package org.djunits.value.vdouble.matrix;
   
   import java.io.Serializable;
   import java.util.Arrays;
   import java.util.stream.IntStream;
   
   import org.djunits.unit.scale.Scale;
   import org.djunits.value.StorageType;
   import org.djunits.value.ValueException;
  import org.djunits.value.vdouble.scalar.DoubleScalarInterface;
  
  /**
   * Stores the data for a DoubleMatrix and carries out basic operations.
   * <p>
   * Copyright (c) 2013-2019 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved. <br>
   * BSD-style license. See <a href="http://opentrafficsim.org/docs/license.html">OpenTrafficSim License</a>.
   * </p>
   * $LastChangedDate: 2015-07-24 02:58:59 +0200 (Fri, 24 Jul 2015) $, @version $Revision: 1147 $, by $Author: averbraeck $,
   * initial version Oct 3, 2015 <br>
   * @author <a href="http://www.tbm.tudelft.nl/averbraeck">Alexander Verbraeck</a>
   * @author <a href="http://www.tudelft.nl/pknoppers">Peter Knoppers</a>
   */
  abstract class DoubleMatrixData implements Serializable
  {
      /** */
      private static final long serialVersionUID = 1L;
  
      /** the internal storage of the Matrix; can be sparse or dense. */
      @SuppressWarnings("checkstyle:visibilitymodifier")
      protected double[] matrixSI;
  
      /** the number of rows of the matrix. */
      @SuppressWarnings("checkstyle:visibilitymodifier")
      protected int rows;
  
      /** the number of columns of the matrix. */
      @SuppressWarnings("checkstyle:visibilitymodifier")
      protected int cols;
  
      /** the data type. */
      private final StorageType storageType;
  
      /**
       * @param storageType StorageType; the data type.
       */
      DoubleMatrixData(final StorageType storageType)
      {
          super();
          this.storageType = storageType;
      }
  
      /* ============================================================================================ */
      /* ====================================== INSTANTIATION ======================================= */
      /* ============================================================================================ */
  
      /**
       * Instantiate a DoubleMatrixData with the right data type.
       * @param values double[][]; the (SI) values to store
       * @param scale Scale; the scale of the unit to use for conversion to SI
       * @param storageType StorageType; the data type to use
       * @return the DoubleMatrixData with the right data type
       * @throws ValueException when values are null, or storageType is null
       */
      public static DoubleMatrixData instantiate(final double[][] values, final Scale scale, final StorageType storageType)
              throws ValueException
      {
          if (values == null || values.length == 0 || values[0].length == 0)
          {
              throw new ValueException("DoubleMatrixData.instantiate: double[][] values is null or "
                      + "values.length == 0 or values[0].length == 0");
          }
  
          final int rows = values.length;
          final int cols = values[0].length;
  
          switch (storageType)
          {
              case DENSE:
                  double[] valuesSI = new double[rows * cols];
                  IntStream.range(0, values.length).parallel().forEach(r -> IntStream.range(0, cols)
                          .forEach(c -> valuesSI[r * cols + c] = scale.toStandardUnit(values[r][c])));
                  return new DoubleMatrixDataDense(valuesSI, rows, cols);
  
              case SPARSE:
                  double[][] matrixSI = new double[rows][cols];
                  IntStream.range(0, values.length).parallel().forEach(
                          r -> IntStream.range(0, cols).forEach(c -> matrixSI[r][c] = scale.toStandardUnit(values[r][c])));
                  return DoubleMatrixDataSparse.instantiate(matrixSI);
  
              default:
                  throw new ValueException("Unknown data type in DoubleMatrixData.instantiate: " + storageType);
          }
      }
  
