package org.djunits.value.vdouble.vector.data;
   
   import java.io.Serializable;
   import java.util.Arrays;
   import java.util.List;
   import java.util.Map.Entry;
   import java.util.SortedMap;
   import java.util.stream.IntStream;
   
  import org.djunits.Throw;
  import org.djunits.unit.Unit;
  import org.djunits.unit.scale.Scale;
  import org.djunits.value.ValueRuntimeException;
  import org.djunits.value.storage.AbstractStorage;
  import org.djunits.value.storage.StorageType;
  import org.djunits.value.vdouble.function.DoubleFunction;
  import org.djunits.value.vdouble.function.DoubleFunction2;
  import org.djunits.value.vdouble.scalar.base.DoubleScalarInterface;
  
  /**
   * Stores the data for a DoubleVector 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="https://opentrafficsim.org/docs/license.html">OpenTrafficSim 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 DoubleVectorData extends AbstractStorage<DoubleVectorData> implements Serializable
  {
      /** */
      private static final long serialVersionUID = 1L;
  
      /** The internal storage of the Vector; can be sparse or dense. */
      @SuppressWarnings("checkstyle:visibilitymodifier")
      protected double[] vectorSI;
  
      /** Threshold to do parallel execution. */
      protected static final int PARALLEL_THRESHOLD = 1000;
  
      /**
       * Construct a new DoubleVectorData object.
       * @param storageType StorageType; the data type.
       */
      DoubleVectorData(final StorageType storageType)
      {
          super(storageType);
      }
  
      /* ============================================================================================ */
      /* ====================================== INSTANTIATION ======================================= */
      /* ============================================================================================ */
  
      /**
       * Instantiate a DoubleVectorData 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 DoubleVectorData; the DoubleVectorData with the right data type
       * @throws ValueRuntimeException when values are null, or storageType is null
       */
      public static DoubleVectorData instantiate(final double[] values, final Scale scale, final StorageType storageType)
              throws ValueRuntimeException
      {
          Throw.whenNull(values, "DoubleVectorData.instantiate: double[] values is null");
          Throw.whenNull(scale, "DoubleVectorData.instantiate: scale is null");
          Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
          Throw.when(values.length == 0, ValueRuntimeException.class,
                  "DoubleVectorData.instantiate: double[] values wrong: values.length == 0");
  
          double[] valuesSI = scale.isBaseSIScale() ? values : new double[values.length];
          if (!scale.isBaseSIScale())
          {
              if (values.length > PARALLEL_THRESHOLD)
              {
                  IntStream.range(0, values.length).parallel().forEach(i -> valuesSI[i] = scale.toStandardUnit(values[i]));
              }
              else
              {
                  IntStream.range(0, values.length).forEach(i -> valuesSI[i] = scale.toStandardUnit(values[i]));
              }
          }
  
          switch (storageType)
          {
              case DENSE:
                  return new DoubleVectorDataDense(valuesSI);
  
              case SPARSE:
                  return DoubleVectorDataSparse.instantiate(valuesSI);
  
              default:
                  throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
          }
      }
  
      /**
       * Instantiate a DoubleVectorData with the right data type.
       * @param values List&lt;Double&gt;; the 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 DoubleVectorData; the DoubleVectorData with the right data type
      * @throws ValueRuntimeException when list is null, or storageType is null
      */
     public static DoubleVectorData instantiate(final List<Double> values, final Scale scale, final StorageType storageType)
             throws ValueRuntimeException
     {
         Throw.whenNull(values, "DoubleVectorData.instantiate: double[] values is null");
         Throw.whenNull(scale, "DoubleVectorData.instantiate: scale is null");
         Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
         Throw.when(values.size() == 0, ValueRuntimeException.class, "DoubleVectorData.instantiate: values.size() == 0");
         Throw.when(values.parallelStream().filter(d -> d == null).count() > 0, NullPointerException.class,
                 "values contains one or more null values");
 
         switch (storageType)
         {
             case DENSE:
             {
                 double[] valuesSI;
                 if (values.size() > PARALLEL_THRESHOLD)
                 {
                     if (scale.isBaseSIScale())
                     {
                         valuesSI = values.parallelStream().mapToDouble(d -> d).toArray();
                     }
                     else
                     {
                         valuesSI = values.parallelStream().mapToDouble(d -> scale.toStandardUnit(d)).toArray();
                     }
                 }
                 else
                 {
                     if (scale.isBaseSIScale())
                     {
                         valuesSI = values.stream().mapToDouble(d -> d).toArray();
                     }
                     else
                     {
                         valuesSI = values.stream().mapToDouble(d -> scale.toStandardUnit(d)).toArray();
                     }
                 }
                 return new DoubleVectorDataDense(valuesSI);
             }
 
