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This example shows how to use the spline commands in Curve Fitting Toolbox™ to fit tensor product splines to bivariate gridded data.

Since Curve Fitting Toolbox can handle splines with *vector* coefficients, it is easy to implement interpolation or approximation to gridded data by tensor product splines. Most spline construction commands in the toolbox take advantage of this.

However, you might be interested in seeing a detailed description of how approximation to gridded data by tensor products is actually done for bivariate data. This will also come in handy when you need some tensor product construction not provided by the commands in the toolbox.

Consider, for example, least-squares approximation to given data

` z(i,j) = f(x(i),y(j)) for i = 1:I, j = 1:J.`

Here are some gridded data, taken from Franke's sample function. Note that the grid is somewhat denser near the boundary, to help pin down the approximation there.

x = sort([(0:10)/10,.03 .07, .93 .97]); y = sort([(0:6)/6,.03 .07, .93 .97]); [xx,yy] = ndgrid(x,y); % note: ndgrid rather than meshgrid z = franke(xx,yy); mesh(x,y,z.'); xlabel('x'); ylabel('y'); view(150,50); title('Data from the Franke Function');

Note that the statements

` [xx,yy] = ndgrid(x,y);`

` z = franke(xx,yy);`

used above make certain that `z(i,j)`

is the value of the function being approximated at the grid point `(x(i),y(j))`

.

However, the MATLAB® command `mesh(x,y,z)`

expects `z(j,i)`

(note the reversed order of `i`

and `j`

) as the value at the grid point `(x(i),y(j))`

. For that reason, the above plot was generated by the statement

` mesh(x,y,z.');`

i.e., using the transpose of the matrix `z`

.

Such transposing would not have been necessary had we used `meshgrid`

instead of `ndgrid`

. But the resulting `z`

would not have followed approximation theory standards.

Next, we choose a spline order `ky`

and a knot sequence `knotsy`

for the y-direction

ky = 3; knotsy = augknt([0,.25,.5,.75,1],ky);

and then obtain

sp = spap2(knotsy,ky,y,z);

a spline curve whose `i`

-th component is an approximation to the curve `(y,z(i,:))`

for `i=1:I`

.

In particular,

yy = -.1:.05:1.1; vals = fnval(sp,yy);

creates the matrix `vals`

whose `(i,j)`

-th element can be taken as an approximation to the value `f(x(i),yy(j))`

of the underlying function `f`

at the grid point `(x(i),yy(j))`

. This is evident when we plot `vals`

.

mesh(x,yy,vals.'); xlabel('x'); ylabel('y'); view(150,50); title('Simultaneous Approximation to All Curves in the Y-Direction');

Note that, for each `x(i)`

, both the first two and the last two values are zero since both the first two and the last two sites in `yy`

are outside the basic interval for the spline `sp`

.

Also note the "ridges" that run along the y-direction, most noticeable near the peaks of the surface. They confirm that we are plotting smooth curves in one direction only.

To get an actual surface, we now have to go one step further. Consider the coefficients `coefsy`

of the spline `sp`

, as obtained by

`coefsy = fnbrk(sp,'c');`

Abstractly, you can think of the spline `sp`

as the vector-valued function

` y |--> sum coefsy(:,r) B_{r,ky}(y)`

` r`

with the `i`

-th element, `coefsy(i,r)`

, of the vector coefficient `coefsy(:,r)`

corresponding to `x(i)`

for `i=1:I`

. This suggests approximating each curve `(x,coefsy(:,r))`

by a spline, using the same order `kx`

and the same appropriate knot sequence `knotsx`

for every `r`

.

kx = 4; knotsx = augknt(0:.2:1,kx); sp2 = spap2(knotsx,kx,x,coefsy.');

The use of the `spap2`

command here needs, perhaps, an explanation.

Recall that `spap2(knots,k,x,fx)`

treats `fx(:,j)`

as the value at `x(j)`

, i.e., takes each *column* of `fx`

as a data value. Since we wanted to fit the value `coefsy(i,:)`

at `x(i)`

, for all `i`

, we have to provide `spap2`

with the *transpose* of `coefsy`

.

