void Eigsym::eig(const MatDoub &A, MatDoub &V, VecDoub &lambda) {
unsigned int n = A.ncols(); /* size of the matrix */
double a[n*n]; /* store initial matrix */
double w[n]; /* store eigenvalues */
int matz = 1; /* return both eigenvalues as well as eigenvectors */
double x[n*n]; /* store eigenvectors */
for(unsigned int i=0; i<n; i++) {
for(unsigned int j=0; j<n; j++) {
a[i*n+j] = A[i][j];
}
}
unsigned int ierr = 0;
ierr = rs ( n, a, w, matz, x );
V.assign(n,n,0.0);
lambda.resize(n);
for(unsigned int i=0; i<n; i++) {
lambda[i] = w[i];
for(unsigned int j=0; j<n; j++) {
V[j][i] = x[i*n+j];
}
}
}
void dump_nrmat( MatDoub &m ) {
for( int r=0; r<m.nrows(); r++ ) {
for( int c=0; c<m.ncols(); c++ ) {
printf( "%+3.2le ", m[r][c] );
}
printf( "\n" );
}
}
void addMatrixRow(MatDoub U, int row, MatDoub &out) {
int dummy = -1000;
for(int i=0; i<out.nrows(); i++) {
out[i][row] = dummy;
out[row][i] = dummy;
}
datain = U.getMatrixArray();
datainO = out.getMatrixArray();
int k = 0;
data [out.nrows()*out.nrows()];
for(int i=0; i < out.nrows()*out.ncols(); i++) {
if( datainO[i] == dummy )
data[i] = 0;
else
data[i] = datain[k++];
}
out = MatDoub( out.nrows(), out.nrows(), data );
}
void copyNRMatToZMat( MatDoub &m, ZMat &z ) {
// account for NR3 is rowmajor, ZMat is colmajor.
int rows = m.nrows();
int cols = m.ncols();
if( z.rows != rows || z.cols != cols ) {
z.alloc( rows, cols, zmatF64 );
}
for( int r=0; r<rows; r++ ) {
for( int c=0; c<cols; c++ ) {
z.putD( r, c, m[r][c] );
}
}
}
/*
Calculte the Modularity matrix when split
into more than two communities, see [2]
in method declarations above.
*/
void calculateB(MatDoub B, MatDoub &Bg) {
int Ng = B.ncols();
if( Bg.ncols() != Ng )
Bg.resize(Ng,Ng);
for(int i=0; i<Ng; i++) {
for(int j=0; j<Ng; j++) {
double sum = 0.0;
for(int k=0; k<Ng; k++)
sum += B[i][k];
Bg[i][j] = B[i][j] -1.0 * delta(i,j) * sum;
}
}
}
/*
Utility method used by Geodesic and RandomWalk algorithms
to set-up the Modularity and Laplacian matrices.
*/
void setupMatrices() {
//--- Matrix size
int N = R.nrows();
//--- 2*m
two_m = elist.size();
//--- _norm
_norm = 1.0/(2.0*two_m);
for(int i=0; i<N; i++) {
C[i] = 0;
for(int j=0; j<N; j++) {
R[i][j] = 0.0;
A[i][j] = 0.0;
}
}
//--- Setup Matrices
//--- Store the current community each vertex belongs too
for(int i=0; i<N; i++) {
R[i][i] = n[i+1].getDegree();
C[i] = n[i+1].c;
}
//--- The Adjacency matrix, A
for(int i=0; i<elist.size(); i++) {
int ind_i = elist[i].so -1;
int ind_j = elist[i].si -1;
if( ind_i == ind_j || ind_j == ind_i )
A[ind_i][ind_j] = 1.0 * elist[i].Globalwe;
else {
A[ind_i][ind_j] = 1.0 * elist[i].Globalwe;
A[ind_j][ind_i] = 1.0 * elist[i].Globalwe;
}
}
for(int i=0; i<N; i++) {
for(int j=0; j<N; j++) {
R[i][j] = R[i][j] - A[i][j];
}
}
//--- The Modularity matrix, Bgi
for(int i=1; i<n.size(); i++) {
for(int j=1; j<n.size(); j++) {
Bgi[i-1][j-1] = A[i-1][j-1] - (n[i].getDegree() * n[j].getDegree() * _norm);
}
}
}
/*
Utility method use by the RandomWalk algorithm to
update the Adjacency and Laplacian matrices.
*/
void upDateMatrices() {
for(int i=0; i<R.nrows(); i++) {
C[i] = 0;
for(int j=0; j<R.nrows(); j++) {
R[i][j] = 0;
}
}
//--- Setup Matrices
//--- Store the current community each vertex belongs too
for(int i=0; i<R.nrows(); i++) {
R[i][i] = n[i+1].getDegree();
C[i] = n[i+1].c;
}
//--- Update the Adjacency matrix, A
for(int i=0; i<elist.size(); i++) {
if( elist[i].removed ) {
int ind_i = elist[i].so -1;
int ind_j = elist[i].si -1;
//if edge has been removed, remove this entry
//from A.
