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trunk/escript/src/DataLazy.cpp revision 2514 by jfenwick, Fri Jul 3 00:57:45 2009 UTC trunk/escriptcore/src/DataLazy.cpp revision 6057 by jfenwick, Thu Mar 10 06:00:58 2016 UTC
# Line 1  Line 1 
1    
2  /*******************************************************  /*****************************************************************************
3  *  *
4  * Copyright (c) 2003-2008 by University of Queensland  * Copyright (c) 2003-2016 by The University of Queensland
5  * Earth Systems Science Computational Center (ESSCC)  * http://www.uq.edu.au
 * http://www.uq.edu.au/esscc  
6  *  *
7  * Primary Business: Queensland, Australia  * Primary Business: Queensland, Australia
8  * Licensed under the Open Software License version 3.0  * Licensed under the Open Software License version 3.0
9  * http://www.opensource.org/licenses/osl-3.0.php  * http://www.opensource.org/licenses/osl-3.0.php
10  *  *
11  *******************************************************/  * Development until 2012 by Earth Systems Science Computational Center (ESSCC)
12    * Development 2012-2013 by School of Earth Sciences
13    * Development from 2014 by Centre for Geoscience Computing (GeoComp)
14    *
15    *****************************************************************************/
16    
17  #include "DataLazy.h"  #include "DataLazy.h"
18    #include "Data.h"
19    #include "DataTypes.h"
20    #include "EscriptParams.h"
21    #include "FunctionSpace.h"
22    #include "UnaryFuncs.h"    // for escript::fsign
23    #include "Utils.h"
24    #include "DataMaths.h"
25    
26  #ifdef USE_NETCDF  #ifdef USE_NETCDF
27  #include <netcdfcpp.h>  #include <netcdfcpp.h>
28  #endif  #endif
 #ifdef PASO_MPI  
 #include <mpi.h>  
 #endif  
 #ifdef _OPENMP  
 #include <omp.h>  
 #endif  
 #include "FunctionSpace.h"  
 #include "DataTypes.h"  
 #include "Data.h"  
 #include "UnaryFuncs.h"     // for escript::fsign  
 #include "Utils.h"  
29    
30  #include "EscriptParams.h"  #include <iomanip> // for some fancy formatting in debug
31    
32  #include <iomanip>      // for some fancy formatting in debug  using namespace escript::DataTypes;
33    
34    #define NO_ARG
35    
36  // #define LAZYDEBUG(X) if (privdebug){X;}  // #define LAZYDEBUG(X) if (privdebug){X;}
37  #define LAZYDEBUG(X)  #define LAZYDEBUG(X)
# Line 44  bool privdebug=false; Line 45  bool privdebug=false;
45    
46  // #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();}  // #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();}
47    
48  #define SIZELIMIT if ((m_height>escript::escriptParams.getTOO_MANY_LEVELS()) || (m_children>escript::escriptParams.getTOO_MANY_NODES())) {resolveToIdentity();}  // #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();}
49    
50    
51    #define SIZELIMIT if (m_height>escript::escriptParams.getTOO_MANY_LEVELS())  {if (escript::escriptParams.getLAZY_VERBOSE()){cerr << "SIZE LIMIT EXCEEDED height=" << m_height << endl;}resolveToIdentity();}
52    
53  /*  /*
54  How does DataLazy work?  How does DataLazy work?
# Line 58  A special operation, IDENTITY, stores an Line 61  A special operation, IDENTITY, stores an
61  This means that all "internal" nodes in the structure are instances of DataLazy.  This means that all "internal" nodes in the structure are instances of DataLazy.
62    
63  Each operation has a string representation as well as an opgroup - eg G_IDENTITY, G_BINARY, ...  Each operation has a string representation as well as an opgroup - eg G_IDENTITY, G_BINARY, ...
64  Note that IDENITY is not considered a unary operation.  Note that IDENTITY is not considered a unary operation.
65    
66  I am avoiding calling the structure formed a tree because it is not guaranteed to be one (eg c=a+a).  I am avoiding calling the structure formed a tree because it is not guaranteed to be one (eg c=a+a).
67  It must however form a DAG (directed acyclic graph).  It must however form a DAG (directed acyclic graph).
# Line 66  I will refer to individual DataLazy obje Line 69  I will refer to individual DataLazy obje
69    
70  Each node also stores:  Each node also stores:
71  - 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
72      evaluated.          evaluated.
73  - m_buffsrequired ~ the larged number of samples which would need to be kept simultaneously in order to  - m_buffsrequired ~ the large number of samples which would need to be kept simultaneously in order to
74      evaluate the expression.          evaluate the expression.
75  - m_samplesize ~ the number of doubles stored in a sample.  - m_samplesize ~ the number of doubles stored in a sample.
76    
77  When a new node is created, the above values are computed based on the values in the child nodes.  When a new node is created, the above values are computed based on the values in the child nodes.
# Line 109  namespace escript Line 112  namespace escript
112  namespace  namespace
113  {  {
114    
115    
116    // enabling this will print out when ever the maximum stacksize used by resolve increases
117    // it assumes _OPENMP is also in use
118    //#define LAZY_STACK_PROF
119    
120    
121    
122    #ifndef _OPENMP
123      #ifdef LAZY_STACK_PROF
124      #undef LAZY_STACK_PROF
125      #endif
126    #endif
127    
128    
129    #ifdef LAZY_STACK_PROF
130    std::vector<void*> stackstart(getNumberOfThreads());
131    std::vector<void*> stackend(getNumberOfThreads());
132    size_t maxstackuse=0;
133    #endif
134    
135  enum ES_opgroup  enum ES_opgroup
136  {  {
137     G_UNKNOWN,     G_UNKNOWN,
138     G_IDENTITY,     G_IDENTITY,
139     G_BINARY,        // pointwise operations with two arguments     G_BINARY,            // pointwise operations with two arguments
140     G_UNARY,     // pointwise operations with one argument     G_UNARY,             // pointwise operations with one argument
141     G_UNARY_P,       // pointwise operations with one argument, requiring a parameter     G_UNARY_P,           // pointwise operations with one argument, requiring a parameter
142     G_NP1OUT,        // non-pointwise op with one output     G_NP1OUT,            // non-pointwise op with one output
143     G_NP1OUT_P,      // non-pointwise op with one output requiring a parameter     G_NP1OUT_P,          // non-pointwise op with one output requiring a parameter
144     G_TENSORPROD,    // general tensor product     G_TENSORPROD,        // general tensor product
145     G_NP1OUT_2P      // non-pointwise op with one output requiring two params     G_NP1OUT_2P,         // non-pointwise op with one output requiring two params
146       G_REDUCTION,         // non-pointwise unary op with a scalar output
147       G_CONDEVAL
148  };  };
149    
150    
151    
152    
153  string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/","^",  string ES_opstrings[]={"UNKNOWN","IDENTITY","+","-","*","/","^",
154              "sin","cos","tan",                          "sin","cos","tan",
155              "asin","acos","atan","sinh","cosh","tanh","erf",                          "asin","acos","atan","sinh","cosh","tanh","erf",
156              "asinh","acosh","atanh",                          "asinh","acosh","atanh",
157              "log10","log","sign","abs","neg","pos","exp","sqrt",                          "log10","log","sign","abs","neg","pos","exp","sqrt",
158              "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",                          "1/","where>0","where<0","where>=0","where<=0", "where<>0","where=0",
159              "symmetric","nonsymmetric",                          "symmetric","nonsymmetric",
160              "prod",                          "prod",
161              "transpose", "trace",                          "transpose", "trace",
162              "swapaxes"};                          "swapaxes",
163  int ES_opcount=41;                          "minval", "maxval",
164                            "condEval"};
165    int ES_opcount=44;
166  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,
167              G_UNARY,G_UNARY,G_UNARY, //10                          G_UNARY,G_UNARY,G_UNARY, //10
168              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
169              G_UNARY,G_UNARY,G_UNARY,                    // 20                          G_UNARY,G_UNARY,G_UNARY,                                        // 20
170              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
171              G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,      // 35                          G_UNARY,G_UNARY,G_UNARY,G_UNARY,G_UNARY, G_UNARY_P, G_UNARY_P,          // 35
172              G_NP1OUT,G_NP1OUT,                          G_NP1OUT,G_NP1OUT,
173              G_TENSORPROD,                          G_TENSORPROD,
174              G_NP1OUT_P, G_NP1OUT_P,                          G_NP1OUT_P, G_NP1OUT_P,
175              G_NP1OUT_2P};                          G_NP1OUT_2P,
176                            G_REDUCTION, G_REDUCTION,
177                            G_CONDEVAL};
178  inline  inline
179  ES_opgroup  ES_opgroup
180  getOpgroup(ES_optype op)  getOpgroup(ES_optype op)
# Line 157  getOpgroup(ES_optype op) Line 186  getOpgroup(ES_optype op)
186  FunctionSpace  FunctionSpace
187  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultFS(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
188  {  {
189      // perhaps this should call interpolate and throw or something?          // perhaps this should call interpolate and throw or something?
190      // maybe we need an interpolate node -          // maybe we need an interpolate node -
191      // 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
192      // programming error exception.          // programming error exception.
193    
194    FunctionSpace l=left->getFunctionSpace();    FunctionSpace l=left->getFunctionSpace();
195    FunctionSpace r=right->getFunctionSpace();    FunctionSpace r=right->getFunctionSpace();
196    if (l!=r)    if (l!=r)
197    {    {
198      if (r.probeInterpolation(l))      signed char res=r.getDomain()->preferredInterpolationOnDomain(r.getTypeCode(), l.getTypeCode());
199        if (res==1)
200      {      {
201      return l;          return l;
202      }      }
203      if (l.probeInterpolation(r))      if (res==-1)
204      {      {
205      return r;          return r;
206      }      }
207      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");      throw DataException("Cannot interpolate between the FunctionSpaces given for operation "+opToString(op)+".");
208    }    }
# Line 184  resultFS(DataAbstract_ptr left, DataAbst Line 214  resultFS(DataAbstract_ptr left, DataAbst
214  DataTypes::ShapeType  DataTypes::ShapeType
215  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  resultShape(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
216  {  {
217      if (left->getShape()!=right->getShape())          if (left->getShape()!=right->getShape())
218      {          {
219        if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))            if ((getOpgroup(op)!=G_BINARY) && (getOpgroup(op)!=G_NP1OUT))
220        {            {
221          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.");
222        }            }
223        if (left->getRank()==0)   // we need to allow scalar * anything  
224        {            if (left->getRank()==0)       // we need to allow scalar * anything
225          return right->getShape();            {
226        }                  return right->getShape();
227        if (right->getRank()==0)            }
228        {            if (right->getRank()==0)
229          return left->getShape();            {
230        }                  return left->getShape();
231        throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");            }
232      }            throw DataException("Shapes not the same - arguments must have matching shapes (or be scalars) for (point)binary operations on lazy data.");
233      return left->getShape();          }
234            return left->getShape();
235  }  }
236    
237  // return the shape for "op left"  // return the shape for "op left"
# Line 208  resultShape(DataAbstract_ptr left, DataA Line 239  resultShape(DataAbstract_ptr left, DataA
239  DataTypes::ShapeType  DataTypes::ShapeType
240  resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)  resultShape(DataAbstract_ptr left, ES_optype op, int axis_offset)
241  {  {
242      switch(op)          switch(op)
243      {          {
244          case TRANS:          case TRANS:
245         {            // for the scoping of variables             {                    // for the scoping of variables
246          const DataTypes::ShapeType& s=left->getShape();                  const DataTypes::ShapeType& s=left->getShape();
247          DataTypes::ShapeType sh;                  DataTypes::ShapeType sh;
248          int rank=left->getRank();                  int rank=left->getRank();
249          if (axis_offset<0 || axis_offset>rank)                  if (axis_offset<0 || axis_offset>rank)
250          {                  {
251                 throw DataException("Error - Data::transpose must have 0 <= axis_offset <= rank=" + rank);              stringstream e;
252              }              e << "Error - Data::transpose must have 0 <= axis_offset <= rank=" << rank;
253              for (int i=0; i<rank; i++)              throw DataException(e.str());
254          {          }
255             int index = (axis_offset+i)%rank;          for (int i=0; i<rank; i++)
256                 sh.push_back(s[index]); // Append to new shape                  {
257              }                     int index = (axis_offset+i)%rank;
258          return sh;             sh.push_back(s[index]); // Append to new shape
259         }          }
260      break;                  return sh;
261      case TRACE:             }
262         {          break;
263          int rank=left->getRank();          case TRACE:
264          if (rank<2)             {
265          {                  int rank=left->getRank();
266             throw DataException("Trace can only be computed for objects with rank 2 or greater.");                  if (rank<2)
267          }                  {
268          if ((axis_offset>rank-2) || (axis_offset<0))                     throw DataException("Trace can only be computed for objects with rank 2 or greater.");
269          {                  }
270             throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");                  if ((axis_offset>rank-2) || (axis_offset<0))
271          }                  {
272          if (rank==2)                     throw DataException("Trace: axis offset must lie between 0 and rank-2 inclusive.");
273          {                  }
274             return DataTypes::scalarShape;                  if (rank==2)
275          }                  {
276          else if (rank==3)                     return DataTypes::scalarShape;
277          {                  }
278             DataTypes::ShapeType sh;                  else if (rank==3)
279                 if (axis_offset==0)                  {
280             {                     DataTypes::ShapeType sh;
281                  sh.push_back(left->getShape()[2]);                     if (axis_offset==0)
282                 }                     {
283                 else     // offset==1                          sh.push_back(left->getShape()[2]);
284             {                     }
285              sh.push_back(left->getShape()[0]);                     else         // offset==1
286                 }                     {
287             return sh;                          sh.push_back(left->getShape()[0]);
288          }                     }
289          else if (rank==4)                     return sh;
290          {                  }
291             DataTypes::ShapeType sh;                  else if (rank==4)
292             const DataTypes::ShapeType& s=left->getShape();                  {
293                 if (axis_offset==0)                     DataTypes::ShapeType sh;
294             {                     const DataTypes::ShapeType& s=left->getShape();
295                  sh.push_back(s[2]);                     if (axis_offset==0)
296                  sh.push_back(s[3]);                     {
297                 }                          sh.push_back(s[2]);
298                 else if (axis_offset==1)                          sh.push_back(s[3]);
299             {                     }
300                  sh.push_back(s[0]);                     else if (axis_offset==1)
301                  sh.push_back(s[3]);                     {
302                 }                          sh.push_back(s[0]);
303             else     // offset==2                          sh.push_back(s[3]);
304             {                     }
305              sh.push_back(s[0]);                     else         // offset==2
306              sh.push_back(s[1]);                     {
307             }                          sh.push_back(s[0]);
308             return sh;                          sh.push_back(s[1]);
309          }                     }
310          else        // unknown rank                     return sh;
311          {                  }
312             throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");                  else            // unknown rank
313          }                  {
314         }                     throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
315      break;                  }
316          default:             }
317      throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");          break;
318      }          default:
319            throw DataException("Programmer error - resultShape(left,op) can't compute shapes for operator "+opToString(op)+".");
320            }
321  }  }
322    
323  DataTypes::ShapeType  DataTypes::ShapeType
# Line 302  SwapShape(DataAbstract_ptr left, const i Line 335  SwapShape(DataAbstract_ptr left, const i
335          throw DataException("Error - Data::swapaxes argument must have at least rank 2.");          throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
336       }       }
337       if (axis0<0 || axis0>rank-1) {       if (axis0<0 || axis0>rank-1) {
338          throw DataException("Error - Data::swapaxes: axis0 must be between 0 and rank-1=" + rank-1);          stringstream e;
339            e << "Error - Data::swapaxes: axis0 must be between 0 and rank-1=" << (rank-1);
340            throw DataException(e.str());
341       }       }
342       if (axis1<0 || axis1>rank-1) {       if (axis1<0 || axis1>rank-1) {
343           throw DataException("Error - Data::swapaxes: axis1 must be between 0 and rank-1=" + rank-1);          stringstream e;
344            e << "Error - Data::swapaxes: axis1 must be between 0 and rank-1=" << (rank-1);
345            throw DataException(e.str());
346       }       }
347       if (axis0 == axis1) {       if (axis0 == axis1) {
348           throw DataException("Error - Data::swapaxes: axis indices must be different.");           throw DataException("Error - Data::swapaxes: axis indices must be different.");
# Line 336  SwapShape(DataAbstract_ptr left, const i Line 373  SwapShape(DataAbstract_ptr left, const i
373  DataTypes::ShapeType  DataTypes::ShapeType
374  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)  GTPShape(DataAbstract_ptr left, DataAbstract_ptr right, int axis_offset, int transpose, int& SL, int& SM, int& SR)
375  {  {
376                
377    // Get rank and shape of inputs    // Get rank and shape of inputs
378    int rank0 = left->getRank();    int rank0 = left->getRank();
379    int rank1 = right->getRank();    int rank1 = right->getRank();
# Line 345  GTPShape(DataAbstract_ptr left, DataAbst Line 382  GTPShape(DataAbstract_ptr left, DataAbst
382    
383    // Prepare for the loops of the product and verify compatibility of shapes    // Prepare for the loops of the product and verify compatibility of shapes
384    int start0=0, start1=0;    int start0=0, start1=0;
385    if (transpose == 0)       {}    if (transpose == 0)           {}
386    else if (transpose == 1)  { start0 = axis_offset; }    else if (transpose == 1)      { start0 = axis_offset; }
387    else if (transpose == 2)  { start1 = rank1-axis_offset; }    else if (transpose == 2)      { start1 = rank1-axis_offset; }
388    else              { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }    else                          { throw DataException("DataLazy GeneralTensorProduct Constructor: Error - transpose should be 0, 1 or 2"); }
389    
390    if (rank0<axis_offset)    if (rank0<axis_offset)
391    {    {
392      throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");          throw DataException("DataLazy GeneralTensorProduct Constructor: Error - rank of left < axisoffset");
393    }    }
394    
395    // Adjust the shapes for transpose    // Adjust the shapes for transpose
396    DataTypes::ShapeType tmpShape0(rank0);    // pre-sizing the vectors rather    DataTypes::ShapeType tmpShape0(rank0);        // pre-sizing the vectors rather
397    DataTypes::ShapeType tmpShape1(rank1);    // than using push_back    DataTypes::ShapeType tmpShape1(rank1);        // than using push_back
398    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }    for (int i=0; i<rank0; i++)   { tmpShape0[i]=shape0[(i+start0)%rank0]; }
399    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }    for (int i=0; i<rank1; i++)   { tmpShape1[i]=shape1[(i+start1)%rank1]; }
400    
401    // Prepare for the loops of the product    // Prepare for the loops of the product
402    SL=1, SM=1, SR=1;    SL=1, SM=1, SR=1;
403    for (int i=0; i<rank0-axis_offset; i++)   {    for (int i=0; i<rank0-axis_offset; i++)       {
404      SL *= tmpShape0[i];      SL *= tmpShape0[i];
405    }    }
406    for (int i=rank0-axis_offset; i<rank0; i++)   {    for (int i=rank0-axis_offset; i<rank0; i++)   {
407      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {      if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
408        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");        throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
409      }      }
410      SM *= tmpShape0[i];      SM *= tmpShape0[i];
411    }    }
412    for (int i=axis_offset; i<rank1; i++)     {    for (int i=axis_offset; i<rank1; i++)         {
413      SR *= tmpShape1[i];      SR *= tmpShape1[i];
414    }    }
415    
416    // Define the shape of the output (rank of shape is the sum of the loop ranges below)    // Define the shape of the output (rank of shape is the sum of the loop ranges below)
417    DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);      
418    {         // block to limit the scope of out_index    {                     // block to limit the scope of out_index
419       int out_index=0;       int out_index=0;
420       for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z       for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
421       for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z       for (int i=axis_offset; i<rank1; i++, ++out_index)   { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
# Line 394  GTPShape(DataAbstract_ptr left, DataAbst Line 431  GTPShape(DataAbstract_ptr left, DataAbst
431    return shape2;    return shape2;
432  }  }
433    
434  // determine the number of samples requires to evaluate an expression combining left and right  }       // end anonymous namespace
 // NP1OUT needs an extra buffer because we can't write the answers over the top of the input.  
 // The same goes for G_TENSORPROD  
 // It might seem that pointwise binary ops (G_BINARY) could be written over the top of the lefts.  
 // This would be true were it not for the possibility that the LHS could be a scalar which needs to be examined  
 // multiple times  
 int  
 calcBuffs(const DataLazy_ptr& left, const DataLazy_ptr& right, ES_optype op)  
 {  
    switch(getOpgroup(op))  
    {  
    case G_IDENTITY: return 1;  
    case G_BINARY: return 1+max(left->getBuffsRequired(),right->getBuffsRequired()+1);  
    case G_UNARY:  
    case G_UNARY_P: return max(left->getBuffsRequired(),1);  
    case G_NP1OUT: return 1+max(left->getBuffsRequired(),1);  
    case G_NP1OUT_P: return 1+max(left->getBuffsRequired(),1);  
    case G_TENSORPROD: return 1+max(left->getBuffsRequired(),right->getBuffsRequired()+1);  
    case G_NP1OUT_2P: return 1+max(left->getBuffsRequired(),1);  
    default:  
     throw DataException("Programmer Error - attempt to calcBuffs() for operator "+opToString(op)+".");  
    }  
 }  
   
