interpolation

I'm trying to write a cubic spline interpolation program. I have written the program but, the graph is not coming out correctly. The spline uses natural boundary conditions(second dervative at start/end node are 0). The code is in Matlab and is shown below, clear all %Function to Interpolate k = 10; %Number of Support Nodes-1 xs(1) = -1; for j = 1:k xs(j+1) = -1 +2*j/k; %Support Nodes(Equidistant) end; fs = 1./(25.*xs.^2+1); %Support Ordinates x = [-0.99:2/(2*k):0.99]; %Places to Evaluate Function fx = 1./(25.*x.^2+1); %Function Evaluated at x %Cubic Spline Code(Coefficients to Calculate 2nd

Note: I asked this question before but it was closed as a duplicate, however, I, along with several others believe it was unduely closed, I explain why in an edit in my original post . So I would like to re-ask this question here again. Does anyone know of a python library that can interpolate between two lines. For example, given the two solid lines below, I would like to produce the dashed line in the middle. In other words, I'd like to get the centreline. The input is a just two numpy arrays of coordinates with size N x 2 and M x 2 respectively. Furthermore, I'd like to know if someone has

I have a list of (x,y) values that are not uniformly spaced. Here is the archive used in this question. I am able to interpolate between the values but what I get are not equispaced interpolating points. Here's what I do: x_data = [0.613,0.615,0.615,...] y_data = [5.919,5.349,5.413,...] # Interpolate values for x and y. t = np.linspace(0, 1, len(x_data)) t2 = np.linspace(0, 1, 100) # One-dimensional linear interpolation. x2 = np.interp(t2, t, x_data) y2 = np.interp(t2, t, y_data) # Plot x,y data. plt.scatter(x_data, y_data, marker='o', color='k', s=40, lw=0.) # Plot interpolated points. plt