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branches/schroedinger_upto1946/escript/src/DataLazy.cpp revision 1993 by phornby, Fri Nov 7 04:52:15 2008 UTC trunk/escript/src/DataLazy.cpp revision 2825 by jfenwick, Thu Dec 17 00:10:06 2009 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*******************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2009 by University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * Earth Systems Science Computational Center (ESSCC)
6  * http://www.uq.edu.au/esscc  * http://www.uq.edu.au/esscc
7  *  *
# Line 28  Line 28 
28  #include "UnaryFuncs.h"     // for escript::fsign  #include "UnaryFuncs.h"     // for escript::fsign
29  #include "Utils.h"  #include "Utils.h"
30    
31    #include "EscriptParams.h"
32    
33    #include <iomanip>      // for some fancy formatting in debug
34    
35    // #define LAZYDEBUG(X) if (privdebug){X;}
36    #define LAZYDEBUG(X)
37    namespace
38    {
39    bool privdebug=false;
40    
41    #define ENABLEDEBUG privdebug=true;
42    #define DISABLEDEBUG privdebug=false;
43    }
44    
45    // #define SIZELIMIT if ((m_height>escript::escriptParams.getTOO_MANY_LEVELS()) || (m_children>escript::escriptParams.getTOO_MANY_NODES())) {cerr << "\n!!!!!!! SIZE LIMIT EXCEEDED " << m_children << ";" << m_height << endl << toString() << endl;resolveToIdentity();}
46    
47    // #define SIZELIMIT if ((m_height>escript::escriptParams.getTOO_MANY_LEVELS()) || (m_children>escript::escriptParams.getTOO_MANY_NODES())) {cerr << "SIZE LIMIT EXCEEDED " << m_height << endl;resolveToIdentity();}
48    
49    
50    #define SIZELIMIT if (m_height>escript::escriptParams.getTOO_MANY_LEVELS())  {if (escript::escriptParams.getLAZY_VERBOSE()){cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;}resolveToIdentity();}
51    
52  /*  /*
53  How does DataLazy work?  How does DataLazy work?
54  ~~~~~~~~~~~~~~~~~~~~~~~  ~~~~~~~~~~~~~~~~~~~~~~~
# Line 48  I will refer to individual DataLazy obje Line 69  I will refer to individual DataLazy obje
69  Each node also stores:  Each node also stores:
70  - m_readytype \in {'E','T','C','?'} ~ indicates what sort of DataReady would be produced if the expression was  - m_readytype \in {'E','T','C','?'} ~ indicates what sort of DataReady would be produced if the expression was
71      evaluated.      evaluated.
 - m_length ~ how many values would be stored in the answer if the expression was evaluated.  
72  - m_buffsrequired ~ the larged number of samples which would need to be kept simultaneously in order to  - m_buffsrequired ~ the larged number of samples which would need to be kept simultaneously in order to
73      evaluate the expression.      evaluate the expression.
74  - m_samplesize ~ the number of doubles stored in a sample.  - m_samplesize ~ the number of doubles stored in a sample.
# Line 70  The convention that I use, is that the r Line 90  The convention that I use, is that the r
90    
91  For expressions which evaluate to Constant or Tagged, there is a different evaluation method.  For expressions which evaluate to Constant or Tagged, there is a different evaluation method.
92  The collapse method invokes the (non-lazy) operations on the Data class to evaluate the expression.  The collapse method invokes the (non-lazy) operations on the Data class to evaluate the expression.
93    
94    To add a new operator you need to do the following (plus anything I might have forgotten - adding a new group for example):
95    1) Add to the ES_optype.
96    2) determine what opgroup your operation belongs to (X)
97    3) add a string for the op to the end of ES_opstrings
98    4) increase ES_opcount
99    5) add an entry (X) to opgroups
100    6) add an entry to the switch in collapseToReady
101    7) add an entry to resolveX
102  */  */
103    
104    
# Line 82  namespace escript Line 111  namespace escript
111  namespace  namespace
112  {  {
113    
114    
115    // enabling this will print out when ever the maximum stacksize used by resolve increases
116    // it assumes _OPENMP is also in use
117    //#define LAZY_STACK_PROF
118    
119    
120    
121    #ifndef _OPENMP
122      #ifdef LAZY_STACK_PROF
123      #undef LAZY_STACK_PROF
124      #endif
125    #endif
126    
127    
128    #ifdef LAZY_STACK_PROF
129    std::vector<void*> stackstart(getNumberOfThreads());
130    std::vector<void*> stackend(getNumberOfThreads());
131    size_t maxstackuse=0;
132    #endif
133    
134  enum ES_opgroup  enum ES_opgroup
135  {  {
136     G_UNKNOWN,     G_UNKNOWN,
137     G_IDENTITY,     G_IDENTITY,
138     G_BINARY,        // pointwise operations with two arguments     G_BINARY,        // pointwise operations with two arguments
139     G_UNARY      // pointwise operations with one argument     G_UNARY,     // pointwise operations with one argument
140       G_UNARY_P,       // pointwise operations with one argument, requiring a parameter
141       G_NP1OUT,        // non-pointwise op with one output
142       G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter
143       G_TENSORPROD,    // general tensor product
144       G_NP1OUT_2P,     // non-pointwise op with one output requiring two params
145       G_REDUCTION      // non-pointwise unary op with a scalar output
146  };  };
147    
148    
# Line 98  string ES_opstrings[]={"UNKNOWN","IDENTI Line 153  string ES_opstrings[]={"UNKNOWN","IDENTI
153              "asin","acos","atan","sinh","cosh","tanh","erf",              "asin","acos","atan","sinh","cosh","tanh","erf",
154              "asinh","acosh","atanh",              "asinh","acosh","atanh",
155              "log10","log","sign","abs","neg","pos","exp","sqrt",              "log10","log","sign","abs","neg","pos","exp","sqrt",
156              "1/","where>0","where<0","where>=0","where<=0"};              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",
157  int ES_opcount=33;              "symmetric","nonsymmetric",
158                "prod",
159                "transpose", "trace",
160                "swapaxes",
161                "minval", "maxval"};
162    int ES_opcount=43;
163  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENTITY,G_BINARY,G_BINARY,G_BINARY,G_BINARY, G_BINARY,  ES_opgroup opgroups[]={G_UNKNOWN,G_IDENTITY,G_BINARY,G_BINARY,G_BINARY,G_BINARY, G_BINARY,
164              G_UNARY,G_UNARY,G_UNARY, //10              G_UNARY,G_UNARY,G_UNARY, //10
165              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,    // 17              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,    // 17
166              G_UNARY,G_UNARY,G_UNARY,                    // 20              G_UNARY,G_UNARY,G_UNARY,                    // 20
167              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,        // 28              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY,    // 28
168              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY};              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,      // 35
169                G_NP1OUT,G_NP1OUT,
170                G_TENSORPROD,
171                G_NP1OUT_P, G_NP1OUT_P,
172                G_NP1OUT_2P,
173                G_REDUCTION, G_REDUCTION};
174  inline  inline
175  ES_opgroup  ES_opgroup
176  getOpgroup(ES_optype op)  getOpgroup(ES_optype op)
# Line 140  resultFS(DataAbstract_ptr left, DataAbst Line 205  resultFS(DataAbstract_ptr left, DataAbst
205  }  }
206    
207  // return the shape of the result of "left op right"  // return the shape of the result of "left op right"
208    // the shapes resulting from tensor product are more complex to compute so are worked out elsewhere
209  DataTypes::ShapeType  DataTypes::ShapeType
210  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
211  {  {
212      if (left->getShape()!=right->getShape())      if (left->getShape()!=right->getShape())
213      {      {
214        if (getOpgroup(op)!=G_BINARY)        if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
215        {        {
216          throw DataException("Shapes not the name - shapes must match for (point)binary operations.");          throw DataException("Shapes not the name - shapes must match for (point)binary operations.");
217        }        }
218    
219        if (left->getRank()==0)   // we need to allow scalar * anything        if (left->getRank()==0)   // we need to allow scalar * anything
220        {        {
221          return right->getShape();          return right->getShape();
