/[escript]/branches/clazy/escriptcore/src/DataLazy.cpp
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branches/schroedinger/escript/src/DataLazy.cpp revision 1910 by jfenwick, Thu Oct 23 03:05:28 2008 UTC trunk/escript/src/DataLazy.cpp revision 2153 by jfenwick, Fri Dec 12 00:18:18 2008 UTC
# Line 26  Line 26 
26  #include "DataTypes.h"  #include "DataTypes.h"
27  #include "Data.h"  #include "Data.h"
28  #include "UnaryFuncs.h"     // for escript::fsign  #include "UnaryFuncs.h"     // for escript::fsign
29    #include "Utils.h"
30    
31    // #define LAZYDEBUG(X) X;
32    #define LAZYDEBUG(X)
33    
34  /*  /*
35  How does DataLazy work?  How does DataLazy work?
# Line 47  I will refer to individual DataLazy obje Line 51  I will refer to individual DataLazy obje
51  Each node also stores:  Each node also stores:
52  - 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
53      evaluated.      evaluated.
 - m_length ~ how many values would be stored in the answer if the expression was evaluated.  
54  - 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
55      evaluate the expression.      evaluate the expression.
56  - m_samplesize ~ the number of doubles stored in a sample.  - m_samplesize ~ the number of doubles stored in a sample.
# Line 69  The convention that I use, is that the r Line 72  The convention that I use, is that the r
72    
73  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.
74  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.
75    
76    To add a new operator you need to do the following (plus anything I might have forgotten - adding a new group for example):
77    1) Add to the ES_optype.
78    2) determine what opgroup your operation belongs to (X)
79    3) add a string for the op to the end of ES_opstrings
80    4) increase ES_opcount
81    5) add an entry (X) to opgroups
82    6) add an entry to the switch in collapseToReady
83    7) add an entry to resolveX
84  */  */
85    
86    
# Line 78  using namespace boost; Line 90  using namespace boost;
90  namespace escript  namespace escript
91  {  {
92    
 const std::string&  
 opToString(ES_optype op);  
   
93  namespace  namespace
94  {  {
95    
# Line 89  enum ES_opgroup Line 98  enum ES_opgroup
98     G_UNKNOWN,     G_UNKNOWN,
99     G_IDENTITY,     G_IDENTITY,
100     G_BINARY,        // pointwise operations with two arguments     G_BINARY,        // pointwise operations with two arguments
101     G_UNARY      // pointwise operations with one argument     G_UNARY,     // pointwise operations with one argument
102       G_UNARY_P,       // pointwise operations with one argument, requiring a parameter
103       G_NP1OUT,        // non-pointwise op with one output
104       G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter
105       G_TENSORPROD     // general tensor product
106  };  };
107    
108    
# Line 100  string ES_opstrings[]={"UNKNOWN","IDENTI Line 113  string ES_opstrings[]={"UNKNOWN","IDENTI
113              "asin","acos","atan","sinh","cosh","tanh","erf",              "asin","acos","atan","sinh","cosh","tanh","erf",
114              "asinh","acosh","atanh",              "asinh","acosh","atanh",
115              "log10","log","sign","abs","neg","pos","exp","sqrt",              "log10","log","sign","abs","neg","pos","exp","sqrt",
116              "1/","where>0","where<0","where>=0","where<=0"};              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",
117  int ES_opcount=32;              "symmetric","nonsymmetric",
118                "prod",
119                "transpose", "trace"};
120    int ES_opcount=40;
121  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,
122              G_UNARY,G_UNARY,G_UNARY, //10              G_UNARY,G_UNARY,G_UNARY, //10
123              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
124              G_UNARY,G_UNARY,G_UNARY,                    // 20              G_UNARY,G_UNARY,G_UNARY,                    // 20
125              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
126              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
127                G_NP1OUT,G_NP1OUT,
128                G_TENSORPROD,
129                G_NP1OUT_P, G_NP1OUT_P};
130  inline  inline
131  ES_opgroup  ES_opgroup
132  getOpgroup(ES_optype op)  getOpgroup(ES_optype op)
# Line 124  resultFS(DataAbstract_ptr left, DataAbst Line 143  resultFS(DataAbstract_ptr left, DataAbst
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    
146      FunctionSpace l=left->getFunctionSpace();
147      if (left->getFunctionSpace()!=right->getFunctionSpace())    FunctionSpace r=right->getFunctionSpace();
148      {    if (l!=r)
149          throw DataException("FunctionSpaces not equal - interpolation not supported on lazy data.");    {
150      }      if (r.probeInterpolation(l))
151      return left->getFunctionSpace();      {
152        return l;
153        }
154        if (l.probeInterpolation(r))
155        {
156        return r;
157        }
158        throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");
159      }
160      return l;
161  }  }
162    
163  // return the shape of the result of "left op right"  // return the shape of the result of "left op right"
164    // the shapes resulting from tensor product are more complex to compute so are worked out elsewhere
165  DataTypes::ShapeType  DataTypes::ShapeType
166  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
167  {  {
168      if (left->getShape()!=right->getShape())      if (left->getShape()!=right->getShape())
169      {      {
170        if (getOpgroup(op)!=G_BINARY)        if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
171        {        {
172          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.");
173        }        }
# Line 155  resultShape(DataAbstract_ptr left, DataA Line 184  resultShape(DataAbstract_ptr left, DataA
184      return left->getShape();      return left->getShape();
185  }  }
186    
187  // determine the number of points in the result of "left op right"  // return the shape for "op left"
188  size_t  
189  resultLength(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataTypes::ShapeType
190    resultShape(DataAbstract_ptr left, ES_optype op)
191  {  {
192     switch (getOpgroup(op))      switch(op)
193     {      {
194     case G_BINARY: return left->getLength();          case TRANS:
195     case G_UNARY: return left->getLength();          return left->getShape();
196     default:      break;
197      throw DataException("Programmer Error - attempt to getLength() for operator "+opToString(op)+".");      case TRACE:
198     }          return DataTypes::scalarShape;
199        break;
200            default:
201        throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
202        }
203  }  }
204    
