Expand description
Spatial interpolation methods
This module provides various methods for interpolating scattered data in 2D and 3D space. These interpolation methods are useful for reconstructing continuous fields from discrete sample points, filling gaps in data, and generating smooth surfaces from irregularly sampled points.
The available interpolation methods include:
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Natural Neighbor interpolation: Local method that creates a weighted average of neighboring points based on their Voronoi cells. Produces smooth surfaces that respect the local structure of the data.
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Radial Basis Function (RBF) interpolation: Uses radial basis functions to create a global interpolation that can represent complex surfaces. Various kernel functions can be selected to control the smoothness and locality of the interpolation.
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Inverse Distance Weighting (IDW): Simple interpolation method that weights neighboring points by the inverse of their distance raised to a power. Fast but can create “bull’s-eye” patterns around sample points.
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Kriging (planned): Geostatistical method that accounts for the spatial correlation of data. Produces an interpolated surface along with an estimate of the prediction error.
Re-exports§
pub use idw::IDWInterpolator;pub use natural_neighbor::NaturalNeighborInterpolator;pub use rbf::RBFInterpolator;pub use rbf::RBFKernel;
Modules§
- idw
- Inverse Distance Weighting interpolation
- natural_
neighbor - Natural Neighbor interpolation methods
- rbf
- Radial Basis Function interpolation