#include "main.h"

#include "mesh.h"
#include "filter.h"
#include "Settings.h"
#include "meshPlotter.h"
#include "Plotty.h"
#include "Plotta.h"

#include <array>
#include <memory>
#include <thread>
#include <filesystem>
#include <chrono>

#include <Indoor/floorplan/v2/FloorplanReader.h>
#include <Indoor/sensors/offline/FileReader.h>
#include <Indoor/geo/Heading.h>
#include <Indoor/geo/Point2.h>
#include <Indoor/sensors/imu/TurnDetection.h>
#include <Indoor/sensors/imu/StepDetection.h>
#include <Indoor/sensors/imu/PoseDetection.h>
#include <Indoor/sensors/imu/MotionDetection.h>
#include <Indoor/sensors/pressure/RelativePressure.h>
#include <Indoor/data/Timestamp.h>


#include <Indoor/math/stats/Statistics.h>

#include "FtmKalman.h"
#include "misc.h"

#include <sys/stat.h>

using namespace std::chrono_literals;

enum class AggregateMethod {
    None,
    Median,
    MovingMedian
};

struct MovingMedianTS2
{
private:
    struct TimeValue {
        Timestamp timestamp;
        double value;
    };

    int timeWindow; // ms
    std::vector<TimeValue> values;
    

public:
    MovingMedianTS2() 
        : timeWindow(0) 
    {}

    MovingMedianTS2(const Timestamp window)
        : timeWindow(window.ms())
    {}

    void add(Timestamp ts, double value)
    {
        values.push_back(TimeValue{ ts, value });
    }

    bool tryGet(const Timestamp ts, double& value)
    {
        int wnd = timeWindow;

        values.erase(std::remove_if(values.begin(),
                                    values.end(),
                                    [wnd, ts](TimeValue tv) { return std::abs((ts - tv.timestamp).ms()) >= wnd; }),
                     values.end());

        if (values.size() == 0)
            return false;

        Stats::Median<double> median;

        for (auto tv : values)
        {
            median.add(tv.value);
        }

        value = median.get();
        return true;
    }
};


std::vector<std::tuple<float, float, float>> getFtmValues(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator, const MACAddress nuc)
{
    std::vector<std::tuple<float, float, float>> result;

    for (const Offline::Entry& e : fr.getEntries())
    {
        if (e.type == Offline::Sensor::WIFI_FTM)
        {
            const Timestamp ts = Timestamp::fromMS(e.ts);

            Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0, 0, 1.3);

            auto wifi = fr.getWifiFtm()[e.idx].data;

            if (wifi.getAP().getMAC() == nuc)
            {
                Point3 apPos = Settings::CurrentPath.NUCs.find(wifi.getAP().getMAC())->second.position;
                float apDist = gtPos.getDistance(apPos);
                float ftmDist = wifi.getFtmDist();
                float rssi = wifi.getRSSI();

                result.push_back({ apDist, ftmDist, rssi });
            }
        }
    }

    return result;
}

std::pair<float, float> optimizeFtm(std::vector<std::tuple<float, float, float>>& values)
{
    std::vector<std::pair<float, float>> error;

    for (float offset = 0; offset < 10.0f; offset += 0.25)
    {
        Stats::Statistics<float> diffs;

        for (const auto& data : values)
        {
            float apDist = std::get<0>(data);
            float ftmDist = std::get<1>(data);
            ftmDist += offset;

            float diff = (apDist - ftmDist);

            diffs.add(diff);
        }

        error.push_back({ offset, diffs.getSquaredSumAvg() });
    }

    auto minElement = std::min_element(error.begin(), error.end(), [](std::pair<float, float> a, std::pair<float, float> b) {
        return a.second < b.second;
    });

    std::cout << "Min ftm offset \t" << minElement->first << "\t" << minElement->second << "\n";

    return *minElement;
}

std::pair<float, float> optimizeRssi(std::vector<std::tuple<float, float, float>>& values)
{
    std::vector<std::pair<float, float>> error;

    for (float pathLoss = 2.0f; pathLoss < 4.0f; pathLoss += 0.125)
    {
        Stats::Statistics<float> diffs;

        for (const auto& data : values)
        {
            float apDist = std::get<0>(data);
            float rssi = std::get<2>(data);
            float rssiDist = LogDistanceModel::rssiToDistance(-40, pathLoss, rssi);

