def update_existing_locations(
vial_http: urllib3.connectionpool.ConnectionPool,
locations: rtree.index.Index,
source_ids: Iterable[str],
) -> None:
"""Updates rtree index with locations with source ids"""
for chunked_ids in misc.batch(source_ids, 20):
updated_locations = search_locations(vial_http, idref=list(chunked_ids))
for loc in updated_locations:
if loc.is_valid and loc.get("geometry"):
locations.insert(_generate_index_row(loc))
def _find_nearest_within_in_index(
self, p: complex, max_dist: float, index: rtree.index.Index
) -> Tuple[Optional[int], Optional[float]]:
"""Find nearest in specific index. Return (idx, dist) tuple, both of which can be None.
"""
# query the index while discarding anything that is no longer available
items = [
item
for item in index.intersection(
[p.real - max_dist, p.imag - max_dist, p.real + max_dist, p.imag + max_dist],
objects=True,
)
if self.available[item.id]
]
if len(items) == 0:
return None, 0
# we want the closest item, and we want it only if it's not too far
item = min(items, key=lambda it: self._item_distance(p, it))
dist = self._item_distance(p, item)
if dist > max_dist:
return None, 0
return item.id, dist
def _find_nearest_in_index(
self, p: complex, index: rtree.index.Index
) -> Tuple[Optional[int], float]:
"""Check the N nearest lines, hopefully find one that is active."""
for item in index.nearest((p.real, p.imag) * 2, 100, objects=True):
if self.available[item.id]:
return item.id, self._item_distance(p, item)
return None, 0.0
def _find_candidates(
source: location.NormalizedLocation,
existing: rtree.index.Index,
candidate_distance: float,
) -> Iterator[dict]:
"""Return a slice of existing locations"""
src_point = shapely.geometry.Point(
source.location.longitude,
source.location.latitude,
)
search_bounds = src_point.buffer(candidate_distance).bounds
yield from existing.intersection(search_bounds, objects="raw")
def _find_candidates(
source: schema.NormalizedLocation,
existing: rtree.index.Index,
) -> Iterator[dict]:
"""Return a slice of existing locations"""
src_point = shapely.geometry.Point(
source.location.longitude,
source.location.latitude,
)
search_bounds = src_point.buffer(CANDIDATE_DEGREES_DISTANCE).bounds
result = existing.intersection(search_bounds, objects=True)
for row in result:
yield row.object
def append_cells_to_spatial_tree(tree: rtree.index.Index, cells: List,
idToNum: Dict):
for element in cells:
tree.insert(idToNum[element.get_feature_id()],
element.get_bounding_box(),
obj=element)