/*
 * PlotSquared, a land and world management plugin for Minecraft.
 * Copyright (C) IntellectualSites <https://intellectualsites.com>
 * Copyright (C) IntellectualSites team and contributors
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <https://www.gnu.org/licenses/>.
 */
package com.plotsquared.core.plot.expiration;

import com.plotsquared.core.PlotSquared;
import com.plotsquared.core.configuration.Settings;
import com.plotsquared.core.generator.HybridUtils;
import com.plotsquared.core.plot.Plot;
import com.plotsquared.core.plot.flag.implementations.AnalysisFlag;
import com.plotsquared.core.util.MathMan;
import com.plotsquared.core.util.query.PlotQuery;
import com.plotsquared.core.util.task.RunnableVal;
import com.plotsquared.core.util.task.TaskManager;
import org.apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;

import java.lang.reflect.Array;
import java.util.ArrayDeque;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.atomic.AtomicInteger;

public class PlotAnalysis {

    private static final Logger LOGGER = LogManager.getLogger("PlotSquared/" + PlotAnalysis.class.getSimpleName());

    public static boolean running = false;
    public int changes;
    public int faces;
    public int data;
    public int air;
    public int variety;
    public int changes_sd;
    public int faces_sd;
    public int data_sd;
    public int air_sd;
    public int variety_sd;
    private int complexity;

    public static PlotAnalysis getAnalysis(Plot plot, Settings.Auto_Clear settings) {
        final List<Integer> values = plot.getFlag(AnalysisFlag.class);
        if (!values.isEmpty()) {
            PlotAnalysis analysis = new PlotAnalysis();
            analysis.changes = values.get(0); // 2126
            analysis.faces = values.get(1); // 90
            analysis.data = values.get(2); // 0
            analysis.air = values.get(3); // 19100
            analysis.variety = values.get(4); // 266

            analysis.changes_sd = values.get(5); // 2104
            analysis.faces_sd = values.get(6); // 89
            analysis.data_sd = values.get(7); // 0
            analysis.air_sd = values.get(8); // 18909
            analysis.variety_sd = values.get(9); // 263

            analysis.complexity = settings != null ? analysis.getComplexity(settings) : 0;
            return analysis;
        }
        return null;
    }

    public static void analyzePlot(Plot plot, RunnableVal<PlotAnalysis> whenDone) {
        PlotSquared.platform().injector().getInstance(HybridUtils.class).analyzePlot(plot, whenDone);
    }

    /**
     * This will set the optimal modifiers for the plot analysis based on the current plot ratings<br>
     * - Will be used to calibrate the threshold for plot clearing
     *
     * @param whenDone  task to run when done
     * @param threshold threshold
     */
    public static void calcOptimalModifiers(final Runnable whenDone, final double threshold) {
        if (running) {
            LOGGER.info("Calibration task already in progress!");
            return;
        }
        if (threshold <= 0 || threshold >= 1) {
            LOGGER.info("Invalid threshold provided! (Cannot be 0 or 100 as then there's no point in calibrating)");
            return;
        }
        running = true;
        final List<Plot> plots = PlotQuery.newQuery().allPlots().asList();
        TaskManager.runTaskAsync(new Runnable() {
            @Override
            public void run() {
                Iterator<Plot> iterator = plots.iterator();
                LOGGER.info("- Reducing {} plots to those with sufficient data", plots.size());
                while (iterator.hasNext()) {
                    Plot plot = iterator.next();
                    if (plot.getSettings().getRatings() == null || plot.getSettings().getRatings()
                            .isEmpty()) {
                        iterator.remove();
                    } else {
                        plot.addRunning();
                    }
                }

                if (plots.size() < 3) {
                    LOGGER.info("Calibration cancelled due to insufficient comparison data, please try again later");
                    running = false;
                    for (Plot plot : plots) {
                        plot.removeRunning();
                    }
                    return;
                }
                LOGGER.info("- Analyzing plot contents (this may take a while)");

                int[] changes = new int[plots.size()];
                int[] faces = new int[plots.size()];
                int[] data = new int[plots.size()];
                int[] air = new int[plots.size()];
                int[] variety = new int[plots.size()];

                int[] changes_sd = new int[plots.size()];
                int[] faces_sd = new int[plots.size()];
                int[] data_sd = new int[plots.size()];
                int[] air_sd = new int[plots.size()];
                int[] variety_sd = new int[plots.size()];

                final int[] ratings = new int[plots.size()];

                final AtomicInteger mi = new AtomicInteger(0);

