存储scipy网格数据使用的权重以供重用 [英] storing the weights used by scipy griddata for re-use

问题描述

我正在尝试将数据从非结构化网格 M1 插值到另一个非结构化网格 M2 .为此，scipy.interpolate.griddata似乎不错.

但是，我需要多次从 M1 插值到 M2 ，仅更改数据而不更改网格.我猜想，在内部，当从 M1 插值到 M2 时，scipy.interpolate.griddata定义了一些权重系数，这可能是计算中昂贵的部分之一.

因此，我想避免每次都重新计算这些权重.有没有办法做到这一点?即，从一个非结构化网格多次插值到另一个非结构化网格，两者均保持不变，以避免重新计算scipy.interpolate.griddata(或等效值)的内部?

解决方案

一种解决方案是使用 可用于非线性插值. griddata使用LinearNDInterpolator或CloughTocher2DInterpolator.

I am trying to interpolate data from an unstructured mesh M1 to another unstructured mesh M2. For this, scipy.interpolate.griddata seems good.

However, I will need to interpolate many times from M1 to M2, changing only the data not the meshes. I guess that, internally, the scipy.interpolate.griddata defines some weight coefficients when interpolating from M1 to M2 and that this may be one of the expensive parts of the computation.

Therefore, I would like to avoid re-compute these weigths each time. Is there a way to do this? I.e., interpolating many times from one unstructured mesh to another unstructured mesh, both kept constant, avoiding to re-compute the internals of scipy.interpolate.griddata (or equivalent)?

解决方案

A solution is to use the LinearNDInterpolator Scipy function with a pre-computed Delaunay triangulation:

from scipy.spatial import Delaunay
from scipy.interpolate import LinearNDInterpolator

tri = Delaunay(mesh1)  # Compute the triangulation

# Perform the interpolation with the given values:
interpolator = LinearNDInterpolator(tri, values_mesh1)
values_mesh2 = interpolator(mesh2)

mesh1 is a (number of points * dim) array.

Note: CloughTocher2DInterpolator could be used for non-linear interpolation. griddata uses either LinearNDInterpolator or CloughTocher2DInterpolator.

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