Use makima to interpolate a cosine curve over unevenly spaced sample points.

x = [0 1 2.5 3.6 5 7 8.1 10];
y = cos(x);
xq = 0:.25:10;
yq = makima(x,y,xq);
plot(x,y,'o',xq,yq,'--')

With oscillatory functions, the Akima algorithm flattens the curve near local extrema. To compensate for this flattening, you can add more sample points near the local extrema.

Add sample points near $\mathit{x}=6.5$ and $\mathit{x}=9$ and replot the interpolation.

x = [0 1 2.5 3.6 5 6.5 7 8.1 9 10];
y = cos(x);
xq = 0:.25:10;
yq = makima(x,y,xq);
plot(x,y,'o',xq,yq,'--')

Compare the interpolation results produced by spline, pchip, and makima for two different data sets. These functions all perform different forms of piecewise cubic Hermite interpolation. Each function differs in how it computes the slopes of the interpolant, leading to different behaviors when the underlying data has flat areas or undulations.

Compare the interpolation results on sample data that connects flat regions. Create vectors of x values, function values at those points y, and query points xq. Compute interpolations at the query points using spline, pchip, and makima. Plot the interpolated function values at the query points for comparison.

x = -3:3; 
y = [-1 -1 -1 0 1 1 1]; 
xq1 = -3:.01:3;
p = pchip(x,y,xq1);
s = spline(x,y,xq1);
m = makima(x,y,xq1);
plot(x,y,'o',xq1,p,'-',xq1,s,'-.',xq1,m,'--')
legend('Sample Points','pchip','spline','makima','Location','SouthEast')

In this case, pchip and makima have similar behavior in that they avoid overshoots and can accurately connect the flat regions.

Perform a second comparison using an oscillatory sample function.

x = 0:15;
y = besselj(1,x);
xq2 = 0:0.01:15;
p = pchip(x,y,xq2);
s = spline(x,y,xq2);
m = makima(x,y,xq2);
plot(x,y,'o',xq2,p,'-',xq2,s,'-.',xq2,m,'--')
legend('Sample Points','pchip','spline','makima')

When the underlying function is oscillatory, spline and makima capture the movement between points better than pchip, which is aggressively flattened near local extrema.

Create vectors for the sample points x and values at those points y. Use makima to construct a piecewise polynomial structure for the data.

x = -5:5;
y = [1 1 1 0 0 1 1 2 2 2 2];
pp = makima(x,y)

pp = struct with fields:
      form: 'pp'
    breaks: [-5 -4 -3 -2 -1 0 1 2 3 4 5]
     coefs: [10x4 double]
    pieces: 10
     order: 4
       dim: 1

The structure contains the information for 10 polynomials of order 4 that span the data. pp.coefs(i,:) contains the coefficients for the polynomial that is valid in the region defined by the breakpoints [breaks(i) breaks(i+1)].

Use the structure with ppval to evaluate the interpolation at several query points, and then plot the results. In regions with three or more constant points, the Akima algorithm connects the points with a straight line.

xq = -5:0.2:5;
m = ppval(pp,xq);
plot(x,y,'o',xq,m,'-.')
ylim([-0.2 2.2])