/[escript]/trunk/escript/src/Data.h
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revision 922 by gross, Fri Jan 5 04:23:05 2007 UTC revision 2723 by jfenwick, Sun Oct 18 23:44:37 2009 UTC
# Line 1  Line 1 
1  // $Id$  
2  /*  /*******************************************************
3   ************************************************************  *
4   *          Copyright 2006 by ACcESS MNRF                   *  * Copyright (c) 2003-2009 by University of Queensland
5   *                                                          *  * Earth Systems Science Computational Center (ESSCC)
6   *              http://www.access.edu.au                    *  * http://www.uq.edu.au/esscc
7   *       Primary Business: Queensland, Australia            *  *
8   *  Licensed under the Open Software License version 3.0    *  * Primary Business: Queensland, Australia
9   *     http://www.opensource.org/licenses/osl-3.0.php       *  * Licensed under the Open Software License version 3.0
10   *                                                          *  * http://www.opensource.org/licenses/osl-3.0.php
11   ************************************************************  *
12  */  *******************************************************/
13    
14    
15  /** \file Data.h */  /** \file Data.h */
16    
# Line 17  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 24  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"  //#include <omp.h>
33  }  }
34    
35  #ifndef PASO_MPI  #include "esysmpi.h"
 #define MPI_Comm long  
 #endif  
   
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 48  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 102  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 129  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 142  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 199  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 214  class Data { Line 176  class Data {
176       Constructor which creates a DataConstant of "shape" with constant value.       Constructor which creates a DataConstant of "shape" with constant value.
177    */    */
178    ESCRIPT_DLL_API    ESCRIPT_DLL_API
179    Data(double value,    Data(double value,
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 226  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    
240    /**    /**
241       \brief       \brief
242       switches on update protection       switches on update protection
243    
244    */    */
245    ESCRIPT_DLL_API    ESCRIPT_DLL_API
# Line 248  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;
   /**  
      \brief  
      Return the values of all data-points as a single python numarray object.  
   */  
   ESCRIPT_DLL_API  
   const boost::python::numeric::array  
   convertToNumArray();  
257    
258    /**  
259       \brief  /**
260       Fills the expanded Data object from values of a python numarray object.     \brief
261    */     Return the value of a data point as a python tuple.
262    */
263    ESCRIPT_DLL_API    ESCRIPT_DLL_API
264    void    const boost::python::object
265    fillFromNumArray(const boost::python::numeric::array);    getValueOfDataPointAsTuple(int dataPointNo);
266    
267    /**    /**
268       \brief       \brief
269       Return the values of a data point on this process       sets the values of a data-point from a python object on this process
270    */    */
271    ESCRIPT_DLL_API    ESCRIPT_DLL_API
272    const boost::python::numeric::array    void
273    getValueOfDataPoint(int dataPointNo);    setValueOfDataPointToPyObject(int dataPointNo, const boost::python::object& py_object);
274    
275    /**    /**
276       \brief       \brief
277       sets the values of a data-point 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 295  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 321  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 330  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 368  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 384  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 399  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 432  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 452  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 493  class Data { Line 538  class Data {
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
556         dumps the object into a netCDF file
557      */
558      ESCRIPT_DLL_API
559      void
560      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 523  class Data { Line 614  class Data {
614    
615    /**    /**
616       \brief       \brief
617       Assign the given value to the data-points referenced by the given       Return a reference into the DataVector which points to the specified data point.
618       reference number.       \param sampleNo - Input -
619         \param dataPointNo - Input -
      The value supplied is a python numarray object.  The data from this numarray  
      is unpacked into a DataArray, and this is used to set the corresponding  
      data-points in the underlying Data object.  
   
      If the underlying Data object cannot be accessed via reference numbers, an  
      exception will be thrown.  
   
      \param ref - Input - reference number.  
      \param value - Input - value to assign to data-points associated with  
                             the given reference number.  
620    */    */
621    ESCRIPT_DLL_API    ESCRIPT_DLL_API
622    void    DataTypes::ValueType::const_reference
623    setRefValue(int ref,    getDataPointRO(int sampleNo, int dataPointNo);
               const boost::python::numeric::array& value);  
624    
625    /**    /**
626       \brief       \brief
627       Return the values associated with the data-points referenced by the given       Return a reference into the DataVector which points to the specified data point.
628       reference number.       \param sampleNo - Input -
629         \param dataPointNo - Input -
      The value supplied is a python numarray object. The data from the corresponding  
      data-points in this Data object are packed into the given numarray object.  
   
      If the underlying Data object cannot be accessed via reference numbers, an  
      exception will be thrown.  
   
      \param ref - Input - reference number.  
      \param value - Output - object to receive values from data-points  
                              associated with the given reference number.  