205    // determine the output shape for the general tensor product operation
206    // the additional parameters return information required later for the product
207    // the majority of this code is copy pasted from C_General_Tensor_Product
208    DataTypes::ShapeType
209    GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
210    {
211        
212      // Get rank and shape of inputs
213      int rank0 = left->getRank();
214      int rank1 = right->getRank();
215      const DataTypes::ShapeType& shape0 = left->getShape();
216      const DataTypes::ShapeType& shape1 = right->getShape();
217    
218      // Prepare for the loops of the product and verify compatibility of shapes
219      int start0=0, start1=0;
220      if (transpose == 0)       {}
221      else if (transpose == 1)  { start0 = axis_offset; }
222      else if (transpose == 2)  { start1 = rank1-axis_offset; }
223      else              { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }
224    
225      if (rank0<axis_offset)
226      {
227        throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
228      }
229    
230      // Adjust the shapes for transpose
231      DataTypes::ShapeType tmpShape0(rank0);    // pre-sizing the vectors rather
232      DataTypes::ShapeType tmpShape1(rank1);    // than using push_back
233      for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
234      for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
235    
236      // Prepare for the loops of the product
237      SL=1, SM=1, SR=1;
238      for (int i=0; i<rank0-axis_offset; i++)   {
239        SL *= tmpShape0[i];
240      }
241      for (int i=rank0-axis_offset; i<rank0; i++)   {
242        if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
243          throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
244        }
245        SM *= tmpShape0[i];
246      }
247      for (int i=axis_offset; i<rank1; i++)     {
248        SR *= tmpShape1[i];
249      }
250    
251      // Define the shape of the output (rank of shape is the sum of the loop ranges below)
252      DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);  
253      {         // block to limit the scope of out_index
254         int out_index=0;
255         for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
256         for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
257      }
258    
259      if (shape2.size()>ESCRIPT_MAX_DATA_RANK)
260      {
261         ostringstream os;
262         os << "C_GeneralTensorProduct: Error - Attempt to create a rank " << shape2.size() << " object. The maximum rank is " << ESCRIPT_MAX_DATA_RANK << ".";
263         throw DataException(os.str());
264      }
265    
266      return shape2;
267    }
268    
269    
270    // determine the number of points in the result of "left op right"
271    // note that determining the resultLength for G_TENSORPROD is more complex and will not be processed here
272    // size_t
273    // resultLength(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
274    // {
275    //    switch (getOpgroup(op))
276    //    {
277    //    case G_BINARY: return left->getLength();
278    //    case G_UNARY: return left->getLength();
279    //    case G_NP1OUT: return left->getLength();
280    //    default:
281    //  throw DataException("Programmer Error - attempt to getLength() for operator "+opToString(op)+".");
282    //    }
283    // }
284    
285  // determine the number of samples requires to evaluate an expression combining left and right  // determine the number of samples requires to evaluate an expression combining left and right
286    // NP1OUT needs an extra buffer because we can't write the answers over the top of the input.
287    // The same goes for G_TENSORPROD
288  int  int
289  calcBuffs(const DataLazy_ptr& left, const DataLazy_ptr& right, ES_optype op)  calcBuffs(const DataLazy_ptr& left, const DataLazy_ptr& right, ES_optype op)
290  {  {
# Line 176  calcBuffs(const DataLazy_ptr& left, cons Line 292  calcBuffs(const DataLazy_ptr& left, cons
292     {     {
293     case G_IDENTITY: return 1;     case G_IDENTITY: return 1;
294     case G_BINARY: return max(left->getBuffsRequired(),right->getBuffsRequired()+1);     case G_BINARY: return max(left->getBuffsRequired(),right->getBuffsRequired()+1);
295     case G_UNARY: return max(left->getBuffsRequired(),1);     case G_UNARY:
296       case G_UNARY_P: return max(left->getBuffsRequired(),1);
297       case G_NP1OUT: return 1+max(left->getBuffsRequired(),1);
298       case G_NP1OUT_P: return 1+max(left->getBuffsRequired(),1);
299       case G_TENSORPROD: return 1+max(left->getBuffsRequired(),right->getBuffsRequired()+1);
300     default:     default:
301      throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");      throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");
302     }     }
# Line 201  opToString(ES_optype op) Line 321  opToString(ES_optype op)
321    
322  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
323      : parent(p->getFunctionSpace(),p->getShape()),      : parent(p->getFunctionSpace(),p->getShape()),
324      m_op(IDENTITY)      m_op(IDENTITY),
325        m_axis_offset(0),
326        m_transpose(0),
327        m_SL(0), m_SM(0), m_SR(0)
328  {  {
329     if (p->isLazy())     if (p->isLazy())
330     {     {
# Line 218  DataLazy::DataLazy(DataAbstract_ptr p) Line 341  DataLazy::DataLazy(DataAbstract_ptr p)
341      else if (p->isTagged()) {m_readytype='T';}      else if (p->isTagged()) {m_readytype='T';}
342      else {throw DataException("Unknown DataReady instance in DataLazy constructor.");}      else {throw DataException("Unknown DataReady instance in DataLazy constructor.");}
343     }     }
    m_length=p->getLength();  
344     m_buffsRequired=1;     m_buffsRequired=1;
345     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
346  cout << "(1)Lazy created with " << m_samplesize << endl;     m_maxsamplesize=m_samplesize;
347    LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
348  }  }
349    
350    
# Line 229  cout << "(1)Lazy created with " << m_sam Line 352  cout << "(1)Lazy created with " << m_sam
352    
353  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
354      : parent(left->getFunctionSpace(),left->getShape()),      : parent(left->getFunctionSpace(),left->getShape()),
355      m_op(op)      m_op(op),
356        m_axis_offset(0),
357        m_transpose(0),
358        m_SL(0), m_SM(0), m_SR(0)
359  {  {
360     if (getOpgroup(op)!=G_UNARY)     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT))
361     {     {
362      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.");
363     }     }
364    
365     DataLazy_ptr lleft;     DataLazy_ptr lleft;
366     if (!left->isLazy())     if (!left->isLazy())
367     {     {