            float diff = (apDist - rssiDist);

            diffs.add(diff);
        }

        error.push_back({ pathLoss, diffs.getSquaredSumAvg() });
    }

    auto minElement = std::min_element(error.begin(), error.end(), [](std::pair<float, float> a, std::pair<float, float> b) {
        return a.second < b.second;
    });

    std::cout << "Min path loss \t" << minElement->first << "\t" << minElement->second << "\n";

    return *minElement;
}

void optimizeWifiParameters(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator)
{
    int i = 1;
    for (auto nuc : { Settings::NUC1, Settings::NUC2, Settings::NUC3, Settings::NUC4 })
    {
        auto values = getFtmValues(fr, gtInterpolator, nuc);
        std::cout << "NUC" << i++ << "\n";

        optimizeFtm(values);
        optimizeRssi(values);
    }
}

void exportFtmValues(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator)
{
    std::fstream fs;
    fs.open("test.txt", std::fstream::out);

    fs << "timestamp;nucid;dist;rssiDist;ftmDist;ftmStdDev;numMeas;numSuccesMeas" << "\n";

    for (const Offline::Entry& e : fr.getEntries())
    {
        if (e.type == Offline::Sensor::WIFI_FTM)
        {
            const Timestamp ts = Timestamp::fromMS(e.ts);

            Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0, 0, 1.3);

            auto wifi = fr.getWifiFtm()[e.idx].data;

            int nucid = Settings::CurrentPath.NUCs.at(wifi.getAP().getMAC()).ID;
            float ftm_offset = Settings::CurrentPath.NUCs.at(wifi.getAP().getMAC()).ftm_offset;
            float rssi_pathloss = Settings::CurrentPath.NUCs.at(wifi.getAP().getMAC()).rssi_pathloss;

            float rssiDist = LogDistanceModel::rssiToDistance(-40, rssi_pathloss, wifi.getRSSI());
            float ftmDist = wifi.getFtmDist() + ftm_offset; //in m; plus static offset
            float ftmStdDev = wifi.getFtmDistStd();
            int numMeas = wifi.getNumAttemptedMeasurements();
            int numSuccessMeas = wifi.getNumSuccessfulMeasurements();

            Point3 apPos = Settings::CurrentPath.NUCs.find(wifi.getAP().getMAC())->second.position;
            float apDist = gtPos.getDistance(apPos);

            fs << ts.ms() << ";" << nucid << ";" << apDist << ";" << rssiDist << ";" << ftmDist << ";" << ftmStdDev << ";" << numMeas << ";" << numSuccessMeas << "\n";
        }

    }
    fs.close();
}

template<typename T>
struct TimeSeries
{
    std::vector<Timestamp> t;
    std::vector<T> values;

    void add(const Timestamp ts, const T value)
    {
        t.push_back(ts);
        values.push_back(value);
    }
};

static float kalman_procNoiseDistStdDev = 1.2f;  // standard deviation of distance for process noise 
static float kalman_procNoiseVelStdDev = 0.1f;   // standard deviation of velocity for process noise 

static CombinedStats<float> run(Settings::DataSetup setup, int walkIdx, std::string folder) {

    // reading file
    std::string currDir = std::filesystem::current_path().string();

    Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(setup.map);
    Offline::FileReader fr(setup.training[walkIdx], setup.HasNanoSecondTimestamps);

    // ground truth
    std::vector<int> gtPath = setup.gtPath;
    Interpolator<uint64_t, Point3> gtInterpolator = fr.getGroundTruthPath(map, gtPath);
    CombinedStats<float> errorStats;

    //calculate distance of path
    std::vector<Interpolator<uint64_t, Point3>::InterpolatorEntry> gtEntries = gtInterpolator.getEntries();
    double gtTotalDistance = 0;
    Stats::Statistics<double> gtWalkingSpeed;
    for (int i = 1; i < gtEntries.size(); ++i) {
        double distance = gtEntries[i].value.getDistance(gtEntries[i - 1].value);
        double timeDiff = static_cast<double>(gtEntries[i].key - gtEntries[i - 1].key);

        double walkingSpeed = distance / (timeDiff / 1000.0f); // m / s

        gtWalkingSpeed.add(walkingSpeed);
        gtTotalDistance += distance;
    }

    std::cout << "Distance of Path: " << gtTotalDistance << std::endl;
    std::cout << "GT walking speed: " << gtWalkingSpeed.getAvg() << "m/s (" << gtWalkingSpeed.getAvg()*3.6f << "km/h)" << std::endl;

