package org.djunits.value.vfloat.matrix.data;
   
   import java.io.Serializable;
   import java.util.Arrays;
   import java.util.Collection;
   import java.util.stream.IntStream;
   
   import org.djunits.unit.Unit;
   import org.djunits.unit.scale.Scale;
  import org.djunits.value.ValueRuntimeException;
  import org.djunits.value.storage.Storage;
  import org.djunits.value.storage.StorageType;
  import org.djunits.value.vfloat.function.FloatFunction;
  import org.djunits.value.vfloat.function.FloatFunction2;
  import org.djunits.value.vfloat.matrix.base.FloatSparseValue;
  import org.djunits.value.vfloat.scalar.base.FloatScalar;
  import org.djutils.exceptions.Throw;
  
  /**
   * Stores the data for a FloatMatrix and carries out basic operations.
   * <p>
   * Copyright (c) 2013-2024 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved. <br>
   * BSD-style license. See <a href="https://djunits.org/docs/license.html">DJUNITS License</a>.
   * </p>
   * @author <a href="https://www.tudelft.nl/averbraeck">Alexander Verbraeck</a>
   * @author <a href="https://www.tudelft.nl/staff/p.knoppers/">Peter Knoppers</a>
   */
  public abstract class FloatMatrixData extends Storage<FloatMatrixData> implements Serializable
  {
      /** */
      private static final long serialVersionUID = 1L;
  
      /** the internal storage of the Matrix; can be sparse or dense. The data is stored in an array. */
      @SuppressWarnings("checkstyle:visibilitymodifier")
      protected float[] 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;
  
      /**
       * Construct a new DoubleMatrixData store.
       * @param storageType StorageType; the data type
       */
      public FloatMatrixData(final StorageType storageType)
      {
          super(storageType);
      }
  
      /* ============================================================================================ */
      /* ====================================== INSTANTIATION ======================================= */
      /* ============================================================================================ */
  
      /**
       * Instantiate a FloatMatrixData with the right data type. The float array is of the form f[rows][columns] so each value can
       * be found with f[row][column].
       * @param values float[][]; 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 FloatMatrixData with the right data type
       * @throws ValueRuntimeException when values is ragged
       * @throws NullPointerException when values are null, or storageType is null
       */
      public static FloatMatrixData instantiate(final float[][] values, final Scale scale, final StorageType storageType)
              throws ValueRuntimeException
      {
          Throw.whenNull(scale, "FloatMatrixData.instantiate: scale is null");
          Throw.whenNull(storageType, "FloatMatrixData.instantiate: storageType is null");
          checkRectangularAndNonNull(values);
  
          int rows = values.length;
          final int cols = rows == 0 ? 0 : values[0].length;
          if (cols == 0)
          {
              rows = 0;
          }
  
          switch (storageType)
          {
              case DENSE:
                  float[] valuesSI = new float[rows * cols];
                  IntStream.range(0, values.length).parallel().forEach(r -> IntStream.range(0, cols)
                          .forEach(c -> valuesSI[r * cols + c] = (float) scale.toStandardUnit(values[r][c])));
                  return new FloatMatrixDataDense(valuesSI, rows, cols);
  
              case SPARSE:
                  return FloatMatrixDataSparse.instantiate(values, scale);
  
              default:
                  throw new ValueRuntimeException("Unknown storage type in FloatMatrixData.instantiate: " + storageType);
          }
      }
  
      /**
       * Instantiate a FloatMatrixData with the right data type.
      * @param values Collection&lt;FloatSparseValue&lt;U, S&gt;&gt;; the (sparse [X, Y, SI]) values to store
      * @param rows int; the number of rows of the matrix
      * @param cols int; the number of columns of the matrix
      * @param storageType StorageType; the data type to use
      * @return the FloatMatrixData with the right data type
      * @throws NullPointerException when values are null, or storageType is null
      * @throws ValueRuntimeException when rows &lt; 0 or cols &lt; 0
      * @param <U> the unit type
      * @param <S> the corresponding scalar type
      */
     public static <U extends Unit<U>, S extends FloatScalar<U, S>> FloatMatrixData instantiate(
             final Collection<FloatSparseValue<U, S>> values, final int rows, final int cols, final StorageType storageType)
             throws NullPointerException
     {
         Throw.whenNull(values, "FloatMatrixData.instantiate: values is null");
         Throw.whenNull(storageType, "FloatMatrixData.instantiate: storageType is null");
         Throw.when(cols < 0, ValueRuntimeException.class, "cols must be >= 0");
         Throw.when(rows < 0, ValueRuntimeException.class, "rows must be >= 0");
         for (FloatSparseValue<U, S> fsp : values)
         {
             Throw.whenNull(fsp, "null value in values");
             Throw.when(fsp.getRow() < 0 || fsp.getRow() >= rows, ValueRuntimeException.class, "row out of range");
             Throw.when(fsp.getColumn() < 0 || fsp.getColumn() >= cols, ValueRuntimeException.class, "column out of range");
         }
 