630    */    */
631    ESCRIPT_DLL_API    ESCRIPT_DLL_API
632    void    DataTypes::ValueType::reference
633    getRefValue(int ref,    getDataPointRW(int sampleNo, int dataPointNo);
634                boost::python::numeric::array& value);  
635    
636    
637    /**    /**
638       \brief       \brief
639       Return a view into the data for the data point specified.       Return the offset for the given sample and point within the sample
      NOTE: Construction of the DataArrayView is a relatively expensive  
      operation.  
      \param sampleNo - Input -  
      \param dataPointNo - Input -  
640    */    */
641    ESCRIPT_DLL_API    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 584  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 609  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. 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.       cannot be converted to a DataTagged object or name cannot be mapped onto a tag key.
703         \param name - Input - name of tag.
704         \param value - Input - Value to associate with given key.
705      */
706      ESCRIPT_DLL_API
707      void
708      setTaggedValueByName(std::string name,
709                           const boost::python::object& value);
710    
711      /**
712         \brief
713         Assign the given value to the tag. Implicitly converts this
714         object to type DataTagged if it is constant.
715    
716       \param tagKey - Input - Integer key.       \param tagKey - Input - Integer key.
717       \param value - Input - Value to associate with given key.       \param value - Input - Value to associate with given key.
718      ==>*      ==>*
# Line 629  class Data { Line 725  class Data {
725    /**    /**
726       \brief       \brief
727       Assign the given value to the tag. Implicitly converts this       Assign the given value to the tag. Implicitly converts this
728       object to type DataTagged. Throws an exception if this object       object to type DataTagged if it is constant.
729       cannot be converted to a DataTagged object.  
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 655  class Data { Line 756  class Data {
756    
757    /**    /**
758       \brief       \brief
759         set all values to zero
760         *
761      */
762      ESCRIPT_DLL_API
763      void
764      setToZero();
765    
766      /**
767         \brief
768       Interpolates this onto the given functionspace and returns       Interpolates this onto the given functionspace and returns
769       the result as a Data object.       the result as a Data object.
770       *       *
# Line 663  class Data { Line 773  class Data {
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 678  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 751  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    
   /**  
      \brief  
      Return the minimum absolute value of this Data object.  
      *  
   */  
910    ESCRIPT_DLL_API    ESCRIPT_DLL_API
911    double    double
912    Linf() const;    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 814  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 882  class Data { Line 1063  class Data {
1063    /**    /**
1064       \brief       \brief
1065       Return the eigenvalues and corresponding eigenvcetors of the symmetric part at each data point of this Data object.       Return the eigenvalues and corresponding eigenvcetors of the symmetric part at each data point of this Data object.
1066       the eigenvalues are ordered in increasing size where eigenvalues with relative difference less than       the eigenvalues are ordered in increasing size where eigenvalues with relative difference less than
1067       tol are treated as equal. The eigenvectors are orthogonal, normalized and the sclaed such that the       tol are treated as equal. The eigenvectors are orthogonal, normalized and the sclaed such that the
1068       first non-zero entry is positive.       first non-zero entry is positive.
1069       Currently this function is restricted to rank 2, square shape, and dimension 3       Currently this function is restricted to rank 2, square shape, and dimension 3
1070       *       *
# Line 1087  class Data { Line 1268  class Data {
1268    /**    /**
1269       \brief       \brief
1270       Return the given power of each data point of this boost python object.       Return the given power of each data point of this boost python object.
1271        
1272       \param right Input - the power to raise the object to.       \param right Input - the power to raise the object to.
1273       *       *
1274     */     */
# Line 1098  class Data { Line 1279  class Data {
1279    /**    /**
1280       \brief       \brief
1281       Return the given power of each data point of this boost python object.       Return the given power of each data point of this boost python object.
1282        
1283       \param left Input - the bases       \param left Input - the bases
1284       *       *
1285     */     */
# Line 1123  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 1134  class Data { Line 1317  class Data {
1317    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1318    Data& operator+=(const boost::python::object& right);    Data& operator+=(const boost::python::object& right);
1319    
1320      ESCRIPT_DLL_API
1321      Data& operator=(const Data& other);
1322    
1323    /**    /**
1324       \brief       \brief
1325       Overloaded operator -=       Overloaded operator -=
# Line 1226  class Data { Line 1412  class Data {
1412    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1413    inline    inline
1414    void    void
1415    unaryOp(UnaryFunction operation);    unaryOp2(UnaryFunction operation);
1416    
1417    /**    /**
1418       \brief       \brief
# Line 1237  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 1250  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);
1440    
1441    /**    /**
1442       \brief       \brief
1443       Archive the current Data object to the given file.       print the data values to stdout. Used for debugging
      \param fileName - Input - file to archive to.  
