grid_spline_6-Cubic_Spline_Approximation

Cubic Spline Approximation

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contributed at 2018-11-06

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This tool approximates irregular scalar 2D data in specified points using C1-continuous bivariate cubic spline.Minimal Number of Points: minimal number of points locally involved in spline calculation (normally = 3)Maximal Number of Points:npmax: maximal number of points locally involved in spline calculation (required > 10, recommended 20 < npmax < 60)Tolerance: relative tolerance multiple in fitting spline coefficients: the higher this value, the higher degree of the locally fitted spline (recommended 80 < k < 200)Points per square: average number of points per square (increase if the point distribution is strongly non-uniform to get larger cells)Author: Pavel Sakov, CSIRO Marine ResearchPurpose: 2D data approximation with bivariate C1 cubic spline. A set of library functions + standalone utility.Description: See J. Haber, F. Zeilfelder, O.Davydov and H.-P. Seidel, Smooth approximation and rendering of large scattered data sets, in 'Proceedings of IEEE Visualization 2001' (Th.Ertl, K.Joy and A.Varshney, Eds.), pp.341-347, 571, IEEE Computer Society, 2001.www.uni-giessen.de/www-Numerische-Mathematik/davydov/VIS2001.ps.gzwww.math.uni-mannheim.de/~lsmath4/paper/VIS2001.pdf.gz

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SAGA (2018). grid_spline_6-Cubic_Spline_Approximation, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/dc33ddb2-43ce-4ef2-8673-204fc4afe800
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contributed at 2018-11-06

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