/*
 * This file is part of McIDAS-V
 *
 * Copyright 2007-2025
 * Space Science and Engineering Center (SSEC)
 * University of Wisconsin - Madison
 * 1225 W. Dayton Street, Madison, WI 53706, USA
 * https://www.ssec.wisc.edu/mcidas/
 * 
 * All Rights Reserved
 * 
 * McIDAS-V is built on Unidata's IDV and SSEC's VisAD libraries, and
 * some McIDAS-V source code is based on IDV and VisAD source code.  
 * 
 * McIDAS-V is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 * 
 * McIDAS-V 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 Lesser Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser Public License
 * along with this program.  If not, see https://www.gnu.org/licenses/.
 */
package edu.wisc.ssec.mcidasv.data.hydra;

import static edu.wisc.ssec.mcidasv.data.StatsTable.fmtMe;
import static java.util.Arrays.asList;

import java.rmi.RemoteException;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Collections;
import java.util.EnumSet;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.apache.commons.math3.random.EmpiricalDistribution;
import org.apache.commons.math3.stat.correlation.PearsonsCorrelation;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
import org.apache.commons.math3.stat.StatUtils;
import org.apache.commons.math3.stat.descriptive.SummaryStatistics;

import visad.Data;
import visad.FlatField;
import visad.FunctionType;
import visad.MathType;
import visad.Real;
import visad.RealTuple;
import visad.RealTupleType;
import visad.RealType;
import visad.TupleType;
import visad.VisADException;

/**
 * Used to obtain various commonly used statistics for VisAD 
 * {@link FlatField FlatFields}.
 */
public class Statistics {
    
    /** 
     * Various types of statistics reported by the 
     * {@link #describe(Object...)} {@literal "function"}. 
     */
    enum DescribeParams {
        HISTOGRAM,
        LENGTH,
        MIN,
        MAX,
        RANGE,
        Q1,
        Q2,
        Q3,
        IQR,
        MEAN,
        MODE,
        KURTOSIS,
        SKEWNESS,
        STDDEV,
        VARIANCE,
        GOODPTS
    }
    
    /** 
     * Characters used to create {@literal "sparklines"}.
     */
    private static final List<Character> CHARS =
        asList('\u2581', '\u2582', '\u2583', '\u2584', '\u2585', '\u2586',
               '\u2587', '\u2588');
    
    DescriptiveStatistics[] descriptiveStats = null;
    
    double[][] values_x;
    double[][] rngVals;
    
    int rngTupLen;
    
    int numPoints;
    
    int[] numGoodPoints;
    
    MathType statType;
    
    PearsonsCorrelation pCorrelation = null;
    
    public Statistics(FlatField fltFld) throws VisADException {
        rngVals = fltFld.getValues(false);
        rngTupLen = rngVals.length;
        numPoints = fltFld.getDomainSet().getLength();
        numGoodPoints = new int[rngTupLen];
        
        values_x = new double[rngTupLen][];
        
        for (int k = 0; k < rngTupLen; k++) {
            values_x[k] = removeMissing(rngVals[k]);
            numGoodPoints[k] = values_x[k].length;
        }
        
        descriptiveStats = new DescriptiveStatistics[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            descriptiveStats[k] = new DescriptiveStatistics(values_x[k]);
        }
        
        MathType rangeType = ((FunctionType) fltFld.getType()).getRange();
        
        if (rangeType instanceof RealTupleType) {
            RealType[] rttypes = ((TupleType) rangeType).getRealComponents();
            if (rngTupLen > 1) {
                statType = new RealTupleType(rttypes);
            } else {
                statType = rttypes[0];
            }
        } else if (rangeType instanceof RealType) {
            statType = rangeType;
        } else {
            throw new VisADException("fltFld must be RealTupleType or RealType");
        }
        
        pCorrelation = new PearsonsCorrelation();
    }
    
    /**
     * Get the number of points in the domain of the {@link FlatField}.
     *
     * @return Number of points.
     */
    public int numPoints() {
        return numPoints;
    }
    