    // error file
    const long int t = static_cast<long int>(time(NULL));
    auto evalDir = std::filesystem::path(Settings::errorDir);
    evalDir.append(folder);

    if (!std::filesystem::exists(evalDir)) {
        std::filesystem::create_directory(evalDir);
    }

    std::ofstream errorFile;
    errorFile.open(evalDir.string() + "/" + std::to_string(walkIdx) + "_" + std::to_string(t) + ".csv");

    // Output dir
    auto outputDir = std::filesystem::path(Settings::outputDir);
    outputDir.append(Settings::CurrentPath.name + "_" + std::to_string(walkIdx));

    if (!std::filesystem::exists(outputDir)) {
        std::filesystem::create_directories(outputDir);
    }

    // wifi
    auto kalmanMap = std::make_shared<std::unordered_map<MACAddress, Kalman>>();

    for (auto& nucConfig : setup.NUCs)
    {
        kalmanMap->insert({ nucConfig.first, Kalman(nucConfig.second.ID, nucConfig.second.kalman_measStdDev, kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev) });
    }

    std::cout << "Optimal wifi parameters for " << setup.training[walkIdx] << "\n";
    optimizeWifiParameters(fr, gtInterpolator);

    // mesh
    NM::NavMeshSettings set;
    set.maxQuality_m = 0.10; // because of narrow hallways and small rooms reduce min. triangle size (default is 0.2)
    MyNavMesh mesh;
    MyNavMeshFactory fac(&mesh, set);
    fac.build(map);

    // debug show
    //MeshPlotter dbg;
    //dbg.addFloors(map);
    //dbg.addOutline(map);
    //dbg.addMesh(mesh);
    ////dbg.addDijkstra(mesh);
    //dbg.draw();

    Plotty plot(map);
    plot.buildFloorplan();
    plot.setGroundTruth(gtPath);
    plot.setView(30, 0);
    // APs Positions
    for (auto& nucConfig : setup.NUCs)
    {
        plot.addCircle(10000 + nucConfig.second.ID, nucConfig.second.position.xy(), 0.1);
    }
    plot.plot();

    // particle-filter
    const int numParticles = 5000;
    //auto init = std::make_unique<MyPFInitFixed>(&mesh, srcPath0);	// known position
    auto init = std::make_unique<MyPFInitUniform>(&mesh);		// uniform distribution
    auto eval = std::make_unique<MyPFEval>();
    eval->ftmKalmanFilters = kalmanMap;

    auto trans = std::make_unique<MyPFTransRandom>();
    //auto trans = std::make_unique<MyPFTransStatic>();

    auto resample = std::make_unique<SMC::ParticleFilterResamplingSimple<MyState>>();
    auto estimate = std::make_unique<SMC::ParticleFilterEstimationWeightedAverage<MyState>>();
    //auto estimate = std::make_unique<SMC::ParticleFilterEstimationMax<MyState>>();

    // setup
    MyFilter pf(numParticles, std::move(init));
    pf.setEvaluation(std::move(eval));
    pf.setTransition(std::move(trans));
    pf.setResampling(std::move(resample));
    pf.setEstimation(std::move(estimate));
    pf.setNEffThreshold(0.85);

    // sensors
    MyControl ctrl;
    MyObservation obs;

    Timestamp lastTimestamp = Timestamp::fromMS(0);
    
    std::vector<float> errorValuesFtm, errorValuesRssi;
    std::vector<int> timestamps;

    std::vector<int> timestampsDist;
    std::vector<std::array<float, 4>> gtDistances, rssiDistances; // distance per AP

    std::array<TimeSeries<std::array<float, 3>>, 4> ftmDistances;
    TimeSeries<std::array<KalmanPrediction, 4>> ftmPredictions;

