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> >
