A fork that adds 3D compatibility to the R-Tree implementation "Pyrtree" by Rhoana (https://github.com/Rhoana/pyrtree).
Taken from https://code.google.com/archive/p/pyrtree/source/default/source (No way to automatically move the versioned source code from code.google.com, so this is copied)
Here's the original project description (https://code.google.com/archive/p/pyrtree/):
This is a pure python implementation of an RTree spatial index -- with no C library dependencies while retaining decent performance.
I wrote it with the following reasons in mind: * Pure cross-platform python; no C library dependencies. * Access to internal nodes in the tree, allowing for custom traversal strategies. * BSD license
The current version targets in-memory insert-then-query workloads -- updates and persistence are not implemented yet -- and performs quite well at doing so. Besides those limitations, the current version only implements a 2-dimensional index. I'm not sure if this will change: R-tree performance drops quickly as you add dimensions, and I anticipate the largest uses of this library will be by GIS developers, where two-dimensional datasets are king. Planned enhancement: saving and loading the index to disk.
from pyrtree import RTree,Rect
... inserting:
t = RTree()
t.insert(some_kind_of_object,Rect(min_x,min_y,max_x,max_y))
... querying:
point_res = t.query_point( (x,y) )
rect_res = t.query_rect( Rect(x,y,xx,yy) )
IMPORTANT: Query results include intermediate nodes which are invalidated as they get iterated over: so if you only want your leaf objects back: (a near-future TODO: a convenience wrapper) real_point_res = [r.leaf_obj() for r in t.query_point( (x,y) ) if r.is_leaf()] ```
An R-tree is a spatial index over axis-aligned rectangles. (The sides of the rectangles are parallel to the X and Y axes.) They're used heavily in GIS as a way to index geospatial data.
They take the form of trees of rectangles where each node's rectangle contains the rectangle of all its children. The challenge is in deciding how to group rectangles in order to arrive at a well-balanced tree; pyrtree uses k-means clustering to do this. (S. Brakatsoulas, D. Pfoser, and Y. Theodoridis. "Revisiting R-Tree Construction Principles", Advances in Databases and Information Systems 2435 (2002): 17-24)