   
 }   // end anonymous namespace  
435    
436    
437    
# Line 434  opToString(ES_optype op) Line 446  opToString(ES_optype op)
446    return ES_opstrings[op];    return ES_opstrings[op];
447  }  }
448    
 #ifdef LAZY_NODE_STORAGE  
449  void DataLazy::LazyNodeSetup()  void DataLazy::LazyNodeSetup()
450  {  {
451  #ifdef _OPENMP  #ifdef _OPENMP
# Line 451  void DataLazy::LazyNodeSetup() Line 462  void DataLazy::LazyNodeSetup()
462      m_sampleids[0]=-1;      m_sampleids[0]=-1;
463  #endif  // _OPENMP  #endif  // _OPENMP
464  }  }
 #endif   // LAZY_NODE_STORAGE  
465    
466    
467  // Creates an identity node  // Creates an identity node
468  DataLazy::DataLazy(DataAbstract_ptr p)  DataLazy::DataLazy(DataAbstract_ptr p)
469      : parent(p->getFunctionSpace(),p->getShape())          : parent(p->getFunctionSpace(),p->getShape())
470  #ifdef LAZY_NODE_STORAGE          ,m_sampleids(0),
471      ,m_sampleids(0),          m_samples(1)
     m_samples(1)  
 #endif  
472  {  {
473     if (p->isLazy())     if (p->isLazy())
474     {     {
475      // I don't want identity of Lazy.          // I don't want identity of Lazy.
476      // Question: Why would that be so bad?          // Question: Why would that be so bad?
477      // Answer: We assume that the child of ID is something we can call getVector on          // Answer: We assume that the child of ID is something we can call getVector on
478      throw DataException("Programmer error - attempt to create identity from a DataLazy.");          throw DataException("Programmer error - attempt to create identity from a DataLazy.");
479     }     }
480     else     else
481     {     {
482      p->makeLazyShared();          p->makeLazyShared();
483      DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);          DataReady_ptr dr=dynamic_pointer_cast<DataReady>(p);
484      makeIdentity(dr);          makeIdentity(dr);
485  LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)  LAZYDEBUG(cout << "Wrapping " << dr.get() << " id=" << m_id.get() << endl;)
486     }     }
487  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(1)Lazy created with " << m_samplesize << endl;)
488  }  }
489    
490  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op)
491      : parent(left->getFunctionSpace(),left->getShape()),          : parent(left->getFunctionSpace(),(getOpgroup(op)!=G_REDUCTION)?left->getShape():DataTypes::scalarShape),
492      m_op(op),          m_op(op),
493      m_axis_offset(0),          m_axis_offset(0),
494      m_transpose(0),          m_transpose(0),
495      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
496  {  {
497     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT))     if ((getOpgroup(op)!=G_UNARY) && (getOpgroup(op)!=G_NP1OUT) && (getOpgroup(op)!=G_REDUCTION))
498     {     {
499      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.");
500     }     }
501    
502     DataLazy_ptr lleft;     DataLazy_ptr lleft;
503     if (!left->isLazy())     if (!left->isLazy())
504     {     {
505      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
506     }     }
507     else     else
508     {     {
509      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
510     }     }
511     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
512     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
513     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());  
514     m_children=m_left->m_children+1;     m_children=m_left->m_children+1;
515     m_height=m_left->m_height+1;     m_height=m_left->m_height+1;
 #ifdef LAZY_NODE_STORAGE  
516     LazyNodeSetup();     LazyNodeSetup();
 #endif  
517     SIZELIMIT     SIZELIMIT
518  }  }
519    
520    
521  // In this constructor we need to consider interpolation  // In this constructor we need to consider interpolation
522  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op)
523      : parent(resultFS(left,right,op), resultShape(left,right,op)),          : parent(resultFS(left,right,op), resultShape(left,right,op)),
524      m_op(op),          m_op(op),
525      m_SL(0), m_SM(0), m_SR(0)          m_SL(0), m_SM(0), m_SR(0)
526  {  {
527  LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)  LAZYDEBUG(cout << "Forming operator with " << left.get() << " " << right.get() << endl;)
528     if ((getOpgroup(op)!=G_BINARY))     if ((getOpgroup(op)!=G_BINARY))
529     {     {
530      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.");
531     }     }
532    
533     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
534     {     {
535      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
536      Data ltemp(left);          Data ltemp(left);
537      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
538      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
539     }     }
540     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
541     {     {
542      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
543      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
544  LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)  LAZYDEBUG(cout << "Right interpolation required " << right.get() << endl;)
545     }     }
546     left->operandCheck(*right);     left->operandCheck(*right);
547    
548     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
549     {     {
550      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
551  LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)  LAZYDEBUG(cout << "Left is " << m_left->toString() << endl;)
552     }     }
553     else     else
554     {     {
555      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
556  LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)  LAZYDEBUG(cout << "Left " << left.get() << " wrapped " << m_left->m_id.get() << endl;)
557     }     }
558     if (right->isLazy())     if (right->isLazy())
559     {     {
560      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
561  LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)  LAZYDEBUG(cout << "Right is " << m_right->toString() << endl;)
562     }     }
563     else     else
564     {     {
565      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
566  LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)  LAZYDEBUG(cout << "Right " << right.get() << " wrapped " << m_right->m_id.get() << endl;)
567     }     }
568     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
569     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
570     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
571     {     {
572      m_readytype='E';          m_readytype='E';
573     }     }
574     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
575     {     {
576      m_readytype='T';          m_readytype='T';
577     }     }
578     else     else
579     {     {
580      m_readytype='C';          m_readytype='C';
581     }     }
582     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());    
    m_buffsRequired=calcBuffs(m_left, m_right,m_op);  
583     m_children=m_left->m_children+m_right->m_children+2;     m_children=m_left->m_children+m_right->m_children+2;
584     m_height=max(m_left->m_height,m_right->m_height)+1;     m_height=max(m_left->m_height,m_right->m_height)+1;
 #ifdef LAZY_NODE_STORAGE  
585     LazyNodeSetup();     LazyNodeSetup();
 #endif  
586     SIZELIMIT     SIZELIMIT
587  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(3)Lazy created with " << m_samplesize << endl;)
588  }  }
589    
590  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)  DataLazy::DataLazy(DataAbstract_ptr left, DataAbstract_ptr right, ES_optype op, int axis_offset, int transpose)
591      : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),          : parent(resultFS(left,right,op), GTPShape(left,right, axis_offset, transpose, m_SL,m_SM, m_SR)),
592      m_op(op),          m_op(op),
593      m_axis_offset(axis_offset),          m_axis_offset(axis_offset),
594      m_transpose(transpose)          m_transpose(transpose)
595  {  {
596     if ((getOpgroup(op)!=G_TENSORPROD))     if ((getOpgroup(op)!=G_TENSORPROD))
597     {     {
598      throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, right, op, ax, tr) will only process BINARY operations which require parameters.");
599     }     }
600     if ((transpose>2) || (transpose<0))     if ((transpose>2) || (transpose<0))
601     {     {
602      throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");          throw DataException("DataLazy GeneralTensorProduct constructor: Error - transpose should be 0, 1 or 2");
603     }     }
604     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated     if (getFunctionSpace()!=left->getFunctionSpace())    // left needs to be interpolated
605     {     {
606      FunctionSpace fs=getFunctionSpace();          FunctionSpace fs=getFunctionSpace();
607      Data ltemp(left);          Data ltemp(left);
608      Data tmp(ltemp,fs);          Data tmp(ltemp,fs);
609      left=tmp.borrowDataPtr();          left=tmp.borrowDataPtr();
610     }     }
611     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated     if (getFunctionSpace()!=right->getFunctionSpace())   // right needs to be interpolated
612     {     {
613      Data tmp(Data(right),getFunctionSpace());          Data tmp(Data(right),getFunctionSpace());
614      right=tmp.borrowDataPtr();          right=tmp.borrowDataPtr();
615     }     }
616  //    left->operandCheck(*right);  //    left->operandCheck(*right);
617    
618     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
619     {     {
620      m_left=dynamic_pointer_cast<DataLazy>(left);          m_left=dynamic_pointer_cast<DataLazy>(left);
621     }     }
622     else     else
623     {     {
624      m_left=DataLazy_ptr(new DataLazy(left));          m_left=DataLazy_ptr(new DataLazy(left));
625     }     }
626     if (right->isLazy())     if (right->isLazy())
627     {     {
628      m_right=dynamic_pointer_cast<DataLazy>(right);          m_right=dynamic_pointer_cast<DataLazy>(right);
629     }     }
630     else     else
631     {     {
632      m_right=DataLazy_ptr(new DataLazy(right));          m_right=DataLazy_ptr(new DataLazy(right));
633     }     }
634     char lt=m_left->m_readytype;     char lt=m_left->m_readytype;
635     char rt=m_right->m_readytype;     char rt=m_right->m_readytype;
636     if (lt=='E' || rt=='E')     if (lt=='E' || rt=='E')
637     {     {
638      m_readytype='E';          m_readytype='E';
639     }     }
640     else if (lt=='T' || rt=='T')     else if (lt=='T' || rt=='T')
641     {     {
642      m_readytype='T';          m_readytype='T';
643     }     }
644     else     else
645     {     {
646      m_readytype='C';          m_readytype='C';
647     }     }
648     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(max(m_samplesize,m_right->getMaxSampleSize()),m_left->getMaxSampleSize());    
    m_buffsRequired=calcBuffs(m_left, m_right,m_op);  
649     m_children=m_left->m_children+m_right->m_children+2;     m_children=m_left->m_children+m_right->m_children+2;
650     m_height=max(m_left->m_height,m_right->m_height)+1;     m_height=max(m_left->m_height,m_right->m_height)+1;
 #ifdef LAZY_NODE_STORAGE  
651     LazyNodeSetup();     LazyNodeSetup();
 #endif  
652     SIZELIMIT     SIZELIMIT
653  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(4)Lazy created with " << m_samplesize << endl;)
654  }  }
655    
656    
657  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, int axis_offset)
658      : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),          : parent(left->getFunctionSpace(), resultShape(left,op, axis_offset)),
659      m_op(op),          m_op(op),
660      m_axis_offset(axis_offset),          m_axis_offset(axis_offset),
661      m_transpose(0),          m_transpose(0),
662      m_tol(0)          m_tol(0)
663  {  {
664     if ((getOpgroup(op)!=G_NP1OUT_P))     if ((getOpgroup(op)!=G_NP1OUT_P))
665     {     {
666      throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, op, ax) will only process UNARY operations which require parameters.");
667     }     }
668     DataLazy_ptr lleft;     DataLazy_ptr lleft;
669     if (!left->isLazy())     if (!left->isLazy())
670     {     {
671      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
672     }     }
673     else     else
674     {     {
675      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
676     }     }
677     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
678     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
679     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());  
680     m_children=m_left->m_children+1;     m_children=m_left->m_children+1;
681     m_height=m_left->m_height+1;     m_height=m_left->m_height+1;
 #ifdef LAZY_NODE_STORAGE  
682     LazyNodeSetup();     LazyNodeSetup();
 #endif  
683     SIZELIMIT     SIZELIMIT
684  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(5)Lazy created with " << m_samplesize << endl;)
685  }  }
686    
687  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, double tol)
688      : parent(left->getFunctionSpace(), left->getShape()),          : parent(left->getFunctionSpace(), left->getShape()),
689      m_op(op),          m_op(op),
690      m_axis_offset(0),          m_axis_offset(0),
691      m_transpose(0),          m_transpose(0),
692      m_tol(tol)          m_tol(tol)
693  {  {
694     if ((getOpgroup(op)!=G_UNARY_P))     if ((getOpgroup(op)!=G_UNARY_P))
695     {     {
696      throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");          throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require parameters.");
697     }     }
698     DataLazy_ptr lleft;     DataLazy_ptr lleft;
699     if (!left->isLazy())     if (!left->isLazy())
700     {     {
701      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
702     }     }
703     else     else
704     {     {
705      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
706     }     }
707     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
708     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
709     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());  
710     m_children=m_left->m_children+1;     m_children=m_left->m_children+1;
711     m_height=m_left->m_height+1;     m_height=m_left->m_height+1;
 #ifdef LAZY_NODE_STORAGE  
712     LazyNodeSetup();     LazyNodeSetup();
 #endif  
713     SIZELIMIT     SIZELIMIT
714  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(6)Lazy created with " << m_samplesize << endl;)
715  }  }
716    
717    
718  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)  DataLazy::DataLazy(DataAbstract_ptr left, ES_optype op, const int axis0, const int axis1)
719      : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),          : parent(left->getFunctionSpace(), SwapShape(left,axis0,axis1)),
720      m_op(op),          m_op(op),
721      m_axis_offset(axis0),          m_axis_offset(axis0),
722      m_transpose(axis1),          m_transpose(axis1),
723      m_tol(0)          m_tol(0)
724  {  {
