Interpolations is very similar, but it currently only supports linear and nearest-neighbor schemes for gridded interpolations:
using Interpolations itp = interpolate((P_NOM,), ETA, Gridded(Linear())) # You pass the x-values as a tuple, since this generalizes to multi-dimensional coordinates println(itp[3.5]) x = linspace(1.5, 14.9, 1024) y = itp[x] plot(x,y) On Saturday, February 27, 2016 at 10:10:28 AM UTC-5, Uwe Fechner wrote: > > Thanks. The following code works: > > using Dierckx > > 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 = Spline1D(P_NOM, ETA, k=1) > > println(calc_eta(3.5)) > > Nevertheless I would be interested how to do that with Interpolations.jl. > Sometimes you don't have Fortran available. > > Best regards: > > Uwe > > On Saturday, February 27, 2016 at 3:58:11 PM UTC+1, Yichao Yu wrote: >> >> >> >> On Sat, Feb 27, 2016 at 9:40 AM, Uwe Fechner <uwe.fec...@gmail.com> >> wrote: >> >>> Hello, >>> >>> I don't think, that this works on a non-uniform grid. The array xg is >>> evenly spaced, and it >>> is NOT passed to the function InterpGrid. >>> >>> >> I've recently tried Dierckx which support non-uniform interpolation. I >> only need very basic functions so I don't know if it has all the >> flexibility you need but it's probably worth a look if you haven't. >> >> >>> Uwe >>> >>> >>> On Saturday, February 27, 2016 at 3:33:06 PM UTC+1, 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> >>>>> >>>> >>