    Plotta::Plotta errorPlot("errorPlot", outputDir.string() + "/errorData.py");
    Plotta::Plotta distsPlot("distsPlot", outputDir.string() + "/distances.py");


    std::unordered_map<MACAddress, MovingMedianTS2> movMedianPerAP;
    for (auto& nucConfig : setup.NUCs)
    {
        movMedianPerAP[nucConfig.first] = MovingMedianTS2(Timestamp::fromMS(500));
    }

    for (const Offline::Entry& e : fr.getEntries())
    {
        if (e.type != Offline::Sensor::WIFI_FTM) {
            continue;
        }

        // TIME
        const Timestamp ts = Timestamp::fromMS(e.ts);

        auto wifiFtm = fr.getWifiFtm()[e.idx].data;
        obs.ftm.push_back(wifiFtm);

        
        if (ts - lastTimestamp >= Timestamp::fromMS(500))
        {
            // Do update step
            Point2 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())).xy();
            plot.setGroundTruth(Point3(gtPos.x, gtPos.y, 0.1));

            // TODO
            //gtDistances.push_back({ gtPos.getDistance(Settings::CurrentPath.nucInfo(0).position.xy()),
            //                        gtPos.getDistance(Settings::CurrentPath.nucInfo(1).position.xy()),
            //                        gtPos.getDistance(Settings::CurrentPath.nucInfo(2).position.xy()),
            //                        gtPos.getDistance(Settings::CurrentPath.nucInfo(3).position.xy()) });

            Point3 estPos;
            float distErrorFtm = 0;
            float distErrorRssi = 0;

            const AggregateMethod aggrMethod = AggregateMethod::None;

            if (aggrMethod == AggregateMethod::Median)
            {
                // Compute median of observations
                std::unordered_map<MACAddress, Stats::Median<double>> apMeas;

                for (WiFiMeasurement wifi : obs.ftm)
                {
                    apMeas[wifi.getAP().getMAC()].add(wifi.getFtmDist());
                }

                obs.ftm.clear();

                for (auto& pair : apMeas)
                {
                    double median = pair.second.get();

                    obs.ftm.push_back(WiFiMeasurement(AccessPoint(pair.first), NAN, ts, median, NAN, 3, 3));
                }
            }
            else if (aggrMethod == AggregateMethod::MovingMedian)
            {
                for (WiFiMeasurement wifi : obs.ftm)
                {
                    movMedianPerAP[wifi.getAP().getMAC()].add(wifi.getTimestamp(), wifi.getFtmDist());
                }

                obs.ftm.clear();

                for (auto& pair : movMedianPerAP)
                {
                    double median = 0;
                    if (pair.second.tryGet(ts, median))
                    {
                        obs.ftm.push_back(WiFiMeasurement(AccessPoint(pair.first), NAN, ts, median, NAN, 3, 3));
                    }
                }
            }

           // Store measurements
            //for (WiFiMeasurement wifi : obs.ftm)
            //{
            //    if (wifi.getNumSuccessfulMeasurements() < 3)
            //    {
            //        continue;
            //    }

            //    Point2 gtPos2 = gtInterpolator.get(static_cast<uint64_t>(wifi.getTimestamp().ms())).xy();
            //    Point2 apPos2 = Settings::CurrentPath.NUCs[wifi.getAP().getMAC()].position.xy();

            //    float gtDist2 = gtPos2.getDistance(apPos2);

            //    // store distances
            //    const int nucIdx = Settings::nucIndex(wifi.getAP().getMAC());
            //    ftmDistances[nucIdx].add(wifi.getTimestamp(), { wifi.getFtmDist(), gtDist2, wifi.getRSSI() }); // TODO
            //}

            // Kalman debugging (can't be used with active PF)
            //{
            //    // Kalman predict & update for available measurments
            //    for (WiFiMeasurement wifi : obs.ftm)
            //    {
            //        kalmanMap->at(wifi.getAP().getMAC()).predictAndUpdate(wifi.getTimestamp(), wifi.getFtmDist());
            //    }

            //    // Kalman prediction only for current timestamp
            //    std::array<KalmanPrediction, 4> pred;
            //    for (size_t i = 0; i < 4; i++)
            //    {
            //        KalmanPrediction prediction = kalmanMap->at(Settings::nucFromIndex(i)).predict(ts);
            //        prediction.P[0] = kalmanMap->at(Settings::nucFromIndex(i)).P[0];
            //        prediction.P[1] = kalmanMap->at(Settings::nucFromIndex(i)).P[1];
            //        prediction.P[2] = kalmanMap->at(Settings::nucFromIndex(i)).P[2];
            //        prediction.P[3] = kalmanMap->at(Settings::nucFromIndex(i)).P[3];

