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Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_mesh_2d_gen_delaunay (d06ab)

## Purpose

nag_mesh_2d_gen_delaunay (d06ab) generates a triangular mesh of a closed polygonal region in 2${ℝ}^{2}$, given a mesh of its boundary. It uses a Delaunay–Voronoi process, based on an incremental method.

## Syntax

[nv, nelt, coor, conn, ifail] = d06ab(nvb, edge, coor, weight, npropa, itrace, 'nvint', nvint, 'nvmax', nvmax, 'nedge', nedge)
[nv, nelt, coor, conn, ifail] = nag_mesh_2d_gen_delaunay(nvb, edge, coor, weight, npropa, itrace, 'nvint', nvint, 'nvmax', nvmax, 'nedge', nedge)

## Description

nag_mesh_2d_gen_delaunay (d06ab) generates the set of interior vertices using a Delaunay–Voronoi process, based on an incremental method. It allows you to specify a number of fixed interior mesh vertices together with weights which allow concentration of the mesh in their neighbourhood. For more details about the triangulation method, consult the D06 Chapter Introduction as well as George and Borouchaki (1998).
This function is derived from material in the MODULEF package from INRIA (Institut National de Recherche en Informatique et Automatique).

## References

George P L and Borouchaki H (1998) Delaunay Triangulation and Meshing: Application to Finite Elements Editions HERMES, Paris

## Parameters

### Compulsory Input Parameters

1:     nvb – int64int32nag_int scalar
The number of vertices in the input boundary mesh.
Constraint: nvb3${\mathbf{nvb}}\ge 3$.
2:     edge(3$3$,nedge) – int64int32nag_int array
The specification of the boundary edges. edge(1,j)${\mathbf{edge}}\left(1,j\right)$ and edge(2,j)${\mathbf{edge}}\left(2,j\right)$ contain the vertex numbers of the two end points of the j$j$th boundary edge. edge(3,j)${\mathbf{edge}}\left(3,j\right)$ is a user-supplied tag for the j$j$th boundary edge and is not used by nag_mesh_2d_gen_delaunay (d06ab).
Constraint: 1edge(i,j)nvb$1\le {\mathbf{edge}}\left(\mathit{i},\mathit{j}\right)\le {\mathbf{nvb}}$ and edge(1,j)edge(2,j)${\mathbf{edge}}\left(1,\mathit{j}\right)\ne {\mathbf{edge}}\left(2,\mathit{j}\right)$, for i = 1,2$\mathit{i}=1,2$ and j = 1,2,,nedge$\mathit{j}=1,2,\dots ,{\mathbf{nedge}}$.
3:     coor(2$2$,nvmax) – double array
coor(1,i)${\mathbf{coor}}\left(1,\mathit{i}\right)$ contains the x$x$ coordinate of the i$\mathit{i}$th input boundary mesh vertex, for i = 1,2,,nvb$\mathit{i}=1,2,\dots ,{\mathbf{nvb}}$. coor(1,i)${\mathbf{coor}}\left(1,\mathit{i}\right)$ contains the x$x$ coordinate of the (invb)$\left(\mathit{i}-{\mathbf{nvb}}\right)$th fixed interior vertex, for i = nvb + 1,,nvb + nvint$\mathit{i}={\mathbf{nvb}}+1,\dots ,{\mathbf{nvb}}+{\mathbf{nvint}}$. For boundary and interior vertices, coor(2,i)${\mathbf{coor}}\left(2,\mathit{i}\right)$ contains the corresponding y$y$ coordinate, for i = 1,2,,nvb + nvint$\mathit{i}=1,2,\dots ,{\mathbf{nvb}}+{\mathbf{nvint}}$.
4:     weight( : $:$) – double array
Note: the dimension of the array weight must be at least max (1,nvint)$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nvint}}\right)$.
The weight of fixed interior vertices. It is the diameter of triangles (length of the longer edge) created around each of the given interior vertices.
Constraint: if nvint > 0${\mathbf{nvint}}>0$, weight(i) > 0.0${\mathbf{weight}}\left(\mathit{i}\right)>0.0$, for i = 1,2,,nvint$\mathit{i}=1,2,\dots ,{\mathbf{nvint}}$.
5:     npropa – int64int32nag_int scalar
The propagation type and coefficient, the parameter npropa is used when the internal points are created. They are distributed in a geometric manner if npropa is positive and in an arithmetic manner if it is negative. For more details see Section [Further Comments].
Constraint: npropa0${\mathbf{npropa}}\ne 0$.
6:     itrace – int64int32nag_int scalar
The level of trace information required from nag_mesh_2d_gen_delaunay (d06ab).
itrace0${\mathbf{itrace}}\le 0$
No output is generated.
itrace1${\mathbf{itrace}}\ge 1$
Output from the meshing solver is printed on the current advisory message unit (see nag_file_set_unit_advisory (x04ab)). This output contains details of the vertices and triangles generated by the process.
You are advised to set itrace = 0${\mathbf{itrace}}=0$, unless you are experienced with finite element mesh generation.

