MCMC.hpp

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// MCMC.cpp : Defines the entry point for the console application.
//
 
#include <vector>
#include "NormalDist.h"
#include <string>
#ifndef mac_version
#include <omp.h>
#endif
#include "MCMC.h"
#include "ProgressWindow.h"
#include "Utilities.h"
 
 
 
//using namespace std;
 
template<class T>
CMCMC<T>::CMCMC(void)
{
}
 
template<class T>
CMCMC<T>::~CMCMC(void)
{
    Params.clear();
    logp1.clear();
    logp.clear();
}
 
 
template<class T>
Parameter* CMCMC<T>::parameter(int i)
{
    return &parameters->at(i);
}
 
 
template <class T>
Observation* CMCMC<T>::observation(int i)
{
    return (&observations->at(i));
}
 
template<class T>
TimeSeriesSet<double> CMCMC<T>::model(vector<double> par)
{
    double sum = 0;
    vector<TimeSeriesSet<double>> res;
 
 
    T G1 = *Model;
    G1.SetSilent(true);
    G1.SetRecordResults(false);
    G1.SetNumThreads(1);
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
        G1.SetParameterValue(i, par[i]);
 
    G1.ApplyParameters();
    G1.Solve();
    sum +=G1.GetObjectiveFunctionValue();
 
    return G1.Outputs.ObservedOutputs;
 
}
 
template<class T>
bool CMCMC<T>::SetProperty(const string &varname, const string &value)
{
 
    if (aquiutils::tolower(varname) == "number_of_samples") {MCMC_Settings.total_number_of_samples = aquiutils::atoi(value); return true;}
    if (aquiutils::tolower(varname) == "number_of_chains") {MCMC_Settings.number_of_chains = aquiutils::atoi(value); return true;}
    if (aquiutils::tolower(varname) == "number_of_burnout_samples") {MCMC_Settings.burnout_samples = aquiutils::atoi(value); return true;}
    if (aquiutils::tolower(varname) == "initial_purturbation_factor") {MCMC_Settings.purturbation_factor = aquiutils::atof(value); return true;}
    if (aquiutils::tolower(varname) == "record_interval") {MCMC_Settings.save_interval = aquiutils::atoi(value); return true;}
    if (aquiutils::tolower(varname) == "initial_purturbation")
    {
        if (aquiutils::tolower(value)=="yes")
            MCMC_Settings.noinipurt = false;
        else
            MCMC_Settings.noinipurt = true;
        return true;
    }
    if (aquiutils::tolower(varname) == "perform_global_sensitivity")
    {
        if (aquiutils::tolower(value)=="yes")
            MCMC_Settings.global_sensitivity = true;
        else
            MCMC_Settings.global_sensitivity = false;
        return true;
    }
    if (aquiutils::tolower(varname) == "continue_based_on_file_name")
    {
        if (value!="")
        {   MCMC_Settings.continue_filename = value;
            MCMC_Settings.continue_mcmc = true;
        }
        else
            MCMC_Settings.continue_mcmc = false;
        return true;
    }
    if (aquiutils::tolower(varname) == "samples_filename")
    {
        if (value!="")
        {
            if (value.find_first_of('/')!=string::npos || value.find_first_of('\\')!=string::npos)
                FileInformation.outputfilename = value;
            else
                FileInformation.outputfilename = FileInformation.outputpath + value;
        }
 
 
        return true;
    }
    if (aquiutils::tolower(varname) == "number_of_post_estimate_realizations")
    {
        MCMC_Settings.number_of_post_estimate_realizations = aquiutils::atoi(value);
        return true;
    }
    if (aquiutils::tolower(varname) == "increment_for_sensitivity_analysis")
    {
        MCMC_Settings.dp_sens = aquiutils::atof(value);
        return true;
    }
    if (aquiutils::tolower(varname) == "add_noise_to_realizations")
    {
        if (aquiutils::tolower(value)=="yes")
            MCMC_Settings.noise_realization_writeout = true;
        else
            MCMC_Settings.noise_realization_writeout = false;
        return true;
 
