def createRtree(data):
"""
Creates an R-Tree from the given data
"""
tree = Rtree()
for index, pair in enumerate(data):
tree.insert(index, (pair[3], pair[4]), obj=pair)
return tree
class Rtree2D(object):
"""Wrapper of `rtree.Index` for supporting friendly 2d operations.
Also forces the uniqueness of the `id` parameter, which is different from
the rtree module's behavior.
"""
def __init__(self):
self._index = Rtree()
self._locations = {}
@staticmethod
def to_coords(location):
return (location[0], location[1], location[0], location[1])
def keys(self):
return self._locations.keys()
def get(self, id, objects=False):
return self._locations.get(id)
def set(self, id, location=None, state=None, obj=None):
# Clean up previous value first if any
old = self._locations.get(id)
if old is not None:
self._index.delete(id, self.to_coords(old[0]))
if old and state is None:
state = old[1]
if old and location is None:
location = old[0]
self._locations[id] = (location, state)
self._index.insert(id, self.to_coords(location), obj=obj)
def update(self, id, state, obj=None):
old = self._locations.get(id)
if old is not None:
self._index.delete(id, self.to_coords(old[0]))
self._locations[id] = (old[0], state)
self._index.insert(id, self.to_coords(old[0]), obj=obj)
def remove(self, id):
self._index.delete(id, self.to_coords(self._locations[id][0]))
del self._locations[id]
def nearest(self, location, count=1, objects=False, max_distance=None):
ids = self._index.nearest(self.to_coords(location), num_results=count,
objects=objects)
if max_distance is not None:
ids = [(id_, self._locations[id_][1]) for id_ in ids
if distance(self._locations[id_][0], location) <= max_distance]
return ids
class EntityIndex:
"""Index of spatial entities.
When an entity is added to the index, it gets an unique id and is kept in
a way than can be queried by id or by spatial coordinates.
"""
__slots__ = ('name', 'id_count', 'entities', 'bounding_rects', 'rtree',
'path_map', 'register_updates', 'simulation', 'stats',
'_updates')
extension = 'shelf'
storage_fields = 'id_count', 'entities', 'path_map'
name: str
id_count: count
entities: Dict[int, Entity]
bounding_rects: Dict[int, BoundingRect]
rtree: Rtree
path_map: PathMap
register_updates: bool
simulation: Simulation
# TODO: Define type for stats instead of Any.
stats: Dict[Type, Dict[Any, Any]]
_updates: Set[int]
def __init__(self, name: Optional[str] = None):
self.reset(name)
@property
def filename(self) -> str:
"""Name with extension added."""
if self.name.endswith(f'.{EntityIndex.extension}'):
return self.name
return f'{self.name}.{EntityIndex.extension}'
def reset(self, name: Optional[str] = None):
"""Reset index and set name."""
self.name = name
self.id_count = count()
self.entities = {}
self.bounding_rects = {}
self.rtree = Rtree()
self.path_map = PathMap()
self.register_updates = False
self.simulation = Simulation()
self.stats = defaultdict(dict)
self._updates = set()
def add(self, entity: Entity):
"""Add entity to index."""
if entity.id is not None:
raise ValueError('Entity already has an id.')
entity.id = next(self.id_count)
self.entities[entity.id] = entity
log.debug('[%s] Added %s', __name__, Entity.__repr__(entity))
def add_static(self, entity: Entity):
"""Add entity as static.
Entity may or not have already been added with the `add` method. It
will be added in case it was not already.
A static entity is an entity with geometric information
(`bounding_rect`) that will rarely change. A spatial index is used to
allow for quick spatial queries. These entities are added to the
updated queue when something about them changes. This queue can be
consumed by a front end application with `consume_updates` to update
the representation only when needed.
"""
if entity.id is None:
self.add(entity)
if entity.id in self.bounding_rects:
raise ValueError('Entity already added as static.')
self.bounding_rects[entity.id] = entity.bounding_rect
self.rtree.insert(entity.id, entity.bounding_rect)
self.updated(entity)
def delete(self, entity: Entity):
"""Delete entity from index."""
to_remove = {entity}
while to_remove:
entity = to_remove.pop()
assert self.entities[entity.id] is entity
del self.entities[entity.id]
delete_result = entity.on_delete()
to_remove.update(delete_result.cascade)
for updated in delete_result.updated:
self.updated(updated)
if entity.id in self.bounding_rects:
self.rtree.delete(entity.id, self.bounding_rects[entity.id])
del self.bounding_rects[entity.id]
self.updated(entity)
self.rebuild_path_map()
log.debug('[%s] Removed %s', __name__, entity)
def update_bounding_rect(self,
entity: Entity,
new_rect: Optional[BoundingRect] = None):
"""Change the bounding rectangle of an entity.
Update the bounding rect to `entity.bounding_rect` or to `new_rect` if
it's not None.
