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temp/escript/src/Data.h revision 1387 by trankine, Fri Jan 11 07:45:26 2008 UTC trunk/escript/src/Data.h revision 2723 by jfenwick, Sun Oct 18 23:44:37 2009 UTC
# Line 1  Line 1 
1    
 /* $Id$ */  
   
2  /*******************************************************  /*******************************************************
3   *  *
4   *           Copyright 2003-2007 by ACceSS MNRF  * Copyright (c) 2003-2009 by University of Queensland
5   *       Copyright 2007 by University of Queensland  * Earth Systems Science Computational Center (ESSCC)
6   *  * http://www.uq.edu.au/esscc
7   *                http://esscc.uq.edu.au  *
8   *        Primary Business: Queensland, Australia  * Primary Business: Queensland, Australia
9   *  Licensed under the Open Software License version 3.0  * Licensed under the Open Software License version 3.0
10   *     http://www.opensource.org/licenses/osl-3.0.php  * http://www.opensource.org/licenses/osl-3.0.php
11   *  *
12   *******************************************************/  *******************************************************/
13    
14    
15  /** \file Data.h */  /** \file Data.h */
16    
# Line 19  Line 18 
18  #define DATA_H  #define DATA_H
19  #include "system_dep.h"  #include "system_dep.h"
20    
21    #include "DataTypes.h"
22  #include "DataAbstract.h"  #include "DataAbstract.h"
23  #include "DataAlgorithm.h"  #include "DataAlgorithm.h"
24  #include "FunctionSpace.h"  #include "FunctionSpace.h"
# Line 26  Line 26 
26  #include "UnaryOp.h"  #include "UnaryOp.h"
27  #include "DataException.h"  #include "DataException.h"
28    
29    
30  extern "C" {  extern "C" {
31  #include "DataC.h"  #include "DataC.h"
32  /* #include "paso/Paso.h" doesn't belong in this file...causes trouble for BruceFactory.cpp */  //#include <omp.h>
33  }  }
34    
35  #include "esysmpi.h"  #include "esysmpi.h"
36  #include <string>  #include <string>
37  #include <algorithm>  #include <algorithm>
38    #include <sstream>
39    
40  #include <boost/shared_ptr.hpp>  #include <boost/shared_ptr.hpp>
41  #include <boost/python/object.hpp>  #include <boost/python/object.hpp>
42  #include <boost/python/tuple.hpp>  #include <boost/python/tuple.hpp>
43  #include <boost/python/numeric.hpp>  
44    #include "BufferGroup.h"
45    
46  namespace escript {  namespace escript {
47    
# Line 47  namespace escript { Line 50  namespace escript {
50  class DataConstant;  class DataConstant;
51  class DataTagged;  class DataTagged;
52  class DataExpanded;  class DataExpanded;
53    class DataLazy;
54    
55  /**  /**
56     \brief     \brief
57     Data creates the appropriate Data object for the given construction     Data represents a collection of datapoints.
    arguments.  
58    
59     Description:     Description:
60     Data is essentially a factory class which creates the appropriate Data     Internally, the datapoints are actually stored by a DataAbstract object.
61     object for the given construction arguments. It retains control over     The specific instance of DataAbstract used may vary over the lifetime
62     the object created for the lifetime of the object.     of the Data object.
63     The type of Data object referred to may change during the lifetime of     Some methods on this class return references (eg getShape()).
64     the Data object.     These references should not be used after an operation which changes the underlying DataAbstract object.
65       Doing so will lead to invalid memory access.
66       This should not affect any methods exposed via boost::python.
67  */  */
68  class Data {  class Data {
69    
# Line 101  class Data { Line 106  class Data {
106         const FunctionSpace& what);         const FunctionSpace& what);
107    
108    /**    /**
109       \brief      \brief Copy Data from an existing vector
110       Constructor which copies data from a DataArrayView.    */
111    
      \param value - Input - Data value for a single point.  
      \param what - Input - A description of what this data represents.  
      \param expanded - Input - Flag, if true fill the entire container with  
                        the value. Otherwise a more efficient storage  
                        mechanism will be used.  
   */  
112    ESCRIPT_DLL_API    ESCRIPT_DLL_API
113    Data(const DataArrayView& value,    Data(const DataTypes::ValueType& value,
114         const FunctionSpace& what=FunctionSpace(),           const DataTypes::ShapeType& shape,
115         bool expanded=false);                   const FunctionSpace& what=FunctionSpace(),
116                     bool expanded=false);
117    
118    /**    /**
119       \brief       \brief
120       Constructor which creates a Data from a DataArrayView shape.       Constructor which creates a Data with points having the specified shape.
121    
122       \param value - Input - Single value applied to all Data.       \param value - Input - Single value applied to all Data.
123       \param dataPointShape - Input - The shape of each data point.       \param dataPointShape - Input - The shape of each data point.
# Line 128  class Data { Line 128  class Data {
128    */    */
129    ESCRIPT_DLL_API    ESCRIPT_DLL_API
130    Data(double value,    Data(double value,
131         const DataArrayView::ShapeType& dataPointShape=DataArrayView::ShapeType(),         const DataTypes::ShapeType& dataPointShape=DataTypes::ShapeType(),
132         const FunctionSpace& what=FunctionSpace(),         const FunctionSpace& what=FunctionSpace(),
133         bool expanded=false);         bool expanded=false);
134    
# Line 141  class Data { Line 141  class Data {
141    */    */
142    ESCRIPT_DLL_API    ESCRIPT_DLL_API
143    Data(const Data& inData,    Data(const Data& inData,
144         const DataArrayView::RegionType& region);         const DataTypes::RegionType& region);
145    
146    /**    /**
147       \brief       \brief
148       Constructor which will create Tagged data if expanded is false.       Constructor which copies data from any object that can be treated like a python array/sequence.
      No attempt is made to ensure the tag keys match the tag keys  
      within the function space.  
   
      \param tagKeys - Input - List of tag values.  
      \param values - Input - List of values, one for each tag.  
      \param defaultValue - Input - A default value, used if tag doesn't exist.  
      \param what - Input - A description of what this data represents.  
      \param expanded - Input - Flag, if true fill the entire container with  
                        the appropriate values.  
     ==>*  
   */  
   ESCRIPT_DLL_API  
   Data(const DataTagged::TagListType& tagKeys,  
        const DataTagged::ValueListType& values,  
        const DataArrayView& defaultValue,  
        const FunctionSpace& what=FunctionSpace(),  
        bool expanded=false);  
   
   /**  
      \brief  
      Constructor which copies data from a python numarray.  
   
      \param value - Input - Data value for a single point.  
      \param what - Input - A description of what this data represents.  
      \param expanded - Input - Flag, if true fill the entire container with  
                        the value. Otherwise a more efficient storage  
                        mechanism will be used.  
   */  
   ESCRIPT_DLL_API  
   Data(const boost::python::numeric::array& value,  
        const FunctionSpace& what=FunctionSpace(),  
        bool expanded=false);  
   