# Line 245  DataLazy::DataLazy(DataAbstract_ptr left Line 372  DataLazy::DataLazy(DataAbstract_ptr left
372      lleft=dynamic_pointer_cast<DataLazy>(left);      lleft=dynamic_pointer_cast<DataLazy>(left);
373     }     }
374     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
    m_length=left->getLength();  
375     m_left=lleft;     m_left=lleft;
376     m_buffsRequired=1;     m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point
377     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
378       m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());
379  }  }
380    
381    
382    // In this constructor we need to consider interpolation
383  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
384      : parent(resultFS(left,right,op), resultShape(left,right,op)),      : parent(resultFS(left,right,op), resultShape(left,right,op)),
385      m_op(op)      m_op(op),
386        m_SL(0), m_SM(0), m_SR(0)
387  {  {
388     if (getOpgroup(op)!=G_BINARY)     if ((getOpgroup(op)!=G_BINARY))
389     {     {
390      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.");
391     }     }
392    
393       if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
394       {
395        FunctionSpace fs=getFunctionSpace();
396        Data ltemp(left);
397        Data tmp(ltemp,fs);
398        left=tmp.borrowDataPtr();
399       }
400       if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
401       {
402        Data tmp(Data(right),getFunctionSpace());
403        right=tmp.borrowDataPtr();
404       }
405       left->operandCheck(*right);
406    
407     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
408     {     {
409      m_left=dynamic_pointer_cast<DataLazy>(left);      m_left=dynamic_pointer_cast<DataLazy>(left);
# Line 290  DataLazy::DataLazy(DataAbstract_ptr left Line 434  DataLazy::DataLazy(DataAbstract_ptr left
434     {     {
435      m_readytype='C';      m_readytype='C';
436     }     }
437     m_length=resultLength(m_left,m_right,m_op);     m_samplesize=getNumDPPSample()*getNoValues();
438     m_samplesize=getNumDPPSample()*getNoValues();         m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());  
439     m_buffsRequired=calcBuffs(m_left, m_right,m_op);     m_buffsRequired=calcBuffs(m_left, m_right,m_op);
440  cout << "(3)Lazy created with " << m_samplesize << endl;  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
441  }  }
442    
443    DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)
444        : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
445        m_op(op),
446        m_axis_offset(axis_offset),
447        m_transpose(transpose)
448    {
449       if ((getOpgroup(op)!=G_TENSORPROD))
450       {
451        throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
452       }
453       if ((transpose>2) || (transpose<0))
454       {
455        throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
456       }
457       if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
458       {
459        FunctionSpace fs=getFunctionSpace();
460        Data ltemp(left);
461        Data tmp(ltemp,fs);
462        left=tmp.borrowDataPtr();
463       }
464       if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
465       {
466        Data tmp(Data(right),getFunctionSpace());
467        right=tmp.borrowDataPtr();
468       }
469       left->operandCheck(*right);
470    
471       if (left->isLazy())          // the children need to be DataLazy. Wrap them in IDENTITY if required
472       {
473        m_left=dynamic_pointer_cast<DataLazy>(left);
474       }
475       else
476       {
477        m_left=DataLazy_ptr(new DataLazy(left));
478       }
479       if (right->isLazy())
480       {
481        m_right=dynamic_pointer_cast<DataLazy>(right);
482       }
483       else
484       {
485        m_right=DataLazy_ptr(new DataLazy(right));
486       }
487       char lt=m_left->m_readytype;
488       char rt=m_right->m_readytype;
489       if (lt=='E' || rt=='E')
490       {
491        m_readytype='E';
492       }
493       else if (lt=='T' || rt=='T')
494       {
495        m_readytype='T';
496       }
497       else
498       {
499        m_readytype='C';
500       }
501       m_samplesize=getNumDPPSample()*getNoValues();
502       m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());  
503       m_buffsRequired=calcBuffs(m_left, m_right,m_op);
504    LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
505    }
506    
507    
508    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
509        : parent(left->getFunctionSpace(), resultShape(left,op)),
510        m_op(op),
511        m_axis_offset(axis_offset),
512        m_transpose(0),
513        m_tol(0)
514    {
515       if ((getOpgroup(op)!=G_NP1OUT_P))
516       {
517        throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
518       }
519       DataLazy_ptr lleft;
520       if (!left->isLazy())
521       {
522        lleft=DataLazy_ptr(new DataLazy(left));
523       }
524       else
525       {
526        lleft=dynamic_pointer_cast<DataLazy>(left);
527       }
528       m_readytype=lleft->m_readytype;
529       m_left=lleft;
530       m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point
531       m_samplesize=getNumDPPSample()*getNoValues();
532       m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());
533    LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
534    }
535    
536    DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
537        : parent(left->getFunctionSpace(), left->getShape()),
538        m_op(op),
539        m_axis_offset(0),
540        m_transpose(0),
541        m_tol(tol)
542    {
543       if ((getOpgroup(op)!=G_UNARY_P))
544       {
545        throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
546       }
547       DataLazy_ptr lleft;
548       if (!left->isLazy())
549       {
550        lleft=DataLazy_ptr(new DataLazy(left));
551       }
552       else
553       {
554        lleft=dynamic_pointer_cast<DataLazy>(left);
555       }
556       m_readytype=lleft->m_readytype;
557       m_left=lleft;
558       m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point
559       m_samplesize=getNumDPPSample()*getNoValues();
560       m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());
561    LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
562    }
563    
564  DataLazy::~DataLazy()  DataLazy::~DataLazy()
565  {  {
# Line 309  DataLazy::getBuffsRequired() const Line 573  DataLazy::getBuffsRequired() const
573  }  }
574    
575    
576    size_t
577    DataLazy::getMaxSampleSize() const
578    {
579        return m_maxsamplesize;
580    }
581    
582  /*  /*
583    \brief Evaluates the expression using methods on Data.    \brief Evaluates the expression using methods on Data.