725     if ((getOpgroup(op)!=G_NP1OUT_2P))     if ((getOpgroup(op)!=G_NP1OUT_2P))
726     {     {
727      throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");          throw DataException("Programmer error - constructor DataLazy(left, op, tol) will only process UNARY operations which require two integer parameters.");
728     }     }
729     DataLazy_ptr lleft;     DataLazy_ptr lleft;
730     if (!left->isLazy())     if (!left->isLazy())
731     {     {
732      lleft=DataLazy_ptr(new DataLazy(left));          lleft=DataLazy_ptr(new DataLazy(left));
733     }     }
734     else     else
735     {     {
736      lleft=dynamic_pointer_cast<DataLazy>(left);          lleft=dynamic_pointer_cast<DataLazy>(left);
737     }     }
738     m_readytype=lleft->m_readytype;     m_readytype=lleft->m_readytype;
739     m_left=lleft;     m_left=lleft;
    m_buffsRequired=calcBuffs(m_left, m_right,m_op); // yeah m_right will be null at this point  
740     m_samplesize=getNumDPPSample()*getNoValues();     m_samplesize=getNumDPPSample()*getNoValues();
    m_maxsamplesize=max(m_samplesize,m_left->getMaxSampleSize());  
741     m_children=m_left->m_children+1;     m_children=m_left->m_children+1;
742     m_height=m_left->m_height+1;     m_height=m_left->m_height+1;
 #ifdef LAZY_NODE_STORAGE  
743     LazyNodeSetup();     LazyNodeSetup();
 #endif  
744     SIZELIMIT     SIZELIMIT
745  LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)  LAZYDEBUG(cout << "(7)Lazy created with " << m_samplesize << endl;)
746  }  }
747    
748  DataLazy::~DataLazy()  
749    namespace
750  {  {
 #ifdef LAZY_NODE_SETUP  
    delete[] m_sampleids;  
    delete[] m_samples;  
 #endif  
 }  
751    
752        inline int max3(int a, int b, int c)
753        {
754            int t=(a>b?a:b);
755            return (t>c?t:c);
756    
757  int      }
 DataLazy::getBuffsRequired() const  
 {  
     return m_buffsRequired;  
758  }  }
759    
760    DataLazy::DataLazy(DataAbstract_ptr mask, DataAbstract_ptr left, DataAbstract_ptr right/*, double tol*/)
761  size_t          : parent(left->getFunctionSpace(), left->getShape()),
762  DataLazy::getMaxSampleSize() const          m_op(CONDEVAL),
763            m_axis_offset(0),
764            m_transpose(0),
765            m_tol(0)
766  {  {
767      return m_maxsamplesize;  
768       DataLazy_ptr lmask;
769       DataLazy_ptr lleft;
770       DataLazy_ptr lright;
771       if (!mask->isLazy())
772       {
773            lmask=DataLazy_ptr(new DataLazy(mask));
774       }
775       else
776       {
777            lmask=dynamic_pointer_cast<DataLazy>(mask);
778       }
779       if (!left->isLazy())
780       {
781            lleft=DataLazy_ptr(new DataLazy(left));
782       }
783       else
784       {
785            lleft=dynamic_pointer_cast<DataLazy>(left);
786       }
787       if (!right->isLazy())
788       {
789            lright=DataLazy_ptr(new DataLazy(right));
790       }
791       else
792       {
793            lright=dynamic_pointer_cast<DataLazy>(right);
794       }
795       m_readytype=lmask->m_readytype;
796       if ((lleft->m_readytype!=lright->m_readytype) || (lmask->m_readytype!=lleft->m_readytype))
797       {
798            throw DataException("Programmer Error - condEval arguments must have the same readytype");
799       }
800       m_left=lleft;
801       m_right=lright;
802       m_mask=lmask;
803       m_samplesize=getNumDPPSample()*getNoValues();
804       m_children=m_left->m_children+m_right->m_children+m_mask->m_children+1;
805       m_height=max3(m_left->m_height,m_right->m_height,m_mask->m_height)+1;
806       LazyNodeSetup();
807       SIZELIMIT
808    LAZYDEBUG(cout << "(8)Lazy created with " << m_samplesize << endl;)
809  }  }
810    
811    
812    
813  size_t  DataLazy::~DataLazy()
 DataLazy::getSampleBufferSize() const  
814  {  {
815      return m_maxsamplesize*(max(1,m_buffsRequired));     delete[] m_sampleids;
816  }  }
817    
818    
819  /*  /*
820    \brief Evaluates the expression using methods on Data.    \brief Evaluates the expression using methods on Data.
821    This does the work for the collapse method.    This does the work for the collapse method.
822    For reasons of efficiency do not call this method on DataExpanded nodes.    For reasons of efficiency do not call this method on DataExpanded nodes.
823  */  */
824  DataReady_ptr  DataReady_ptr
825  DataLazy::collapseToReady()  DataLazy::collapseToReady() const
826  {  {
827    if (m_readytype=='E')    if (m_readytype=='E')
828    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
829      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
830    }    }
831    if (m_op==IDENTITY)    if (m_op==IDENTITY)
# Line 818  DataLazy::collapseToReady() Line 843  DataLazy::collapseToReady()
843    switch(m_op)    switch(m_op)
844    {    {
845      case ADD:      case ADD:
846      result=left+right;          result=left+right;
847      break;          break;
848      case SUB:            case SUB:          
849      result=left-right;          result=left-right;
850      break;          break;
851      case MUL:            case MUL:          
852      result=left*right;          result=left*right;
853      break;          break;
854      case DIV:            case DIV:          
855      result=left/right;          result=left/right;
856      break;          break;
857      case SIN:      case SIN:
858      result=left.sin();            result=left.sin();      
859      break;          break;
860      case COS:      case COS:
861      result=left.cos();          result=left.cos();
862      break;          break;
863      case TAN:      case TAN:
864      result=left.tan();          result=left.tan();
865      break;          break;
866      case ASIN:      case ASIN:
867      result=left.asin();          result=left.asin();
868      break;          break;
869      case ACOS:      case ACOS:
870      result=left.acos();          result=left.acos();
871      break;          break;
872      case ATAN:      case ATAN:
873      result=left.atan();          result=left.atan();
874      break;          break;
875      case SINH:      case SINH:
876      result=left.sinh();          result=left.sinh();
877      break;          break;
878      case COSH:      case COSH:
879      result=left.cosh();          result=left.cosh();
880      break;          break;
881      case TANH:      case TANH:
882      result=left.tanh();          result=left.tanh();
883      break;          break;
884      case ERF:      case ERF:
885      result=left.erf();          result=left.erf();
886      break;          break;
887     case ASINH:     case ASINH:
888      result=left.asinh();          result=left.asinh();
889      break;          break;
890     case ACOSH:     case ACOSH:
891      result=left.acosh();          result=left.acosh();
892      break;          break;
893     case ATANH:     case ATANH:
894      result=left.atanh();          result=left.atanh();
895      break;          break;
896      case LOG10:      case LOG10:
897      result=left.log10();          result=left.log10();
898      break;          break;
899      case LOG:      case LOG:
900      result=left.log();          result=left.log();
901      break;          break;
902      case SIGN:      case SIGN:
903      result=left.sign();          result=left.sign();
904      break;          break;
905      case ABS:      case ABS:
906      result=left.abs();          result=left.abs();
907      break;          break;
908      case NEG:      case NEG:
909      result=left.neg();          result=left.neg();
910      break;          break;
911      case POS:      case POS:
912      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
913      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
914      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
915      break;          break;
916      case EXP:      case EXP:
917      result=left.exp();          result=left.exp();
918      break;          break;
919      case SQRT:      case SQRT:
920      result=left.sqrt();          result=left.sqrt();
921      break;          break;
922      case RECIP:      case RECIP:
923      result=left.oneOver();          result=left.oneOver();
924      break;          break;
925      case GZ:      case GZ:
926      result=left.wherePositive();          result=left.wherePositive();
927      break;          break;
928      case LZ:      case LZ:
929      result=left.whereNegative();          result=left.whereNegative();
930      break;          break;
931      case GEZ:      case GEZ:
932      result=left.whereNonNegative();          result=left.whereNonNegative();
933      break;          break;
934      case LEZ:      case LEZ:
935      result=left.whereNonPositive();          result=left.whereNonPositive();
936      break;          break;
937      case NEZ:      case NEZ:
938      result=left.whereNonZero(m_tol);          result=left.whereNonZero(m_tol);
939      break;          break;
940      case EZ:      case EZ:
941      result=left.whereZero(m_tol);          result=left.whereZero(m_tol);
942      break;          break;
943      case SYM:      case SYM:
944      result=left.symmetric();          result=left.symmetric();
945      break;          break;
946      case NSYM:      case NSYM:
947      result=left.nonsymmetric();          result=left.nonsymmetric();
948      break;          break;
949      case PROD:      case PROD:
950      result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);          result=C_GeneralTensorProduct(left,right,m_axis_offset, m_transpose);
951      break;          break;
952      case TRANS:      case TRANS:
953      result=left.transpose(m_axis_offset);          result=left.transpose(m_axis_offset);
954      break;          break;
955      case TRACE:      case TRACE:
956      result=left.trace(m_axis_offset);          result=left.trace(m_axis_offset);
957      break;          break;
958      case SWAP:      case SWAP:
959      result=left.swapaxes(m_axis_offset, m_transpose);          result=left.swapaxes(m_axis_offset, m_transpose);
960      break;          break;
961        case MINVAL:
962            result=left.minval();
963            break;
964        case MAXVAL:
965            result=left.minval();
966            break;
967      default:      default:
968      throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - collapseToReady does not know how to resolve operator "+opToString(m_op)+".");
969    }    }
970    return result.borrowReadyPtr();    return result.borrowReadyPtr();
971  }  }
# Line 946  DataLazy::collapseToReady() Line 977  DataLazy::collapseToReady()
977     the purpose of using DataLazy in the first place).     the purpose of using DataLazy in the first place).
978  */  */
979  void  void
980  DataLazy::collapse()  DataLazy::collapse() const
981  {  {
982    if (m_op==IDENTITY)    if (m_op==IDENTITY)
983    {    {
984      return;          return;
985    }    }
986    if (m_readytype=='E')    if (m_readytype=='E')
987    { // this is more an efficiency concern than anything else    {     // this is more an efficiency concern than anything else
988      throw DataException("Programmer Error - do not use collapse on Expanded data.");      throw DataException("Programmer Error - do not use collapse on Expanded data.");
989    }    }
990    m_id=collapseToReady();    m_id=collapseToReady();
991    m_op=IDENTITY;    m_op=IDENTITY;
992  }  }
993    
 /*  
   \brief Compute the value of the expression (unary 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.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveUnary(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
 {  
     // we assume that any collapsing has been done before we get here  
     // since we only have one argument we don't need to think about only  
     // processing single points.  
   if (m_readytype!='E')  
   {  
     throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");  
   }  
   const ValueType* vleft=m_left->resolveSample(v,offset,sampleNo,roffset);  
   const double* left=&((*vleft)[roffset]);  
   double* result=&(v[offset]);  
   roffset=offset;  
   switch (m_op)  
   {  
     case SIN:    
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);  
     break;  
     case COS:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);  
     break;  
     case TAN:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);  
     break;  
     case ASIN:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);  
     break;  
     case ACOS:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);  
     break;  
     case ATAN:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);  
     break;  
     case SINH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);  
     break;  
     case COSH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);  
     break;  
     case TANH:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);  
     break;  
     case ERF:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::erf);  
     break;  
 #endif  
    case ASINH:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::asinh);  
 #endif    
     break;  
    case ACOSH:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::acosh);  
 #endif    
     break;  
    case ATANH:  
 #if defined (_WIN32) && !defined(__INTEL_COMPILER)  
     tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);  
 #else  
     tensor_unary_operation(m_samplesize, left, result, ::atanh);  
 #endif    
     break;  
     case LOG10:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);  
     break;  
     case LOG:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);  
     break;  
     case SIGN:  
     tensor_unary_operation(m_samplesize, left, result, escript::fsign);  
     break;  
     case ABS:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);  
     break;  
     case NEG:  
     tensor_unary_operation(m_samplesize, left, result, negate<double>());  
     break;  
     case POS:  
     // it doesn't mean anything for delayed.  
     // it will just trigger a deep copy of the lazy object  
     throw DataException("Programmer error - POS not supported for lazy data.");  
     break;  
     case EXP:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);  
     break;  
     case SQRT:  
     tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);  
     break;  
     case RECIP:  
     tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));  
     break;  
     case GZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));  
     break;  
     case LZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));  
     break;  
     case GEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));  
     break;  
     case LEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(less_equal<double>(),0.0));  
     break;  
 // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently  
     case NEZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));  
     break;  
     case EZ:  
     tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));  
     break;  
   