   /**  
      \brief  
      Constructor which copies data from any object that can be converted into  
      a python numarray.  
149    
150       \param value - Input - Input data.       \param value - Input - Input data.
151       \param what - Input - A description of what this data represents.       \param what - Input - A description of what this data represents.
# Line 198  class Data { Line 161  class Data {
161    /**    /**
162       \brief       \brief
163       Constructor which creates a DataConstant.       Constructor which creates a DataConstant.
164       Copies data from any object that can be converted       Copies data from any object that can be treated like a python array/sequence.
165       into a numarray. All other parameters are copied from other.       All other parameters are copied from other.
166    
167       \param value - Input - Input data.       \param value - Input - Input data.
168       \param other - Input - contains all other parameters.       \param other - Input - contains all other parameters.
# Line 217  class Data { Line 180  class Data {
180         const boost::python::tuple& shape=boost::python::make_tuple(),         const boost::python::tuple& shape=boost::python::make_tuple(),
181         const FunctionSpace& what=FunctionSpace(),         const FunctionSpace& what=FunctionSpace(),
182         bool expanded=false);         bool expanded=false);
183    
184    
185    
186      /**
187        \brief Create a Data using an existing DataAbstract. Warning: The new object assumes ownership of the pointer!
188        Once you have passed the pointer, do not delete it.
189      */
190      ESCRIPT_DLL_API
191      explicit Data(DataAbstract* underlyingdata);
192    
193      /**
194        \brief Create a Data based on the supplied DataAbstract
195      */
196      ESCRIPT_DLL_API
197      explicit Data(DataAbstract_ptr underlyingdata);
198    
199    /**    /**
200       \brief       \brief
201       Destructor       Destructor
# Line 225  class Data { Line 204  class Data {
204    ~Data();    ~Data();
205    
206    /**    /**
207       \brief       \brief Make this object a deep copy of "other".
      Perform a deep copy.  
208    */    */
209    ESCRIPT_DLL_API    ESCRIPT_DLL_API
210    void    void
211    copy(const Data& other);    copy(const Data& other);
212    
213    /**    /**
214         \brief Return a pointer to a deep copy of this object.
215      */
216      ESCRIPT_DLL_API
217      Data
218      copySelf();
219    
220    
221      /**
222         \brief produce a delayed evaluation version of this Data.
223      */
224      ESCRIPT_DLL_API
225      Data
226      delay();
227    
228      /**
229         \brief convert the current data into lazy data.
230      */
231      ESCRIPT_DLL_API
232      void
233      delaySelf();
234    
235    
236      /**
237       Member access methods.       Member access methods.
238    */    */
239    
# Line 247  class Data { Line 248  class Data {
248    
249    /**    /**
250       \brief       \brief
251       Returns trueif the data object is protected against update       Returns true, if the data object is protected against update
252    
253    */    */
254    ESCRIPT_DLL_API    ESCRIPT_DLL_API
255    bool    bool
256    isProtected() const;    isProtected() const;
257    
258    /**  
259       \brief  /**
260       Return the values of a data point on this process     \brief
261    */     Return the value of a data point as a python tuple.
262    */
263    ESCRIPT_DLL_API    ESCRIPT_DLL_API
264    const boost::python::numeric::array    const boost::python::object
265    getValueOfDataPoint(int dataPointNo);    getValueOfDataPointAsTuple(int dataPointNo);
266    
267    /**    /**
268       \brief       \brief
# Line 272  class Data { Line 274  class Data {
274    
275    /**    /**
276       \brief       \brief
277       sets the values of a data-point from a numarray object on this process       sets the values of a data-point from a array-like object on this process
278    */    */
279    ESCRIPT_DLL_API    ESCRIPT_DLL_API
280    void    void
281    setValueOfDataPointToArray(int dataPointNo, const boost::python::numeric::array&);    setValueOfDataPointToArray(int dataPointNo, const boost::python::object&);
282    
283    /**    /**
284       \brief       \brief
# Line 287  class Data { Line 289  class Data {
289    setValueOfDataPoint(int dataPointNo, const double);    setValueOfDataPoint(int dataPointNo, const double);
290    
291    /**    /**
292       \brief       \brief Return a data point across all processors as a python tuple.
      Return the value of the specified data-point across all processors  
293    */    */
294    ESCRIPT_DLL_API    ESCRIPT_DLL_API
295    const boost::python::numeric::array    const boost::python::object
296    getValueOfGlobalDataPoint(int procNo, int dataPointNo);    getValueOfGlobalDataPointAsTuple(int procNo, int dataPointNo);
297    
298    /**    /**
299       \brief       \brief
300       Return the tag number associated with the given data-point.       Return the tag number associated with the given data-point.
301    
      The data-point number here corresponds to the data-point number in the  
      numarray returned by convertToNumArray.  
302    */    */
303    ESCRIPT_DLL_API    ESCRIPT_DLL_API
304    int    int
# Line 313  class Data { Line 312  class Data {
312    escriptDataC    escriptDataC
313    getDataC();    getDataC();
314    
315    
316    
317    /**    /**
318       \brief       \brief
319       Return the C wrapper for the Data object - const version.       Return the C wrapper for the Data object - const version.
# Line 322  class Data { Line 323  class Data {
323    getDataC() const;    getDataC() const;
324    
325    /**    /**
326       \brief      \brief How much space is required to evaulate a sample of the Data.
      Write the data as a string.  
327    */    */
328    ESCRIPT_DLL_API    ESCRIPT_DLL_API
329    inline    size_t
330    std::string    getSampleBufferSize() const;
331    toString() const  
332    {  
     return m_data->toString();  
   }  
333    
334    /**    /**
335       \brief       \brief
336       Return the DataArrayView of the point data. This essentially contains       Write the data as a string. For large amounts of data, a summary is printed.
      the shape information for each data point although it also may be used  
      to manipulate the point data.  
337    */    */
338    ESCRIPT_DLL_API    ESCRIPT_DLL_API
339    inline    std::string
340    const DataArrayView&    toString() const;
   getPointDataView() const  
   {  
      return m_data->getPointDataView();  
   }  
341    
342    /**    /**
343       \brief       \brief
# Line 360  class Data { Line 352  class Data {
352       If possible convert this Data to DataTagged. This will only allow       If possible convert this Data to DataTagged. This will only allow
353       Constant data to be converted to tagged. An attempt to convert       Constant data to be converted to tagged. An attempt to convert
354       Expanded data to tagged will throw an exception.       Expanded data to tagged will throw an exception.
     ==>*  
355    */    */
356    ESCRIPT_DLL_API    ESCRIPT_DLL_API
357    void    void
358    tag();    tag();
359    
360    /**    /**
361        \brief If this data is lazy, then convert it to ready data.
362        What type of ready data depends on the expression. For example, Constant+Tagged==Tagged.
363      */
364      ESCRIPT_DLL_API
365      void
366      resolve();
367    
368    
369      /**
370       \brief Ensures data is ready for write access.
371      This means that the data will be resolved if lazy and will be copied if shared with another Data object.
372      \warning This method should only be called in single threaded sections of code. (It modifies m_data).
373      Do not create any Data objects from this one between calling requireWrite and getSampleDataRW.
374      Doing so might introduce additional sharing.
375      */
376      ESCRIPT_DLL_API
377      void
378      requireWrite();
379    
380      /**
381       \brief       \brief
382       Return true if this Data is expanded.       Return true if this Data is expanded.
383         \note To determine if a sample will contain separate values for each datapoint. Use actsExpanded instead.
384    */    */
385    ESCRIPT_DLL_API    ESCRIPT_DLL_API
386    bool    bool
# Line 376  class Data { Line 388  class Data {
388    
389    /**    /**
390       \brief       \brief
391         Return true if this Data is expanded or resolves to expanded.
392         That is, if it has a separate value for each datapoint in the sample.
393      */
394      ESCRIPT_DLL_API
395      bool
396      actsExpanded() const;
397      
398    
399      /**
400         \brief
401       Return true if this Data is tagged.       Return true if this Data is tagged.
402    */    */
403    ESCRIPT_DLL_API    ESCRIPT_DLL_API
# Line 391  class Data { Line 413  class Data {
413    isConstant() const;    isConstant() const;
414    
415    /**    /**
416         \brief Return true if this Data is lazy.
417      */
418      ESCRIPT_DLL_API
419      bool
420      isLazy() const;
421    
422      /**
423         \brief Return true if this data is ready.
424      */
425      ESCRIPT_DLL_API
426      bool
427      isReady() const;
428    
429      /**
430       \brief       \brief
431       Return true if this Data is empty.       Return true if this Data holds an instance of DataEmpty. This is _not_ the same as asking if the object
432    contains datapoints.
433    */    */
434    ESCRIPT_DLL_API    ESCRIPT_DLL_API
435    bool    bool
# Line 424  class Data { Line 461  class Data {
461    */    */
462    ESCRIPT_DLL_API    ESCRIPT_DLL_API
463    inline    inline
464    const AbstractDomain&  //   const AbstractDomain&
465      const_Domain_ptr
466    getDomain() const    getDomain() const
467    {    {
468       return getFunctionSpace().getDomain();       return getFunctionSpace().getDomain();
469    }    }
470    
471    
472      /**
473         \brief
474         Return the domain.
475         TODO: For internal use only.   This should be removed.
476      */
477      ESCRIPT_DLL_API
478      inline
479    //   const AbstractDomain&
480      Domain_ptr
481      getDomainPython() const
482      {
483         return getFunctionSpace().getDomainPython();
484      }
485    
486    /**    /**
487       \brief       \brief
488       Return a copy of the domain.       Return a copy of the domain.
# Line 444  class Data { Line 497  class Data {
497    */    */
498    ESCRIPT_DLL_API    ESCRIPT_DLL_API
499    inline    inline
500    int    unsigned int
501    getDataPointRank() const    getDataPointRank() const
502    {    {
503      return m_data->getPointDataView().getRank();      return m_data->getRank();
504    }    }
505    
506    /**    /**
# Line 484  class Data { Line 537  class Data {
537    {    {
538      return m_data->getNumDPPSample();      return m_data->getNumDPPSample();
539    }    }
540    
541    
542      /**
543        \brief
544        Return the number of values in the shape for this object.
545      */
546      ESCRIPT_DLL_API
547      int
548      getNoValues() const
549      {
550        return m_data->getNoValues();
551      }
552    
553    
554    /**    /**
555       \brief       \brief
556       dumps the object into a netCDF file       dumps the object into a netCDF file
# Line 491  class Data { Line 558  class Data {
558    ESCRIPT_DLL_API    ESCRIPT_DLL_API
559    void    void
560    dump(const std::string fileName) const;    dump(const std::string fileName) const;
561    
562     /**
563      \brief returns the values of the object as a list of tuples (one for each datapoint).
564    
565      \param scalarastuple If true, scalar data will produce single valued tuples [(1,) (2,) ...]
566    If false, the result is a list of scalars [1, 2, ...]
567     */
568      ESCRIPT_DLL_API
569      const boost::python::object
570      toListOfTuples(bool scalarastuple=true);
571    
572    
573     /**
574        \brief
575        Return the sample data for the given sample no. This is not the
576        preferred interface but is provided for use by C code.
577        The bufferg parameter is only required for LazyData.
578        \param sampleNo - Input - the given sample no.
579        \param bufferg - A buffer to compute (and store) sample data in will be selected from this group.
580        \return pointer to the sample data.
