spline

Cubic spline data interpolation

Description

example

s = spline(x,y,xq) returns a vector of interpolated values s corresponding to the query points in xq. The values of s are determined by cubic spline interpolation of x and y.

example

pp = spline(x,y) returns a piecewise polynomial structure for use by ppval and the spline utility unmkpp.

Examples

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Use spline to interpolate a sine curve over unevenly-spaced sample points.

x = [0 1 2.5 3.6 5 7 8.1 10];
y = sin(x);
xx = 0:.25:10;
yy = spline(x,y,xx);
plot(x,y,'o',xx,yy) Use clamped or complete spline interpolation when endpoint slopes are known. To do this, you can specify the values vector $\mathit{y}$ with two extra elements, one at the beginning and one at the end, to define the endpoint slopes.

Create a vector of data $\mathit{y}$ and another vector with the $\mathit{x}$-coordinates of the data.

x = -4:4;
y = [0 .15 1.12 2.36 2.36 1.46 .49 .06 0];

Interpolate the data using spline and plot the results. Specify the second input with two extra values [0 y 0] to signify that the endpoint slopes are both zero. Use ppval to evaluate the spline fit over 101 points in the interpolation interval.

cs = spline(x,[0 y 0]);
xx = linspace(-4,4,101);
plot(x,y,'o',xx,ppval(cs,xx),'-'); Extrapolate a data set to predict population growth.

Create two vectors to represent the census years from 1900 to 1990 (t) and the corresponding United States population in millions of people (p).

t = 1900:10:1990;
p = [ 75.995  91.972  105.711  123.203  131.669 ...
150.697 179.323  203.212  226.505  249.633 ];

Extrapolate and predict the population in the year 2000 using a cubic spline.

spline(t,p,2000)
ans = 270.6060

Generate the plot of a circle, with the five data points y(:,2),...,y(:,6) marked with o's. The matrix y contains two more columns than does x. Therefore, spline uses y(:,1) and y(:,end) as the endslopes. The circle starts and ends at the point (1,0), so that point is plotted twice.

x = pi*[0:.5:2];
y = [0  1  0 -1  0  1  0;
1  0  1  0 -1  0  1];
pp = spline(x,y);
yy = ppval(pp, linspace(0,2*pi,101));
plot(yy(1,:),yy(2,:),'-b',y(1,2:5),y(2,2:5),'or')
axis equal Use spline to sample a function over a finer mesh.

Generate sine and cosine curves for a few values between 0 and 1. Use spline interpolation to sample the functions over a finer mesh.

x = 0:.25:1;
Y = [sin(x); cos(x)];
xx = 0:.1:1;
YY = spline(x,Y,xx);
plot(x,Y(1,:),'o',xx,YY(1,:),'-')
hold on
plot(x,Y(2,:),'o',xx,YY(2,:),':')
hold off 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.

Input Arguments

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x-coordinates, specified as a vector. The vector x specifies the points at which the data y is given. The elements of x must be unique.

Data Types: single | double

Function values at x-coordinates, specified as a numeric vector, matrix, or array. x and y typically have the same length, but y also can have exactly two more elements than x to specify endslopes.

If y is a matrix or array, then the values in the last dimension, y(:,...,:,j), are taken as the values to match with x. In that case, the last dimension of y must be the same length as x or have exactly two more elements.

The endslopes of the cubic spline follow these rules:

• If x and y are vectors of the same size, then the not-a-knot end conditions are used.

• If x or y is a scalar, then it is expanded to have the same length as the other and the not-a-knot end conditions are used.

• If y is a vector that contains two more values than x has entries, then spline uses the first and last values in y as the endslopes for the cubic spline. For example, if y is a vector, then:

• y(2:end-1) gives the function values at each point in x

• y(1) gives the slope at the beginning of the interval located at min(x)

• y(end) gives the slope at the end of the interval located at max(x)

• Similarly, if y is a matrix or an N-dimensional array with size(y,N) equal to length(x)+2, then:

• y(:,...,:,j+1) gives the function values at each point in x for j = 1:length(x)

• y(:,:,...:,1) gives the slopes at the beginning of the intervals located at min(x)

• y(:,:,...:,end) gives the slopes at the end of the intervals located at max(x)

Data Types: single | double

Query points, specified as a scalar, vector, matrix, or array. The points specified in xq are the x-coordinates for the interpolated function values yq computed by spline.

Data Types: single | double

Output Arguments

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Interpolated values at query points, returned as a scalar, vector, matrix, or array.

The size of s is related to the sizes of y and xq:

• If y is a vector, then s has the same size as xq.

• If y is an array of size Ny = size(y), then these conditions apply:

• If xq is a scalar or vector, then size(s) returns [Ny(1:end-1) length(xq)].

• If xq is an array, then size(s) returns [Ny(1:end-1) size(xq)].

Piecewise polynomial, returned as a structure. Use this structure with the ppval function to evaluate the piecewise polynomial at one or more query points. The structure has these fields.

FieldDescription
form

'pp' for piecewise polynomial

breaks

Vector of length L+1  with strictly increasing elements that represent the start and end of each of L intervals

coefs

L-by-k  matrix with each row coefs(i,:) containing the local coefficients of an order k polynomial on the ith interval, [breaks(i),breaks(i+1)]

pieces

Number of pieces, L

order

Order of the polynomials

dim

Dimensionality of target

Since the polynomial coefficients in coefs are local coefficients for each interval, you must subtract the lower endpoint of the corresponding knot interval to use the coefficients in a conventional polynomial equation. In other words, for the coefficients [a,b,c,d] on the interval [x1,x2], the corresponding polynomial is

$f\left(x\right)=a{\left(x-{x}_{1}\right)}^{3}+b{\left(x-{x}_{1}\right)}^{2}+c\left(x-{x}_{1}\right)+d\text{\hspace{0.17em}}.$

Tips

• You also can perform spline interpolation using the interp1 function with the command interp1(x,y,xq,'spline'). While spline performs interpolation on rows of an input matrix, interp1 performs interpolation on columns of an input matrix.

Algorithms

A tridiagonal linear system (possibly with several right-hand sides) is solved for the information needed to describe the coefficients of the various cubic polynomials that make up the interpolating spline. spline uses the functions ppval, mkpp, and unmkpp. These routines form a small suite of functions for working with piecewise polynomials. For access to more advanced features, see interp1 or the Curve Fitting Toolbox™ spline functions.

 de Boor, Carl. A Practical Guide to Splines. Springer-Verlag, New York: 1978.