         switch (storageType)
         {
             case DENSE:
                 float[] valuesSI = new float[rows * cols];
                 values.stream().parallel().forEach(v -> valuesSI[v.getRow() * cols + v.getColumn()] = v.getValueSI());
                 return new FloatMatrixDataDense(valuesSI, rows, cols);
 
             case SPARSE:
                 return new FloatMatrixDataSparse(values, rows, cols);
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in FloatMatrixData.instantiate: " + storageType);
         }
     }
 
     /**
      * Instantiate a FloatMatrixData with the right data type. The FloatScalar array is of the form fs[rows][columns] so each
      * value can be found with fs[row][column].
      * @param values S[][]; the values to store
      * @param storageType StorageType; the data type to use
      * @return the FloatMatrixData with the right data type
      * @throws NullPointerException when values is null, or storageType is null
      * @throws ValueRuntimeException when values is ragged
      * @param <U> the unit type
      * @param <S> the corresponding scalar type
      */
     public static <U extends Unit<U>, S extends FloatScalar<U, S>> FloatMatrixData instantiate(final S[][] values,
             final StorageType storageType) throws ValueRuntimeException
     {
         Throw.whenNull(storageType, "FloatMatrixData.instantiate: storageType is null");
         checkRectangularAndNonNull(values);
 
         int rows = values.length;
         final int cols = rows == 0 ? 0 : values[0].length;
         if (cols == 0)
         {
             rows = 0;
         }
 
         switch (storageType)
         {
             case DENSE:
                 float[] valuesSI = new float[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 FloatMatrixDataDense(valuesSI, rows, cols);
 
             case SPARSE:
                 float[][] matrixSI = new float[rows][cols];
                 IntStream.range(0, values.length).parallel()
                         .forEach(r -> IntStream.range(0, cols).forEach(c -> matrixSI[r][c] = values[r][c].getSI()));
                 return FloatMatrixDataSparse.instantiate(matrixSI);
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in FloatMatrixData.instantiate: " + storageType);
         }
     }
 
     /* ============================================================================================ */
     /* ==================================== UTILITY FUNCTIONS ===================================== */
     /* ============================================================================================ */
 
     /**
      * Retrieve the row count.
      * @return int; the number of rows of the matrix
      */
     public int rows()
     {
         return this.rows;
     }
 
     /**
      * Retrieve the column count.
      * @return int; the number of columns of the matrix
      */
     public int cols()
     {
         return this.cols;
     }
 
     /**
      * Return the data of this matrix in dense storage format.
      * @return FloatMatrixDataDense; the dense transformation of this data
      */
     public abstract FloatMatrixDataDense toDense();
 
     /**
      * Return the data of this matrix in sparse storage format.
      * @return FloatMatrixDataSparse; the sparse transformation of this data
      */
     public abstract FloatMatrixDataSparse toSparse();
 
     /**
      * Retrieve one value from this data.
      * @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 float 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 float; the value at the index
      */
     public abstract void setSI(int row, int col, float valueSI);
 
     /**
      * Compute and return the sum of the values of all cells of this matrix.
      * @return float; the sum of the values of all cells
      */
     public final float zSum()
     {
         // this does not copy the data. See http://stackoverflow.com/questions/23106093/how-to-get-a-stream-from-a-float
         return (float) IntStream.range(0, this.matrixSI.length).parallel().mapToDouble(i -> this.matrixSI[i]).sum();
     }
 
     /**
      * Create and return a deep copy of the data in dense format. The float array is of the form f[rows][columns] so each value
      * can be found with f[row][column].
      * @return float[][]; a safe, dense copy of matrixSI as a matrix
      */
     public abstract float[][] getDenseMatrixSI();
 
     /**
      * Create and return a deep copy of the data in dense format. The double array is of the form d[rows][columns] so each value
      * can be found with d[row][column].
      * @return double[][]; a safe, dense copy of matrixSI as a matrix
      */
     public abstract double[][] getDoubleDenseMatrixSI();
 
