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branches/schroedinger/escript/src/DataLazy.cpp revision 1868 by jfenwick, Thu Oct 9 06:30:49 2008 UTC branches/clazy/escriptcore/src/DataLazy.cpp revision 6511 by jfenwick, Fri Mar 3 01:41:39 2017 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*****************************************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2016 by The University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * http://www.uq.edu.au
 * http://www.uq.edu.au/esscc  
6  *  *
7  * Primary Business: Queensland, Australia  * Primary Business: Queensland, Australia
8  * Licensed under the Open Software License version 3.0  * Licensed under the Apache License, version 2.0
9  * http://www.opensource.org/licenses/osl-3.0.php  * http://www.apache.org/licenses/LICENSE-2.0
10  *  *
11  *******************************************************/  * Development until 2012 by Earth Systems Science Computational Center (ESSCC)
12    * Development 2012-2013 by School of Earth Sciences
13    * Development from 2014 by Centre for Geoscience Computing (GeoComp)
14    *
15    *****************************************************************************/
16    
17  #include "DataLazy.h"  #include "DataLazy.h"
 #ifdef USE_NETCDF  
 #include <netcdfcpp.h>  
 #endif  
 #ifdef PASO_MPI  
 #include <mpi.h>  
 #endif  
 #include "FunctionSpace.h"  
 #include "DataTypes.h"  
18  #include "Data.h"  #include "Data.h"
19    #include "DataTypes.h"
20    #include "EscriptParams.h"
21    #include "FunctionSpace.h"
22    #include "Utils.h"
23    #include "DataVectorOps.h"
24    
25    #include <iomanip> // for some fancy formatting in debug
26    
27    using namespace escript::DataTypes;
28    
29    #define NO_ARG
30    
31    // #define LAZYDEBUG(X) if (privdebug){X;}
32    #define LAZYDEBUG(X)
33    namespace
34    {
35    bool privdebug=false;
36    
37    #define ENABLEDEBUG privdebug=true;
38    #define DISABLEDEBUG privdebug=false;
39    }
40    
41    //#define SIZELIMIT if ((m_height>escript::escriptParams.getInt("TOO_MANY_LEVELS")) || (m_children>escript::escriptParams.getInt("TOO_MANY_NODES"))) {cerr << "\n!!!!!!! SIZE LIMIT EXCEEDED " << m_children << ";" << m_height << endl << toString() << endl;resolveToIdentity();}
42    
43    //#define SIZELIMIT if ((m_height>escript::escriptParams.getInt("TOO_MANY_LEVELS")) || (m_children>escript::escriptParams.getInt("TOO_MANY_NODES"))) {cerr << "SIZE LIMIT EXCEEDED " << m_height << endl;resolveToIdentity();}
44    
45    
46    #define SIZELIMIT \
47        if (m_height > escript::escriptParams.getTooManyLevels()) {\
48            if (escript::escriptParams.getLazyVerbose()) {\
49                cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;\
50            }\
51            resolveToIdentity();\
52        }
53    
54    /*
55    How does DataLazy work?
56    ~~~~~~~~~~~~~~~~~~~~~~~
57    
58    Each instance represents a single operation on one or two other DataLazy instances. These arguments are normally
59    denoted left and right.
60    
61    A special operation, IDENTITY, stores an instance of DataReady in the m_id member.
62    This means that all "internal" nodes in the structure are instances of DataLazy.
63    
64    Each operation has a string representation as well as an opgroup - eg G_IDENTITY, G_BINARY, ...
65    Note that IDENTITY is not considered a unary operation.
66    
67    I am avoiding calling the structure formed a tree because it is not guaranteed to be one (eg c=a+a).
68    It must however form a DAG (directed acyclic graph).
69    I will refer to individual DataLazy objects with the structure as nodes.
70    
71    Each node also stores:
72    - m_readytype \in {'E','T','C','?'} ~ indicates what sort of DataReady would be produced if the expression was
73            evaluated.
74    - m_buffsrequired ~ the large number of samples which would need to be kept simultaneously in order to
75            evaluate the expression.
76    - m_samplesize ~ the number of doubles stored in a sample.
77    
78    When a new node is created, the above values are computed based on the values in the child nodes.
79    Eg: if left requires 4 samples and right requires 6 then left+right requires 7 samples.
80    
81    The resolve method, which produces a DataReady from a DataLazy, does the following:
82    1) Create a DataReady to hold the new result.
83    2) Allocate a vector (v) big enough to hold m_buffsrequired samples.
84    3) For each sample, call resolveSample with v, to get its values and copy them into the result object.
85    
86    (In the case of OMP, multiple samples are resolved in parallel so the vector needs to be larger.)
87    
88    resolveSample returns a Vector* and an offset within that vector where the result is stored.
89    Normally, this would be v, but for identity nodes their internal vector is returned instead.
90    
91    The convention that I use, is that the resolve methods should store their results starting at the offset they are passed.
92    
93    For expressions which evaluate to Constant or Tagged, there is a different evaluation method.
94    The collapse method invokes the (non-lazy) operations on the Data class to evaluate the expression.
95    
96    To add a new operator you need to do the following (plus anything I might have forgotten - adding a new group for example):
97    1) Add to the ES_optype.
98    2) determine what opgroup your operation belongs to (X)
99    3) add a string for the op to the end of ES_opstrings
100    4) increase ES_opcount
101    5) add an entry (X) to opgroups
102    6) add an entry to the switch in collapseToReady
103    7) add an entry to resolveX
104    */
105    
106    
107  using namespace std;  using namespace std;
108  using namespace boost;  using namespace boost;
# Line 29  using namespace boost; Line 110  using namespace boost;
110  namespace escript  namespace escript
111  {  {
112    
 const std::string&  
 opToString(ES_optype op);  
   
113  namespace  namespace
114  {  {
115    
116    
117    // enabling this will print out when ever the maximum stacksize used by resolve increases
118    // it assumes _OPENMP is also in use
119    //#define LAZY_STACK_PROF
120    
121    
122    
123    #ifndef _OPENMP
124      #ifdef LAZY_STACK_PROF
125      #undef LAZY_STACK_PROF
126      #endif
127    #endif
128    
129    
130    #ifdef LAZY_STACK_PROF
131    std::vector<void*> stackstart(getNumberOfThreads());
132    std::vector<void*> stackend(getNumberOfThreads());
133    size_t maxstackuse=0;
134    #endif
135    
136    
137  // return the FunctionSpace of the result of "left op right"  // return the FunctionSpace of the result of "left op right"
138  FunctionSpace  FunctionSpace
139  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
140  {  {
141      // perhaps this should call interpolate and throw or something?          // perhaps this should call interpolate and throw or something?
142      // maybe we need an interpolate node -          // maybe we need an interpolate node -
143      // that way, if interpolate is required in any other op we can just throw a          // that way, if interpolate is required in any other op we can just throw a
144      // programming error exception.          // programming error exception.