            //        pred[i] = prediction;
            //    }
            //    ftmPredictions.add(ts, pred);
            //}


            // Run PF
            obs.currentTime = ts;
            ctrl.currentTime = ts;

            MyState est = pf.update(&ctrl, obs);
            ctrl.afterEval();
            lastTimestamp = ts;

            estPos = est.pos.pos;
            ctrl.lastEstimate = estPos;

            plot.setCurEst(Point3(estPos.x, estPos.y, 0.1));
            plot.addEstimationNode(Point3(estPos.x, estPos.y, 0.1));

            // Error
            distErrorFtm = gtPos.getDistance(estPos.xy());
            errorStats.ftm.add(distErrorFtm);

            // draw wifi ranges
            plot.clearDistanceCircles();

            for (size_t i = 0; i < obs.ftm.size(); i++)
            {
                WiFiMeasurement wifi2 = obs.ftm[i];

                Point3 apPos = Settings::CurrentPath.nuc(wifi2.getAP().getMAC()).position;

                K::GnuplotColor color;
                switch (Settings::CurrentPath.nuc(wifi2.getAP().getMAC()).ID)
                {
                case 1:  color = K::GnuplotColor::fromRGB(0, 255, 0); break;
                case 2:  color = K::GnuplotColor::fromRGB(0, 0, 255); break;
                case 3:  color = K::GnuplotColor::fromRGB(255, 255, 0); break;
                default: color = K::GnuplotColor::fromRGB(255, 0, 0); break;
                }


                //float rssiDist = LogDistanceModel::rssiToDistance(-40, 2.5, wifi2.getRSSI());
                //plot.addDistanceCircle(apPos.xy(), rssiDist, color);

                plot.addDistanceCircle(apPos.xy(), wifi2.getFtmDist(), color);
            }

            
            obs.wifi.clear();
            obs.ftm.clear();


            errorValuesFtm.push_back(distErrorFtm);
            errorValuesRssi.push_back(distErrorRssi);
            timestamps.push_back(ts.ms());

            // Error plot
            errorPlot.add("t", timestamps);
            errorPlot.add("errorFtm", errorValuesFtm);
            errorPlot.add("errorRssi", errorValuesRssi);
            errorPlot.frame();

            // Distances plot
            //distsPlot.add("t", timestamps);
            //distsPlot.add("gtDists", gtDistances);
            //distsPlot.add("ftmDists", ftmDistances);
            //distsPlot.frame();

            // Plotting
            plot.showParticles(pf.getParticles());
            plot.setCurEst(estPos);
            plot.setGroundTruth(Point3(gtPos.x, gtPos.y, 0.1));

            plot.addEstimationNode(estPos);
            //plot.setActivity((int)act.get());
            //plot.splot.getView().setEnabled(false);
            //plot.splot.getView().setCamera(0, 0);
            //plot.splot.getView().setEqualXY(true);

            plot.plot();
            std::this_thread::sleep_for(100ms);
        }
    }
    
    printErrorStats(errorStats);

    std::ofstream plot_out;
    plot_out.open(outputDir.string() + "/plot.gp");

    plot.clearDistanceCircles();
    plot.saveToFile(plot_out);

    std::ofstream errorStats_out;
    errorStats_out.open(outputDir.string() + "/error_stats.txt");
    printErrorStats(errorStats_out, errorStats);

    errorPlot.frame();

    // MATLAB output
    //{
    //    std::ofstream matlab_error_out;
    //    matlab_error_out.open(outputDir.string() + "/error.csv");

    //    matlab_error_out << "t;ftmError" << "\n";

    //    for (size_t i = 0; i < timestamps.size(); i++)
    //    {
    //        matlab_error_out << timestamps[i] << ";" << errorValuesFtm[i] << "\n";
    //    }
    //}

    //{
    //    std::ofstream matlab_gt_out;
    //    matlab_gt_out.open(outputDir.string() + "/distance_gt.csv");

    //    matlab_gt_out << "t;distGT1;distGT2;distGT3;distGT4" << "\n";