# Line 162  resultShape(DataAbstract_ptr left, DataA Line 229  resultShape(DataAbstract_ptr left, DataA
229      return left->getShape();      return left->getShape();
230  }  }
231    
232  // determine the number of points in the result of "left op right"  // return the shape for "op left"
233  size_t  
234  resultLength(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataTypes::ShapeType
235    resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
236  {  {
237     switch (getOpgroup(op))      switch(op)
238     {      {
239     case G_BINARY: return left->getLength();          case TRANS:
240     case G_UNARY: return left->getLength();         {            // for the scoping of variables
241     default:          const DataTypes::ShapeType& s=left->getShape();
242      throw DataException("Programmer Error - attempt to getLength() for operator "+opToString(op)+".");          DataTypes::ShapeType sh;
243     }          int rank=left->getRank();
244            if (axis_offset<0 || axis_offset>rank)
245            {
246                stringstream e;
247                e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
248                throw DataException(e.str());
249            }
250            for (int i=0; i<rank; i++)
251            {
252               int index = (axis_offset+i)%rank;
253               sh.push_back(s[index]); // Append to new shape
254            }
255            return sh;
256           }
257        break;
258        case TRACE:
259           {
260            int rank=left->getRank();
261            if (rank<2)
262            {
263               throw DataException("Trace can only be computed for objects with rank 2 or greater.");
264            }
265            if ((axis_offset>rank-2) || (axis_offset<0))
266            {
267               throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
268            }
269            if (rank==2)
270            {
271               return DataTypes::scalarShape;
272            }
273            else if (rank==3)
274            {
275               DataTypes::ShapeType sh;
276                   if (axis_offset==0)
277               {
278                    sh.push_back(left->getShape()[2]);
279                   }
280                   else     // offset==1
281               {
282                sh.push_back(left->getShape()[0]);
283                   }
284               return sh;
285            }
286            else if (rank==4)
287            {
288               DataTypes::ShapeType sh;
289               const DataTypes::ShapeType& s=left->getShape();
290                   if (axis_offset==0)
291               {
292                    sh.push_back(s[2]);
293                    sh.push_back(s[3]);
294                   }
295                   else if (axis_offset==1)
296               {
297                    sh.push_back(s[0]);
298                    sh.push_back(s[3]);
299                   }
300               else     // offset==2
301               {
302                sh.push_back(s[0]);
303                sh.push_back(s[1]);
304               }
305               return sh;
306            }
307            else        // unknown rank
308            {
309               throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
310            }
311           }
312        break;
313            default:
314        throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
315        }
316  }  }
317    
318  // determine the number of samples requires to evaluate an expression combining left and right  DataTypes::ShapeType
319  int  SwapShape(DataAbstract_ptr left, const int axis0, const int axis1)
 calcBuffs(const DataLazy_ptr& left, const DataLazy_ptr& right, ES_optype op)  
320  {  {
321     switch(getOpgroup(op))       // This code taken from the Data.cpp swapaxes() method
322     {       // Some of the checks are probably redundant here
323     case G_IDENTITY: return 1;       int axis0_tmp,axis1_tmp;
324     case G_BINARY: return max(left->getBuffsRequired(),right->getBuffsRequired()+1);       const DataTypes::ShapeType& s=left->getShape();
325     case G_UNARY: return max(left->getBuffsRequired(),1);       DataTypes::ShapeType out_shape;
326     default:       // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
327      throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");       // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
328     }       int rank=left->getRank();
329         if (rank<2) {
330            throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
331         }
332         if (axis0<0 || axis0>rank-1) {
333            stringstream e;
334            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
335            throw DataException(e.str());
336         }
337         if (axis1<0 || axis1>rank-1) {
338            stringstream e;
339            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
340            throw DataException(e.str());
341         }
342         if (axis0 == axis1) {
343             throw DataException("Error - Data::swapaxes: axis indices must be different.");
344         }
345         if (axis0 > axis1) {
346             axis0_tmp=axis1;
347             axis1_tmp=axis0;
348         } else {
349             axis0_tmp=axis0;
350             axis1_tmp=axis1;
351         }
352         for (int i=0; i<rank; i++) {
353           if (i == axis0_tmp) {
354              out_shape.push_back(s[axis1_tmp]);
355           } else if (i == axis1_tmp) {
356              out_shape.push_back(s[axis0_tmp]);
357           } else {
358              out_shape.push_back(s[i]);
359           }
360         }
361        return out_shape;
362  }  }
363    
364    
365    // determine the output shape for the general tensor product operation
366    // the additional parameters return information required later for the product
367    // the majority of this code is copy pasted from C_General_Tensor_Product
368    DataTypes::ShapeType
369    GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
370    {
371        
372      // Get rank and shape of inputs
373      int rank0 = left->getRank();
374      int rank1 = right->getRank();
375      const DataTypes::ShapeType& shape0 = left->getShape();
376      const DataTypes::ShapeType& shape1 = right->getShape();
377    
378      // Prepare for the loops of the product and verify compatibility of shapes
379      int start0=0, start1=0;
380      if (transpose == 0)       {}
381      else if (transpose == 1)  { start0 = axis_offset; }
382      else if (transpose == 2)  { start1 = rank1-axis_offset; }
383      else              { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }
384    
385      if (rank0<axis_offset)
386      {
387        throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
388      }
389    
390      // Adjust the shapes for transpose
391      DataTypes::ShapeType tmpShape0(rank0);    // pre-sizing the vectors rather
392      DataTypes::ShapeType tmpShape1(rank1);    // than using push_back
393      for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
394      for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
395    
396      // Prepare for the loops of the product
397      SL=1, SM=1, SR=1;
398      for (int i=0; i<rank0-axis_offset; i++)   {
399        SL *= tmpShape0[i];
400      }
401      for (int i=rank0-axis_offset; i<rank0; i++)   {
402        if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
403          throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
404        }
405        SM *= tmpShape0[i];
406      }
407      for (int i=axis_offset; i<rank1; i++)     {
408        SR *= tmpShape1[i];
409      }
410    
411      // Define the shape of the output (rank of shape is the sum of the loop ranges below)
412      DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);  
413      {         // block to limit the scope of out_index
414         int out_index=0;
415         for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
416         for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
417      }
418    
419      if (shape2.size()>ESCRIPT_MAX_DATA_RANK)
420      {
421         ostringstream os;
422         os << "C_GeneralTensorProduct: Error - Attempt to create a rank " << shape2.size() << " object. The maximum rank is " << ESCRIPT_MAX_DATA_RANK << ".";
423         throw DataException(os.str());
424      }
425    
426      return shape2;
427    }
428    
429  }   // end anonymous namespace  }   // end anonymous namespace
