#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab 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.
 *
 * HeuristicLab 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 HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Globalization;
using System.IO;
using System.Linq;
using System.Text;

namespace HeuristicLab.Problems.Instances.PermutationProblems.LinearOrdering {
  public class LOPParser {
    public string Name { get; set; }
    public string Description { get; set; }
    public int Dimension { get; set; }
    public double[,] Matrix { get; set; }
    public int[] BestKnownPermutation { get; set; }
    public double? BestKnownQuality { get; set; }

    public LOPParser() {
      Reset();
    }

    public void Reset() {
      Name = Description = String.Empty;
      Dimension = 0;
      BestKnownQuality = 0;
      Matrix = null;
      BestKnownPermutation = null;
    }

    public void Parse(string file) {
      using (Stream stream = new FileStream(file, FileMode.Open, FileAccess.Read)) {
        Parse(stream);
      }
    }

    public void Export(string file) {
      using (Stream stream = new FileStream(file, FileMode.Create, FileAccess.Write)) {
        Export(stream);
      }
    }

    public void Parse(Stream stream) {
      using (var reader = new StreamReader(stream, Encoding.UTF8, true, 4092, true)) {
        Name = reader.ReadLine().Trim();
        Description = reader.ReadLine().Trim();
        BestKnownQuality = double.Parse(reader.ReadLine().Trim());
        var delim = new char[] { ' ', '\t' };

        Dimension = int.Parse(reader.ReadLine().Trim());
        Matrix = new double[Dimension, Dimension];

        for (int k = 0; k < Dimension; k++) {
          if (reader.EndOfStream) throw new InvalidDataException("Unexpected End of Stream.");
          var valLine = reader.ReadLine();
          while (String.IsNullOrWhiteSpace(valLine)) valLine = reader.ReadLine();
          var vals = valLine.Split(delim, StringSplitOptions.RemoveEmptyEntries);


          for (int i = 0; i < Dimension; i++) {
            Matrix[k, i] = double.Parse(vals[i], CultureInfo.InvariantCulture.NumberFormat);
          }
        }

        while (!reader.EndOfStream) {
          var vals = reader.ReadLine().Split(delim, StringSplitOptions.RemoveEmptyEntries);
          if (vals.Count() == Dimension) { BestKnownPermutation = vals.Select(val => Int32.Parse(val)).ToArray(); }
        }
      }
    }

    public void Export(Stream stream) {
      using (var writer = new StreamWriter(stream, Encoding.UTF8, 4092, true)) {
        writer.WriteLine(Name);
        writer.WriteLine(Description);
        writer.WriteLine(BestKnownQuality);
        writer.WriteLine(Dimension);

        for (int k = 0; k < Dimension; k++) {
          for (int i = 0; i < Dimension; i++) {
            writer.Write(Matrix[k, i] + '\t');
          }
          writer.WriteLine();
        }

        if (BestKnownPermutation != null) {
          writer.WriteLine(BestKnownPermutation);
        }
        writer.Flush();
      }
    }
  }
}