1444    */    */
1445    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1446    void    void
1447    archiveData(const std::string fileName);          print(void);
1448    
1449    /**    /**
1450       \brief       \brief
1451       Extract the Data object archived in the given file, overwriting       return the MPI rank number of the local data
1452       the current Data object.                   MPI_COMM_WORLD is assumed and the result of MPI_Comm_size()
1453       Note - the current object must be of type DataEmpty.                   is returned
      \param fileName - Input - file to extract from.  
      \param fspace - Input - a suitable FunctionSpace descibing the data.  
1454    */    */
1455    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1456    void          int
1457    extractData(const std::string fileName,          get_MPIRank(void) const;
               const FunctionSpace& fspace);  
   
1458    
1459    /**    /**
1460       \brief       \brief
1461       print the data values to stdout. Used for debugging       return the MPI rank number of the local data
1462                     MPI_COMM_WORLD is assumed and the result of MPI_Comm_rank()
1463                     is returned
1464    */    */
1465    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1466    void          int
1467      print(void);          get_MPISize(void) const;
1468    
1469    /**    /**
1470       \brief       \brief
1471       return the MPI rank number of the local data       return the MPI rank number of the local data
1472           MPI_COMM_WORLD is assumed and the result of MPI_Comm_size()                   MPI_COMM_WORLD is assumed and returned.
          is returned  
1473    */    */
1474    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1475      int          MPI_Comm
1476      get_MPIRank(void) const;          get_MPIComm(void) const;
1477    
1478    /**    /**
1479       \brief       \brief
1480       return the MPI rank number of the local data       return the object produced by the factory, which is a DataConstant or DataExpanded
1481           MPI_COMM_WORLD is assumed and the result of MPI_Comm_rank()      TODO Ownership of this object should be explained in doco.
          is returned  
1482    */    */
1483    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1484      int          DataAbstract*
1485      get_MPISize(void) const;          borrowData(void) const;
1486    
1487      ESCRIPT_DLL_API
1488            DataAbstract_ptr
1489            borrowDataPtr(void) const;
1490    
   /**  
      \brief  
      return the MPI rank number of the local data  
          MPI_COMM_WORLD is assumed and returned.  
   */  
1491    ESCRIPT_DLL_API    ESCRIPT_DLL_API
1492      MPI_Comm          DataReady_ptr
1493      get_MPIComm(void) const;          borrowReadyPtr(void) const;
1494    
1495    
1496    
1497    /**    /**
1498       \brief       \brief
1499       return the object produced by the factory, which is a DataConstant or DataExpanded       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    ESCRIPT_DLL_API
1510      DataAbstract*          DataTypes::ValueType::reference
1511      borrowData(void) const;          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 1395  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      DataReady*
1666      getReady();
1667    
1668    
1669    // 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    // pointer to the internal profiling data    // This means that the only transition we need to consider, is when a previously shared object is
1700    struct profDataEntry *profData;    // not shared anymore. ie. the other objects have been destroyed or a deep copy has been made.
1701      // In those cases the m_shared flag changes to false after m_data has completed changing.
1702      // For any threads executing before the flag switches they will assume the object is still shared.
1703      bool isShared() const
1704      {
1705        return m_shared;
1706    /*  if (m_shared) return true;
1707        if (m_data->isShared())        
1708        {                  
1709            updateShareStatus(true);
1710            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  template <class IValueType>  }   // end namespace escript
1774  void  
1775  Data::initialise(const IValueType& value,  
1776                   const FunctionSpace& what,  // No, this is not supposed to be at the top of the file
1777                   bool expanded)  // 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    // Construct a Data object of the appropriate type.  inline
1786    // Construct the object first as there seems to be a bug which causes  const DataReady*
1787    // undefined behaviour if an exception is thrown during construction  Data::getReady() const
1788    // within the shared_ptr constructor.  {
1789    if (expanded) {     const DataReady* dr=dynamic_cast<const DataReady*>(m_data.get());
1790      DataAbstract* temp=new DataExpanded(value,what);     EsysAssert((dr!=0), "Error - casting to DataReady.");
1791      boost::shared_ptr<DataAbstract> temp_data(temp);     return dr;
1792      m_data=temp_data;  }
1793    } else {  
1794      DataAbstract* temp=new DataConstant(value,what);  inline
1795      boost::shared_ptr<DataAbstract> temp_data(temp);  DataReady*
1796      m_data=temp_data;  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  */  */
1864  inline double rpow(double x,double y)  inline double rpow(double x,double y)
1865  {  {
1866      return pow(y,x);      return pow(y,x);
1867  };  }
1868    
1869  /**  /**
1870    \brief    \brief
# Line 1537  ESCRIPT_DLL_API Data operator*(const boo Line 1958  ESCRIPT_DLL_API Data operator*(const boo
1958  */  */
1959  ESCRIPT_DLL_API Data operator/(const boost::python::object& left, const Data& right);  ESCRIPT_DLL_API Data operator/(const boost::python::object& left, const Data& right);
1960    
1961    
1962    
1963  /**  /**
1964    \brief    \brief
1965    Output operator    Output operator
# Line 1546  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    
1984  /**  /**
1985    \brief    \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);  
   
 /**  
   \brief  
1986    Perform the given binary operation with this and right as operands.    Perform the given binary operation with this and right as operands.