430    
431    
# Line 205  opToString(ES_optype op) Line 441  opToString(ES_optype op)
441    return ES_opstrings[op];    return ES_opstrings[op];
442  }  }
443    
444    void DataLazy::LazyNodeSetup()
445    {
446    #ifdef _OPENMP
447        int numthreads=omp_get_max_threads();
448        m_samples.resize(numthreads*m_samplesize);
449        m_sampleids=new int[numthreads];
450        for (int i=0;i<numthreads;++i)
451        {
452            m_sampleids[i]=-1;  
453        }
454    #else
455        m_samples.resize(m_samplesize);
456        m_sampleids=new int[1];
457        m_sampleids[0]=-1;
458    #endif  // _OPENMP
459    }
460    
461    
462    // Creates an identity node
463  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
464      : parent(p->getFunctionSpace(),p->getShape()),      : parent(p->getFunctionSpace(),p->getShape())
465      m_op(IDENTITY)      ,m_sampleids(0),
466        m_samples(1)
467  {  {
468     if (p->isLazy())     if (p->isLazy())
469     {     {
# Line 219  DataLazy::DataLazy(DataAbstract_ptr p) Line 474  DataLazy::DataLazy(DataAbstract_ptr p)
474     }     }
475     else     else
476     {     {
477      m_id=dynamic_pointer_cast<DataReady>(p);      p->makeLazyShared();
478      if(p->isConstant()) {m_readytype='C';}      DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
479      else if(p->isExpanded()) {m_readytype='E';}      makeIdentity(dr);
480      else if (p->isTagged()) {m_readytype='T';}  LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)
     else {throw DataException("Unknown DataReady instance in DataLazy constructor.");}  
481     }     }
482     m_length=p->getLength();  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
    m_buffsRequired=1;  
    m_samplesize=getNumDPPSample()*getNoValues();  
 cout << "(1)Lazy created with " << m_samplesize << endl;  
483  }  }
484    
   
   
   
485  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
486      : parent(left->getFunctionSpace(),left->getShape()),      : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
487      m_op(op)      m_op(op),
488        m_axis_offset(0),
489        m_transpose(0),
490        m_SL(0), m_SM(0), m_SR(0)
491  {  {
492     if (getOpgroup(op)!=G_UNARY)     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
493     {     {
494      throw DataException("Programmer error - constructor DataLazy(left, op) will only process UNARY operations.");      throw DataException("Programmer error - constructor DataLazy(left, op) will only process UNARY operations.");
495     }     }
496    
497     DataLazy_ptr lleft;     DataLazy_ptr lleft;
498     if (!left->isLazy())     if (!left->isLazy())
499     {     {
# Line 252  DataLazy::DataLazy(DataAbstract_ptr left Line 504  DataLazy::DataLazy(DataAbstract_ptr left
504      lleft=dynamic_pointer_cast<DataLazy>(left);      lleft=dynamic_pointer_cast<DataLazy>(left);
505     }     }
506     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
    m_length=left->getLength();  
507     m_left=lleft;     m_left=lleft;
    m_buffsRequired=1;  
508     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
509       m_children=m_left->m_children+1;
510       m_height=m_left->m_height+1;
511       LazyNodeSetup();
512       SIZELIMIT
513  }  }
514    
515    
516  // In this constructor we need to consider interpolation  // In this constructor we need to consider interpolation
517  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
518      : parent(resultFS(left,right,op), resultShape(left,right,op)),      : parent(resultFS(left,right,op), resultShape(left,right,op)),
519      m_op(op)      m_op(op),
520        m_SL(0), m_SM(0), m_SR(0)
521  {  {
522     if (getOpgroup(op)!=G_BINARY)  LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
523       if ((getOpgroup(op)!=G_BINARY))
524     {     {
525      throw DataException("Programmer error - constructor DataLazy(left, right, op) will only process BINARY operations.");      throw DataException("Programmer error - constructor DataLazy(left, right, op) will only process BINARY operations.");
526     }     }
# Line 276  DataLazy::DataLazy(DataAbstract_ptr left Line 532  DataLazy::DataLazy(DataAbstract_ptr left
532      Data tmp(ltemp,fs);      Data tmp(ltemp,fs);
533      left=tmp.borrowDataPtr();      left=tmp.borrowDataPtr();
534     }     }
535     if (getFunctionSpace()!=right->getFunctionSpace())   // left needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
536     {     {
537      Data tmp(Data(right),getFunctionSpace());      Data tmp(Data(right),getFunctionSpace());
538      right=tmp.borrowDataPtr();      right=tmp.borrowDataPtr();
539    LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
540     }     }
541     left->operandCheck(*right);     left->operandCheck(*right);
542    
543     if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required     if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required
544     {     {
545      m_left=dynamic_pointer_cast<DataLazy>(left);      m_left=dynamic_pointer_cast<DataLazy>(left);
546    LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
547       }
548       else
549       {
550        m_left=DataLazy_ptr(new DataLazy(left));
551    LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
552       }
553       if (right->isLazy())
554       {
555        m_right=dynamic_pointer_cast<DataLazy>(right);
556    LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
557       }
558       else
559       {
560        m_right=DataLazy_ptr(new DataLazy(right));
561    LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
562       }
563       char lt=m_left->m_readytype;
564       char rt=m_right->m_readytype;
565       if (lt=='E' || rt=='E')
566       {
567        m_readytype='E';
568       }
569       else if (lt=='T' || rt=='T')
570       {
571        m_readytype='T';
572       }
573       else
574       {
575        m_readytype='C';
576       }
577       m_samplesize=getNumDPPSample()*getNoValues();
578       m_children=m_left->m_children+m_right->m_children+2;
579       m_height=max(m_left->m_height,m_right->m_height)+1;
580       LazyNodeSetup();
581       SIZELIMIT
582    LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
583    }
584    
585    DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)
586        : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
587        m_op(op),
588        m_axis_offset(axis_offset),
589        m_transpose(transpose)
590    {
591       if ((getOpgroup(op)!=G_TENSORPROD))
592       {
593        throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
594       }
595       if ((transpose>2) || (transpose<0))
596       {
597        throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
598       }
599       if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
600       {
601        FunctionSpace fs=getFunctionSpace();
602        Data ltemp(left);
603        Data tmp(ltemp,fs);
604        left=tmp.borrowDataPtr();
605       }
606       if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
607       {
608        Data tmp(Data(right),getFunctionSpace());
609        right=tmp.borrowDataPtr();
610       }
611    //    left->operandCheck(*right);
612    
613       if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required
614       {
615        m_left=dynamic_pointer_cast<DataLazy>(left);
616     }     }
617     else     else
618     {     {
# Line 313  DataLazy::DataLazy(DataAbstract_ptr left Line 640  DataLazy::DataLazy(DataAbstract_ptr left
640     {     {
641      m_readytype='C';      m_readytype='C';
642     }     }
643     m_length=resultLength(m_left,m_right,m_op);     m_samplesize=getNumDPPSample()*getNoValues();
644     m_samplesize=getNumDPPSample()*getNoValues();         m_children=m_left->m_children+m_right->m_children+2;
645     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_height=max(m_left->m_height,m_right->m_height)+1;
646  cout << "(3)Lazy created with " << m_samplesize << endl;     LazyNodeSetup();
647       SIZELIMIT
648    LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
649  }  }
650    
651    
652  DataLazy::~DataLazy()  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
653        : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
654        m_op(op),
655        m_axis_offset(axis_offset),
656        m_transpose(0),
657        m_tol(0)
658  {  {
659       if ((getOpgroup(op)!=G_NP1OUT_P))
660       {