             case SPARSE:
             {
                 int nonZeroCount;
                 if (values.size() > PARALLEL_THRESHOLD)
                 {
                     if (scale.isBaseSIScale())
                     {
                         nonZeroCount = (int) values.parallelStream().filter(d -> d != 0d).count();
                     }
                     else
                     {
                         nonZeroCount = (int) values.parallelStream().filter(d -> scale.toStandardUnit(d) != 0d).count();
                     }
                 }
                 else
                 {
                     if (scale.isBaseSIScale())
                     {
                         nonZeroCount = (int) values.stream().filter(d -> d != 0d).count();
                     }
                     else
                     {
                         nonZeroCount = values.size()
                                 - (int) values.parallelStream().filter(d -> scale.toStandardUnit(d) == 0d).count();
                     }
                 }
                 int[] indices = new int[nonZeroCount];
                 double[] valuesSI = new double[nonZeroCount];
                 // Counting non zeros could be done in parallel; but filling the arrays has to be done sequentially
                 int index = 0;
                 for (int i = 0; i < values.size(); i++)
                 {
                     double d = scale.toStandardUnit(values.get(i));
                     if (d != 0.0)
                     {
                         indices[index] = i;
                         valuesSI[index] = d;
                         index++;
                     }
                 }
                 return new DoubleVectorDataSparse(valuesSI, indices, values.size());
             }
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
         }
     }
 
     /**
      * Instantiate a DoubleVectorData with the right data type.
      * @param values DoubleScalarInterface[]; the values to store
      * @param storageType StorageType; the data type to use
      * @return DoubleVectorData; the DoubleVectorData with the right data type
      * @throws ValueRuntimeException when values is null, or storageType is null
      * @param <U> the unit type
      * @param <S> the corresponding scalar type
      */
     public static <U extends Unit<U>, S extends DoubleScalarInterface<U, S>> DoubleVectorData instantiate(final S[] values,
             final StorageType storageType) throws ValueRuntimeException
     {
         Throw.whenNull(values, "DoubleVectorData.instantiate: double[] values is null");
         Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
         Throw.when(values.length == 0, ValueRuntimeException.class,
                 "DoubleVectorData.instantiate: DoubleScalar[] values wrong: values.length == 0");
         for (S s : values)
         {
             Throw.whenNull(s, "null value in values");
         }
 
         switch (storageType)
         {
             case DENSE:
             {
                 double[] valuesSI;
                 if (values.length > PARALLEL_THRESHOLD)
                 {
                     valuesSI = Arrays.stream(values).parallel().mapToDouble(s -> s.getSI()).toArray();
                 }
                 else
                 {
                     valuesSI = Arrays.stream(values).mapToDouble(s -> s.getSI()).toArray();
                 }
                 return new DoubleVectorDataDense(valuesSI);
             }
 
             case SPARSE:
             {
                 int nonZeroCount;
                 if (values.length > PARALLEL_THRESHOLD)
                 {
                     nonZeroCount = (int) Arrays.stream(values).parallel().filter(s -> s.getSI() != 0).count();
                 }
                 else
                 {
                     nonZeroCount = (int) Arrays.stream(values).filter(s -> s.getSI() != 0.0).count();
                 }
                 int[] indices = new int[nonZeroCount];
                 double[] valuesSI = new double[nonZeroCount];
                 // Counting non zeros could be done in parallel; but filling the arrays has to be done sequentially
                 int index = 0;
                 for (int i = 0; i < values.length; i++)
                 {
                     double d = values[i].getSI();
                     if (d != 0.0)
                     {
                         indices[index] = i;
                         valuesSI[index] = d;
                         index++;
                     }
                 }
                 return new DoubleVectorDataSparse(valuesSI, indices, values.length);
             }
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
         }
     }
 