1987    Right is a Data object.    Right is a Data object.
1988  */  */
# Line 1579  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         //         //
2012         // an interpolation is required so create a new Data         // an interpolation is required so create a new Data
2013         tempRight=Data(right,this->getFunctionSpace());         tempRight=Data(right,this->getFunctionSpace());
2014       } else if (probeInterpolation(right.getFunctionSpace())) {       } else if (probeInterpolation(right.getFunctionSpace())) {
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 1610  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 1632  Data::binaryOp(const Data& right, Line 2054  Data::binaryOp(const Data& right,
2054       EsysAssert((leftC!=0 && rightC!=0), "Programming error - casting to DataConstant.");       EsysAssert((leftC!=0 && rightC!=0), "Programming error - casting to DataConstant.");
2055       escript::binaryOp(*leftC,*rightC,operation);       escript::binaryOp(*leftC,*rightC,operation);
2056     }     }
    #if defined DOPROF  
    profData->binary++;  
    #endif  
 }  
   
 /**  
   \brief  
   Perform the given unary operation on other and return the result.  
   Given operation is performed on each element of each data point, thus  
   argument object is a rank n Data object, and returned object is a rank n  
   Data object.  
   Calls Data::unaryOp.  
 */  
 template <class UnaryFunction>  
 inline  
 Data  
 unaryOp(const Data& other,  
         UnaryFunction operation)  
 {  
   Data result;  
   result.copy(other);  
   result.unaryOp(operation);  
   return result;  
 }  
   
 /**  
   \brief  
   Perform the given unary operation on this.  
   Given operation is performed on each element of each data point.  
   Calls escript::unaryOp.  
 */  
 template <class UnaryFunction>  
 inline  
 void  
 Data::unaryOp(UnaryFunction operation)  
 {  
   if (isExpanded()) {  
     DataExpanded* leftC=dynamic_cast<DataExpanded*>(m_data.get());  
     EsysAssert((leftC!=0), "Programming error - casting to DataExpanded.");  
     escript::unaryOp(*leftC,operation);  
   } else if (isTagged()) {  
     DataTagged* leftC=dynamic_cast<DataTagged*>(m_data.get());  
     EsysAssert((leftC!=0), "Programming error - casting to DataTagged.");  
     escript::unaryOp(*leftC,operation);  
   } else if (isConstant()) {  
     DataConstant* leftC=dynamic_cast<DataConstant*>(m_data.get());  
     EsysAssert((leftC!=0), "Programming error - casting to DataConstant.");  
     escript::unaryOp(*leftC,operation);  
   }  
2057  }  }
2058    
2059  /**  /**
# Line 1707  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  /**  /**
2093    \brief    \brief
2094    Perform the given data point reduction algorithm on data and return the result.    Perform the given data point reduction algorithm on data and return the result.
2095    Given operation combines each element within each data point into a scalar,    Given operation combines each element within each data point into a scalar,
2096    thus argument object is a rank n Data object, and returned object is a    thus argument object is a rank n Data object, and returned object is a
2097    rank 0 Data object.    rank 0 Data object.
