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NAG Toolbox: nag_interp_2d_scat_shep (e01sg)
Purpose
nag_interp_2d_scat_shep (e01sg) generates a two-dimensional interpolant to a set of scattered data points, using a modified Shepard method.
Syntax
Description
nag_interp_2d_scat_shep (e01sg) constructs a smooth function which interpolates a set of scattered data points , for , using a modification of Shepard's method. The surface is continuous and has continuous first partial derivatives.
The basic
Shepard (1968) method interpolates the input data with the weighted mean
where
,
and
.
The basic method is global in that the interpolated value at any point depends on all the data, but this function uses a modification (see
Franke and Nielson (1980) and
Renka (1988a)), whereby the method becomes local by adjusting each
to be zero outside a circle with centre
and some radius
. Also, to improve the performance of the basic method, each
above is replaced by a function
, which is a quadratic fitted by weighted least squares to data local to
and forced to interpolate
. In this context, a point
is defined to be local to another point if it lies within some distance
of it. Computation of these quadratics constitutes the main work done by this function.
The efficiency of the function is further enhanced by using a cell method for nearest neighbour searching due to
Bentley and Friedman (1979).
The radii
and
are chosen to be just large enough to include
and
data points, respectively, for user-supplied constants
and
. Default values of these arguments are provided by the function, and advice on alternatives is given in
Choice of and .
This function is derived from the function QSHEP2 described by
Renka (1988b).
Values of the interpolant
generated by this function, and its first partial derivatives, can subsequently be evaluated for points in the domain of the data by a call to
nag_interp_2d_scat_shep_eval (e01sh).
References
Bentley J L and Friedman J H (1979) Data structures for range searching ACM Comput. Surv. 11 397–409
Franke R and Nielson G (1980) Smooth interpolation of large sets of scattered data Internat. J. Num. Methods Engrg. 15 1691–1704
Renka R J (1988a) Multivariate interpolation of large sets of scattered data ACM Trans. Math. Software 14 139–148
Renka R J (1988b) Algorithm 660: QSHEP2D: Quadratic Shepard method for bivariate interpolation of scattered data ACM Trans. Math. Software 14 149–150
Shepard D (1968) A two-dimensional interpolation function for irregularly spaced data Proc. 23rd Nat. Conf. ACM 517–523 Brandon/Systems Press Inc., Princeton
Parameters
Compulsory Input Parameters
- 1:
– double array
- 2:
– double array
-
The Cartesian coordinates of the data points
, for .
Constraint:
these coordinates must be distinct, and must not all be collinear.
- 3:
– double array
-
must be set to the data value , for .
- 4:
– int64int32nag_int scalar
-
The number
of data points that determines each radius of influence
, appearing in the definition of each of the weights
, for
(see
Description). Note that
is different for each weight. If
the default value
is used instead.
Constraint:
.
- 5:
– int64int32nag_int scalar
-
The number
of data points to be used in the least squares fit for coefficients defining the nodal functions
(see
Description). If
the default value
is used instead.
Constraint:
or .
Optional Input Parameters
- 1:
– int64int32nag_int scalar
-
Default:
the dimension of the arrays
x,
y,
f. (An error is raised if these dimensions are not equal.)
, the number of data points.
Constraint:
.
Output Parameters
- 1:
– int64int32nag_int array
-
.
Integer data defining the interpolant .
- 2:
– double array
-
.
Real data defining the interpolant .
- 3:
– int64int32nag_int scalar
unless the function detects an error (see
Error Indicators and Warnings).
Error Indicators and Warnings
Errors or warnings detected by the function:
-
-
On entry, | , |
or | , |
or | , |
or | , |
or | , |
or | . |
-
-
On entry, | for some . |
-
-
On entry, | all the data points are collinear. No unique solution exists. |
-
An unexpected error has been triggered by this routine. Please
contact
NAG.
-
Your licence key may have expired or may not have been installed correctly.
-
Dynamic memory allocation failed.
Accuracy
On successful exit, the function generated interpolates the input data exactly and has quadratic accuracy.
Further Comments
Timing
The time taken for a call to
nag_interp_2d_scat_shep (e01sg) will depend in general on the distribution of the data points. If
x and
y are uniformly randomly distributed, then the time taken should be
. At worst
time will be required.
Choice of Nw and Nq
Default values of the arguments and may be selected by calling nag_interp_2d_scat_shep (e01sg) with and . These default values may well be satisfactory for many applications.
If non-default values are required they must be supplied to
nag_interp_2d_scat_shep (e01sg) through positive values of
nw and
nq. Increasing these arguments makes the method less local. This may increase the accuracy of the resulting interpolant at the expense of increased computational cost. The default values
and
have been chosen on the basis of experimental results reported in
Renka (1988a). In these experiments the error norm was found to vary smoothly with
and
, generally increasing monotonically and slowly with distance from the optimal pair. The method is not therefore thought to be particularly sensitive to the argument values. For further advice on the choice of these arguments see
Renka (1988a).
Example
This program reads in a set of
data points and calls
nag_interp_2d_scat_shep (e01sg) to construct an interpolating function
. It then calls
nag_interp_2d_scat_shep_eval (e01sh) to evaluate the interpolant at a set of points.
Note that this example is not typical of a realistic problem: the number of data points would normally be larger.
Open in the MATLAB editor:
e01sg_example
function e01sg_example
fprintf('e01sg example results\n\n');
x = [11.16; 12.85; 19.85; 19.72; 15.91; 0.00; 20.87; 3.45; 14.26; ...
17.43; 22.80; 7.58; 25.00; 0.00; 9.66; 5.22; 17.25; 25.00; ...
12.13; 22.23; 11.52; 15.20; 7.54; 17.32; 2.14; 0.51; 22.69; ...
5.47; 21.67; 3.31];
y = [ 1.24; 3.06; 10.72; 1.39; 7.74; 20.00; 20.00; 12.78; 17.87; ...
3.46; 12.39; 1.98; 11.87; 0.00; 20.00; 14.66; 19.57; 3.87; ...
10.79; 6.21; 8.53; 0.00; 10.69; 13.78; 15.03; 8.37; 19.63; ...
17.13; 14.36; 0.33];
f = [22.15; 22.11; 7.97; 16.83; 15.30; 34.60; 5.74; 41.24; 10.74; ...
18.60; 5.47; 29.87; 4.40; 58.20; 4.73; 40.36; 6.43; 8.74; ...
13.71; 10.25; 15.74; 21.60; 19.31; 12.11; 53.10; 49.43; 3.25; ...
28.63; 5.52; 44.08];
nw = int64(0);
nq = int64(0);
[iq, rq, ifail] = e01sg(x, y, f, nw, nq);
u = [20.00; 6.41; 7.54; 9.91; 12.30];
v = [ 3.14; 15.44; 10.69; 18.27; 9.22];
[q, qx, qy, ifail] = e01sh(x, y, f, iq, rq, u, v);
fprintf('Interpolated values Q and its derivatives at (u,v)\n');
fprintf(' u v q qx qy\n');
for i = 1:size(u,1)
fprintf('%7.2f%7.2f%7.2f%7.2f%7.2f\n', u(i), v(i), q(i), qx(i), qy(i));
end
e01sg example results
Interpolated values Q and its derivatives at (u,v)
u v q qx qy
20.00 3.14 15.89 -1.28 -0.63
6.41 15.44 34.05 -3.62 -3.56
7.54 10.69 19.31 -2.84 0.81
9.91 18.27 13.68 -1.59 -4.71
12.30 9.22 14.56 -0.68 -0.78
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