### Optional Input Parameters

1:     nvint – int64int32nag_int scalar
Default: The dimension of the array weight.
The number of fixed interior mesh vertices to which a weight will be applied.
Constraint: nvint0${\mathbf{nvint}}\ge 0$.
2:     nvmax – int64int32nag_int scalar
Default: The dimension of the array coor.
The maximum number of vertices in the mesh to be generated.
Constraint: ${\mathbf{nvmax}}\ge {\mathbf{nvb}}+{\mathbf{nvint}}$.
3:     nedge – int64int32nag_int scalar
Default: The dimension of the array edge.
The number of boundary edges in the input mesh.
Constraint: nedge1${\mathbf{nedge}}\ge 1$.

### Input Parameters Omitted from the MATLAB Interface

rwork lrwork iwork liwork

### Output Parameters

1:     nv – int64int32nag_int scalar
The total number of vertices in the output mesh (including both boundary and interior vertices). If ${\mathbf{nvb}}+{\mathbf{nvint}}={\mathbf{nvmax}}$, no interior vertices will be generated and ${\mathbf{nv}}={\mathbf{nvmax}}$.
2:     nelt – int64int32nag_int scalar
The number of triangular elements in the mesh.
3:     coor(2$2$,nvmax) – double array
coor(1,i)${\mathbf{coor}}\left(1,\mathit{i}\right)$ will contain the x$x$ coordinate of the (invbnvint)$\left(\mathit{i}-{\mathbf{nvb}}-{\mathbf{nvint}}\right)$th generated interior mesh vertex, for i = nvb + nvint + 1,,nv$\mathit{i}={\mathbf{nvb}}+{\mathbf{nvint}}+1,\dots ,{\mathbf{nv}}$; while coor(2,i)${\mathbf{coor}}\left(2,i\right)$ will contain the corresponding y$y$ coordinate. The remaining elements are unchanged.
4:     conn(3$3$,2 × nvmax + 5$2×{\mathbf{nvmax}}+5$) – int64int32nag_int array
The connectivity of the mesh between triangles and vertices. For each triangle j$\mathit{j}$, conn(i,j)${\mathbf{conn}}\left(\mathit{i},\mathit{j}\right)$ gives the indices of its three vertices (in anticlockwise order), for i = 1,2,3$\mathit{i}=1,2,3$ and j = 1,2,,nelt$\mathit{j}=1,2,\dots ,{\mathbf{nelt}}$.
5:     ifail – int64int32nag_int scalar
${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see [Error Indicators and Warnings]).