661        throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
662       }
663       DataLazy_ptr lleft;
664       if (!left->isLazy())
665       {
666        lleft=DataLazy_ptr(new DataLazy(left));
667       }
668       else
669       {
670        lleft=dynamic_pointer_cast<DataLazy>(left);
671       }
672       m_readytype=lleft->m_readytype;
673       m_left=lleft;
674       m_samplesize=getNumDPPSample()*getNoValues();
675       m_children=m_left->m_children+1;
676       m_height=m_left->m_height+1;
677       LazyNodeSetup();
678       SIZELIMIT
679    LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
680  }  }
681    
682    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
683        : parent(left->getFunctionSpace(), left->getShape()),
684        m_op(op),
685        m_axis_offset(0),
686        m_transpose(0),
687        m_tol(tol)
688    {
689       if ((getOpgroup(op)!=G_UNARY_P))
690       {
691        throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
692       }
693       DataLazy_ptr lleft;
694       if (!left->isLazy())
695       {
696        lleft=DataLazy_ptr(new DataLazy(left));
697       }
698       else
699       {
700        lleft=dynamic_pointer_cast<DataLazy>(left);
701       }
702       m_readytype=lleft->m_readytype;
703       m_left=lleft;
704       m_samplesize=getNumDPPSample()*getNoValues();
705       m_children=m_left->m_children+1;
706       m_height=m_left->m_height+1;
707       LazyNodeSetup();
708       SIZELIMIT
709    LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
710    }
711    
712  int  
713  DataLazy::getBuffsRequired() const  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
714        : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
715        m_op(op),
716        m_axis_offset(axis0),
717        m_transpose(axis1),
718        m_tol(0)
719  {  {
720      return m_buffsRequired;     if ((getOpgroup(op)!=G_NP1OUT_2P))
721       {
722        throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
723       }
724       DataLazy_ptr lleft;
725       if (!left->isLazy())
726       {
727        lleft=DataLazy_ptr(new DataLazy(left));
728       }
729       else
730       {
731        lleft=dynamic_pointer_cast<DataLazy>(left);
732       }
733       m_readytype=lleft->m_readytype;
734       m_left=lleft;
735       m_samplesize=getNumDPPSample()*getNoValues();
736       m_children=m_left->m_children+1;
737       m_height=m_left->m_height+1;
738       LazyNodeSetup();
739       SIZELIMIT
740    LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
741    }
742    
743    DataLazy::~DataLazy()
744    {
745       delete[] m_sampleids;
746  }  }
747    
748    
# Line 351  DataLazy::collapseToReady() Line 765  DataLazy::collapseToReady()
765    DataReady_ptr pleft=m_left->collapseToReady();    DataReady_ptr pleft=m_left->collapseToReady();
766    Data left(pleft);    Data left(pleft);
767    Data right;    Data right;
768    if (getOpgroup(m_op)==G_BINARY)    if ((getOpgroup(m_op)==G_BINARY) || (getOpgroup(m_op)==G_TENSORPROD))
769    {    {
770      right=Data(m_right->collapseToReady());      right=Data(m_right->collapseToReady());
771    }    }
# Line 450  DataLazy::collapseToReady() Line 864  DataLazy::collapseToReady()
864      case LEZ:      case LEZ:
865      result=left.whereNonPositive();      result=left.whereNonPositive();
866      break;      break;
867        case NEZ:
868        result=left.whereNonZero(m_tol);
869        break;
870        case EZ:
871        result=left.whereZero(m_tol);
872        break;
873        case SYM:
874        result=left.symmetric();
875        break;
876        case NSYM:
877        result=left.nonsymmetric();
878        break;
879        case PROD:
880        result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
881        break;
882        case TRANS:
883        result=left.transpose(m_axis_offset);
884        break;
885        case TRACE:
886        result=left.trace(m_axis_offset);
887        break;
888        case SWAP:
889        result=left.swapaxes(m_axis_offset, m_transpose);
890        break;
891        case MINVAL:
892        result=left.minval();
893        break;
894        case MAXVAL:
895        result=left.minval();
896        break;
897      default:      default:
898      throw DataException("Programmer error - do not know how to resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");
899    }    }
900    return result.borrowReadyPtr();    return result.borrowReadyPtr();
901  }  }
# Line 477  DataLazy::collapse() Line 921  DataLazy::collapse()
921    m_op=IDENTITY;    m_op=IDENTITY;
922  }  }
923    
924  /*  
925    \brief Compute the value of the expression (binary operation) for the given sample.  
926    \return Vector which stores the value of the subexpression for the given sample.  
927    \param v A vector to store intermediate results.  
928    \param offset Index in v to begin storing results.  
929    \param sampleNo Sample number to evaluate.  #define PROC_OP(TYPE,X)                               \
930    \param roffset (output parameter) the offset in the return vector where the result begins.      for (int j=0;j<onumsteps;++j)\
931        {\
932    The return value will be an existing vector so do not deallocate it.        for (int i=0;i<numsteps;++i,resultp+=resultStep) \
933    If the result is stored in v it should be stored at the offset given.        { \
934    Everything from offset to the end of v should be considered available for this method to use.  LAZYDEBUG(cout << "[left,right]=[" << lroffset << "," << rroffset << "]" << endl;)\
935  */  LAZYDEBUG(cout << "{left,right}={" << (*left)[lroffset] << "," << (*right)[rroffset] << "}\n";)\
936  DataTypes::ValueType*           tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \
937  DataLazy::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  LAZYDEBUG(cout << " result=      " << resultp[0] << endl;) \
938             lroffset+=leftstep; \
939             rroffset+=rightstep; \
940          }\
941          lroffset+=oleftstep;\
942          rroffset+=orightstep;\
943        }
944    
945    
946    // The result will be stored in m_samples
947    // The return value is a pointer to the DataVector, offset is the offset within the return value
948    const DataTypes::ValueType*
949    DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset)
950    {
951    LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
952        // collapse so we have a 'E' node or an IDENTITY for some other type
953      if (m_readytype!='E' && m_op!=IDENTITY)
954      {
955        collapse();
956      }
957      if (m_op==IDENTITY)  
958      {
959        const ValueType& vec=m_id->getVectorRO();
960        roffset=m_id->getPointOffset(sampleNo, 0);
961    #ifdef LAZY_STACK_PROF
962    int x;
963    if (&x<stackend[omp_get_thread_num()])
964    {
965           stackend[omp_get_thread_num()]=&x;
966    }
967    #endif
968        return &(vec);
969      }
970      if (m_readytype!='E')
971      {
972        throw DataException("Programmer Error - Collapse did not produce an expanded node.");
973      }
974      if (m_sampleids[tid]==sampleNo)
975      {
976        roffset=tid*m_samplesize;
977        return &(m_samples);        // sample is already resolved
978      }
979      m_sampleids[tid]=sampleNo;
980      switch (getOpgroup(m_op))
981      {
982      case G_UNARY:
983      case G_UNARY_P: return resolveNodeUnary(tid, sampleNo, roffset);
984      case G_BINARY: return resolveNodeBinary(tid, sampleNo, roffset);
985      case G_NP1OUT: return resolveNodeNP1OUT(tid, sampleNo, roffset);
986      case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
987      case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
988      case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
989      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
990      default:
991        throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
992      }
993    }
994    
995    const DataTypes::ValueType*
996    DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset)
997  {  {
998      // we assume that any collapsing has been done before we get here      // we assume that any collapsing has been done before we get here
999      // since we only have one argument we don't need to think about only      // since we only have one argument we don't need to think about only
1000      // processing single points.      // processing single points.
