def _spatialJoinDist(self, ldf, rdf, lName, rName, lTree, polygon, dist):
(polyOffset, ringOffset, xPoints, yPoints) = polygon
(points, tree) = lTree
boundingBox = cuspatial.polygon_bounding_boxes(polyOffset, ringOffset,
xPoints, yPoints)
joinFilter = cuspatial.join_quadtree_and_bounding_boxes(
tree, boundingBox, 0.0, 1.0, 0.0, 1.0, 1.0, 15)
joinPolygon = cuspatial.quadtree_point_in_polygon(
joinFilter,
tree,
points,
ldf[lName + 'X'],
ldf[lName + 'Y'],
polyOffset,
ringOffset,
xPoints,
yPoints,
)
# https://github.com/rapidsai/cuspatial/issues/284
lGather = ldf.take(points.take(
joinPolygon['point_index'])).reset_index(drop=True)
rGather = rdf.take(joinPolygon['polygon_index']).reset_index(drop=True)
dfConcat = cudf.concat([lGather, rGather], axis=1)
dfConcat['distPred'] = False
@cuda.jit
def distPredFunc(lX, lY, rX, rY, out, dist):
i = cuda.grid(1)
if i < lX.shape[0]:
dX = lX[i] - rX[i]
dY = lY[i] - rY[i]
dSquare = (dX * dX) + (dY * dY)
out[i] = dSquare < (dist * dist)
numbaTime = 0.0
if dist > 0.0:
startTime = time.time()
distPredFunc.forall(dfConcat.shape[0])(dfConcat[lName + 'X'],
dfConcat[lName + 'Y'],
dfConcat[rName + 'X'],
dfConcat[rName + 'Y'],
dfConcat['distPred'], dist)
endTime = time.time()
numbaTime = endTime - startTime
dfConcat = dfConcat[dfConcat['distPred']]
return (dfConcat, numbaTime)
def get_updated_df_quadtree_pip(lat, lon, nodes_df):
min_x, min_y, max_x, max_y = (nodes_df["x"].min(),
nodes_df["y"].min(),
nodes_df["x"].max(),
nodes_df["y"].max())
max_depth = 6
min_size = 50
scale = max(max_x - min_x, max_y - min_y) // (1 << max_depth)
point_indices, quadtree = cuspatial.quadtree_on_points(
nodes_df.x,
nodes_df.y,
min_x,
max_x,
min_y,
max_y,
scale,
max_depth,
min_size,
)
poly_offsets, ring_offsets = cudf.Series([0], index=["selection"]),[0]
poly_bboxes = cuspatial.polygon_bounding_boxes(
poly_offsets, ring_offsets, lat, lon,
)
intersections = cuspatial.join_quadtree_and_bounding_boxes(
quadtree, poly_bboxes, min_x, max_x, min_y, max_y, scale, max_depth,
)
polygons_and_points = cuspatial.quadtree_point_in_polygon(
intersections,
quadtree,
point_indices,
nodes_df.x,
nodes_df.y,
poly_offsets,
ring_offsets,
lat,
lon,
)
return nodes_df.loc[polygons_and_points.point_index]
def test_quadtree_point_in_polygon_small(dtype):
x_min = 0
x_max = 8
y_min = 0
y_max = 8
scale = 1
max_depth = 3
min_size = 12
points_x = small_points_x.astype(dtype)
points_y = small_points_y.astype(dtype)
poly_points_x = small_poly_xs.astype(dtype)
poly_points_y = small_poly_ys.astype(dtype)
point_indices, quadtree = cuspatial.quadtree_on_points(
points_x,
points_y,
x_min,
x_max,
y_min,
y_max,
scale,
max_depth,
min_size,
)
poly_bboxes = cuspatial.polygon_bounding_boxes(
small_poly_offsets,
small_ring_offsets,
poly_points_x,
poly_points_y,
)
intersections = cuspatial.join_quadtree_and_bounding_boxes(
quadtree,
poly_bboxes,
x_min,
x_max,
y_min,
y_max,
scale,
max_depth,
)
polygons_and_points = cuspatial.quadtree_point_in_polygon(
intersections,
quadtree,
point_indices,
points_x,
points_y,
small_poly_offsets,
small_ring_offsets,
poly_points_x,
poly_points_y,
)
cudf.testing.assert_frame_equal(
polygons_and_points,
cudf.DataFrame({
"polygon_index":
cudf.Series(
[3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3],
dtype=np.uint32,
),
"point_index":
cudf.Series(
[
28,
29,
30,
31,
32,
33,
34,
35,
45,
46,
47,
48,
49,
50,
51,
52,
54,
62,
60,
],
dtype=np.uint32,
),
}),
)