## Error Indicators and Warnings

Errors or warnings detected by the function:
ifail = 1${\mathbf{ifail}}=1$
 On entry, nvb < 3${\mathbf{nvb}}<3$, or nvint < 0${\mathbf{nvint}}<0$, or ${\mathbf{nvb}}+{\mathbf{nvint}}>{\mathbf{nvmax}}$, or nedge < 1${\mathbf{nedge}}<1$, or edge(i,j) < 1${\mathbf{edge}}\left(i,j\right)<1$ or edge(i,j) > nvb${\mathbf{edge}}\left(i,j\right)>{\mathbf{nvb}}$, for some i = 1,2$i=1,2$ and j = 1,2, … ,nedge$j=1,2,\dots ,{\mathbf{nedge}}$, or edge(1,j) = edge(2,j)${\mathbf{edge}}\left(1,j\right)={\mathbf{edge}}\left(2,j\right)$, for some j = 1,2, … ,nedge$j=1,2,\dots ,{\mathbf{nedge}}$, or npropa = 0${\mathbf{npropa}}=0$; or if nvint > 0${\mathbf{nvint}}>0$, weight(i) ≤ 0.0${\mathbf{weight}}\left(i\right)\le 0.0$, for some i = 1,2, … ,nvint$i=1,2,\dots ,{\mathbf{nvint}}$; or lrwork < 12 × nvmax + 15$\mathit{lrwork}<12×{\mathbf{nvmax}}+15$, or liwork < 6 × nedge + 32 × nvmax + 2 × nvb + 78$\mathit{liwork}<6×{\mathbf{nedge}}+32×{\mathbf{nvmax}}+2×{\mathbf{nvb}}+78$.
ifail = 2${\mathbf{ifail}}=2$
An error has occurred during the generation of the interior mesh. Check the definition of the boundary (arguments coor and edge) as well as the orientation of the boundary (especially in the case of a multiple connected component boundary). Setting itrace > 0${\mathbf{itrace}}>0$ may provide more details.
ifail = 3${\mathbf{ifail}}=3$
An error has occurred during the generation of the boundary mesh. It appears that nvmax is not large enough.

## Accuracy

Not applicable.

The position of the internal vertices is a function position of the vertices on the given boundary. A fine mesh on the boundary results in a fine mesh in the interior. To dilute the influence of the data on the interior of the domain, the value of npropa can be changed. The propagation coefficient is calculated as: ω = 1 + (a1.0)/20.0 $\omega =1+\frac{a-1.0}{20.0}$, where a$a$ is the absolute value of npropa. During the process vertices are generated on edges of the mesh Ti${\mathcal{T}}_{i}$ to obtain the mesh Ti + 1${\mathcal{T}}_{i+1}$ in the general incremental method (consult the D06 Chapter Introduction or George and Borouchaki (1998)). This generation uses the coefficient ω$\omega$, and it is geometric if npropa > 0${\mathbf{npropa}}>0$, and arithmetic otherwise. But increasing the value of a$a$ may lead to failure of the process, due to precision, especially in geometries with holes. So you are advised to manipulate the parameter npropa with care.
You are advised to take care to set the boundary inputs properly, especially for a boundary with multiply connected components. The orientation of the interior boundaries should be in clockwise order and opposite to that of the exterior boundary. If the boundary has only one connected component, its orientation should be anticlockwise.