    //    for (size_t i = 0; i < gtDistances.size(); i++)
    //    {
    //        matlab_gt_out << timestamps[i] << ";" << gtDistances[i][0] << ";" << gtDistances[i][1] << ";" << gtDistances[i][2] << ";" << gtDistances[i][3] << "\n";
    //    }
    //}

    //for (size_t i = 0; i < 4; i++)
    //{
    //    std::ofstream matlab_out;
    //    matlab_out.open(outputDir.string() + "/distance_ap" + std::to_string(i+1) + ".csv");

    //    matlab_out << "t;distAp;distGT;rssi" << "\n";

    //    for (size_t j = 0; j < ftmDistances[i].t.size(); j++)
    //    {
    //        matlab_out << ftmDistances[i].t[j].ms() 
    //                   << ";" << ftmDistances[i].values[j][0] 
    //                   << ";" << ftmDistances[i].values[j][1] 
    //                   << ";" << ftmDistances[i].values[j][2]
    //                   << "\n";
    //    }
    //}

    //{
    //    std::ofstream matlab_prediction_out;
    //    matlab_prediction_out.open(outputDir.string() + "/predictions.csv");

    //    matlab_prediction_out << "t;pAP1d;pAP1dDev;pAP1s;pAP1sDev;pAP2d;pAP2dDev;pAP2s;pAP2sDev;pAP3d;pAP3dDev;pAP3s;pAP3sDev;pAP4d;pAP4dDev;pAP4s;pAP4sDev" << "\n";
    //    for (size_t i = 0; i < ftmPredictions.values.size(); i++)
    //    {
    //        matlab_prediction_out << ftmPredictions.t[i].ms();
    //        for (size_t j = 0; j < 4; j++)
    //        {
    //            const KalmanPrediction v = ftmPredictions.values[i][j];

    //            if (isnan(v.distance))
    //                matlab_prediction_out << ";nan";
    //            else
    //                matlab_prediction_out << ";" << v.distance;

    //            if (isnan(v.P[0]))
    //                matlab_prediction_out << ";nan";
    //            else
    //                matlab_prediction_out << ";" << std::sqrt(v.P[0]);

    //            if (isnan(v.speed))
    //                matlab_prediction_out << ";nan";
    //            else
    //                matlab_prediction_out << ";" << v.speed;

    //            if (isnan(v.P[2]))
    //                matlab_prediction_out << ";nan";
    //            else
    //                matlab_prediction_out << ";" << std::sqrt(v.P[2]);

    //        }
    //        matlab_prediction_out << "\n";
    //    }
    //}

    return errorStats;
}

int main(int argc, char** argv) 
{
    CmdArguments args(argc, argv);

    if (args.hasFlag("prob"))
    {
        std::cout << "Probabilistic" << "\n";
        return mainProp(argc, argv);
    }
    else if (args.hasFlag("trilat"))
    {
        std::cout << "Trilateration" << "\n";
        return mainTrilat(argc, argv);
    }

    CombinedStats<float> statsAVG;
    CombinedStats<float> statsMedian;
    CombinedStats<float> statsSTD;
    CombinedStats<float> statsQuantil;
    CombinedStats<float> tmp;

    std::string evaluationName = "prologic/tmp";

    std::vector<Settings::DataSetup> setupsToRun = { 
        //Settings::data.Path5,
        //Settings::data.Path7,
        //Settings::data.Path8,
        //Settings::data.Path9,
        //Settings::data.Path10,
        //Settings::data.Path11
        //Settings::data.Path20,
        //Settings::data.Path21,
        Settings::data.Path22,
    };
    
    for (Settings::DataSetup setupToRun : setupsToRun)
    {
        Settings::CurrentPath = setupToRun;


        for (size_t walkIdx = 0; walkIdx < Settings::CurrentPath.training.size(); walkIdx++)
        {
            std::cout << "Executing walk " << walkIdx << "\n";
            for (int i = 0; i < 1; ++i)
            {
                std::cout << "Start of iteration " << i << "\n";

                tmp = run(Settings::CurrentPath, walkIdx, evaluationName);

                statsAVG.ftm.add(tmp.ftm.getAvg());
                statsMedian.ftm.add(tmp.ftm.getMedian());
                statsSTD.ftm.add(tmp.ftm.getStdDev());
                statsQuantil.ftm.add(tmp.ftm.getQuantile(0.75));

