natinterp3d 0.1.0

Natural Neighbor Interpolation in 3D

Natural Neighbor Interpolation in 3D

This is a Python package for 3D natural neighbor interpolation (Sibson interpolation).

Natural neighbor interpolation is a form of scattered data interpolation, where you have a set of sample values of a function at arbitrary locations in 3D space (let's call the locations keys), and you want to interpolate the function value at other points (let's call them queries).

Specifically, in natural neighbor interpolation, the interpolated value is a weighted average of the function values of the query point's "natural neighbors", which are the vertices of the Voronoi cell that contains the query point. The weights are proportional to the volumes of the sub-Voronoi cells corresponding to each natural neighbor that we would obtain if the query point was inserted into the Voronoi tetrahedralization.

This package uses Cython to wrap my modified version of Ross Hemsley's interpolate3d. The modifications are:

• parallelization via OpenMP
• option to extract the natural neighbor weights (Sibson coordinates) directly (the original version only gives the final interpolated value, but not the weights)
• k-d tree for faster search for the containing simplex of an inserted point

Usage

Simplest is to call natinterp3d.interpolate(queries, keys, values) or natinterp3d.get_weights(queries, keys):

import natinterp3d
import numpy as np

# The positions of the data points where the function values are known
keys = np.array([[x1, y1, z1], [x2, y2, z2], ...])

# The values can also be a 2D array of shape (N, values_dim) with D dimensional vectors as values at each data point
values = np.array([v1, v2, v3, ...])  

# The positions where we want to interpolate the function values
queries = np.array([[qx1, qy1, qz1], [qx2, qy2, qz2], ...])

# Returns either [num_queries] or [num_queries, values_dim], the array of interpolated values
interpolated_values = natinterp3d.interpolate(queries, keys, values)

# or get the interpolation weights as a sparse matrix of size [num_queries, num_keys] (scipy.sparse.csr_matrix)
weights = natinterp3d.get_weights(queries, keys)

For more control, e.g., if you want to interpolate queries and/or values on the same key positions, you can use the natinterp3d.Interpolator class as:

import natinterp3d

keys = np.array([[x1, y1, z1], [x2, y2, z2], ...])
interpolator = natinterp3d.Interpolator(keys)

values = np.array([v1, v2, v3, ...])  # or a 2D array of shape (N, values_dim)
queries = np.array([[qx1, qy1, qz1], [qx2, qy2, qz2], ...])
interpolated_values = interpolator.interpolate(queries, values)

# or:
weights = interpolator.get_weights(queries)

Multithreaded computation is automatically enabled. To customize, use the argument parallel=True/False and num_threads in interpolate or get_weights. With num_threads=None (default), the number of threads is automatically determined based on the available CPU cores.

License

GNU GPL v3