C++ input::insert_item方法代码示例

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();
}