A[ind_i][ind_j] = 0;
A[ind_j][ind_i] = 0;
}
}
//--- Update the Lapacian matrix, R
for(int i=0; i<R.nrows(); i++) {
for(int j=0; j<R.nrows(); j++) {
R[i][j] = R[i][j] - A[i][j];
}
}
}
/*
Utility method used by RandomWalk algorithm to
resize the Graph Laplacian for each community, com,
within the network.
*/
void getSubMatrix(int com, vector<node> &Nodes) {
int dummy = -1000;
int rows = 0;
Rh.resize(R.nrows(), R.nrows());
Rh = R;
//--- NR style
for( int i=0; i< C.size(); i++) {
if( C[i] == com )
Nodes.push_back(node(rows++,0.0,0.0));
else {
for( int k=0; k<Rh.nrows(); k++) {
Rh[i][k] = dummy;
Rh[k][i] = dummy;
}
}
}
datain = Rh.getMatrixArray();
data [Rh.nrows()*Rh.nrows()];
int ind = 0;
for(int i=0; i < Rh.nrows()*Rh.ncols(); i++) {
double ele = datain[i];
if(ele != dummy)
data[ind++] = ele;
}
Ri.resize(rows,rows);
Ri = MatDoub( rows, rows, data );
}
/*
Update the index vectors, si and SI, for each node in the
current split such that:
si(i) = 1 if eigenvector_max(i) > 0
= -1 if eigenvector_max(i) < 0
SI(i,0) = 1
SI(i,1) = 0 if eigenvector_max(i) > 0
= 0
= 1 if eigenvector_max(i) < 0
*/
void maximiseIndexVectors( int ind ) {
int Ng = u.ncols();
si.resize(Ng);
SI.resize(Ng,2);
for(int i=0; i<Ng; i++) {
if(u[i][ind] < 0) {
si[i] = -1;
SI[i][0] = 0;
SI[i][1] = 1;
} else {
si[i] = 1;
SI[i][0] = 1;
SI[i][1] = 0;
}
}
}
/*
Calculate the eigenvalues, betai, and eigenvectors, u, for
the current Modularity matrix Bgi.
*/
void calculateEigenVectors() {
int Ng = Bgi.ncols();
if(u.ncols() != Ng) {
u.resize(Ng,Ng);
betai.resize(Ng);
}
u.resize(Ng,Ng);
betai.resize(Ng);
Symmeig h(Bgi, true);
for(int i=0; i<Ng; i++) {
betai[i] = h.d[i];
for(int j=0; j<Ng; j++) {
u[j][i] = h.z[j][i];
}
}
}
/*
Find the leading eigenvector, i.e.
the one which corresponds to the most positive
eigenvalue.
*/
void findLeadingEigenVectors(int &ind) {
int Ng = Bgi.ncols();
int ind_max = 0;
int ind_min = 0;
double max = betai[ind_max];
double min = betai[ind_min];
for(int i=0; i<Ng-1; i++) {
if( betai[i] > max ) {
max = betai[i];
ind_max = i;
}
}
ind = ind_max;
}
void removeMatrixRow( MatDoub Unr, MatDoub &outnr ) {
int dummy = -1000;
datain = Unr.getMatrixArray();
data [outnr.nrows()*outnr.nrows()];
int k=0;
for(int i=0; i < Unr.nrows()*Unr.nrows(); i++) {
double ele = datain[i];
if(ele != dummy)
data[k++] = ele;
}
outnr = MatDoub( outnr.nrows(), outnr.nrows(), data );
}
void removeMatrixRow( MatDoub &out ) {
int dummy = -1000;
datain = Ri.getMatrixArray();
data [Ri.nrows()*Ri.nrows()];
int k=0;
for(int i=0; i < Ri.nrows()*Ri.ncols(); i++) {
double ele = datain[i];
if(ele != dummy)
data[k++] = ele;
}
out = MatDoub( out.nrows(), out.nrows(), data );
}
double interpolate( util::matrix_t<double> &data,
util::matrix_t<double> &par,
double I, double T, int idx, bool quiet )
{
MatDoub tempirr;
std::vector<double> parvals;
std::vector<sp_point> pts, hull;
double maxz = -1e99;
double tmin = 1e99;
double tmax = -1e99;
double imin = 1e99;
double imax = -1e99;
double dist = 1e99;
int idist = -1;
for( size_t i=0;i<data.nrows();i++ )
{
double z = par(i,idx);
if ( !std::isfinite( z ) )
continue;
double temp = data(i,TC);//x value
double irr = data(i,IRR);//y value
if ( temp < tmin ) tmin = temp;
if ( temp > tmax ) tmax = temp;
if ( irr < imin ) imin = irr;
if ( irr > imax ) imax = irr;