1001        // we will also know we won't get identity nodes
1002    if (m_readytype!='E')    if (m_readytype!='E')
1003    {    {
1004      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1005    }    }
1006    const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);    if (m_op==IDENTITY)
1007    const double* left=&((*vleft)[roffset]);    {
1008    double* result=&(v[offset]);      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1009    roffset=offset;    }
1010      const DataTypes::ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1011      const double* left=&((*leftres)[roffset]);
1012      roffset=m_samplesize*tid;
1013      double* result=&(m_samples[roffset]);
1014    switch (m_op)    switch (m_op)
1015    {    {
1016      case SIN:        case SIN:  
# Line 533  DataLazy::resolveUnary(ValueType& v, siz Line 1041  DataLazy::resolveUnary(ValueType& v, siz
1041      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);
1042      break;      break;
1043      case ERF:      case ERF:
1044  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1045      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
1046  #else  #else
1047      tensor_unary_operation(m_samplesize, left, result, ::erf);      tensor_unary_operation(m_samplesize, left, result, ::erf);
1048      break;      break;
1049  #endif  #endif
1050     case ASINH:     case ASINH:
1051  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1052      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);
1053  #else  #else
1054      tensor_unary_operation(m_samplesize, left, result, ::asinh);      tensor_unary_operation(m_samplesize, left, result, ::asinh);
1055  #endif    #endif  
1056      break;      break;
1057     case ACOSH:     case ACOSH:
1058  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1059      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);
1060  #else  #else
1061      tensor_unary_operation(m_samplesize, left, result, ::acosh);      tensor_unary_operation(m_samplesize, left, result, ::acosh);
1062  #endif    #endif  
1063      break;      break;
1064     case ATANH:     case ATANH:
1065  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1066      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);
1067  #else  #else
1068      tensor_unary_operation(m_samplesize, left, result, ::atanh);      tensor_unary_operation(m_samplesize, left, result, ::atanh);
# Line 601  DataLazy::resolveUnary(ValueType& v, siz Line 1109  DataLazy::resolveUnary(ValueType& v, siz
1109      case LEZ:      case LEZ:
1110      tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));      tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));
1111      break;      break;
1112    // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently
1113        case NEZ:
1114        tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));
1115        break;
1116        case EZ:
1117        tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));
1118        break;
1119    
1120      default:      default:
1121      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1122    }    }
1123    return &v;    return &(m_samples);
1124  }  }
1125    
1126    
1127    const DataTypes::ValueType*
1128    DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset)
1129    {
1130        // we assume that any collapsing has been done before we get here
1131        // since we only have one argument we don't need to think about only
1132        // processing single points.
1133        // we will also know we won't get identity nodes
1134      if (m_readytype!='E')
1135      {
1136        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1137      }
1138      if (m_op==IDENTITY)
1139      {
1140        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1141      }
1142      size_t loffset=0;
1143      const DataTypes::ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1144    
1145      roffset=m_samplesize*tid;
1146      unsigned int ndpps=getNumDPPSample();
1147      unsigned int psize=DataTypes::noValues(getShape());
1148      double* result=&(m_samples[roffset]);
1149      switch (m_op)
1150      {
1151        case MINVAL:
1152        {
1153          for (unsigned int z=0;z<ndpps;++z)
1154          {
1155            FMin op;
1156            *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1157            loffset+=psize;
1158            result++;
1159          }
1160        }
1161        break;
1162        case MAXVAL:
1163        {
1164          for (unsigned int z=0;z<ndpps;++z)
1165          {
1166          FMax op;
1167          *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1168          loffset+=psize;
1169          result++;
1170          }
1171        }
1172        break;
1173        default:
1174        throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1175      }
1176      return &(m_samples);
1177    }
1178    
1179    const DataTypes::ValueType*
1180    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset)
1181    {
1182        // we assume that any collapsing has been done before we get here
1183        // since we only have one argument we don't need to think about only
1184        // processing single points.
1185      if (m_readytype!='E')
1186      {
1187        throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
1188      }
1189      if (m_op==IDENTITY)
1190      {
1191        throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1192      }
1193      size_t subroffset;
1194      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1195      roffset=m_samplesize*tid;
1196      size_t loop=0;
1197      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1198      size_t step=getNoValues();
1199      size_t offset=roffset;
1200      switch (m_op)
1201      {
1202        case SYM:
1203        for (loop=0;loop<numsteps;++loop)
1204        {
1205            DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1206            subroffset+=step;
1207            offset+=step;
1208        }
1209        break;
1210        case NSYM:
1211        for (loop=0;loop<numsteps;++loop)
1212        {
1213            DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1214            subroffset+=step;
1215            offset+=step;
1216        }
1217        break;
1218        default:
1219        throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1220      }
1221      return &m_samples;
1222    }
1223    
1224  #define PROC_OP(TYPE,X)                               \  const DataTypes::ValueType*
1225      for (int i=0;i<steps;++i,resultp+=resultStep) \  DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset)
1226      { \  {
1227         tensor_binary_operation##TYPE(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \      // we assume that any collapsing has been done before we get here
1228         lroffset+=leftStep; \      // since we only have one argument we don't need to think about only
1229         rroffset+=rightStep; \      // processing single points.
1230      if (m_readytype!='E')
1231      {
1232        throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
1233      }
1234      if (m_op==IDENTITY)
1235      {
1236        throw DataException("Programmer error - resolveNodeNP1OUT_P should not be called on identity nodes.");
1237      }
1238      size_t subroffset;
1239      size_t offset;
1240      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1241      roffset=m_samplesize*tid;
1242      offset=roffset;
1243      size_t loop=0;
1244      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1245      size_t outstep=getNoValues();
1246      size_t instep=m_left->getNoValues();
1247      switch (m_op)
1248      {
1249        case TRACE:
1250        for (loop=0;loop<numsteps;++loop)
1251        {
1252                DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1253            subroffset+=instep;
1254            offset+=outstep;
1255        }
1256        break;
1257        case TRANS:
1258        for (loop=0;loop<numsteps;++loop)
1259        {
1260                DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1261            subroffset+=instep;
1262            offset+=outstep;
1263      }      }
1264        break;
1265        default:
1266        throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1267      }
1268      return &m_samples;
1269    }
1270    
1271    
1272    const DataTypes::ValueType*
1273    DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset)
1274    {
1275      if (m_readytype!='E')
1276      {
1277        throw DataException("Programmer error - resolveNodeNP1OUT_2P should only be called on expanded Data.");
1278      }
1279      if (m_op==IDENTITY)
1280      {
1281        throw DataException("Programmer error - resolveNodeNP1OUT_2P should not be called on identity nodes.");
1282      }
1283      size_t subroffset;
1284      size_t offset;
1285      const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1286      roffset=m_samplesize*tid;
1287      offset=roffset;
1288      size_t loop=0;
1289      size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
1290      size_t outstep=getNoValues();
1291      size_t instep=m_left->getNoValues();
1292      switch (m_op)
1293      {
1294        case SWAP:
1295        for (loop=0;loop<numsteps;++loop)
1296        {
1297                DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1298            subroffset+=instep;
1299            offset+=outstep;
1300        }
1301        break;
1302        default:
1303        throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1304      }
1305      return &m_samples;
1306    }
1307    
1308    
1309    
 /*  
   \brief Compute the value of the expression (binary operation) for the given sample.  
   \return Vector which stores the value of the subexpression for the given sample.  
   \param v A vector to store intermediate results.  
   \param offset Index in v to begin storing results.  
   \param sampleNo Sample number to evaluate.  
   \param roffset (output parameter) the offset in the return vector where the result begins.  
   
   The return value will be an existing vector so do not deallocate it.  
   If the result is stored in v it should be stored at the offset given.  
   Everything from offset to the end of v should be considered available for this method to use.  
 */  
1310  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data  // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1311  // have already been collapsed to IDENTITY. So we must have at least one expanded child.  // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1312  // If both children are expanded, then we can process them in a single operation (we treat  // If both children are expanded, then we can process them in a single operation (we treat
# Line 641  DataLazy::resolveUnary(ValueType& v, siz Line 1316  DataLazy::resolveUnary(ValueType& v, siz
1316  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1317  // For example, 2+Vector.  // For example, 2+Vector.
