/*****************************************************************************************
 *                                                                                       *
 * OpenSpace                                                                             *
 *                                                                                       *
 * Copyright (c) 2014-2018                                                               *
 *                                                                                       *
 * Permission is hereby granted, free of charge, to any person obtaining a copy of this  *
 * software and associated documentation files (the "Software"), to deal in the Software *
 * without restriction, including without limitation the rights to use, copy, modify,    *
 * merge, publish, distribute, sublicense, and/or sell copies of the Software, and to    *
 * permit persons to whom the Software is furnished to do so, subject to the following   *
 * conditions:                                                                           *
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 * The above copyright notice and this permission notice shall be included in all copies *
 * or substantial portions of the Software.                                              *
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 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,   *
 * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A         *
 * PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT    *
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 * OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.                                         *
 ****************************************************************************************/

#include <modules/iswa/util/dataprocessor.h>
#include <openspace/util/histogram.h>

#include <fstream>

namespace openspace {

DataProcessor::DataProcessor()
    :_useLog(false)
    ,_useHistogram(false)
    ,_normValues(glm::vec2(1.0))
    ,_filterValues(glm::vec2(0.0))
    ,_histNormValues(glm::vec2(10.f, 10.f))
{
    _coordinateVariables = {"x", "y", "z", "phi", "theta"};
}

DataProcessor::~DataProcessor() {}

void DataProcessor::useLog(bool useLog) {
    _useLog = useLog;
}

void DataProcessor::useHistogram(bool useHistogram) {
    _useHistogram = useHistogram;
}

void DataProcessor::normValues(glm::vec2 normValues) {
    _normValues = normValues;
}

glm::size3_t DataProcessor::dimensions() {
    return _dimensions;
}

glm::vec2 DataProcessor::filterValues() {
    return _filterValues;
}

void DataProcessor::clear() {
    _min.clear();
    _max.clear();
    _sum.clear();
    _standardDeviation.clear();
    _histograms.clear();
    _numValues.clear();
}


float DataProcessor::processDataPoint(float value, int option) {
    if (_numValues.empty()) {
        return 0.f;
    }
    std::shared_ptr<Histogram> histogram = _histograms[option];
    float mean = (1.f / _numValues[option]) * _sum[option];
    float sd = _standardDeviation[option];

    float v;
    if (_useHistogram) {
        v = histogram->equalize(
            normalizeWithStandardScore(value, mean, sd, _histNormValues)
        ) / 512.f;
    } else {
        v = normalizeWithStandardScore(value, mean, sd, _normValues);
    }

    // float v = normalizeWithStandardScore(value, mean, sd, _normValues);
    return v;
}

float DataProcessor::normalizeWithStandardScore(float value, float mean, float sd,
                                                glm::vec2 normalizationValues)
{
    float zScoreMin = normalizationValues.x; //10.0f;//_normValues.x;
    float zScoreMax = normalizationValues.y; //10.0f;//_normValues.y;
    float standardScore = ( value - mean ) / sd;
    // Clamp intresting values
    standardScore = glm::clamp(standardScore, -zScoreMin, zScoreMax);
    //return and normalize
    return ( standardScore + zScoreMin )/(zScoreMin + zScoreMax );
}

float DataProcessor::unnormalizeWithStandardScore(float standardScore, float mean,
                                                  float sd, glm::vec2 normalizationValues)
{
    float zScoreMin = normalizationValues.x;
    float zScoreMax = normalizationValues.y;

    float value = standardScore*(zScoreMax+zScoreMin)-zScoreMin;
    value = value*sd+mean;

    // std::cout << value << std::endl;
    return value;
    // float standardScore = ( value - mean ) / sd;
    // // Clamp intresting values
    // standardScore = glm::clamp(standardScore, -zScoreMin, zScoreMax);
    // //return and normalize
    // return ( standardScore + zScoreMin )/(zScoreMin + zScoreMax );
}

void DataProcessor::initializeVectors(int numOptions){
    if (_min.empty()) {
        _min = std::vector<float>(numOptions, std::numeric_limits<float>::max());
    }
    if (_max.empty()) {
        _max = std::vector<float>(numOptions, std::numeric_limits<float>::min());
    }
    if (_sum.empty()) {
        _sum = std::vector<float>(numOptions, 0.0f);
    }
    if (_standardDeviation.empty()) {
        _standardDeviation = std::vector<float>(numOptions, 0.0f);
    }
    if (_numValues.empty()) {
        _numValues = std::vector<float>(numOptions, 0.0f);
    }
    if (_histograms.empty()) {
        _histograms = std::vector<std::shared_ptr<Histogram>>(numOptions, nullptr);
    }
}

void DataProcessor::calculateFilterValues(std::vector<int> selectedOptions) {
    int numSelected = static_cast<int>(selectedOptions.size());