     default:  
     throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");  
   }  
   return &v;  
 }  
   
   
   
   
   
   
 /*  
   \brief Compute the value of the expression (unary 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.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveNP1OUT(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
 {  
     // we assume that any collapsing has been done before we get here  
     // since we only have one argument we don't need to think about only  
     // processing single points.  
   if (m_readytype!='E')  
   {  
     throw DataException("Programmer error - resolveNP1OUT should only be called on expanded Data.");  
   }  
     // since we can't write the result over the input, we need a result offset further along  
   size_t subroffset=roffset+m_samplesize;  
 LAZYDEBUG(cerr << "subroffset=" << subroffset << endl;)  
   const ValueType* vleft=m_left->resolveSample(v,offset+m_left->m_samplesize,sampleNo,subroffset);  
   roffset=offset;  
   size_t loop=0;  
   size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;  
   size_t step=getNoValues();  
   switch (m_op)  
   {  
     case SYM:  
     for (loop=0;loop<numsteps;++loop)  
     {  
         DataMaths::symmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);  
         subroffset+=step;  
         offset+=step;  
     }  
     break;  
     case NSYM:  
     for (loop=0;loop<numsteps;++loop)  
     {  
         DataMaths::nonsymmetric(*vleft,m_left->getShape(),subroffset, v, getShape(), offset);  
         subroffset+=step;  
         offset+=step;  
     }  
     break;  
     default:  
     throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");  
   }  
   return &v;  
 }  
   
 /*  
   \brief Compute the value of the expression (unary 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.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveNP1OUT_P(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
 {  
     // we assume that any collapsing has been done before we get here  
     // since we only have one argument we don't need to think about only  
     // processing single points.  
   if (m_readytype!='E')  
   {  
     throw DataException("Programmer error - resolveNP1OUT_P should only be called on expanded Data.");  
   }  
     // since we can't write the result over the input, we need a result offset further along  
   size_t subroffset;  
   const ValueType* vleft=m_left->resolveSample(v,offset+m_left->m_samplesize,sampleNo,subroffset);  
 LAZYDEBUG(cerr << "srcsamplesize=" << offset+m_left->m_samplesize << " beg=" << subroffset << endl;)  
 LAZYDEBUG(cerr << "Offset for 5800=" << getPointOffset(5800/getNumDPPSample(),5800%getNumDPPSample()) << endl;)  
   roffset=offset;  
   size_t loop=0;  
   size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;  
   size_t outstep=getNoValues();  
   size_t instep=m_left->getNoValues();  
 LAZYDEBUG(cerr << "instep=" << instep << " outstep=" << outstep<< " numsteps=" << numsteps << endl;)  
   switch (m_op)  
   {  
     case TRACE:  
     for (loop=0;loop<numsteps;++loop)  
     {  
 size_t zz=sampleNo*getNumDPPSample()+loop;  
 if (zz==5800)  
 {  
 LAZYDEBUG(cerr << "point=" <<  zz<< endl;)  
 LAZYDEBUG(cerr << "Input to  trace=" << DataTypes::pointToString(*vleft,m_left->getShape(),subroffset,"") << endl;)  
 LAZYDEBUG(cerr << "Offset for point=" << getPointOffset(5800/getNumDPPSample(),5800%getNumDPPSample()) << " vs ";)  
 LAZYDEBUG(cerr << subroffset << endl;)  
 LAZYDEBUG(cerr << "output=" << offset << endl;)  
 }  
             DataMaths::trace(*vleft,m_left->getShape(),subroffset, v ,getShape(),offset,m_axis_offset);  
 if (zz==5800)  
 {  
 LAZYDEBUG(cerr << "Result of trace=" << DataTypes::pointToString(v,getShape(),offset,"") << endl;)  
 }  
         subroffset+=instep;  
         offset+=outstep;  
     }  
     break;  
     case TRANS:  
     for (loop=0;loop<numsteps;++loop)  
     {  
             DataMaths::transpose(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset);  
         subroffset+=instep;  
         offset+=outstep;  
     }  
     break;  
     default:  
     throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");  
   }  
   return &v;  
 }  
   
   
 /*  
   \brief Compute the value of the expression (unary operation with int params) 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.  
 */  
 DataTypes::ValueType*  
 DataLazy::resolveNP1OUT_2P(ValueType& v, size_t offset, int sampleNo, size_t& roffset) const  
 {  
     // we assume that any collapsing has been done before we get here  
     // since we only have one argument we don't need to think about only  
     // processing single points.  
   if (m_readytype!='E')  
   {  
     throw DataException("Programmer error - resolveNP1OUT_2P should only be called on expanded Data.");  
   }  
     // since we can't write the result over the input, we need a result offset further along  
   size_t subroffset;  
   const ValueType* vleft=m_left->resolveSample(v,offset+m_left->m_samplesize,sampleNo,subroffset);  
 LAZYDEBUG(cerr << "srcsamplesize=" << offset+m_left->m_samplesize << " beg=" << subroffset << endl;)  
 LAZYDEBUG(cerr << "Offset for 5800=" << getPointOffset(5800/getNumDPPSample(),5800%getNumDPPSample()) << endl;)  
   roffset=offset;  
   size_t loop=0;  
   size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;  
   size_t outstep=getNoValues();  
   size_t instep=m_left->getNoValues();  
 LAZYDEBUG(cerr << "instep=" << instep << " outstep=" << outstep<< " numsteps=" << numsteps << endl;)  
   switch (m_op)  
   {  
     case SWAP:  
     for (loop=0;loop<numsteps;++loop)  
     {  
             DataMaths::swapaxes(*vleft,m_left->getShape(),subroffset, v,getShape(),offset,m_axis_offset, m_transpose);  
         subroffset+=instep;  
         offset+=outstep;  
     }  
     break;  
     default:  
     throw DataException("Programmer error - resolveNP1OUT2P can not resolve operator "+opToString(m_op)+".");  
   }  
   return &v;  
 }  
   
   
   
