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【资源】如何绘制高水平三维图像，by www.mathworks.com
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Introduction Gridfit is a surface modeling tool, fitting a surface of the form z(x,y) to scattered (or regular) data. As it is not an interpolant, it allows the existence of replicates in your data with no problems. What do I mean by an "interpolant"? An interpolant is a code that is designed to always exactly predict all supplied data. Griddata and interp1 are examples of interpolants. Gridfit is more accurately described as an approximant. It produces a surface which represents the behavior of the supplied data as closely as possible, allowing for noise in the data and for replicate data. A nice feature of gridfit is its ability to smoothly extrapolate beyond the convex hull of your data, something that griddata cannot do (except by the slow, memory intensive 'v4' method.) Finally, gridfit does its extrapolation in a well behaved manner, unlike how polynomial models (for example) might behave in extrapolation. Gridfit also allows you to build a gridded surface directly from your data, rather than interpolating a linear approximation to a surface from a delaunay triangulation. This document describes the ideas behind gridfit. [ Last edited by nono2009 on 2009-9-23 at 16:13 ] |
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