     /**
      * Check that a 2D array of float is not null, not empty and not jagged; i.e. all rows have the same length.
      * @param values float[][]; the 2D array to check
      * @return the values in case the method is used in a constructor
      * @throws NullPointerException when <code>values</code> is null
      * @throws ValueRuntimeException when <code>values</code> is jagged
      */
     protected static float[][] checkRectangularAndNonNull(final float[][] values) throws ValueRuntimeException
     {
         Throw.when(null == values, NullPointerException.class, "Cannot create a matrix from a null float[][]");
         for (int row = 0; row < values.length; row++)
         {
             Throw.when(null == values[row], ValueRuntimeException.class,
                     "Cannot create a matrix from float[][] containing null row(s)");
             Throw.when(values[row].length != values[0].length, ValueRuntimeException.class,
                     "Cannot create a matrix from a jagged float[][]");
         }
         return values;
     }
 
     /**
      * Check that a 2D array of float is not null, not empty and not jagged; i.e. all rows have the same length.
      * @param values S[][]; the 2D array to check
      * @return the values in case the method is used in a constructor
      * @throws NullPointerException when <code>values</code> is null
      * @throws ValueRuntimeException when <code>values</code> is jagged
      * @param <U> the unit type
      * @param <S> the corresponding scalar type
      */
     protected static <U extends Unit<U>, S extends FloatScalar<U, S>> S[][] checkRectangularAndNonNull(
             final S[][] values) throws ValueRuntimeException
     {
         Throw.when(null == values, NullPointerException.class, "Cannot create a matrix from a null Scalar[][]");
         for (int row = 0; row < values.length; row++)
         {
             Throw.when(null == values[row], ValueRuntimeException.class,
                     "Cannot create a matrix from Scalar[][] containing null row(s)");
             Throw.when(values[row].length != values[0].length, ValueRuntimeException.class,
                     "Cannot create a matrix from a jagged Scalar[][]");
             for (int col = 0; col < values[row].length; col++)
             {
                 Throw.whenNull(values[row][col], "Cannot create a matrix from Scalar[][] containing null(s)");
             }
         }
         return values;
     }
 
     /**
      * Check the sizes of this data object and the other data object.
      * @param other FloatMatrixData; the other data object
      * @throws ValueRuntimeException if matrices have different lengths
      */
     protected void checkSizes(final FloatMatrixData other) throws ValueRuntimeException
     {
         if (this.rows() != other.rows() || this.cols() != other.cols())
         {
             throw new ValueRuntimeException("Two data objects used in a FloatMatrix operation do not have the same size");
         }
     }
 
     /* ============================================================================================ */
     /* ================================== CALCULATION FUNCTIONS =================================== */
     /* ============================================================================================ */
 
     /**
      * Apply an operation to each cell.
      * @param doubleFunction FloatFunction; the operation to apply
      * @return FloatMatrixData; this (modified) double vector data object
      */
     public abstract FloatMatrixData assign(FloatFunction doubleFunction);
 
     /**
      * Apply a binary operation on a cell by cell basis.
      * @param floatFunction FloatFunction2; the binary operation to apply
      * @param right FloatMatrixData; the right operand for the binary operation
      * @return FloatMatrixData; this (modified) double matrix data object
      * @throws ValueRuntimeException when the sizes of the vectors do not match
      */
     abstract FloatMatrixData assign(FloatFunction2 floatFunction, FloatMatrixData right) throws ValueRuntimeException;
 
     /**
      * 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 FloatMatrixData; the other data object to add
      * @return the sum of this data object and the other data object
      * @throws ValueRuntimeException if matrices have different lengths
      */
     public abstract FloatMatrixData plus(FloatMatrixData right) throws ValueRuntimeException;
 
     /**
      * Add a matrix to this matrix on a cell-by-cell basis. The type of matrix (sparse, dense) stays the same.
      * @param right FloatMatrixData; the other data object to add
      * @return FloatMatrixData; this modified float matrix data object
      * @throws ValueRuntimeException if matrices have different lengths
      */
     public final FloatMatrixData incrementBy(final FloatMatrixData right) throws ValueRuntimeException
     {
         return assign(new FloatFunction2()
         {
             @Override
             public float apply(final float leftValue, final float rightValue)
             {
                 return leftValue + rightValue;
             }
         }, right);
     }
 
     /**
      * 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 FloatMatrixData; the other data object to subtract
      * @return the sum of this data object and the other data object
      * @throws ValueRuntimeException if matrices have different lengths
      */
     public abstract FloatMatrixData minus(FloatMatrixData right) throws ValueRuntimeException;
 
