.game_state_updated = True
# Generate our corridor map, capturing useful information out of the map
self.corridor_graph = corridormap.build_graph(self.level.blockHeights,
self.level.width,
self.level.height)
# We use scipy to perform a nearest neighbour look-up.
# This allows use to work out which node the bots are closest to.
self.kdtree = scipy.spatial.cKDTree([
self.corridor_graph.node[n]["position"]
for n in self.corridor_graph.nodes()
])
# Using the nearest neighbour tree. For each grid block, which is 1m x 1m, what is the closest node.
self.lookup = [[None for y in range(self.level.height)]
for x in range(self.level.width)]
node_count = len(self.corridor_graph.nodes())
for x, y in itertools.product(range(self.level.width),
range(self.level.height)):
closest = None
for d, i in zip(
*self.kdtree.query((x, y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
# Initialise all nodes to not having been visited.
self.explored = [
False for x in range(len(self.corridor_graph.nodes()))
]
self.initialise = True
def __init__(self, level, clone=False):
if clone:
self.level = level
self.verbose = False
else:
self.iterations = 0
self.level = level
self.verbose = False
self.positions = []
self.corridor_graph = corridormap.build_graph(
level.blockHeights, level.width, level.height)
self.kdtree = scipy.spatial.cKDTree([
self.corridor_graph.node[n]["position"]
for n in self.corridor_graph.nodes()
])
self.lookup = [[None for y in range(level.height)]
for x in range(level.width)]
node_count = len(self.corridor_graph.nodes())
for x, y in itertools.product(range(level.width),
range(level.height)):
closest = None
for d, i in zip(
*self.kdtree.query((x,
y), 2, distance_upper_bound=8.0)):
if i >= node_count:
continue
if closest is None or d < closest:
self.lookup[x][y] = i
closest = d
self.explored = [
False for x in range(len(self.corridor_graph.nodes()))
]
# Initialise the seen grids to false.
self.seen = [[False for y in range(level.height)]
for x in range(level.width)]
# Initialise the visibility to false.
self.visibility = [[False for y in range(level.height)]
for x in range(level.width)]