      /**
       * Instantiate a DoubleMatrixData with the right data type.
       * @param values DoubleScalarInterface[][]; the values to store
       * @param storageType StorageType; the data type to use
       * @return the DoubleMatrixData with the right data type
      * @throws ValueException when values is null, or storageType is null
      */
     public static DoubleMatrixData instantiate(final DoubleScalarInterface[][] values, final StorageType storageType)
             throws ValueException
     {
         if (values == null)
         {
             throw new ValueException("DoubleMatrixData.instantiate: DoubleScalar[] values is null");
         }
 
         if (values == null || values.length == 0 || values[0].length == 0)
         {
             throw new ValueException("DoubleMatrixData.instantiate: DoubleScalar[][] values is null or "
                     + "values.length == 0 or values[0].length == 0");
         }
 
         final int rows = values.length;
         final int cols = values[0].length;
 
         switch (storageType)
         {
             case DENSE:
                 double[] valuesSI = new double[rows * cols];
                 IntStream.range(0, rows).parallel()
                         .forEach(r -> IntStream.range(0, cols).forEach(c -> valuesSI[r * cols + c] = values[r][c].getSI()));
                 return new DoubleMatrixDataDense(valuesSI, rows, cols);
 
             case SPARSE:
                 double[][] matrixSI = new double[rows][cols];
                 IntStream.range(0, values.length).parallel()
                         .forEach(r -> IntStream.range(0, cols).forEach(c -> matrixSI[r][c] = values[r][c].getSI()));
                 return DoubleMatrixDataSparse.instantiate(matrixSI);
 
             default:
                 throw new ValueException("Unknown data type in DoubleMatrixData.instantiate: " + storageType);
         }
     }
 
     /* ============================================================================================ */
     /* ==================================== UTILITY FUNCTIONS ===================================== */
     /* ============================================================================================ */
 
     /**
      * Return the StorageType (DENSE, SPARSE, etc.) for the stored Matrix.
      * @return the StorageType (DENSE, SPARSE, etc.) for the stored Matrix
      */
     public final StorageType getStorageType()
     {
         return this.storageType;
     }
 
     /**
      * @return the number of rows of the matrix
      */
     public int rows()
     {
         return this.rows;
     }
 
     /**
      * @return the number of columns of the matrix
      */
     public int cols()
     {
         return this.cols;
     }
 
     /**
      * @return whether data type is sparse.
      */
     public boolean isSparse()
     {
         return this.storageType.equals(StorageType.SPARSE);
     }
 
     /**
      * @return the sparse transformation of this data
      */
     public DoubleMatrixDataSparse toSparse()
     {
         return isSparse() ? (DoubleMatrixDataSparse) this : ((DoubleMatrixDataDense) this).toSparse();
     }
 
     /**
      * @return whether data type is dense.
      */
     public boolean isDense()
     {
         return this.storageType.equals(StorageType.DENSE);
     }
 
     /**
      * @return the dense transformation of this data
      */
     public DoubleMatrixDataDense toDense()
     {
         return isDense() ? (DoubleMatrixDataDense) this : ((DoubleMatrixDataSparse) this).toDense();
     }
 
     /**
      * @param row int; the row number to get the value for
      * @param col int; the column number to get the value for
      * @return the value at the [row, col] point
      */
     public abstract double getSI(int row, int col);
 
     /**
      * Sets a value at the [row, col] point in the matrix.
      * @param row int; the row number to set the value for
      * @param col int; the column number to set the value for
      * @param valueSI double; the value at the index
      */
     public abstract void setSI(int row, int col, double valueSI);
 
     /**
      * @return the number of non-zero cells.
      */
     public final int cardinality()
     {
         return (int) Arrays.stream(this.matrixSI).parallel().filter(d -> d != 0.0).count();
     }
 
     /**
      * @return the sum of the values of all cells.
      */
     public final double zSum()
     {
         return Arrays.stream(this.matrixSI).parallel().sum();
     }
 
     /**
      * @return a deep copy of the data.
      */
     public abstract DoubleMatrixData copy();
 
     /**
      * @return a safe dense copy of matrixSI as a matrix
      */
     public abstract double[][] getDenseMatrixSI();
 
     /**
      * Check the sizes of this data object and the other data object.
      * @param other DoubleMatrixData; the other data object
      * @throws ValueException if matrices have different lengths
      */
     private void checkSizes(final DoubleMatrixData other) throws ValueException
     {
         if (this.rows() != other.rows() || this.cols() != other.cols())
         {
             throw new ValueException("Two data objects used in a DoubleMatrix operation do not have the same size");
         }
     }
 