581    */
582      ESCRIPT_DLL_API
583      inline
584      const DataAbstract::ValueType::value_type*
585      getSampleDataRO(DataAbstract::ValueType::size_type sampleNo, BufferGroup* bufferg=0);
586    
587    
588    /**    /**
589       \brief       \brief
590       Return the sample data for the given sample no. This is not the       Return the sample data for the given sample no. This is not the
591       preferred interface but is provided for use by C code.       preferred interface but is provided for use by C code.
592       \param sampleNo - Input - the given sample no.       \param sampleNo - Input - the given sample no.
593         \return pointer to the sample data.
594    */    */
595    ESCRIPT_DLL_API    ESCRIPT_DLL_API
596    inline    inline
597    DataAbstract::ValueType::value_type*    DataAbstract::ValueType::value_type*
598    getSampleData(DataAbstract::ValueType::size_type sampleNo)    getSampleDataRW(DataAbstract::ValueType::size_type sampleNo);
599    {  
     return m_data->getSampleData(sampleNo);  
   }  
600    
601    /**    /**
602       \brief       \brief
# Line 521  class Data { Line 614  class Data {
614    
615    /**    /**
616       \brief       \brief
617       Return a view into the data for the data point specified.       Return a reference into the DataVector which points to the specified data point.
618       NOTE: Construction of the DataArrayView is a relatively expensive       \param sampleNo - Input -
619       operation.       \param dataPointNo - Input -
620      */
621      ESCRIPT_DLL_API
622      DataTypes::ValueType::const_reference
623      getDataPointRO(int sampleNo, int dataPointNo);
624    
625      /**
626         \brief
627         Return a reference into the DataVector which points to the specified data point.
628       \param sampleNo - Input -       \param sampleNo - Input -
629       \param dataPointNo - Input -       \param dataPointNo - Input -
630    */    */
631    ESCRIPT_DLL_API    ESCRIPT_DLL_API
632      DataTypes::ValueType::reference
633      getDataPointRW(int sampleNo, int dataPointNo);
634    
635    
636    
637      /**
638         \brief
639         Return the offset for the given sample and point within the sample
640      */
641      ESCRIPT_DLL_API
642    inline    inline
643    DataArrayView    DataTypes::ValueType::size_type
644    getDataPoint(int sampleNo,    getDataOffset(int sampleNo,
645                 int dataPointNo)                 int dataPointNo)
646    {    {
647                  return m_data->getDataPoint(sampleNo,dataPointNo);        return m_data->getPointOffset(sampleNo,dataPointNo);
648    }    }
649    
650    /**    /**
# Line 541  class Data { Line 652  class Data {
652       Return a reference to the data point shape.       Return a reference to the data point shape.
653    */    */
654    ESCRIPT_DLL_API    ESCRIPT_DLL_API
655    const DataArrayView::ShapeType&    inline
656    getDataPointShape() const;    const DataTypes::ShapeType&
657      getDataPointShape() const
658      {
659        return m_data->getShape();
660      }
661    
662    /**    /**
663       \brief       \brief
# Line 566  class Data { Line 681  class Data {
681       Return the number of doubles stored for this Data.       Return the number of doubles stored for this Data.
682    */    */
683    ESCRIPT_DLL_API    ESCRIPT_DLL_API
684    DataArrayView::ValueType::size_type    DataTypes::ValueType::size_type
685    getLength() const;    getLength() const;
686    
687      /**
688      \brief Return true if this object contains no samples.
689      This is not the same as isEmpty()
690      */
691      ESCRIPT_DLL_API
692      bool
693      hasNoSamples() const
694      {
695        return getLength()==0;
696      }
697    
698    /**    /**
699       \brief       \brief
700       Assign the given value to the tag assocciated with name. Implicitly converts this       Assign the given value to the tag assocciated with name. Implicitly converts this
701       object to type DataTagged. Throws an exception if this object       object to type DataTagged. Throws an exception if this object
702       cannot be converted to a DataTagged object or name cannot be mapped onto a tag key.       cannot be converted to a DataTagged object or name cannot be mapped onto a tag key.
703       \param tagKey - Input - Integer key.       \param name - Input - name of tag.
704       \param value - Input - Value to associate with given key.       \param value - Input - Value to associate with given key.
     ==>*  
705    */    */
706    ESCRIPT_DLL_API    ESCRIPT_DLL_API
707    void    void
# Line 605  class Data { Line 728  class Data {
728       object to type DataTagged if it is constant.       object to type DataTagged if it is constant.
729    
730       \param tagKey - Input - Integer key.       \param tagKey - Input - Integer key.
731         \param pointshape - Input - The shape of the value parameter
732       \param value - Input - Value to associate with given key.       \param value - Input - Value to associate with given key.
733      ==>*       \param dataOffset - Input - Offset of the begining of the point within the value parameter
734    */    */
735    ESCRIPT_DLL_API    ESCRIPT_DLL_API
736    void    void
737    setTaggedValueFromCPP(int tagKey,    setTaggedValueFromCPP(int tagKey,
738                          const DataArrayView& value);              const DataTypes::ShapeType& pointshape,
739                            const DataTypes::ValueType& value,
740                int dataOffset=0);
741    
742    
743    
744    /**    /**
745      \brief      \brief
# Line 644  class Data { Line 772  class Data {
772    ESCRIPT_DLL_API    ESCRIPT_DLL_API
773    Data    Data
774    interpolate(const FunctionSpace& functionspace) const;    interpolate(const FunctionSpace& functionspace) const;
775    
776    
777      ESCRIPT_DLL_API
778      Data
779      interpolateFromTable2D(const WrappedArray& table, double Amin, double Astep,
780                           double undef, Data& B, double Bmin, double Bstep,bool check_boundaries);
781    
782      ESCRIPT_DLL_API
783      Data
784      interpolateFromTable1D(const WrappedArray& table, double Amin, double Astep,
785                           double undef,bool check_boundaries);
786    
787    
788    
789    
790      ESCRIPT_DLL_API
791      Data
792      interpolateFromTable2DP(boost::python::object table, double Amin, double Astep,
793                            Data& B, double Bmin, double Bstep, double undef,bool check_boundaries);
794    
795      ESCRIPT_DLL_API
796      Data
797      interpolateFromTable1DP(boost::python::object table, double Amin, double Astep,
798                            double undef,bool check_boundaries);
799    
800    /**    /**
801       \brief       \brief
802       Calculates the gradient of the data at the data points of functionspace.       Calculates the gradient of the data at the data points of functionspace.
# Line 659  class Data { Line 812  class Data {
812    grad() const;    grad() const;
813    
814    /**    /**
815       \brief      \brief
816       Calculate the integral over the function space domain.       Calculate the integral over the function space domain as a python tuple.
817       *    */
818      ESCRIPT_DLL_API
819      boost::python::object
820      integrateToTuple_const() const;
821    
822    
823      /**
824        \brief
825         Calculate the integral over the function space domain as a python tuple.
826    */    */
827    ESCRIPT_DLL_API    ESCRIPT_DLL_API
828    boost::python::numeric::array    boost::python::object
829    integrate() const;    integrateToTuple();
830    
831    
832    
833    /**    /**
834       \brief       \brief
# Line 732  class Data { Line 895  class Data {
895    /**    /**
896       \brief       \brief
897       Return the maximum absolute value of this Data object.       Return the maximum absolute value of this Data object.
898       *  
899         The method is not const because lazy data needs to be expanded before Lsup can be computed.
900         The _const form can be used when the Data object is const, however this will only work for
901         Data which is not Lazy.
902    
903         For Data which contain no samples (or tagged Data for which no tags in use have a value)
904         zero is returned.
905    */    */
906    ESCRIPT_DLL_API    ESCRIPT_DLL_API
907    double    double
908    Lsup() const;    Lsup();
909    
910      ESCRIPT_DLL_API
911      double
912      Lsup_const() const;
913    
914    
915    /**    /**
916       \brief       \brief
917       Return the maximum value of this Data object.       Return the maximum value of this Data object.
918       *  
919         The method is not const because lazy data needs to be expanded before sup can be computed.
920         The _const form can be used when the Data object is const, however this will only work for
921         Data which is not Lazy.
922    
923         For Data which contain no samples (or tagged Data for which no tags in use have a value)
924         a large negative value is returned.
925    */    */
926    ESCRIPT_DLL_API    ESCRIPT_DLL_API
927    double    double
928    sup() const;    sup();
929    
930      ESCRIPT_DLL_API
931      double
932      sup_const() const;
933    
934    
935    /**    /**
936       \brief       \brief
937       Return the minimum value of this Data object.       Return the minimum value of this Data object.
938       *  
939         The method is not const because lazy data needs to be expanded before inf can be computed.
940         The _const form can be used when the Data object is const, however this will only work for
941         Data which is not Lazy.
942    
943         For Data which contain no samples (or tagged Data for which no tags in use have a value)
944         a large positive value is returned.
945    */    */
946    ESCRIPT_DLL_API    ESCRIPT_DLL_API
947    double    double
948    inf() const;    inf();
949    
950      ESCRIPT_DLL_API
951      double
952      inf_const() const;
953    
954    
955    
956    /**    /**
957       \brief       \brief
# Line 786  class Data { Line 983  class Data {
983    /**    /**
984       \brief       \brief
985       Return the (sample number, data-point number) of the data point with       Return the (sample number, data-point number) of the data point with
986       the minimum value in this Data object.       the minimum component value in this Data object.
987         \note If you are working in python, please consider using Locator
988    instead of manually manipulating process and point IDs.
989    */    */
990    ESCRIPT_DLL_API    ESCRIPT_DLL_API
991    const boost::python::tuple    const boost::python::tuple
992    minGlobalDataPoint() const;    minGlobalDataPoint() const;
993    
994      /**
995         \brief
996         Return the (sample number, data-point number) of the data point with
997         the minimum component value in this Data object.
998         \note If you are working in python, please consider using Locator
999    instead of manually manipulating process and point IDs.
1000      */
1001    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1002    void    const boost::python::tuple
1003    calc_minGlobalDataPoint(int& ProcNo,  int& DataPointNo) const;    maxGlobalDataPoint() const;
1004    
1005    
1006    
1007    /**    /**
1008       \brief       \brief
1009       Return the sign of each data point of this Data object.       Return the sign of each data point of this Data object.
# Line 1095  class Data { Line 1304  class Data {
1304    void    void
1305    saveVTK(std::string fileName) const;    saveVTK(std::string fileName) const;
1306    
1307    
1308    
1309    /**    /**
1310       \brief       \brief
1311       Overloaded operator +=       Overloaded operator +=
# Line 1212  class Data { Line 1423  class Data {
1423    */    */
1424    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1425    Data    Data
1426    getSlice(const DataArrayView::RegionType& region) const;    getSlice(const DataTypes::RegionType& region) const;
1427    
1428    /**    /**
1429       \brief       \brief
# Line 1225  class Data { Line 1436  class Data {
1436    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1437    void    void
1438    setSlice(const Data& value,    setSlice(const Data& value,
1439             const DataArrayView::RegionType& region);             const DataTypes::RegionType& region);
   