## Example

```function nag_mesh_2d_gen_delaunay_example
nvb = int64(296);
edge = zeros(3, 296, 'int64');
edge(1,(1:296)) = ...
[int64(1),2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,...
27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,...
53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,...
79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,...
104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,...
124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,...
144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,...
164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,...
184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,...
204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,...
224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,...
244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,...
264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,...
284,285,286,287,288,289,290,291,292,293,294,295,296];
edge(2,(1:296)) = ...
[int64(2),3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,...
31,32,33,34,35,36,37,38,39,40,1,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,...
58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,...
85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,...
109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,...
129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,...
149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,...
41,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,...
189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,...
209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,...
229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,...
249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,...
269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,...
289,290,291,292,293,294,295,296,169];
coor = zeros(2, 6000);
coor(1,(1:336)) = [4,3.96307,3.85317,3.67302,3.42705,3.12132,2.76336,2.36197,1.92705,...
1.4693,1,0.530697,0.072949,-0.361971,-0.763356,-1.12132,-1.42705,-1.67302,...
-1.85317,-1.96307,-2,-1.96307,-1.85317,-1.67302,-1.42705,-1.12132,-0.763356,...
-0.361971,0.072949,0.530697,1,1.4693,1.92705,2.36197,2.76336,3.12132,3.42705,...
3.67302,3.85317,3.96307,0,0.000602,0.002408,0.005412,0.009607,0.014984,0.02153,...
0.029228,0.03806,0.048005,0.059039,0.071136,0.084265,0.098396,0.113495,0.129524,...
0.146447,0.164221,0.182803,0.20215,0.222215,0.242949,0.264302,0.286222,0.308658,...
0.331555,0.354858,0.37851,0.402455,0.426635,0.450991,0.475466,0.5,0.524534,...
0.549009,0.573365,0.597545,0.62149,0.645142,0.668445,0.691342,0.713778,0.735698,...
0.757051,0.777785,0.79785,0.817197,0.835779,0.853553,0.870476,0.886505,0.901604,...
0.915735,0.928864,0.940961,0.951995,0.96194,0.970772,0.97847,0.985016,0.990393,...
0.994588,0.997592,0.999398,1,0.999398,0.997592,0.994588,0.990393,0.985016,0.97847,...
0.970772,0.96194,0.951995,0.940961,0.928864,0.915735,0.901604,0.886505,0.870476,...
0.853553,0.835779,0.817197,0.79785,0.777785,0.757051,0.735698,0.713778,0.691342,...
0.668445,0.645142,0.62149,0.597545,0.573365,0.549009,0.524534,0.5,0.475466,...
