A is the array of values you want to interpolate. x are the positions. Effectively, `A = [f(xx) for xx in x]` assuming you want to interpolate the function `f`.
--Tim On Wednesday, March 09, 2016 02:25:24 PM jmarcellopere...@ufpi.edu.br wrote: > I did not understand how to use the "A". "A" is a vector tuple ...? > > Em domingo, 28 de fevereiro de 2016 18:32:23 UTC-3, Tomas Lycken escreveu: > > Gridded here is the *interpolation scheme*, as opposed to (implicitly > > uniform) BSpline interpolation; it’s simply the way Interpolations.jl > > lets you specify which algorithm you want to use. Compare the following > > incantations: > > > > itp = interpolate(A, BSpline(Linear()), OnGrid()) # linear b-spline > > interpolation; x is implicitly 1:length(A) itp = interpolate((x,), A, > > Gridded(Linear())) # linear, "gridded" interpolation; x can be irregular > > itp = interpolate(A, BSpline(Cubic(Flat())), OnCell()) # cubic b-spline > > with free boundary condition > > > > That is; you give the data to be interpolated (A and, where applicable, x) > > as well as one or more arguments specifying the algortihm you want to use > > (for details on OnGrid/OnCell, see the readme). Gridded is what just > > we’ve called the family of algorithms that support irregular grids. > > > > This is all documented in the readme, but documentation is not just about > > putting the information in writing - it’s also about putting it in the > > correct place, where it seems obvious to look for it. If you have > > suggestions on how this information can be made easier to find and digest, > > please file an issue or PR. All feedback is most welcome! :) > > > > // T > > > > On Sunday, February 28, 2016 at 7:37:42 PM UTC+1, Uwe Fechner wrote: > > > > Hello, > > > >> thanks for your explanations. > >> > >> Nevertheless I like the syntax of the package Dierckx much more. > >> The expression Gridded(Linear()) is very confusing for me. What is > >> gridded, > >> in this example? > >> > >> Furthermore the Readme file of Interpolations is missing the information, > >> how > >> to use it for the given problem. > >> > >> Best regards: > >> > >> Uwe > >> > >> On Saturday, February 27, 2016 at 4:57:07 PM UTC+1, Matt Bauman wrote: > >>> 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/AAAAAAA > >>>>>>>> AAQI/UTLksCCMIPo/s1600/LinearInterpolation.png>>>>>> > >>>>>