     /* ============================================================================================ */
     /* ================================== CALCULATION FUNCTIONS =================================== */
     /* ============================================================================================ */
 
     /**
      * Add two matrices on a cell-by-cell basis. If both matrices are sparse, a sparse matrix is returned, otherwise a dense
      * matrix is returned.
      * @param right DoubleMatrixData; the other data object to add
      * @return the sum of this data object and the other data object
      * @throws ValueException if matrices have different lengths
      */
     public DoubleMatrixData plus(final DoubleMatrixData right) throws ValueException
     {
         checkSizes(right);
         double[] dm = new double[this.rows * this.cols];
         IntStream.range(0, this.rows).parallel().forEach(
                 r -> IntStream.range(0, this.cols).forEach(c -> dm[r * this.cols + c] = getSI(r, c) + right.getSI(r, c)));
         if (this instanceof DoubleMatrixDataSparse && right instanceof DoubleMatrixDataSparse)
         {
             return new DoubleMatrixDataDense(dm, this.rows, this.cols).toSparse();
         }
         return new DoubleMatrixDataDense(dm, this.rows, this.cols);
     }
 
     /**
      * Add a matrix to this matrix on a cell-by-cell basis. The type of matrix (sparse, dense) stays the same.
      * @param right DoubleMatrixData; the other data object to add
      * @throws ValueException if matrices have different lengths
      */
     public abstract void incrementBy(DoubleMatrixData right) throws ValueException;
 
     /**
      * Add a number to this matrix on a cell-by-cell basis.
      * @param valueSI double; the value to add
      */
     public void incrementBy(final double valueSI)
     {
         IntStream.range(0, this.matrixSI.length).parallel().forEach(i -> this.matrixSI[i] += valueSI);
     }
 
     /**
      * Subtract two matrices on a cell-by-cell basis. If both matrices are sparse, a sparse matrix is returned, otherwise a
      * dense matrix is returned.
      * @param right DoubleMatrixData; the other data object to subtract
      * @return the sum of this data object and the other data object
      * @throws ValueException if matrices have different lengths
      */
     public DoubleMatrixData minus(final DoubleMatrixData right) throws ValueException
     {
         checkSizes(right);
         double[] dm = new double[this.rows * this.cols];
         IntStream.range(0, this.rows).parallel().forEach(
                 r -> IntStream.range(0, this.cols).forEach(c -> dm[r * this.cols + c] = getSI(r, c) - right.getSI(r, c)));
         if (this instanceof DoubleMatrixDataSparse && right instanceof DoubleMatrixDataSparse)
         {
             return new DoubleMatrixDataDense(dm, this.rows, this.cols).toSparse();
         }
         return new DoubleMatrixDataDense(dm, this.rows, this.cols);
     }
 
     /**
      * Subtract a matrix from this matrix on a cell-by-cell basis. The type of matrix (sparse, dense) stays the same.
      * @param right DoubleMatrixData; the other data object to subtract
      * @throws ValueException if matrices have different lengths
      */
     public abstract void decrementBy(DoubleMatrixData right) throws ValueException;
 
     /**
      * Subtract a number from this matrix on a cell-by-cell basis.
      * @param valueSI double; the value to subtract
      */
     public void decrementBy(final double valueSI)
     {
         IntStream.range(0, this.matrixSI.length).parallel().forEach(i -> this.matrixSI[i] -= valueSI);
     }
 
     /**
      * Multiply two matrix on a cell-by-cell basis. If both matrices are dense, a dense matrix is returned, otherwise a sparse
      * matrix is returned.
      * @param right DoubleMatrixData; the other data object to multiply with
      * @return the sum of this data object and the other data object
      * @throws ValueException if matrices have different lengths
      */
     public DoubleMatrixData times(final DoubleMatrixData right) throws ValueException
     {
         checkSizes(right);
         double[] dm = new double[this.rows * this.cols];
         IntStream.range(0, this.rows).parallel().forEach(
                 r -> IntStream.range(0, this.cols).forEach(c -> dm[r * this.cols + c] = getSI(r, c) * right.getSI(r, c)));
         if (this instanceof DoubleMatrixDataDense && right instanceof DoubleMatrixDataDense)
         {
             return new DoubleMatrixDataDense(dm, this.rows, this.cols);
         }
         return new DoubleMatrixDataDense(dm, this.rows, this.cols).toSparse();
     }
 