    }
    if (aquiutils::tolower(varname) == "number_of_threads")
    {
        MCMC_Settings.numberOfThreads = aquiutils::atoi(value);
        return true;
    }
    if (aquiutils::tolower(varname) == "acceptance_rate")
    {
        MCMC_Settings.acceptance_rate = aquiutils::atof(value);
        return true;
    }
    if (aquiutils::tolower(varname) == "purturbation_change_scale")
    {
        MCMC_Settings.purt_change_scale = aquiutils::atof(value);
        return true;
    }
    if (aquiutils::tolower(varname) == "dissolve_chains")
    {
        if (aquiutils::tolower(value)=="true")
            MCMC_Settings.dissolve_chains = true;
        else
            MCMC_Settings.dissolve_chains = false;
        return true;
    }
 
    last_error = "Property '" + varname + "' was not found!";
    return false;
}
 
 
template<class T>
double CMCMC<T>::posterior(vector<double> par, int chain_counter)
{
    double sum = 0;
 
    for (int i = 0; i < MCMC_Settings.number_of_parameters; i++)
        CopiedModels[chain_counter].SetParameterValue(i, par[i]);
 
    for (int i = 0; i < MCMC_Settings.number_of_parameters; i++)
        sum+=parameter(i)->CalcLogPriorProbability(par[i]);
 
 
    sum+= -CopiedModels[chain_counter].GetObjectiveFunctionValue();
    temp_predicted[chain_counter] = CopiedModels[chain_counter].GetPredictedValues();
 
    return sum;
}
 
template<class T>
void CMCMC<T>::model(T *Model1, vector<double> par)
{
 
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        Model1->SetSilent(true);
        Model1->SetRecordResults(false);
        Model1->SetNumThreads(1);
        for (unsigned int i = 0; i < MCMC_Settings.number_of_parameters; i++)
            Model1->SetParameterValue(i, par[i]);
        Model1->ApplyParameters();
    }
 
    Model1->Solve();
 
}
 
template<class T>
void CMCMC<T>::initialize(CMBTimeSeriesSet *results, bool random)
{
    parameters = &Model->Parameters();
    Params.resize(MCMC_Settings.total_number_of_samples);
    logp.resize(MCMC_Settings.total_number_of_samples);
    logp1.resize(MCMC_Settings.total_number_of_samples);
    pertcoeff.resize(parameters->size());
    MCMC_Settings.number_of_parameters = Model->Parameters().size();
 
    for (unsigned int i=0; i<MCMC_Settings.total_number_of_samples; i++)
        Params[i].resize(MCMC_Settings.number_of_parameters);
 
    results->resize(MCMC_Settings.number_of_parameters);
    results->SetAllSeriesSize(MCMC_Settings.total_number_of_samples);
    predicted.resize(Model->ObservationsCount());
    predicted.SetAllSeriesSize(MCMC_Settings.total_number_of_samples);
    temp_predicted.resize(MCMC_Settings.number_of_chains);
    CopiedModels.resize(MCMC_Settings.number_of_chains);
    for (unsigned int i=0; i<MCMC_Settings.number_of_chains; i++)
    {
        CopiedModels[i] = *Model;
    }
    double pp=0;
    for (int j = 0; j<MCMC_Settings.number_of_parameters; j++)
    {
        if (parameter(j)->GetPriorDistribution()=="normal" || parameter(j)->GetPriorDistribution()=="uniform")
        {
            pertcoeff[j] = MCMC_Settings.purturbation_factor*(-parameter(j)->GetRange(_range::low) + parameter(j)->GetRange(_range::high));
        }
        if (parameter(j)->GetPriorDistribution()=="log-normal")
        {
            pertcoeff[j] = MCMC_Settings.purturbation_factor*(-log(parameter(j)->GetRange(_range::low)) + log(parameter(j)->GetRange(_range::high)));
        }
    }
 
    if (random)
    {   for (int j=0; j<MCMC_Settings.number_of_chains; j++)
        {
            double posterior_value = -2e6;
            while (posterior_value<-1e6)
            {   for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
                {   if (parameter(i)->GetPriorDistribution()=="log-normal")
                        Params[j][i] = exp(log(parameter(i)->GetRange(_range::low))+(log(parameter(i)->GetRange(_range::high))-log(parameter(i)->GetRange(_range::low)))*unitrandom());
                    else
                        Params[j][i] = parameter(i)->GetRange(_range::low)+(parameter(i)->GetRange(_range::high)-parameter(i)->GetRange(_range::low))*unitrandom();
                    if (parameter(i)->GetPriorDistribution()=="log-normal")
                        pp += log(Params[j][i]);
                }
 
                logp[j] = posterior(Params[j],j);
                posterior_value = logp[j];
                logp1[j] = logp[j]+pp;
            }
            results->SetRow(j,j,Params[j]);
            cout<<"success!";
            predicted.SetRow(j,j,CopiedModels[j].GetPredictedValues().vec);
            temp_predicted[j] = CopiedModels[j].GetPredictedValues();
        }
    }
 
    else
    {
        for (int j=0; j<MCMC_Settings.number_of_chains; j++)
        {   for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
            {   Params[j][i] = parameter(i)->GetValue();
                if (parameter(i)->GetPriorDistribution()=="log-normal")
                    pp += log(Params[j][i]);
            }
            logp[j] = posterior(Params[j],j);
            logp1[j] = logp[j]+pp;
        }
    }
 