145      return left->getFunctionSpace();  
146      FunctionSpace l=left->getFunctionSpace();
147      FunctionSpace r=right->getFunctionSpace();
148      if (l!=r)
149      {
150        signed char res=r.getDomain()->preferredInterpolationOnDomain(r.getTypeCode(), l.getTypeCode());
151        if (res==1)
152        {
153            return l;
154        }
155        if (res==-1)
156        {
157            return r;
158        }
159        throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");
160      }
161      return l;
162  }  }
163    
164  // return the shape of the result of "left op right"  // return the shape of the result of "left op right"
165    // the shapes resulting from tensor product are more complex to compute so are worked out elsewhere
166  DataTypes::ShapeType  DataTypes::ShapeType
167  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
168  {  {
169      return DataTypes::scalarShape;          if (left->getShape()!=right->getShape())
170            {
171              if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
172              {
173                    throw DataException("Shapes not the name - shapes must match for (point)binary operations.");
174              }
175    
176              if (left->getRank()==0)       // we need to allow scalar * anything
177              {
178                    return right->getShape();
179              }
180              if (right->getRank()==0)
181              {
182                    return left->getShape();
183              }
184              throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");
185            }
186            return left->getShape();
187  }  }
188    
189  size_t  // return the shape for "op left"
190  resultLength(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  
191    DataTypes::ShapeType
192    resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
193  {  {
194     switch(op)          switch(op)
195     {          {
196     case IDENTITY: return left->getLength();          case TRANS:
197     case ADD:    // the length is preserved in these ops             {                    // for the scoping of variables
198     case SUB:                  const DataTypes::ShapeType& s=left->getShape();
199     case MUL:                  DataTypes::ShapeType sh;
200     case DIV: return left->getLength();                  int rank=left->getRank();
201     default:                  if (axis_offset<0 || axis_offset>rank)
202      throw DataException("Programmer Error - attempt to getLength() for operator "+opToString(op)+".");                  {
203                stringstream e;
204                e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
205                throw DataException(e.str());
206            }
207            for (int i=0; i<rank; i++)
208                    {
209                       int index = (axis_offset+i)%rank;
210               sh.push_back(s[index]); // Append to new shape
211            }
212                    return sh;
213               }
214            break;
215            case TRACE:
216               {
217                    int rank=left->getRank();
218                    if (rank<2)
219                    {
220                       throw DataException("Trace can only be computed for objects with rank 2 or greater.");
221                    }
222                    if ((axis_offset>rank-2) || (axis_offset<0))
223                    {
224                       throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
225                    }
226                    if (rank==2)
227                    {
228                       return DataTypes::scalarShape;
229                    }
230                    else if (rank==3)
231                    {
232                       DataTypes::ShapeType sh;
233                       if (axis_offset==0)
234                       {
235                            sh.push_back(left->getShape()[2]);
236                       }
237                       else         // offset==1
238                       {
239                            sh.push_back(left->getShape()[0]);
240                       }
241                       return sh;
242                    }
243                    else if (rank==4)
244                    {
245                       DataTypes::ShapeType sh;
246                       const DataTypes::ShapeType& s=left->getShape();
247                       if (axis_offset==0)
248                       {
249                            sh.push_back(s[2]);
250                            sh.push_back(s[3]);
251                       }
252                       else if (axis_offset==1)
253                       {
254                            sh.push_back(s[0]);
255                            sh.push_back(s[3]);
256                       }
257                       else         // offset==2
258                       {
259                            sh.push_back(s[0]);
260                            sh.push_back(s[1]);
261                       }
262                       return sh;
263                    }
264                    else            // unknown rank
265                    {
266                       throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
267                    }
268               }
269            break;
270            default:
271            throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
272            }
273    }
274    
275     }  DataTypes::ShapeType
276    SwapShape(DataAbstract_ptr left, const int axis0, const int axis1)
277    {
278         // This code taken from the Data.cpp swapaxes() method
279         // Some of the checks are probably redundant here
280         int axis0_tmp,axis1_tmp;
281         const DataTypes::ShapeType& s=left->getShape();
282         DataTypes::ShapeType out_shape;
283         // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
284         // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
285         int rank=left->getRank();
286         if (rank<2) {
287            throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
288         }
289         if (axis0<0 || axis0>rank-1) {
290            stringstream e;
291            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
292            throw DataException(e.str());
293         }
294         if (axis1<0 || axis1>rank-1) {
295            stringstream e;
296            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
297            throw DataException(e.str());
298         }
299         if (axis0 == axis1) {
300             throw DataException("Error - Data::swapaxes: axis indices must be different.");
301         }
302         if (axis0 > axis1) {
303             axis0_tmp=axis1;
304             axis1_tmp=axis0;
305         } else {
306             axis0_tmp=axis0;
307             axis1_tmp=axis1;
308         }
309         for (int i=0; i<rank; i++) {
310           if (i == axis0_tmp) {
311              out_shape.push_back(s[axis1_tmp]);
312           } else if (i == axis1_tmp) {
313              out_shape.push_back(s[axis0_tmp]);
314           } else {
315              out_shape.push_back(s[i]);
316           }
317         }
318        return out_shape;
319  }  }
320    
 string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/"};  
 int ES_opcount=5;  
321    
322  }   // end anonymous namespace  // determine the output shape for the general tensor product operation
323    // the additional parameters return information required later for the product
324    // the majority of this code is copy pasted from C_General_Tensor_Product
325    DataTypes::ShapeType
326    GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
327    {
328            
329      // Get rank and shape of inputs
330      int rank0 = left->getRank();
331      int rank1 = right->getRank();
332      const DataTypes::ShapeType& shape0 = left->getShape();
333      const DataTypes::ShapeType& shape1 = right->getShape();
334    
335      // Prepare for the loops of the product and verify compatibility of shapes
336      int start0=0, start1=0;
337      if (transpose == 0)           {}
338      else if (transpose == 1)      { start0 = axis_offset; }
339      else if (transpose == 2)      { start1 = rank1-axis_offset; }
340      else                          { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }
341    
342      if (rank0<axis_offset)
343      {
344            throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
345      }
346    
347      // Adjust the shapes for transpose
348      DataTypes::ShapeType tmpShape0(rank0);        // pre-sizing the vectors rather
349      DataTypes::ShapeType tmpShape1(rank1);        // than using push_back
350      for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
351      for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
352    
353      // Prepare for the loops of the product
354      SL=1, SM=1, SR=1;
355      for (int i=0; i<rank0-axis_offset; i++)       {
356        SL *= tmpShape0[i];
357      }
358      for (int i=rank0-axis_offset; i<rank0; i++)   {
359        if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
360          throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
361        }
362        SM *= tmpShape0[i];
363      }
364      for (int i=axis_offset; i<rank1; i++)         {
365        SR *= tmpShape1[i];
366      }
367    
368  const std::string&    // Define the shape of the output (rank of shape is the sum of the loop ranges below)
369  opToString(ES_optype op)    DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      
370  {    {                     // block to limit the scope of out_index
371    if (op<0 || op>=ES_opcount)       int out_index=0;
372         for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
373         for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
374      }
375    
376      if (shape2.size()>ESCRIPT_MAX_DATA_RANK)
377    {    {
378      op=UNKNOWNOP;       ostringstream os;
379         os << "C_GeneralTensorProduct: Error - Attempt to create a rank " << shape2.size() << " object. The maximum rank is " << ESCRIPT_MAX_DATA_RANK << ".";
380         throw DataException(os.str());
381    }    }
382    return ES_opstrings[op];  
383      return shape2;
384  }  }
385    
386    }       // end anonymous namespace
387    
388    void DataLazy::LazyNodeSetup()
389    {
390    #ifdef _OPENMP
391        int numthreads=omp_get_max_threads();
392        m_samples.resize(numthreads*m_samplesize);
393        m_sampleids=new int[numthreads];
394        for (int i=0;i<numthreads;++i)
395        {
396            m_sampleids[i]=-1;  
397        }
398    #else
399        m_samples.resize(m_samplesize);
400        m_sampleids=new int[1];
401        m_sampleids[0]=-1;
402    #endif  // _OPENMP
403    }
404    
405    
406    // Creates an identity node
407  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
408      : parent(p->getFunctionSpace(),p->getShape()),          : parent(p->getFunctionSpace(),p->getShape())
409      m_left(p),          ,m_sampleids(0),
410      m_op(IDENTITY)          m_samples(1)
411    {
412       if (p->isLazy())
413       {
414            // I don't want identity of Lazy.
415            // Question: Why would that be so bad?
