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Python implementation of Priority R-Tree

Project description


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.


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
cd python_prtree
python install


import numpy as np
from python_prtree import PRTree2D

idxes = np.array([1, 2])

# rects is a list of (xmin, ymin, xmax, ymax)
rects = np.array([[0.0, 0.0, 1.0, 0.5],
                  [1.0, 1.5, 1.2, 3.0]])

prtree = PRTree2D(idxes, rects)

# batch query
q = np.array([[0.5, 0.2, 0.6, 0.3],
              [0.8, 0.5, 1.5, 3.5]])
result = prtree.batch_query(q)
# [[1], [1, 2]]

# You can insert an additional rectangle by insert method,
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)
# [[1], [1, 2, 3]]

# Plus, you can erase by an index.
result = prtree.batch_query(q)
# [[1], [1, 3]]

# Non-batch query is also supported.
print(prtree.query(0.5, 0.5))
# [1]
print(prtree.query((0.5, 0.5)))
# [1]
import numpy as np
from python_prtree import PRTree2D

objs = [{"name": "foo"}, (1, 2, 3)]  # must NOT be unique but pickable
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)

# returns indexes genereted by incremental rule.
result = prtree.query((0, 0, 1, 1))
# [1]

# returns objects when you specify the keyword argment return_obj=True
result = prtree.query((0, 0, 1, 1), return_obj=True)
# [{'name': 'foo'}]

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])
# [1]

result = prtree.batch_query(q[0])
# [[1]]

You can also erase(delete) by index and insert a new one.

prtree.erase(1)  # delete the rectangle with idx=1 from the PRTree

prtree.insert(3, np.array([0.3, 0.1, 0.5, 0.2]))  # add a new rectangle to the PRTree

You can save and load a binary file as follows.

# save'tree.bin')

# load with binary file
prtree = PRTree('tree.bin')

# or defered load
prtree = PRTree()

Note that cross-version compatibility is NOT guaranteed, so please reconstruct your tree when you update this package.







Query and batch query





Delete and insert





New Features and Changes


  • 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.


  • You can use PRTree4D.


  • Add compression for pickled objects.


You can use pickable Python objects instead of int64 indexes for insert and query methods:


  • 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.


  • You can use PRTree3D:


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

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