}
 
 
 
template<class T>
void CMCMC<T>::initialize(vector<double> par)
{
 
    if (MCMC_Settings.sensbasedpurt == true)
    {
        CVector X = sensitivity(1e-4, par);
 
        for (int j = 0; j<MCMC_Settings.number_of_parameters; j++)
        {
            if (parameter(j).Get_Distribution()=="normal" || parameter(j).Get_Distribution()=="uniform")
            {
                pertcoeff[j] = MCMC_Settings.purturbation_factor / fabs(X[getparamno(j, 0)]);
            }
            if (parameter(j).Get_Distribution()=="log-normal")
            {
                pertcoeff[j] = MCMC_Settings.purturbation_factor / fabs(sqrt(par[j])*X[getparamno(j, 0)]);
            }
 
        }
    }
    else
        for (int j = 0; j<MCMC_Settings.number_of_parameters; j++)
        {
            if (parameter(j).Get_Distribution()=="normal" || parameter(j).Get_Distribution()=="uniform")
            {
                pertcoeff[j] = MCMC_Settings.purturbation_factor*(-parameter(j)->GetRange().low + -parameter(j)->GetRange().high);
            }
            if (parameter(j).Get_Distribution()=="log-normal")
            {
                pertcoeff[j] = MCMC_Settings.purturbation_factor*(-log(parameter(j)->GetRange().low) + log(parameter(j)->GetRange().high));
            }
        }
    double alpha;
    if (MCMC_Settings.noinipurt == true) alpha = 0; else alpha = 1;
 
 
    for (int j = 0; j<MCMC_Settings.number_of_chains; j++)
    {
        Params[j].resize(MCMC_Settings.number_of_parameters);
        double pp = 0;
        for (int i = 0; i<MCMC_Settings.number_of_parameters; i++)
        {
            if (parameter(i).Get_Distribution()=="normal" || parameter(i).Get_Distribution()=="uniform")
                Params[j][i] = par[i] + alpha*getnormalrand(0, pertcoeff[i]);
            else
            {   Params[j][i] = par[i] * exp(alpha*getnormalrand(0, pertcoeff[i]));
                pp+=log(par[i]);
            }
            if (parameter(i).Get_Distribution()=="uniform")
                while ((Params[j][i]<parameter(i).GetRange().low) || (Params[j][i]>parameter(i).GetRange(_range::high)))
                    Params[j][i] = par[i] + alpha*getnormalrand(0, pertcoeff[i]);
 
        }
        logp[j] = posterior(Params[j]);
        logp1[j] = logp[j] + pp;
    }
}
 
template<class T>
bool CMCMC<T>::step(int k, int chain_counter)
{
 
    vector<double> X = purturb(k-MCMC_Settings.number_of_chains);
    double pp =0;
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        if (parameter(i)->GetPriorDistribution()=="log-normal")
            pp += log(X[i]);
    }
    double logp_0 = posterior(X,chain_counter) + pp;
 
 
    double logp_1 = logp_0;
    bool res;
 
 
    if (unitrandom() <exp(logp_0-logp[k-MCMC_Settings.number_of_chains]) && !isnan(logp_0))
    {
        res=true;
        Params[k] = X;
        logp[k] = logp_0;
        logp1[k] = logp_1;
        predicted.SetRow(k,k,temp_predicted[chain_counter].vec);
 
        //accepted_count += 1;
    }
    else
    {
        res = false;
        Params[k] = Params[k-MCMC_Settings.number_of_chains];
        logp[k] = logp[k-MCMC_Settings.number_of_chains];
        logp1[k] = logp_1;
        predicted.SetRow(k,k,predicted.getrow(k-MCMC_Settings.number_of_chains));
    }
    //total_count += 1;
    return res;
}
 
template<class T>
vector<double> CMCMC<T>::purturb(int k)
{
    vector<double> X;
    X.resize(MCMC_Settings.number_of_parameters);
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        if (parameter(i)->GetPriorDistribution() == "log-normal")
            X[i] = Params[k][i]*exp(pertcoeff[i]*getstdnormalrand());
        else
            X[i] = Params[k][i]+pertcoeff[i]*getstdnormalrand();
 