     /**
      * Instantiate a DoubleVectorData with the right data type.
      * @param valueList List&lt;S&gt;; the values to store
      * @param storageType StorageType; the data type to use
      * @return DoubleVectorData; the DoubleVectorData with the right data type
      * @throws ValueRuntimeException when values is null, or storageType is null
      * @param <U> the unit type
      * @param <S> the corresponding scalar type
      */
     public static <U extends Unit<U>, S extends DoubleScalarInterface<U, S>> DoubleVectorData instantiateList(
             final List<S> valueList, final StorageType storageType) throws ValueRuntimeException
     {
         Throw.whenNull(valueList, "DoubleVectorData.instantiate: valueList is null");
         Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
         Throw.when(valueList.size() == 0, ValueRuntimeException.class, "DoubleVectorData.instantiate: valueList.size() == 0");
         for (S s : valueList)
         {
             Throw.whenNull(s, "null value in valueList");
         }
 
         switch (storageType)
         {
             case DENSE:
             {
                 double[] valuesSI;
                 if (valueList.size() > PARALLEL_THRESHOLD)
                 {
                     valuesSI = valueList.stream().parallel().mapToDouble(s -> s.getSI()).toArray();
                 }
                 else
                 {
                     valuesSI = valueList.stream().mapToDouble(s -> s.getSI()).toArray();
                 }
                 return new DoubleVectorDataDense(valuesSI);
             }
 
             case SPARSE:
             {
                 int nonZeroCount = (int) valueList.parallelStream().filter(s -> s.getSI() != 0.0).count();
                 int[] indices = new int[nonZeroCount];
                 double[] valuesSI = new double[nonZeroCount];
                 // Counting non zeros could be done in parallel; but filling the arrays has to be done sequentially
                 int index = 0;
                 for (int i = 0; i < valueList.size(); i++)
                 {
                     double d = valueList.get(i).getSI();
                     if (d != 0.0)
                     {
                         indices[index] = i;
                         valuesSI[index] = d;
                         index++;
                     }
                 }
                 return new DoubleVectorDataSparse(valuesSI, indices, valueList.size());
             }
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
         }
     }
 
     /**
      * Instantiate a DoubleVectorData with the right data type.
      * @param valueMap SortedMap&lt;Integer,Double&gt;; the DoubleScalar values to store
      * @param length int; the length of the vector to pad with 0 after last entry in map
      * @param scale Scale; the scale of the unit to use for conversion to SI
      * @param storageType StorageType; the data type to use
      * @return DoubleVectorData; the DoubleVectorData with the right data type
      * @throws ValueRuntimeException when values is null, or storageType is null
      */
     public static DoubleVectorData instantiate(final SortedMap<Integer, Double> valueMap, final int length, final Scale scale,
             final StorageType storageType) throws ValueRuntimeException
     {
         Throw.whenNull(valueMap, "DoubleVectorData.instantiate: values is null");
         Throw.when(length < 1, ValueRuntimeException.class, "Length must be > 0");
         Throw.whenNull(scale, "DoubleVectorData.instantiate: scale is null");
         Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
         for (Integer key : valueMap.keySet())
         {
             Throw.when(key < 0 || key >= length, ValueRuntimeException.class, "Key in values out of range");
         }
 
         switch (storageType)
         {
             case DENSE:
             {
                 double[] valuesSI = new double[length];
                 if (scale.isBaseSIScale())
                 {
                     valueMap.entrySet().parallelStream().forEach(entry -> valuesSI[entry.getKey()] = entry.getValue());
                 }
                 else
                 {
                     Arrays.fill(valuesSI, scale.toStandardUnit(0.0));
                     valueMap.entrySet().parallelStream()
                             .forEach(entry -> valuesSI[entry.getKey()] = scale.toStandardUnit(entry.getValue()));
                 }
                 return new DoubleVectorDataDense(valuesSI);
             }
 
             case SPARSE:
             {
                 int nonZeroCount;
                 if (scale.isBaseSIScale())
                 {
                     nonZeroCount = (int) valueMap.keySet().parallelStream().filter(d -> d != 0d).count();
                 }
                 else
                 {
                     // Much harder, and the result is unlikely to be very sparse
                     nonZeroCount = length
                             - (int) valueMap.values().parallelStream().filter(d -> scale.toStandardUnit(d) == 0d).count();
                 }
                 int[] indices = new int[nonZeroCount];
                 double[] valuesSI = new double[nonZeroCount];
                 if (scale.isBaseSIScale())
                 {
                     int index = 0;
                     for (Integer key : valueMap.keySet())
                     {
                         double value = valueMap.get(key);
                         if (0.0 != value)
                         {
                             indices[index] = key;
                             valuesSI[index] = value;
                             index++;
                         }
                     }
                 }
                 else
                 {
                     Arrays.fill(valuesSI, scale.toStandardUnit(0.0));
                     int index = 0;
                     int lastKey = 0;
                     for (Integer key : valueMap.keySet())
                     {
                         for (int i = lastKey; i < key; i++)
                         {
                             indices[index++] = i;
                         }
                         lastKey = key;
                         double value = scale.toStandardUnit(valueMap.get(key));
                         if (0.0 != value)
                         {
                             indices[index] = key;
                             valuesSI[index] = value;
                             index++;
                         }
                         lastKey = key + 1;
                     }
                     while (index < indices.length)
                     {
                         indices[index++] = lastKey++;
                     }
                 }
                 return new DoubleVectorDataSparse(valuesSI, indices, length);
             }
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
         }
     }
 
