Problem
interp_weights can be slow for 3D interpolation for grids with large aspect ratios (x/y spans of tens of kilometers vs. z spans of tens to hundreds of meters). This flat point cloud is difficult for scipy.spatial.Delaunay to tessellate efficiently.
Fix
Scale the source and destination coordinates so each dimension has the same range before building the tessellation. This can follow the same approach as scipy.interpolate.griddata with rescale=True. The interpolation results would be unchanged because the same scaling is applied to both source and destination points.
Problem
interp_weightscan be slow for 3D interpolation for grids with large aspect ratios (x/y spans of tens of kilometers vs. z spans of tens to hundreds of meters). This flat point cloud is difficult forscipy.spatial.Delaunayto tessellate efficiently.Fix
Scale the source and destination coordinates so each dimension has the same range before building the tessellation. This can follow the same approach as
scipy.interpolate.griddatawithrescale=True. The interpolation results would be unchanged because the same scaling is applied to both source and destination points.