    }
    return X;
}
 
template<class T>
bool CMCMC<T>::step(int k, int nsamps, string filename, CMBTimeSeriesSet *results, ProgressWindow *rtw)
{
    FILE *file;
    if (!MCMC_Settings.continue_mcmc)
    {   file = fopen(filename.c_str(),"w");
        fclose(file);
    }
    qDebug()<<5;
    if (!MCMC_Settings.continue_mcmc)
    {
        file = fopen(filename.c_str(),"a");
        fprintf(file,"%s, ", "no.");
        for (unsigned int i=0; i<MCMC_Settings.number_of_parameters; i++)
        {   fprintf(file, "%s, ", parameter(i)->Name().c_str());
            results->setname(i,parameter(i)->Name());
        }
        fprintf(file,"%s, %s, %s,", "logp", "logp_1", "stuck_counter");
        for (unsigned int j=0; j<pertcoeff.size(); j++) fprintf(file,"%s,", string("purt_coeff_" + QString("%1").arg(j).toStdString()).c_str());
        fprintf(file, "\n");
        fclose(file);
    }
 
    CVector stuckcounter(MCMC_Settings.number_of_chains);
    CVector accepted(MCMC_Settings.number_of_chains);
 
    MCMC_Settings.ini_purt_fact = pertcoeff[0];
    int k_0 = k;
    qDebug()<<6;
    for (unsigned int kk=k; kk<k+nsamps+MCMC_Settings.number_of_chains; kk+=MCMC_Settings.number_of_chains)
    {
        QCoreApplication::processEvents(QEventLoop::AllEvents,10*1000);
 
#ifndef NO_OPENMP
        omp_set_num_threads(MCMC_Settings.numberOfThreads);
#endif
 
 
#ifdef WIN64
#pragma omp parallel
        {
            srand(int(time(NULL)) ^ omp_get_thread_num() + kk);
        }
#endif
qDebug()<<7;
#pragma omp parallel for
        for (int jj = kk; jj < min(kk + MCMC_Settings.number_of_chains, MCMC_Settings.total_number_of_samples); jj++)
        {
            qDebug() << "Starting step: " + QString::number(jj);
            bool stepstuck = !step(jj,jj-kk);
            qDebug() << "Step: " + QString::number(jj) + "Done!";
            if (stepstuck)
            {   stuckcounter[jj - kk]++;
                accepted[jj-kk]=0;
            }
            else
            {   stuckcounter[jj - kk] = 0;
                accepted[jj-kk]=1;
            }
 
        }
 
        accepted_count += accepted.sum();
        total_count += accepted.num;
        QCoreApplication::processEvents(QEventLoop::AllEvents,100*1000);
 
        if ((kk-k_0) % (50 * MCMC_Settings.number_of_chains) == 0 || kk == k + nsamps + MCMC_Settings.number_of_chains - 1)
        {
 
            if (MCMC_Settings.dissolve_chains)
            {   int_value_pair minchain = Min(logp,min(kk + MCMC_Settings.number_of_chains, MCMC_Settings.total_number_of_samples),MCMC_Settings.number_of_chains);
                int_value_pair maxchain = Max(logp,min(kk + MCMC_Settings.number_of_chains, MCMC_Settings.total_number_of_samples),MCMC_Settings.number_of_chains);
                double ratio = maxchain.value-minchain.value;
                qDebug()<<"Ratio:"<<ratio;
                if (ratio>5)
                {
                    Params[minchain.counter] = Params[maxchain.counter];
                    logp[minchain.counter] = logp[maxchain.counter];
                    logp1[minchain.counter] = logp1[maxchain.counter];
                }
            }
            file = fopen(filename.c_str(), "a");
            for (int jj = max(int(min(kk + MCMC_Settings.number_of_chains, MCMC_Settings.total_number_of_samples) - 50 * MCMC_Settings.number_of_chains), k_0); jj < min(kk + MCMC_Settings.number_of_chains, MCMC_Settings.total_number_of_samples); jj++)
            {
                if (jj%MCMC_Settings.save_interval == 0)
                {
                    //QCoreApplication::processEvents(QEventLoop::AllEvents,100*1000);
 