2098    Calls escript::dp_algorithm.    Calls escript::dp_algorithm.
2099  */  */
# Line 1724  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      }
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>
2149    inline
2150    Data
2151    C_TensorBinaryOperation(Data const &arg_0,
2152                            Data const &arg_1,
2153                            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
2164      Data arg_0_Z, arg_1_Z;
2165      if (arg_0.getFunctionSpace()!=arg_1.getFunctionSpace()) {
2166        if (arg_0.probeInterpolation(arg_1.getFunctionSpace())) {
2167          arg_0_Z = arg_0.interpolate(arg_1.getFunctionSpace());
2168          arg_1_Z = Data(arg_1);
2169        }
2170        else if (arg_1.probeInterpolation(arg_0.getFunctionSpace())) {
2171          arg_1_Z=arg_1.interpolate(arg_0.getFunctionSpace());
2172          arg_0_Z =Data(arg_0);
2173        }
2174        else {
2175          throw DataException("Error - C_TensorBinaryOperation: arguments have incompatible function spaces.");
2176        }
2177      } else {
2178          arg_0_Z = Data(arg_0);
2179          arg_1_Z = Data(arg_1);
2180      }
2181      // Get rank and shape of inputs
2182      int rank0 = arg_0_Z.getDataPointRank();
2183      int rank1 = arg_1_Z.getDataPointRank();
2184      DataTypes::ShapeType shape0 = arg_0_Z.getDataPointShape();
2185      DataTypes::ShapeType shape1 = arg_1_Z.getDataPointShape();
2186      int size0 = arg_0_Z.getDataPointSize();
2187      int size1 = arg_1_Z.getDataPointSize();
2188      // Declare output Data object
2189      Data res;
2190    
2191      if (shape0 == shape1) {
2192        if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2193          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output
2194          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2195          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2196          double *ptr_2 = &(res.getDataAtOffsetRW(0));
2197    
2198          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2199        }
2200        else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
2201    
2202          // Prepare the DataConstant input
2203          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2204    
2205          // Borrow DataTagged input from Data object
2206          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2207    
2208          // Prepare a DataTagged output 2
2209          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataTagged output
2210          res.tag();
2211          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2212    
2213          // Prepare offset into DataConstant
2214          int offset_0 = tmp_0->getPointOffset(0,0);
2215          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2216    
2217          // Get the pointers to the actual data
2218          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2219          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2220    
2221          // Compute a result for the default
2222          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2223          // Compute a result for each tag
2224          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
2226          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2227            tmp_2->addTag(i->first);
2228            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2229            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2230    
2231            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()) {
2236          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2237          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2238          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2239          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2240    
2241          int sampleNo_1,dataPointNo_1;
2242          int numSamples_1 = arg_1_Z.getNumSamples();
2243          int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2244          int offset_0 = tmp_0->getPointOffset(0,0);
2245          res.requireWrite();
2246          #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2247          for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2248            for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2249              int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2250              int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2251              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2252              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2253              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2254              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()) {
2260          // Borrow DataTagged input from Data object
2261          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2262    
2263          // Prepare the DataConstant input
2264          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2265    
2266          // Prepare a DataTagged output 2
2267          res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());      // DataTagged output
2268          res.tag();
2269          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2270    
2271          // Prepare offset into DataConstant
2272          int offset_1 = tmp_1->getPointOffset(0,0);
2273    
2274          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2275          // Get the pointers to the actual data
2276          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2277          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2278          // Compute a result for the default
2279          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2280          // Compute a result for each tag
2281          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
2283          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2284            tmp_2->addTag(i->first);
2285            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2286            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2287            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()) {
2292          // Borrow DataTagged input from Data object
2293          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2294    
2295          // Borrow DataTagged input from Data object
2296          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2297    
2298          // Prepare a DataTagged output 2
2299          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());
2300          res.tag();        // DataTagged output
2301          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2302    
2303          // Get the pointers to the actual data
2304          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2305          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2306          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2307    
2308          // Compute a result for the default
2309          tensor_binary_operation(size0, ptr_0, ptr_1, ptr_2, operation);
2310          // Merge the tags
2311          DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2312          const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2313          const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2314          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2315            tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2316          }
2317          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2318            tmp_2->addTag(i->first);
2319          }
2320          // Compute a result for each tag
2321          const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2322          for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2323    
2324            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2325            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2326            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2327    
2328            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()) {
2333          // 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
2335          DataTagged*   tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2336          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2337          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2338    
2339          int sampleNo_0,dataPointNo_0;
2340          int numSamples_0 = arg_0_Z.getNumSamples();
2341          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2342          res.requireWrite();
2343          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2344          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
2346            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2347            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2348              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2349              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2350              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2351              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2352              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()) {
2358          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2359          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2360          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2361          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2362    
2363          int sampleNo_0,dataPointNo_0;
2364          int numSamples_0 = arg_0_Z.getNumSamples();
2365          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2366          int offset_1 = tmp_1->getPointOffset(0,0);
2367          res.requireWrite();
2368          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2369          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2370            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2371              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2372              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2373    
2374              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2375              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);
2380            }
