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/AAAAAAAAAQI/UTLksCCMIPo/s1600/LinearInterpolation.png> >>>>>>>> >>>>>>> >>>>> >
