Chaste Commit::ca8ccdedf819b6e02855bc0e8e6f50bdecbc5208
PottsElement.cpp
1/*
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34*/
35#include "PottsElement.hpp"
36#include "RandomNumberGenerator.hpp"
37
38
39template<unsigned DIM>
40PottsElement<DIM>::PottsElement(unsigned index, const std::vector<Node<DIM>*>& rNodes)
41 : MutableElement<DIM,DIM>(index, rNodes)
42{
43 this->RegisterWithNodes();
44}
45
46template<unsigned DIM>
50
51template<unsigned DIM>
52void PottsElement<DIM>::AddNode(Node<DIM>* pNode, const unsigned& rIndex)
53{
54 // Add element to this node
55 pNode->AddElement(this->mIndex);
56
57 // Add pNode to mNodes
58 this->mNodes.push_back(pNode);
59}
60
61template<unsigned DIM>
63{
64 assert(DIM == 2); // LCOV_EXCL_LINE
65 assert(this->GetNumNodes() != 0);
66
67 if (this->GetNumNodes() <= 2)
68 {
69 return 1.0;
70 }
71
72 double eig_max;
73 double eig_min;
74
75 // See http://stackoverflow.com/questions/7059841/estimating-aspect-ratio-of-a-convex-hull for how to do it.
76 switch (DIM)
77 {
78 case 2:
79 {
80 // Calculate entries of covariance matrix (var_x,cov_xy;cov_xy,var_y)
81 double mean_x=0;
82 double mean_y=0;
83
84 for (unsigned i=0; i<this->GetNumNodes(); i++)
85 {
86 mean_x += this->mNodes[i]->rGetLocation()[0];
87 mean_y += this->mNodes[i]->rGetLocation()[1];
88 }
89 mean_x /= this->GetNumNodes();
90 mean_y /= this->GetNumNodes();
91
92 double variance_x = 0;
93 double variance_y = 0;
94 double covariance_xy = 0;
95
96 for (unsigned i=0; i<this->GetNumNodes(); i++)
97 {
98 variance_x += pow((this->mNodes[i]->rGetLocation()[0]-mean_x),2);
99 variance_y += pow((this->mNodes[i]->rGetLocation()[1]-mean_y),2);
100 covariance_xy += (this->mNodes[i]->rGetLocation()[0]-mean_x)*(this->mNodes[i]->rGetLocation()[1]-mean_y);
101 }
102 variance_x /= this->GetNumNodes();
103 variance_y /= this->GetNumNodes();
104 covariance_xy /= this->GetNumNodes();
105
106 // Calculate max/min eigenvalues
107 double trace = variance_x+variance_y;
108 double det = variance_x*variance_y - covariance_xy*covariance_xy;
109
110 eig_max = 0.5*(trace+sqrt(trace*trace - 4*det));
111 eig_min = 0.5*(trace-sqrt(trace*trace - 4*det));
112
113 break;
114 }
115// case 3:
116// {
117// double mean_x = 0;
118// double mean_y = 0;
119// double mean_z = 0;
120//
121// for (unsigned i=0; i<this->GetNumNodes(); i++)
122// {
123// mean_x += this->mNodes[i]->rGetLocation()[0];
124// mean_y += this->mNodes[i]->rGetLocation()[1];
125// mean_z += this->mNodes[i]->rGetLocation()[2];
126// }
127// mean_x /= this->GetNumNodes();
128// mean_y /= this->GetNumNodes();
129// mean_z /= this->GetNumNodes();
130//
131// double variance_x = 0;
132// double variance_y = 0;
133// double variance_z = 0;
134//
135// double covariance_xy = 0;
136// double covariance_xz = 0;
137// double covariance_yz = 0;
138//
139// for (unsigned i=0; i<this->GetNumNodes(); i++)
140// {
141// double diff_x = this->mNodes[i]->rGetLocation()[0]-mean_x;
142// double diff_y = this->mNodes[i]->rGetLocation()[1]-mean_y;
143// double diff_z = this->mNodes[i]->rGetLocation()[2]-mean_z;
144//
145// variance_x += diff_x*diff_x;
146// variance_y += diff_y*diff_y;
147// variance_z += diff_z*diff_z;
148// covariance_xy += diff_x*diff_y;
149// covariance_xz += diff_x*diff_z;
150// covariance_yz += diff_y*diff_z;
151// }
152// variance_x /= this->GetNumNodes();
153// variance_y /= this->GetNumNodes();
154// variance_z /= this->GetNumNodes();
155// covariance_xy /= this->GetNumNodes();
156// covariance_xz /= this->GetNumNodes();
157// covariance_yz /= this->GetNumNodes();
158//
159// c_matrix<PetscScalar, 3, 3> covariance_matrix;
160//
161// covariance_matrix(0,0) = variance_x;
162// covariance_matrix(0,1) = covariance_xy;
163// covariance_matrix(0,2) = covariance_xz;
164//
165// covariance_matrix(1,0) = covariance_xy;
166// covariance_matrix(1,1) = variance_y;
167// covariance_matrix(1,2) = covariance_yz;
168//
169// covariance_matrix(2,0) = covariance_xz;
170// covariance_matrix(2,1) = covariance_yz;
171// covariance_matrix(2,2) = variance_z;
172//
173// const char N = 'N';
174// const char L = 'L';
175// PetscBLASInt size = DIM;
176// PetscReal eigs[DIM];
177// // Optimal work_size (102 entries) computed with a special call to LAPACKsyev_ (see documentation)
178// // and rounded to the next power of 2
179// const PetscBLASInt work_size = 128;
180// PetscScalar workspace[work_size];
181// PetscBLASInt info;
182// LAPACKsyev_(&N, &L, &size, covariance_matrix.data(), &size, eigs, workspace, (PetscBLASInt*) &work_size, &info);
183// assert(info == 0);
184//
185// // Lapack returns eigenvalues in ascending order
186// eig_max = eigs[DIM-1];
187// eig_min = eigs[0] +eigs[1];
188// break;
189// }
190 default:
192 }
193
194 // As matrix is symmetric positive semidefinite
195 assert(eig_min >= 0);
196 assert(eig_max >= 0);
197
198 if (eig_min == 0)
199 {
200 EXCEPTION("All nodes in an element lie in the same line/plane (2D/3D) so aspect ratio is infinite. This interferes with calculation of the Hamiltonian.");
201 }
202
203 return eig_max/eig_min;
204}
205
206// Explicit instantiation
207template class PottsElement<1>;
208template class PottsElement<2>;
209template class PottsElement<3>;
#define EXCEPTION(message)
#define NEVER_REACHED
Definition Node.hpp:59
void AddElement(unsigned index)
Definition Node.cpp:268
void AddNode(Node< DIM > *pNode, const unsigned &rIndex=UINT_MAX)
PottsElement(unsigned index, const std::vector< Node< DIM > * > &rNodes)
double GetAspectRatio()