416            // Answer: We assume that the child of ID is something we can call getVector on
417            throw DataException("Programmer error - attempt to create identity from a DataLazy.");
418       }
419       else
420       {
421            DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
422            makeIdentity(dr);
423    LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)
424       }
425    LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
426    }
427    
428    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
429            : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
430            m_op(op),
431            m_axis_offset(0),
432            m_transpose(0),
433            m_SL(0), m_SM(0), m_SR(0)
434  {  {
435     length=resultLength(m_left,m_right,m_op);     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
436       {
437            throw DataException("Programmer error - constructor DataLazy(left, op) will only process UNARY operations.");
438       }
439    
440       DataLazy_ptr lleft;
441       if (!left->isLazy())
442       {
443            lleft=DataLazy_ptr(new DataLazy(left));
444       }
445       else
446       {
447            lleft=dynamic_pointer_cast<DataLazy>(left);
448       }
449       m_readytype=lleft->m_readytype;
450       m_left=lleft;
451       m_samplesize=getNumDPPSample()*getNoValues();
452       m_children=m_left->m_children+1;
453       m_height=m_left->m_height+1;
454       LazyNodeSetup();
455       SIZELIMIT
456  }  }
457    
458    
459    // In this constructor we need to consider interpolation
460  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
461      : parent(resultFS(left,right,op), resultShape(left,right,op)),          : parent(resultFS(left,right,op), resultShape(left,right,op)),
462      m_left(left),          m_op(op),
463      m_right(right),          m_SL(0), m_SM(0), m_SR(0)
464      m_op(op)  {
465    LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
466       if ((getOpgroup(op)!=G_BINARY))
467       {
468            throw DataException("Programmer error - constructor DataLazy(left, right, op) will only process BINARY operations.");
469       }
470    
471       if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
472       {
473            FunctionSpace fs=getFunctionSpace();
474            Data ltemp(left);
475            Data tmp(ltemp,fs);
476            left=tmp.borrowDataPtr();
477       }
478       if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
479       {
480            Data tmp(Data(right),getFunctionSpace());
481            right=tmp.borrowDataPtr();
482    LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
483       }
484       left->operandCheck(*right);
485    
486       if (left->isLazy())                  // the children need to be DataLazy. Wrap them in IDENTITY if required
487       {
488            m_left=dynamic_pointer_cast<DataLazy>(left);
489    LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
490       }
491       else
492       {
493            m_left=DataLazy_ptr(new DataLazy(left));
494    LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
495       }
496       if (right->isLazy())
497       {
498            m_right=dynamic_pointer_cast<DataLazy>(right);
499    LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
500       }
501       else
502       {
503            m_right=DataLazy_ptr(new DataLazy(right));
504    LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
505       }
506       char lt=m_left->m_readytype;
507       char rt=m_right->m_readytype;
508       if (lt=='E' || rt=='E')
509       {
510            m_readytype='E';
511       }
512       else if (lt=='T' || rt=='T')
513       {
514            m_readytype='T';
515       }
516       else
517       {
518            m_readytype='C';
519       }
520       m_samplesize=getNumDPPSample()*getNoValues();
521       m_children=m_left->m_children+m_right->m_children+2;
522       m_height=max(m_left->m_height,m_right->m_height)+1;
523       LazyNodeSetup();
524       SIZELIMIT
525    LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
526    }
527    
528    DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)
529            : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
530            m_op(op),
531            m_axis_offset(axis_offset),
532            m_transpose(transpose)
533    {
534       if ((getOpgroup(op)!=G_TENSORPROD))
535       {
536            throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
537       }
538       if ((transpose>2) || (transpose<0))
539       {
540            throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
541       }
542       if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
543       {
544            FunctionSpace fs=getFunctionSpace();
545            Data ltemp(left);
546            Data tmp(ltemp,fs);
547            left=tmp.borrowDataPtr();
548       }
549       if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
550       {
551            Data tmp(Data(right),getFunctionSpace());
552            right=tmp.borrowDataPtr();
553       }
554    //    left->operandCheck(*right);
555    
556       if (left->isLazy())                  // the children need to be DataLazy. Wrap them in IDENTITY if required
557       {
558            m_left=dynamic_pointer_cast<DataLazy>(left);
559       }
560       else
561       {
562            m_left=DataLazy_ptr(new DataLazy(left));
563       }
564       if (right->isLazy())
565       {
566            m_right=dynamic_pointer_cast<DataLazy>(right);
567       }
568       else
569       {
570            m_right=DataLazy_ptr(new DataLazy(right));
571       }
572       char lt=m_left->m_readytype;
573       char rt=m_right->m_readytype;
574       if (lt=='E' || rt=='E')
575       {
576            m_readytype='E';
577       }
578       else if (lt=='T' || rt=='T')
579       {
580            m_readytype='T';
581       }
582       else
583       {
584            m_readytype='C';
585       }
586       m_samplesize=getNumDPPSample()*getNoValues();
587       m_children=m_left->m_children+m_right->m_children+2;
588       m_height=max(m_left->m_height,m_right->m_height)+1;
589       LazyNodeSetup();
590       SIZELIMIT
591    LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
592    }
593    
594    
595    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
596            : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
597            m_op(op),
598            m_axis_offset(axis_offset),
599            m_transpose(0),
600            m_tol(0)
601    {
602       if ((getOpgroup(op)!=G_NP1OUT_P))
603       {
604            throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
605       }
606       DataLazy_ptr lleft;
607       if (!left->isLazy())
608       {
609            lleft=DataLazy_ptr(new DataLazy(left));
610       }
611       else
612       {
613            lleft=dynamic_pointer_cast<DataLazy>(left);
614       }
615       m_readytype=lleft->m_readytype;
616       m_left=lleft;
617       m_samplesize=getNumDPPSample()*getNoValues();
618       m_children=m_left->m_children+1;
619       m_height=m_left->m_height+1;
620       LazyNodeSetup();
621       SIZELIMIT
622    LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
623    }
624    
625    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
626            : parent(left->getFunctionSpace(), left->getShape()),
627            m_op(op),
628            m_axis_offset(0),
629            m_transpose(0),
630            m_tol(tol)
631    {
632       if ((getOpgroup(op)!=G_UNARY_P))
633       {
634            throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
635       }
636       DataLazy_ptr lleft;
637       if (!left->isLazy())
638       {
639            lleft=DataLazy_ptr(new DataLazy(left));
640       }
641       else
642       {
643            lleft=dynamic_pointer_cast<DataLazy>(left);
644       }
645       m_readytype=lleft->m_readytype;
646       m_left=lleft;
647       m_samplesize=getNumDPPSample()*getNoValues();
648       m_children=m_left->m_children+1;
649       m_height=m_left->m_height+1;
650       LazyNodeSetup();
651       SIZELIMIT
652    LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
653    }
654    
655    
656    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
657            : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
658            m_op(op),
659            m_axis_offset(axis0),
660            m_transpose(axis1),
661            m_tol(0)
662    {
663       if ((getOpgroup(op)!=G_NP1OUT_2P))
664       {
665            throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
666       }
667       DataLazy_ptr lleft;
668       if (!left->isLazy())
669       {
670            lleft=DataLazy_ptr(new DataLazy(left));
671       }
672       else
673       {
674            lleft=dynamic_pointer_cast<DataLazy>(left);
675       }
676       m_readytype=lleft->m_readytype;
677       m_left=lleft;
678       m_samplesize=getNumDPPSample()*getNoValues();
679       m_children=m_left->m_children+1;
680       m_height=m_left->m_height+1;
681       LazyNodeSetup();
682       SIZELIMIT
683    LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
684    }
685    
686    
687    namespace
688  {  {
689     length=resultLength(m_left,m_right,m_op);  
690        inline int max3(int a, int b, int c)
691        {
692            int t=(a>b?a:b);
693            return (t>c?t:c);
694    
695        }
696    }
697    
698    DataLazy::DataLazy(DataAbstract_ptr mask, DataAbstract_ptr left, DataAbstract_ptr right/*, double tol*/)
699            : parent(left->getFunctionSpace(), left->getShape()),
700            m_op(CONDEVAL),
701            m_axis_offset(0),
702            m_transpose(0),
703            m_tol(0)
704    {
705    
706       DataLazy_ptr lmask;
707       DataLazy_ptr lleft;
708       DataLazy_ptr lright;
709       if (!mask->isLazy())
710       {
711            lmask=DataLazy_ptr(new DataLazy(mask));
712       }
713       else
714       {
715            lmask=dynamic_pointer_cast<DataLazy>(mask);
716       }
717       if (!left->isLazy())
718       {
719            lleft=DataLazy_ptr(new DataLazy(left));
720       }
721       else
722       {
723            lleft=dynamic_pointer_cast<DataLazy>(left);
724       }
725       if (!right->isLazy())
726       {
727            lright=DataLazy_ptr(new DataLazy(right));
728       }
729       else
730       {
731            lright=dynamic_pointer_cast<DataLazy>(right);
732       }
733       m_readytype=lmask->m_readytype;
734       if ((lleft->m_readytype!=lright->m_readytype) || (lmask->m_readytype!=lleft->m_readytype))
735       {
736            throw DataException("Programmer Error - condEval arguments must have the same readytype");
737       }
738       m_left=lleft;
739       m_right=lright;
740       m_mask=lmask;
741       m_samplesize=getNumDPPSample()*getNoValues();
742       m_children=m_left->m_children+m_right->m_children+m_mask->m_children+1;
743       m_height=max3(m_left->m_height,m_right->m_height,m_mask->m_height)+1;
744       LazyNodeSetup();
745       SIZELIMIT
746    LAZYDEBUG(cout << "(8)Lazy created with " << m_samplesize << endl;)
747  }  }
748    
749    
750    
751  DataLazy::~DataLazy()  DataLazy::~DataLazy()
752  {  {
753       delete[] m_sampleids;
754  }  }
755    
756  // If resolving records a pointer to the resolved Data we may need to rethink the const on this method  
757    /*
758      \brief Evaluates the expression using methods on Data.