Now consider the *transpose* of the coefficient matrix of the resulting spline "curve" `sp2`

, obtained as

`coefs = fnbrk(sp2,'c').';`

`coefs`

provides the *bivariate* spline approximation

` (x,y) |--> sum sum coefs(q,r) B_{q,kx}(x) B_{r,ky}(y)`

` q r`

to the original data

` (x(i),y(j)) |--> f(x(i),y(j)) = z(i,j).`

We use `spcol`

to provide the values `B_{q,kx}(xv(i))`

and `B_{r,ky}(yv(j))`

needed to evaluate this spline surface at some grid points `(xv(i),yv(j))`

and then plot the values.

xv = 0:.025:1; yv = 0:.025:1; values = spcol(knotsx,kx,xv)*coefs*spcol(knotsy,ky,yv).'; mesh(xv,yv,values.'); xlabel('x'); ylabel('y'); view(150,50); title('The Spline Approximant');

The statement

` values = spcol(knotsx,kx,xv) * coefs * spcol(knotsy,ky,yv).'`

used above makes good sense since, for example, `spcol(knotsx,kx,xv)`

is the matrix whose `(i,q)`

-th entry equals the value `B_{q,kx}(xv(i))`

at `xv(i)`

of the `q`

-th B-spline of order `kx`

for the knot sequence `knotsx`

, while we want to evaluate the expression

` sum sum coefs(q,r) B_{q,kx}(x) B_{r,ky}(y)`

` q r`

` = sum sum B_{q,kx}(x) coefs(q,r) B_{r,ky}(y)`

` q r`

at `(x,y) = (xv(i),yv(j))`

.

Since the matrices `spcol(knotsx,kx,xv)`

and `spcol(knotsy,ky,yv)`

are banded, it may be more efficient for "large" `xv`

and `yv`

(though perhaps more memory-consuming) to make use of `fnval`

.

value2 = fnval(spmak(knotsx,fnval(spmak(knotsy,coefs),yv).'),xv).';

In fact, `fnval`

and `spmak`

can deal directly with multivariate splines, hence the above statement can be replaced by

value3 = fnval( spmak({knotsx,knotsy},coefs), {xv,yv} );

Better yet, the construction of the approximation can be done by *one* call to `spap2`

, therefore we can obtain these values directly from the given data by the statement

value4 = fnval( spap2({knotsx,knotsy},[kx ky],{x,y},z), {xv,yv} );

Here is a check, specifically, the *relative* difference between the values computed in these four different ways.

diffs = abs(values-value2) + abs(values-value3) + abs(values-value4); max(max(diffs)) / max(max(abs(values)))

ans = 1.6809e-15

The four methods return the same values, up to round-off error.

Here is a plot of the error, i.e., the difference between the given data value and the value of the spline approximation at those data sites.

errors = z - spcol(knotsx,kx,x)*coefs*spcol(knotsy,ky,y).'; mesh(x,y,errors.'); xlabel('x'); ylabel('y'); view(150,50); title('Error at the Given Data Sites');

The *relative* error is

max(max(abs(errors))) / max(max(abs(z)))

ans = 0.0539

This is perhaps not too impressive. On the other hand, the ratio

`(degrees of freedom used) / (number of data points)`

is only

numel(coefs)/numel(z)

ans = 0.2909

The approach followed here seems `biased`

: We first think of the given data values `z`

as describing a vector-valued function of `y`

, and then we treat the matrix formed by the vector coefficients of the approximating curve as describing a vector-valued function of `x`

.

What happens when we take things in the opposite order, i.e., think of `z`

as describing a vector-valued function of `x`

, and then treat the matrix made up from the vector coefficients of the approximating curve as describing a vector-valued function of `y`

?

Perhaps surprisingly, the final approximation is the same, up to roundoff. The next section contains the numerical experiment confirming that.

First, we fit a spline curve to the data, but this time with `x`

as the independent variable, hence it is the *rows* of `z`

which now become the data values. Correspondingly, we must supply `z.'`

(rather than `z`

) to `spap2`

, and obtain

spb = spap2(knotsx,kx,x,z.');

a spline approximation to all the curves `(x,z(:,j))`

for `j=1:J`

. In particular,

valsb = fnval(spb,xv).';

creates a matrix whose `(i,j)`

-th element can be taken as an approximation to the value `f(xv(i),y(j))`

of the underlying function `f`

at the grid point `(xv(i),y(j))`

. This is evident when we plot `valsb`

.

mesh(xv,y,valsb.'); xlabel('x'); ylabel('y'); view(150,50); title('Simultaneous Approximation to All Curves in the X-Direction');

Again note the ridges, this time running along the x-direction. They confirm that, once again, we are plotting smooth curves in one direction only.

Now comes the second step, to get the actual surface.