584    This does the work for the collapse method.    This does the work for the collapse method.
# Line 328  DataLazy::collapseToReady() Line 598  DataLazy::collapseToReady()
598    DataReady_ptr pleft=m_left->collapseToReady();    DataReady_ptr pleft=m_left->collapseToReady();
599    Data left(pleft);    Data left(pleft);
600    Data right;    Data right;
601    if (getOpgroup(m_op)==G_BINARY)    if ((getOpgroup(m_op)==G_BINARY) || (getOpgroup(m_op)==G_TENSORPROD))
602    {    {
603      right=Data(m_right->collapseToReady());      right=Data(m_right->collapseToReady());
604    }    }
# Line 427  DataLazy::collapseToReady() Line 697  DataLazy::collapseToReady()
697      case LEZ:      case LEZ:
698      result=left.whereNonPositive();      result=left.whereNonPositive();
699      break;      break;
700        case NEZ:
701        result=left.whereNonZero(m_tol);
702        break;
703        case EZ:
704        result=left.whereZero(m_tol);
705        break;
706        case SYM:
707        result=left.symmetric();
708        break;
709        case NSYM:
710        result=left.nonsymmetric();
711        break;
712        case PROD:
713        result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
714        break;
715        case TRANS:
716        result=left.transpose(m_axis_offset);
717        break;
718        case TRACE:
719        result=left.trace(m_axis_offset);
720        break;
721      default:      default:
722      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)+".");
723    }    }
724    return result.borrowReadyPtr();    return result.borrowReadyPtr();
725  }  }
# Line 455  DataLazy::collapse() Line 746  DataLazy::collapse()
746  }  }
747    
748  /*  /*
749    \brief Compute the value of the expression (binary operation) for the given sample.    \brief Compute the value of the expression (unary operation) for the given sample.
750    \return Vector which stores the value of the subexpression for the given sample.    \return Vector which stores the value of the subexpression for the given sample.
751    \param v A vector to store intermediate results.    \param v A vector to store intermediate results.
752    \param offset Index in v to begin storing results.    \param offset Index in v to begin storing results.
# Line 483  DataLazy::resolveUnary(ValueType& v, siz Line 774  DataLazy::resolveUnary(ValueType& v, siz
774    switch (m_op)    switch (m_op)
775    {    {
776      case SIN:        case SIN:  
777      tensor_unary_operation(m_samplesize, left, result, ::sin);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);
778      break;      break;
779      case COS:      case COS:
780      tensor_unary_operation(m_samplesize, left, result, ::cos);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);
781      break;      break;
782      case TAN:      case TAN:
783      tensor_unary_operation(m_samplesize, left, result, ::tan);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);
784      break;      break;
785      case ASIN:      case ASIN:
786      tensor_unary_operation(m_samplesize, left, result, ::asin);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);
787      break;      break;
788      case ACOS:      case ACOS:
789      tensor_unary_operation(m_samplesize, left, result, ::acos);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);
790      break;      break;
791      case ATAN:      case ATAN:
792      tensor_unary_operation(m_samplesize, left, result, ::atan);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);
793      break;      break;
794      case SINH:      case SINH:
795      tensor_unary_operation(m_samplesize, left, result, ::sinh);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);
796      break;      break;
797      case COSH:      case COSH:
798      tensor_unary_operation(m_samplesize, left, result, ::cosh);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);
799      break;      break;
800      case TANH:      case TANH:
801      tensor_unary_operation(m_samplesize, left, result, ::tanh);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);
802      break;      break;
803      case ERF:      case ERF:
804  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
805      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
806  #else  #else
807      tensor_unary_operation(m_samplesize, left, result, ::erf);      tensor_unary_operation(m_samplesize, left, result, ::erf);
808      break;      break;
809  #endif  #endif
810     case ASINH:     case ASINH:
811  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
812      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);
813  #else  #else
814      tensor_unary_operation(m_samplesize, left, result, ::asinh);      tensor_unary_operation(m_samplesize, left, result, ::asinh);
815  #endif    #endif  
816      break;      break;
817     case ACOSH:     case ACOSH:
818  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
819      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);
820  #else  #else
821      tensor_unary_operation(m_samplesize, left, result, ::acosh);      tensor_unary_operation(m_samplesize, left, result, ::acosh);
822  #endif    #endif  
823      break;      break;
824     case ATANH:     case ATANH:
825  #ifdef _WIN32  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
826      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);
827  #else  #else
828      tensor_unary_operation(m_samplesize, left, result, ::atanh);      tensor_unary_operation(m_samplesize, left, result, ::atanh);
829  #endif    #endif  
830      break;      break;
831      case LOG10:      case LOG10:
832      tensor_unary_operation(m_samplesize, left, result, ::log10);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);
833      break;      break;
834      case LOG:      case LOG:
835      tensor_unary_operation(m_samplesize, left, result, ::log);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);
836      break;      break;
837      case SIGN:      case SIGN:
838      tensor_unary_operation(m_samplesize, left, result, escript::fsign);      tensor_unary_operation(m_samplesize, left, result, escript::fsign);
839      break;      break;
840      case ABS:      case ABS:
841      tensor_unary_operation(m_samplesize, left, result, ::fabs);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);
842      break;      break;
843      case NEG:      case NEG:
844      tensor_unary_operation(m_samplesize, left, result, negate<double>());      tensor_unary_operation(m_samplesize, left, result, negate<double>());
# Line 558  DataLazy::resolveUnary(ValueType& v, siz Line 849  DataLazy::resolveUnary(ValueType& v, siz
849      throw DataException("Programmer error - POS not supported for lazy data.");      throw DataException("Programmer error - POS not supported for lazy data.");
850      break;      break;
851      case EXP:      case EXP:
852      tensor_unary_operation(m_samplesize, left, result, ::exp);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);
853      break;      break;
854      case SQRT:      case SQRT:
855      tensor_unary_operation(m_samplesize, left, result, ::sqrt);      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);
856      break;      break;
857      case RECIP:      case RECIP:
858      tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));      tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));
# Line 578  DataLazy::resolveUnary(ValueType& v, siz Line 869  DataLazy::resolveUnary(ValueType& v, siz
869      case LEZ:      case LEZ:
870      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));
871      break;      break;
872    // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently
873        case NEZ:
874        tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));
875        break;
876        case EZ:
877        tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));
878        break;
879    
880      default:      default:
881      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
# Line 589  DataLazy::resolveUnary(ValueType& v, siz Line 887  DataLazy::resolveUnary(ValueType& v, siz
887    
888    
889    
890  #define PROC_OP(X) \  
891      for (int i=0;i<steps;++i,resultp+=resultStep) \  /*
892      { \    \brief Compute the value of the expression (unary operation) for the given sample.
