Hello, I am looking to determine multiple regression lines from a single power spectral dataset (log power vs radial wavenumber), and was wondering if it is feasible in Scilab to compute something similar to the attached plot?
I did locate a Matlab code for finding a turning point in a plot and perhaps this may be a route to find the change in curvature? clear clf() //to find the "knee" dt = 0.01; t = 0:dt:1; y = exp(-10*t); //Compute first and second derivatives by finite differencing (centred) //yp = nan(size(y)); //ypp = nan(size(y)); yp=%nan*y; // similar behaviour to Matlab nan(size(y)) ypp=%nan*y; yp(2:$-1) = (y(3:$) - y(1:$-2))/(2*dt); // first derivative ypp(2:$-1) = (y(3:$) + y(1:$-2) - 2*y(2:$-1)) / (dt^2); // second derivative //Compute the curvature k = abs(ypp) ./ (1 + yp.^2).^1.5 //Find the maximum curvature point and plot it on the curve [kmax, idx] = max(k); plot(t, y, 'b', t(idx), y(idx), 'ro') //plot the curvature figure() plot(t, k) Any pointers or ideas would be welcome. Thanks Lester <http://mailinglists.scilab.org/file/t495709/Capture_Radial-averaged-power_spectra.jpg> -- Sent from: http://mailinglists.scilab.org/Scilab-users-Mailing-Lists-Archives-f2602246.html _______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users