void GlobalsCollector::visit(ast::FunctionDec & e)
{
locals.emplace(e.getSymbol());
DeclaredMacroDef dmd(&e);
GlobalsCollector gc(dmd);
for (const auto global : gc.globals)
{
if (locals.find(global) == locals.end())
{
globals.emplace(global);
}
}
}
void c_elegans::update(){
if(has_gone){
has_gone=false;//prevent more than one update happening without call to rhs
if(!first_round){
if(next_comb(abl_neur, num_neur)){
holder->write_dat();
char ind_str[20];//won't ever have a 20 digit index
//handy buffer to overflow for those hacking this.
sprintf(ind_str, "%d", cur_ind);
err(std::string("Combinations exhausted in index ") + ind_str,
"c_elegans::update","rhs/c_elegans.cpp", FATAL_ERROR);
}
auto dat_inds =
std::shared_ptr<writer>(new writer(true));
dat_inds->add_data(data::create("Ablations", abl_neur.data(), abl_neur.size()), writer::OTHER);
holder->add_writer(dat_inds);
}
else{
first_round=false;
}
Matrix<double, Dynamic, Dynamic> ag_dense(num_neur, num_neur);
//insert code to zero it out later
auto ag_m = ag_full;
AEchem_trans = AEchem_trans_full;
auto fncval = [this](int i, int j, double val)->bool{
for(int kk = 0; kk < (int)this->abl_neur.size(); kk++){
if((int)this->abl_neur[kk] == i || (int)this->abl_neur[kk] == j){
return false;
}
}
return true;
};
AEchem_trans.prune(fncval);
ag_m.prune(fncval);
ag_dense = ag_m * Matrix<double, num_neur, num_neur, ColMajor>::Identity();
dmd(ag_dense);
auto sumvals = ag_dense.colwise().sum();
sparse_type temp(num_neur, num_neur);
//generate the sparse diagonal matrix to build the lapacian
std::vector<Triplet<double, int> > temp_tr;
for(int i = 0; i < (int)num_neur; i++){
temp_tr.push_back(Triplet<double, int>(i, i, sumvals[i]));
}
temp.setFromTriplets(temp_tr.begin(), temp_tr.end());
laplacian = temp - ag_m;
//initialize the Echem array
for(size_t i = 0; i < num_neur; i++){
if(GABAergic[i]){
Echem[i] = EchemInh;
}
else{
Echem[i] = EchemEx;
}
}
//Initialize the sig array
for(size_t i = 0; i < num_neur; i++){
sig[i] = 0.5;
}
eqS = sig * (ar/(ar*sig + ad));
//more initialization of temporary dense matrices
Matrix<double, Dynamic, Dynamic> ldense(num_neur,num_neur);
ldense = laplacian*Matrix<double, num_neur, num_neur>::Identity();
Matrix<double, Dynamic, Dynamic> aedense(num_neur, num_neur);
aedense= AEchem_trans*Matrix<double, num_neur, num_neur>::Identity();
Matrix<double, Dynamic, Dynamic> C(num_neur, num_neur);
//create the C matrix
C= memG*Matrix<double, num_neur, num_neur>::Identity() + gelec*ldense;
//initialize matrix to modify diagonal part of C
Matrix<double, num_neur, 1> tmp =(gchem * aedense * eqS.matrix());
for(size_t i = 0; i < num_neur; i++){
C(i, i) += tmp(i);
}
Matrix<double, num_neur, 1> Ivals;
Ivals.setZero();
double amp=2e4;
Ivals[276]=amp;
Ivals[278]=amp;
Matrix<double, num_neur, 1> b;
//create B vector
b= gchem*aedense*(eqS * Echem).matrix();
for(size_t i = 0; i < num_neur; i++){
b[i] += (memG*memV + Ivals[i]);
}
//calculate eqV
eqV.matrix() = C.inverse()*b;
vmean = eqV+(1.0/beta) * (1.0/sig - 1).log();
for(auto val : abl_neur){
eqV[val] = vmean [val] = eqS[val] = 0;
};
}
}