     /**
      * Subtract a matrix from this matrix on a cell-by-cell basis. The type of matrix (sparse, dense) stays the same.
      * @param decrement FloatMatrixData; the other data object to subtract
      * @return FloatMatrixData; this modified float matrix data object
      * @throws ValueRuntimeException if matrices have different lengths
      */
     public final FloatMatrixData decrementBy(final FloatMatrixData decrement) throws ValueRuntimeException
     {
         return assign(new FloatFunction2()
         {
             @Override
             public float apply(final float leftValue, final float rightValue)
             {
                 return leftValue - rightValue;
             }
         }, decrement);
     }
 
     /**
      * Multiply 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 FloatMatrixData; the other data object to multiply with
      * @return FloatMatrixData; a new double matrix data store holding the result of the multiplications
      * @throws ValueRuntimeException if matrices have different sizes
      */
     public abstract FloatMatrixData times(FloatMatrixData right) throws ValueRuntimeException;
 
     /**
      * 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 FloatMatrixData; the other data object to multiply with
      * @return FloatMatrixData; this modified data store
      * @throws ValueRuntimeException if matrices have different sizes
      */
     public final FloatMatrixData multiplyBy(final FloatMatrixData right) throws ValueRuntimeException
     {
         return assign(new FloatFunction2()
         {
             @Override
             public float apply(final float leftValue, final float rightValue)
             {
                 return leftValue * rightValue;
             }
         }, right);
     }
 
     /**
      * 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 FloatMatrixData; the other data object to divide by
      * @return the sum of this data object and the other data object
      * @throws ValueRuntimeException if matrices have different sizes
      */
     public abstract FloatMatrixData divide(FloatMatrixData right) throws ValueRuntimeException;
 
     /**
      * 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 FloatMatrixData; the other data object to divide by
      * @return FloatMatrixData; this modified data store
      * @throws ValueRuntimeException if matrices have different sizes
      */
     public final FloatMatrixData divideBy(final FloatMatrixData right) throws ValueRuntimeException
     {
         return assign(new FloatFunction2()
         {
             @Override
             public float apply(final float leftValue, final float rightValue)
             {
                 return leftValue / rightValue;
             }
         }, right);
     }
 
     /* ============================================================================================ */
     /* =============================== EQUALS, HASHCODE, TOSTRING ================================= */
     /* ============================================================================================ */
 
     @Override
     public int hashCode()
     {
         final int prime = 31;
         int result = 1;
         result = prime * result + this.rows;
         result = prime * result + this.cols;
         for (int row = 0; row < this.rows; row++)
         {
             for (int col = 0; col < this.cols; col++)
             {
                 result = 31 * result + Float.floatToIntBits(getSI(row, col));
             }
         }
         return result;
     }
 
     /**
      * Compare contents of a dense and a sparse matrix.
      * @param dm FloatMatrixDataDense; the dense matrix
      * @param sm FloatMatrixDataSparse; the sparse matrix
      * @return boolean; true if the contents are equal
      */
     protected boolean compareDenseMatrixWithSparseMatrix(final FloatMatrixDataDense dm, final FloatMatrixDataSparse sm)
     {
         for (int row = 0; row < dm.rows; row++)
         {
             for (int col = 0; col < dm.cols; col++)
             {
                 if (dm.getSI(row, col) != sm.getSI(row, col))
                 {
                     return false;
                 }
             }
         }
         return true;
     }
 
     @Override
     @SuppressWarnings("checkstyle:needbraces")
     public boolean equals(final Object obj)
     {
         if (this == obj)
             return true;
         if (obj == null)
             return false;
         if (!(obj instanceof FloatMatrixData))
             return false;
         FloatMatrixData other = (FloatMatrixData) obj;
         if (this.rows != other.rows)
             return false;
         if (this.cols != other.cols)
             return false;
         if (other instanceof FloatMatrixDataSparse && this instanceof FloatMatrixDataDense)
         {
             return compareDenseMatrixWithSparseMatrix((FloatMatrixDataDense) this, (FloatMatrixDataSparse) other);
         }
         else if (other instanceof FloatMatrixDataDense && this instanceof FloatMatrixDataSparse)
         {
             return compareDenseMatrixWithSparseMatrix((FloatMatrixDataDense) other, (FloatMatrixDataSparse) this);
         }
         // Both are dense (both sparse is handled in FloatMatrixDataSparse class)
         return Arrays.equals(this.matrixSI, other.matrixSI);
     }
 
     @Override
     public String toString()
     {
         return "FloatMatrixData [storageType=" + getStorageType() + ", matrixSI=" + Arrays.toString(this.matrixSI) + "]";
     }
 
 }