Skip to main content

Python wrappers around Cell-Tree 2D spatial index

Project description

A module that provides the CellTree data structure as described by Garth and Joy in their 2010 paper:

http://graphics.cs.ucdavis.edu/~joy/NSF-IIS-0916289/Papers/GarthTVCG2011.pdf

This implementation is 2D specific and includes some additions useful to answering one question:

“What is the index of the polygon that contains this point?”

Algorithm Notes

There are two major benefits to this algorithm over other types of BVHs. First is that overlaps in volumes bounded by nodes do not create duplicates, decreasing the memory footprint. Secondly, the tree is balanced by splitting a node along the plane that minimizes a cost function that ‘weighs’ each half. The result is a tree with no duplicates and that becomes increasingly balanced as the number of buckets used when building the tree increases (though this linearly increases build time).

This is a 2D version of the algorithm that is oriented towards maximum lookup speed and immediate answer checking. The basic CellTree does not hold enough information to give conclusive point-in-polygon answers; the best it can do is provide the (short) list of cell bounding boxes that contain the point. Since a point can be within the bounds of two different cells, and it is very possible both children of a parent node will need to be searched, implementing immediate point-in-polygon checks on each cell as they are encountered is highly beneficial, as an early success will avoid all further tree traversal.

Usage Notes

The tree needs certain information to be built:

  1. ‘verts’ - A 2xV numpy array containing x/y coordinates of the V vertices
  2. ‘faces’ - A PxN numpy array containing N arrays of P indices of vertices that describe one ‘face’ or polygon of degree P
  3. ‘num_buckets’ - The # of buckets desired. Must be >= 2 The default is 4. Values higher than 8 begin to provide diminishing returns.
  4. ‘cells_per_leaf’ - The # of polygons per leaf node. The default is 2. Using 1 is possible, but doubles memory footprint for only slightly. faster lookup. If memory is a concern, this value can be increased, but lookup performance will quickly be impacted

IMPORTANT: ‘verts’ and ‘faces’ MUST describe a properly formed unstructured grid. Assume that degenerate (0 area) or overlapping polygons will cause a build failure. If the construction of the tree causes a segfault, this is probably the cause.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cell_tree2d, version 0.3.0
Filename, size File type Python version Upload date Hashes
Filename, size cell_tree2d-0.3.0.tar.gz (126.8 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page