"""
assert self.entities[entity.id] is entity
if new_rect is None:
new_rect = entity.bounding_rect
old_rect = self.bounding_rects.get(entity.id, None)
if old_rect is None or old_rect == new_rect:
return
self.rtree.delete(entity.id, old_rect)
self.bounding_rects[entity.id] = new_rect
self.rtree.insert(entity.id, new_rect)
def updated(self, entity: Union[Entity, int]):
"""Mark entity as updated."""
if self.register_updates:
try:
self._updates.add(entity.id)
except AttributeError:
self._updates.add(entity)
def clear_updates(self):
"""Clear entity updates."""
self._updates.clear()
def consume_updates(self) -> Iterator[int]:
"""Get generator that pops and returns updates."""
while self._updates:
yield self._updates.pop()
def generate_rtree_from_entities(self):
"""Create an rtree with all entities with bounding rectangles."""
self.bounding_rects = {
id_: e.bounding_rect
for id_, e in self.entities.items() if hasattr(e, 'bounding_rect')
}
self.rtree = Rtree(
(id_, rect, None) for id_, rect in self.bounding_rects.items())
def load(self, name: Optional[str] = None):
"""Load entities from shelf.
Load enities using the this index name. If a name is passed as
argument, will set the index name before loading.
"""
if name is not None:
self.name = name
with shelve.open(self.filename) as data:
for key in EntityIndex.storage_fields:
log.info('Loading %s', key)
value = data.get(key, None)
if value:
setattr(self, key, value)
log.info('Loaded %s', self.name)
self.generate_rtree_from_entities()
if not hasattr(self, 'path_map'):
self.rebuild_path_map()
def save(self):
"""Save entities to shelf."""
with shelve.open(self.filename) as data:
for key in EntityIndex.storage_fields:
log.info('Saving %s', key)
data[key] = getattr(self, key)
def get_all(self,
of_type: Type[Entity] = None,
where: Callable[[Entity], bool] = None) -> Iterator[Entity]:
"""Get all entities with optional filters."""
def type_filter(entity):
return isinstance(entity, of_type)
filters = []
if of_type is not None:
filters.append(type_filter)
if where is not None:
filters.append(where)
yield from filter(lambda e: all(f(e) for f in filters),
self.entities.values())
def get_at(self,
point: Point,
of_type: Type[Entity] = None,
where: Callable[[Entity], bool] = None) -> List[Entity]:
"""Get entities at given coordinates.
Get a list with entities intersecting the given point. If of_type is
not None, will return only entities of the given type. If where is not
None, where must be a function that receives an Entity and returns True
or False, meaning whether the entity will be returned.
"""
def polygon_filter(entity: Entity) -> bool:
return point_in_polygon(point, entity.polygon)
def type_filter(entity: Entity) -> bool:
return isinstance(entity, of_type)
filters = [polygon_filter]
if of_type is not None:
filters.append(type_filter)
if where is not None:
filters.append(where)
return list(
filter(
lambda e: all(f(e) for f in filters),
map(self.entities.get,
self.rtree.intersection(point.bounding_rect))))
def rebuild_path_map(self):
"""Rebuild the path map, invalidating the old map."""
self.path_map = PathMap()
def main(train, qgram_size):
logger = logging.getLogger('build_rtree')
logger.setLevel(logging.DEBUG)
fh = logging.FileHandler('./log/%s' % train)
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('---------------------------- Build R-tree ----------------------------')
qgram_tag = 'q_%d' % qgram_size
train_path = './data/processed/%s.txt' % train
data = load_trajectory(train_path)
logger.info('Load train trajectory: %s' % train_path)
trajectory, id_list = build_qgram(data, qgram_size)
id_to_key_dict = build_id_to_key(id_list)
# order_key_dict = build_order_dict(id_list)
#save orderId-key mapping
#key: trajectory id in string, value: encoded key
rtree_id_dict_path = './data/interim/%s/rtree_id_dict_%s.txt' % (train, qgram_tag)
save_pickle(id_to_key_dict, rtree_id_dict_path)
logger.info('Output rtree_id_dict: %s' % rtree_id_dict_path)
#key: key, value: trajectory id in string
# filename = '../data/processed/order_key_dict.txt'
# outfile = open(filename,'wb')
# pickle.dump(order_key_dict,outfile)
# outfile.close()
# R-tree constructor
# parameter: 'data_full' is the filename of R-tree storage
# 2 files are created: data_full.dat, data_full.idx
# return: r-tree index
rtree_path = './data/interim/%s/my_rtree_%s' % (train, qgram_tag)
data_idx = Rtree(rtree_path)
logger.info('Output R-tree: %s' % rtree_path)
# put all trajectories into r-tree in the form of bounding box
node_id = 0
start_time = time.time()
for key, qgrams in trajectory.items():
for qgram in qgrams:
# parameters:
# 1. node id
# 2. bounding box(point): (x,y,x,y)
# 3. data inside each node: trajectory's key from order_dict
x = np.around(qgram[0], decimals=5)
y = np.around(qgram[1], decimals=5)
data_idx.insert(node_id, (x, y, x, y), obj=(id_to_key_dict[key]))
node_id += 1
del data_idx
end_time = time.time()
logger.info("exec time: "+str(end_time-start_time))
logger.info('Finished building R-tree')