   /**  
      \brief  
      Archive the current Data object to the given file.  
      \param fileName - Input - file to archive to.  
   */  
   ESCRIPT_DLL_API  
   void  
   archiveData(const std::string fileName);  
   
   /**  
      \brief  
      Extract the Data object archived in the given file, overwriting  
      the current Data object.  
      Note - the current object must be of type DataEmpty.  
      \param fileName - Input - file to extract from.  
      \param fspace - Input - a suitable FunctionSpace descibing the data.  
   */  
   ESCRIPT_DLL_API  
   void  
   extractData(const std::string fileName,  
               const FunctionSpace& fspace);  
   
1440    
1441    /**    /**
1442       \brief       \brief
# Line 1290  class Data { Line 1478  class Data {
1478    /**    /**
1479       \brief       \brief
1480       return the object produced by the factory, which is a DataConstant or DataExpanded       return the object produced by the factory, which is a DataConstant or DataExpanded
1481        TODO Ownership of this object should be explained in doco.
1482    */    */
1483    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1484          DataAbstract*          DataAbstract*
1485          borrowData(void) const;          borrowData(void) const;
1486    
1487      ESCRIPT_DLL_API
1488            DataAbstract_ptr
1489            borrowDataPtr(void) const;
1490    
1491      ESCRIPT_DLL_API
1492            DataReady_ptr
1493            borrowReadyPtr(void) const;
1494    
1495    
1496    
1497      /**
1498         \brief
1499         Return a pointer to the beginning of the datapoint at the specified offset.
1500         TODO Eventually these should be inlined.
1501         \param i - position(offset) in the underlying datastructure
1502      */
1503    
1504      ESCRIPT_DLL_API
1505            DataTypes::ValueType::const_reference
1506            getDataAtOffsetRO(DataTypes::ValueType::size_type i);
1507    
1508    
1509      ESCRIPT_DLL_API
1510            DataTypes::ValueType::reference
1511            getDataAtOffsetRW(DataTypes::ValueType::size_type i);
1512    
1513    
1514    
1515    /**
1516       \brief Create a buffer for use by getSample
1517       Allocates a DataVector large enough for DataLazy::resolveSample to operate on for the current Data.
1518       Do not use this buffer for other Data instances (unless you are sure they will be the same size).
1519      
1520       In multi-threaded sections, this needs to be called on each thread.
1521    
1522       \return A BufferGroup* if Data is lazy, NULL otherwise.
1523       \warning This pointer must be deallocated using freeSampleBuffer to avoid cross library memory issues.
1524    */
1525      ESCRIPT_DLL_API
1526      BufferGroup*
1527      allocSampleBuffer() const;
1528    
1529    /**
1530       \brief Free a buffer allocated with allocSampleBuffer.
1531       \param buffer Input - pointer to the buffer to deallocate.
1532    */
1533    ESCRIPT_DLL_API void freeSampleBuffer(BufferGroup* buffer);
1534    
1535   protected:   protected:
1536    
1537   private:   private:
1538    
1539    template <class BinaryOp>
1540      double
1541    #ifdef PASO_MPI
1542      lazyAlgWorker(double init, MPI_Op mpiop_type);
1543    #else
1544      lazyAlgWorker(double init);
1545    #endif
1546    
1547      double
1548      LsupWorker() const;
1549    
1550      double
1551      supWorker() const;
1552    
1553      double
1554      infWorker() const;
1555    
1556      boost::python::object
1557      integrateWorker() const;
1558    
1559      void
1560      calc_minGlobalDataPoint(int& ProcNo,  int& DataPointNo) const;
1561    
1562      void
1563      calc_maxGlobalDataPoint(int& ProcNo,  int& DataPointNo) const;
1564    
1565    
1566    /**    /**
1567       \brief       \brief
1568       Check *this and the right operand are compatible. Throws       Check *this and the right operand are compatible. Throws
# Line 1370  class Data { Line 1634  class Data {
1634       \brief       \brief
1635       Construct a Data object of the appropriate type.       Construct a Data object of the appropriate type.
1636    */    */
1637    template <class IValueType>  
1638    void    void
1639    initialise(const IValueType& value,    initialise(const DataTypes::ValueType& value,
1640             const DataTypes::ShapeType& shape,
1641               const FunctionSpace& what,               const FunctionSpace& what,
1642               bool expanded);               bool expanded);
1643    
1644      void
1645      initialise(const WrappedArray& value,
1646                     const FunctionSpace& what,
1647                     bool expanded);
1648    
1649    //    //
1650    // flag to protect the data object against any update    // flag to protect the data object against any update
1651    bool m_protected;    bool m_protected;
1652      mutable bool m_shared;
1653      bool m_lazy;
1654    
1655    //    //
1656    // pointer to the actual data object    // pointer to the actual data object
1657    boost::shared_ptr<DataAbstract> m_data;  //   boost::shared_ptr<DataAbstract> m_data;
1658      DataAbstract_ptr m_data;
1659    
1660  };  // If possible please use getReadyPtr instead.
1661    // But see warning below.
1662      const DataReady*
1663      getReady() const;
1664    
1665  template <class IValueType>    DataReady*
1666  void    getReady();
1667  Data::initialise(const IValueType& value,  
1668                   const FunctionSpace& what,  
1669                   bool expanded)  // Be wary of using this for local operations since it (temporarily) increases reference count.
1670  {  // If you are just using this to call a method on DataReady instead of DataAbstract consider using
1671    // getReady() instead
1672      DataReady_ptr
1673      getReadyPtr();
1674    
1675      const_DataReady_ptr
1676      getReadyPtr() const;
1677    
1678    
1679      /**
1680       \brief Update the Data's shared flag
1681       This indicates that the DataAbstract used by this object is now shared (or no longer shared).
1682       For internal use only.
1683      */
1684      void updateShareStatus(bool nowshared) const
1685      {
1686        m_shared=nowshared;     // m_shared is mutable
1687      }
1688    
1689      // In the isShared() method below:
1690      // A problem would occur if m_data (the address pointed to) were being modified
1691      // while the call m_data->is_shared is being executed.
1692      //
1693      // Q: So why do I think this code can be thread safe/correct?
1694      // A: We need to make some assumptions.
1695      // 1. We assume it is acceptable to return true under some conditions when we aren't shared.
1696      // 2. We assume that no constructions or assignments which will share previously unshared
1697      //    will occur while this call is executing. This is consistent with the way Data:: and C are written.
1698    //    //
1699    // Construct a Data object of the appropriate type.    // This means that the only transition we need to consider, is when a previously shared object is
1700    // Construct the object first as there seems to be a bug which causes    // not shared anymore. ie. the other objects have been destroyed or a deep copy has been made.
1701    // undefined behaviour if an exception is thrown during construction    // In those cases the m_shared flag changes to false after m_data has completed changing.
1702    // within the shared_ptr constructor.    // For any threads executing before the flag switches they will assume the object is still shared.
1703    if (expanded) {    bool isShared() const
1704      DataAbstract* temp=new DataExpanded(value,what);    {
1705      boost::shared_ptr<DataAbstract> temp_data(temp);      return m_shared;
1706      m_data=temp_data;  /*  if (m_shared) return true;
1707    } else {      if (m_data->isShared())        
1708      DataAbstract* temp=new DataConstant(value,what);      {                  
1709      boost::shared_ptr<DataAbstract> temp_data(temp);          updateShareStatus(true);
1710      m_data=temp_data;          return true;
1711        }
1712        return false;*/
1713    }    }
1714    
1715      void forceResolve()
1716      {
1717        if (isLazy())
1718        {
1719            #ifdef _OPENMP
1720            if (omp_in_parallel())
1721            {   // Yes this is throwing an exception out of an omp thread which is forbidden.
1722            throw DataException("Please do not call forceResolve() in a parallel region.");
1723            }
1724            #endif
1725            resolve();
1726        }
1727      }
1728    
1729      /**
1730      \brief if another object is sharing out member data make a copy to work with instead.
1731      This code should only be called from single threaded sections of code.
1732      */
1733      void exclusiveWrite()
1734      {
1735    #ifdef _OPENMP
1736      if (omp_in_parallel())
1737      {
1738    // *((int*)0)=17;
1739        throw DataException("Programming error. Please do not run exclusiveWrite() in multi-threaded sections.");
1740      }
1741    #endif
1742        forceResolve();
1743        if (isShared())
1744        {
1745            DataAbstract* t=m_data->deepCopy();
1746            set_m_data(DataAbstract_ptr(t));
1747        }
1748      }
1749    
1750      /**
1751      \brief checks if caller can have exclusive write to the object
1752      */
1753      void checkExclusiveWrite()
1754      {
1755        if  (isLazy() || isShared())
1756        {
1757            throw DataException("Programming error. ExclusiveWrite required - please call requireWrite()");
1758        }
1759      }
1760    
1761      /**
1762      \brief Modify the data abstract hosted by this Data object
1763      For internal use only.
1764      Passing a pointer to null is permitted (do this in the destructor)
1765      \warning Only to be called in single threaded code or inside a single/critical section. This method needs to be atomic.
1766      */
1767      void set_m_data(DataAbstract_ptr p);
1768    
1769      friend class DataAbstract;        // To allow calls to updateShareStatus
1770    
1771    };
1772    
1773    }   // end namespace escript
1774    
1775    
1776    // No, this is not supposed to be at the top of the file
1777    // DataAbstact needs to be declared first, then DataReady needs to be fully declared
1778    // so that I can dynamic cast between them below.
1779    #include "DataReady.h"
1780    #include "DataLazy.h"
1781    
1782    namespace escript
1783    {
1784    
1785    inline
1786    const DataReady*
1787    Data::getReady() const
1788    {
1789       const DataReady* dr=dynamic_cast<const DataReady*>(m_data.get());
1790       EsysAssert((dr!=0), "Error - casting to DataReady.");
1791       return dr;
1792    }
1793    
1794    inline
1795    DataReady*
1796    Data::getReady()
1797    {
1798       DataReady* dr=dynamic_cast<DataReady*>(m_data.get());
1799       EsysAssert((dr!=0), "Error - casting to DataReady.");
1800       return dr;
1801    }
1802    
1803    // Be wary of using this for local operations since it (temporarily) increases reference count.
1804    // If you are just using this to call a method on DataReady instead of DataAbstract consider using
1805    // getReady() instead
1806    inline
1807    DataReady_ptr
1808    Data::getReadyPtr()
1809    {
1810       DataReady_ptr dr=boost::dynamic_pointer_cast<DataReady>(m_data);
1811       EsysAssert((dr.get()!=0), "Error - casting to DataReady.");
1812       return dr;
1813    }
1814    
1815    
1816    inline
1817    const_DataReady_ptr
1818    Data::getReadyPtr() const
1819    {
1820       const_DataReady_ptr dr=boost::dynamic_pointer_cast<const DataReady>(m_data);
1821       EsysAssert((dr.get()!=0), "Error - casting to DataReady.");
1822       return dr;
1823  }  }
1824    
1825    inline
1826    DataAbstract::ValueType::value_type*
1827    Data::getSampleDataRW(DataAbstract::ValueType::size_type sampleNo)
1828    {
1829       if (isLazy())
1830       {
1831        throw DataException("Error, attempt to acquire RW access to lazy data. Please call requireWrite() first.");
1832       }
1833       return getReady()->getSampleDataRW(sampleNo);
1834    }
1835    
1836    inline
1837    const DataAbstract::ValueType::value_type*
1838    Data::getSampleDataRO(DataAbstract::ValueType::size_type sampleNo, BufferGroup* bufferg)
1839    {
1840       DataLazy* l=dynamic_cast<DataLazy*>(m_data.get());
1841       if (l!=0)
1842       {
1843        size_t offset=0;
1844        if (bufferg==NULL)
1845        {
1846            throw DataException("Error, attempt to getSampleDataRO for lazy Data with buffer==NULL");
1847        }
1848        const DataTypes::ValueType* res=l->resolveSample(*bufferg,sampleNo,offset);
1849        return &((*res)[offset]);
1850       }
1851       return getReady()->getSampleDataRO(sampleNo);
1852    }
1853    
1854    
1855    
1856    /**
1857       Modify a filename for MPI parallel output to multiple files
1858    */
1859    char *Escript_MPI_appendRankToFileName(const char *, int, int);
1860    
1861  /**  /**
1862     Binary Data object operators.     Binary Data object operators.
1863  */  */
# Line 1519  ESCRIPT_DLL_API std::ostream& operator<< Line 1969  ESCRIPT_DLL_API std::ostream& operator<<
1969  /**  /**
1970    \brief    \brief
1971    Compute a tensor product of two Data objects    Compute a tensor product of two Data objects
1972    \param arg0 - Input - Data object    \param arg_0 - Input - Data object
1973    \param arg1 - Input - Data object    \param arg_1 - Input - Data object
1974    \param axis_offset - Input - axis offset    \param axis_offset - Input - axis offset
1975    \param transpose - Input - 0: transpose neither, 1: transpose arg0, 2: transpose arg1    \param transpose - Input - 0: transpose neither, 1: transpose arg0, 2: transpose arg1
1976  */  */
1977  ESCRIPT_DLL_API  ESCRIPT_DLL_API
1978  Data  Data
1979  C_GeneralTensorProduct(Data& arg0,  C_GeneralTensorProduct(Data& arg_0,
1980                       Data& arg1,                       Data& arg_1,
1981                       int axis_offset=0,                       int axis_offset=0,
1982                       int transpose=0);                       int transpose=0);
1983    
   