    /**
     * Get the number of non-missing points in each range component.
     *
     * @return Number of non-missing points.
     */
    public int[] getNumGoodPoints() {
        return numGoodPoints;
    }
    
    /** 
     * Get the original range values.
     *
     * @return Original range values.
     */
    public double[][] getRngVals() {
        return rngVals;
    }
    
    /** 
     * Get the range values actually used (missing removed).
     * 
     * @return Range values used.
     */
    public double[][] getValues() {
        return values_x;
    }
    
    private double[] removeMissing(double[] vals) {
        int num = vals.length;
        int cnt = 0;
        int[] good = new int[num];
        for (int k = 0; k < num; k++) {
            if (!(Double.isNaN(vals[k]))) {
                good[cnt] = k;
                cnt++;
            }
        }
        
        if (cnt == num) {
            return vals;
        }
        
        double[] newVals = new double[cnt];
        for (int k = 0; k < cnt; k++) {
            newVals[k] = vals[good[k]];
        }
        
        return newVals;
    }
    
    private double[][] removeMissing(double[][] vals) {
        int tupLen = vals.length;
        double[][] newVals = new double[tupLen][];
        for (int k = 0; k < tupLen; k++) {
            newVals[k] = removeMissing(vals[k]);
        }
        return newVals;
    }
    
    public Data mean() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getMean();
        }
        return makeStat(stats);
    }
    
    public Data geometricMean() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getGeometricMean();
        }
        return makeStat(stats);
    }
    
    
    public Data max() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getMax();
        }
        return makeStat(stats);
    }
    
    public Data min() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getMin();
        }
        return makeStat(stats);
    }
    
    public Data median() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getPercentile(50.0);
        }
        return makeStat(stats);
    }
    
    public Data percentile(double p) throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getPercentile(p);
        }
        return makeStat(stats);
    }
    
    public Data variance() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getVariance();
        }
        return makeStat(stats);
    }
    
    public Data kurtosis() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getKurtosis();
        }
        return makeStat(stats);
    }
    
    public Data standardDeviation() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getStandardDeviation();
        }
        return makeStat(stats);
    }
    
    public Data skewness() throws VisADException, RemoteException {
        double[] stats = new double[rngTupLen];
        for (int k = 0; k < rngTupLen; k++) {
            stats[k] = descriptiveStats[k].getSkewness();
        }
        return makeStat(stats);
    }
    
    public Data correlation(FlatField fltFld)
        throws VisADException, RemoteException {
        double[][] values_x = this.rngVals;
        double[][] values_y = fltFld.getValues(false);
        
        if (values_y.length != rngTupLen) {
            throw new VisADException("fields must have same range tuple length");
        }
        
        double[] stats = new double[rngTupLen];
        
        for (int k = 0; k < rngTupLen; k++) {
            double[][] newVals = removeMissingAND(values_x[k], values_y[k]);
            stats[k] = pCorrelation.correlation(newVals[0], newVals[1]);
        }
        
        return makeStat(stats);
    }
    
    private Data makeStat(double[] stats)
        throws VisADException, RemoteException {
        if (statType instanceof RealType) {
            return new Real((RealType) statType, stats[0]);
        } else if (statType instanceof RealTupleType) {
            return new RealTuple((RealTupleType) statType, stats);
        }
        return null;
    }
    
    private double[][] removeMissingAND(double[] vals_x, double[] vals_y) {
        int cnt = 0;
        int[] good = new int[vals_x.length];
        for (int k = 0; k < vals_x.length; k++) {
            if (!(Double.isNaN(vals_x[k])) && !(Double.isNaN(vals_y[k]))) {
                good[cnt] = k;
                cnt++;
            }
        }
        
        if (cnt == vals_x.length) {
            return new double[][]{vals_x, vals_y};
        } else {
            double[] newVals_x = new double[cnt];
            double[] newVals_y = new double[cnt];
            for (int k = 0; k < cnt; k++) {
                newVals_x[k] = vals_x[good[k]];
                newVals_y[k] = vals_y[good[k]];
            }
            return new double[][]{newVals_x, newVals_y};
        }
    }
    