                    fprintf(file, "%i, ", jj);
                    for (int i = 0; i < MCMC_Settings.number_of_parameters; i++)
                        fprintf(file, "%le, ", Params[jj][i]);
                    if (!results->SetRow(jj, jj,Params[jj]))
                    {
                        cout<<"not enough room!";
                    }
 
                    fprintf(file, "%le, %le, %f,", logp[jj], logp1[jj], stuckcounter[jj%MCMC_Settings.number_of_chains]);
                    for (int j = 0; j < pertcoeff.size(); j++) fprintf(file, "%le,", pertcoeff[j]);
                    fprintf(file, "\n");
 
                }
 
                //cout << jj << "," << pertcoeff[0] << "," << stuckcounter.max() << "," << stuckcounter.min() << endl;
                //if (jj<n_burnout)
            }
            fclose(file);
        }
 
        if ((kk-k_0) % (50*MCMC_Settings.number_of_chains) == 0)
        {
 
            if (double(accepted_count) / double(total_count)>MCMC_Settings.acceptance_rate)
                for (int i = 0; i < MCMC_Settings.number_of_parameters; i++) pertcoeff[i] /= MCMC_Settings.purt_change_scale;
            else
                for (int i = 0; i < MCMC_Settings.number_of_parameters; i++) pertcoeff[i] *= MCMC_Settings.purt_change_scale;
            accepted_count = 0;
            total_count = 0;
 
        }
 
 
        if (rtw)
        {
            double average_log_p = CVector::Extract(logp,kk-100,kk).mean();
            double progress = double(kk) / double(nsamps)*0.98;
            rtw->SetProgress(progress);
            rtw->AppendPoint(kk,double(accepted_count) / double(total_count),0);
            rtw->AppendPoint(kk,double(pertcoeff[0] / MCMC_Settings.ini_purt_fact),1);
            rtw->AppendPoint(kk,double(average_log_p),2);
            QCoreApplication::processEvents();
        }
    }
    qDebug()<<"MCMC done!";
    return 0;
}
 
template<class T>
void CMCMC<T>::SetRunTimeWindow(ProgressWindow *_rtw)
{
    rtw = _rtw;
}
 
template<class T>
CMCMC<T>::CMCMC(T *_system)
{
    Model = _system;
    MCMC_Settings.number_of_parameters = 0;
    MCMC_Settings.numberOfThreads = 20;
    FileInformation.outputpath = Model->OutputPath();
 
    for (unsigned int i=0; i<Model->Parameters().size(); i++)
    {
        MCMC_Settings.number_of_parameters++;
        params.push_back(i);
    }
    parameters = &Model->Parameters();
    observations = Model->Observations();
    pertcoeff.resize(parameters->size());
}
 
template<class T>
CVector CMCMC<T>::sensitivity(double d, vector<double> par)
{
 
    double base = posterior(par);
    CVector X(MCMC_Settings.number_of_parameters);
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        vector<double> par1 = par;
        par1[i]=par[i]*(1+d);
        double base_1 = posterior(par1);
 
        X[i] = (sqrt(fabs(base))-sqrt(fabs(base_1)))/(d*par[i]);
    }
    return X;
}
 
 
template<class T>
CVector CMCMC<T>::sensitivity_ln(double d, vector<double> par)
{
 
    CVector X = sensitivity(d, par);
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        X[i] = par[i]*X[i];
    }
    return X;
}
 
template<class T>
int CMCMC<T>::readfromfile(string filename)
{
    ifstream file(filename);
    vector<string> s;
    s = aquiutils::getline(file);
    int jj=0;
    while (file.eof() == false)
    {
        s = aquiutils::getline(file);
        if (s.size() == 2*MCMC_Settings.number_of_parameters+4)
        {   Params[jj].resize(MCMC_Settings.number_of_parameters);
            for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
            {
                Params[jj][i] = atof(s[i+1].c_str());
                pertcoeff[i] =  atof(s[MCMC_Settings.number_of_parameters+i+4].c_str());
            }
            logp[jj] = atof(s[MCMC_Settings.number_of_parameters+1].c_str());
            logp1[jj] = atof(s[MCMC_Settings.number_of_parameters+2].c_str());
            jj++;
        }
    }
    file.close();
    return jj;
}
 