                Thread ratingAnalysis = new Thread(() -> {
                    for (; mi.intValue() < plots.size(); mi.incrementAndGet()) {
                        int i = mi.intValue();
                        Plot plot = plots.get(i);
                        ratings[i] = (int) (
                                (plot.getAverageRating() + plot.getSettings().getRatings().size())
                                        * 100);
                        LOGGER.info(" | {} (rating) {}", plot, ratings[i]);

                    }
                });
                ratingAnalysis.start();

                ArrayDeque<Plot> plotsQueue = new ArrayDeque<>(plots);
                while (true) {
                    final Plot queuePlot = plotsQueue.poll();
                    if (queuePlot == null) {
                        break;
                    }
                    LOGGER.info(" | {}", queuePlot);

                    final Object lock = new Object();
                    TaskManager.runTask(new Runnable() {
                        @Override
                        public void run() {
                            analyzePlot(queuePlot, new RunnableVal<>() {
                                @Override
                                public void run(PlotAnalysis value) {
                                    try {
                                        synchronized (this) {
                                            wait(10000);
                                        }
                                    } catch (InterruptedException e) {
                                        e.printStackTrace();
                                    }
                                    synchronized (lock) {
                                        queuePlot.removeRunning();
                                        lock.notify();
                                    }
                                }
                            });
                        }
                    });
                    try {
                        synchronized (lock) {
                            lock.wait();
                        }
                    } catch (InterruptedException e) {
                        e.printStackTrace();
                    }
                }

                LOGGER.info("- Waiting on plot rating thread: {}%", mi.intValue() * 100 / plots.size());

                try {
                    ratingAnalysis.join();
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }

                LOGGER.info("- Processing and grouping single plot analysis for bulk processing");

                for (int i = 0; i < plots.size(); i++) {
                    Plot plot = plots.get(i);
                    LOGGER.info("| {}", plot);

                    PlotAnalysis analysis = plot.getComplexity(null);

                    changes[i] = analysis.changes;
                    faces[i] = analysis.faces;
                    data[i] = analysis.data;
                    air[i] = analysis.air;
                    variety[i] = analysis.variety;

                    changes_sd[i] = analysis.changes_sd;
                    faces_sd[i] = analysis.faces_sd;
                    data_sd[i] = analysis.data_sd;
                    air_sd[i] = analysis.air_sd;
                    variety_sd[i] = analysis.variety_sd;
                }

                LOGGER.info("- Calculating rankings");

                int[] rankRatings = rank(ratings);
                int n = rankRatings.length;

                int optimalIndex = (int) Math.round((1 - threshold) * (n - 1));

                LOGGER.info("- Calculating rank correlation: ");
                LOGGER.info(
                        "- The analyzed plots which were processed and put into bulk data will be compared and correlated to the plot ranking");
                LOGGER.info(
                        "- The calculated correlation constant will then be used to calibrate the threshold for auto plot clearing");

                Settings.Auto_Clear settings = new Settings.Auto_Clear();

                int[] rankChanges = rank(changes);
                int[] sdChanges = getSD(rankChanges, rankRatings);
                int[] varianceChanges = square(sdChanges);
                int sumChanges = sum(varianceChanges);
                double factorChanges = getCC(n, sumChanges);
                settings.CALIBRATION.CHANGES = factorChanges == 1 ?
                        0 :
                        (int) (factorChanges * 1000 / MathMan.getMean(changes));