759      This does the work for the collapse method.
760      For reasons of efficiency do not call this method on DataExpanded nodes.
761    */
762  DataReady_ptr  DataReady_ptr
763  DataLazy::resolve()  DataLazy::collapseToReady() const
764    {
765      if (m_readytype=='E')
766      {     // this is more an efficiency concern than anything else
767        throw DataException("Programmer Error - do not use collapse on Expanded data.");
768      }
769      if (m_op==IDENTITY)
770      {
771        return m_id;
772      }
773      DataReady_ptr pleft=m_left->collapseToReady();
774      Data left(pleft);
775      Data right;
776      if ((getOpgroup(m_op)==G_BINARY) || (getOpgroup(m_op)==G_TENSORPROD))
777      {
778        right=Data(m_right->collapseToReady());
779      }
780      Data result;
781      switch(m_op)
782      {
783        case ADD:
784            result=left+right;
785            break;
786        case SUB:          
787            result=left-right;
788            break;
789        case MUL:          
790            result=left*right;
791            break;
792        case DIV:          
793            result=left/right;
794            break;
795        case SIN:
796            result=left.sin();      
797            break;
798        case COS:
799            result=left.cos();
800            break;
801        case TAN:
802            result=left.tan();
803            break;
804        case ASIN:
805            result=left.asin();
806            break;
807        case ACOS:
808            result=left.acos();
809            break;
810        case ATAN:
811            result=left.atan();
812            break;
813        case SINH:
814            result=left.sinh();
815            break;
816        case COSH:
817            result=left.cosh();
818            break;
819        case TANH:
820            result=left.tanh();
821            break;
822        case ERF:
823            result=left.erf();
824            break;
825       case ASINH:
826            result=left.asinh();
827            break;
828       case ACOSH:
829            result=left.acosh();
830            break;
831       case ATANH:
832            result=left.atanh();
833            break;
834        case LOG10:
835            result=left.log10();
836            break;
837        case LOG:
838            result=left.log();
839            break;
840        case SIGN:
841            result=left.sign();
842            break;
843        case ABS:
844            result=left.abs();
845            break;
846        case NEG:
847            result=left.neg();
848            break;
849        case POS:
850            // it doesn't mean anything for delayed.
851            // it will just trigger a deep copy of the lazy object
852            throw DataException("Programmer error - POS not supported for lazy data.");
853            break;
854        case EXP:
855            result=left.exp();
856            break;
857        case SQRT:
858            result=left.sqrt();
859            break;
860        case RECIP:
861            result=left.oneOver();
862            break;
863        case GZ:
864            result=left.wherePositive();
865            break;
866        case LZ:
867            result=left.whereNegative();
868            break;
869        case GEZ:
870            result=left.whereNonNegative();
871            break;
872        case LEZ:
873            result=left.whereNonPositive();
874            break;
875        case NEZ:
876            result=left.whereNonZero(m_tol);
877            break;
878        case EZ:
879            result=left.whereZero(m_tol);
880            break;
881        case SYM:
882            result=left.symmetric();
883            break;
884        case NSYM:
885            result=left.antisymmetric();
886            break;
887        case PROD:
888            result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
889            break;
890        case TRANS:
891            result=left.transpose(m_axis_offset);
892            break;
893        case TRACE:
894            result=left.trace(m_axis_offset);
895            break;
896        case SWAP:
897            result=left.swapaxes(m_axis_offset, m_transpose);
898            break;
899        case MINVAL:
900            result=left.minval();
901            break;
902        case MAXVAL:
903            result=left.minval();
904            break;
905        case HER:
906        result=left.hermitian();
907        break;
908        default:
909            throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");
910      }
911      return result.borrowReadyPtr();
912    }
913    
914    /*
915       \brief Converts the DataLazy into an IDENTITY storing the value of the expression.
916       This method uses the original methods on the Data class to evaluate the expressions.
917       For this reason, it should not be used on DataExpanded instances. (To do so would defeat
918       the purpose of using DataLazy in the first place).
919    */
920    void
921    DataLazy::collapse() const
922    {
923      if (m_op==IDENTITY)
924      {
925            return;
926      }
927      if (m_readytype=='E')
928      {     // this is more an efficiency concern than anything else
929        throw DataException("Programmer Error - do not use collapse on Expanded data.");
930      }
931      m_id=collapseToReady();
932      m_op=IDENTITY;
933    }
934    
935    // The result will be stored in m_samples
936    // The return value is a pointer to the DataVector, offset is the offset within the return value
937    const DataTypes::RealVectorType*
938    DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
939    {
940    LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
941            // collapse so we have a 'E' node or an IDENTITY for some other type
942      if (m_readytype!='E' && m_op!=IDENTITY)
943      {
944            collapse();
945      }
946      if (m_op==IDENTITY)  
947      {
948        const RealVectorType& vec=m_id->getVectorRO();
949        roffset=m_id->getPointOffset(sampleNo, 0);
950    #ifdef LAZY_STACK_PROF
951    int x;
952    if (&x<stackend[omp_get_thread_num()])
953    {
954           stackend[omp_get_thread_num()]=&x;
955    }
956    #endif
957        return &(vec);
958      }
959      if (m_readytype!='E')
960      {
961        throw DataException("Programmer Error - Collapse did not produce an expanded node.");
962      }
963      if (m_sampleids[tid]==sampleNo)
964      {
965            roffset=tid*m_samplesize;
966            return &(m_samples);            // sample is already resolved
967      }
968      m_sampleids[tid]=sampleNo;
969    
970      switch (getOpgroup(m_op))
971      {
972      case G_UNARY:
973      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
974      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
975      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
976      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
977      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
978      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
979      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
980      case G_CONDEVAL: return resolveNodeCondEval(tid, sampleNo, roffset);
981      default:
982        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
983      }
984    }
985    
986    const DataTypes::RealVectorType*
987    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset) const
988    {
989            // we assume that any collapsing has been done before we get here
990            // since we only have one argument we don't need to think about only
991            // processing single points.