double d = sqrt( (irr-I)*(irr-I) + (temp-T)*(temp-T) );
if ( d < dist )
{
dist = d;
idist = (int)i;
}
std::vector<double> it(2,0.0);
it[0] = temp; it[1] = irr;
tempirr.push_back( it );
parvals.push_back( z );
if ( z > maxz ) maxz = z;
pts.push_back( sp_point( temp, irr, z ) );
}
Toolbox::convex_hull( pts, hull );
if ( Toolbox::pointInPolygon( hull, sp_point(T, I, 0.0) ) )
{
// scale values based on max - helps GM interp routine
for( size_t i=0;i<parvals.size();i++)
parvals[i] /= maxz;
Powvargram vgram( tempirr, parvals, 1.75, 0. );
GaussMarkov gm( tempirr, parvals, vgram );
// test the fit against the data
double err_fit = 0.;
for( size_t i=0;i<parvals.size();i++ )
{
double zref = parvals[i];
double zfit = gm.interp( tempirr[i] );
double dz = zref - zfit;
err_fit += dz*dz;
}
err_fit = sqrt(err_fit);
if ( err_fit > 0.01 )
{
log( util::format("interpolation function for iec61853 parameter '%s' at I=%lg T=%lg is poor: %lg RMS",
parnames[idx], I, T, err_fit ),
SSC_WARNING );
}
std::vector<double> q(2,0.0);
q[0] = T;
q[1] = I;
// now interpolate and return the value
return gm.interp( q ) * maxz;
}
else
{
// if we're pretty close, return the nearest known value
if ( dist < 30. )
{
if ( !quiet )
log( util::format("query point (%lg, %lg) is outside convex hull of data but close... returning nearest value from data table at (%lg, %lg)=%lg",
T, I, data(idist,TC), data(idist,IRR), par(idist,idx) ),
SSC_WARNING );
return par(idist,idx);
}
// fall back to the 5 parameter model's auxiliary equations
// to estimate the parameter values outside the convex hull
int idx_stc = -1;
for( size_t i=0;i<data.nrows();i++)
if ( data(i,IRR) == 1000.0
&& data(i,TC) == 25.0 )
idx_stc = (int)i;
if ( idx_stc < 0 )
throw general_error("STC conditions required to be supplied in the temperature/irradiance data");
double value = par(idist,idx);;
if ( idx == A )
{
double a_nearest = par( idist, A );
double T_nearest = data( idist, TC );
double a_est = a_nearest * T/T_nearest;
value = a_est;
}
else if ( idx == IL )
{
double IL_nearest = par( idist, IL );
double I_nearest = data(idist, IRR );
double IL_est = IL_nearest * I/I_nearest;
value = IL_est;
}/*
else if ( idx == IO )
{
#define Tc_ref 298.15
#define Eg_ref 1.12
#define KB 8.618e-5
double IO_stc = par(idx_stc,IO);
double TK = T+273.15;
double EG = Eg_ref * (1-0.0002677*(TK-Tc_ref));
double IO_oper = IO_stc * pow(TK/Tc_ref, 3) * exp( 1/KB*(Eg_ref/Tc_ref - EG/TK) );
value = IO_oper;
}*/
else if ( idx == RSH )
{
double RSH_nearest = par( idist, RSH );
double I_nearest = data(idist, IRR );
double RSH_est = RSH_nearest * I_nearest/I;
value = RSH_est;
}
if ( !quiet )
log( util::format("query point (%lg, %lg) is too far out of convex hull of data (dist=%lg)... estimating value from 5 parameter modele at (%lg, %lg)=%lg",
T, I, dist, data(idist,TC), data(idist,IRR), value ),
SSC_WARNING );
return value;
}
}
/*
Calculate the split of nodes belonging to the last group of nodes
with negative eigenvector values.
*/
void splitN(MatDoub Bg, VecInt keys, int dummy, double tol) {
cout << "> In splitN method... " << endl;
int N = Bg.nrows();
MatDoub Bgii(N,N);
MatDoub Bgiii(N,N);
VecInt keysi_n (N);
//--- Starting from the group Modularity matrix Bg,
//--- resize matrices: Bgi, keysi_p, keysi_n, u and betai.