0.450991,0.426635,0.402455,0.37851,0.354858,0.331555,0.308658,0.286222,0.264302,...
0.242949,0.222215,0.20215,0.182803,0.164221,0.146447,0.129524,0.113495,0.098396,...
0.084265,0.071136,0.059039,0.048005,0.03806,0.029228,0.02153,0.014984,0.009607,...
0.005412,0.002408,0.000602,0.991481,0.992042,0.993065,0.994547,0.996485,0.998874,...
1.00171,1.00498,1.00869,1.01283,1.01739,1.02235,1.0277,1.03343,1.03953,1.04598,...
1.05277,1.05987,1.06727,1.07496,1.0829,1.09109,1.0995,1.10812,1.11691,1.12586,...
1.13495,1.14416,1.15346,1.16282,1.17223,1.18166,1.19109,1.20049,1.20983,1.2191,...
1.22828,1.23734,1.24626,1.25503,1.26362,1.27203,1.28022,1.28819,1.29591,1.30337,...
1.31055,1.31744,1.32402,1.33027,1.33619,1.34176,1.34696,1.35179,1.35624,1.36029,...
1.36394,1.36717,1.36999,1.37238,1.37435,1.37588,1.37697,1.37763,1.37785,1.37762,...
1.37694,1.37579,1.37419,1.37214,1.36963,1.36667,1.36327,1.35943,1.35515,1.35044,...
1.34531,1.33977,1.33384,1.32751,1.32082,1.31378,1.30639,1.29869,1.29068,1.28239,...
1.27385,1.26506,1.25606,1.24687,1.23751,1.22802,1.21842,1.20873,1.19898,1.18921,...
1.17943,1.16969,1.16001,1.15042,1.14095,1.13162,1.12246,1.11347,1.10469,1.09611,...
1.08776,1.07966,1.07181,1.06423,1.05695,1.04999,1.04334,1.03704,1.0311,1.02552,...
1.02033,1.01553,1.01114,1.00717,1.00363,1.00053,0.997863,0.995651,0.993893,...
0.992595,0.991759,0.991387,1.440975609756098,1.501951219512195,1.562926829268293,...
1.62390243902439,1.684878048780488,1.745853658536585,1.806829268292683,...
1.86780487804878,1.928780487804878,1.989756097560976,2.050731707317073,...
2.11170731707317,2.172682926829268,2.233658536585366,2.294634146341463,...
2.355609756097561,2.416585365853658,2.477560975609756,2.538536585365854,...
2.599512195121951,2.660487804878048,2.721463414634146,2.782439024390244,...
2.843414634146341,2.904390243902439,2.965365853658537,3.026341463414634,...
3.087317073170731,3.148292682926829,3.209268292682927,3.270243902439024,...
3.331219512195122,3.392195121951219,3.453170731707317,3.514146341463415,...
3.575121951219512,3.636097560975609,3.697073170731707,3.758048780487805,...
3.819024390243902];
coor(2,(1:336))=[0,0.469303,0.927051,1.36197,1.76336,2.12132,2.42705,2.67302,2.85317,...
2.96307,3,2.96307,2.85317,2.67302,2.42705,2.12132,1.76336,1.36197,0.927051,...
0.469303,3.67394e-16,-0.469303,-0.927051,-1.36197,-1.76336,-2.12132,-2.42705,...
-2.67302,-2.85317,-2.96307,-3,-2.96307,-2.85317,-2.67302,-2.42705,-2.12132,...
-1.76336,-1.36197,-0.927051,-0.469303,0,0.003165,0.006306,0.009416,0.01248,...
0.015489,0.018441,0.021348,0.024219,0.027062,0.029874,0.032644,0.03536,0.038011,...
0.040585,0.043071,0.045457,0.047729,0.049874,0.051885,0.053753,0.05547,0.057026,...
0.058414,0.059629,0.06066,0.061497,0.062133,0.062562,0.062779,0.062774,0.06253,...
0.062029,0.061254,0.060194,0.058845,0.057218,0.055344,0.053258,0.050993,0.048575,...
0.046029,0.043377,0.040641,0.037847,0.035017,0.032176,0.029347,0.026554,0.023817,...
0.021153,0.01858,0.016113,0.013769,0.011562,0.009508,0.007622,0.005915,0.004401,...
0.003092,0.002001,0.001137,0.00051,0.000128,0,3.5e-05,0.000137,0.000296,0.000497,...
0.000719,0.000935,0.001112,0.001212,0.001197,0.001033,0.000694,0.000157,-0.0006,...