                LOGGER.info("- | changes {}", factorChanges);

                int[] rankFaces = rank(faces);
                int[] sdFaces = getSD(rankFaces, rankRatings);
                int[] varianceFaces = square(sdFaces);
                int sumFaces = sum(varianceFaces);
                double factorFaces = getCC(n, sumFaces);
                settings.CALIBRATION.FACES =
                        factorFaces == 1 ? 0 : (int) (factorFaces * 1000 / MathMan.getMean(faces));

                LOGGER.info("- | faces {}", factorFaces);

                int[] rankData = rank(data);
                int[] sdData = getSD(rankData, rankRatings);
                int[] variance_data = square(sdData);
                int sum_data = sum(variance_data);
                double factor_data = getCC(n, sum_data);
                settings.CALIBRATION.DATA =
                        factor_data == 1 ? 0 : (int) (factor_data * 1000 / MathMan.getMean(data));

                LOGGER.info("- | data {}", factor_data);

                int[] rank_air = rank(air);
                int[] sd_air = getSD(rank_air, rankRatings);
                int[] variance_air = square(sd_air);
                int sum_air = sum(variance_air);
                double factor_air = getCC(n, sum_air);
                settings.CALIBRATION.AIR =
                        factor_air == 1 ? 0 : (int) (factor_air * 1000 / MathMan.getMean(air));

                LOGGER.info("- | air {}", factor_air);


                int[] rank_variety = rank(variety);
                int[] sd_variety = getSD(rank_variety, rankRatings);
                int[] variance_variety = square(sd_variety);
                int sum_variety = sum(variance_variety);
                double factor_variety = getCC(n, sum_variety);
                settings.CALIBRATION.VARIETY = factor_variety == 1 ?
                        0 :
                        (int) (factor_variety * 1000 / MathMan.getMean(variety));

                LOGGER.info("- | variety {}", factor_variety);

                int[] rank_changes_sd = rank(changes_sd);
                int[] sd_changes_sd = getSD(rank_changes_sd, rankRatings);
                int[] variance_changes_sd = square(sd_changes_sd);
                int sum_changes_sd = sum(variance_changes_sd);
                double factor_changes_sd = getCC(n, sum_changes_sd);
                settings.CALIBRATION.CHANGES_SD = factor_changes_sd == 1 ?
                        0 :
                        (int) (factor_changes_sd * 1000 / MathMan.getMean(changes_sd));

                LOGGER.info("- | changed_sd {}", factor_changes_sd);

                int[] rank_faces_sd = rank(faces_sd);
                int[] sd_faces_sd = getSD(rank_faces_sd, rankRatings);
                int[] variance_faces_sd = square(sd_faces_sd);
                int sum_faces_sd = sum(variance_faces_sd);
                double factor_faces_sd = getCC(n, sum_faces_sd);
                settings.CALIBRATION.FACES_SD = factor_faces_sd == 1 ?
                        0 :
                        (int) (factor_faces_sd * 1000 / MathMan.getMean(faces_sd));

                LOGGER.info("- | faced_sd {}", factor_faces_sd);

                int[] rank_data_sd = rank(data_sd);
                int[] sd_data_sd = getSD(rank_data_sd, rankRatings);
                int[] variance_data_sd = square(sd_data_sd);
                int sum_data_sd = sum(variance_data_sd);
                double factor_data_sd = getCC(n, sum_data_sd);
                settings.CALIBRATION.DATA_SD = factor_data_sd == 1 ?
                        0 :
                        (int) (factor_data_sd * 1000 / MathMan.getMean(data_sd));

                LOGGER.info("- | data_sd {}", factor_data_sd);

                int[] rank_air_sd = rank(air_sd);
                int[] sd_air_sd = getSD(rank_air_sd, rankRatings);
                int[] variance_air_sd = square(sd_air_sd);
                int sum_air_sd = sum(variance_air_sd);
                double factor_air_sd = getCC(n, sum_air_sd);
                settings.CALIBRATION.AIR_SD =
                        factor_air_sd == 1 ? 0 : (int) (factor_air_sd * 1000 / MathMan.getMean(air_sd));