    /**
     * Creates a {@literal "description"} of any {@link FlatField FlatFields} 
     * in {@code params}.
     * 
     * <p>This is mostly useful from within the Jython Shell.</p>
     * 
     * <p>Some notes about {@code params}:
     * <ul>
     *     <li>Understands {@code FlatField} and {@code String} objects.</li>
     *     <li>Strings must be found within {@link DescribeParams}.</li>
     *     <li>Strings control descriptions returned for all fields in 
     *     {@code params}.</li>
     *     <li>{@code FlatField} and {@code String} objects may appear in any order.</li>
     * </ul>
     * </p>
     * 
     * @param params See description of this method. If {@code null} or empty,
     *               nothing will happen.
     * 
     * @return String descriptions of any {@code FlatField} objects in 
     *         {@code params}, with relevant strings in {@code params} 
     *         controlling what shows up in all descriptions.
     * 
     * @throws VisADException if VisAD had problems.
     * @throws RemoteException if VisAD had problems.
     */
    public static String describe(Object... params)
        throws VisADException, RemoteException {
        String result = "";
        if (params != null) {
            List<FlatField> fields = new ArrayList<>(params.length);
            List<String> descs = new ArrayList<>(params.length);
            for (Object param : params) {
                if (param instanceof FlatField) {
                    fields.add((FlatField) param);
                } else if (param instanceof String) {
                    descs.add((String) param);
                }
            }
            
            // 350 is typical size of a single, "complete" describe call with
            // one field.
            StringBuilder buf = new StringBuilder(350 * descs.size());
            for (FlatField field : fields) {
                Description d = new Description(field, descs);
                buf.append(d.makeDescription());
            }
            result = buf.toString();
        }
        return result;
    }
    
    /**
     * Creates a {@literal "binned sparkline"} of the given 
     * {@link FlatField FlatFields}.
     * 
     * @param fields {@code FlatField} objects to {@literal "visualize"} with 
     *               sparklines.
     * 
     * @return String sparkline for each {@code FlatField} in {@code fields}. 
     * 
     * @throws VisADException if VisAD had problems.
     * @throws RemoteException if VisAD had problems.
     * 
     * @see <a href="https://en.wikipedia.org/wiki/Sparkline" target="_top">Sparkline Wikipedia Article</a>
     */
    public static String sparkline(FlatField... fields)
        throws VisADException, RemoteException
    {
        // assuming sparkline is only using 20 bins
        StringBuilder sb = new StringBuilder(25 * fields.length);
        for (FlatField field : fields) {
            Statistics s = new Statistics(field);
            sb.append(sparkline(field, s)).append('\n');
        }
        return sb.toString();
    }
    
    public static Long[] histogram(FlatField field, int bins)
        throws VisADException {
        Long[] histogram = new Long[bins];
        EmpiricalDistribution distribution = new EmpiricalDistribution(bins);
        distribution.load(field.getValues(false)[0]);
        int k = 0;
        for (SummaryStatistics stats : distribution.getBinStats()) {
            histogram[k++] = stats.getN();
        }
        return histogram;
    }
    
    public static String sparkline(FlatField field, Statistics s)
        throws VisADException, RemoteException 
    {
        Long[] values = histogram(field, 20);
        Real sMin = (Real) s.min();
        Real sMax = (Real) s.max();
        Collection<Long> collection = asList(values);
        long max = Collections.max(collection);
        long min = Collections.min(collection);
        float scale = (max - min) / 7f;
        final StringBuilder buf = new StringBuilder(values.length);
        