-0.001592,-0.002829,-0.004314,-0.006048,-0.008027,-0.010244,-0.01269,-0.015357,...
-0.018232,-0.021289,-0.024495,-0.027814,-0.031207,-0.034631,-0.038043,-0.041397,...
-0.044642,-0.047719,-0.050563,-0.053099,-0.055257,-0.056979,-0.058224,-0.058974,...
-0.059236,-0.059046,-0.058459,-0.057547,-0.056376,-0.054994,-0.053427,-0.051694,...
-0.049805,-0.047773,-0.04561,-0.043326,-0.040929,-0.038431,-0.035843,-0.03317,...
-0.030416,-0.027586,-0.024685,-0.021722,-0.018707,-0.015649,-0.012559,-0.009443,...
-0.006308,-0.00316,-0.0647048,-0.0635442,-0.0625176,-0.061627,-0.0608774,...
-0.0602715,-0.0598087,-0.0594824,-0.0592875,-0.0592187,-0.0592745,-0.0594566,...
-0.0597665,-0.0602051,-0.0607738,-0.0614727,-0.0623028,-0.0632651,-0.0643601,...
-0.065586,-0.0669416,-0.0684247,-0.0700342,-0.0717672,-0.0736205,-0.0755926,...
-0.0776817,-0.0798847,-0.0821979,-0.0846173,-0.0871408,-0.0897689,-0.0925024,...
-0.0953418,-0.0982852,-0.101328,-0.10446,-0.107663,-0.110917,-0.114205,-0.11751,...
-0.120816,-0.12411,-0.127378,-0.130604,-0.133775,-0.136875,-0.139892,-0.142811,...
-0.145621,-0.14831,-0.150867,-0.153283,-0.155548,-0.157653,-0.159589,-0.161347,...
-0.162921,-0.164303,-0.165486,-0.166465,-0.167233,-0.167786,-0.168121,-0.168232,...
-0.168157,-0.16793,-0.167558,-0.167046,-0.166403,-0.165642,-0.164777,-0.163824,...
-0.1628,-0.161721,-0.1606,-0.159448,-0.158277,-0.157098,-0.155916,-0.154738,...
-0.153568,-0.152409,-0.151262,-0.15013,-0.149014,-0.147914,-0.146826,-0.145742,...
-0.144654,-0.143552,-0.142427,-0.141266,-0.140058,-0.138791,-0.137446,-0.136005,...
-0.134445,-0.132744,-0.130888,-0.128866,-0.126677,-0.124329,-0.121843,-0.119246,...
-0.116571,-0.113849,-0.111105,-0.108353,-0.105606,-0.102873,-0.100164,-0.0974884,...
-0.094854,-0.0922684,-0.0897401,-0.0872772,-0.0848852,-0.0825688,-0.080333,...
-0.0781826,-0.0761234,-0.0741615,-0.0723023,-0.0705518,-0.0689135,-0.0673913,...
-0.065988,-0.1890634146341463,-0.2055268292682927,-0.221990243902439,...
-0.2384536585365853,-0.2549170731707317,-0.271380487804878,-0.2878439024390244,...
-0.3043073170731707,-0.3207707317073171,-0.3372341463414634,-0.3536975609756098,...
-0.370160975609756,-0.3866243902439024,-0.4030878048780487,-0.4195512195121951,...
-0.4360146341463415,-0.4524780487804878,-0.4689414634146342,-0.4854048780487805,...
-0.5018682926829269,-0.5183317073170731,-0.5347951219512195,-0.5512585365853658,...
-0.5677219512195122,-0.5841853658536585,-0.6006487804878049,-0.6171121951219513,...
-0.6335756097560975,-0.6500390243902439,-0.6665024390243902,-0.6829658536585366,...
-0.6994292682926829,-0.7158926829268293,-0.7323560975609756,-0.748819512195122,...
-0.7652829268292684,-0.7817463414634146,-0.798209756097561,-0.8146731707317073,...
-0.8311365853658537];
weight = [0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;...
0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;...
0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01];
npropa = int64(-5);
itrace = int64(0);
[nv, nelt, coorOut, conn, ifail] = ...
nag_mesh_2d_gen_delaunay(nvb, edge, coor, weight, npropa, itrace);
nv
nelt
ifail
```
```

nv =

2321

nelt =

4348

ifail =

0