992            // we will also know we won't get identity nodes
993      if (m_readytype!='E')
994      {
995        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
996      }
997      if (m_op==IDENTITY)
998      {
999        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1000      }
1001      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1002      const double* left=&((*leftres)[roffset]);
1003      roffset=m_samplesize*tid;
1004      double* result=&(m_samples[roffset]);
1005      if (m_op==POS)
1006      {
1007        // this should be prevented earlier
1008        // operation is meaningless for lazy
1009            throw DataException("Programmer error - POS not supported for lazy data.");    
1010      }
1011      tensor_unary_array_operation(m_samplesize,
1012                                 left,
1013                                 result,
1014                                 m_op,
1015                                 m_tol);  
1016      return &(m_samples);
1017    }
1018    
1019    
1020    const DataTypes::RealVectorType*
1021    DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset) const
1022    {
1023            // we assume that any collapsing has been done before we get here
1024            // since we only have one argument we don't need to think about only
1025            // processing single points.
1026            // we will also know we won't get identity nodes
1027      if (m_readytype!='E')
1028      {
1029        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1030      }
1031      if (m_op==IDENTITY)
1032      {
1033        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1034      }
1035      size_t loffset=0;
1036      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1037    
1038      roffset=m_samplesize*tid;
1039      unsigned int ndpps=getNumDPPSample();
1040      unsigned int psize=DataTypes::noValues(m_left->getShape());
1041      double* result=&(m_samples[roffset]);
1042      switch (m_op)
1043      {
1044        case MINVAL:
1045            {
1046              for (unsigned int z=0;z<ndpps;++z)
1047              {
1048                FMin op;
1049                *result=escript::reductionOpVector(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1050                loffset+=psize;
1051                result++;
1052              }
1053            }
1054            break;
1055        case MAXVAL:
1056            {
1057              for (unsigned int z=0;z<ndpps;++z)
1058              {
1059              FMax op;
1060              *result=escript::reductionOpVector(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1061              loffset+=psize;
1062              result++;
1063              }
1064            }
1065            break;
1066        default:
1067            throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1068      }
1069      return &(m_samples);
1070    }
1071    
1072    const DataTypes::RealVectorType*
1073    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset) const
1074    {
1075            // we assume that any collapsing has been done before we get here
1076            // since we only have one argument we don't need to think about only
1077            // processing single points.
1078      if (m_readytype!='E')
1079      {
1080        throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1081      }
1082      if (m_op==IDENTITY)
1083      {
1084        throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1085      }
1086      size_t subroffset;
1087      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1088      roffset=m_samplesize*tid;
1089      size_t loop=0;
1090      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1091      size_t step=getNoValues();
1092      size_t offset=roffset;
1093      switch (m_op)
1094      {
1095        case SYM:
1096            for (loop=0;loop<numsteps;++loop)
1097            {
1098                escript::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1099                subroffset+=step;
1100                offset+=step;
1101            }
1102            break;
1103        case NSYM:
1104            for (loop=0;loop<numsteps;++loop)
1105            {
1106                escript::antisymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1107                subroffset+=step;
1108                offset+=step;
1109            }
1110            break;
1111        default:
1112            throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1113      }
1114      return &m_samples;
1115    }
1116    
1117    const DataTypes::RealVectorType*
1118    DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset) const
1119    {
1120            // we assume that any collapsing has been done before we get here
1121            // since we only have one argument we don't need to think about only
1122            // processing single points.
1123      if (m_readytype!='E')
1124      {
1125        throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
1126      }
1127      if (m_op==IDENTITY)
1128      {
1129        throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1130      }
1131      size_t subroffset;
1132      size_t offset;
1133      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1134      roffset=m_samplesize*tid;
1135      offset=roffset;
1136      size_t loop=0;
1137      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1138      size_t outstep=getNoValues();
1139      size_t instep=m_left->getNoValues();
1140      switch (m_op)
1141      {
1142        case TRACE:
1143            for (loop=0;loop<numsteps;++loop)
1144            {
1145                escript::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1146                subroffset+=instep;
1147                offset+=outstep;
1148            }
1149            break;
1150        case TRANS:
1151            for (loop=0;loop<numsteps;++loop)
1152            {
1153                escript::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1154                subroffset+=instep;
1155                offset+=outstep;
1156            }
1157            break;
1158        default:
1159            throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1160      }
1161      return &m_samples;
1162    }
1163    
1164    
1165    const DataTypes::RealVectorType*
1166    DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1167  {  {
1168    DataReady_ptr left;    if (m_readytype!='E')
   DataReady_ptr right;  
   if (m_left.get()!=0)  
1169    {    {
1170      left=m_left->resolve();      throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1171    }    }
1172    if (m_right.get()!=0)    if (m_op==IDENTITY)
1173    {    {
1174      right=m_right->resolve();      throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1175    }    }
1176      size_t subroffset;
1177      size_t offset;
1178      const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1179      roffset=m_samplesize*tid;
1180      offset=roffset;
1181      size_t loop=0;
1182      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1183      size_t outstep=getNoValues();
1184      size_t instep=m_left->getNoValues();
1185    switch (m_op)    switch (m_op)
1186    {    {
1187      case IDENTITY: return left;      case SWAP:
1188            for (loop=0;loop<numsteps;++loop)
1189            {
1190                escript::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1191                subroffset+=instep;
1192                offset+=outstep;
1193            }
1194            break;
1195        default:
1196            throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1197      }
1198      return &m_samples;
1199    }
1200    
1201    const DataTypes::RealVectorType*
1202    DataLazy::resolveNodeCondEval(int tid, int sampleNo, size_t& roffset) const
1203    {
1204      if (m_readytype!='E')
1205      {
1206        throw DataException("Programmer error - resolveNodeCondEval should only be called on expanded Data.");
1207      }
1208      if (m_op!=CONDEVAL)
1209      {
1210        throw DataException("Programmer error - resolveNodeCondEval should only be called on CONDEVAL nodes.");
1211      }
1212      size_t subroffset;
1213    
1214      const RealVectorType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1215      const RealVectorType* srcres=0;
1216      if ((*maskres)[subroffset]>0)
1217      {
1218            srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1219      }
1220      else
1221      {
1222            srcres=m_right->resolveNodeSample(tid, sampleNo, subroffset);
1223      }
1224    
1225      // Now we need to copy the result
1226    
1227      roffset=m_samplesize*tid;
1228      for (int i=0;i<m_samplesize;++i)
1229      {
1230            m_samples[roffset+i]=(*srcres)[subroffset+i];  
1231      }
1232    
1233      return &m_samples;
1234    }
1235    
1236    // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1237    // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1238    // If both children are expanded, then we can process them in a single operation (we treat
1239    // the whole sample as one big datapoint.
1240    // If one of the children is not expanded, then we need to treat each point in the sample
1241    // individually.
1242    // There is an additional complication when scalar operations are considered.
1243    // For example, 2+Vector.