Bgiii = Bg;
int Ng = 0;
for(int i=0; i<keys.size(); i++) {
if(keys[i] != dummy) {
Ng++;
} else {
for(int k=0; k<Bgiii.nrows(); k++) {
Bgiii[i][k] = dummy;
Bgiii[k][i] = dummy;
}
}
}
keysi_n.resize(Ng);
VecInt keysi_p(Ng);
int k=0;
for(int i=0; i<keys.size(); i++) {
if(keys[i] != dummy)
keysi_n[k++] = keys[i];
}
Bgii.resize(Ng,Ng);
removeMatrixRow(Bgiii,Bgii);
Bgi.resize(Bgii.nrows(),Bgii.nrows());
//--- Calculate the Modularity matrix Bgi for the new node group
calculateB(Bgii, Bgi);
u.resize(Ng,Ng);
betai.resize(Ng);
//--- Calculate eigenvectors, and values, from Bgi...
calculateEigenVectors();
int ind = 0;
findLeadingEigenVectors(ind);
//--- Check that maximum eigenvalue is greater than the tolerance.
cout << "> max EigenValue is " << betai[ind] << " with ind " << ind << endl;
if(betai[ind] > tol ) {
//--- set up the index vectors, si and SI, for the initial split
maximiseIndexVectors(ind);
double deltaQ_old = 0.0;
double deltaQ_new = 0.0;
int cp = 0;
int cn = 0;
//--- Calculate the Spectral Modularity
deltaModularity(deltaQ_old);
cout << "> Spectral Q: " << deltaQ_old << endl;
double diff = fabs(deltaQ_old);
int count = 0;
//--- Fine tuning stage to maximum deltaModularity for the initial split
while( diff > tol ) {
modifySplit( tol, Ng );
deltaModularity(deltaQ_new);
cout << "> Modified Q: " << deltaQ_new << endl;
diff = fabs( deltaQ_new - deltaQ_old );
deltaQ_old = deltaQ_new;
}
//--- Keep recorded of maximum fine-tuned Modularity value.
specQ += deltaQ_old;
for(int i=0; i<Ng; i++) {
si[i] = si[i];
if(si[i] > 0) cp++;
else cn++;
}
if(cp < 1 || cn < 1) {
cout << "> Stop splitting. " << endl;
return;
}
int Ncomn = maxCommunity() + 1;
int Ncomp = Ncomn + 1;
cout << "> node list " << endl;
for(int i=0; i<keysi_n.size(); i++) {
if( si[i] < 0) {
keysi_n[i] = keysi_n[i];
keysi_p[i] = dummy;
n[(int)keysi_n[i]].c = Ncomn;
cout << "> Node: " << keysi_n[i] << " c = " << n[(int)keysi_n[i]].c << endl;
} else {
keysi_p[i] = keysi_n[i];
keysi_n[i] = dummy;
cout << "> Node: " << keysi_p[i] << " c = " << n[(int)keysi_p[i]].c << endl;
}
}
//--- Recursively split the group of positive eigenvector nodes
splitP(Bgii, keysi_p, dummy, tol);
//--- Recursively split the group of negative eigenvector nodes
splitN(Bgii, keysi_n, dummy, tol);
} else {
cout << "> Stop splitting. " << endl;
return ;
}
}
//--- MAIN PROGRAM
//-------------------------------------------------------------------------------------
int main(int argc, char * argv[]) {
int seed;
int a_type;
int w_type;
string title;
string if_weighted;
string if_help;
const char *file_network;
const char *file_names;
if ( argc != 5 ) {
printHelpMessage( argv[0] );
}
if_help = argv[1];
if( if_help.compare("-h") == 0 || if_help.compare("-help") == 0 ) {
printHelpMessage( argv[0] );
}
seed = atoi(argv[1]);
cout << "> seed is " << seed << endl;
//--- Initialize random seed:
_rand.setSeed(seed);
a_type = atoi(argv[2]);
if( a_type < 1 || a_type > 3 ) {
cout << "argument 2: the type of algorithm to run needs to be either (1,2,3): " << endl;
cout << " : 1 = Geodesic edge Betweenness" << endl;
cout << " : 2 = Random edge Betweenness" << endl;
cout << " : 3 = Spectral Betweenness" << endl;
exit(1);
}
switch(a_type) {
case 1:
cout << "> Using Geodesic edge Betweenness." << endl;
title = "Geodesic edge Betweenness.";
break;
case 2:
cout << "> Using Random edge Betweenness." << endl;
title = "RandomWalk edge Betweenness.";
break;
case 3:
cout << "> Using Spectral Betweenness." << endl;
title = "Spectral Betweenness.";
break;
default:
break;
}
if_weighted = argv[3];
if( if_weighted.compare("w") == 0 ) {
w_type = 3;
cout << "> Using a weighted network " << endl;
} else {
if( if_weighted.compare("nw") == 0 ) {
w_type = 2;
cout << "> Using a non-weighted network " << endl;
} else {
cout << "argument 3: specify if network file is weighted or not: " << endl;
cout << " : w = Using a weighted network file " << endl;
cout << " : nw = Using a non-weighted network file " << endl;
exit(1);
}
}
file_network = argv[4];
//--- Default values for parameters which may be modified from the commandline
ihelper = Helper();
reader.readFile(file_network, w_type);
Gn = reader.getNodeSet();
Gelist = reader.getEdgeSet();
vector<int> key_listi;
vector<int> key_listj;
vector<int> key_listk;
cout << "> The Global node list..." << endl;
for(int i=1; i<Gn.size(); i++) {
key_listi.push_back(Gn[i].ID);
key_listj.push_back(Gn[i].ID);
key_listk.push_back(-1);
Gn[i].print();
Gn[i].printEdges();
}
//--- To use getSubSample, comment following two lines, and
//--- uncomment getSubSample(key_listj).