```
```function d06ab_example
nvb = int64(296);
edge = zeros(3, 296, 'int64');
edge(1,(1:296)) = ...
[int64(1),2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,...
27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,...
53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,...
79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,...
104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,...
124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,...
144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,...
164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,...
184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,...
204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,...
224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,...
244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,...
264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,...
284,285,286,287,288,289,290,291,292,293,294,295,296];
edge(2,(1:296)) = ...
[int64(2),3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,...
31,32,33,34,35,36,37,38,39,40,1,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,...
58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,...
85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,...
109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,...
129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,...
149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,...
41,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,...
189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,...
209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,...
229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,...
249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,...
269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,...
289,290,291,292,293,294,295,296,169];
coor = zeros(2, 6000);
coor(1,(1:336)) = [4,3.96307,3.85317,3.67302,3.42705,3.12132,2.76336,2.36197,1.92705,...
1.4693,1,0.530697,0.072949,-0.361971,-0.763356,-1.12132,-1.42705,-1.67302,...
-1.85317,-1.96307,-2,-1.96307,-1.85317,-1.67302,-1.42705,-1.12132,-0.763356,...
-0.361971,0.072949,0.530697,1,1.4693,1.92705,2.36197,2.76336,3.12132,3.42705,...
3.67302,3.85317,3.96307,0,0.000602,0.002408,0.005412,0.009607,0.014984,0.02153,...
0.029228,0.03806,0.048005,0.059039,0.071136,0.084265,0.098396,0.113495,0.129524,...
0.146447,0.164221,0.182803,0.20215,0.222215,0.242949,0.264302,0.286222,0.308658,...
0.331555,0.354858,0.37851,0.402455,0.426635,0.450991,0.475466,0.5,0.524534,...
0.549009,0.573365,0.597545,0.62149,0.645142,0.668445,0.691342,0.713778,0.735698,...
0.757051,0.777785,0.79785,0.817197,0.835779,0.853553,0.870476,0.886505,0.901604,...
0.915735,0.928864,0.940961,0.951995,0.96194,0.970772,0.97847,0.985016,0.990393,...
0.994588,0.997592,0.999398,1,0.999398,0.997592,0.994588,0.990393,0.985016,0.97847,...
0.970772,0.96194,0.951995,0.940961,0.928864,0.915735,0.901604,0.886505,0.870476,...
0.853553,0.835779,0.817197,0.79785,0.777785,0.757051,0.735698,0.713778,0.691342,...
0.668445,0.645142,0.62149,0.597545,0.573365,0.549009,0.524534,0.5,0.475466,...
0.450991,0.426635,0.402455,0.37851,0.354858,0.331555,0.308658,0.286222,0.264302,...
0.242949,0.222215,0.20215,0.182803,0.164221,0.146447,0.129524,0.113495,0.098396,...
0.084265,0.071136,0.059039,0.048005,0.03806,0.029228,0.02153,0.014984,0.009607,...
0.005412,0.002408,0.000602,0.991481,0.992042,0.993065,0.994547,0.996485,0.998874,...
1.00171,1.00498,1.00869,1.01283,1.01739,1.02235,1.0277,1.03343,1.03953,1.04598,...
1.05277,1.05987,1.06727,1.07496,1.0829,1.09109,1.0995,1.10812,1.11691,1.12586,...
1.13495,1.14416,1.15346,1.16282,1.17223,1.18166,1.19109,1.20049,1.20983,1.2191,...
1.22828,1.23734,1.24626,1.25503,1.26362,1.27203,1.28022,1.28819,1.29591,1.30337,...
1.31055,1.31744,1.32402,1.33027,1.33619,1.34176,1.34696,1.35179,1.35624,1.36029,...
1.36394,1.36717,1.36999,1.37238,1.37435,1.37588,1.37697,1.37763,1.37785,1.37762,...
1.37694,1.37579,1.37419,1.37214,1.36963,1.36667,1.36327,1.35943,1.35515,1.35044,...
1.34531,1.33977,1.33384,1.32751,1.32082,1.31378,1.30639,1.29869,1.29068,1.28239,...
1.27385,1.26506,1.25606,1.24687,1.23751,1.22802,1.21842,1.20873,1.19898,1.18921,...
1.17943,1.16969,1.16001,1.15042,1.14095,1.13162,1.12246,1.11347,1.10469,1.09611,...
1.08776,1.07966,1.07181,1.06423,1.05695,1.04999,1.04334,1.03704,1.0311,1.02552,...
1.02033,1.01553,1.01114,1.00717,1.00363,1.00053,0.997863,0.995651,0.993893,...
0.992595,0.991759,0.991387,1.440975609756098,1.501951219512195,1.562926829268293,...
1.62390243902439,1.684878048780488,1.745853658536585,1.806829268292683,...
1.86780487804878,1.928780487804878,1.989756097560976,2.050731707317073,...
2.11170731707317,2.172682926829268,2.233658536585366,2.294634146341463,...