        // TJJ Mar 2018 - sandwich with min/max
        // http://mcidas.ssec.wisc.edu/inquiry-v/?inquiry=2548
        buf.append(fmtMe((sMin).getValue()));
        for (Long value : values) {
            int index = Math.round((value - min) / scale);
            buf.append(CHARS.get(index));
        }
        buf.append(fmtMe((sMax).getValue()));
        
        return buf.toString();
    }
    
    private static EnumSet<DescribeParams> parseParams(List<String> ps) {
        Set<DescribeParams> params = Collections.emptySet();
        if (ps != null) {
            params = new HashSet<>(ps.size());
            for (String p : ps) {
                params.add(DescribeParams.valueOf(p.toUpperCase()));
            }
        }
        return EnumSet.copyOf(params);
    }
    
    public static class Description {
        
        private final FlatField field;
        
        private final EnumSet<DescribeParams> params;
        
        public Description(FlatField field, List<String> params) {
            this.field = field;
            if ((params == null) || params.isEmpty()) {
                this.params = EnumSet.allOf(DescribeParams.class);
            } else {
                this.params = parseParams(params);
            }
        }
        
        public String makeDescription()
            throws VisADException, RemoteException {
            StringBuilder sb = new StringBuilder(1024);
            Statistics s = new Statistics(field);
            double max = ((Real) s.max()).getValue();
            double min = ((Real) s.min()).getValue();
            double q1 = ((Real) s.percentile(25.0)).getValue();
            double q3 = ((Real) s.percentile(75.0)).getValue();
            double[] modes = StatUtils.mode(field.getValues(false)[0]);
            
            StringBuilder tmp = new StringBuilder(128);
            for (int i = 0; i < modes.length; i++) {
                tmp.append(fmtMe(modes[i]));
                if ((i + 1) < modes.length) {
                    tmp.append(", ");
                }
            }
            
            String temp;
            char endl = '\n';
            if (params.contains(DescribeParams.HISTOGRAM)) {
                temp = sparkline(field, s);
                sb.append("Histogram :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.LENGTH)) {
                temp = String.format("%d", s.numPoints());
                sb.append("Length    :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.MIN)) {
                temp = fmtMe(((Real) s.min()).getValue());
                sb.append("Min       :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.MAX)) {
                temp = fmtMe(((Real) s.max()).getValue());
                sb.append("Max       :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.RANGE)) {
                temp = fmtMe(max - min);
                sb.append("Range     :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.Q1)) {
                sb.append("Q1        :  ").append(fmtMe(q1)).append(endl);
            }
            if (params.contains(DescribeParams.Q2)) {
                temp = fmtMe(((Real) s.percentile(50.0)).getValue());
                sb.append("Q2        :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.Q3)) {
                sb.append("Q3        :  ").append(fmtMe(q3)).append(endl);
            }
            if (params.contains(DescribeParams.IQR)) {
                temp = fmtMe(q3 - q1);
                sb.append("IQR       :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.MEAN)) {
                temp = fmtMe(((Real) s.mean()).getValue());
                sb.append("Mean      :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.MODE)) {
                sb.append("Mode      :  ").append(tmp).append(endl);
            }
            if (params.contains(DescribeParams.KURTOSIS)) {
                temp = fmtMe(((Real) s.kurtosis()).getValue());
                sb.append("Kurtosis  :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.SKEWNESS)) {
                temp = fmtMe(((Real) s.skewness()).getValue());
                sb.append("Skewness  :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.STDDEV)) {
                temp = fmtMe(((Real) s.standardDeviation()).getValue());
                sb.append("Std Dev   :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.VARIANCE)) {
                temp = fmtMe(((Real) s.variance()).getValue());
                sb.append("Variance  :  ").append(temp).append(endl);
            }
            if (params.contains(DescribeParams.GOODPTS)) {
                temp = String.format("%d", s.getNumGoodPoints()[0]);
                sb.append("# Good Pts:  ").append(temp).append(endl);
            }
            return sb.toString();
        }
    }
}