n = Gn;
elist = Gelist;
//getSubSample(key_listj);
//cout << "The sub-node list ... " << endl;
//for(int i=1; i<n.size(); i++){
//n[i].print();
//n[i].printEdges();
//}
cout << "> The Global edge list..." << endl;
for(int i=0; i<elist.size(); i++) {
elist[i].print();
}
forcytoscape = new fstream("OUT/communities_newman.txt",ios_base::out);
(*forcytoscape) << "communities" << endl;
removededges = new fstream("OUT/removededges.txt",ios_base::out);
(*removededges) << "Removed Edges" << endl;
(*removededges) << "so \t IDso \t si \t IDsi \t we \t Globalweight \t key" << endl;
totallist = ihelper.cloneEdgeList(elist);
com_max = 0;
specQ = 0.0;
double Q = 0.0;
double Q_SD = 0.0;
double Q_old = 0.0;
double Q_SD_old = 0.0;
int loop = elist.size();
int E = loop;
double Q_max = 0.0;
double Q_limit = 1.0;
bool stopping = false;
int N = n.size()-1;
R.resize(N,N);
Ri.resize(N,N);
A.resize(N,N);
Ai.resize(N,N);
Bi.resize(N,N);
C.resize(N);
S.resize(N,1);
V.resize(N,1);
T.resize(N,N);
Ti.resize(N,N);
Rc.resize((N-1),(N-1));
Vi.resize(C.size(),1);
B.resize(N,N);
Bm.resize(N,N);
Bgi.resize(N,N);
keys_p.resize(N);
keys_n.resize(N);
u.resize(N,N); //eigenvectors
betai.resize(N);//eigenvalues
SI.resize(N,2);
si.resize(N);
visited.resize(N);
setupMatrices();
cout << "> Running " << title.c_str() << endl;
cstart = clock();
if( a_type == 3 ) {
//--- Calculate betweenness using the Spectral algorithm
calculateSpectralModularity();
} else {
while( loop !=0 && !stopping ) {
int old_max_com = com_max;
//--- Calculate betweenness using Geodesic or RandomWalk algorithms
if( a_type == 1 )
calculateEdgeBetweennessGeodesic();
else
calculateEdgeBetweennessRandom();
//--- Calculate the Modularity
Q_old = Q;
Q_SD_old = Q_SD;
Q = 0.0;
Q_SD = 0.0;
Modularity(Q, Q_SD);
//--- Store networks state if Modularity has increased during this iteraction
if(com_max > old_max_com) {
vec_mod.push_back(Q);
vec_mod_err.push_back(Q_SD);
vec_com_max.push_back(com_max);
vec_nodes.push_back(storeNodes());
}
//--- Record the maximum Modularity value
if( Q > Q_max ) {
Q_max = Q;
} else {
if( Q_max > 0.0 && (Q_max - Q)/Q_max > Q_limit ) stopping = true;
}
//--- Find edge with maximum edge betweenness score and remove
edge _max;
_max = totallist[1].Clone();
for(int i=1; i<totallist.size(); i++) {
if( totallist[i].removed == false ) {
if(totallist[i].we >= _max.we) {
if(totallist[i].we > _max.we)
_max = totallist[i];
else {
int rdm = rand()%2;
if(rdm == 1) _max = totallist[i];
}
}
}
totallist[i].we = 0;
}
//--- Record the removed edges.