1244    // In this case each double within the point is treated individually
1245    const DataTypes::RealVectorType*
1246    DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset) const
1247    {
1248    LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1249    
1250      size_t lroffset=0, rroffset=0;        // offsets in the left and right result vectors
1251            // first work out which of the children are expanded
1252      bool leftExp=(m_left->m_readytype=='E');
1253      bool rightExp=(m_right->m_readytype=='E');
1254      if (!leftExp && !rightExp)
1255      {
1256            throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");
1257      }
1258      bool leftScalar=(m_left->getRank()==0);
1259      bool rightScalar=(m_right->getRank()==0);
1260      if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1261      {
1262            throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1263      }
1264      size_t leftsize=m_left->getNoValues();
1265      size_t rightsize=m_right->getNoValues();
1266      size_t chunksize=1;                   // how many doubles will be processed in one go
1267      int leftstep=0;               // how far should the left offset advance after each step
1268      int rightstep=0;
1269      int numsteps=0;               // total number of steps for the inner loop
1270      int oleftstep=0;      // the o variables refer to the outer loop
1271      int orightstep=0;     // The outer loop is only required in cases where there is an extended scalar
1272      int onumsteps=1;
1273      
1274      bool LES=(leftExp && leftScalar);     // Left is an expanded scalar
1275      bool RES=(rightExp && rightScalar);
1276      bool LS=(!leftExp && leftScalar);     // left is a single scalar
1277      bool RS=(!rightExp && rightScalar);
1278      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1279      bool RN=(!rightExp && !rightScalar);
1280      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1281      bool REN=(rightExp && !rightScalar);
1282    
1283      if ((LES && RES) || (LEN && REN))     // both are Expanded scalars or both are expanded non-scalars
1284      {
1285            chunksize=m_left->getNumDPPSample()*leftsize;
1286            leftstep=0;
1287            rightstep=0;
1288            numsteps=1;
1289      }
1290      else if (LES || RES)
1291      {
1292            chunksize=1;
1293            if (LES)                // left is an expanded scalar
1294            {
1295                    if (RS)
1296                    {
1297                       leftstep=1;
1298                       rightstep=0;
1299                       numsteps=m_left->getNumDPPSample();
1300                    }
1301                    else            // RN or REN
1302                    {
1303                       leftstep=0;
1304                       oleftstep=1;
1305                       rightstep=1;
1306                       orightstep=(RN ? -(int)rightsize : 0);
1307                       numsteps=rightsize;
1308                       onumsteps=m_left->getNumDPPSample();
1309                    }
1310            }
1311            else            // right is an expanded scalar
1312            {
1313                    if (LS)
1314                    {
1315                       rightstep=1;
1316                       leftstep=0;
1317                       numsteps=m_right->getNumDPPSample();
1318                    }
1319                    else
1320                    {
1321                       rightstep=0;
1322                       orightstep=1;
1323                       leftstep=1;
1324                       oleftstep=(LN ? -(int)leftsize : 0);
1325                       numsteps=leftsize;
1326                       onumsteps=m_right->getNumDPPSample();
1327                    }
1328            }
1329      }
1330      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1331      {
1332            if (LEN)        // and Right will be a single value
1333            {
1334                    chunksize=rightsize;
1335                    leftstep=rightsize;
1336                    rightstep=0;
1337                    numsteps=m_left->getNumDPPSample();
1338                    if (RS)
1339                    {
1340                       numsteps*=leftsize;
1341                    }
1342            }
1343            else    // REN
1344            {
1345                    chunksize=leftsize;
1346                    rightstep=leftsize;
1347                    leftstep=0;
1348                    numsteps=m_right->getNumDPPSample();
1349                    if (LS)
1350                    {
1351                       numsteps*=rightsize;
1352                    }
1353            }
1354      }
1355    
1356      int resultStep=max(leftstep,rightstep);       // only one (at most) should be !=0
1357            // Get the values of sub-expressions
1358      const RealVectorType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      
1359      const RealVectorType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1360    LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1361    LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1362    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1363    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1364    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1365    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1366    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1367    
1368    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1369    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1370    
1371    
1372      roffset=m_samplesize*tid;
1373      double* resultp=&(m_samples[roffset]);                // results are stored at the vector offset we received
1374      switch(m_op)
1375      {
1376      case ADD:      case ADD:
1377      // Hmm we could get interpolation here, better be careful          //PROC_OP(NO_ARG,plus<double>());
1378        return C_TensorBinaryOperation(Data(left),Data(right),plus<double>()).borrowReadyPtr();        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1379                 &(*left)[0],
1380                 &(*right)[0],
1381                 chunksize,
1382                 onumsteps,
1383                 numsteps,
1384                 resultStep,
1385                 leftstep,
1386                 rightstep,
1387                 oleftstep,
1388                 orightstep,
1389                 lroffset,
1390                 rroffset,
1391                 escript::ES_optype::ADD);  
1392            break;
1393      case SUB:      case SUB:
1394        return C_TensorBinaryOperation(Data(left),Data(right),minus<double>()).borrowReadyPtr();        escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1395                 &(*left)[0],
1396                 &(*right)[0],
1397                 chunksize,
1398                 onumsteps,
1399                 numsteps,
1400                 resultStep,
1401                 leftstep,
1402                 rightstep,
1403                 oleftstep,
1404                 orightstep,
1405                 lroffset,
1406                 rroffset,
1407                 escript::ES_optype::SUB);        
1408            //PROC_OP(NO_ARG,minus<double>());
1409            break;
1410      case MUL:      case MUL:
1411        return C_TensorBinaryOperation(Data(left),Data(right),multiplies<double>()).borrowReadyPtr();          //PROC_OP(NO_ARG,multiplies<double>());
1412          escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1413                 &(*left)[0],
1414                 &(*right)[0],
1415                 chunksize,
1416                 onumsteps,
1417                 numsteps,
1418                 resultStep,
1419                 leftstep,
1420                 rightstep,
1421                 oleftstep,
1422                 orightstep,
1423                 lroffset,
1424                 rroffset,
1425                 escript::ES_optype::MUL);        
1426            break;
1427      case DIV:      case DIV:
1428        return C_TensorBinaryOperation(Data(left),Data(right),divides<double>()).borrowReadyPtr();          //PROC_OP(NO_ARG,divides<double>());
1429          escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1430                 &(*left)[0],
1431                 &(*right)[0],
1432                 chunksize,
1433                 onumsteps,
1434                 numsteps,
1435                 resultStep,
1436                 leftstep,
1437                 rightstep,
1438                 oleftstep,
1439                 orightstep,
1440                 lroffset,
1441                 rroffset,
1442                 escript::ES_optype::DIV);        
1443            break;
1444        case POW:
1445           //PROC_OP(double (double,double),::pow);
1446          escript::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1447                 &(*left)[0],
1448                 &(*right)[0],
1449                 chunksize,
1450                 onumsteps,
1451                 numsteps,
1452                 resultStep,
1453                 leftstep,
1454                 rightstep,
1455                 oleftstep,
1456                 orightstep,
1457                 lroffset,
1458                 rroffset,
1459                 escript::ES_optype::POW);        
1460            break;
1461        default:
1462            throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1463      }
1464    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1465      return &m_samples;
1466    }
1467    
1468    
1469    // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1470    // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1471    // unlike the other resolve helpers, we must treat these datapoints separately.
