Actually, nevermind, I misread the documentation, Grid is for a regularly-spaced grid of points.
On Saturday, February 27, 2016 at 9:33:06 AM UTC-5, Cedric St-Jean wrote: > > Hi Uwe, > > Have you tried Grid.jl? I haven't tried it, but this example looks like it > might work with a non-uniform grid. > > # Let's define a quadratic function in one dimension, and evaluate it on an > evenly-spaced grid of 5 points: > c = 2.3 # center > a = 8.1 # quadratic coefficient > o = 1.6 # vertical offset > qfunc = x -> a*(x-c).^2 + o > xg = Float64[1:5] > y = qfunc(xg) > yi = InterpGrid(y, BCnil, InterpQuadratic) > > > > > On Saturday, February 27, 2016 at 9:21:53 AM UTC-5, Uwe Fechner wrote: >> >> Hello, >> >> I am trying to port the following function from python to julia: >> >> # -*- coding: utf-8 -*- >> from scipy.interpolate import InterpolatedUnivariateSpline >> import numpy as np >> from pylab import plot >> >> P_NOM = [1.5, 2.2, 3.7, 5.6, 7.5, 11.2, 14.9] >> ETA = [93., 94., 94., 95., 95., 95.5, 95.5] >> >> calc_eta = InterpolatedUnivariateSpline(P_NOM, ETA, k=1) >> >> # plotting code, only for testing >> if __name__ == "__main__": >> X = np.linspace(1.5, 14.9, 1024, endpoint=True) >> ETA = [] >> for alpha in X: >> eta = calc_eta(alpha) >> ETA.append(eta) >> plot(X, ETA) >> >> The resulting plot is shown at the end of this posting. >> >> How can I port this to Julia? >> >> I am trying to use the package "Interpolations.jl", but I do not see any >> example, that shows the interpolation on a non-uniform grid. >> >> For now I need only linear interpolation, but I want to use B-Splines >> later. >> >> Any hint appreciated! >> >> Uwe Fechner >> >> >> >> <https://lh3.googleusercontent.com/-8OofwCQWohg/VtGwKR-1BOI/AAAAAAAAAQI/UTLksCCMIPo/s1600/LinearInterpolation.png> >> >
