Description:
geoindexrs 0.1.0
geoindex-rs
Fast, memory-efficient 2D spatial indexes for Python.
API
KDTree
KDTree.from_interleaved
Construct an KDTree from a 2D numpy array of x and y. This must have two dimensions, with the second dimension having length two.
KDTree.from_separated
Construct an KDTree from two separated 1D numpy arrays of x and y. Each array must have one dimension and both arrays must have the same length.
KDTree.range
Search the index for items within a given bounding box.
Arguments:
min_x: float
min_y: float
max_x: float
max_y: float
Returns indices of found items
KDTree.within
Search the index for items within a given radius.
qx (float): x value of query point
qy (float): y value of query point
r (float): radius
Returns indices of found items
RTree
RTree.from_interleaved
Construct an RTree from a 2D numpy array of minx, miny, maxx, maxy. This must have two dimensions, with the second dimension having length four.
For example, the output of shapely.bounds is in this format.
import numpy as np
from geoindex_rs import RTree
geometries = shapely.polygons(...)
bounds = shapely.bounds(geometries)
tree = RTree.from_separated(bounds)
RTree.from_separated
Construct an RTree from four separate 1D numpy arrays of minx, miny, maxx, maxy. Each array must have one dimension and all arrays must have the same length.
import numpy as np
from geoindex_rs import RTree
minx = np.array([-10, ...], dtype=np.float64)
miny = np.array([-20, ...], dtype=np.float64)
maxx = np.array([10, ...], dtype=np.float64)
maxy = np.array([20, ...], dtype=np.float64)
tree = RTree.from_separated(minx, miny, maxx, maxy)
RTree.search
Search within an RTree for the given bounding box. Returns the indices of the input array that match the output.
tree = RTree.from_separated(...)
tree.search(minx, miny, maxx, maxy)
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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