1472    const DataTypes::RealVectorType*
1473    DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1474    {
1475    LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1476    
1477      size_t lroffset=0, rroffset=0;        // offsets in the left and right result vectors
1478            // first work out which of the children are expanded
1479      bool leftExp=(m_left->m_readytype=='E');
1480      bool rightExp=(m_right->m_readytype=='E');
1481      int steps=getNumDPPSample();
1482      int leftStep=(leftExp? m_left->getNoValues() : 0);            // do not have scalars as input to this method
1483      int rightStep=(rightExp?m_right->getNoValues() : 0);
1484    
1485      int resultStep=getNoValues();
1486      roffset=m_samplesize*tid;
1487      size_t offset=roffset;
1488    
1489      const RealVectorType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1490    
1491      const RealVectorType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1492    
1493    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1494    cout << getNoValues() << endl;)
1495    
1496    
1497    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1498    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1499    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1500    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1501    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1502    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1503    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1504    
1505      double* resultp=&(m_samples[offset]);         // results are stored at the vector offset we received
1506      switch(m_op)
1507      {
1508        case PROD:
1509            for (int i=0;i<steps;++i,resultp+=resultStep)
1510            {
1511              const double *ptr_0 = &((*left)[lroffset]);
1512              const double *ptr_1 = &((*right)[rroffset]);
1513    
1514    LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1515    LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1516    
1517              matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1518    
1519              lroffset+=leftStep;
1520              rroffset+=rightStep;
1521            }
1522            break;
1523      default:      default:
1524      throw DataException("Programmer error - do not know how to resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1525      }
1526      roffset=offset;
1527      return &m_samples;
1528    }
1529    
1530    
1531    const DataTypes::RealVectorType*
1532    DataLazy::resolveSample(int sampleNo, size_t& roffset) const
1533    {
1534    #ifdef _OPENMP
1535            int tid=omp_get_thread_num();
1536    #else
1537            int tid=0;
1538    #endif
1539    
1540    #ifdef LAZY_STACK_PROF
1541            stackstart[tid]=&tid;
1542            stackend[tid]=&tid;
1543            const DataTypes::RealVectorType* r=resolveNodeSample(tid, sampleNo, roffset);
1544            size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1545            #pragma omp critical
1546            if (d>maxstackuse)
1547            {
1548    cout << "Max resolve Stack use " << d << endl;
1549                    maxstackuse=d;
1550            }
1551            return r;
1552    #else
1553            return resolveNodeSample(tid, sampleNo, roffset);
1554    #endif
1555    }
1556    
1557    
1558    // This needs to do the work of the identity constructor
1559    void
1560    DataLazy::resolveToIdentity()
1561    {
1562       if (m_op==IDENTITY)
1563            return;
1564       DataReady_ptr p=resolveNodeWorker();
1565       makeIdentity(p);
1566    }
1567    
1568    void DataLazy::makeIdentity(const DataReady_ptr& p)
1569    {
1570       m_op=IDENTITY;
1571       m_axis_offset=0;
1572       m_transpose=0;
1573       m_SL=m_SM=m_SR=0;
1574       m_children=m_height=0;
1575       m_id=p;
1576       if(p->isConstant()) {m_readytype='C';}
1577       else if(p->isExpanded()) {m_readytype='E';}
1578       else if (p->isTagged()) {m_readytype='T';}
1579       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1580       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1581       m_left.reset();
1582       m_right.reset();
1583    }
1584    
1585    
1586    DataReady_ptr
1587    DataLazy::resolve()
1588    {
1589        resolveToIdentity();
1590        return m_id;
1591    }
1592    
1593    
1594    /* This is really a static method but I think that caused problems in windows */
1595    void
1596    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1597    {
1598      if (dats.empty())
1599      {
1600            return;
1601      }
1602      vector<DataLazy*> work;
1603      FunctionSpace fs=dats[0]->getFunctionSpace();
1604      bool match=true;
1605      for (int i=dats.size()-1;i>=0;--i)
1606      {
1607            if (dats[i]->m_readytype!='E')
1608            {
1609                    dats[i]->collapse();
1610            }
1611            if (dats[i]->m_op!=IDENTITY)
1612            {
1613                    work.push_back(dats[i]);
1614                    if (fs!=dats[i]->getFunctionSpace())
1615                    {
1616                            match=false;
1617                    }
1618            }
1619      }
1620      if (work.empty())
1621      {
1622            return;         // no work to do
1623      }
1624      if (match)    // all functionspaces match.  Yes I realise this is overly strict
1625      {             // it is possible that dats[0] is one of the objects which we discarded and
1626                    // all the other functionspaces match.
1627            vector<DataExpanded*> dep;
1628            vector<RealVectorType*> vecs;
1629            for (int i=0;i<work.size();++i)
1630            {
1631                    dep.push_back(new DataExpanded(fs,work[i]->getShape(), RealVectorType(work[i]->getNoValues())));
1632                    vecs.push_back(&(dep[i]->getVectorRW()));
1633            }
1634            int totalsamples=work[0]->getNumSamples();
1635            const RealVectorType* res=0; // Storage for answer
1636            int sample;
1637            #pragma omp parallel private(sample, res)
1638            {
1639                size_t roffset=0;
1640                #pragma omp for schedule(static)
1641                for (sample=0;sample<totalsamples;++sample)
1642                {
1643                    roffset=0;
1644                    int j;
1645                    for (j=work.size()-1;j>=0;--j)
1646                    {
1647    #ifdef _OPENMP
1648                        res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1649    #else
1650                        res=work[j]->resolveNodeSample(0,sample,roffset);
1651    #endif
1652                        RealVectorType::size_type outoffset=dep[j]->getPointOffset(sample,0);
1653                        memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(RealVectorType::ElementType));
1654                    }
1655                }
1656            }
1657            // Now we need to load the new results as identity ops into the lazy nodes
1658            for (int i=work.size()-1;i>=0;--i)
1659            {
1660                work[i]->makeIdentity(REFCOUNTNS::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1661            }
1662      }
1663      else  // functionspaces do not match
1664      {
1665            for (int i=0;i<work.size();++i)
1666            {
1667                    work[i]->resolveToIdentity();
1668            }
1669      }
1670    }
1671    
1672    
1673    
1674    // This version of resolve uses storage in each node to hold results
1675    DataReady_ptr
1676    DataLazy::resolveNodeWorker()
1677    {
1678      if (m_readytype!='E')         // if the whole sub-expression is Constant or Tagged, then evaluate it normally
1679      {
1680        collapse();
1681      }
1682      if (m_op==IDENTITY)           // So a lazy expression of Constant or Tagged data will be returned here.
1683      {
1684        return m_id;
1685      }
1686            // from this point on we must have m_op!=IDENTITY and m_readytype=='E'
1687      DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  RealVectorType(getNoValues()));
1688      RealVectorType& resvec=result->getVectorRW();
1689      DataReady_ptr resptr=DataReady_ptr(result);
1690    
1691      int sample;
1692      int totalsamples=getNumSamples();
1693      const RealVectorType* res=0;       // Storage for answer
1694    LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1695      #pragma omp parallel private(sample,res)
1696      {
1697            size_t roffset=0;
1698    #ifdef LAZY_STACK_PROF
1699            stackstart[omp_get_thread_num()]=&roffset;
1700            stackend[omp_get_thread_num()]=&roffset;
1701    #endif
1702            #pragma omp for schedule(static)
1703            for (sample=0;sample<totalsamples;++sample)
1704            {
1705                    roffset=0;
1706    #ifdef _OPENMP
1707                    res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1708    #else
1709                    res=resolveNodeSample(0,sample,roffset);
1710    #endif
1711    LAZYDEBUG(cout << "Sample #" << sample << endl;)
1712    LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1713                    RealVectorType::size_type outoffset=result->getPointOffset(sample,0);
1714                    memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(RealVectorType::ElementType));
1715            }
1716      }
1717    #ifdef LAZY_STACK_PROF
1718      for (int i=0;i<getNumberOfThreads();++i)
1719      {
1720            size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1721    //      cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1722            if (r>maxstackuse)
1723            {
1724                    maxstackuse=r;
1725            }
1726    }    }
1727      cout << "Max resolve Stack use=" << maxstackuse << endl;
1728    #endif
1729      return resptr;
1730  }  }
