Integer type:  int32  int64  nag_int  show int32  show int32  show int64  show int64  show nag_int  show nag_int

Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_interp_2d_triangulate (e01ea)

## Purpose

nag_interp_2d_triangulate (e01ea) generates a triangulation for a given set of two-dimensional points using the method of Renka and Cline.

## Syntax

[triang, ifail] = e01ea(x, y, 'n', n)
[triang, ifail] = nag_interp_2d_triangulate(x, y, 'n', n)

## Description

nag_interp_2d_triangulate (e01ea) creates a Thiessen triangulation with a given set of two-dimensional data points as nodes. This triangulation will be as equiangular as possible (Cline and Renka (1984)). See Renka and Cline (1984) for more detailed information on the algorithm, a development of that by Lawson (1977). The code is derived from Renka (1984).
The computed triangulation is returned in a form suitable for passing to nag_interp_2d_triang_bary_eval (e01eb) which, for a set of nodal function values, computes interpolated values at a set of points.

## References

Cline A K and Renka R L (1984) A storage-efficient method for construction of a Thiessen triangulation Rocky Mountain J. Math. 14 119–139
Lawson C L (1977) Software for ${C}^{1}$ surface interpolation Mathematical Software III (ed J R Rice) 161–194 Academic Press
Renka R L (1984) Algorithm 624: triangulation and interpolation of arbitrarily distributed points in the plane ACM Trans. Math. Software 10 440–442
Renka R L and Cline A K (1984) A triangle-based ${C}^{1}$ interpolation method Rocky Mountain J. Math. 14 223–237

## Parameters

### Compulsory Input Parameters

1:     $\mathrm{x}\left({\mathbf{n}}\right)$ – double array
The $x$ coordinates of the $n$ data points.
2:     $\mathrm{y}\left({\mathbf{n}}\right)$ – double array
The $y$ coordinates of the $n$ data points.

### Optional Input Parameters

1:     $\mathrm{n}$int64int32nag_int scalar
Default: the dimension of the arrays x, y. (An error is raised if these dimensions are not equal.)
$n$, the number of data points.
Constraint: ${\mathbf{n}}\ge 3$.

### Output Parameters

1:     $\mathrm{triang}\left(7×{\mathbf{n}}\right)$int64int32nag_int array
A data structure defining the computed triangulation, in a form suitable for passing to nag_interp_2d_triang_bary_eval (e01eb). Details of how the triangulation is encoded in triang are given in Further Comments. These details are most likely to be of use when plotting the computed triangulation which is demonstrated in Example.
2:     $\mathrm{ifail}$int64int32nag_int scalar
${\mathbf{ifail}}={\mathbf{0}}$ unless the function detects an error (see Error Indicators and Warnings).

## Error Indicators and Warnings

Errors or warnings detected by the function:
${\mathbf{ifail}}=1$
Constraint: ${\mathbf{n}}\ge 3$.
${\mathbf{ifail}}=2$
On entry, all the $\left(x,y\right)$ pairs are collinear.
${\mathbf{ifail}}=-99$
${\mathbf{ifail}}=-399$
Your licence key may have expired or may not have been installed correctly.
${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.

## Accuracy

Not applicable.

The time taken for a call of nag_interp_2d_triangulate (e01ea) is approximately proportional to the number of data points, $n$. The function is more efficient if, before entry, the $\left(x,y\right)$ pairs are arranged in x and y such that the $x$ values are in ascending order.
The triangulation is encoded in triang as follows:
• set ${j}_{0}=0$; for each node, $k=1,2,\dots ,n$, (using the ordering inferred from x and y)
• ${i}_{k}={j}_{k-1}+1$
• ${j}_{k}={\mathbf{triang}}\left(6×{\mathbf{n}}+k\right)$
• ${\mathbf{triang}}\left(\mathit{j}\right)$, for $\mathit{j}={i}_{k},\dots ,{\mathit{j}}_{k}$, contains the list of nodes to which node $k$ is connected. If ${\mathbf{triang}}\left({j}_{k}\right)=0$ then node $k$ is on the boundary of the mesh.

## Example

In this example, nag_interp_2d_triangulate (e01ea) creates a triangulation from a set of data points. nag_interp_2d_triang_bary_eval (e01eb) then evaluates the interpolant at a sample of points using this triangulation. Note that this example is not typical of a realistic problem: the number of data points would normally be larger, so that interpolants can be more accurately evaluated at the fine triangulated grid.
```function e01ea_example

fprintf('e01ea example results\n\n');

% Scattered Grid Data
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; 15.2;   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];
% Triangulate on grid
[triang] = e01ea(x, y);

% Convert to form that MATLAB trimesh function can use
n = size(x,1);
t = int64([0;0;0]);
max_t = int64(2*n-5);
tri = int64(zeros(max_t,3));
iend = int64(0);
k = int64(0);
for i = 1:n
% set up loop of nodes connected to node i
ibeg = iend + 1;
iend = triang(6*n+i);
% first connection setup
t(1) = i;
t(2) = triang(ibeg);
% loop over remaining connections
ibeg = ibeg + 1;
for j = ibeg:iend
t(3) = triang(j);
if t(3)>0
if t(2)>i || t(3)>i
% new triangle here
k = k + 1;
tri(k,1:3) = t(1:3);
end
% shuffle down for next connected node
t(2) = t(3);
end
end
end

fprintf('Number of triangles in triangulation = %d\n',k);

%display mesh
fig1 = figure;
trimesh(tri(1:k,1:3),x,y)
xlabel('x');
ylabel('y');
title('Triangulation of scattered data using e01ea');
% Interpolate function values on mesh
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];
px = [2.0500 3.7500 5.0000 8.5400 9.1400];
py = [1.7750 3.2500 5.0000 2.0500 4.4500];

[pf, ifail] = e01eb(...
x, y, f, triang, px, py);

fprintf('\n      px        py     Interpolated Value\n');
disp([px' py' pf]);

```
```e01ea example results

Number of triangles in triangulation = 88

px        py     Interpolated Value
2.0500    1.7750   48.2100
3.7500    3.2500   41.4195
5.0000    5.0000   36.1613
8.5400    2.0500   28.2458
9.1400    4.4500   24.4543

``` 