_max.print( removededges );
n[elist[_max.key-1].so].removeEdge(_max.key);
n[elist[_max.key-1].si].removeEdge(_max.key);
n[elist[_max.key-1].so].setDegree( (n[elist[_max.key-1].so].getDegree() - 1) );
n[elist[_max.key-1].si].setDegree( (n[elist[_max.key-1].si].getDegree() - 1) );
totallist[_max.key].removed = true;
elist[_max.key-1].removed = true;
--loop;
//--- Calculate the remaining processor time
DrawProgressBar( 20, ((double)E - (double)loop)/(double)E );
}
}
//--- Recored the CPU-time taken
cend = clock();
double cpu_time_used = ((double) (cend - cstart)) / CLOCKS_PER_SEC;
cout << "" << endl;
cout << "> cputime: " << cpu_time_used << " seconds " << endl;
cout << "> Network (nodes): " << N << " (edges): " << E << endl;
if( a_type != 3 ) {
//--- Print all stored Modularity values
modularityscore = new fstream("OUT/modularityscore.txt",ios_base::out);
(*modularityscore) << title.c_str() << endl;
for(int i=0; i<vec_mod.size(); i++) {
(*modularityscore) << vec_mod[i] << " " << vec_mod_err[i] << " " << vec_com_max[i] << endl;
}
modularityscore->close();
int ind = findMax(vec_mod);
int com = 1;
int _size = 0;
int c_max = com_max;
//--- Print node communities for maximum Modularity value, for Geodesic or RandomWalk runs
communityout = new fstream("OUT/communityout.txt",ios_base::out);
(*communityout) << "Max Q: " << vec_mod[ind] << " +- " << vec_mod_err[ind] << endl;
(*communityout) << "cputime: " << cpu_time_used << " seconds " << endl;
(*communityout) << "Network (nodes): " << N << " (edges): " << E << endl;
while(com<(c_max+1)) {
_size = 0;
for(int i=0; i<vec_nodes[ind].size(); i++) {
if(vec_nodes[ind][i].c == com ) {
(*communityout) << vec_nodes[ind][i].ID << "\t" << vec_nodes[ind][i].c << endl;
//vec_nodes[ind][i].print( communityout );
_size++;
}
}
if(_size != 0)
(*communityout) << "community: " << com << " size: " << _size << endl;
com++;
}
for(int i=0; i<vec_nodes[ind].size(); i++) {
for(int j=0; j<key_listi.size(); j++) {
if(vec_nodes[ind][i].ID == key_listi[j]) {
key_listk[j] = vec_nodes[ind][i].c;
break;
}
}
}
//--- Print node communities for maximum Modularity for the consensus matrix
consensusout = new fstream("OUT/consensusout.txt",ios_base::out);
(*consensusout) << "key list" << endl;
for(int i=0; i<key_listi.size(); i++) {
if(key_listk[i] == -1 && key_listj[i] != -1)
key_listj[i] = -1;
(*consensusout) << key_listi[i] << " " << key_listj[i] << " " << key_listk[i] << endl;
//(*consensusout) << key_listi[i] << " = " << key_listk[i] << endl;
(*forcytoscape) << key_listi[i] << " = " << key_listk[i] << endl;
cout << key_listi[i] << " " << key_listj[i] << " " << key_listk[i] << endl;
}
} else {
int com = 1;
int _size = 0;
int c_max = maxCommunity();
//--- Store node communities for maximum Modularity for the Spectral Modularity run
communityout = new fstream("OUT/communityout.txt",ios_base::out);
(*communityout) << "communityout" << endl;
(*communityout) << "Max Q: " << specQ << endl;
(*communityout) << "cputime: " << cpu_time_used << " seconds " << endl;
(*communityout) << "Network (nodes): " << N << " (edges): " << E << endl;
while(com<(c_max+1)) {
_size = 0;
for(int i=0; i<n.size(); i++) {
if(n[i].c == com ) {
n[i].print( communityout );
_size++;
}
}
if(_size != 0)
(*communityout) << "community: " << com << " size: " << _size << endl;
com++;
}
for(int i=1; i<n.size(); i++) {
for(int j=0; j<key_listi.size(); j++) {
if(n[i].ID == key_listi[j]) {
key_listk[j] = n[i].c;
break;
}
}
}
//--- Print node communities for maximum Modularity the consensus matrix
consensusout = new fstream("OUT/consensusout.txt",ios_base::out);
(*consensusout) << "key list" << endl;
for(int i=0; i<key_listi.size(); i++) {
if(key_listk[i] == -1 && key_listj[i] != -1)
key_listj[i] = -1;
(*consensusout) << key_listi[i] << " " << key_listj[i] << " " << key_listk[i] << endl;
//(*consensusout) << key_listi[i] << " = " << key_listk[i] << endl;
(*forcytoscape) << key_listi[i] << " = " << key_listk[i] << endl;
cout << key_listi[i] << " " << key_listj[i] << " " << key_listk[i] << endl;
}
}
//--- Remove data structures
communityout->close();
forcytoscape->close();
vec_mod.clear();
vec_mod_err.clear();
vec_nodes.clear();
exit(1);
}
void C_csp_gen_collector_receiver::init(const C_csp_collector_receiver::S_csp_cr_init_inputs init_inputs,
C_csp_collector_receiver::S_csp_cr_solved_params & solved_params)
{
// Check that ms_params are set
check_double_params_are_set();
// Could sanity-check other parameters here...
