This code implements the PR-Quadtree data structure, which is useful for indexing spatial data. The data structure has defined boundaries and a capacity for each node. Points are inserted into the nodes, and once the capacity is exceeded, the node decomposes into four children and its points are then reinserted into these new leaf nodes

There are a few ways to interact with the PR-Quadtree. A point can be inserted with a coordinate pair and a value, a query can be performed to determine if a point exists in the quadtree, a point can be deleted from the quadtree, and a preorder traversal can be performed on the quadtree.

It also includes a visualizer tool built on matplotlib

Below are some examples from the visualizer. This quadtree has a capacity of 1 and a width of 10. Below is a visualization of the tree after points are inserted:

Then, after the green and red points are deleted: It should be noted that the colors are not stored in the points. They are assigned when the visualization is created based on the order of insertion