893         tensor_binary_operation(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \    \return Vector which stores the value of the subexpression for the given sample.
894         lroffset+=leftStep; \    \param v A vector to store intermediate results.
895         rroffset+=rightStep; \    \param offset Index in v to begin storing results.
896      \param sampleNo Sample number to evaluate.
897      \param roffset (output parameter) the offset in the return vector where the result begins.
898    
899      The return value will be an existing vector so do not deallocate it.
900      If the result is stored in v it should be stored at the offset given.
901      Everything from offset to the end of v should be considered available for this method to use.
902    */
903    DataTypes::ValueType*
904    DataLazy::resolveNP1OUT(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const
905    {
906        // we assume that any collapsing has been done before we get here
907        // since we only have one argument we don't need to think about only
908        // processing single points.
909      if (m_readytype!='E')
910      {
911        throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");
912      }
913        // since we can't write the result over the input, we need a result offset further along
914      size_t subroffset=roffset+m_samplesize;
915      const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);
916      roffset=offset;
917      switch (m_op)
918      {
919        case SYM:
920        DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);
921        break;
922        case NSYM:
923        DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);
924        break;
925        default:
926        throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
927      }
928      return &v;
929    }
930    
931    /*
932      \brief Compute the value of the expression (unary operation) for the given sample.
933      \return Vector which stores the value of the subexpression for the given sample.
934      \param v A vector to store intermediate results.
935      \param offset Index in v to begin storing results.
936      \param sampleNo Sample number to evaluate.
937      \param roffset (output parameter) the offset in the return vector where the result begins.
938    
939      The return value will be an existing vector so do not deallocate it.
940      If the result is stored in v it should be stored at the offset given.
941      Everything from offset to the end of v should be considered available for this method to use.
942    */
943    DataTypes::ValueType*
944    DataLazy::resolveNP1OUT_P(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const
945    {
946        // we assume that any collapsing has been done before we get here
947        // since we only have one argument we don't need to think about only
948        // processing single points.
949      if (m_readytype!='E')
950      {
951        throw DataException("Programmer error - resolveNP1OUT_P should only be called on expanded Data.");
952      }
953        // since we can't write the result over the input, we need a result offset further along
954      size_t subroffset=roffset+m_samplesize;
955      const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,subroffset);
956      roffset=offset;
957      switch (m_op)
958      {
959        case TRACE:
960             DataMaths::trace(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);
961        break;
962        case TRANS:
963             DataMaths::transpose(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);
964        break;
965        default:
966        throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
967      }
968      return &v;
969    }
970    
971    
972    #define PROC_OP(TYPE,X)                               \
973        for (int j=0;j<onumsteps;++j)\
974        {\
975          for (int i=0;i<numsteps;++i,resultp+=resultStep) \
976          { \
977    LAZYDEBUG(cout << "[left,right]=[" << lroffset << "," << rroffset << "]" << endl;)\
978             tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \
979             lroffset+=leftstep; \
980             rroffset+=rightstep; \
981          }\
982          lroffset+=oleftstep;\
983          rroffset+=orightstep;\
984      }      }
985    
986  /*  /*
# Line 621  DataLazy::resolveUnary(ValueType& v, siz Line 1007  DataLazy::resolveUnary(ValueType& v, siz
1007  DataTypes::ValueType*  DataTypes::ValueType*
1008  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const
1009  {  {
1010  cout << "Resolve binary: " << toString() << endl;  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1011    
1012    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
1013      // first work out which of the children are expanded      // first work out which of the children are expanded
1014    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1015    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1016    bool bigloops=((leftExp && rightExp) || (!leftExp && !rightExp)); // is processing in single step?    if (!leftExp && !rightExp)
1017    int steps=(bigloops?1:getNumDPPSample());    {
1018    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'.");
1019    if (m_left->getRank()!=m_right->getRank())    // need to deal with scalar * ? ops    }
1020    {    bool leftScalar=(m_left->getRank()==0);
1021      EsysAssert((m_left->getRank()==0) || (m_right->getRank()==0), "Error - Ranks must match unless one is 0.");    bool rightScalar=(m_right->getRank()==0);
1022      steps=getNumDPPSample()*max(m_left->getNoValues(),m_right->getNoValues());    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1023      chunksize=1;    // for scalar    {
1024    }          throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1025    int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);    }
1026    int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);    size_t leftsize=m_left->getNoValues();
1027    int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0    size_t rightsize=m_right->getNoValues();
1028      size_t chunksize=1;           // how many doubles will be processed in one go
1029      int leftstep=0;       // how far should the left offset advance after each step
1030      int rightstep=0;
1031      int numsteps=0;       // total number of steps for the inner loop
1032      int oleftstep=0;  // the o variables refer to the outer loop
1033      int orightstep=0; // The outer loop is only required in cases where there is an extended scalar
1034      int onumsteps=1;
1035      
