class SymbolMap(object):
def __init__(self, min_v):
self._list = SortedCollection((), lambda x : x[0])
self._min_vaddr = min_v
def add_symbol(self, start, length, name):
tuple = (start, length, name)
self._list.insert(tuple)
def find(self, addr):
try:
tuple = self._list.find_le(addr)
if addr < tuple[0] + tuple[1]:
return tuple[2]
return None
except ValueError:
return None
def copy(self):
ret = SymbolMap()
ret._list = self._list.copy()
return ret
def __str__(self):
return "SymbolMap: " + self._list.__str__()
def __repr__(self):
return self.__str__()
class MmapState(object):
def __init__(self):
self._list = SortedCollection((), lambda x : x[0])
def add_map(self, start, length, pgoff, name):
tuple = (start, length, pgoff, name)
self._list.insert(tuple)
def find(self, addr):
try:
tuple = self._list.find_le(addr)
if addr < tuple[0] + tuple[1]:
return tuple
return None
except ValueError:
return None
def copy(self):
ret = MmapState()
ret._list = self._list.copy()
return ret
def __str__(self):
return "MmapState: " + self._list.__str__()
def __repr__(self):
return self.__str__()
class ElvesReady:
def __init__(self):
self.training_elf_list = SortedCollection(key=attrgetter('rating'))
self.high_performance_elf_list = []
def get_elf_with_best_fit_rating(self, rating):
return self.training_elf_list.find_ge(rating)
def add_elf(self, elf):
if elf.rating > 3.95:
self.high_performance_elf_list.append(elf)
else:
self.training_elf_list.insert(elf)
def add_elves(self, elves):
for elf in elves:
self.add_elf(elf)
def remove_from_training_list(self, elf):
self.training_elf_list.remove(elf)
def remove_from_high_performance_list(self, elf):
self.high_performance_elf_list.remove(elf)
#todo: remove elves that are chosen.
class SymbolMap(object):
def __init__(self, min_v):
self._list = SortedCollection((), lambda x: x[0])
self._min_vaddr = min_v
def add_symbol(self, start, length, name):
tuple = (start, length, name)
self._list.insert(tuple)
def find(self, addr):
try:
tuple = self._list.find_le(addr)
if addr < tuple[0] + tuple[1]:
return tuple[2]
return None
except ValueError:
return None
def copy(self):
ret = SymbolMap()
ret._list = self._list.copy()
return ret
def __str__(self):
return "SymbolMap: " + self._list.__str__()
def __repr__(self):
return self.__str__()
class MmapState(object):
def __init__(self):
self._list = SortedCollection((), lambda x: x[0])
def add_map(self, start, length, pgoff, name):
map_tuple = (start, length, pgoff, name)
self._list.insert(map_tuple)
def find(self, addr):
try:
map_tuple = self._list.find_le(addr)
if addr < map_tuple[0] + map_tuple[1]:
return map_tuple
return None
except ValueError:
return None
def copy(self):
ret = MmapState()
ret._list = self._list.copy()
return ret
def __str__(self):
return 'MmapState: ' + self._list.__str__()
def __repr__(self):
return self.__str__()
def compute_path(self, orig, goal):
orig = map(int, orig)
goal = tuple(goal)
queue = SortedCollection(key=itemgetter(0))
queue.insert((0, orig, 0))
pred = {}
pred[tuple(orig)] = None
while len(queue) > 0:
score, (px, py), steps = queue[0]
queue.remove_first()
if (px, py) == goal:
# follow trail back greedily
pos = (px, py)
plan = [pos]
while pred[pos]:
plan.append(pred[pos])
pos = pred[pos]
return plan[::-1][1:]
for dx, dy in CognitiveMonkey.Directions:
nx, ny = px + dx, py + dy
if (nx, ny) in pred:
continue
if nx < 0 or ny < 0 or nx >= self.world.height or ny >= self.world.width:
continue
if self.explore_map[nx][ny] == CognitiveMonkey.Obstacle:
continue
pred[(nx, ny)] = (px, py)
nscore = steps + 1 + Collisions.distance((nx, ny), goal)
queue.insert((nscore, (nx, ny), steps + 1))
return []