if(ms_params.m_interp_arr < 1 || ms_params.m_interp_arr > 2)
{
std::string msg = util::format("The interpolation code must be 1 (interpolate) or 2 (nearest neighbor)"
"The input value was %d, so it was reset to 1", ms_params.m_interp_arr);
mc_csp_messages.add_notice(msg);
ms_params.m_interp_arr = 1;
}
if(ms_params.m_rad_type < 1 || ms_params.m_rad_type > 3)
{ // Fairly important to know the intent of this input, so throw an exception if it's not one of the three options
std::string msg = util::format("The solar resource radiation type must be 1 (DNI), 2 (Beam horizontal), or "
"3 (Total horizontal). The input value was %d.");
throw(C_csp_exception("C_csp_gen_collector_receiver::init",msg));
}
if(ms_params.mv_sfhlQ_coefs.size() < 1)
{
throw(C_csp_exception("C_csp_gen_collector_receiver::init","The model requires at least one irradiation-based "
"thermal loss adjustment coefficient (mv_sfhlQ_coefs)"));
}
if(ms_params.mv_sfhlT_coefs.size() < 1)
{
throw(C_csp_exception("C_csp_gen_collector_receiver::init", "The model requires at least one temperature-based "
"thermal loss adjustment coefficient (mv_sfhlT_coefs)"));
}
if( ms_params.mv_sfhlV_coefs.size() < 1 )
{
throw(C_csp_exception("C_csp_gen_collector_receiver::init", "The model requires at least one wind-based "
"thermal loss adjustment coefficient (mv_sfhlV_coefs)"));
}
// Unit conversions
ms_params.m_latitude *= CSP::pi/180.0; //[rad], convert from deg
ms_params.m_longitude *= CSP::pi / 180.0; //[rad], convert from deg
ms_params.m_theta_stow *= CSP::pi / 180.0; //[rad], convert from deg
ms_params.m_theta_dep *= CSP::pi / 180.0; //[rad], convert from deg
ms_params.m_T_sfdes += 273.15; //[K], convert from C
if( !ms_params.m_is_table_unsorted )
{
/*
Standard azimuth-elevation table
*/
//does the table look right?
if( (ms_params.m_optical_table.nrows() < 5 && ms_params.m_optical_table.ncols() > 3) ||
(ms_params.m_optical_table.ncols() == 3 && ms_params.m_optical_table.nrows() > 4) )
{
mc_csp_messages.add_message(C_csp_messages::WARNING, "The optical efficiency table option flag may not match the specified table format. If running SSC, ensure \"IsTableUnsorted\""
" =0 if regularly-spaced azimuth-zenith matrix is used and =1 if azimuth,zenith,efficiency points are specified.");
}
if( ms_params.m_optical_table.nrows() <= 0 || ms_params.m_optical_table.ncols() <= 0 ) // If these were not set correctly, it will create memory allocation crash not caught by error handling.
{
throw(C_csp_exception("C_csp_gen_collector_receiver::init","The optical table must have a positive number of rows and columns"));
}
double *xax = new double[ms_params.m_optical_table.ncols() - 1];
double *yax = new double[ms_params.m_optical_table.nrows() - 1];
double *data = new double[(ms_params.m_optical_table.ncols() - 1) * (ms_params.m_optical_table.nrows() - 1)];
//get the xaxis data values
for( size_t i = 1; i<ms_params.m_optical_table.ncols(); i++ ){
xax[i - 1] = ms_params.m_optical_table(0,i)*CSP::pi/180.0;
}
//get the yaxis data values
for( size_t j = 1; j<ms_params.m_optical_table.nrows(); j++ ){
yax[j - 1] = ms_params.m_optical_table(j,0)*CSP::pi / 180.0;
}
//Get the data values
for( size_t j = 1; j<ms_params.m_optical_table.nrows(); j++ ){
for( size_t i = 1; i<ms_params.m_optical_table.ncols(); i++ ){
data[i - 1 + (ms_params.m_optical_table.ncols() - 1)*(j - 1)] = ms_params.m_optical_table(j, i);
}
}
mc_optical_table.AddXAxis(xax, (int)ms_params.m_optical_table.ncols() - 1);
mc_optical_table.AddYAxis(yax, (int)ms_params.m_optical_table.nrows() - 1);
mc_optical_table.AddData(data);
delete[] xax, yax, data;
}
else
{
/*
Use the unstructured data table
*/
/*
------------------------------------------------------------------------------
Create the regression fit on the efficiency map
------------------------------------------------------------------------------
*/