1036      bool LES=(leftExp && leftScalar); // Left is an expanded scalar
1037      bool RES=(rightExp && rightScalar);
1038      bool LS=(!leftExp && leftScalar); // left is a single scalar
1039      bool RS=(!rightExp && rightScalar);
1040      bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1041      bool RN=(!rightExp && !rightScalar);
1042      bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1043      bool REN=(rightExp && !rightScalar);
1044    
1045      if ((LES && RES) || (LEN && REN)) // both are Expanded scalars or both are expanded non-scalars
1046      {
1047        chunksize=m_left->getNumDPPSample()*leftsize;
1048        leftstep=0;
1049        rightstep=0;
1050        numsteps=1;
1051      }
1052      else if (LES || RES)
1053      {
1054        chunksize=1;
1055        if (LES)        // left is an expanded scalar
1056        {
1057            if (RS)
1058            {
1059               leftstep=1;
1060               rightstep=0;
1061               numsteps=m_left->getNumDPPSample();
1062            }
1063            else        // RN or REN
1064            {
1065               leftstep=0;
1066               oleftstep=1;
1067               rightstep=1;
1068               orightstep=(RN?-rightsize:0);
1069               numsteps=rightsize;
1070               onumsteps=m_left->getNumDPPSample();
1071            }
1072        }
1073        else        // right is an expanded scalar
1074        {
1075            if (LS)
1076            {
1077               rightstep=1;
1078               leftstep=0;
1079               numsteps=m_right->getNumDPPSample();
1080            }
1081            else
1082            {
1083               rightstep=0;
1084               orightstep=1;
1085               leftstep=1;
1086               oleftstep=(LN?-leftsize:0);
1087               numsteps=leftsize;
1088               onumsteps=m_right->getNumDPPSample();
1089            }
1090        }
1091      }
1092      else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1093      {
1094        if (LEN)    // and Right will be a single value
1095        {
1096            chunksize=rightsize;
1097            leftstep=rightsize;
1098            rightstep=0;
1099            numsteps=m_left->getNumDPPSample();
1100            if (RS)
1101            {
1102               numsteps*=leftsize;
1103            }
1104        }
1105        else    // REN
1106        {
1107            chunksize=leftsize;
1108            rightstep=leftsize;
1109            leftstep=0;
1110            numsteps=m_right->getNumDPPSample();
1111            if (LS)
1112            {
1113               numsteps*=rightsize;
1114            }
1115        }
1116      }
1117    
1118      int resultStep=max(leftstep,rightstep);   // only one (at most) should be !=0
1119      // Get the values of sub-expressions      // Get the values of sub-expressions
1120    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);    const ValueType* left=m_left->resolveSample(v,offset,sampleNo,lroffset);
1121    const ValueType* right=m_right->resolveSample(v,offset+m_samplesize,sampleNo,rroffset); // Note    const ValueType* right=m_right->resolveSample(v,offset+m_left->getMaxSampleSize(),sampleNo,rroffset); // Note
1122      // the right child starts further along.      // the right child starts further along.
1123    LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1124    LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1125    LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
1126    LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)
1127    LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)
1128    LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)
1129    LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)
1130    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved    double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved
1131    switch(m_op)    switch(m_op)
1132    {    {
1133      case ADD:      case ADD:
1134      PROC_OP(plus<double>());          PROC_OP(NO_ARG,plus<double>());
1135      break;      break;
1136      case SUB:      case SUB:
1137      PROC_OP(minus<double>());      PROC_OP(NO_ARG,minus<double>());
1138      break;      break;
1139      case MUL:      case MUL:
1140      PROC_OP(multiplies<double>());      PROC_OP(NO_ARG,multiplies<double>());
1141      break;      break;
1142      case DIV:      case DIV:
1143      PROC_OP(divides<double>());      PROC_OP(NO_ARG,divides<double>());
1144      break;      break;
1145      case POW:      case POW:
1146      PROC_OP(::pow);         PROC_OP(double (double,double),::pow);
1147      break;      break;
1148      default:      default:
1149      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
# Line 671  cout << "Resolve binary: " << toString() Line 1155  cout << "Resolve binary: " << toString()
1155    
1156    
1157  /*  /*
1158      \brief Compute the value of the expression (tensor product) for the given sample.
1159      \return Vector which stores the value of the subexpression for the given sample.
1160      \param v A vector to store intermediate results.
1161      \param offset Index in v to begin storing results.
1162      \param sampleNo Sample number to evaluate.
1163      \param roffset (output parameter) the offset in the return vector where the result begins.
1164    
1165      The return value will be an existing vector so do not deallocate it.
1166      If the result is stored in v it should be stored at the offset given.
1167      Everything from offset to the end of v should be considered available for this method to use.
1168    */
1169    // This method assumes that any subexpressions which evaluate to Constant or Tagged Data
1170    // have already been collapsed to IDENTITY. So we must have at least one expanded child.
1171    // unlike the other resolve helpers, we must treat these datapoints separately.
1172    DataTypes::ValueType*
1173    DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const
1174    {
1175    LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1176    
1177      size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors
1178        // first work out which of the children are expanded
1179      bool leftExp=(m_left->m_readytype=='E');
1180      bool rightExp=(m_right->m_readytype=='E');
1181      int steps=getNumDPPSample();
1182      int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);
1183      int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);
1184      int resultStep=max(leftStep,rightStep);   // only one (at most) should be !=0
1185        // Get the values of sub-expressions (leave a gap of one sample for the result).