     /**
      * Instantiate a DoubleVectorData with the right data type.
      * @param values SortedMap&lt;Integer,S&gt;; the DoubleScalar values to store
      * @param length int; the length of the vector to pad with 0 after last entry in map
      * @param storageType StorageType; the data type to use
      * @return DoubleVectorData; the DoubleVectorData with the right data type
      * @throws ValueRuntimeException when values is null, or storageType is null
      * @param <U> the unit
      * @param <S> the corresponding scalar type
      */
     public static <U extends Unit<U>, S extends DoubleScalarInterface<U, S>> DoubleVectorData instantiateMap(
             final SortedMap<Integer, S> values, final int length, final StorageType storageType) throws ValueRuntimeException
     {
         Throw.whenNull(values, "DoubleVectorData.instantiate: values is null");
         Throw.when(length < 1, ValueRuntimeException.class, "Length must be > 0");
         Throw.whenNull(storageType, "DoubleVectorData.instantiate: storageType is null");
         for (Entry<Integer, S> e : values.entrySet())
         {
             Throw.when(e.getKey() < 0 || e.getKey() >= length, ValueRuntimeException.class, "Key in values out of range");
             Throw.whenNull(e.getValue(), "null value in map");
         }
 
         switch (storageType)
         {
             case DENSE:
             {
                 double[] valuesSI = new double[length];
                 values.entrySet().parallelStream().forEach(entry -> valuesSI[entry.getKey()] = entry.getValue().getSI());
                 return new DoubleVectorDataDense(valuesSI);
             }
 
             case SPARSE:
             {
                 int nonZeroCount = (int) values.values().parallelStream().filter(s -> s.getSI() != 0d).count();
                 int[] indices = new int[nonZeroCount];
                 double[] valuesSI = new double[nonZeroCount];
                 int index = 0;
                 for (Integer key : values.keySet())
                 {
                     double value = values.get(key).getSI();
                     if (0.0 != value)
                     {
                         indices[index] = key;
                         valuesSI[index] = value;
                         index++;
                     }
                 }
                 return new DoubleVectorDataSparse(valuesSI, indices, length);
             }
 
             default:
                 throw new ValueRuntimeException("Unknown storage type in DoubleVectorData.instantiate: " + storageType);
         }
     }
 
     /* ============================================================================================ */
     /* ==================================== UTILITY FUNCTIONS ===================================== */
     /* ============================================================================================ */
 
     /**
      * Retrieve the size of the vector.
      * @return int; the size of the vector
      */
     public abstract int size();
 
     /**
      * Return the densely stored equivalent of this data.
      * @return DoubleVectorDataDense; the dense transformation of this data
      */
     public abstract DoubleVectorDataDense toDense();
 
     /**
      * Return the sparsely stored equivalent of this data.
      * @return DoubleVectorDataSparse; the sparse transformation of this data
      */
     public abstract DoubleVectorDataSparse toSparse();
 
     /**
      * Retrieve the SI value of one element of this data.
      * @param index int; the index to get the value for
      * @return double; the value at the index
      */
     public abstract double getSI(int index);
 
     /**
      * Sets a value at the index in the vector.
      * @param index int; the index to set the value for
      * @param valueSI double; the value at the index
      */
     public abstract void setSI(int index, double valueSI);
 
     /**
      * Compute and return the sum of all values.
      * @return double; the sum of the values of all cells
      */
     public final double zSum()
     {
         return Arrays.stream(this.vectorSI).parallel().sum();
     }
 
     /**
      * Create and return a dense copy of the data.
      * @return double[]; a safe copy of VectorSI
      */
     public abstract double[] getDenseVectorSI();
 