 /**  
   \brief  
   Compute a tensor operation with two Data objects  
   \param arg0 - Input - Data object  
   \param arg1 - Input - Data object  
   \param operation - Input - Binary op functor  
 */  
 template <typename BinaryFunction>  
 ESCRIPT_DLL_API  
 Data  
 C_TensorBinaryOperation(Data const &arg0,  
                         Data const &arg1,  
                         BinaryFunction operation);  
   
 /**  
   \brief  
   Return true if operands are equivalent, else return false.  
   NB: this operator does very little at this point, and isn't to  
   be relied on. Requires further implementation.  
 */  
 // ESCRIPT_DLL_API bool operator==(const Data& left, const Data& right);  
   
1984  /**  /**
1985    \brief    \brief
1986    Perform the given binary operation with this and right as operands.    Perform the given binary operation with this and right as operands.
# Line 1567  Data::binaryOp(const Data& right, Line 1994  Data::binaryOp(const Data& right,
1994  {  {
1995     //     //
1996     // if this has a rank of zero promote it to the rank of the RHS     // if this has a rank of zero promote it to the rank of the RHS
1997     if (getPointDataView().getRank()==0 && right.getPointDataView().getRank()!=0) {     if (getDataPointRank()==0 && right.getDataPointRank()!=0) {
1998       throw DataException("Error - attempt to update rank zero object with object with rank bigger than zero.");       throw DataException("Error - attempt to update rank zero object with object with rank bigger than zero.");
1999     }     }
2000    
2001       if (isLazy() || right.isLazy())
2002       {
2003         throw DataException("Programmer error - attempt to call binaryOp with Lazy Data.");
2004       }
2005     //     //
2006     // initially make the temporary a shallow copy     // initially make the temporary a shallow copy
2007     Data tempRight(right);     Data tempRight(right);
2008    
2009     if (getFunctionSpace()!=right.getFunctionSpace()) {     if (getFunctionSpace()!=right.getFunctionSpace()) {
2010       if (right.probeInterpolation(getFunctionSpace())) {       if (right.probeInterpolation(getFunctionSpace())) {
2011         //         //
# Line 1582  Data::binaryOp(const Data& right, Line 2015  Data::binaryOp(const Data& right,
2015         //         //
2016         // interpolate onto the RHS function space         // interpolate onto the RHS function space
2017         Data tempLeft(*this,right.getFunctionSpace());         Data tempLeft(*this,right.getFunctionSpace());
2018         m_data=tempLeft.m_data;  //        m_data=tempLeft.m_data;
2019           set_m_data(tempLeft.m_data);
2020       }       }
2021     }     }
2022     operandCheck(tempRight);     operandCheck(tempRight);
# Line 1598  Data::binaryOp(const Data& right, Line 2032  Data::binaryOp(const Data& right,
2032       // of any data type       // of any data type
2033       DataExpanded* leftC=dynamic_cast<DataExpanded*>(m_data.get());       DataExpanded* leftC=dynamic_cast<DataExpanded*>(m_data.get());
2034       EsysAssert((leftC!=0), "Programming error - casting to DataExpanded.");       EsysAssert((leftC!=0), "Programming error - casting to DataExpanded.");
2035       escript::binaryOp(*leftC,*(tempRight.m_data.get()),operation);       escript::binaryOp(*leftC,*(tempRight.getReady()),operation);
2036     } else if (isTagged()) {     } else if (isTagged()) {
2037       //       //
2038       // Tagged data is operated on serially, the right hand side can be       // Tagged data is operated on serially, the right hand side can be
# Line 1646  Data::algorithm(BinaryFunction operation Line 2080  Data::algorithm(BinaryFunction operation
2080      DataConstant* leftC=dynamic_cast<DataConstant*>(m_data.get());      DataConstant* leftC=dynamic_cast<DataConstant*>(m_data.get());
2081      EsysAssert((leftC!=0), "Programming error - casting to DataConstant.");      EsysAssert((leftC!=0), "Programming error - casting to DataConstant.");
2082      return escript::algorithm(*leftC,operation,initial_value);      return escript::algorithm(*leftC,operation,initial_value);
2083      } else if (isEmpty()) {
2084        throw DataException("Error - Operations not permitted on instances of DataEmpty.");
2085      } else if (isLazy()) {
2086        throw DataException("Error - Operations not permitted on instances of DataLazy.");
2087      } else {
2088        throw DataException("Error - Data encapsulates an unknown type.");
2089    }    }
   return 0;  
2090  }  }
2091    
2092  /**  /**
# Line 1663  inline Line 2102  inline
2102  Data  Data
2103  Data::dp_algorithm(BinaryFunction operation, double initial_value) const  Data::dp_algorithm(BinaryFunction operation, double initial_value) const
2104  {  {
2105    if (isExpanded()) {    if (isEmpty()) {
2106      Data result(0,DataArrayView::ShapeType(),getFunctionSpace(),isExpanded());      throw DataException("Error - Operations not permitted on instances of DataEmpty.");
2107      }
2108      else if (isExpanded()) {
2109        Data result(0,DataTypes::ShapeType(),getFunctionSpace(),isExpanded());
2110      DataExpanded* dataE=dynamic_cast<DataExpanded*>(m_data.get());      DataExpanded* dataE=dynamic_cast<DataExpanded*>(m_data.get());
2111      DataExpanded* resultE=dynamic_cast<DataExpanded*>(result.m_data.get());      DataExpanded* resultE=dynamic_cast<DataExpanded*>(result.m_data.get());
2112      EsysAssert((dataE!=0), "Programming error - casting data to DataExpanded.");      EsysAssert((dataE!=0), "Programming error - casting data to DataExpanded.");
2113      EsysAssert((resultE!=0), "Programming error - casting result to DataExpanded.");      EsysAssert((resultE!=0), "Programming error - casting result to DataExpanded.");
2114      escript::dp_algorithm(*dataE,*resultE,operation,initial_value);      escript::dp_algorithm(*dataE,*resultE,operation,initial_value);
2115      return result;      return result;
2116    } else if (isTagged()) {    }
2117      else if (isTagged()) {
2118      DataTagged* dataT=dynamic_cast<DataTagged*>(m_data.get());      DataTagged* dataT=dynamic_cast<DataTagged*>(m_data.get());
     DataArrayView::ShapeType viewShape;  
     DataArrayView::ValueType viewData(1);  
     viewData[0]=0;  
     DataArrayView defaultValue(viewData,viewShape);  
     DataTagged::TagListType keys;  
     DataTagged::ValueListType values;  
     DataTagged::DataMapType::const_iterator i;  
     for (i=dataT->getTagLookup().begin();i!=dataT->getTagLookup().end();i++) {  
       keys.push_back(i->first);  
       values.push_back(defaultValue);  
     }  
     Data result(keys,values,defaultValue,getFunctionSpace());  
     DataTagged* resultT=dynamic_cast<DataTagged*>(result.m_data.get());  
2119      EsysAssert((dataT!=0), "Programming error - casting data to DataTagged.");      EsysAssert((dataT!=0), "Programming error - casting data to DataTagged.");
2120      EsysAssert((resultT!=0), "Programming error - casting result to DataTagged.");      DataTypes::ValueType defval(1);
2121        defval[0]=0;
2122        DataTagged* resultT=new DataTagged(getFunctionSpace(), DataTypes::scalarShape, defval, dataT);
2123      escript::dp_algorithm(*dataT,*resultT,operation,initial_value);      escript::dp_algorithm(*dataT,*resultT,operation,initial_value);
2124      return result;      return Data(resultT);   // note: the Data object now owns the resultT pointer
2125    } else if (isConstant()) {    }
2126      Data result(0,DataArrayView::ShapeType(),getFunctionSpace(),isExpanded());    else if (isConstant()) {
2127        Data result(0,DataTypes::ShapeType(),getFunctionSpace(),isExpanded());
2128      DataConstant* dataC=dynamic_cast<DataConstant*>(m_data.get());      DataConstant* dataC=dynamic_cast<DataConstant*>(m_data.get());
2129      DataConstant* resultC=dynamic_cast<DataConstant*>(result.m_data.get());      DataConstant* resultC=dynamic_cast<DataConstant*>(result.m_data.get());
2130      EsysAssert((dataC!=0), "Programming error - casting data to DataConstant.");      EsysAssert((dataC!=0), "Programming error - casting data to DataConstant.");
2131      EsysAssert((resultC!=0), "Programming error - casting result to DataConstant.");      EsysAssert((resultC!=0), "Programming error - casting result to DataConstant.");
2132      escript::dp_algorithm(*dataC,*resultC,operation,initial_value);      escript::dp_algorithm(*dataC,*resultC,operation,initial_value);