template<class T>
TimeSeriesSet<double> CMCMC<T>::prior_distribution(int n_bins)
{
    TimeSeriesSet<double> prior_dist(MCMC_Settings.number_of_parameters);
    TimeSeries<double> B(n_bins);
 
    double min_range , max_range;
 
    for (int i=0; i<MCMC_Settings.number_of_parameters; i++)
    {
        if (parameter(i).GetDistribution() != "log-normal")
        {
            min_range = parameter(i)->mean() - 4*parameter(i)->std();
            max_range = parameter(i)->mean() + 4*parameter(i)->std();;
        }
        if (parameter(i).GetDistribution() == "log-normal")
        {
            min_range = parameter(i)->mean() * exp(-4*parameter(i)->std());
            max_range = parameter(i)->mean() * exp(4*parameter(i)->std());
        }
 
 
        double dp = abs(max_range - min_range) / n_bins;
 
        B.setTime(0, min_range + dp/2);
        for (int j=0; j<n_bins-1; j++)
            B.setTime(j+1, B.getTime(j) + dp);
 
        if (parameter(i).GetDistribution() != "log-normal")
            for (int j=0; j<n_bins; j++)
                B.setValue(j , exp(-pow(B.getTime(j)-parameter(i)->mean(),2)/(2.0*pow(parameter(i)->std(),2)))/(parameter(i)->std()*pow(6.28,0.5)));
 
        if (parameter(i).GetDistribution() == "log-normal")
            for (int j=0; j<n_bins; j++)
                B.setValue(j, exp(-pow(log(B.getTime(j))-log(parameter(i)->mean()),2)/(2.0*pow(parameter(i)->std(),2)))/(B.getTime(j)*parameter(i)->std()*pow(6.28,0.5)));
 
        prior_dist.at(i) = B;
    }
 
    return prior_dist;
}
 
template<class T>
void CMCMC<T>::ProduceRealizations(TimeSeriesSet<double> &MCMCout)
{
 
    vector<TimeSeriesSet<double>> realized_timeseries(observations->size());
    vector<TimeSeriesSet<double>> predicted_percentiles(observations->size());
 
    for (unsigned int jj = 0; jj <=MCMC_Settings.number_of_post_estimate_realizations/MCMC_Settings.numberOfThreads; jj++)
    {
        vector<T> Sys1(MCMC_Settings.numberOfThreads);
#ifndef NO_OPENMP
        omp_set_num_threads(MCMC_Settings.numberOfThreads);
#endif
#pragma omp parallel for
        for (int j = 0; j < min(MCMC_Settings.numberOfThreads, MCMC_Settings.number_of_post_estimate_realizations - jj*MCMC_Settings.numberOfThreads); j++)
        {
            Sys1[j] = *Model;
            vector<double> sampled_parameters = MCMCout.getrandom(MCMC_Settings.burnout_samples);
            model(&Sys1[j],sampled_parameters);
        }
 
        for (unsigned int j = 0; j < min(MCMC_Settings.numberOfThreads, MCMC_Settings.number_of_post_estimate_realizations - jj*MCMC_Settings.numberOfThreads); j++)
            for (unsigned int i=0; i<observations->size(); i++)
                realized_timeseries[i].append(*(Sys1[j].observation(i)->GetModeledTimeSeries()));
 
        if (rtw)
        {
            double progress = double(jj*MCMC_Settings.numberOfThreads) / double(MCMC_Settings.number_of_post_estimate_realizations);
            rtw->SetProgress(progress);
        }
    }
    vector<double> percents; percents.push_back(0.025); percents.push_back(0.5); percents.push_back(0.975);
    for (unsigned int i=0; i<observations->size(); i++)
    {
        realized_timeseries[i].write(FileInformation.outputpath + "Realizations_" + observation(i)->GetName() + ".txt");
        predicted_percentiles[i] = realized_timeseries[i].getpercentiles(percents);
        observation(i)->SetPercentile95(predicted_percentiles[i]);
        predicted_percentiles[i].write(FileInformation.outputpath + "Predicted_95p_Bracket" + observation(i)->GetName() + ".txt");
        observation(i)->SetRealizations(realized_timeseries[i]);
 
    }
    rtw->SetProgress(1);
}
 
template<class T>
void CMCMC<T>::get_outputpercentiles(TimeSeriesSet<double> &MCMCout)
{
 
    ProduceRealizations(MCMCout);
    int n_BTCout_obs = Model->ObservationsCount();
 