1318  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1319  DataTypes::ValueType*  const DataTypes::ValueType*
1320  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset)
1321  {  {
1322  cout << "Resolve binary: " << toString() << endl;  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1323    
1324    size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors    size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors
1325      // first work out which of the children are expanded      // first work out which of the children are expanded
1326    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1327    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1328    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if (!leftExp && !rightExp)
1329    int steps=(bigloops?1:getNumDPPSample());    {
1330    size_t chunksize=(bigloops? m_samplesize : getNoValues());    // if bigloops, pretend the whole sample is a datapoint      throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");
1331    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1332    {    bool leftScalar=(m_left->getRank()==0);
1333      EsysAssert((m_left->getRank()==0) || (m_right->getRank()==0), "Error - Ranks must match unless one is 0.");    bool rightScalar=(m_right->getRank()==0);
1334      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1335      chunksize=1;    // for scalar    {
1336    }          throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1337    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    }
1338    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    size_t leftsize=m_left->getNoValues();
1339    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    size_t rightsize=m_right->getNoValues();
1340      size_t chunksize=1;           // how many doubles will be processed in one go
1341      int leftstep=0;       // how far should the left offset advance after each step
1342      int rightstep=0;
1343      int numsteps=0;       // total number of steps for the inner loop
1344      int oleftstep=0;  // the o variables refer to the outer loop
1345      int orightstep=0; // The outer loop is only required in cases where there is an extended scalar
1346      int onumsteps=1;
1347      
1348      bool LES=(leftExp && leftScalar); // Left is an expanded scalar
1349      bool RES=(rightExp && rightScalar);
1350      bool LS=(!leftExp && leftScalar); // left is a single scalar
1351      bool RS=(!rightExp && rightScalar);
1352      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1353      bool RN=(!rightExp && !rightScalar);
1354      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1355      bool REN=(rightExp && !rightScalar);
1356    
1357      if ((LES && RES) || (LEN && REN)) // both are Expanded scalars or both are expanded non-scalars
1358      {
1359        chunksize=m_left->getNumDPPSample()*leftsize;
1360        leftstep=0;
1361        rightstep=0;
1362        numsteps=1;
1363      }
1364      else if (LES || RES)
1365      {
1366        chunksize=1;
1367        if (LES)        // left is an expanded scalar
1368        {
1369            if (RS)
1370            {
1371               leftstep=1;
1372               rightstep=0;
1373               numsteps=m_left->getNumDPPSample();
1374            }
1375            else        // RN or REN
1376            {
1377               leftstep=0;
1378               oleftstep=1;
1379               rightstep=1;
1380               orightstep=(RN ? -(int)rightsize : 0);
1381               numsteps=rightsize;
1382               onumsteps=m_left->getNumDPPSample();
1383            }
1384        }
1385        else        // right is an expanded scalar
1386        {
1387            if (LS)
1388            {
1389               rightstep=1;
1390               leftstep=0;
1391               numsteps=m_right->getNumDPPSample();
1392            }
1393            else
1394            {
1395               rightstep=0;
1396               orightstep=1;
1397               leftstep=1;
1398               oleftstep=(LN ? -(int)leftsize : 0);
1399               numsteps=leftsize;
1400               onumsteps=m_right->getNumDPPSample();
1401            }
1402        }
1403      }
1404      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1405      {
1406        if (LEN)    // and Right will be a single value
1407        {
1408            chunksize=rightsize;
1409            leftstep=rightsize;
1410            rightstep=0;
1411            numsteps=m_left->getNumDPPSample();
1412            if (RS)
1413            {
1414               numsteps*=leftsize;
1415            }
1416        }
1417        else    // REN
1418        {
1419            chunksize=leftsize;
1420            rightstep=leftsize;
1421            leftstep=0;
1422            numsteps=m_right->getNumDPPSample();
1423            if (LS)
1424            {
1425               numsteps*=rightsize;
1426            }
1427        }
1428      }
1429    
1430      int resultStep=max(leftstep,rightstep);   // only one (at most) should be !=0
1431      // Get the values of sub-expressions      // Get the values of sub-expressions
1432    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    const ValueType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);  
1433    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    const ValueType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1434      // the right child starts further along.  LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1435    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1436    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1437    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1438    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1439    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1440    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1441    
1442    LAZYDEBUG(cout << "Left res["<< lroffset<< "]=" << (*left)[lroffset] << endl;)
1443    LAZYDEBUG(cout << "Right res["<< rroffset<< "]=" << (*right)[rroffset] << endl;)
1444    
1445    
1446      roffset=m_samplesize*tid;
1447      double* resultp=&(m_samples[roffset]);        // results are stored at the vector offset we recieved
1448    switch(m_op)    switch(m_op)
1449    {    {
1450      case ADD:      case ADD:
1451          PROC_OP(/**/,plus<double>());          PROC_OP(NO_ARG,plus<double>());
1452      break;      break;
1453      case SUB:      case SUB:
1454      PROC_OP(/**/,minus<double>());      PROC_OP(NO_ARG,minus<double>());
1455      break;      break;
1456      case MUL:      case MUL:
1457      PROC_OP(/**/,multiplies<double>());      PROC_OP(NO_ARG,multiplies<double>());
1458      break;      break;
1459      case DIV:      case DIV:
1460      PROC_OP(/**/,divides<double>());      PROC_OP(NO_ARG,divides<double>());
1461      break;      break;
1462      case POW:      case POW:
1463         PROC_OP(<double (double,double)>,::pow);         PROC_OP(double (double,double),::pow);
1464      break;      break;
1465      default:      default:
1466      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1467    }    }
1468    roffset=offset;    LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1469    return &v;    return &m_samples;
1470  }  }
1471    
1472    
1473    // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1474    // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1475    // unlike the other resolve helpers, we must treat these datapoints separately.
1476    const DataTypes::ValueType*
1477    DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset)
1478    {
1479    LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1480    
1481  /*    size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors
1482    \brief Compute the value of the expression for the given sample.      // first work out which of the children are expanded
1483    \return Vector which stores the value of the subexpression for the given sample.    bool leftExp=(m_left->m_readytype=='E');
1484    \param v A vector to store intermediate results.    bool rightExp=(m_right->m_readytype=='E');
1485    \param offset Index in v to begin storing results.    int steps=getNumDPPSample();
1486    \param sampleNo Sample number to evaluate.    int leftStep=(leftExp? m_left->getNoValues() : 0);        // do not have scalars as input to this method
1487    \param roffset (output parameter) the offset in the return vector where the result begins.    int rightStep=(rightExp?m_right->getNoValues() : 0);
1488    
1489      int resultStep=getNoValues();
1490      roffset=m_samplesize*tid;
1491      size_t offset=roffset;
1492    
1493      const ValueType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1494    
1495      const ValueType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1496    
1497    LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1498    cout << getNoValues() << endl;)
1499    
1500    
1501    LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)
1502    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1503    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1504    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1505    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1506    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1507    LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1508    
1509      double* resultp=&(m_samples[offset]);     // results are stored at the vector offset we recieved
1510      switch(m_op)
1511      {
1512        case PROD:
1513        for (int i=0;i<steps;++i,resultp+=resultStep)
1514        {
1515              const double *ptr_0 = &((*left)[lroffset]);
1516              const double *ptr_1 = &((*right)[rroffset]);
1517    
1518    LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1519    LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1520    
1521              matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1522    
1523          lroffset+=leftStep;
1524          rroffset+=rightStep;
1525        }
1526        break;
1527        default:
1528        throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1529      }
1530      roffset=offset;
1531      return &m_samples;
1532    }
1533    
   The return value will be an existing vector so do not deallocate it.  