if( ms_params.m_optical_table.ncols() != 3 )
{
std::string msg = util::format("The heliostat field efficiency file is not formatted correctly. Type expects 3 columns"
" (zenith angle, azimuth angle, efficiency value) and instead has %d cols.", ms_params.m_optical_table.ncols());
throw(C_csp_exception("C_csp_gen_collector_receiver::init", msg));
}
MatDoub sunpos;
vector<double> effs;
int nrows = (int)ms_params.m_optical_table.nrows();
//read the data from the array into the local storage arrays
sunpos.resize(nrows, VectDoub(2));
effs.resize(nrows);
double eff_maxval = -9.e9;
for( int i = 0; i<nrows; i++ )
{
sunpos.at(i).at(0) = ms_params.m_optical_table(i,0) / az_scale * CSP::pi / 180.;
sunpos.at(i).at(1) = ms_params.m_optical_table(i,1) / zen_scale * CSP::pi / 180.;
double eff = ms_params.m_optical_table(i,2);
effs.at(i) = eff;
if( eff > eff_maxval ) eff_maxval = eff;
}
//scale values based on maximum. This helps the GM interpolation routine
m_eff_scale = eff_maxval;
for( int i = 0; i<nrows; i++ )
effs.at(i) /= m_eff_scale;
//Create the field efficiency table
Powvargram vgram(sunpos, effs, 1.99, 0.);
mpc_optical_table_uns = new GaussMarkov(sunpos, effs, vgram);
//test how well the fit matches the data
double err_fit = 0.;
int npoints = (int)sunpos.size();
for( int i = 0; i<npoints; i++ ){
double zref = effs.at(i);
double zfit = mpc_optical_table_uns->interp(sunpos.at(i));
double dz = zref - zfit;
err_fit += dz * dz;
}
err_fit = sqrt(err_fit);
if( err_fit > 0.01 )
{
std::string msg = util::format("The heliostat field interpolation function fit is poor! (err_fit=%f RMS)", err_fit);
mc_csp_messages.add_message(C_csp_messages::WARNING, msg);
}
} // end unstructured data table
init_sf();
m_mode = C_csp_collector_receiver::OFF; //[-] 0 = requires startup, 1 = starting up, 2 = running
m_mode_prev = m_mode;
return;
}
void removeMatrixRow(int row, MatDoub &out) {
int dummy = -1000;
Rh.resize(Ri.nrows(),Ri.nrows());
Rh = Ri;
for(int i=0; i<Rh.nrows(); i++) {
Rh[row][i] = dummy;
Rh[i][row] = dummy;
}
datain = Rh.getMatrixArray();
data [Rh.nrows()*Rh.nrows()];
int k=0;
for(int i=0; i < Rh.nrows()*Rh.nrows(); i++) {
double ele = datain[i];
if(ele != dummy)
data[k++] = ele;
}
out = MatDoub( out.nrows(), out.nrows(), data );
}
/*
Utility method used by the RandomWalk algorithm to
invert the Graph Laplacian and accummulate the random-path
contributions from each source-sink node pair, in
accordance with [2] (see method declarations above).
*/
void calculateRandomWalk(int c, vector<node> Nodes) {
queue<node> termNodes;
for(int i=0; i<Nodes.size(); i++)
termNodes.push(Nodes[i]);
int N = Nodes.size();
S.resize(N,1);
V.resize(N,1);
T.resize(N,N);
Rc.resize((N-1),(N-1));
Vi.resize(C.size(),1);
//--- Remove arbitrary termination ('sink') state '0'.
removeMatrixRow(0,Rc);
//--- Invert Matrix.
LUdcmp lu( Rc );
Ti.resize(Rc.nrows(),Rc.nrows());
lu.inverse( Ti );
for(int i=0; i< T.nrows(); i++) {
for(int j=0; j< T.nrows(); j++) {
T[i][j] = 0;
}
}
//--- Add back arbitrary termination ('sink') state '0'.
addMatrixRow(Ti,0,T);
while ( !termNodes.empty() ) {
node termNode = termNodes.front();
termNodes.pop();
for(int t=0; t< Nodes.size(); t++) {
//--- Take the next start ('source') state.
node startNode = Nodes[t];
if( startNode.k != termNode.k ) {
for(int i=0; i<S.nrows(); i++) {
S[i][0] = 0;
V[i][0] = 0;
Vi[i][0] = 0;
}
S[startNode.k][0] = 1;
S[termNode.k][0] = -1;
//--- V = T * S
for(int i=0; i<T.nrows(); i++) {
double sum = 0.0;
for(int j=0; j<T.nrows(); j++) {
sum += T[i][j] * S[j][0];
}
V[i][0] = sum;
}
addMatrixRows(V, c, Vi);
//--- Edge Betweenness, i.e.
//--- the currents (potential differences) alone each edge!
for(int i=0; i<elist.size(); i++) {
if( !elist[i].removed ) {
int Ni = elist[i].so-1;
int Nj = elist[i].si-1;
totallist[i+1].we += fabs(Vi[Ni][0] - Vi[Nj][0]);
}
}//elist
}//Nodes
}//startNodes
}
}