 #define PROC_OP(TYPE,X)                               \  
     for (int j=0;j<onumsteps;++j)\  
     {\  
       for (int i=0;i<numsteps;++i,resultp+=resultStep) \  
       { \  
 LAZYDEBUG(cout << "[left,right]=[" << lroffset << "," << rroffset << "]" << endl;)\  
 LAZYDEBUG(cout << "{left,right}={" << (*left)[lroffset] << "," << (*right)[rroffset] << "}\n";)\  
          tensor_binary_operation< TYPE >(chunksize, &((*left)[lroffset]), &((*right)[rroffset]), resultp, X); \  
 LAZYDEBUG(cout << " result=      " << resultp[0] << endl;) \  
          lroffset+=leftstep; \  
          rroffset+=rightstep; \  
       }\  
       lroffset+=oleftstep;\  
       rroffset+=orightstep;\  
     }  
   
 /*  
   \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.  
 */  
 // This method assumes that any subexpressions which evaluate to Constant or Tagged Data  
 // have already been collapsed to IDENTITY. So we must have at least one expanded child.  
 // If both children are expanded, then we can process them in a single operation (we treat  
 // the whole sample as one big datapoint.  
 // If one of the children is not expanded, then we need to treat each point in the sample  
 // individually.  
 // There is an additional complication when scalar operations are considered.  
 // For example, 2+Vector.  
 // In this case each double within the point is treated individually  
 DataTypes::ValueType*  
 DataLazy::resolveBinary(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  
 {  
 LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)  
   
   size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors  
     // first work out which of the children are expanded  
   bool leftExp=(m_left->m_readytype=='E');  
   bool rightExp=(m_right->m_readytype=='E');  
   if (!leftExp && !rightExp)  
   {  
     throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");  
   }  
   bool leftScalar=(m_left->getRank()==0);  
   bool rightScalar=(m_right->getRank()==0);  
   if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))  
   {  
     throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");  
   }  
   size_t leftsize=m_left->getNoValues();  
   size_t rightsize=m_right->getNoValues();  
   size_t chunksize=1;           // how many doubles will be processed in one go  
   int leftstep=0;       // how far should the left offset advance after each step  
   int rightstep=0;  
   int numsteps=0;       // total number of steps for the inner loop  
   int oleftstep=0;  // the o variables refer to the outer loop  
   int orightstep=0; // The outer loop is only required in cases where there is an extended scalar  
   int onumsteps=1;  
     
   bool LES=(leftExp && leftScalar); // Left is an expanded scalar  
   bool RES=(rightExp && rightScalar);  
   bool LS=(!leftExp && leftScalar); // left is a single scalar  
   bool RS=(!rightExp && rightScalar);  
   bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar  
   bool RN=(!rightExp && !rightScalar);  
   bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar  
   bool REN=(rightExp && !rightScalar);  
   
   if ((LES && RES) || (LEN && REN)) // both are Expanded scalars or both are expanded non-scalars  
   {  
     chunksize=m_left->getNumDPPSample()*leftsize;  
     leftstep=0;  
     rightstep=0;  
     numsteps=1;  
   }  
   else if (LES || RES)  
   {  
     chunksize=1;  
     if (LES)        // left is an expanded scalar  
     {  
         if (RS)  
         {  
            leftstep=1;  
            rightstep=0;  
            numsteps=m_left->getNumDPPSample();  
         }  
         else        // RN or REN  
         {  
            leftstep=0;  
            oleftstep=1;  
            rightstep=1;  
            orightstep=(RN ? -(int)rightsize : 0);  
            numsteps=rightsize;  
            onumsteps=m_left->getNumDPPSample();  
         }  
     }  
     else        // right is an expanded scalar  
     {  
         if (LS)  
         {  
            rightstep=1;  
            leftstep=0;  
            numsteps=m_right->getNumDPPSample();  
         }  
         else  
         {  
            rightstep=0;  
            orightstep=1;  
            leftstep=1;  
            oleftstep=(LN ? -(int)leftsize : 0);  
            numsteps=leftsize;  
            onumsteps=m_right->getNumDPPSample();  
         }  
     }  
   }  
   else  // this leaves (LEN, RS), (LEN, RN) and their transposes  
   {  
     if (LEN)    // and Right will be a single value  
     {  
         chunksize=rightsize;  
         leftstep=rightsize;  
         rightstep=0;  
         numsteps=m_left->getNumDPPSample();  
         if (RS)  
         {  
            numsteps*=leftsize;  
         }  
     }  
     else    // REN  
     {  
         chunksize=leftsize;  
         rightstep=leftsize;  
         leftstep=0;  
         numsteps=m_right->getNumDPPSample();  
         if (LS)  
         {  
            numsteps*=rightsize;  
         }  
     }  
   }  
   
   int resultStep=max(leftstep,rightstep);   // only one (at most) should be !=0  
     // Get the values of sub-expressions  
   const ValueType* left=m_left->resolveSample(v,offset+getMaxSampleSize(),sampleNo,lroffset);   // see note on  
     // calcBufss for why we can't put offset as the 2nd param above  
   const ValueType* right=m_right->resolveSample(v,offset+2*getMaxSampleSize(),sampleNo,rroffset); // Note  
     // the right child starts further along.  
 LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)  
 LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)  
 LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)  
 LAZYDEBUG(cout << " numsteps=" << numsteps << endl << "oleftstep=" << oleftstep << " orightstep=" << orightstep;)  
 LAZYDEBUG(cout << "onumsteps=" << onumsteps << endl;)  
 LAZYDEBUG(cout << " DPPS=" << m_left->getNumDPPSample() << "," <<m_right->getNumDPPSample() << endl;)  
 LAZYDEBUG(cout << "" << LS << RS << LN << RN << LES << RES <<LEN << REN <<   endl;)  
   
   
   double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
   switch(m_op)  
   {  
     case ADD:  
         PROC_OP(NO_ARG,plus<double>());  
     break;  
     case SUB:  
     PROC_OP(NO_ARG,minus<double>());  
     break;  
     case MUL:  
     PROC_OP(NO_ARG,multiplies<double>());  
     break;  
     case DIV:  
     PROC_OP(NO_ARG,divides<double>());  
     break;  
     case POW:  
        PROC_OP(double (double,double),::pow);  
     break;  
     default:  
     throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");  
   }  
   roffset=offset;  
   return &v;  
 }  
   
   
   
 /*  
   \brief Compute the value of the expression (tensor product) 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.  
 */  
 // This method assumes that any subexpressions which evaluate to Constant or Tagged Data  
 // have already been collapsed to IDENTITY. So we must have at least one expanded child.  
 // unlike the other resolve helpers, we must treat these datapoints separately.  
 DataTypes::ValueType*  
 DataLazy::resolveTProd(ValueType& v,  size_t offset, int sampleNo, size_t& roffset) const  
 {  
 LAZYDEBUG(cout << "Resolve TensorProduct: " << toString()  << " to offset " << offset<< endl;)  
   
   size_t lroffset=0, rroffset=0;    // offsets in the left and right result vectors  
     // first work out which of the children are expanded  
   bool leftExp=(m_left->m_readytype=='E');  
   bool rightExp=(m_right->m_readytype=='E');  
   int steps=getNumDPPSample();  
 /*  int leftStep=((leftExp && !rightExp)? m_right->getNoValues() : 0);  
   int rightStep=((rightExp && !leftExp)? m_left->getNoValues() : 0);*/  
   int leftStep=(leftExp? m_left->getNoValues() : 0);        // do not have scalars as input to this method  
   int rightStep=(rightExp?m_right->getNoValues() : 0);  
   
   int resultStep=getNoValues();  
     // Get the values of sub-expressions (leave a gap of one sample for the result).  
   int gap=offset+m_samplesize;    
   
 LAZYDEBUG(cout << "Query left with offset=" << gap << endl;)  
   
   const ValueType* left=m_left->resolveSample(v,gap,sampleNo,lroffset);  
   gap+=m_left->getMaxSampleSize();  
   
   
 LAZYDEBUG(cout << "Query right with offset=" << gap << endl;)  
   
   
   const ValueType* right=m_right->resolveSample(v,gap,sampleNo,rroffset);  
   
 LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;  
 cout << getNoValues() << endl;)  
 LAZYDEBUG(cerr << "Result of left=";)  
 LAZYDEBUG(cerr << "[" << lroffset << " .. " << lroffset+m_left->getNoValues() << "]" << endl;  
   
 for (int i=lroffset, limit=lroffset+(leftExp?m_left->getNoValues()*m_left->getNumDPPSample():m_left->getNoValues());i<limit;++i)  
 {  
 cout << "[" << setw(2) << i-lroffset << "] " << setw(10) << (*left)[i] << " ";  
 if (i%4==0) cout << endl;  
 })  
 LAZYDEBUG(cerr << "\nResult of right=" << endl;)  
 LAZYDEBUG(  
 for (int i=rroffset, limit=rroffset+(rightExp?m_right->getNoValues()*m_right->getNumDPPSample():m_right->getNoValues());i<limit;++i)  
 {  
 cerr << "[" <<  setw(2)<< i-rroffset << "] " << setw(10) << (*right)[i] << " ";  
 if (i%4==0) cout << endl;  
 }  
 cerr << endl;  
 )  
 LAZYDEBUG(cerr << "Post sub calls: " << toString() << endl;)  
 LAZYDEBUG(cout << "LeftExp=" << leftExp << " rightExp=" << rightExp << endl;)  
 LAZYDEBUG(cout << "LeftR=" << m_left->getRank() << " rightExp=" << m_right->getRank() << endl;)  
 LAZYDEBUG(cout << "LeftSize=" << m_left->getNoValues() << " RightSize=" << m_right->getNoValues() << endl;)  
 LAZYDEBUG(cout << "m_samplesize=" << m_samplesize << endl;)  
 LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)  
 LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)  
   
   double* resultp=&(v[offset]);     // results are stored at the vector offset we recieved  
   switch(m_op)  
   {  
     case PROD:  
     for (int i=0;i<steps;++i,resultp+=resultStep)  
     {  
   
 LAZYDEBUG(cout << "lroffset=" << lroffset << "rroffset=" << rroffset << endl;)  
 LAZYDEBUG(cout << "l*=" << left << " r*=" << right << endl;)  
 LAZYDEBUG(cout << "m_SL=" << m_SL << " m_SM=" << m_SM << " m_SR=" << m_SR << endl;)  
   
           const double *ptr_0 = &((*left)[lroffset]);  
           const double *ptr_1 = &((*right)[rroffset]);  
   
 LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)  
 LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)  
   
           matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);  
   
 LAZYDEBUG(cout << "Results--\n";  
 {  
   DataVector dv(getNoValues());  
 for (int z=0;z<getNoValues();++z)  
 {  
   cout << "[" << setw(2) << z<< "] " << setw(10) << resultp[z] << " ";  
   if (z%4==0) cout << endl;  
   dv[z]=resultp[z];  
 }  
 cout << endl << DataTypes::pointToString(dv,getShape(),0,"RESLT");  
 cout << "\nWritten to: " << resultp << " resultStep=" << resultStep << endl;  
 }  
 )  
       lroffset+=leftStep;  
       rroffset+=rightStep;  
     }  
     break;  
     default:  
     throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");  
   }  
   roffset=offset;  
   return &v;  
 }  
   
