void	nag_3d_shep_interp (Integer m, const double x[], const double y[], const double z[], const double f[], Integer nw, Integer nq, Integer iq[], double rq[], NagError *fail)

3 Description

nag_3d_shep_interp (e01tgc) constructs a smooth function

Q (x, y, z)

which interpolates a set of

m

scattered data points

(x_{r}, y_{r}, z_{r}, f_{r})

, for

r = 1, 2, \dots, m

, using a modification of Shepard's method. The surface is continuous and has continuous first partial derivatives.

The basic Shepard method, which is a generalization of the two-dimensional method described in Shepard (1968), interpolates the input data with the weighted mean

Q (x, y, z) = \frac{\sum_{r = 1}^{m} w_{r} (x, y, z) q_{r}}{\sum_{r = 1}^{m} w_{r} (x, y, z)},

where

q_{r} = f_{r} ​ and ​ w_{r} (x, y, z) = \frac{1}{d_{r}^{2}} ​ and ​ d_{r}^{2} = {(x - x_{r})}^{2} + {(y - y_{r})}^{2} + {(z - z_{r})}^{2} .

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

w_{r} (x, y, z)

to be zero outside a sphere with centre

(x_{r}, y_{r}, z_{r})

and some radius

R_{w}

. Also, to improve the performance of the basic method, each

q_{r}

above is replaced by a function

q_{r} (x, y, z)

, which is a quadratic fitted by weighted least squares to data local to

(x_{r}, y_{r}, z_{r})

and forced to interpolate

(x_{r}, y_{r}, z_{r}, f_{r})

. In this context, a point

(x, y, z)

is defined to be local to another point if it lies within some distance

R_{q}

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

R_{w}

and

R_{q}

are chosen to be just large enough to include

N_{w}

and

N_{q}

data points, respectively, for user-supplied constants

N_{w}

and

N_{q}

. Default values of these arguments are provided by the function, and advice on alternatives is given in Section 9.2.

This function is derived from the function QSHEP3 described by Renka (1988b).

Values of the interpolant

Q (x, y, z)

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_3d_shep_eval (e01thc).

4 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 661: QSHEP3D: Quadratic Shepard method for trivariate interpolation of scattered data ACM Trans. Math. Software 14 151–152

Shepard D (1968) A two-dimensional interpolation function for irregularly spaced data Proc. 23rd Nat. Conf. ACM 517–523 Brandon/Systems Press Inc., Princeton

5 Arguments

1: $m$ – IntegerInput: On entry: $m$ , the number of data points.

Constraint: $m \geq 10$ .
2: $x [m]$ – const doubleInput
3: $y [m]$ – const doubleInput
4: $z [m]$ – const doubleInput: On entry: $x [r - 1]$ , $y [r - 1]$ , $z [r - 1]$ must be set to the Cartesian coordinates of the data point $(x_{r}, y_{r}, z_{r})$ , for $r = 1, 2, \dots, m$ .

Constraint: these coordinates must be distinct, and must not all be coplanar.
5: $f [m]$ – const doubleInput: On entry: $f [r - 1]$ must be set to the data value $f_{r}$ , for $r = 1, 2, \dots, m$ .
6: $nw$ – IntegerInput: On entry: the number $N_{w}$ of data points that determines each radius of influence $R_{w}$ , appearing in the definition of each of the weights $w_{r}$ , for $r = 1, 2, \dots, m$ (see Section 3). Note that $R_{w}$ is different for each weight. If $nw \leq 0$ the default value $nw = \min (32, m - 1)$ is used instead.

Constraint: $nw \leq \min (40, m - 1)$ .
7: $nq$ – IntegerInput: On entry: the number $N_{q}$ of data points to be used in the least squares fit for coefficients defining the nodal functions $q_{r} (x, y, z)$ (see Section 3). If $nq \leq 0$ the default value $nq = \min (17, m - 1)$ is used instead.

Constraint: $nq \leq 0$ or $9 \leq nq \leq \min (40, m - 1)$ .
8: $iq [(2 \times m + 1)]$ – IntegerOutput: On exit: integer data defining the interpolant $Q (x, y, z)$ .
9: $rq [(10 \times m + 7)]$ – doubleOutput: On exit: real data defining the interpolant $Q (x, y, z)$ .
10: $fail$ – NagError *Input/Output: The NAG error argument (see Section 2.7 in How to Use the NAG Library and its Documentation).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_DATA_COPLANAR: All nodes are coplanar. There is no unique solution.
NE_DUPLICATE_NODE: There are duplicate nodes in the dataset. $(x [I - 1], y [I - 1], z [I - 1]) = (x [J - 1], y [J - 1], z [J - 1])$ for: $I = 〈value〉$ and $J = 〈value〉$ . The interpolant cannot be derived.
NE_INT: On entry, $m = 〈value〉$ .
Constraint: $m \geq 10$ .

On entry, $nq = 〈value〉$ .
Constraint: $nq \leq 0$ or $nq \geq 9$ .
NE_INT_2: On entry, $nq = 〈value〉$ and $m = 〈value〉$ .
Constraint: $nq \leq \min (40, m - 1)$ .

On entry, $nw = 〈value〉$ and $m = 〈value〉$ .
Constraint: $nw \leq \min (40, m - 1)$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.

An unexpected error has been triggered by this function. Please contact NAG.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.

7 Accuracy

On successful exit, the function generated interpolates the input data exactly and has quadratic accuracy.

8 Parallelism and Performance

nag_3d_shep_interp (e01tgc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_3d_shep_interp (e01tgc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.

Please consult the x06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

9.1 Timing

The time taken for a call to nag_3d_shep_interp (e01tgc) will depend in general on the distribution of the data points. If x, y and z are uniformly randomly distributed, then the time taken should be O(m). At worst

O (m^{2})

time will be required.

9.2 Choice of $N_{w}$ and $N_{q}$

Default values of the arguments

N_{w}

and

N_{q}

may be selected by calling nag_3d_shep_interp (e01tgc) with

nw \leq 0

and

nq \leq 0

. These default values may well be satisfactory for many applications.

If non-default values are required they must be supplied to nag_3d_shep_interp (e01tgc) 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

nw = \min (32, m - 1)

and

nq = \min (17, m - 1)

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

N_{w}

and

N_{q}

, 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).

10 Example

This program reads in a set of

30

data points and calls nag_3d_shep_interp (e01tgc) to construct an interpolating function

Q (x, y, z)

. It then calls nag_3d_shep_eval (e01thc) 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.

NAG Library Function Documentnag_3d_shep_interp (e01tgc)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Timing

9.2 Choice of Nw and Nq

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_3d_shep_interp (e01tgc)

9.2 Choice of $N_{w}$ and $N_{q}$