2133      return result;      return result;
2134      } else if (isLazy()) {
2135        throw DataException("Error - Operations not permitted on instances of DataLazy.");
2136      } else {
2137        throw DataException("Error - Data encapsulates an unknown type.");
2138    }    }
   Data falseRetVal; // to keep compiler quiet  
   return falseRetVal;  
2139  }  }
2140    
2141    /**
2142      \brief
2143      Compute a tensor operation with two Data objects
2144      \param arg_0 - Input - Data object
2145      \param arg_1 - Input - Data object
2146      \param operation - Input - Binary op functor
2147    */
2148  template <typename BinaryFunction>  template <typename BinaryFunction>
2149    inline
2150  Data  Data
2151  C_TensorBinaryOperation(Data const &arg_0,  C_TensorBinaryOperation(Data const &arg_0,
2152                          Data const &arg_1,                          Data const &arg_1,
2153                          BinaryFunction operation)                          BinaryFunction operation)
2154  {  {
2155      if (arg_0.isEmpty() || arg_1.isEmpty())
2156      {
2157         throw DataException("Error - Operations not permitted on instances of DataEmpty.");
2158      }
2159      if (arg_0.isLazy() || arg_1.isLazy())
2160      {
2161         throw DataException("Error - Operations not permitted on lazy data.");
2162      }
2163    // Interpolate if necessary and find an appropriate function space    // Interpolate if necessary and find an appropriate function space
2164    Data arg_0_Z, arg_1_Z;    Data arg_0_Z, arg_1_Z;
2165    if (arg_0.getFunctionSpace()!=arg_1.getFunctionSpace()) {    if (arg_0.getFunctionSpace()!=arg_1.getFunctionSpace()) {
# Line 1730  C_TensorBinaryOperation(Data const &arg_ Line 2181  C_TensorBinaryOperation(Data const &arg_
2181    // Get rank and shape of inputs    // Get rank and shape of inputs
2182    int rank0 = arg_0_Z.getDataPointRank();    int rank0 = arg_0_Z.getDataPointRank();
2183    int rank1 = arg_1_Z.getDataPointRank();    int rank1 = arg_1_Z.getDataPointRank();
2184    DataArrayView::ShapeType shape0 = arg_0_Z.getDataPointShape();    DataTypes::ShapeType shape0 = arg_0_Z.getDataPointShape();
2185    DataArrayView::ShapeType shape1 = arg_1_Z.getDataPointShape();    DataTypes::ShapeType shape1 = arg_1_Z.getDataPointShape();
2186    int size0 = arg_0_Z.getDataPointSize();    int size0 = arg_0_Z.getDataPointSize();
2187    int size1 = arg_1_Z.getDataPointSize();    int size1 = arg_1_Z.getDataPointSize();
   
2188    // Declare output Data object    // Declare output Data object
2189    Data res;    Data res;
2190    
2191    if (shape0 == shape1) {    if (shape0 == shape1) {
   
2192      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2193        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output
2194        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2195        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[0]);        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2196        double *ptr_2 = &((res.getPointDataView().getData())[0]);        double *ptr_2 = &(res.getDataAtOffsetRW(0));
2197    
2198        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2199      }      }
2200      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
# Line 1762  C_TensorBinaryOperation(Data const &arg_ Line 2212  C_TensorBinaryOperation(Data const &arg_
2212    
2213        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2214        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2215        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2216        // Get the views  
       DataArrayView view_1 = tmp_1->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2217        // Get the pointers to the actual data        // Get the pointers to the actual data
2218        double *ptr_1 = &((view_1.getData())[0]);        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2219        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2220    
2221        // Compute a result for the default        // Compute a result for the default
2222        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2223        // Compute a result for each tag        // Compute a result for each tag
2224        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2225        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2226        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2227          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2228          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2229          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2230          double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2231          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2232        }        }
2233    
2234      }      }
2235      else if (arg_0_Z.isConstant()   && arg_1_Z.isExpanded()) {      else if (arg_0_Z.isConstant()   && arg_1_Z.isExpanded()) {
   
2236        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2237        DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());        DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2238        DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());        DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
# Line 1795  C_TensorBinaryOperation(Data const &arg_ Line 2242  C_TensorBinaryOperation(Data const &arg_
2242        int numSamples_1 = arg_1_Z.getNumSamples();        int numSamples_1 = arg_1_Z.getNumSamples();
2243        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2244        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2245          res.requireWrite();
2246        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2247        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2248          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2249            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2250            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2251            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2252            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2253            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2254            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2255          }          }
2256        }        }
2257    
2258      }      }
2259      else if (arg_0_Z.isTagged()     && arg_1_Z.isConstant()) {      else if (arg_0_Z.isTagged()     && arg_1_Z.isConstant()) {
   
2260        // Borrow DataTagged input from Data object        // Borrow DataTagged input from Data object
2261        DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());        DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2262    
# Line 1823  C_TensorBinaryOperation(Data const &arg_ Line 2270  C_TensorBinaryOperation(Data const &arg_
2270    
2271        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2272        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2273        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);  
2274        // Get the views        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2275        // Get the pointers to the actual data        // Get the pointers to the actual data
2276        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2277        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2278        // Compute a result for the default        // Compute a result for the default
2279        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2280        // Compute a result for each tag        // Compute a result for each tag
2281        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2282        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2283        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2284          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2285          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2286          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
         double *ptr_0 = &view_0.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2287          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2288        }        }
2289    
2290      }      }
2291      else if (arg_0_Z.isTagged()     && arg_1_Z.isTagged()) {      else if (arg_0_Z.isTagged()     && arg_1_Z.isTagged()) {
   