   
 #ifdef LAZY_NODE_STORAGE  
   
994  // The result will be stored in m_samples  // The result will be stored in m_samples
995  // The return value is a pointer to the DataVector, offset is the offset within the return value  // The return value is a pointer to the DataVector, offset is the offset within the return value
996  const DataTypes::ValueType*  const DataTypes::RealVectorType*
997  DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeSample(int tid, int sampleNo, size_t& roffset) const
998  {  {
999  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)  LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)
1000      // 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
1001    if (m_readytype!='E' && m_op!=IDENTITY)    if (m_readytype!='E' && m_op!=IDENTITY)
1002    {    {
1003      collapse();          collapse();
1004    }    }
1005    if (m_op==IDENTITY)      if (m_op==IDENTITY)  
1006    {    {
1007      const ValueType& vec=m_id->getVectorRO();      const RealVectorType& vec=m_id->getVectorRO();
 //     if (m_readytype=='C')  
 //     {  
 //  roffset=0;      // all samples read from the same position  
 //  return &(m_samples);  
 //     }  
1008      roffset=m_id->getPointOffset(sampleNo, 0);      roffset=m_id->getPointOffset(sampleNo, 0);
1009    #ifdef LAZY_STACK_PROF
1010    int x;
1011    if (&x<stackend[omp_get_thread_num()])
1012    {
1013           stackend[omp_get_thread_num()]=&x;
1014    }
1015    #endif
1016      return &(vec);      return &(vec);
1017    }    }
1018    if (m_readytype!='E')    if (m_readytype!='E')
# Line 1615  LAZYDEBUG(cout << "Resolve sample " << t Line 1021  LAZYDEBUG(cout << "Resolve sample " << t
1021    }    }
1022    if (m_sampleids[tid]==sampleNo)    if (m_sampleids[tid]==sampleNo)
1023    {    {
1024      roffset=tid*m_samplesize;          roffset=tid*m_samplesize;
1025      return &(m_samples);        // sample is already resolved          return &(m_samples);            // sample is already resolved
1026    }    }
1027    m_sampleids[tid]=sampleNo;    m_sampleids[tid]=sampleNo;
1028    
1029    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1030    {    {
1031    case G_UNARY:    case G_UNARY:
# Line 1628  LAZYDEBUG(cout << "Resolve sample " << t Line 1035  LAZYDEBUG(cout << "Resolve sample " << t
1035    case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);    case G_NP1OUT_P: return resolveNodeNP1OUT_P(tid, sampleNo, roffset);
1036    case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);    case G_TENSORPROD: return resolveNodeTProd(tid, sampleNo, roffset);
1037    case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);    case G_NP1OUT_2P: return resolveNodeNP1OUT_2P(tid, sampleNo, roffset);
1038      case G_REDUCTION: return resolveNodeReduction(tid, sampleNo, roffset);
1039      case G_CONDEVAL: return resolveNodeCondEval(tid, sampleNo, roffset);
1040    default:    default:
1041      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)+".");
1042    }    }
1043  }  }
1044    
1045  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1046  DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeUnary(int tid, int sampleNo, size_t& roffset) const
1047  {  {
1048      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1049      // 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
1050      // processing single points.          // processing single points.
1051      // we will also know we won't get identity nodes          // we will also know we won't get identity nodes
1052    if (m_readytype!='E')    if (m_readytype!='E')
1053    {    {
1054      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");      throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
# Line 1648  DataLazy::resolveNodeUnary(int tid, int Line 1057  DataLazy::resolveNodeUnary(int tid, int
1057    {    {
1058      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");      throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1059    }    }
1060    const DataTypes::ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);    const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, roffset);
1061    const double* left=&((*leftres)[roffset]);    const double* left=&((*leftres)[roffset]);
1062    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1063    double* result=&(m_samples[roffset]);    double* result=&(m_samples[roffset]);
1064    switch (m_op)    switch (m_op)
1065    {    {
1066      case SIN:        case SIN:  
1067      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sin);
1068      break;          break;
1069      case COS:      case COS:
1070      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cos);
1071      break;          break;
1072      case TAN:      case TAN:
1073      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tan);
1074      break;          break;
1075      case ASIN:      case ASIN:
1076      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::asin);
1077      break;          break;
1078      case ACOS:      case ACOS:
1079      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::acos);
1080      break;          break;
1081      case ATAN:      case ATAN:
1082      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::atan);
1083      break;          break;
1084      case SINH:      case SINH:
1085      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sinh);
1086      break;          break;
1087      case COSH:      case COSH:
1088      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::cosh);
1089      break;          break;
1090      case TANH:      case TANH:
1091      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::tanh);
1092      break;          break;
1093      case ERF:      case ERF:
1094  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1095      throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");          throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
1096  #else  #else
1097      tensor_unary_operation(m_samplesize, left, result, ::erf);          tensor_unary_operation(m_samplesize, left, result, ::erf);
1098      break;          break;
1099  #endif  #endif
1100     case ASINH:     case ASINH:
1101  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1102      tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::asinh_substitute);
1103  #else  #else
1104      tensor_unary_operation(m_samplesize, left, result, ::asinh);          tensor_unary_operation(m_samplesize, left, result, ::asinh);
1105  #endif    #endif  
1106      break;          break;
1107     case ACOSH:     case ACOSH:
1108  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1109      tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::acosh_substitute);
1110  #else  #else
1111      tensor_unary_operation(m_samplesize, left, result, ::acosh);          tensor_unary_operation(m_samplesize, left, result, ::acosh);
1112  #endif    #endif  
1113      break;          break;
1114     case ATANH:     case ATANH:
1115  #if defined (_WIN32) && !defined(__INTEL_COMPILER)  #if defined (_WIN32) && !defined(__INTEL_COMPILER)
1116      tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);          tensor_unary_operation(m_samplesize, left, result, escript::atanh_substitute);
1117  #else  #else
1118      tensor_unary_operation(m_samplesize, left, result, ::atanh);          tensor_unary_operation(m_samplesize, left, result, ::atanh);
1119  #endif    #endif  
1120      break;          break;
1121      case LOG10:      case LOG10:
1122      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log10);
1123      break;          break;
1124      case LOG:      case LOG:
1125      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::log);
1126      break;          break;
1127      case SIGN:      case SIGN:
1128      tensor_unary_operation(m_samplesize, left, result, escript::fsign);          tensor_unary_operation(m_samplesize, left, result, escript::fsign);
1129      break;          break;
1130      case ABS:      case ABS:
1131      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::fabs);
1132      break;          break;
1133      case NEG:      case NEG:
1134      tensor_unary_operation(m_samplesize, left, result, negate<double>());          tensor_unary_operation(m_samplesize, left, result, negate<double>());
1135      break;          break;
1136      case POS:      case POS:
1137      // it doesn't mean anything for delayed.          // it doesn't mean anything for delayed.
1138      // it will just trigger a deep copy of the lazy object          // it will just trigger a deep copy of the lazy object
1139      throw DataException("Programmer error - POS not supported for lazy data.");          throw DataException("Programmer error - POS not supported for lazy data.");
1140      break;          break;
1141      case EXP:      case EXP:
1142      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::exp);
1143      break;          break;
1144      case SQRT:      case SQRT:
1145      tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);          tensor_unary_operation<double (*)(double)>(m_samplesize, left, result, ::sqrt);
1146      break;          break;
1147      case RECIP:      case RECIP:
1148      tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));          tensor_unary_operation(m_samplesize, left, result, bind1st(divides<double>(),1.));
1149      break;          break;
1150      case GZ:      case GZ:
1151      tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(greater<double>(),0.0));
1152      break;          break;
1153      case LZ:      case LZ:
1154      tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(less<double>(),0.0));
1155      break;          break;
1156      case GEZ:      case GEZ:
1157      tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));          tensor_unary_operation(m_samplesize, left, result, bind2nd(greater_equal<double>(),0.0));
1158      break;          break;
1159      case LEZ:      case LEZ:
1160      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));
1161      break;          break;
1162  // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently  // There are actually G_UNARY_P but I don't see a compelling reason to treat them differently
1163      case NEZ:      case NEZ:
1164      tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));          tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsGT(),m_tol));
1165      break;          break;
1166      case EZ:      case EZ:
1167      tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));          tensor_unary_operation(m_samplesize, left, result, bind2nd(AbsLTE(),m_tol));
1168      break;          break;
1169    
1170      default:      default:
1171      throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1172    }    }
1173    return &(m_samples);    return &(m_samples);
1174  }  }
1175    
1176    
1177  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1178  DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeReduction(int tid, int sampleNo, size_t& roffset) const
1179  {  {
1180      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1181      // 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
1182      // processing single points.          // processing single points.
1183            // we will also know we won't get identity nodes
1184      if (m_readytype!='E')
1185      {
1186        throw DataException("Programmer error - resolveUnary should only be called on expanded Data.");
1187      }
1188      if (m_op==IDENTITY)
1189      {
1190        throw DataException("Programmer error - resolveNodeUnary should not be called on identity nodes.");
1191      }
1192      size_t loffset=0;
1193      const DataTypes::RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, loffset);
1194    
1195      roffset=m_samplesize*tid;
1196      unsigned int ndpps=getNumDPPSample();
1197      unsigned int psize=DataTypes::noValues(m_left->getShape());
1198      double* result=&(m_samples[roffset]);
1199      switch (m_op)
1200      {
1201        case MINVAL:
1202            {
1203              for (unsigned int z=0;z<ndpps;++z)
1204              {
1205                FMin op;
1206                *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max());
1207                loffset+=psize;
1208                result++;
1209              }
1210            }
1211            break;
1212        case MAXVAL:
1213            {
1214              for (unsigned int z=0;z<ndpps;++z)
1215              {
1216              FMax op;
1217              *result=DataMaths::reductionOp(*leftres, m_left->getShape(), loffset, op, numeric_limits<double>::max()*-1);
1218              loffset+=psize;
1219              result++;
1220              }
1221            }
1222            break;
1223        default:
1224            throw DataException("Programmer error - resolveUnary can not resolve operator "+opToString(m_op)+".");
1225      }
1226      return &(m_samples);
1227    }
1228    
1229    const DataTypes::RealVectorType*
1230    DataLazy::resolveNodeNP1OUT(int tid, int sampleNo, size_t& roffset) const
1231    {
1232            // we assume that any collapsing has been done before we get here
1233            // since we only have one argument we don't need to think about only
1234            // processing single points.
1235    if (m_readytype!='E')    if (m_readytype!='E')
1236    {    {
1237      throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");      throw DataException("Programmer error - resolveNodeNP1OUT should only be called on expanded Data.");
# Line 1780  DataLazy::resolveNodeNP1OUT(int tid, int Line 1241  DataLazy::resolveNodeNP1OUT(int tid, int
1241      throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");      throw DataException("Programmer error - resolveNodeNP1OUT should not be called on identity nodes.");
1242    }    }
1243    size_t subroffset;    size_t subroffset;
1244    const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);    const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1245    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1246    size_t loop=0;    size_t loop=0;
1247    size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;    size_t numsteps=(m_readytype=='E')?getNumDPPSample():1;
# Line 1789  DataLazy::resolveNodeNP1OUT(int tid, int Line 1250  DataLazy::resolveNodeNP1OUT(int tid, int
1250    switch (m_op)    switch (m_op)
1251    {    {
1252      case SYM:      case SYM:
1253      for (loop=0;loop<numsteps;++loop)          for (loop=0;loop<numsteps;++loop)
1254      {          {
1255          DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);              DataMaths::symmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1256          subroffset+=step;              subroffset+=step;
1257          offset+=step;              offset+=step;
1258      }          }
1259      break;          break;
1260      case NSYM:      case NSYM:
1261      for (loop=0;loop<numsteps;++loop)          for (loop=0;loop<numsteps;++loop)
1262      {          {
1263          DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);              DataMaths::nonsymmetric(*leftres,m_left->getShape(),subroffset, m_samples, getShape(), offset);
1264          subroffset+=step;              subroffset+=step;
1265          offset+=step;              offset+=step;
1266      }          }
1267      break;          break;
1268      default:      default:
1269      throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUT can not resolve operator "+opToString(m_op)+".");
1270    }    }
1271    return &m_samples;    return &m_samples;
1272  }  }
1273    
1274  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1275  DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeNP1OUT_P(int tid, int sampleNo, size_t& roffset) const
1276  {  {
1277      // we assume that any collapsing has been done before we get here          // we assume that any collapsing has been done before we get here
1278      // 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
1279      // processing single points.          // processing single points.
1280    if (m_readytype!='E')    if (m_readytype!='E')
1281    {    {
1282      throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");      throw DataException("Programmer error - resolveNodeNP1OUT_P should only be called on expanded Data.");
# Line 1826  DataLazy::resolveNodeNP1OUT_P(int tid, i Line 1287  DataLazy::resolveNodeNP1OUT_P(int tid, i
1287    }    }
1288    size_t subroffset;    size_t subroffset;
1289    size_t offset;    size_t offset;
1290    const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);    const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1291    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1292    offset=roffset;    offset=roffset;
1293    size_t loop=0;    size_t loop=0;
# Line 1836  DataLazy::resolveNodeNP1OUT_P(int tid, i Line 1297  DataLazy::resolveNodeNP1OUT_P(int tid, i
1297    switch (m_op)    switch (m_op)
1298    {    {
1299      case TRACE:      case TRACE:
1300      for (loop=0;loop<numsteps;++loop)          for (loop=0;loop<numsteps;++loop)
1301      {          {
1302              DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);              DataMaths::trace(*leftres,m_left->getShape(),subroffset, m_samples ,getShape(),offset,m_axis_offset);
1303          subroffset+=instep;              subroffset+=instep;
1304          offset+=outstep;              offset+=outstep;
1305      }          }
1306      break;          break;
1307      case TRANS:      case TRANS:
1308      for (loop=0;loop<numsteps;++loop)          for (loop=0;loop<numsteps;++loop)
1309      {          {
1310              DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);              DataMaths::transpose(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset,m_axis_offset);
1311          subroffset+=instep;              subroffset+=instep;
1312          offset+=outstep;              offset+=outstep;
1313      }          }
1314      break;          break;
1315      default:      default:
1316      throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNP1OUTP can not resolve operator "+opToString(m_op)+".");
1317    }    }
1318    return &m_samples;    return &m_samples;
1319  }  }
1320    
1321    
1322  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1323  DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeNP1OUT_2P(int tid, int sampleNo, size_t& roffset) const
1324  {  {
1325    if (m_readytype!='E')    if (m_readytype!='E')
1326    {    {
# Line 1871  DataLazy::resolveNodeNP1OUT_2P(int tid, Line 1332  DataLazy::resolveNodeNP1OUT_2P(int tid,
1332    }    }
1333    size_t subroffset;    size_t subroffset;
1334    size_t offset;    size_t offset;
1335    const ValueType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);    const RealVectorType* leftres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1336    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1337    offset=roffset;    offset=roffset;
1338    size_t loop=0;    size_t loop=0;
# Line 1881  DataLazy::resolveNodeNP1OUT_2P(int tid, Line 1342  DataLazy::resolveNodeNP1OUT_2P(int tid,
1342    switch (m_op)    switch (m_op)
1343    {    {
1344      case SWAP:      case SWAP:
1345      for (loop=0;loop<numsteps;++loop)          for (loop=0;loop<numsteps;++loop)
1346      {          {
1347              DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);              DataMaths::swapaxes(*leftres,m_left->getShape(),subroffset, m_samples, getShape(),offset, m_axis_offset, m_transpose);
1348          subroffset+=instep;              subroffset+=instep;
1349          offset+=outstep;              offset+=outstep;
1350      }          }
1351      break;          break;
1352      default:      default:
