template<typename Scalar, int Dim, int Options> void transform_products()
{
typedef Matrix<Scalar,Dim+1,Dim+1> Mat;
typedef Transform<Scalar,Dim,Projective,Options> Proj;
typedef Transform<Scalar,Dim,Affine,Options> Aff;
typedef Transform<Scalar,Dim,AffineCompact,Options> AffC;
Proj p; p.matrix().setRandom();
Aff a; a.linear().setRandom(); a.translation().setRandom();
AffC ac = a;
Mat p_m(p.matrix()), a_m(a.matrix());
VERIFY_IS_APPROX((p*p).matrix(), p_m*p_m);
VERIFY_IS_APPROX((a*a).matrix(), a_m*a_m);
VERIFY_IS_APPROX((p*a).matrix(), p_m*a_m);
VERIFY_IS_APPROX((a*p).matrix(), a_m*p_m);
VERIFY_IS_APPROX((ac*a).matrix(), a_m*a_m);
VERIFY_IS_APPROX((a*ac).matrix(), a_m*a_m);
VERIFY_IS_APPROX((p*ac).matrix(), p_m*a_m);
VERIFY_IS_APPROX((ac*p).matrix(), a_m*p_m);
}
/*!
* This function does the processing for the c_elegans class.
*
* It initializes the various matrices and reads values from the input files
*/
void c_elegans::postprocess(input& in){
rhs_type::postprocess(in);
if(dimension != num_neur*2){
err("Dimension must be 558, which is double the number of neurons",
"", "", FATAL_ERROR);
}
in.retrieve(beta, "beta", this);
in.retrieve(tau, "tau", this);
in.retrieve(gelec, "gelec", this);
in.retrieve(gchem, "gchem", this);
in.retrieve(memV, "memV", this);
in.retrieve(memG, "memG", this);
in.retrieve(EchemEx, "EchemEx", this);
in.retrieve(EchemInh, "EchemInh", this);
in.retrieve(ar, "ar", this);
in.retrieve(ad, "ad", this);
std::string ag_fname, a_fname;
in.retrieve(ag_fname, "ag_mat", this);
in.retrieve(a_fname, "a_mat", this);
sparse_type a_m(num_neur, num_neur);
ag_full.resize(num_neur, num_neur);
laplacian.resize(num_neur, num_neur);
read_mat(ag_fname, ag_full);
read_mat(a_fname, a_m);
//create transposed sparse matrix AEchem
AEchem_trans_full.resize(num_neur, num_neur);
AEchem_trans_full = a_m.transpose();
AEchem_trans.resize(num_neur, num_neur);
//do any needed fake iterations, must make more general at some point
size_t num_comb;
int iterations;
in.retrieve(num_comb, "num_comb", this);
in.retrieve(iterations, "iterations", this);
in.retrieve(cur_ind, "!start_ind", this);
abl_neur.resize(num_comb);
for(auto& val : abl_neur){
val = 0;
}
if(abl_neur.size() != 1){
next_comb(abl_neur, num_neur);
}
for(int i = 0; i < cur_ind; i++){
for(int j = 0; j < iterations; j++){
if(next_comb(abl_neur, num_neur)){
char ind_str[20];//won't ever have a 20 digit index
//handy buffer to overflow for those hacking this.
sprintf(ind_str, "%d", (int)cur_ind);
err(std::string("Combinations exhausted in index ") + ind_str,
"c_elegans::postprocess","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);
//write first ablation data
//set up dummy connection to toroidal controller for now
controller* cont;
in.retrieve(cont, "controller", this);
auto val = std::make_shared<variable>();
val->setname("c_elegans_quickfix");
val->holder = holder;
val->parse("0.1");
in.insert_item(val);
cont->addvar(val);
in.retrieve(dummy, val->name(), this);
has_gone=true; //is true at first to allow update of zero index to occur
first_round=true;
update();
}