                statsAVG.rssi.add(tmp.rssi.getAvg());
                statsMedian.rssi.add(tmp.rssi.getMedian());
                statsSTD.rssi.add(tmp.rssi.getStdDev());
                statsQuantil.rssi.add(tmp.rssi.getQuantile(0.75));

                std::cout << "Iteration " << i << " completed" << std::endl;
            }
        }

        std::cout << "Results for path " << Settings::CurrentPath.name << std::endl;
        std::cout << "==========================================================" << std::endl;
        std::cout << "Average of all statistical data FTM: " << std::endl;
        std::cout << "Median: " << statsMedian.ftm.getAvg() << std::endl;
        std::cout << "Average: " << statsAVG.ftm.getAvg() << std::endl;
        std::cout << "Standard Deviation: " << statsSTD.ftm.getAvg() << std::endl;
        std::cout << "75 Quantil: " << statsQuantil.ftm.getAvg() << std::endl;
        std::cout << "==========================================================" << std::endl;

        std::cout << "==========================================================" << std::endl;
        std::cout << "Average of all statistical data RSSI: " << std::endl;
        std::cout << "Median: " << statsMedian.rssi.getAvg() << std::endl;
        std::cout << "Average: " << statsAVG.rssi.getAvg() << std::endl;
        std::cout << "Standard Deviation: " << statsSTD.rssi.getAvg() << std::endl;
        std::cout << "75 Quantil: " << statsQuantil.rssi.getAvg() << std::endl;
        std::cout << "==========================================================" << std::endl;
    }





    //std::vector<std::array<float, 3>> error;
    //std::ofstream error_out;
    //error_out.open(Settings::errorDir + evaluationName + "_error_path1" +  ".csv", std::ios_base::app);


    //for (kalman_procNoiseDistStdDev = 0.8f; kalman_procNoiseDistStdDev < 1.5f; kalman_procNoiseDistStdDev += 0.1f)
    //{
    //    for (kalman_procNoiseVelStdDev = 0.1f; kalman_procNoiseVelStdDev < 0.5f; kalman_procNoiseVelStdDev += 0.1f)
    //    {

    //for (size_t walkIdx = 0; walkIdx < Settings::data.CurrentPath.training.size(); walkIdx++)
    //{
    //    std::cout << "Executing walk " << walkIdx << "\n";
    //    for (int i = 0; i < 1; ++i) 
    //    {
    //        std::cout << "Start of iteration " << i << "\n";

    //        tmp = run(Settings::data.CurrentPath, walkIdx, evaluationName);
    //        statsMedian.add(tmp.getMedian());
    //        statsAVG.add(tmp.getAvg());
    //        statsSTD.add(tmp.getStdDev());
    //        statsQuantil.add(tmp.getQuantile(0.75));

    //        std::cout << kalman_procNoiseDistStdDev << " " << kalman_procNoiseVelStdDev << std::endl;
    //        std::cout << "Iteration " << i << " completed" << std::endl;

    //    }
    //}


    //        error.push_back({{ kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev, statsAVG.getAvg() }});

    //        auto minElement = std::min_element(error.begin(), error.end(), [](std::array<float, 3> a, std::array<float, 3> b) {
    //            return a[2] < b[2];
    //        });

    //        std::cout << "Current min error " << (*minElement)[2] << "\t Q(0)=\t" << (*minElement)[0] << "\t Q(1)=" << (*minElement)[1] << "\n";

    //        error_out << kalman_procNoiseDistStdDev << ";" << kalman_procNoiseVelStdDev << ";" << statsAVG.getAvg() << std::endl;

    //        // reset stats
    //        statsAVG.reset();
    //        statsMedian.reset();
    //        statsSTD.reset();
    //        statsQuantil.reset();
    //    }
    //}

    //auto minElement = std::min_element(error.begin(), error.end(), [](std::array<float, 3> a, std::array<float, 3> b) {
    //    return a[2] < b[2];
    //});

    //std::cout << "Global Min error " << (*minElement)[2] << "\t Q(0)=\t" << (*minElement)[0] << "\t Q(1)=" << (*minElement)[1] << "\n";

    //error_out.close();



    //std::this_thread::sleep_for(std::chrono::seconds(60));

}