2292        // Borrow DataTagged input from Data object        // Borrow DataTagged input from Data object
2293        DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());        DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2294    
# Line 1858  C_TensorBinaryOperation(Data const &arg_ Line 2300  C_TensorBinaryOperation(Data const &arg_
2300        res.tag();        // DataTagged output        res.tag();        // DataTagged output
2301        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2302    
       // Get the views  
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_1 = tmp_1->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2303        // Get the pointers to the actual data        // Get the pointers to the actual data
2304        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2305        double *ptr_1 = &((view_1.getData())[0]);        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2306        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2307    
2308        // Compute a result for the default        // Compute a result for the default
2309        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2310        // Merge the tags        // Merge the tags
# Line 1873  C_TensorBinaryOperation(Data const &arg_ Line 2312  C_TensorBinaryOperation(Data const &arg_
2312        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2313        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2314        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2315          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue()); // use tmp_2 to get correct shape          tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2316        }        }
2317        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2318          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2319        }        }
2320        // Compute a result for each tag        // Compute a result for each tag
2321        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2322        for (i=lookup_2.begin();i!=lookup_2.end();i++) {        for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2323          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);  
2324          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2325          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2326          double *ptr_0 = &view_0.getData(0);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2327          double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2328          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2329        }        }
2330    
2331      }      }
2332      else if (arg_0_Z.isTagged()     && arg_1_Z.isExpanded()) {      else if (arg_0_Z.isTagged()     && arg_1_Z.isExpanded()) {
   
2333        // After finding a common function space above the two inputs have the same numSamples and num DPPS        // After finding a common function space above the two inputs have the same numSamples and num DPPS
2334        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2335        DataTagged*   tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());        DataTagged*   tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
# Line 1902  C_TensorBinaryOperation(Data const &arg_ Line 2339  C_TensorBinaryOperation(Data const &arg_
2339        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2340        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2341        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2342          res.requireWrite();
2343        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2344        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2345          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0
2346          double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2347          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2348            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2349            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2350            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2351            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2352            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2353          }          }
2354        }        }
2355    
2356      }      }
2357      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {
   
2358        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2359        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2360        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
# Line 1927  C_TensorBinaryOperation(Data const &arg_ Line 2364  C_TensorBinaryOperation(Data const &arg_
2364        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2365        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2366        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2367          res.requireWrite();
2368        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2369        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2370          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2371            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2372            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2373            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);  
2374            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2375            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2376              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2377    
2378    
2379            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2380          }          }
2381        }        }
2382    
2383      }      }
2384      else if (arg_0_Z.isExpanded()   && arg_1_Z.isTagged()) {      else if (arg_0_Z.isExpanded()   && arg_1_Z.isTagged()) {
   
2385        // After finding a common function space above the two inputs have the same numSamples and num DPPS        // After finding a common function space above the two inputs have the same numSamples and num DPPS
2386        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2387        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
# Line 1951  C_TensorBinaryOperation(Data const &arg_ Line 2391  C_TensorBinaryOperation(Data const &arg_
2391        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2392        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2393        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2394          res.requireWrite();
2395        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2396        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2397          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2398          double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2399          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2400            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2401            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2402            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2403            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2404            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2405          }          }
2406        }        }
2407    
2408      }      }
2409      else if (arg_0_Z.isExpanded()   && arg_1_Z.isExpanded()) {      else if (arg_0_Z.isExpanded()   && arg_1_Z.isExpanded()) {
   
2410        // After finding a common function space above the two inputs have the same numSamples and num DPPS        // After finding a common function space above the two inputs have the same numSamples and num DPPS
2411        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2412        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
# Line 1976  C_TensorBinaryOperation(Data const &arg_ Line 2416  C_TensorBinaryOperation(Data const &arg_
2416        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2417        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2418        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2419          res.requireWrite();
2420        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2421        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2422          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {        dataPointNo_0=0;
2423    //        for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2424            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2425            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2426            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2427            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2428            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2429            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2430            tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);            tensor_binary_operation(size0*numDataPointsPerSample_0, ptr_0, ptr_1, ptr_2, operation);
2431          }  //       }
2432        }        }
2433    
2434      }      }
# Line 1995  C_TensorBinaryOperation(Data const &arg_ Line 2437  C_TensorBinaryOperation(Data const &arg_
2437      }      }
2438    
2439    } else if (0 == rank0) {    } else if (0 == rank0) {
   
2440      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2441        res = Data(0.0, shape1, arg_1_Z.getFunctionSpace());      // DataConstant output        res = Data(0.0, shape1, arg_1_Z.getFunctionSpace());      // DataConstant output
2442        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2443        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[0]);        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2444        double *ptr_2 = &((res.getPointDataView().getData())[0]);        double *ptr_2 = &(res.getDataAtOffsetRW(0));
2445        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2446      }      }
2447      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
# Line 2018  C_TensorBinaryOperation(Data const &arg_ Line 2459  C_TensorBinaryOperation(Data const &arg_
2459    
2460        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2461        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2462        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2463        // Get the views  
2464        DataArrayView view_1 = tmp_1->getDefaultValue();        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2465        DataArrayView view_2 = tmp_2->getDefaultValue();        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2466        // Get the pointers to the actual data  
       double *ptr_1 = &((view_1.getData())[0]);  
       double *ptr_2 = &((view_2.getData())[0]);  
2467        // Compute a result for the default        // Compute a result for the default
2468        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2469        // Compute a result for each tag        // Compute a result for each tag
2470        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2471        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2472        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2473          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2474          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2475          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
         double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2476          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2477        }        }
2478    
# Line 2051  C_TensorBinaryOperation(Data const &arg_ Line 2488  C_TensorBinaryOperation(Data const &arg_
2488        int numSamples_1 = arg_1_Z.getNumSamples();        int numSamples_1 = arg_1_Z.getNumSamples();
2489        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2490        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2491          res.requireWrite();
2492        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2493        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2494          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2495            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2496            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2497            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2498            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2499            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2500            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2501    
2502          }          }
# Line 2080  C_TensorBinaryOperation(Data const &arg_ Line 2518  C_TensorBinaryOperation(Data const &arg_
2518    
2519        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2520        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2521        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2522        // Get the views  
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2523        // Get the pointers to the actual data        // Get the pointers to the actual data
2524        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2525        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2526    
2527    
2528        // Compute a result for the default        // Compute a result for the default
2529        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2530        // Compute a result for each tag        // Compute a result for each tag
2531        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2532        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2533        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2534          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2535          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2536          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2537          double *ptr_0 = &view_0.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2538          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2539        }        }
2540    
# Line 2115  C_TensorBinaryOperation(Data const &arg_ Line 2552  C_TensorBinaryOperation(Data const &arg_
2552        res.tag();        // DataTagged output        res.tag();        // DataTagged output
2553        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2554    
       // Get the views  
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_1 = tmp_1->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2555        // Get the pointers to the actual data        // Get the pointers to the actual data
2556        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2557        double *ptr_1 = &((view_1.getData())[0]);        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2558        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2559    
2560        // Compute a result for the default        // Compute a result for the default
2561        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);        tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2562        // Merge the tags        // Merge the tags
# Line 2130  C_TensorBinaryOperation(Data const &arg_ Line 2564  C_TensorBinaryOperation(Data const &arg_
2564        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2565        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2566        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2567          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue()); // use tmp_2 to get correct shape          tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2568        }        }
2569        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2570          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2571        }        }
2572        // Compute a result for each tag        // Compute a result for each tag
2573        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2574        for (i=lookup_2.begin();i!=lookup_2.end();i++) {        for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2575          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2576          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2577          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2578          double *ptr_0 = &view_0.getData(0);  
         double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2579          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2580        }        }
2581    
# Line 2159  C_TensorBinaryOperation(Data const &arg_ Line 2591  C_TensorBinaryOperation(Data const &arg_
2591        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2592        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2593        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2594          res.requireWrite();
2595        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2596        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2597          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0
2598          double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2599          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2600            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2601            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2602            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2603            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2604            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2605          }          }
2606        }        }
2607    
2608      }      }
2609      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {
   
2610        res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2611        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2612        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
# Line 2184  C_TensorBinaryOperation(Data const &arg_ Line 2616  C_TensorBinaryOperation(Data const &arg_
2616        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2617        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2618        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2619          res.requireWrite();
2620        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2621        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2622          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2623            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2624            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2625            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2626            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2627            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2628            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2629          }          }
2630        }        }
# Line 2209  C_TensorBinaryOperation(Data const &arg_ Line 2642  C_TensorBinaryOperation(Data const &arg_
2642        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2643        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2644        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2645          res.requireWrite();
2646        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2647        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2648          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2649          double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2650          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2651            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2652            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2653            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2654            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2655            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2656          }          }
2657        }        }
# Line 2234  C_TensorBinaryOperation(Data const &arg_ Line 2668  C_TensorBinaryOperation(Data const &arg_
2668        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2669        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2670        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2671          res.requireWrite();
2672        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2673        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2674          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2675            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2676            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2677            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2678            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2679            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2680            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2681            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2682          }          }
2683        }        }
# Line 2253  C_TensorBinaryOperation(Data const &arg_ Line 2688  C_TensorBinaryOperation(Data const &arg_
2688      }      }
2689    
2690    } else if (0 == rank1) {    } else if (0 == rank1) {
   
2691      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {      if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2692        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output
2693        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2694        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[0]);        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2695        double *ptr_2 = &((res.getPointDataView().getData())[0]);        double *ptr_2 = &(res.getDataAtOffsetRW(0));
2696        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2697      }      }
2698      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {      else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
# Line 2276  C_TensorBinaryOperation(Data const &arg_ Line 2710  C_TensorBinaryOperation(Data const &arg_
2710    
2711        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2712        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2713        double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2714        // Get the views  
2715        DataArrayView view_1 = tmp_1->getDefaultValue();        //Get the pointers to the actual data
2716        DataArrayView view_2 = tmp_2->getDefaultValue();        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2717        // Get the pointers to the actual data        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2718        double *ptr_1 = &((view_1.getData())[0]);  
       double *ptr_2 = &((view_2.getData())[0]);  
2719        // Compute a result for the default        // Compute a result for the default
2720        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2721        // Compute a result for each tag        // Compute a result for each tag
2722        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2723        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2724        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2725          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2726          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2727          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
         double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2728          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2729        }        }
   