    _filterValues = glm::vec2(0.0);
    if (numSelected <= 0) {
        return;
    }

    if (!_histograms.empty()) {
        for (int option : selectedOptions) {
            float filterMid;
            float filterWidth;
            if (!_useHistogram) {
                float mean = (1.f / _numValues[option]) * _sum[option];
                float standardDeviation = _standardDeviation[option];
                std::shared_ptr<Histogram> histogram = _histograms[option];

                filterMid = histogram->highestBinValue(_useHistogram);
                filterWidth = mean+histogram->binWidth();

                filterMid = normalizeWithStandardScore(
                    filterMid,
                    mean,
                    standardDeviation,
                    _normValues
                );
                filterWidth = fabs(0.5f-normalizeWithStandardScore(
                    filterWidth,
                    mean,
                    standardDeviation,
                    _normValues)
                );
            } else {
                Histogram hist = _histograms[option]->equalize();
                filterMid = hist.highestBinValue(true);
                std::cout << filterMid << std::endl;
                filterWidth = 1.f / 512.f;
            }

             _filterValues += glm::vec2(filterMid, filterWidth);

        }
        _filterValues /= numSelected;
    }
}

void DataProcessor::add(std::vector<std::vector<float>>& optionValues,
                        std::vector<float>& sum)
{
    int numOptions = static_cast<int>(optionValues.size());
    int numValues;
    float mean, value, variance, standardDeviation;

    for (int i=0; i<numOptions; i++) {
        std::vector<float> values = optionValues[i];
        numValues = static_cast<int>(values.size());

        variance = 0;
        mean = (1.0f/numValues)*sum[i];

        for (int j=0; j<numValues; j++) {
            value = values[j];
            variance +=  pow(value-mean, 2);
        }

        standardDeviation = sqrt(variance/ numValues);

        float oldStandardDeviation = _standardDeviation[i];
        float oldMean = (1.0f/_numValues[i])*_sum[i];

        _sum[i] += sum[i];
        _standardDeviation[i] = sqrt(pow(standardDeviation, 2) +
                                pow(_standardDeviation[i], 2));
        _numValues[i] += numValues;


        mean = (1.0f/_numValues[i])*_sum[i];
        float min = normalizeWithStandardScore(
            _min[i],
            mean,
            _standardDeviation[i],
            _histNormValues
        );
        float max = normalizeWithStandardScore(
            _max[i],
            mean,
            _standardDeviation[i],
            _histNormValues
        );

        if (!_histograms[i]) {
             _histograms[i] = std::make_shared<Histogram>(min, max, 512);
        }
        else {
            const float* histData = _histograms[i]->data();
            float histMin = _histograms[i]->minValue();
            float histMax = _histograms[i]->maxValue();
            int numBins = _histograms[i]->numBins();

            float unNormHistMin = unnormalizeWithStandardScore(
                histMin,
                oldMean,
                oldStandardDeviation,
                _histNormValues
            );
            float unNormHistMax = unnormalizeWithStandardScore(
                histMax,
                oldMean,
                oldStandardDeviation,
                _histNormValues
            );
            //unnormalize histMin, histMax
            // min = std::min(min, histMin)
            std::shared_ptr<Histogram> newHist = std::make_shared<Histogram>(
                std::min(min, normalizeWithStandardScore(
                    unNormHistMin,
                    mean,
                    _standardDeviation[i],
                    _histNormValues
                )),
                std::min(max, normalizeWithStandardScore(
                    unNormHistMax,
                    mean,
                    _standardDeviation[i],
                    _histNormValues
                )),
                numBins
            );

            for (int j = 0; j < numBins; j++) {
                value = j * (histMax-histMin)+histMin;
                value = unnormalizeWithStandardScore(
                    value,
                    oldMean,
                    oldStandardDeviation,
                    _histNormValues
                );
                _histograms[i]->add(normalizeWithStandardScore(
                    value,
                    mean,
                    _standardDeviation[i],
                    _histNormValues
                ), histData[j]);
            }
            // _histograms[i]->changeRange(min, max);
            _histograms[i] = newHist;
        }

        for (int j = 0; j < numValues; j++) {
            value = values[j];
            _histograms[i]->add(normalizeWithStandardScore(
                value,
                mean,
                _standardDeviation[i],
                _histNormValues
            ), 1);
        }

        _histograms[i]->generateEqualizer();

        std::cout << std::endl;
        _histograms[i]->print();
        std::cout << std::endl;
        std::cout << "Eq: ";
        Histogram hist = _histograms[i]->equalize();
        hist.print();
    }
}

} // namespace