1731    
1732  std::string  std::string
1733  DataLazy::toString() const  DataLazy::toString() const
1734  {  {
1735    return "Lazy evaluation object. No details available.";    ostringstream oss;
1736      oss << "Lazy Data: [depth=" << m_height<< "] ";
1737      switch (escriptParams.getLazyStrFmt())
1738      {
1739      case 1:       // tree format
1740            oss << endl;
1741            intoTreeString(oss,"");
1742            break;
1743      case 2:       // just the depth
1744            break;
1745      default:
1746            intoString(oss);
1747            break;
1748      }
1749      return oss.str();
1750    }
1751    
1752    
1753    void
1754    DataLazy::intoString(ostringstream& oss) const
1755    {
1756    //    oss << "[" << m_children <<";"<<m_height <<"]";
1757      switch (getOpgroup(m_op))
1758      {
1759      case G_IDENTITY:
1760            if (m_id->isExpanded())
1761            {
1762               oss << "E";
1763            }
1764            else if (m_id->isTagged())
1765            {
1766              oss << "T";
1767            }
1768            else if (m_id->isConstant())
1769            {
1770              oss << "C";
1771            }
1772            else
1773            {
1774              oss << "?";
1775            }
1776            oss << '@' << m_id.get();
1777            break;
1778      case G_BINARY:
1779            oss << '(';
1780            m_left->intoString(oss);
1781            oss << ' ' << opToString(m_op) << ' ';
1782            m_right->intoString(oss);
1783            oss << ')';
1784            break;
1785      case G_UNARY:
1786      case G_UNARY_P:
1787      case G_NP1OUT:
1788      case G_NP1OUT_P:
1789      case G_REDUCTION:
1790            oss << opToString(m_op) << '(';
1791            m_left->intoString(oss);
1792            oss << ')';
1793            break;
1794      case G_TENSORPROD:
1795            oss << opToString(m_op) << '(';
1796            m_left->intoString(oss);
1797            oss << ", ";
1798            m_right->intoString(oss);
1799            oss << ')';
1800            break;
1801      case G_NP1OUT_2P:
1802            oss << opToString(m_op) << '(';
1803            m_left->intoString(oss);
1804            oss << ", " << m_axis_offset << ", " << m_transpose;
1805            oss << ')';
1806            break;
1807      case G_CONDEVAL:
1808            oss << opToString(m_op)<< '(' ;
1809            m_mask->intoString(oss);
1810            oss << " ? ";
1811            m_left->intoString(oss);
1812            oss << " : ";
1813            m_right->intoString(oss);
1814            oss << ')';
1815            break;
1816      default:
1817            oss << "UNKNOWN";
1818      }
1819    }
1820    
1821    
1822    void
1823    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1824    {
1825      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1826      switch (getOpgroup(m_op))
1827      {
1828      case G_IDENTITY:
1829            if (m_id->isExpanded())
1830            {
1831               oss << "E";
1832            }
1833            else if (m_id->isTagged())
1834            {
1835              oss << "T";
1836            }
1837            else if (m_id->isConstant())
1838            {
1839              oss << "C";
1840            }
1841            else
1842            {
1843              oss << "?";
1844            }
1845            oss << '@' << m_id.get() << endl;
1846            break;
1847      case G_BINARY:
1848            oss << opToString(m_op) << endl;
1849            indent+='.';
1850            m_left->intoTreeString(oss, indent);
1851            m_right->intoTreeString(oss, indent);
1852            break;
1853      case G_UNARY:
1854      case G_UNARY_P:
1855      case G_NP1OUT:
1856      case G_NP1OUT_P:
1857      case G_REDUCTION:
1858            oss << opToString(m_op) << endl;
1859            indent+='.';
1860            m_left->intoTreeString(oss, indent);
1861            break;
1862      case G_TENSORPROD:
1863            oss << opToString(m_op) << endl;
1864            indent+='.';
1865            m_left->intoTreeString(oss, indent);
1866            m_right->intoTreeString(oss, indent);
1867            break;
1868      case G_NP1OUT_2P:
1869            oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1870            indent+='.';
1871            m_left->intoTreeString(oss, indent);
1872            break;
1873      default:
1874            oss << "UNKNOWN";
1875      }
1876  }  }
1877    
1878  // Note that in this case, deepCopy does not make copies of the leaves.  
 // Hopefully copy on write (or whatever we end up using) will take care of this.  
1879  DataAbstract*  DataAbstract*
1880  DataLazy::deepCopy()  DataLazy::deepCopy() const
1881  {  {
1882    if (m_op==IDENTITY)    switch (getOpgroup(m_op))
1883    {    {
1884      return new DataLazy(m_left);    // we don't need to copy the child here    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1885      case G_UNARY:
1886      case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1887      case G_UNARY_P:       return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
1888      case G_BINARY:        return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
1889      case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);
1890      case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1891      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
1892      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1893      default:
1894            throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");
1895    }    }
   return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);  
1896  }  }
1897    
1898    // For this, we don't care what op you were doing because the answer is now zero
1899    DataAbstract*
1900    DataLazy::zeroedCopy() const
1901    {
1902      return new DataLazy(m_id->zeroedCopy()->getPtr());
1903    }
1904    
1905  DataTypes::ValueType::size_type  // There is no single, natural interpretation of getLength on DataLazy.
1906    // Instances of DataReady can look at the size of their vectors.
1907    // For lazy though, it could be the size the data would be if it were resolved;
1908    // or it could be some function of the lengths of the DataReady instances which
1909    // form part of the expression.
1910    // Rather than have people making assumptions, I have disabled the method.
1911    DataTypes::RealVectorType::size_type
1912  DataLazy::getLength() const  DataLazy::getLength() const
1913  {  {
1914    return length;    throw DataException("getLength() does not make sense for lazy data.");
1915  }  }
1916    
1917    
1918  DataAbstract*  DataAbstract*
1919  DataLazy::getSlice(const DataTypes::RegionType& region) const  DataLazy::getSlice(const DataTypes::RegionType& region) const
1920  {  {
1921    // this seems like a really good one to include I just haven't added it yet    throw DataException("getSlice - not implemented for Lazy objects.");
   throw DataException("getSlice - not implemented for Lazy objects - yet.");  
1922  }  }
1923    
1924  DataTypes::ValueType::size_type  
1925    // To do this we need to rely on our child nodes
1926    DataTypes::RealVectorType::size_type
1927    DataLazy::getPointOffset(int sampleNo,
1928                     int dataPointNo)
1929    {
1930      if (m_op==IDENTITY)
1931      {
1932            return m_id->getPointOffset(sampleNo,dataPointNo);
1933      }
1934      if (m_readytype!='E')
1935      {
1936            collapse();
1937            return m_id->getPointOffset(sampleNo,dataPointNo);
1938      }
1939      // at this point we do not have an identity node and the expression will be Expanded
1940      // so we only need to know which child to ask
1941      if (m_left->m_readytype=='E')
1942      {
1943            return m_left->getPointOffset(sampleNo,dataPointNo);
1944      }
1945      else
1946      {
1947            return m_right->getPointOffset(sampleNo,dataPointNo);
1948      }
1949    }
1950    
1951    // To do this we need to rely on our child nodes
1952    DataTypes::RealVectorType::size_type
1953  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
1954                   int dataPointNo) const                   int dataPointNo) const
1955  {  {
1956    throw DataException("getPointOffset - not implemented for Lazy objects - yet.");    if (m_op==IDENTITY)
1957      {
1958            return m_id->getPointOffset(sampleNo,dataPointNo);
1959      }
1960      if (m_readytype=='E')
1961      {
1962        // at this point we do not have an identity node and the expression will be Expanded
1963        // so we only need to know which child to ask
1964        if (m_left->m_readytype=='E')
1965        {
1966            return m_left->getPointOffset(sampleNo,dataPointNo);
1967        }
1968        else
1969        {
1970            return m_right->getPointOffset(sampleNo,dataPointNo);
1971        }
1972      }
1973      if (m_readytype=='C')
1974      {
1975            return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
1976      }
1977      throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");
1978    }
1979    
1980    
1981    // I have decided to let Data:: handle this issue.
1982    void
1983    DataLazy::setToZero()
1984    {
1985    //   DataTypes::RealVectorType v(getNoValues(),0);
1986    //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
1987    //   m_op=IDENTITY;
1988    //   m_right.reset();  
1989    //   m_left.reset();
1990    //   m_readytype='C';
1991    //   m_buffsRequired=1;
1992    
1993      (void)privdebug;  // to stop the compiler complaining about unused privdebug
1994      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
1995    }
1996    
1997    bool
1998    DataLazy::actsExpanded() const
1999    {
2000            return (m_readytype=='E');
2001  }  }
2002    
2003  }   // end namespace  } // end namespace
2004    

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