     /**
      * Check the sizes of this data object and the other data object.
      * @param other DoubleVectorData; the other data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     protected void checkSizes(final DoubleVectorData other) throws ValueRuntimeException
     {
         if (this.size() != other.size())
         {
             throw new ValueRuntimeException("Two data objects used in a DoubleVector operation do not have the same size");
         }
     }
 
     /* ============================================================================================ */
     /* ================================== CALCULATION FUNCTIONS =================================== */
     /* ============================================================================================ */
 
     /**
      * Apply an operation to each cell.
      * @param doubleFunction DoubleFunction; the operation to apply
      * @return DoubleVectorData; this (modified) double vector data object
      */
     public abstract DoubleVectorData assign(DoubleFunction doubleFunction);
 
     /**
      * Apply a binary operation on a cell by cell basis.
      * @param doubleFunction2 DoubleFunction2; the binary operation to apply
      * @param right DoubleVectorData; the right operand for the binary operation
      * @return DoubleVectorData; this (modified) double vector data object
      * @throws ValueRuntimeException when the sizes of the vectors do not match
      */
     abstract DoubleVectorDatable/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData assign(DoubleFunction2 doubleFunction2, DoubleVectorData right) throws ValueRuntimeException;
 
     /**
      * Add two vectors on a cell-by-cell basis. If both vectors are sparse, a sparse vector is returned, otherwise a dense
      * vector is returned. Neither of the two objects is changed.
      * @param right DoubleVectorData; the other data object to add
      * @return DoubleVectorData; the sum of this data object and the other data object as a new data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public abstract DoubleVectorData/../../../../org/djunits/value/vdouble/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData plus(DoubleVectorData right) throws ValueRuntimeException;
 
     /**
      * Add a vector to this vector on a cell-by-cell basis. The type of vector (sparse, dense) stays the same.
      * @param right DoubleVectorData; the other data object to add
      * @return DoubleVectorData; this modified double vector data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public final DoubleVectorData/DoubleVectorData.html#DoubleVectorData">DoubleVectorData incrementBy(final DoubleVectorData right) throws ValueRuntimeException
     {
         return assign(new DoubleFunction2()
         {
             @Override
             public double apply(final double leftValue, final double rightValue)
             {
                 return leftValue + rightValue;
             }
         }, right);
     }
 
     /**
      * Subtract two vectors on a cell-by-cell basis. If both vectors are sparse, a sparse vector is returned, otherwise a dense
      * vector is returned. Neither of the two objects is changed.
      * @param right DoubleVectorData; the other data object to subtract
      * @return DoubleVectorData; the difference of this data object and the other data object as a new data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public abstract DoubleVectorData../../../../org/djunits/value/vdouble/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData minus(DoubleVectorData right) throws ValueRuntimeException;
 
     /**
      * Subtract a vector from this vector on a cell-by-cell basis. The type of vector (sparse, dense) stays the same.
      * @param right DoubleVectorData; the other data object to subtract
      * @return DoubleVectorData; this modified double vector data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public final DoubleVectorData/DoubleVectorData.html#DoubleVectorData">DoubleVectorData decrementBy(final DoubleVectorData right) throws ValueRuntimeException
     {
         return assign(new DoubleFunction2()
         {
             @Override
             public double apply(final double leftValue, final double rightValue)
             {
                 return leftValue - rightValue;
             }
         }, right);
     }
 
     /**
      * Multiply two vectors on a cell-by-cell basis. If both vectors are dense, a dense vector is returned, otherwise a sparse
      * vector is returned.
      * @param right DoubleVectorData; the other data object to multiply with
      * @return DoubleVectorData; a new double vector data store holding the result of the multiplications
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public abstract DoubleVectorData../../../../org/djunits/value/vdouble/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData times(DoubleVectorData right) throws ValueRuntimeException;
 
     /**
      * Multiply a vector with the values of another vector on a cell-by-cell basis. The type of vector (sparse, dense) stays the
      * same.
      * @param right DoubleVectorData; the other data object to multiply with
      * @return DoubleVectordata; this modified double vector data store
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public final DoubleVectorDataa/DoubleVectorData.html#DoubleVectorData">DoubleVectorData multiplyBy(final DoubleVectorData right) throws ValueRuntimeException
     {
         assign(new DoubleFunction2()
         {
             @Override
             public double apply(final double leftValue, final double rightValue)
             {
                 return leftValue * rightValue;
             }
         }, right);
         return this;
     }
 