    BTCout_obs_prcntle.resize(1); for (int j = 0; j < 1; j++) BTCout_obs_prcntle[j].resize(n_BTCout_obs);
    BTCout_obs_prcntle_noise.resize(1); for (int j = 0; j < 1; j++) BTCout_obs_prcntle_noise[j].resize(n_BTCout_obs);
 
    if (calc_output_percentiles.size()>0)
        for (int i = 0; i < n_BTCout_obs; i++)
        {
            for (int j = 0; j < 1; j++)
            {
                BTCout_obs_prcntle[j][i] = BTCout_obs[j][i].getpercentiles(calc_output_percentiles);
 
                BTCout_obs_prcntle[j][i].write(FileInformation.outputpath + "BTC_obs_prcntl_" + Model->Observation(i)->GetName() + ".txt");
 
                if (MCMC_Settings.noise_realization_writeout)
                    BTCout_obs_prcntle_noise[j][i] = BTCout_obs_noise[j][i].getpercentiles(calc_output_percentiles);
 
                BTCout_obs_prcntle_noise[j][i].write(FileInformation.outputpath + "BTC_obs_prcntl_noise_" + Model->Observation(i)->GetName() + ".txt");
 
            }
        }
 
}
 
template<class T>
void CMCMC<T>::Perform()
{
    initialize(false);
    int mcmcstart = MCMC_Settings.number_of_chains;
    if (MCMC_Settings.continue_mcmc)
    {
        //if (rtw) rtw->AppendText("Reading samples from ... " + MCMC_Settings.continue_filename);
        mcmcstart = readfromfile(MCMC_Settings.continue_filename);
    }
    //if (rtw) rtw->AppendText(string("Generating samples ... "));
    step(mcmcstart, int((MCMC_Settings.total_number_of_samples - mcmcstart) / MCMC_Settings.number_of_chains)*MCMC_Settings.number_of_chains, FileInformation.outputfilename , rtw);
    //if (rtw) rtw->AppendText(string("Creating posterior distribution ..."));
    TimeSeriesSet<double> all_posterior_distributions;
    TimeSeriesSet<double> parameter_samples;
    for (unsigned int i=0; i<parameters->size(); i++)
    {
        TimeSeriesSet<double> chain_values(MCMC_Settings.number_of_chains);
        TimeSeries<double> all_samples;
        for (unsigned int i=0; i<MCMC_Settings.number_of_chains; i++)
        {
            chain_values.setname(i,"Chain_" + aquiutils::numbertostring(i));
        }
        for (unsigned int j=MCMC_Settings.burnout_samples; j<MCMC_Settings.total_number_of_samples; j++)
            chain_values[j%MCMC_Settings.number_of_chains].append(j,Params[j][i]);
 
        for (unsigned int i=0; i<MCMC_Settings.number_of_chains; i++)
        {
            all_samples.append(chain_values[i]);
        }
 
        //chain_values.name = parameter(i)->GetName();
        parameter(i)->SetMCMCSamples(chain_values);
 
        TimeSeries<double> posterior_distribution = all_samples.distribution(all_samples.size()/100,0);
        all_posterior_distributions.append(posterior_distribution,parameter(i)->GetName());
        posterior_distribution.setName("Posterior density");
        parameter(i)->SetPosteriorDistribution(posterior_distribution);
        parameter_samples.append(all_samples);
 
    }
    all_posterior_distributions.write(FileInformation.outputpath + "Posterior_distributions.txt");
    //if (rtw) rtw->AppendText(string("Generating Realizations ..."));
    ProduceRealizations(parameter_samples);
}
 
template<class T>
int_value_pair CMCMC<T>::Min(const vector<double> &vec, int current_counter, int n_chains)
{
    int_value_pair out;
    out.value = 1e12;
    for (unsigned int i=max(current_counter-n_chains,0); i<current_counter; i++)
    {
        if (vec[i]<out.value)
        {
            out.value = vec[i];
            out.counter = i;
        }
    }
    return out;
}
 
template<class T>
int_value_pair CMCMC<T>::Max(const vector<double> &vec, int current_counter, int n_chains)
{
    int_value_pair out;
    out.value = -1e12;
    for (unsigned int i=max(current_counter-n_chains,0); i<current_counter; i++)
    {
        if (vec[i]>out.value)
        {
            out.value = vec[i];
            out.counter = i;
        }
    }
    return out;
}