2381          }
2382    
2383        }
2384        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
2386          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2387          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2388          DataTagged*   tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2389          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2390    
2391          int sampleNo_0,dataPointNo_0;
2392          int numSamples_0 = arg_0_Z.getNumSamples();
2393          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2394          res.requireWrite();
2395          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2396          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2397            int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2398            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2399            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2400              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2401              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2402              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2403              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2404              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()) {
2410          // 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
2412          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2413          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2414          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2415    
2416          int sampleNo_0,dataPointNo_0;
2417          int numSamples_0 = arg_0_Z.getNumSamples();
2418          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2419          res.requireWrite();
2420          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2421          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2422          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);
2425              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2426              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2427              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2428              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2429              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2430              tensor_binary_operation(size0*numDataPointsPerSample_0, ptr_0, ptr_1, ptr_2, operation);
2431    //       }
2432          }
2433    
2434        }
2435        else {
2436          throw DataException("Error - C_TensorBinaryOperation: unknown combination of inputs");
2437        }
2438    
2439      } else if (0 == rank0) {
2440        if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2441          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace());      // DataConstant output
2442          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2443          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2444          double *ptr_2 = &(res.getDataAtOffsetRW(0));
2445          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2446        }
2447        else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
2448    
2449          // Prepare the DataConstant input
2450          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2451    
2452          // Borrow DataTagged input from Data object
2453          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2454    
2455          // Prepare a DataTagged output 2
2456          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace());      // DataTagged output
2457          res.tag();
2458          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2459    
2460          // Prepare offset into DataConstant
2461          int offset_0 = tmp_0->getPointOffset(0,0);
2462          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2463    
2464          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2465          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2466    
2467          // Compute a result for the default
2468          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2469          // Compute a result for each tag
2470          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
2472          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2473            tmp_2->addTag(i->first);
2474            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2475            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2476            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2477          }
2478    
2479        }
2480        else if (arg_0_Z.isConstant()   && arg_1_Z.isExpanded()) {
2481    
2482          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2483          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2484          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2485          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2486    
2487          int sampleNo_1,dataPointNo_1;
2488          int numSamples_1 = arg_1_Z.getNumSamples();
2489          int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2490          int offset_0 = tmp_0->getPointOffset(0,0);
2491          res.requireWrite();
2492          #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2493          for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2494            for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2495              int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2496              int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2497              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2498              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2499              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2500              tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2501    
2502            }
2503          }
2504    
2505        }
2506        else if (arg_0_Z.isTagged()     && arg_1_Z.isConstant()) {
2507    
2508          // Borrow DataTagged input from Data object
2509          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2510    
2511          // Prepare the DataConstant input
2512          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2513    
2514          // Prepare a DataTagged output 2
2515          res = Data(0.0, shape1, arg_0_Z.getFunctionSpace());      // DataTagged output
2516          res.tag();
2517          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2518    
2519          // Prepare offset into DataConstant
2520          int offset_1 = tmp_1->getPointOffset(0,0);
2521          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2522    
2523          // Get the pointers to the actual data
2524          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2525          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2526    
2527    
2528          // Compute a result for the default
2529          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2530          // Compute a result for each tag
2531          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
2533          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2534            tmp_2->addTag(i->first);
2535            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2536            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2537    
2538            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2539          }
2540    
2541        }
2542        else if (arg_0_Z.isTagged()     && arg_1_Z.isTagged()) {
2543    
2544          // Borrow DataTagged input from Data object
2545          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2546    
2547          // Borrow DataTagged input from Data object
2548          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2549    
2550          // Prepare a DataTagged output 2
2551          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace());
2552          res.tag();        // DataTagged output
2553          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2554    
2555          // Get the pointers to the actual data
2556          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2557          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2558          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2559    
2560          // Compute a result for the default
2561          tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2562          // Merge the tags
2563          DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2564          const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2565          const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2566          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2567            tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2568          }
2569          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2570            tmp_2->addTag(i->first);
2571          }
2572          // Compute a result for each tag
2573          const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2574          for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2575            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2576            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2577            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2578    
2579            tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2580          }
2581    
2582        }
2583        else if (arg_0_Z.isTagged()     && arg_1_Z.isExpanded()) {
2584    
2585          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2586          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2587          DataTagged*   tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2588          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2589          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2590    
2591          int sampleNo_0,dataPointNo_0;
2592          int numSamples_0 = arg_0_Z.getNumSamples();
2593          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2594          res.requireWrite();
2595          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2596          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