2.355609756097561,2.416585365853658,2.477560975609756,2.538536585365854,...
2.599512195121951,2.660487804878048,2.721463414634146,2.782439024390244,...
2.843414634146341,2.904390243902439,2.965365853658537,3.026341463414634,...
3.087317073170731,3.148292682926829,3.209268292682927,3.270243902439024,...
3.331219512195122,3.392195121951219,3.453170731707317,3.514146341463415,...
3.575121951219512,3.636097560975609,3.697073170731707,3.758048780487805,...
3.819024390243902];
coor(2,(1:336))=[0,0.469303,0.927051,1.36197,1.76336,2.12132,2.42705,2.67302,2.85317,...
2.96307,3,2.96307,2.85317,2.67302,2.42705,2.12132,1.76336,1.36197,0.927051,...
0.469303,3.67394e-16,-0.469303,-0.927051,-1.36197,-1.76336,-2.12132,-2.42705,...
-2.67302,-2.85317,-2.96307,-3,-2.96307,-2.85317,-2.67302,-2.42705,-2.12132,...
-1.76336,-1.36197,-0.927051,-0.469303,0,0.003165,0.006306,0.009416,0.01248,...
0.015489,0.018441,0.021348,0.024219,0.027062,0.029874,0.032644,0.03536,0.038011,...
0.040585,0.043071,0.045457,0.047729,0.049874,0.051885,0.053753,0.05547,0.057026,...
0.058414,0.059629,0.06066,0.061497,0.062133,0.062562,0.062779,0.062774,0.06253,...
0.062029,0.061254,0.060194,0.058845,0.057218,0.055344,0.053258,0.050993,0.048575,...
0.046029,0.043377,0.040641,0.037847,0.035017,0.032176,0.029347,0.026554,0.023817,...
0.021153,0.01858,0.016113,0.013769,0.011562,0.009508,0.007622,0.005915,0.004401,...
0.003092,0.002001,0.001137,0.00051,0.000128,0,3.5e-05,0.000137,0.000296,0.000497,...
0.000719,0.000935,0.001112,0.001212,0.001197,0.001033,0.000694,0.000157,-0.0006,...
-0.001592,-0.002829,-0.004314,-0.006048,-0.008027,-0.010244,-0.01269,-0.015357,...
-0.018232,-0.021289,-0.024495,-0.027814,-0.031207,-0.034631,-0.038043,-0.041397,...
-0.044642,-0.047719,-0.050563,-0.053099,-0.055257,-0.056979,-0.058224,-0.058974,...
-0.059236,-0.059046,-0.058459,-0.057547,-0.056376,-0.054994,-0.053427,-0.051694,...
-0.049805,-0.047773,-0.04561,-0.043326,-0.040929,-0.038431,-0.035843,-0.03317,...
-0.030416,-0.027586,-0.024685,-0.021722,-0.018707,-0.015649,-0.012559,-0.009443,...
-0.006308,-0.00316,-0.0647048,-0.0635442,-0.0625176,-0.061627,-0.0608774,...
-0.0602715,-0.0598087,-0.0594824,-0.0592875,-0.0592187,-0.0592745,-0.0594566,...
-0.0597665,-0.0602051,-0.0607738,-0.0614727,-0.0623028,-0.0632651,-0.0643601,...
-0.065586,-0.0669416,-0.0684247,-0.0700342,-0.0717672,-0.0736205,-0.0755926,...
-0.0776817,-0.0798847,-0.0821979,-0.0846173,-0.0871408,-0.0897689,-0.0925024,...
-0.0953418,-0.0982852,-0.101328,-0.10446,-0.107663,-0.110917,-0.114205,-0.11751,...
-0.120816,-0.12411,-0.127378,-0.130604,-0.133775,-0.136875,-0.139892,-0.142811,...
-0.145621,-0.14831,-0.150867,-0.153283,-0.155548,-0.157653,-0.159589,-0.161347,...
-0.162921,-0.164303,-0.165486,-0.166465,-0.167233,-0.167786,-0.168121,-0.168232,...
-0.168157,-0.16793,-0.167558,-0.167046,-0.166403,-0.165642,-0.164777,-0.163824,...
-0.1628,-0.161721,-0.1606,-0.159448,-0.158277,-0.157098,-0.155916,-0.154738,...
-0.153568,-0.152409,-0.151262,-0.15013,-0.149014,-0.147914,-0.146826,-0.145742,...
-0.144654,-0.143552,-0.142427,-0.141266,-0.140058,-0.138791,-0.137446,-0.136005,...
-0.134445,-0.132744,-0.130888,-0.128866,-0.126677,-0.124329,-0.121843,-0.119246,...
-0.116571,-0.113849,-0.111105,-0.108353,-0.105606,-0.102873,-0.100164,-0.0974884,...
-0.094854,-0.0922684,-0.0897401,-0.0872772,-0.0848852,-0.0825688,-0.080333,...
-0.0781826,-0.0761234,-0.0741615,-0.0723023,-0.0705518,-0.0689135,-0.0673913,...
-0.065988,-0.1890634146341463,-0.2055268292682927,-0.221990243902439,...
-0.2384536585365853,-0.2549170731707317,-0.271380487804878,-0.2878439024390244,...
-0.3043073170731707,-0.3207707317073171,-0.3372341463414634,-0.3536975609756098,...
-0.370160975609756,-0.3866243902439024,-0.4030878048780487,-0.4195512195121951,...
-0.4360146341463415,-0.4524780487804878,-0.4689414634146342,-0.4854048780487805,...
-0.5018682926829269,-0.5183317073170731,-0.5347951219512195,-0.5512585365853658,...
-0.5677219512195122,-0.5841853658536585,-0.6006487804878049,-0.6171121951219513,...
-0.6335756097560975,-0.6500390243902439,-0.6665024390243902,-0.6829658536585366,...
-0.6994292682926829,-0.7158926829268293,-0.7323560975609756,-0.748819512195122,...
-0.7652829268292684,-0.7817463414634146,-0.798209756097561,-0.8146731707317073,...
-0.8311365853658537];
weight = [0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;...
0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;...
0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01;0.01];
npropa = int64(-5);
itrace = int64(0);
[nv, nelt, coorOut, conn, ifail] = d06ab(nvb, edge, coor, weight, npropa, itrace);
nv
nelt
ifail
```
```

nv =

2321

nelt =

4348

ifail =

0

```