                LOGGER.info("- | air_sd {}", factor_air_sd);

                int[] rank_variety_sd = rank(variety_sd);
                int[] sd_variety_sd = getSD(rank_variety_sd, rankRatings);
                int[] variance_variety_sd = square(sd_variety_sd);
                int sum_variety_sd = sum(variance_variety_sd);
                double factor_variety_sd = getCC(n, sum_variety_sd);
                settings.CALIBRATION.VARIETY_SD = factor_variety_sd == 1 ?
                        0 :
                        (int) (factor_variety_sd * 1000 / MathMan.getMean(variety_sd));

                LOGGER.info("- | variety_sd {}", factor_variety_sd);

                int[] complexity = new int[n];

                LOGGER.info("Calculating threshold");

                int max = 0;
                int min = 0;
                for (int i = 0; i < n; i++) {
                    Plot plot = plots.get(i);
                    PlotAnalysis analysis = plot.getComplexity(settings);
                    complexity[i] = analysis.complexity;
                    if (analysis.complexity < min) {
                        min = analysis.complexity;
                    } else if (analysis.complexity > max) {
                        max = analysis.complexity;
                    }
                }
                int optimalComplexity = Integer.MAX_VALUE;
                if (min > 0 && max < 102400) { // If low size, use my fast ranking algorithm
                    int[] rankComplexity = rank(complexity, max + 1);
                    for (int i = 0; i < n; i++) {
                        if (rankComplexity[i] == optimalIndex) {
                            optimalComplexity = complexity[i];
                            break;
                        }
                    }
                    logln("Complexity: ");
                    logln(rankComplexity);
                    logln("Ratings: ");
                    logln(rankRatings);
                    logln("Correlation: ");
                    logln(getCC(n, sum(square(getSD(rankComplexity, rankRatings)))));
                    if (optimalComplexity == Integer.MAX_VALUE) {
                        LOGGER.info("Insufficient data to determine correlation! {} | {}",
                                optimalIndex, n
                        );
                        running = false;
                        for (Plot plot : plots) {
                            plot.removeRunning();
                        }
                        return;
                    }
                } else { // Use the fast radix sort algorithm
                    int[] sorted = complexity.clone();
                    sort(sorted);
                    logln("Complexity: ");
                    logln(complexity);
                    logln("Ratings: ");
                    logln(rankRatings);
                }

                // Save calibration
                LOGGER.info("Saving calibration");
                Settings.AUTO_CLEAR.put("auto-calibrated", settings);
                Settings.save(PlotSquared.get().getWorldsFile());
                running = false;
                for (Plot plot : plots) {
                    plot.removeRunning();
                }
                LOGGER.info("Done!");
                whenDone.run();
            }
        });
    }

    public static void logln(Object obj) {
        LOGGER.info("" + log(obj));
    }

    public static String log(Object obj) {
        StringBuilder result = new StringBuilder();
        if (obj.getClass().isArray()) {
            String prefix = "";

            for (int i = 0; i < Array.getLength(obj); i++) {
                result.append(prefix).append(log(Array.get(obj, i)));
                prefix = ",";
            }
            return "( " + result + " )";
        } else if (obj instanceof List<?>) {
            String prefix = "";
            for (Object element : (List<?>) obj) {
                result.append(prefix).append(log(element));
                prefix = ",";
            }
            return "[ " + result + " ]";
        } else {
            return obj.toString();
        }
    }

    /**
     * Get correlation coefficient.
     *
     * @param n   n
     * @param sum sum
     * @return result
     */
    public static double getCC(int n, int sum) {
        return 1 - 6 * (double) sum / (n * (n * n - 1));
    }

    /**
     * Calls {@code Arrays.stream(array).sum()}
     *
     * @param array array
     * @return sum
     */
    public static int sum(int[] array) {
        return Arrays.stream(array).sum();
    }