2598            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2599            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2600              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2601              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2602              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2603              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2604              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()) {
2610          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2611          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2612          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2613          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2614    
2615          int sampleNo_0,dataPointNo_0;
2616          int numSamples_0 = arg_0_Z.getNumSamples();
2617          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2618          int offset_1 = tmp_1->getPointOffset(0,0);
2619          res.requireWrite();
2620          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2621          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2622            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2623              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2624              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2625              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2626              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2627              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2628              tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2629            }
2630          }
2631    
2632    
2633        }
2634        else if (arg_0_Z.isExpanded()   && arg_1_Z.isTagged()) {
2635    
2636          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2637          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2638          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2639          DataTagged*   tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2640          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2641    
2642          int sampleNo_0,dataPointNo_0;
2643          int numSamples_0 = arg_0_Z.getNumSamples();
2644          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2645          res.requireWrite();
2646          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2647          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2648            int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2649            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2650            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2651              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2652              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2653              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2654              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2655              tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2656            }
2657          }
2658    
2659        }
2660        else if (arg_0_Z.isExpanded()   && arg_1_Z.isExpanded()) {
2661    
2662          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2663          res = Data(0.0, shape1, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2664          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2665          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2666          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2667    
2668          int sampleNo_0,dataPointNo_0;
2669          int numSamples_0 = arg_0_Z.getNumSamples();
2670          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2671          res.requireWrite();
2672          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2673          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2674            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2675              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2676              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2677              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2678              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2679              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2680              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2681              tensor_binary_operation(size1, ptr_0[0], ptr_1, ptr_2, operation);
2682            }
2683          }
2684    
2685        }
2686        else {
2687          throw DataException("Error - C_TensorBinaryOperation: unknown combination of inputs");
2688        }
2689    
2690      } else if (0 == rank1) {
2691        if (arg_0_Z.isConstant()   && arg_1_Z.isConstant()) {
2692          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataConstant output
2693          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2694          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(0));
2695          double *ptr_2 = &(res.getDataAtOffsetRW(0));
2696          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2697        }
2698        else if (arg_0_Z.isConstant()   && arg_1_Z.isTagged()) {
2699    
2700          // Prepare the DataConstant input
2701          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2702    
2703          // Borrow DataTagged input from Data object
2704          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2705    
2706          // Prepare a DataTagged output 2
2707          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());      // DataTagged output
2708          res.tag();
2709          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2710    
2711          // Prepare offset into DataConstant
2712          int offset_0 = tmp_0->getPointOffset(0,0);
2713          const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2714    
2715          //Get the pointers to the actual data
2716          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2717          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2718    
2719          // Compute a result for the default
2720          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2721          // Compute a result for each tag
2722          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
2724          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2725            tmp_2->addTag(i->first);
2726            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2727            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2728            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()) {
2732    
2733          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2734          DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2735          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2736          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2737    
2738          int sampleNo_1,dataPointNo_1;
2739          int numSamples_1 = arg_1_Z.getNumSamples();
2740          int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2741          int offset_0 = tmp_0->getPointOffset(0,0);
2742          res.requireWrite();
2743          #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2744          for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2745            for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2746              int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2747              int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2748              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2749              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2750              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2751              tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2752            }
2753          }
2754    
2755        }
2756        else if (arg_0_Z.isTagged()     && arg_1_Z.isConstant()) {
2757    
2758          // Borrow DataTagged input from Data object
2759          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2760    
2761          // Prepare the DataConstant input
2762          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2763    
2764          // Prepare a DataTagged output 2
2765          res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());      // DataTagged output
2766          res.tag();
2767          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2768    
2769          // Prepare offset into DataConstant
2770          int offset_1 = tmp_1->getPointOffset(0,0);
2771          const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2772          // Get the pointers to the actual data
2773          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2774          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2775          // Compute a result for the default
2776          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2777          // Compute a result for each tag
2778          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
2780          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2781            tmp_2->addTag(i->first);
2782            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2783            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2784            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2785          }
2786    
2787        }
2788        else if (arg_0_Z.isTagged()     && arg_1_Z.isTagged()) {
2789    
2790          // Borrow DataTagged input from Data object
2791          DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2792    
2793          // Borrow DataTagged input from Data object
2794          DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2795    
2796          // Prepare a DataTagged output 2
2797          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace());
2798          res.tag();        // DataTagged output
2799          DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2800    
2801          // Get the pointers to the actual data
2802          const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2803          const double *ptr_1 = &(tmp_1->getDefaultValueRO(0));
