Plot(dense, interpolate(dense), label = 'InterpolatedUnivariateSpline', linewidth = 2) Interpolate = ip.InterpolatedUnivariateSpline(sparse, fsparse) Scipy interp1d and matlab interp1 Ask Question Asked 9 years, 5 months ago Modified 9 years, 5 months ago Viewed 19k times 9 The following are the inputs for my interpolation: x -1.01, 5.66, 5.69, 13.77, 20.89 y 0.28773, 1.036889, 1.043178, 1.595322, 1. #Plot the different interpolation results Plot(dense, fdense, label = 'True function') Plot(sparse, fsparse, label = 'Sparse samples', linestyle = 'None', marker = 'o') #Plot the sparse samples and the true function #Define function and calculate dependent variable
#Python matlab interp1 code
The code to reproduce the figure is shown below. It's like having a recipe (X and V), and you want to make a variation of it (Xq), and 'interp1' is your personal chef, giving you the result (Vq). The figure below illustrates the difference. Here's the basic syntax: Vq interp1( X, V, Xq) In this equation, 'X' and 'V' are your known data points, 'Xq' are the points you want to interpolate, and 'Vq' are the interpolated values.
The former smoothes the data whereas the latter is a more conventional interpolation method and reproduces the results expected from interp1d. Vector xq contains the coordinates of the query points. Vector x contains the sample points, and v contains the corresponding values, v ( x ). UnivariateSpline is a 'one-dimensional smoothing spline fit to a given set of data points' whereas InterpolatedUnivariateSpline is a 'one-dimensional interpolating spline for a given set of data points'. Description example vq interp1 (x,v,xq) returns interpolated values of a 1-D function at specific query points using linear interpolation. Use InterpolatedUnivariateSpline instead: f = InterpolatedUnivariateSpline(row1, row2)
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