1353      throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveNodeNP1OUT2P can not resolve operator "+opToString(m_op)+".");
1354    }    }
1355    return &m_samples;    return &m_samples;
1356  }  }
1357    
1358    const DataTypes::RealVectorType*
1359    DataLazy::resolveNodeCondEval(int tid, int sampleNo, size_t& roffset) const
1360    {
1361      if (m_readytype!='E')
1362      {
1363        throw DataException("Programmer error - resolveNodeCondEval should only be called on expanded Data.");
1364      }
1365      if (m_op!=CONDEVAL)
1366      {
1367        throw DataException("Programmer error - resolveNodeCondEval should only be called on CONDEVAL nodes.");
1368      }
1369      size_t subroffset;
1370    
1371      const RealVectorType* maskres=m_mask->resolveNodeSample(tid, sampleNo, subroffset);
1372      const RealVectorType* srcres=0;
1373      if ((*maskres)[subroffset]>0)
1374      {
1375            srcres=m_left->resolveNodeSample(tid, sampleNo, subroffset);
1376      }
1377      else
1378      {
1379            srcres=m_right->resolveNodeSample(tid, sampleNo, subroffset);
1380      }
1381    
1382      // Now we need to copy the result
1383    
1384      roffset=m_samplesize*tid;
1385      for (int i=0;i<m_samplesize;++i)
1386      {
1387            m_samples[roffset+i]=(*srcres)[subroffset+i];  
1388      }
1389    
1390      return &m_samples;
1391    }
1392    
1393  // 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
1394  // 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.
# Line 1905  DataLazy::resolveNodeNP1OUT_2P(int tid, Line 1399  DataLazy::resolveNodeNP1OUT_2P(int tid,
1399  // There is an additional complication when scalar operations are considered.  // There is an additional complication when scalar operations are considered.
1400  // For example, 2+Vector.  // For example, 2+Vector.
1401  // In this case each double within the point is treated individually  // In this case each double within the point is treated individually
1402  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1403  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeBinary(int tid, int sampleNo, size_t& roffset) const
1404  {  {
1405  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve binary: " << toString() << endl;)
1406    
1407    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
1408      // first work out which of the children are expanded          // first work out which of the children are expanded
1409    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1410    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1411    if (!leftExp && !rightExp)    if (!leftExp && !rightExp)
1412    {    {
1413      throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");          throw DataException("Programmer Error - please use collapse if neither argument has type 'E'.");
1414    }    }
1415    bool leftScalar=(m_left->getRank()==0);    bool leftScalar=(m_left->getRank()==0);
1416    bool rightScalar=(m_right->getRank()==0);    bool rightScalar=(m_right->getRank()==0);
1417    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))    if ((m_left->getRank()!=m_right->getRank()) && (!leftScalar && !rightScalar))
1418    {    {
1419      throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");          throw DataException("resolveBinary - ranks of arguments must match unless one of them is scalar.");
1420    }    }
1421    size_t leftsize=m_left->getNoValues();    size_t leftsize=m_left->getNoValues();
1422    size_t rightsize=m_right->getNoValues();    size_t rightsize=m_right->getNoValues();
1423    size_t chunksize=1;           // how many doubles will be processed in one go    size_t chunksize=1;                   // how many doubles will be processed in one go
1424    int leftstep=0;       // how far should the left offset advance after each step    int leftstep=0;               // how far should the left offset advance after each step
1425    int rightstep=0;    int rightstep=0;
1426    int numsteps=0;       // total number of steps for the inner loop    int numsteps=0;               // total number of steps for the inner loop
1427    int oleftstep=0;  // the o variables refer to the outer loop    int oleftstep=0;      // the o variables refer to the outer loop
1428    int orightstep=0; // The outer loop is only required in cases where there is an extended scalar    int orightstep=0;     // The outer loop is only required in cases where there is an extended scalar
1429    int onumsteps=1;    int onumsteps=1;
1430        
1431    bool LES=(leftExp && leftScalar); // Left is an expanded scalar    bool LES=(leftExp && leftScalar);     // Left is an expanded scalar
1432    bool RES=(rightExp && rightScalar);    bool RES=(rightExp && rightScalar);
1433    bool LS=(!leftExp && leftScalar); // left is a single scalar    bool LS=(!leftExp && leftScalar);     // left is a single scalar
1434    bool RS=(!rightExp && rightScalar);    bool RS=(!rightExp && rightScalar);
1435    bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar    bool LN=(!leftExp && !leftScalar);    // left is a single non-scalar
1436    bool RN=(!rightExp && !rightScalar);    bool RN=(!rightExp && !rightScalar);
1437    bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar    bool LEN=(leftExp && !leftScalar);    // left is an expanded non-scalar
1438    bool REN=(rightExp && !rightScalar);    bool REN=(rightExp && !rightScalar);
1439    
1440    if ((LES && RES) || (LEN && REN)) // both are Expanded scalars or both are expanded non-scalars    if ((LES && RES) || (LEN && REN))     // both are Expanded scalars or both are expanded non-scalars
1441    {    {
1442      chunksize=m_left->getNumDPPSample()*leftsize;          chunksize=m_left->getNumDPPSample()*leftsize;
1443      leftstep=0;          leftstep=0;
1444      rightstep=0;          rightstep=0;
1445      numsteps=1;          numsteps=1;
1446    }    }
1447    else if (LES || RES)    else if (LES || RES)
1448    {    {
1449      chunksize=1;          chunksize=1;
1450      if (LES)        // left is an expanded scalar          if (LES)                // left is an expanded scalar
1451      {          {
1452          if (RS)                  if (RS)
1453          {                  {
1454             leftstep=1;                     leftstep=1;
1455             rightstep=0;                     rightstep=0;
1456             numsteps=m_left->getNumDPPSample();                     numsteps=m_left->getNumDPPSample();
1457          }                  }
1458          else        // RN or REN                  else            // RN or REN
1459          {                  {
1460             leftstep=0;                     leftstep=0;
1461             oleftstep=1;                     oleftstep=1;
1462             rightstep=1;                     rightstep=1;
1463             orightstep=(RN ? -(int)rightsize : 0);                     orightstep=(RN ? -(int)rightsize : 0);
1464             numsteps=rightsize;                     numsteps=rightsize;
1465             onumsteps=m_left->getNumDPPSample();                     onumsteps=m_left->getNumDPPSample();
1466          }                  }
1467      }          }
1468      else        // right is an expanded scalar          else            // right is an expanded scalar
1469      {          {
1470          if (LS)                  if (LS)
1471          {                  {
1472             rightstep=1;                     rightstep=1;
1473             leftstep=0;                     leftstep=0;
1474             numsteps=m_right->getNumDPPSample();                     numsteps=m_right->getNumDPPSample();
1475          }                  }
1476          else                  else
1477          {                  {
1478             rightstep=0;                     rightstep=0;
1479             orightstep=1;                     orightstep=1;
1480             leftstep=1;                     leftstep=1;
1481             oleftstep=(LN ? -(int)leftsize : 0);                     oleftstep=(LN ? -(int)leftsize : 0);
1482             numsteps=leftsize;                     numsteps=leftsize;
1483             onumsteps=m_right->getNumDPPSample();                     onumsteps=m_right->getNumDPPSample();
1484          }                  }
1485      }          }
1486    }    }
1487    else  // this leaves (LEN, RS), (LEN, RN) and their transposes    else  // this leaves (LEN, RS), (LEN, RN) and their transposes
1488    {    {
1489      if (LEN)    // and Right will be a single value          if (LEN)        // and Right will be a single value
1490      {          {
1491          chunksize=rightsize;                  chunksize=rightsize;
1492          leftstep=rightsize;                  leftstep=rightsize;
1493          rightstep=0;                  rightstep=0;
1494          numsteps=m_left->getNumDPPSample();                  numsteps=m_left->getNumDPPSample();
1495          if (RS)                  if (RS)
1496          {                  {
1497             numsteps*=leftsize;                     numsteps*=leftsize;
1498          }                  }
1499      }          }
1500      else    // REN          else    // REN
1501      {          {
1502          chunksize=leftsize;                  chunksize=leftsize;
1503          rightstep=leftsize;                  rightstep=leftsize;
1504          leftstep=0;                  leftstep=0;
1505          numsteps=m_right->getNumDPPSample();                  numsteps=m_right->getNumDPPSample();
1506          if (LS)                  if (LS)
1507          {                  {
1508             numsteps*=rightsize;                     numsteps*=rightsize;
1509          }                  }
1510      }          }
1511    }    }
1512    
1513    int resultStep=max(leftstep,rightstep);   // only one (at most) should be !=0    int resultStep=max(leftstep,rightstep);       // only one (at most) should be !=0
1514      // Get the values of sub-expressions          // Get the values of sub-expressions
1515    const ValueType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      const RealVectorType* left=m_left->resolveNodeSample(tid,sampleNo,lroffset);      
1516    const ValueType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);    const RealVectorType* right=m_right->resolveNodeSample(tid,sampleNo,rroffset);
1517  LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)  LAZYDEBUG(cout << "Post sub calls in " << toString() << endl;)
1518  LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)  LAZYDEBUG(cout << "shapes=" << DataTypes::shapeToString(m_left->getShape()) << "," << DataTypes::shapeToString(m_right->getShape()) << endl;)
1519  LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)  LAZYDEBUG(cout << "chunksize=" << chunksize << endl << "leftstep=" << leftstep << " rightstep=" << rightstep;)
# Line 2033  LAZYDEBUG(cout << "Right res["<< rroffse Line 1527  LAZYDEBUG(cout << "Right res["<< rroffse
1527    
1528    
1529    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1530    double* resultp=&(m_samples[roffset]);        // results are stored at the vector offset we recieved    double* resultp=&(m_samples[roffset]);                // results are stored at the vector offset we received
1531    switch(m_op)    switch(m_op)
1532    {    {
1533      case ADD:      case ADD:
1534          PROC_OP(NO_ARG,plus<double>());          //PROC_OP(NO_ARG,plus<double>());
1535      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1536                 &(*left)[0],
1537                 &(*right)[0],
1538                 chunksize,
1539                 onumsteps,
1540                 numsteps,
1541                 resultStep,
1542                 leftstep,
1543                 rightstep,
1544                 oleftstep,
1545                 orightstep,
1546                 lroffset,
1547                 rroffset,
1548                 escript::ESFunction::PLUSF);  
1549            break;
1550      case SUB:      case SUB:
1551      PROC_OP(NO_ARG,minus<double>());        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1552      break;               &(*left)[0],
1553                 &(*right)[0],
1554                 chunksize,
1555                 onumsteps,
1556                 numsteps,
1557                 resultStep,
1558                 leftstep,
1559                 rightstep,
1560                 oleftstep,
1561                 orightstep,
1562                 lroffset,
1563                 rroffset,
1564                 escript::ESFunction::MINUSF);        
1565            //PROC_OP(NO_ARG,minus<double>());
1566            break;
1567      case MUL:      case MUL:
1568      PROC_OP(NO_ARG,multiplies<double>());          //PROC_OP(NO_ARG,multiplies<double>());
1569      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1570                 &(*left)[0],
1571                 &(*right)[0],
1572                 chunksize,
1573                 onumsteps,
1574                 numsteps,
1575                 resultStep,
1576                 leftstep,
1577                 rightstep,
1578                 oleftstep,
1579                 orightstep,
1580                 lroffset,
1581                 rroffset,
1582                 escript::ESFunction::MULTIPLIESF);      
1583            break;
1584      case DIV:      case DIV:
1585      PROC_OP(NO_ARG,divides<double>());          //PROC_OP(NO_ARG,divides<double>());
1586      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1587                 &(*left)[0],
1588                 &(*right)[0],
1589                 chunksize,
1590                 onumsteps,
1591                 numsteps,
1592                 resultStep,
1593                 leftstep,
1594                 rightstep,
1595                 oleftstep,
1596                 orightstep,
1597                 lroffset,
1598                 rroffset,
1599                 escript::ESFunction::DIVIDESF);          
1600            break;
1601      case POW:      case POW:
1602         PROC_OP(double (double,double),::pow);         //PROC_OP(double (double,double),::pow);
1603      break;        DataMaths::binaryOpVectorLazyHelper<real_t, real_t, real_t>(resultp,
1604                 &(*left)[0],
1605                 &(*right)[0],
1606                 chunksize,
1607                 onumsteps,
1608                 numsteps,
1609                 resultStep,
1610                 leftstep,
1611                 rightstep,
1612                 oleftstep,
1613                 orightstep,
1614                 lroffset,
1615                 rroffset,
1616                 escript::ESFunction::POWF);          
1617            break;
1618      default:      default:
1619      throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveBinary can not resolve operator "+opToString(m_op)+".");
1620    }    }
1621  LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)  LAZYDEBUG(cout << "Result res[" << roffset<< "]" << m_samples[roffset] << endl;)
1622    return &m_samples;    return &m_samples;
# Line 2062  LAZYDEBUG(cout << "Result res[" << roffs Line 1626  LAZYDEBUG(cout << "Result res[" << roffs
1626  // 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
1627  // 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.
1628  // unlike the other resolve helpers, we must treat these datapoints separately.  // unlike the other resolve helpers, we must treat these datapoints separately.
1629  const DataTypes::ValueType*  const DataTypes::RealVectorType*
1630  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset)  DataLazy::resolveNodeTProd(int tid, int sampleNo, size_t& roffset) const
1631  {  {
1632  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)  LAZYDEBUG(cout << "Resolve TensorProduct: " << toString() << endl;)
1633    
1634    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
1635      // first work out which of the children are expanded          // first work out which of the children are expanded
1636    bool leftExp=(m_left->m_readytype=='E');    bool leftExp=(m_left->m_readytype=='E');
1637    bool rightExp=(m_right->m_readytype=='E');    bool rightExp=(m_right->m_readytype=='E');
1638    int steps=getNumDPPSample();    int steps=getNumDPPSample();
1639    int leftStep=(leftExp? m_left->getNoValues() : 0);        // do not have scalars as input to this method    int leftStep=(leftExp? m_left->getNoValues() : 0);            // do not have scalars as input to this method
1640    int rightStep=(rightExp?m_right->getNoValues() : 0);    int rightStep=(rightExp?m_right->getNoValues() : 0);
1641    
1642    int resultStep=getNoValues();    int resultStep=getNoValues();
1643    roffset=m_samplesize*tid;    roffset=m_samplesize*tid;
1644    size_t offset=roffset;    size_t offset=roffset;
1645    
1646    const ValueType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);    const RealVectorType* left=m_left->resolveNodeSample(tid, sampleNo, lroffset);
1647    
1648    const ValueType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);    const RealVectorType* right=m_right->resolveNodeSample(tid, sampleNo, rroffset);
1649    
1650  LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;  LAZYDEBUG(cerr << "[Left shape]=" << DataTypes::shapeToString(m_left->getShape()) << "\n[Right shape]=" << DataTypes::shapeToString(m_right->getShape()) << " result=" <<DataTypes::shapeToString(getShape()) <<  endl;
1651  cout << getNoValues() << endl;)  cout << getNoValues() << endl;)
# Line 2095  LAZYDEBUG(cout << "m_samplesize=" << m_s Line 1659  LAZYDEBUG(cout << "m_samplesize=" << m_s
1659  LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)  LAZYDEBUG(cout << "outputshape=" << DataTypes::shapeToString(getShape()) << endl;)
1660  LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)  LAZYDEBUG(cout << "DPPS=" << m_right->getNumDPPSample() <<"."<<endl;)
1661    
1662    double* resultp=&(m_samples[offset]);     // results are stored at the vector offset we recieved    double* resultp=&(m_samples[offset]);         // results are stored at the vector offset we received
1663    switch(m_op)    switch(m_op)
1664    {    {
1665      case PROD:      case PROD:
1666      for (int i=0;i<steps;++i,resultp+=resultStep)          for (int i=0;i<steps;++i,resultp+=resultStep)
1667      {          {
1668            const double *ptr_0 = &((*left)[lroffset]);            const double *ptr_0 = &((*left)[lroffset]);
1669            const double *ptr_1 = &((*right)[rroffset]);            const double *ptr_1 = &((*right)[rroffset]);
1670    
1671  LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)  LAZYDEBUG(cout << DataTypes::pointToString(*left, m_left->getShape(),lroffset,"LEFT") << endl;)
1672  LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)  LAZYDEBUG(cout << DataTypes::pointToString(*right,m_right->getShape(),rroffset, "RIGHT") << endl;)
1673    
1674            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);            matrix_matrix_product(m_SL, m_SM, m_SR, ptr_0, ptr_1, resultp, m_transpose);
1675    
1676        lroffset+=leftStep;            lroffset+=leftStep;
1677        rroffset+=rightStep;            rroffset+=rightStep;
1678      }          }
1679      break;          break;
1680      default:      default:
1681      throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");          throw DataException("Programmer error - resolveTProduct can not resolve operator "+opToString(m_op)+".");
1682    }    }
1683    roffset=offset;    roffset=offset;
1684    return &m_samples;    return &m_samples;
1685  }  }
 #endif //LAZY_NODE_STORAGE  
1686    
 /*  
   \brief Compute the value of the expression 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.  
1687    
1688    The return value will be an existing vector so do not deallocate it.  const DataTypes::RealVectorType*
1689  */  DataLazy::resolveSample(int sampleNo, size_t& roffset) const
 // 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.  
   
