There are several things going on every time you make a call to scipy.interpolate.griddata: First, a call to sp.spatial.qhull.Delaunay is made to triangulate the irregular grid coordinates. Then, for each point in the new grid, the triangulation is searched to find in which triangle (actually, in which simplex, which in your 3D case will be … Read more
I was originally going to show you how much of a difference it makes for 2d interpolation if your input data are oriented along the coordinate axes rather than in some general direction, but it turns out that the result would be even messier than I had anticipated. I tried using a random dataset over … Read more
You need to use interp1 with ‘linear’ interpolation method: >> v = [1 2 3 4 10]; >> newNum = 13; % new number of elements in the “buffed” vector >> iv = interp1( linspace(0,1,numel(v)), v, linspace(0,1,newNum) ) iv = 1.0000 1.3333 1.6667 2.0000 2.3333 2.6667 3.0000 3.3333 3.6667 4.0000 6.0000 8.0000 10.0000
Here is an example using scatteredInterpolant: %# get some 2D matrix, and plot as surface A = peaks(15); subplot(121), surf(A) %# create interpolant [X,Y] = meshgrid(1:size(A,2), 1:size(A,1)); F = scatteredInterpolant(X(:), Y(:), A(:), ‘linear’); %# interpolate over a finer grid [U,V] = meshgrid(linspace(1,size(A,2),50), linspace(1,size(A,1),50)); subplot(122), surf(U,V, F(U,V)) Note that you can evaluate the interpolant object at … Read more
The other answers give you linear interpolations — these don’t really work for complex, nonlinear data. You want a spline fit, (spline interpolation) I believe. Spline fits describe regions of the data using a set of control points from the data, then apply a polynomial interpolation between control points. More control points gives you a … Read more
Your closed path can be considered as a parametric curve, x=f(u), y=g(u) where u is distance along the curve, bounded on the interval [0, 1). You can use scipy.interpolate.splprep with per=True to treat your x and y points as periodic, then evaluate the fitted splines using scipy.interpolate.splev: import numpy as np from scipy import interpolate … Read more
In the same ticket you have linked, there is an example implementation of what they call tensor product interpolation, showing the proper way to nest recursive calls to interp1d. This is equivalent to quadrilinear interpolation if you choose the default kind=’linear’ parameter for your interp1d‘s. While this may be good enough, this is not linear … Read more
Sure! There are two options that do different things but both exploit the regularly-gridded nature of the original data. The first is scipy.ndimage.zoom. If you just want to produce a denser regular grid based on interpolating the original data, this is the way to go. The second is scipy.ndimage.map_coordinates. If you’d like to interpolate a … Read more
My favorite resource for audio interpolating (especially in resampling applications) is Olli Niemitalo’s “Elephant” paper. I’ve used a couple of these and they sound terrific (much better than a straight cubic solution, which is relatively noisy). There are spline forms, Hermite forms, Watte, parabolic, etc. And they are discussed from an audio point-of-view. This is … Read more
You can pass anchored offsets to resample, among other options they cover this case. For example the weekly frequency from Monday: ts.resample(‘W-MON’)