1186      int gap=offset+m_left->getMaxSampleSize();    // actually only needs to be m_left->m_samplesize
1187      const ValueType* left=m_left->resolveSample(v,gap,sampleNo,lroffset);
1188      gap+=m_right->getMaxSampleSize();
1189      const ValueType* right=m_right->resolveSample(v,gap,sampleNo,rroffset);
1190    LAZYDEBUG(cout << "Post sub calls: " << toString() << endl;)
1191    LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)
1192    LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)
1193    LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)
1194    LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)
1195    LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1196      double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved
1197      switch(m_op)
1198      {
1199        case PROD:
1200        for (int i=0;i<steps;++i,resultp+=resultStep)
1201        {
1202    LAZYDEBUG(cout << "lroffset=" << lroffset << "rroffset=" << rroffset << endl;)
1203    LAZYDEBUG(cout << "l*=" << left << " r*=" << right << endl;)
1204    LAZYDEBUG(cout << "m_SL=" << m_SL << " m_SM=" << m_SM << " m_SR=" << m_SR << endl;)
1205              const double *ptr_0 = &((*left)[lroffset]);
1206              const double *ptr_1 = &((*right)[rroffset]);
1207    LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1208    LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1209              matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1210          lroffset+=leftStep;
1211          rroffset+=rightStep;
1212        }
1213        break;
1214        default:
1215        throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1216      }
1217      roffset=offset;
1218      return &v;
1219    }
1220    
1221    
1222    
1223    /*
1224    \brief Compute the value of the expression for the given sample.    \brief Compute the value of the expression for the given sample.
1225    \return Vector which stores the value of the subexpression for the given sample.    \return Vector which stores the value of the subexpression for the given sample.
1226    \param v A vector to store intermediate results.    \param v A vector to store intermediate results.
# Line 689  cout << "Resolve binary: " << toString() Line 1239  cout << "Resolve binary: " << toString()
1239  const DataTypes::ValueType*  const DataTypes::ValueType*
1240  DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)
1241  {  {
1242  cout << "Resolve sample " << toString() << endl;  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
1243      // collapse so we have a 'E' node or an IDENTITY for some other type      // collapse so we have a 'E' node or an IDENTITY for some other type
1244    if (m_readytype!='E' && m_op!=IDENTITY)    if (m_readytype!='E' && m_op!=IDENTITY)
1245    {    {
# Line 701  cout << "Resolve sample " << toString() Line 1251  cout << "Resolve sample " << toString()
1251      if (m_readytype=='C')      if (m_readytype=='C')
1252      {      {
1253      roffset=0;      roffset=0;
1254    LAZYDEBUG(cout << "Finish  sample " << toString() << endl;)
1255      return &(vec);      return &(vec);
1256      }      }
1257      roffset=m_id->getPointOffset(sampleNo, 0);      roffset=m_id->getPointOffset(sampleNo, 0);
1258    LAZYDEBUG(cout << "Finish  sample " << toString() << endl;)
1259      return &(vec);      return &(vec);
1260    }    }
1261    if (m_readytype!='E')    if (m_readytype!='E')
# Line 712  cout << "Resolve sample " << toString() Line 1264  cout << "Resolve sample " << toString()
1264    }    }
1265    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1266    {    {
1267    case G_UNARY: return resolveUnary(v, offset,sampleNo,roffset);    case G_UNARY:
1268      case G_UNARY_P: return resolveUnary(v, offset,sampleNo,roffset);
1269    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);    case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);
1270      case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);
1271      case G_NP1OUT_P: return resolveNP1OUT_P(v, offset, sampleNo,roffset);
1272      case G_TENSORPROD: return resolveTProd(v,offset, sampleNo,roffset);
1273    default:    default:
1274      throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");      throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");
1275    }    }
1276    
1277  }  }
1278    
1279    
# Line 726  DataReady_ptr Line 1283  DataReady_ptr
1283  DataLazy::resolve()  DataLazy::resolve()
1284  {  {
1285    
1286  cout << "Sample size=" << m_samplesize << endl;  LAZYDEBUG(cout << "Sample size=" << m_samplesize << endl;)
1287  cout << "Buffers=" << m_buffsRequired << endl;  LAZYDEBUG(cout << "Buffers=" << m_buffsRequired << endl;)
1288    
1289    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
1290    {    {
# Line 738  cout << "Buffers=" << m_buffsRequired << Line 1295  cout << "Buffers=" << m_buffsRequired <<
1295      return m_id;      return m_id;
1296    }    }
1297      // 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'
1298    size_t threadbuffersize=m_samplesize*(max(1,m_buffsRequired));    // Each thread needs to have enough    size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough
1299      // storage to evaluate its expression      // storage to evaluate its expression
1300    int numthreads=1;    int numthreads=1;
1301  #ifdef _OPENMP  #ifdef _OPENMP
1302    numthreads=getNumberOfThreads();    numthreads=getNumberOfThreads();
   int threadnum=0;  
1303  #endif  #endif
1304    ValueType v(numthreads*threadbuffersize);    ValueType v(numthreads*threadbuffersize);
1305  cout << "Buffer created with size=" << v.size() << endl;  LAZYDEBUG(cout << "Buffer created with size=" << v.size() << endl;)
1306    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));
1307    ValueType& resvec=result->getVector();    ValueType& resvec=result->getVector();
1308    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
# Line 755  cout << "Buffer created with size=" << v Line 1311  cout << "Buffer created with size=" << v
1311    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1312    const ValueType* res=0;   // Vector storing the answer    const ValueType* res=0;   // Vector storing the answer
1313    size_t resoffset=0;       // where in the vector to find the answer    size_t resoffset=0;       // where in the vector to find the answer
1314    #pragma omp parallel for private(sample,resoffset,outoffset,threadnum,res) schedule(static)  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1315      #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)
1316    for (sample=0;sample<totalsamples;++sample)    for (sample=0;sample<totalsamples;++sample)
1317    {    {
1318  cout << "################################# " << sample << endl;  LAZYDEBUG(cout << "################################# " << sample << endl;)
1319  #ifdef _OPENMP  #ifdef _OPENMP
1320      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);
1321  #else  #else
1322      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.
