Package: numba-celltree
Finding your way around in an unstructured meshes is difficult. Numba Celltree
provides methods for searching for points, lines, boxes, and cells (convex
polygons) in a two dimensional unstructured mesh.importnumpyasnpfromnumba_celltreeimportCellTree2dvertices,faces=demo.generate_disk(5,5)vertices+=1.0vertices*=5.0tree=CellTree2d(vertices,faces,-1)# Intersection with two trianglestriangle_vertices=np.array([[5.0,3.0],[7.0,3.0],[7.0,5.0],[0.0,6.0],[4.0,4.0],[6.0,10.0],])triangles=np.array([[0,1,2],[3,4,5]])tri_i,cell_i,area=tree.intersect_faces(triangle_vertices,triangles,-1)# Intersection with two linesedge_coords=np.array([[[0.0,0.0],[10.0,10.0]],[[0.0,10.0],[10.0,0.0]],])edge_i,cell_i,intersections=tree.intersect_edges(edge_coords)Installationpip install numba_celltreeDocumentationBackgroundThis package provides the cell tree data structure described in:Garth, C., & Joy, K. I. (2010). Fast, memory-efficient cell location in
unstructured grids for visualization. IEEE Transactions on Visualization and
Computer Graphics, 16(6), 1541-1550.This paper can be readhere.The tree building code is a direction translation of the (public domain)C++
codeby Jay Hennen, which is available in thecell_tree2dpython package. This
implementation is currently specialized for two spatial dimensions, but
extension to three dimensions is relatively straightforward. Another (BSD
licensed) implementation which supports three dimensions can be found in VTK’sCellTreeLocator.The cell tree of thecell_tree2dcurrently only locates points. I’ve added
additional methods for locating and clipping lines and convex polygons.Just-In-Time Compilation: NumbaThis package relies onNumbato just-in-time
compile Python code into fast machine code. This has the benefit of keeping
this package a “pure” Python package, albeit with a dependency on Numba.With regards to performance:Building the tree is marginally faster compared to the C++ implementation
(~15%).Serial point queries are somewhat slower (~50%), but Numba’s automatic
parallellization speeds things up significantly (down to 20% runtime on my 4
core laptop). (Of course the C++ code can be parallellized in the same manner
withpragma omp parallel for.)The other queries have not been compared, as the C++ code lacks the
functionality.In traversing the tree, recursion in Numba appears to be less performant than
maintaining a stack of nodes to traverse. The VTK implementation also uses
a stack rather than recursion.Numba (like itsLLVM JIT sister Julia) does not
allocate small arrays on the stack automatically, like C++ and Fortran
compilers do. However, it can be donemanually. This cuts down runtimes by
at least a factor 2, more so in parallel. However, these stack allocated
arrays work only in the context of Numba. They must be disabled when running
in uncompiled Python – there is some code innumba_celltree.utilswhich
takes care of this.All methods have been carefully written to keep heap allocations to a
minimum. This also helps in parallellization, as at the time of writing
Numba’s lists arenot thread safe.  Unfortunately, this means we
have to query twice when the number of answers is unknown: once to count,
after which we can allocate, then another time to store the answers. Since
parallelization results in speedups over a factor 2, this still results in a
net gain.To debug, set the environmental variableNUMBA_DISABLE_JIT=1. Re-enable by
settingNUMBA_DISABLE_JIT=0.exportNUMBA_DISABLE_JIT=1In Windows Command Prompt:set NUMBA_DISABLE_JIT=1In Windows Powershell:$env:NUMBA_DISABLE_JIT=1In Python itself:importosos.environ["NUMBA_DISABLE_JIT"]="1"This must be done before importing the package to have effect.