2730      }      }
2731      else if (arg_0_Z.isConstant()   && arg_1_Z.isExpanded()) {      else if (arg_0_Z.isConstant()   && arg_1_Z.isExpanded()) {
2732    
# Line 2309  C_TensorBinaryOperation(Data const &arg_ Line 2739  C_TensorBinaryOperation(Data const &arg_
2739        int numSamples_1 = arg_1_Z.getNumSamples();        int numSamples_1 = arg_1_Z.getNumSamples();
2740        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2741        int offset_0 = tmp_0->getPointOffset(0,0);        int offset_0 = tmp_0->getPointOffset(0,0);
2742          res.requireWrite();
2743        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)        #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2744        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {        for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2745          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {          for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2746            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2747            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);            int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2748            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2749            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2750            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2751            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2752          }          }
2753        }        }
# Line 2337  C_TensorBinaryOperation(Data const &arg_ Line 2768  C_TensorBinaryOperation(Data const &arg_
2768    
2769        // Prepare offset into DataConstant        // Prepare offset into DataConstant
2770        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2771        double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);        const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
       // Get the views  
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2772        // Get the pointers to the actual data        // Get the pointers to the actual data
2773        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2774        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2775        // Compute a result for the default        // Compute a result for the default
2776        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2777        // Compute a result for each tag        // Compute a result for each tag
2778        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2779        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory        DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2780        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2781          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2782          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2783          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
         double *ptr_0 = &view_0.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2784          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2785        }        }
2786    
# Line 2372  C_TensorBinaryOperation(Data const &arg_ Line 2798  C_TensorBinaryOperation(Data const &arg_
2798        res.tag();        // DataTagged output        res.tag();        // DataTagged output
2799        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2800    
       // Get the views  
       DataArrayView view_0 = tmp_0->getDefaultValue();  
       DataArrayView view_1 = tmp_1->getDefaultValue();  
       DataArrayView view_2 = tmp_2->getDefaultValue();  
2801        // Get the pointers to the actual data        // Get the pointers to the actual data
2802        double *ptr_0 = &((view_0.getData())[0]);        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2803        double *ptr_1 = &((view_1.getData())[0]);        const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2804        double *ptr_2 = &((view_2.getData())[0]);        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2805    
2806        // Compute a result for the default        // Compute a result for the default
2807        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);        tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2808        // Merge the tags        // Merge the tags
# Line 2387  C_TensorBinaryOperation(Data const &arg_ Line 2810  C_TensorBinaryOperation(Data const &arg_
2810        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();        const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2811        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();        const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2812        for (i=lookup_0.begin();i!=lookup_0.end();i++) {        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2813          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue()); // use tmp_2 to get correct shape          tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2814        }        }
2815        for (i=lookup_1.begin();i!=lookup_1.end();i++) {        for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2816          tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());          tmp_2->addTag(i->first);
2817        }        }
2818        // Compute a result for each tag        // Compute a result for each tag
2819        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();        const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2820        for (i=lookup_2.begin();i!=lookup_2.end();i++) {        for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2821          DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2822          DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);          const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2823          DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
         double *ptr_0 = &view_0.getData(0);  
         double *ptr_1 = &view_1.getData(0);  
         double *ptr_2 = &view_2.getData(0);  
2824          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2825        }        }
2826    
# Line 2416  C_TensorBinaryOperation(Data const &arg_ Line 2836  C_TensorBinaryOperation(Data const &arg_
2836        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2837        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2838        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2839          res.requireWrite();
2840        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2841        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2842          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0          int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0
2843          double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2844          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2845            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2846            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2847            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2848            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2849            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2850          }          }
2851        }        }
2852    
2853      }      }
2854      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {      else if (arg_0_Z.isExpanded()   && arg_1_Z.isConstant()) {
   
2855        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output        res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2856        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2857        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());        DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
# Line 2441  C_TensorBinaryOperation(Data const &arg_ Line 2861  C_TensorBinaryOperation(Data const &arg_
2861        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2862        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2863        int offset_1 = tmp_1->getPointOffset(0,0);        int offset_1 = tmp_1->getPointOffset(0,0);
2864          res.requireWrite();
2865        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2866        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2867          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2868            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2869            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2870            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2871            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2872            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2873            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2874          }          }
2875        }        }
# Line 2466  C_TensorBinaryOperation(Data const &arg_ Line 2887  C_TensorBinaryOperation(Data const &arg_
2887        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2888        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2889        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2890          res.requireWrite();
2891        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2892        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2893          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);          int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2894          double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2895          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2896            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2897            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2898            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2899            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2900            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2901          }          }
2902        }        }
# Line 2491  C_TensorBinaryOperation(Data const &arg_ Line 2913  C_TensorBinaryOperation(Data const &arg_
2913        int sampleNo_0,dataPointNo_0;        int sampleNo_0,dataPointNo_0;
2914        int numSamples_0 = arg_0_Z.getNumSamples();        int numSamples_0 = arg_0_Z.getNumSamples();
2915        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2916          res.requireWrite();
2917        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2918        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2919          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {          for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2920            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2921            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2922            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2923            double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2924            double *ptr_1 = &((arg_1_Z.getPointDataView().getData())[offset_1]);            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2925            double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2926            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2927          }          }
2928        }        }
# Line 2521  Data Line 2944  Data
2944  C_TensorUnaryOperation(Data const &arg_0,  C_TensorUnaryOperation(Data const &arg_0,
2945                         UnaryFunction operation)                         UnaryFunction operation)
2946  {  {
2947      if (arg_0.isEmpty())  // do this before we attempt to interpolate
2948      {
2949         throw DataException("Error - Operations not permitted on instances of DataEmpty.");
2950      }
2951      if (arg_0.isLazy())
2952      {
2953         throw DataException("Error - Operations not permitted on lazy data.");
2954      }
2955    // Interpolate if necessary and find an appropriate function space    // Interpolate if necessary and find an appropriate function space
2956    Data arg_0_Z = Data(arg_0);    Data arg_0_Z = Data(arg_0);
2957    
2958    // Get rank and shape of inputs    // Get rank and shape of inputs
2959    int rank0 = arg_0_Z.getDataPointRank();    const DataTypes::ShapeType& shape0 = arg_0_Z.getDataPointShape();
   DataArrayView::ShapeType shape0 = arg_0_Z.getDataPointShape();  
2960    int size0 = arg_0_Z.getDataPointSize();    int size0 = arg_0_Z.getDataPointSize();
2961    
2962    // Declare output Data object    // Declare output Data object
# Line 2534  C_TensorUnaryOperation(Data const &arg_0 Line 2964  C_TensorUnaryOperation(Data const &arg_0
2964    
2965    if (arg_0_Z.isConstant()) {    if (arg_0_Z.isConstant()) {
2966      res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());      // DataConstant output      res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());      // DataConstant output
2967      double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[0]);      const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2968      double *ptr_2 = &((res.getPointDataView().getData())[0]);      double *ptr_2 = &(res.getDataAtOffsetRW(0));
2969      tensor_unary_operation(size0, ptr_0, ptr_2, operation);      tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2970    }    }
2971    else if (arg_0_Z.isTagged()) {    else if (arg_0_Z.isTagged()) {
# Line 2548  C_TensorUnaryOperation(Data const &arg_0 Line 2978  C_TensorUnaryOperation(Data const &arg_0
2978      res.tag();      res.tag();
2979      DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());      DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2980    
     // Get the views  
     DataArrayView view_0 = tmp_0->getDefaultValue();  
     DataArrayView view_2 = tmp_2->getDefaultValue();  
2981      // Get the pointers to the actual data      // Get the pointers to the actual data
2982      double *ptr_0 = &((view_0.getData())[0]);      const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2983      double *ptr_2 = &((view_2.getData())[0]);      double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2984      // Compute a result for the default      // Compute a result for the default
2985      tensor_unary_operation(size0, ptr_0, ptr_2, operation);      tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2986      // Compute a result for each tag      // Compute a result for each tag
2987      const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();      const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2988      DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory      DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2989      for (i=lookup_0.begin();i!=lookup_0.end();i++) {      for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2990        tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());        tmp_2->addTag(i->first);
2991        DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);        const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2992        DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);        double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
       double *ptr_0 = &view_0.getData(0);  
       double *ptr_2 = &view_2.getData(0);  
2993        tensor_unary_operation(size0, ptr_0, ptr_2, operation);        tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2994      }      }
2995    
# Line 2580  C_TensorUnaryOperation(Data const &arg_0 Line 3005  C_TensorUnaryOperation(Data const &arg_0
3005      int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();      int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
3006      #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)      #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
3007      for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {      for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
3008        for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {      dataPointNo_0=0;
3009    //      for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
3010          int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);          int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
3011          int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);          int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
3012          double *ptr_0 = &((arg_0_Z.getPointDataView().getData())[offset_0]);          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
3013          double *ptr_2 = &((res.getPointDataView().getData())[offset_2]);          double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
3014          tensor_unary_operation(size0, ptr_0, ptr_2, operation);          tensor_unary_operation(size0*numDataPointsPerSample_0, ptr_0, ptr_2, operation);
3015        }  //      }
3016      }      }
   
3017    }    }
3018    else {    else {
3019      throw DataException("Error - C_TensorUnaryOperation: unknown combination of inputs");      throw DataException("Error - C_TensorUnaryOperation: unknown combination of inputs");

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