     /**
      * Multiply the values of this vector with a number on a cell-by-cell basis.
      * @param valueSI double; the value to multiply with
      */
     public final void multiplyBy(final double valueSI)
     {
         assign(new DoubleFunction()
         {
             @Override
             public double apply(final double value)
             {
                 return value * valueSI;
             }
         });
     }
 
     /**
      * Divide two vectors on a cell-by-cell basis. If this vector is sparse and <code>right</code> is dense, a sparse vector is
      * returned, otherwise a dense vector is returned.
      * @param right DoubleVectorData; the other data object to divide by
      * @return DoubleVectorData; the ratios of the values of this data object and the other data object
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public abstract DoubleVectorData./../../../org/djunits/value/vdouble/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData divide(DoubleVectorData right) throws ValueRuntimeException;
 
     /**
      * Divide the values of a vector by the values of another vector on a cell-by-cell basis. The type of vector (sparse, dense)
      * stays the same.
      * @param right DoubleVectorData; the other data object to divide by
      * @return DoubleVectorData; this modified double vector data store
      * @throws ValueRuntimeException if vectors have different lengths
      */
     public final DoubleVectorDataata/DoubleVectorData.html#DoubleVectorData">DoubleVectorData divideBy(final DoubleVectorData right) throws ValueRuntimeException
     {
         return assign(new DoubleFunction2()
         {
             @Override
             public double apply(final double leftValue, final double rightValue)
             {
                 return leftValue / rightValue;
             }
         }, right);
     }
 
     /**
      * Divide the values of this vector by a number on a cell-by-cell basis.
      * @param valueSI double; the value to multiply with
      */
     public final void divideBy(final double valueSI)
     {
         assign(new DoubleFunction()
         {
             @Override
             public double apply(final double value)
             {
                 return value / valueSI;
             }
         });
     }
 
     /* ============================================================================================ */
     /* =============================== EQUALS, HASHCODE, TOSTRING ================================= */
     /* ============================================================================================ */
 
     /** {@inheritDoc} */
     @Override
     public int hashCode()
     {
         final int prime = 31;
         int result = 1;
         result = prime * result + this.size();
         for (int index = 0; index < this.size(); index++)
         {
             long bits = Double.doubleToLongBits(getSI(index));
             result = 31 * result + (int) (bits ^ (bits >>> 32));
         }
         return result;
     }
 
     /**
      * Compare contents of a dense and a sparse vector.
      * @param dm DoubleVectorDataDense; the dense vector
      * @param sm DoubleVectorDataSparse; the sparse vector
      * @return boolean; true if the contents are equal
      */
     protected boolean compareDenseVectorWithSparseVector(final DoubleVectorDataDense dm, final DoubleVectorDataSparse sm)
     {
         for (int index = 0; index < dm.size(); index++)
         {
             if (dm.getSI(index) != sm.getSI(index))
             {
                 return false;
             }
         }
         return true;
     }
 
     /** {@inheritDoc} */
     @Override
     @SuppressWarnings("checkstyle:needbraces")
     public boolean equals(final Object obj)
     {
         if (this == obj)
             return true;
         if (obj == null)
             return false;
         if (!(obj instanceof DoubleVectorData))
             return false;
         DoubleVectorData../../../org/djunits/value/vdouble/vector/data/DoubleVectorData.html#DoubleVectorData">DoubleVectorData other = (DoubleVectorData) obj;
         if (this.size() != other.size())
             return false;
         if (other instanceof DoubleVectorDataSparse && this instanceof DoubleVectorDataDense)
         {
             return compareDenseVectorWithSparseVector((DoubleVectorDataDense) this, (DoubleVectorDataSparse) other);
         }
         else if (other instanceof DoubleVectorDataDense && this instanceof DoubleVectorDataSparse)
         {
             return compareDenseVectorWithSparseVector((DoubleVectorDataDense) other, (DoubleVectorDataSparse) this);
         }
         // Both are dense (both sparse is handled in DoubleVectorDataSparse class)
         return Arrays.equals(this.vectorSI, other.vectorSI);
     }
 
     /** {@inheritDoc} */
     @Override
     public String toString()
     {
         return "DoubleVectorData [storageType=" + getStorageType() + ", vectorSI=" + Arrays.toString(this.vectorSI) + "]";
     }
 
 }