2804          double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2805    
2806          // Compute a result for the default
2807          tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2808          // Merge the tags
2809          DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2810          const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2811          const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2812          for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2813            tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2814          }
2815          for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2816            tmp_2->addTag(i->first);
2817          }
2818          // Compute a result for each tag
2819          const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2820          for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2821            const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2822            const double *ptr_1 = &(tmp_1->getDataByTagRO(i->first,0));
2823            double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2824            tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2825          }
2826    
2827        }
2828        else if (arg_0_Z.isTagged()     && arg_1_Z.isExpanded()) {
2829    
2830          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2831          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2832          DataTagged*   tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2833          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2834          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2835    
2836          int sampleNo_0,dataPointNo_0;
2837          int numSamples_0 = arg_0_Z.getNumSamples();
2838          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2839          res.requireWrite();
2840          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2841          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
2843            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2844            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2845              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2846              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2847              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2848              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2849              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()) {
2855          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2856          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2857          DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2858          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2859    
2860          int sampleNo_0,dataPointNo_0;
2861          int numSamples_0 = arg_0_Z.getNumSamples();
2862          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2863          int offset_1 = tmp_1->getPointOffset(0,0);
2864          res.requireWrite();
2865          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2866          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2867            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2868              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2869              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2870              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2871              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2872              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2873              tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2874            }
2875          }
2876    
2877    
2878        }
2879        else if (arg_0_Z.isExpanded()   && arg_1_Z.isTagged()) {
2880    
2881          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2882          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2883          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2884          DataTagged*   tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2885          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2886    
2887          int sampleNo_0,dataPointNo_0;
2888          int numSamples_0 = arg_0_Z.getNumSamples();
2889          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2890          res.requireWrite();
2891          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2892          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2893            int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2894            const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2895            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2896              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2897              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2898              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2899              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2900              tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2901            }
2902          }
2903    
2904        }
2905        else if (arg_0_Z.isExpanded()   && arg_1_Z.isExpanded()) {
2906    
2907          // After finding a common function space above the two inputs have the same numSamples and num DPPS
2908          res = Data(0.0, shape0, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2909          DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2910          DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2911          DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2912    
2913          int sampleNo_0,dataPointNo_0;
2914          int numSamples_0 = arg_0_Z.getNumSamples();
2915          int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2916          res.requireWrite();
2917          #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2918          for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2919            for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2920              int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2921              int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2922              int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2923              const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
2924              const double *ptr_1 = &(arg_1_Z.getDataAtOffsetRO(offset_1));
2925              double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
2926              tensor_binary_operation(size0, ptr_0, ptr_1[0], ptr_2, operation);
2927            }
2928          }
2929    
2930        }
2931        else {
2932          throw DataException("Error - C_TensorBinaryOperation: unknown combination of inputs");
2933        }
2934    
2935      } else {
2936        throw DataException("Error - C_TensorBinaryOperation: arguments have incompatible shapes");
2937      }
2938    
2939      return res;
2940    }
2941    
2942    template <typename UnaryFunction>
2943    Data
2944    C_TensorUnaryOperation(Data const &arg_0,
2945                           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
2956      Data arg_0_Z = Data(arg_0);
2957    
2958      // Get rank and shape of inputs
2959      const DataTypes::ShapeType& shape0 = arg_0_Z.getDataPointShape();
2960      int size0 = arg_0_Z.getDataPointSize();
2961    
2962      // Declare output Data object
2963      Data res;
2964    
2965      if (arg_0_Z.isConstant()) {
2966        res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());      // DataConstant output
2967        const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(0));
2968        double *ptr_2 = &(res.getDataAtOffsetRW(0));
2969        tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2970      }
2971      else if (arg_0_Z.isTagged()) {
2972    
2973        // Borrow DataTagged input from Data object
2974        DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2975    
2976        // Prepare a DataTagged output 2
2977        res = Data(0.0, shape0, arg_0_Z.getFunctionSpace());   // DataTagged output
2978        res.tag();
2979        DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2980    
2981        // Get the pointers to the actual data
2982        const double *ptr_0 = &(tmp_0->getDefaultValueRO(0));
2983        double *ptr_2 = &(tmp_2->getDefaultValueRW(0));
2984        // Compute a result for the default
2985        tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2986        // Compute a result for each tag
2987        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
2989        for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2990          tmp_2->addTag(i->first);
2991          const double *ptr_0 = &(tmp_0->getDataByTagRO(i->first,0));
2992          double *ptr_2 = &(tmp_2->getDataByTagRW(i->first,0));
2993          tensor_unary_operation(size0, ptr_0, ptr_2, operation);
2994        }
2995    
2996    }    }
2997    Data falseRetVal; // to keep compiler quiet    else if (arg_0_Z.isExpanded()) {
2998    return falseRetVal;  
2999        res = Data(0.0, shape0, arg_0_Z.getFunctionSpace(),true); // DataExpanded output
3000        DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
3001        DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
3002    
3003        int sampleNo_0,dataPointNo_0;
3004        int numSamples_0 = arg_0_Z.getNumSamples();
3005        int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
3006        #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
3007        for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
3008        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);
3011            int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
3012            const double *ptr_0 = &(arg_0_Z.getDataAtOffsetRO(offset_0));
3013            double *ptr_2 = &(res.getDataAtOffsetRW(offset_2));
3014            tensor_unary_operation(size0*numDataPointsPerSample_0, ptr_0, ptr_2, operation);
3015    //      }
3016        }
3017      }
3018      else {
3019        throw DataException("Error - C_TensorUnaryOperation: unknown combination of inputs");
3020      }
3021    
3022      return res;
3023  }  }
3024    
3025  }  }

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