    /**
     * A simple array squaring algorithm.
     * - Used for calculating the variance
     *
     * @param array array
     * @return result
     */
    public static int[] square(int[] array) {
        array = array.clone();
        for (int i = 0; i < array.length; i++) {
            array[i] *= array[i];
        }
        return array;
    }

    /**
     * An optimized lossy standard deviation algorithm.
     *
     * @param ranks ranks
     * @return result
     */
    public static int[] getSD(int[]... ranks) {
        if (ranks.length == 0) {
            return null;
        }
        int[] result = new int[ranks[0].length];
        for (int j = 0; j < ranks[0].length; j++) {
            int sum = 0;
            for (int[] rank : ranks) {
                sum += rank[j];
            }
            int mean = sum / ranks.length;
            int sd = 0;
            for (int[] rank : ranks) {
                int value = rank[j];
                sd += value < mean ? mean - value : value - mean;
            }
            result[j] = sd;
        }
        return result;
    }

    /**
     * An optimized algorithm for ranking a very specific set of inputs<br>
     * - Input is an array of int with a max size of 102400<br>
     * - A reduced sample space allows for sorting (and ranking in this case) in linear time
     *
     * @param input input
     * @return result
     */
    public static int[] rank(int[] input) {
        return rank(input, 102400);
    }

    /**
     * An optimized algorithm for ranking a very specific set of inputs
     *
     * @param input input
     * @param size  size
     * @return result
     */
    public static int[] rank(int[] input, int size) {
        int[] cache = new int[size];
        int max = 0;
        if (input.length < size) {
            for (int value : input) {
                if (value > max) {
                    max = value;
                }
                cache[value]++;
            }
        } else {
            max = cache.length - 1;
            for (int value : input) {
                cache[value]++;
            }
        }
        int last = 0;
        for (int i = max; i >= 0; i--) {
            if (cache[i] != 0) {
                cache[i] += last;
                last = cache[i];
                if (last == input.length) {
                    break;
                }
            }
        }

        int[] ranks = new int[input.length];
        for (int i = 0; i < input.length; i++) {
            int index = input[i];
            ranks[i] = cache[index];
            cache[index]--;
        }
        return ranks;
    }

    @SuppressWarnings("unchecked")
    public static void sort(int[] input) {
        int SIZE = 10;
        List<Integer>[] bucket = new ArrayList[SIZE];
        for (int i = 0; i < bucket.length; i++) {
            bucket[i] = new ArrayList<>();
        }
        boolean maxLength = false;
        int placement = 1;
        while (!maxLength) {
            maxLength = true;
            for (Integer i : input) {
                int tmp = i / placement;
                bucket[tmp % SIZE].add(i);
                if (maxLength && tmp > 0) {
                    maxLength = false;
                }
            }
            int a = 0;
            for (int b = 0; b < SIZE; b++) {
                for (Integer i : bucket[b]) {
                    input[a++] = i;
                }
                bucket[b].clear();
            }
            placement *= SIZE;
        }
    }

    public List<Integer> asList() {
        return Arrays
                .asList(this.changes, this.faces, this.data, this.air, this.variety, this.changes_sd,
                        this.faces_sd, this.data_sd, this.air_sd, this.variety_sd
                );
    }

    public int getComplexity(Settings.Auto_Clear settings) {
        Settings.Auto_Clear.CALIBRATION modifiers = settings.CALIBRATION;
        if (this.complexity != 0) {
            return this.complexity;
        }
        this.complexity = this.changes * modifiers.CHANGES + this.faces * modifiers.FACES
                + this.data * modifiers.DATA + this.air * modifiers.AIR
                + this.variety * modifiers.VARIETY + this.changes_sd * modifiers.CHANGES_SD
                + this.faces_sd * modifiers.FACES_SD + this.data_sd * modifiers.DATA_SD
                + this.air_sd * modifiers.AIR_SD + this.variety_sd * modifiers.VARIETY_SD;
        return this.complexity;
    }

}