 */  
 // the vector and the offset are a place where the method could write its data if it wishes  
 // it is not obligated to do so. For example, if it has its own storage already, it can use that.  
 // Hence the return value to indicate where the data is actually stored.  
 // Regardless, the storage should be assumed to be used, even if it isn't.  
1534    
 // the roffset is the offset within the returned vector where the data begins  
1535  const DataTypes::ValueType*  const DataTypes::ValueType*
1536  DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  DataLazy::resolveSample(int sampleNo, size_t& roffset)
1537  {  {
1538  cout << "Resolve sample " << toString() << endl;  #ifdef _OPENMP
1539      // collapse so we have a 'E' node or an IDENTITY for some other type      int tid=omp_get_thread_num();
1540    if (m_readytype!='E' && m_op!=IDENTITY)  #else
1541        int tid=0;
1542    #endif
1543    
1544    #ifdef LAZY_STACK_PROF
1545        stackstart[tid]=&tid;
1546        stackend[tid]=&tid;
1547        const DataTypes::ValueType* r=resolveNodeSample(tid, sampleNo, roffset);
1548        size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1549        #pragma omp critical
1550        if (d>maxstackuse)
1551        {
1552    cout << "Max resolve Stack use " << d << endl;
1553            maxstackuse=d;
1554        }
1555        return r;
1556    #else
1557        return resolveNodeSample(tid, sampleNo, roffset);
1558    #endif
1559    }
1560    
1561    
1562    // This needs to do the work of the identity constructor
1563    void
1564    DataLazy::resolveToIdentity()
1565    {
1566       if (m_op==IDENTITY)
1567        return;
1568       DataReady_ptr p=resolveNodeWorker();
1569       makeIdentity(p);
1570    }
1571    
1572    void DataLazy::makeIdentity(const DataReady_ptr& p)
1573    {
1574       m_op=IDENTITY;
1575       m_axis_offset=0;
1576       m_transpose=0;
1577       m_SL=m_SM=m_SR=0;
1578       m_children=m_height=0;
1579       m_id=p;
1580       if(p->isConstant()) {m_readytype='C';}
1581       else if(p->isExpanded()) {m_readytype='E';}
1582       else if (p->isTagged()) {m_readytype='T';}
1583       else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
1584       m_samplesize=p->getNumDPPSample()*p->getNoValues();
1585       m_left.reset();
1586       m_right.reset();
1587    }
1588    
1589    
1590    DataReady_ptr
1591    DataLazy::resolve()
1592    {
1593        resolveToIdentity();
1594        return m_id;
1595    }
1596    
1597    
1598    /* This is really a static method but I think that caused problems in windows */
1599    void
1600    DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1601    {
1602      if (dats.empty())
1603    {    {
1604      collapse();      return;
1605    }    }
1606    if (m_op==IDENTITY)      vector<DataLazy*> work;
1607      FunctionSpace fs=dats[0]->getFunctionSpace();
1608      bool match=true;
1609      for (int i=dats.size()-1;i>=0;--i)
1610    {    {
1611      const ValueType& vec=m_id->getVector();      if (dats[i]->m_readytype!='E')
1612      if (m_readytype=='C')      {
1613      {          dats[i]->collapse();
1614      roffset=0;      }
1615      return &(vec);      if (dats[i]->m_op!=IDENTITY)
1616      }      {
1617      roffset=m_id->getPointOffset(sampleNo, 0);          work.push_back(dats[i]);
1618      return &(vec);          if (fs!=dats[i]->getFunctionSpace())
1619            {
1620                match=false;
1621            }
1622        }
1623    }    }
1624    if (m_readytype!='E')    if (work.empty())
1625    {    {
1626      throw DataException("Programmer Error - Collapse did not produce an expanded node.");      return;     // no work to do
1627    }    }
1628    switch (getOpgroup(m_op))    if (match)    // all functionspaces match.  Yes I realise this is overly strict
1629      {     // it is possible that dats[0] is one of the objects which we discarded and
1630            // all the other functionspaces match.
1631        vector<DataExpanded*> dep;
1632        vector<ValueType*> vecs;
1633        for (int i=0;i<work.size();++i)
1634        {
1635            dep.push_back(new DataExpanded(fs,work[i]->getShape(), ValueType(work[i]->getNoValues())));
1636            vecs.push_back(&(dep[i]->getVectorRW()));
1637        }
1638        int totalsamples=work[0]->getNumSamples();
1639        const ValueType* res=0; // Storage for answer
1640        int sample;
1641        #pragma omp parallel private(sample, res)
1642        {
1643            size_t roffset=0;
1644            #pragma omp for schedule(static)
1645            for (sample=0;sample<totalsamples;++sample)
1646            {
1647            roffset=0;
1648            int j;
1649            for (j=work.size()-1;j>=0;--j)
1650            {
1651    #ifdef _OPENMP
1652                    res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1653    #else
1654                    res=work[j]->resolveNodeSample(0,sample,roffset);
1655    #endif
1656                    DataVector::size_type outoffset=dep[j]->getPointOffset(sample,0);
1657                    memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(DataVector::ElementType));
1658            }
1659            }
1660        }
1661        // Now we need to load the new results as identity ops into the lazy nodes
1662        for (int i=work.size()-1;i>=0;--i)
1663        {
1664            work[i]->makeIdentity(boost::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1665        }
1666      }
1667      else  // functionspaces do not match
1668    {    {
1669    case G_UNARY: return resolveUnary(v, offset,sampleNo,roffset);      for (int i=0;i<work.size();++i)
1670    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);      {
1671    default:          work[i]->resolveToIdentity();
1672      throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");      }
1673    }    }
1674  }  }
1675    
1676    
1677  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1678  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1679  DataReady_ptr  DataReady_ptr
1680  DataLazy::resolve()  DataLazy::resolveNodeWorker()
1681  {  {
   
 cout << "Sample size=" << m_samplesize << endl;  
 cout << "Buffers=" << m_buffsRequired << endl;  
   
1682    if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally    if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally
1683    {    {
1684      collapse();      collapse();
# Line 761  cout << "Buffers=" << m_buffsRequired << Line 1688  cout << "Buffers=" << m_buffsRequired <<
1688      return m_id;      return m_id;
1689    }    }
1690      // from this point on we must have m_op!=IDENTITY and m_readytype=='E'      // from this point on we must have m_op!=IDENTITY and m_readytype=='E'
   size_t threadbuffersize=m_samplesize*(max(1,m_buffsRequired));    // Each thread needs to have enough  
     // storage to evaluate its expression  
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=getNumberOfThreads();  
   int threadnum=0;  
 #endif  
   ValueType v(numthreads*threadbuffersize);  
 cout << "Buffer created with size=" << v.size() << endl;  
1691    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));
1692    ValueType& resvec=result->getVector();    ValueType& resvec=result->getVectorRW();
1693    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1694    
1695    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1696    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1697    const ValueType* res=0;   // Vector storing the answer    const ValueType* res=0;   // Storage for answer
1698    size_t resoffset=0;       // where in the vector to find the answer  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1699    #pragma omp parallel for private(sample,resoffset,outoffset,threadnum,res) schedule(static)    #pragma omp parallel private(sample,res)
1700    for (sample=0;sample<totalsamples;++sample)    {
1701    {      size_t roffset=0;
1702  cout << "################################# " << sample << endl;  #ifdef LAZY_STACK_PROF
1703        stackstart[omp_get_thread_num()]=&roffset;
1704        stackend[omp_get_thread_num()]=&roffset;
1705    #endif
1706        #pragma omp for schedule(static)
1707        for (sample=0;sample<totalsamples;++sample)
1708        {
1709            roffset=0;
1710  #ifdef _OPENMP  #ifdef _OPENMP
1711      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);              res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1712  #else  #else
