void jones_optical::postprocess(input& invals){ #ifdef gen_t_dat func_dat=fopen("python/grad_data2.out", "w"); func_score = fopen("python/score_data2.out", "w"); #else func_dat=fopen("python/grad_data.out", "w"); func_score = fopen("python/score_data.out", "w"); #endif stable_spectral_pde_1d_tmpl<comp>::postprocess(invals); if(dimension%2){ err("System jones_optical requires an even dimension", "jones_optical::postprocess", "system/jones_optical.cpp", FATAL_ERROR); } int num_segments; invals.retrieve(num_segments, "num_jones_segments", this); if(num_segments < 0){ err("Number of jones segments must be greater than or equal to zero", "jones_optical::postprocess", "system/jones_optical.cpp", FATAL_ERROR); } invals.retrieve(jones_int_dist, "jones_int_dist", this); if(jones_int_dist<0){ err("The distance between jones segments, jones_int_dist, must be greater than or equal to zero", "jones_optical::postprocess", "system/jones_optical.cpp", FATAL_ERROR); } memp.add(dimension, &nvec1); memp.add(nts, &help, &t, &kurtosis_help, &phold, &nvec2); double dt = 60.0/nts; gaussian_noise(ucur, dimension, 0, 0.2); for(size_t i = 0; i < nts; i++){ t[i] = dt*(i-nts/2.0); } for(size_t i = 0; i < nts; i++){ ucur[i] = ucur[i+nts] = 1.00/cosh(t[i]/2.0); help[i] = _real(ucur[i]); nvec1[i] = ucur[i]; } for(size_t i = 0; i < num_pulses; i++){ fft(nvec1 + i*nts, nvec1 +1*nts, nts); } //generate variables for the jones matrices //create jones matrices std::string name_base = "jones_system_vars"; std::string mat_base = "jones_system_matrices"; for(int i = 0; i < num_segments; i++){ std::vector<std::shared_ptr<variable> > vv(4); for(auto& val : vv){ val = std::make_shared<variable>(); val->setname(get_unique_name(name_base)); val->holder=holder; val->parse("0.1"); #ifdef gen_t_dat val->set(0*2*3.1415*(rand()*1.0/RAND_MAX)); #else val->set(0); #endif invals.insert_item(val); cont->addvar(val); } std::shared_ptr<jones_matrix> m = std::make_shared<jones_matrix>(get_unique_name(mat_base), i, holder); invals.insert_item(m); m->setup(vv); jones_matrices.push_back(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(); }