     /**
      * Multiply a matrix with the values of another matrix on a cell-by-cell basis. The type of matrix (sparse, dense) stays the
      * same.
      * @param right DoubleMatrixData; the other data object to multiply with
      * @throws ValueException if matrices have different lengths
      */
     public abstract void multiplyBy(DoubleMatrixData right) throws ValueException;
 
     /**
      * Multiply the values of this matrix with a number on a cell-by-cell basis.
      * @param valueSI double; the value to multiply with
      */
     public void multiplyBy(final double valueSI)
     {
         IntStream.range(0, this.matrixSI.length).parallel().forEach(i -> this.matrixSI[i] *= valueSI);
     }
 
     /**
      * Divide two matrices on a cell-by-cell basis. If both matrices are dense, a dense matrix is returned, otherwise a sparse
      * matrix is returned.
      * @param right DoubleMatrixData; the other data object to divide by
      * @return the sum of this data object and the other data object
      * @throws ValueException if matrices have different lengths
      */
     public DoubleMatrixData divide(final DoubleMatrixData right) throws ValueException
     {
         checkSizes(right);
         double[] dm = new double[this.rows * this.cols];
         IntStream.range(0, this.rows).parallel().forEach(
                 r -> IntStream.range(0, this.cols).forEach(c -> dm[r * this.cols + c] = getSI(r, c) / right.getSI(r, c)));
         if (this instanceof DoubleMatrixDataDense && right instanceof DoubleMatrixDataDense)
         {
             return new DoubleMatrixDataDense(dm, this.rows, this.cols);
         }
         return new DoubleMatrixDataDense(dm, this.rows, this.cols).toSparse();
     }
 
     /**
      * Divide the values of a matrix by the values of another matrix on a cell-by-cell basis. The type of matrix (sparse, dense)
      * stays the same.
      * @param right DoubleMatrixData; the other data object to divide by
      * @throws ValueException if matrices have different lengths
      */
     public abstract void divideBy(DoubleMatrixData right) throws ValueException;
 
     /**
      * Divide the values of this matrix by a number on a cell-by-cell basis.
      * @param valueSI double; the value to multiply with
      */
     public void divideBy(final double valueSI)
     {
         IntStream.range(0, this.matrixSI.length).parallel().forEach(i -> this.matrixSI[i] /= valueSI);
     }
 
     /* ============================================================================================ */
     /* =============================== EQUALS, HASHCODE, TOSTRING ================================= */
     /* ============================================================================================ */
 
     /** {@inheritDoc} */
     @Override
     public int hashCode()
     {
         final int prime = 31;
         int result = 1;
         result = prime * result + this.rows;
         result = prime * result + this.cols;
         result = prime * result + ((this.storageType == null) ? 0 : this.storageType.hashCode());
         result = prime * result + Arrays.hashCode(this.matrixSI);
         return result;
     }
 
     /** {@inheritDoc} */
     @Override
     @SuppressWarnings("checkstyle:needbraces")
     public boolean equals(final Object obj)
     {
         if (this == obj)
             return true;
         if (obj == null)
             return false;
         if (getClass() != obj.getClass())
             return false;
         DoubleMatrixData other = (DoubleMatrixData) obj;
         if (this.rows != other.rows)
             return false;
         if (this.cols != other.cols)
             return false;
         if (this.storageType != other.storageType)
             return false;
         if (!Arrays.equals(this.matrixSI, other.matrixSI))
             return false;
         return true;
     }
 
     /** {@inheritDoc} */
     @Override
     public String toString()
     {
         return "DoubleMatrixData [storageType=" + this.storageType + ", matrixSI=" + Arrays.toString(this.matrixSI) + "]";
     }
 }