1713      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.              res=resolveNodeSample(0,sample,roffset);
1714  #endif  #endif
1715  cerr << "-------------------------------- " << endl;  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1716      outoffset=result->getPointOffset(sample,0);  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1717  cerr << "offset=" << outoffset << endl;              DataVector::size_type outoffset=result->getPointOffset(sample,0);
1718      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector              memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(DataVector::ElementType));
1719      {      }
1720      resvec[outoffset]=(*res)[resoffset];    }
1721      }  #ifdef LAZY_STACK_PROF
1722  cerr << "*********************************" << endl;    for (int i=0;i<getNumberOfThreads();++i)
1723      {
1724        size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1725    //  cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1726        if (r>maxstackuse)
1727        {
1728            maxstackuse=r;
1729        }
1730    }    }
1731      cout << "Max resolve Stack use=" << maxstackuse << endl;
1732    #endif
1733    return resptr;    return resptr;
1734  }  }
1735    
# Line 803  std::string Line 1737  std::string
1737  DataLazy::toString() const  DataLazy::toString() const
1738  {  {
1739    ostringstream oss;    ostringstream oss;
1740    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1741    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1742      {
1743      case 1:   // tree format
1744        oss << endl;
1745        intoTreeString(oss,"");
1746        break;
1747      case 2:   // just the depth
1748        break;
1749      default:
1750        intoString(oss);
1751        break;
1752      }
1753    return oss.str();    return oss.str();
1754  }  }
1755    
# Line 812  DataLazy::toString() const Line 1757  DataLazy::toString() const
1757  void  void
1758  DataLazy::intoString(ostringstream& oss) const  DataLazy::intoString(ostringstream& oss) const
1759  {  {
1760    //    oss << "[" << m_children <<";"<<m_height <<"]";
1761    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1762    {    {
1763    case G_IDENTITY:    case G_IDENTITY:
# Line 841  DataLazy::intoString(ostringstream& oss) Line 1787  DataLazy::intoString(ostringstream& oss)
1787      oss << ')';      oss << ')';
1788      break;      break;
1789    case G_UNARY:    case G_UNARY:
1790      case G_UNARY_P:
1791      case G_NP1OUT:
1792      case G_NP1OUT_P:
1793      case G_REDUCTION:
1794        oss << opToString(m_op) << '(';
1795        m_left->intoString(oss);
1796        oss << ')';
1797        break;
1798      case G_TENSORPROD:
1799        oss << opToString(m_op) << '(';
1800        m_left->intoString(oss);
1801        oss << ", ";
1802        m_right->intoString(oss);
1803        oss << ')';
1804        break;
1805      case G_NP1OUT_2P:
1806      oss << opToString(m_op) << '(';      oss << opToString(m_op) << '(';
1807      m_left->intoString(oss);      m_left->intoString(oss);
1808        oss << ", " << m_axis_offset << ", " << m_transpose;
1809      oss << ')';      oss << ')';
1810      break;      break;
1811    default:    default:
# Line 850  DataLazy::intoString(ostringstream& oss) Line 1813  DataLazy::intoString(ostringstream& oss)
1813    }    }
1814  }  }
1815    
1816    
1817    void
1818    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1819    {
1820      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1821      switch (getOpgroup(m_op))
1822      {
1823      case G_IDENTITY:
1824        if (m_id->isExpanded())
1825        {
1826           oss << "E";
1827        }
1828        else if (m_id->isTagged())
1829        {
1830          oss << "T";
1831        }
1832        else if (m_id->isConstant())
1833        {
1834          oss << "C";
1835        }
1836        else
1837        {
1838          oss << "?";
1839        }
1840        oss << '@' << m_id.get() << endl;
1841        break;
1842      case G_BINARY:
1843        oss << opToString(m_op) << endl;
1844        indent+='.';
1845        m_left->intoTreeString(oss, indent);
1846        m_right->intoTreeString(oss, indent);
1847        break;
1848      case G_UNARY:
1849      case G_UNARY_P:
1850      case G_NP1OUT:
1851      case G_NP1OUT_P:
1852      case G_REDUCTION:
1853        oss << opToString(m_op) << endl;
1854        indent+='.';
1855        m_left->intoTreeString(oss, indent);
1856        break;
1857      case G_TENSORPROD:
1858        oss << opToString(m_op) << endl;
1859        indent+='.';
1860        m_left->intoTreeString(oss, indent);
1861        m_right->intoTreeString(oss, indent);
1862        break;
1863      case G_NP1OUT_2P:
1864        oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
1865        indent+='.';
1866        m_left->intoTreeString(oss, indent);
1867        break;
1868      default:
1869        oss << "UNKNOWN";
1870      }
1871    }
1872    
1873    
1874  DataAbstract*  DataAbstract*
1875  DataLazy::deepCopy()  DataLazy::deepCopy()
1876  {  {
1877    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1878    {    {
1879    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1880    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
1881      case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1882      case G_UNARY_P:   return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
1883    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
1884      case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);
1885      case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1886      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
1887      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1888    default:    default:
1889      throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");      throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");
1890    }    }
1891  }  }
1892    
1893    
1894    
1895    // There is no single, natural interpretation of getLength on DataLazy.
1896    // Instances of DataReady can look at the size of their vectors.
1897    // For lazy though, it could be the size the data would be if it were resolved;
1898    // or it could be some function of the lengths of the DataReady instances which
1899    // form part of the expression.
1900    // Rather than have people making assumptions, I have disabled the method.
1901  DataTypes::ValueType::size_type  DataTypes::ValueType::size_type
1902  DataLazy::getLength() const  DataLazy::getLength() const
1903  {  {
1904    return m_length;    throw DataException("getLength() does not make sense for lazy data.");
1905  }  }
1906    
1907    
# Line 933  DataLazy::getPointOffset(int sampleNo, Line 1967  DataLazy::getPointOffset(int sampleNo,
1967    throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");    throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");
1968  }  }
1969    
1970  // It would seem that DataTagged will need to be treated differently since even after setting all tags  
1971  // to zero, all the tags from all the DataTags would be in the result.  // I have decided to let Data:: handle this issue.
 // However since they all have the same value (0) whether they are there or not should not matter.  
 // So I have decided that for all types this method will create a constant 0.  
 // It can be promoted up as required.  
 // A possible efficiency concern might be expanded->constant->expanded which has an extra memory management  
 // but we can deal with that if it arrises.  
1972  void  void
1973  DataLazy::setToZero()  DataLazy::setToZero()
1974  {  {
1975    DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::ValueType v(getNoValues(),0);
1976    m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
1977    m_op=IDENTITY;  //   m_op=IDENTITY;
1978    m_right.reset();    //   m_right.reset();  
1979    m_left.reset();  //   m_left.reset();
1980    m_readytype='C';  //   m_readytype='C';
1981    m_buffsRequired=1;  //   m_buffsRequired=1;
1982    
1983      privdebug=privdebug;  // to stop the compiler complaining about unused privdebug
1984      throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
1985    }
1986    
1987    bool
1988    DataLazy::actsExpanded() const
1989    {
1990        return (m_readytype=='E');
1991  }  }
1992    
1993  }   // end namespace  }   // end namespace

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