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Yy = griddata((X.flatten(), Y.flatten()),V.flatten(),(data_input,data_input),method='linear')īut from what I was seeing the griddata function is not well implemented, I also tried with np.interp2d but I didn't get the same thing. Xx = griddata((X.flatten(), Y.flatten()),U.flatten(),(data_input,data_input),method='linear') The response surfaces were plotted in Matlab 2019. Since the interpolated function is C 1 continuous, optimal conditions were computed by the gradient-based line search optimization method (Nocedal and Wright, 2006). = np.meshgrid(np.linspace(1, 20, 20), np.linspace(1, 20, 20)) In this work the Natural Neighbor interpolation method was implemented by using the Matlab function scatteredInterpolant. I was testing and in Python what I managed to do is the following: import numpy as np I note the fact that the matrices are actually much larger and are not randomly generated, but they are useful to understand the effect. What it would do would be to perform the interpolation between two matrices and apply them to some input data, a way to call the function would be: data_out = test_interp (rand(10,2))
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The simplified Matlab function would be as follows function data_out = test_interp (data_input)Īux_array = ,1) reshape(V,1) reshape(X,1) reshape(Y,1)] I have searched for more information and found this other entry but I have not been able to adapt the code, so I was wondering if anyone would know. The main problem I have is to find an interpolation function equivalent to Matlab's scatteredInterpolant.
#SCATTEREDINTERPOLANT MATLAB 2012 HOW TO#
I was wondering if anyone would know how to translate a function I have from Matlab to Python and C++.