python_prtree
python_prtree is a python/c++ implementation of the Priority R-Tree (see references below), an alternative to R-Tree. The supported futures are as follows:
- Construct a Priority R-Tree (PRTree) from an array of rectangles.
PRTree2D
, PRTree3D
and PRTree4D
(2D, 3D and 4D respectively)
insert
and erase
- The
insert
method can be passed pickable Python objects instead of int64 indexes.
query
and batch_query
batch_query
is parallelized by std::thread
and is much faster than the query
method.- The
query
method has an optional keyword argument return_obj
; if return_obj=True
, a Python object is returned.
rebuild
- It improves performance when many insert/delete operations are called since the last rebuild.
- Note that if the size changes more than 1.5 times, the insert/erase method also performs
rebuild
.
This package is mainly for mostly static situations where insertion and deletion events rarely occur.
Installation
You can install python_prtree with the pip command:
pip install python-prtree
If the pip installation does not work, please git clone clone and install as follows:
pip install -U cmake pybind11
git clone --recursive https://github.com/atksh/python_prtree
cd python_prtree
python setup.py install
Examples
import numpy as np
from python_prtree import PRTree2D
idxes = np.array([1, 2])
rects = np.array([[0.0, 0.0, 1.0, 0.5],
[1.0, 1.5, 1.2, 3.0]])
prtree = PRTree2D(idxes, rects)
q = np.array([[0.5, 0.2, 0.6, 0.3],
[0.8, 0.5, 1.5, 3.5]])
result = prtree.batch_query(q)
print(result)
prtree.insert(3, np.array([1.0, 1.0, 2.0, 2.0]))
q = np.array([[0.5, 0.2, 0.6, 0.3],
[0.8, 0.5, 1.5, 3.5]])
result = prtree.batch_query(q)
print(result)
prtree.erase(2)
result = prtree.batch_query(q)
print(result)
print(prtree.query([0.5, 0.5, 1.0, 1.0]))
print(prtree.query([0.5, 0.5]))
print(prtree.query(0.5, 0.5))
import numpy as np
from python_prtree import PRTree2D
objs = [{"name": "foo"}, (1, 2, 3)]
rects = np.array([[0.0, 0.0, 1.0, 0.5],
[1.0, 1.5, 1.2, 3.0]])
prtree = PRTree2D()
for obj, rect in zip(objs, rects):
prtree.insert(bb=rect, obj=obj)
result = prtree.query((0, 0, 1, 1))
print(result)
result = prtree.query((0, 0, 1, 1), return_obj=True)
print(result)
The 1d-array batch query will be implicitly treated as a batch with size = 1.
If you want 1d result, please use query
method.
result = prtree.query(q[0])
print(result)
result = prtree.batch_query(q[0])
print(result)
You can also erase(delete) by index and insert a new one.
prtree.erase(1)
prtree.insert(3, np.array([0.3, 0.1, 0.5, 0.2]))
You can save and load a binary file as follows.
prtree.save('tree.bin')
prtree = PRTree('tree.bin')
prtree = PRTree()
prtree.load('tree.bin')
Note that cross-version compatibility is NOT guaranteed, so please reconstruct your tree when you update this package.
Performance
Construction
2d
3d
Query and batch query
2d
3d
Delete and insert
2d
3d
New Features and Changes
python-prtree>=0.5.8
- The insert method has been improved to select the node with the smallest mbb expansion.
- The erase method now also executes rebuild when the size changes by a factor of 1.5 or more.
python-prtree>=0.5.7
python-prtree>=0.5.3
- Add compression for pickled objects.
python-prtree>=0.5.2
You can use pickable Python objects instead of int64 indexes for insert
and query
methods:
python-prtree>=0.5.0
- Changed the input order from (xmin, xmax, ymin, ymax, ...) to (xmin, ymin, xmax, ymax, ...).
- Added rebuild method to build the PRTree from scratch using the already given data.
- Fixed a bug that prevented insertion into an empty PRTree.
python-prtree>=0.4.0
Reference
The Priority R-Tree: A Practically Efficient and Worst-Case Optimal R-Tree
Lars Arge, Mark de Berg, Herman Haverkort, and Ke Yi
Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD '04), Paris, France, June 2004, 347-358. Journal version in ACM Transactions on Algorithms.
author's page