Let `coefsx`

be the coefficients for `spb`

, i.e.,

`coefsx = fnbrk(spb,'c');`

Abstractly, you can think of the spline `spb`

as the vector-valued function

` x |--> sum coefsx(r,:) B_{r,kx}(x)`

` r`

with the `j`

-th entry `coefsx(r,j)`

of the vector coefficient `coefsx(r,:)`

corresponding to `y(j)`

, for all `j`

. Thus, we now fit each curve `(y,coefsx(r,:))`

by a spline, using the same order `ky`

and the same appropriate knot sequence `knotsy`

for each `r`

.

spb2 = spap2(knotsy,ky,y,coefsx.');

In the construction of `spb2`

, we again need to transpose the coefficient matrix from `spb`

, since `spap2`

takes the columns of its last input argument as the data values.

For this reason, there is now no need to transpose the coefficient matrix `coefsb`

of the resulting "curve".

`coefsb = fnbrk(spb2,'c');`

Claim: `coefsb`

equals the earlier coefficient array `coefs`

, up to round-off. For a proof of this, see the discussion of the tensor product construct in Curve Fitting Toolbox documentation. Here, we simply make the following check.

max(max(abs(coefs - coefsb)))

ans = 8.8818e-16

Thus, the *bivariate* spline approximation

` (x,y) |--> sum sum coefsb(q,r) B_{q,kx}(x) B_{r,ky}(y)`

` q r`

to the original data

` (x(i),y(j)) |--> f(x(i),y(j)) = z(i,j)`

obtained coincides with the earlier one, which generated `coefs`

rather than `coefsb`

.

As observed earlier, you can carry out the entire construction we just went through (in two ways), using just two statements, one for the construction of the least-squares approximant, the other for its evaluation at a rectangular mesh.

tsp = spap2({knotsx,knotsy},[kx,ky],{x,y},z); valuet = fnval(tsp,{xv,yv});

Here, as another check, is the relative difference between the values computed earlier and those computed now:

max(max(abs(values-valuet))) / max(max(abs(values)))

ans = 5.6029e-16

Since the data come from a smooth function, we should be interpolating it, i.e., using `spapi`

instead of `spap2`

, or, equivalently, use `spap2`

with the appropriate knot sequences. For illustration, here is the same process done with `spapi`

.

To recall, the (univariate) data sites were

x

```
x =
Columns 1 through 7
0 0.0300 0.0700 0.1000 0.2000 0.3000 0.4000
Columns 8 through 14
0.5000 0.6000 0.7000 0.8000 0.9000 0.9300 0.9700
Column 15
1.0000
```

y

```
y =
Columns 1 through 7
0 0.0300 0.0700 0.1667 0.3333 0.5000 0.6667
Columns 8 through 11
0.8333 0.9300 0.9700 1.0000
```

We use again quadratic splines in `y`

, hence use knots midway between data sites.

```
knotsy = augknt( [0 1 (y(2:(end-2))+y(3:(end-1)))/2 ], ky);
spi = spapi(knotsy,y,z);
coefsy = fnbrk(spi,'c');
```

We use again cubic splines in `x`

, and use the not-a-knot condition. We therefore use all but the second and the second-to-last data points as knots.

```
knotsx = augknt(x([1,3:(end-2),end]), kx);
spi2 = spapi(knotsx,x,coefsy.');
icoefs = fnbrk(spi2,'c').';
```

We are now ready to evaluate the interpolant

ivalues = spcol(knotsx,kx,xv)*icoefs*spcol(knotsy,ky,yv).';

and plot the interpolant at a fine mesh.

mesh(xv,yv,ivalues.'); xlabel('x'); ylabel('y'); view(150,50); title('The Spline Interpolant');

Again, the steps above can be carried out using just two statements, one for the construction of the interpolant, the other for its evaluation at a rectangular mesh.

tsp = spapi({knotsx,knotsy},{x,y},z); valuet = fnval(tsp,{xv,yv});

For a check, we also compute the relative difference between the values computed earlier and those computed now.

max(max(abs(ivalues-valuet))) / max(max(abs(ivalues)))

ans = 5.5068e-16

Next, we compute the error of the interpolant as an approximation to the Franke function.

fvalues = franke(repmat(xv.',1,length(yv)),repmat(yv,length(xv),1)); error = fvalues - ivalues; mesh(xv,yv,error.'); xlabel('x'); ylabel('y'); view(150,50); title('Interpolation error');

The *relative* approximation error is

max(max(abs(error))) / max(max(abs(fvalues)))

ans = 0.0409

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