1323  #endif  #endif
1324  cerr << "-------------------------------- " << endl;  LAZYDEBUG(cerr << "-------------------------------- " << endl;)
1325      outoffset=result->getPointOffset(sample,0);      outoffset=result->getPointOffset(sample,0);
1326  cerr << "offset=" << outoffset << endl;  LAZYDEBUG(cerr << "offset=" << outoffset << endl;)
1327      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector
1328      {      {
1329      resvec[outoffset]=(*res)[resoffset];      resvec[outoffset]=(*res)[resoffset];
1330      }      }
1331  cerr << "*********************************" << endl;  LAZYDEBUG(cerr << "*********************************" << endl;)
1332    }    }
1333    return resptr;    return resptr;
1334  }  }
# Line 818  DataLazy::intoString(ostringstream& oss) Line 1375  DataLazy::intoString(ostringstream& oss)
1375      oss << ')';      oss << ')';
1376      break;      break;
1377    case G_UNARY:    case G_UNARY:
1378      case G_UNARY_P:
1379      case G_NP1OUT:
1380      case G_NP1OUT_P:
1381      oss << opToString(m_op) << '(';      oss << opToString(m_op) << '(';
1382      m_left->intoString(oss);      m_left->intoString(oss);
1383      oss << ')';      oss << ')';
1384      break;      break;
1385      case G_TENSORPROD:
1386        oss << opToString(m_op) << '(';
1387        m_left->intoString(oss);
1388        oss << ", ";
1389        m_right->intoString(oss);
1390        oss << ')';
1391        break;
1392    default:    default:
1393      oss << "UNKNOWN";      oss << "UNKNOWN";
1394    }    }
1395  }  }
1396    
 // 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.  
1397  DataAbstract*  DataAbstract*
1398  DataLazy::deepCopy()  DataLazy::deepCopy()
1399  {  {
1400    if (m_op==IDENTITY)    switch (getOpgroup(m_op))
1401    {    {
1402      return new DataLazy(m_left);    // we don't need to copy the child here    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
1403      case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
1404      case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
1405      case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);
1406      case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
1407      default:
1408        throw DataException("Programmer error - do not know how to deepcopy operator "+opToString(m_op)+".");
1409    }    }
   return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);  
1410  }  }
1411    
1412    
1413    // There is no single, natural interpretation of getLength on DataLazy.
1414    // Instances of DataReady can look at the size of their vectors.
1415    // For lazy though, it could be the size the data would be if it were resolved;
1416    // or it could be some function of the lengths of the DataReady instances which
1417    // form part of the expression.
1418    // Rather than have people making assumptions, I have disabled the method.
1419  DataTypes::ValueType::size_type  DataTypes::ValueType::size_type
1420  DataLazy::getLength() const  DataLazy::getLength() const
1421  {  {
1422    return m_length;    throw DataException("getLength() does not make sense for lazy data.");
1423  }  }
1424    
1425    
# Line 853  DataLazy::getSlice(const DataTypes::Regi Line 1429  DataLazy::getSlice(const DataTypes::Regi
1429    throw DataException("getSlice - not implemented for Lazy objects.");    throw DataException("getSlice - not implemented for Lazy objects.");
1430  }  }
1431    
1432    
1433    // To do this we need to rely on our child nodes
1434    DataTypes::ValueType::size_type
1435    DataLazy::getPointOffset(int sampleNo,
1436                     int dataPointNo)
1437    {
1438      if (m_op==IDENTITY)
1439      {
1440        return m_id->getPointOffset(sampleNo,dataPointNo);
1441      }
1442      if (m_readytype!='E')
1443      {
1444        collapse();
1445        return m_id->getPointOffset(sampleNo,dataPointNo);
1446      }
1447      // at this point we do not have an identity node and the expression will be Expanded
1448      // so we only need to know which child to ask
1449      if (m_left->m_readytype=='E')
1450      {
1451        return m_left->getPointOffset(sampleNo,dataPointNo);
1452      }
1453      else
1454      {
1455        return m_right->getPointOffset(sampleNo,dataPointNo);
1456      }
1457    }
1458    
1459    // To do this we need to rely on our child nodes
1460  DataTypes::ValueType::size_type  DataTypes::ValueType::size_type
1461  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
1462                   int dataPointNo) const                   int dataPointNo) const
1463  {  {
1464    throw DataException("getPointOffset - not implemented for Lazy objects - yet.");    if (m_op==IDENTITY)
1465      {
1466        return m_id->getPointOffset(sampleNo,dataPointNo);
1467      }
1468      if (m_readytype=='E')
1469      {
1470        // at this point we do not have an identity node and the expression will be Expanded
1471        // so we only need to know which child to ask
1472        if (m_left->m_readytype=='E')
1473        {
1474        return m_left->getPointOffset(sampleNo,dataPointNo);
1475        }
1476        else
1477        {
1478        return m_right->getPointOffset(sampleNo,dataPointNo);
1479        }
1480      }
1481      if (m_readytype=='C')
1482      {
1483        return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
1484      }
1485      throw DataException("Programmer error - getPointOffset on lazy data may require collapsing (but this object is marked const).");
1486  }  }
1487    
1488  // It would seem that DataTagged will need to be treated differently since even after setting all tags  // It would seem that DataTagged will need to be treated differently since even after setting all tags

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