 // the roffset is the offset within the returned vector where the data begins  
 const DataTypes::ValueType*  
 DataLazy::resolveSample(ValueType& v, size_t offset, int sampleNo, size_t& roffset)  
 {  
 LAZYDEBUG(cout << "Resolve sample " << toString() << endl;)  
     // collapse so we have a 'E' node or an IDENTITY for some other type  
   if (m_readytype!='E' && m_op!=IDENTITY)  
   {  
     collapse();  
   }  
   if (m_op==IDENTITY)    
   {  
     const ValueType& vec=m_id->getVectorRO();  
     if (m_readytype=='C')  
     {  
     roffset=0;  
 LAZYDEBUG(cout << "Finish  sample " << toString() << endl;)  
     return &(vec);  
     }  
     roffset=m_id->getPointOffset(sampleNo, 0);  
 LAZYDEBUG(cout << "Finish  sample " << toString() << endl;)  
     return &(vec);  
   }  
   if (m_readytype!='E')  
   {  
     throw DataException("Programmer Error - Collapse did not produce an expanded node.");  
   }  
   switch (getOpgroup(m_op))  
   {  
   case G_UNARY:  
   case G_UNARY_P: return resolveUnary(v, offset,sampleNo,roffset);  
   case G_BINARY: return resolveBinary(v, offset,sampleNo,roffset);  
   case G_NP1OUT: return resolveNP1OUT(v, offset, sampleNo,roffset);  
   case G_NP1OUT_P: return resolveNP1OUT_P(v, offset, sampleNo,roffset);  
   case G_TENSORPROD: return resolveTProd(v,offset, sampleNo,roffset);  
   case G_NP1OUT_2P: return resolveNP1OUT_2P(v, offset, sampleNo, roffset);  
   default:  
     throw DataException("Programmer Error - resolveSample does not know how to process "+opToString(m_op)+".");  
   }  
   
 }  
   
 const DataTypes::ValueType*  
 DataLazy::resolveSample(BufferGroup& bg, int sampleNo, size_t& roffset)  
1690  {  {
1691  #ifdef _OPENMP  #ifdef _OPENMP
1692      int tid=omp_get_thread_num();          int tid=omp_get_thread_num();
1693  #else  #else
1694      int tid=0;          int tid=0;
1695  #endif  #endif
1696      return resolveSample(bg.getBuffer(tid),bg.getOffset(tid),sampleNo,roffset);  
1697    #ifdef LAZY_STACK_PROF
1698            stackstart[tid]=&tid;
1699            stackend[tid]=&tid;
1700            const DataTypes::RealVectorType* r=resolveNodeSample(tid, sampleNo, roffset);
1701            size_t d=(size_t)stackstart[tid]-(size_t)stackend[tid];
1702            #pragma omp critical
1703            if (d>maxstackuse)
1704            {
1705    cout << "Max resolve Stack use " << d << endl;
1706                    maxstackuse=d;
1707            }
1708            return r;
1709    #else
1710            return resolveNodeSample(tid, sampleNo, roffset);
1711    #endif
1712  }  }
1713    
1714    
# Line 2195  void Line 1717  void
1717  DataLazy::resolveToIdentity()  DataLazy::resolveToIdentity()
1718  {  {
1719     if (m_op==IDENTITY)     if (m_op==IDENTITY)
1720      return;          return;
 #ifndef LAZY_NODE_STORAGE  
    DataReady_ptr p=resolveVectorWorker();  
 #else  
1721     DataReady_ptr p=resolveNodeWorker();     DataReady_ptr p=resolveNodeWorker();
 #endif  
1722     makeIdentity(p);     makeIdentity(p);
1723  }  }
1724    
# Line 2216  void DataLazy::makeIdentity(const DataRe Line 1734  void DataLazy::makeIdentity(const DataRe
1734     else if(p->isExpanded()) {m_readytype='E';}     else if(p->isExpanded()) {m_readytype='E';}
1735     else if (p->isTagged()) {m_readytype='T';}     else if (p->isTagged()) {m_readytype='T';}
1736     else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}     else {throw DataException("Unknown DataReady instance in convertToIdentity constructor.");}
    m_buffsRequired=1;  
1737     m_samplesize=p->getNumDPPSample()*p->getNoValues();     m_samplesize=p->getNumDPPSample()*p->getNoValues();
    m_maxsamplesize=m_samplesize;  
1738     m_left.reset();     m_left.reset();
1739     m_right.reset();     m_right.reset();
1740  }  }
# Line 2231  DataLazy::resolve() Line 1747  DataLazy::resolve()
1747      return m_id;      return m_id;
1748  }  }
1749    
 #ifdef LAZY_NODE_STORAGE  
1750    
1751  // This version of resolve uses storage in each node to hold results  /* This is really a static method but I think that caused problems in windows */
1752  DataReady_ptr  void
1753  DataLazy::resolveNodeWorker()  DataLazy::resolveGroupWorker(std::vector<DataLazy*>& dats)
1754  {  {
1755    if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally    if (dats.empty())
   {  
     collapse();  
   }  
   if (m_op==IDENTITY)       // So a lazy expression of Constant or Tagged data will be returned here.  
1756    {    {
1757      return m_id;          return;
1758    }    }
1759      // from this point on we must have m_op!=IDENTITY and m_readytype=='E'    vector<DataLazy*> work;
1760    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));    FunctionSpace fs=dats[0]->getFunctionSpace();
1761    ValueType& resvec=result->getVectorRW();    bool match=true;
1762    DataReady_ptr resptr=DataReady_ptr(result);    for (int i=dats.size()-1;i>=0;--i)
1763      {
1764    int sample;          if (dats[i]->m_readytype!='E')
1765    int totalsamples=getNumSamples();          {
1766    const ValueType* res=0;   // Storage for answer                  dats[i]->collapse();
1767  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)          }
1768    #pragma omp parallel for private(sample,res) schedule(static)          if (dats[i]->m_op!=IDENTITY)
1769    for (sample=0;sample<totalsamples;++sample)          {
1770    {                  work.push_back(dats[i]);
1771      size_t roffset=0;                  if (fs!=dats[i]->getFunctionSpace())
1772                    {
1773                            match=false;
1774                    }
1775            }
1776      }
1777      if (work.empty())
1778      {
1779            return;         // no work to do
1780      }
1781      if (match)    // all functionspaces match.  Yes I realise this is overly strict
1782      {             // it is possible that dats[0] is one of the objects which we discarded and
1783                    // all the other functionspaces match.
1784            vector<DataExpanded*> dep;
1785            vector<RealVectorType*> vecs;
1786            for (int i=0;i<work.size();++i)
1787            {
1788                    dep.push_back(new DataExpanded(fs,work[i]->getShape(), RealVectorType(work[i]->getNoValues())));
1789                    vecs.push_back(&(dep[i]->getVectorRW()));
1790            }
1791            int totalsamples=work[0]->getNumSamples();
1792            const RealVectorType* res=0; // Storage for answer
1793            int sample;
1794            #pragma omp parallel private(sample, res)
1795            {
1796                size_t roffset=0;
1797                #pragma omp for schedule(static)
1798                for (sample=0;sample<totalsamples;++sample)
1799                {
1800                    roffset=0;
1801                    int j;
1802                    for (j=work.size()-1;j>=0;--j)
1803                    {
1804  #ifdef _OPENMP  #ifdef _OPENMP
1805      res=resolveNodeSample(omp_get_thread_num(),sample,roffset);                      res=work[j]->resolveNodeSample(omp_get_thread_num(),sample,roffset);
1806  #else  #else
1807      res=resolveNodeSample(0,sample,roffset);                      res=work[j]->resolveNodeSample(0,sample,roffset);
1808  #endif  #endif
1809  LAZYDEBUG(cout << "Sample #" << sample << endl;)                      RealVectorType::size_type outoffset=dep[j]->getPointOffset(sample,0);
1810  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )                      memcpy(&((*vecs[j])[outoffset]),&((*res)[roffset]),work[j]->m_samplesize*sizeof(RealVectorType::ElementType));
1811      DataVector::size_type outoffset=result->getPointOffset(sample,0);                  }
1812      memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(DataVector::ElementType));              }
1813            }
1814            // Now we need to load the new results as identity ops into the lazy nodes
1815            for (int i=work.size()-1;i>=0;--i)
1816            {
1817                work[i]->makeIdentity(REFCOUNTNS::dynamic_pointer_cast<DataReady>(dep[i]->getPtr()));
1818            }
1819      }
1820      else  // functionspaces do not match
1821      {
1822            for (int i=0;i<work.size();++i)
1823            {
1824                    work[i]->resolveToIdentity();
1825            }
1826    }    }
   return resptr;  
1827  }  }
1828    
 #endif // LAZY_NODE_STORAGE  
1829    
1830  // To simplify the memory management, all threads operate on one large vector, rather than one each.  
1831  // Each sample is evaluated independently and copied into the result DataExpanded.  // This version of resolve uses storage in each node to hold results
1832  DataReady_ptr  DataReady_ptr
1833  DataLazy::resolveVectorWorker()  DataLazy::resolveNodeWorker()
1834  {  {
1835      if (m_readytype!='E')         // if the whole sub-expression is Constant or Tagged, then evaluate it normally
 LAZYDEBUG(cout << "Sample size=" << m_samplesize << endl;)  
 LAZYDEBUG(cout << "Buffers=" << m_buffsRequired << endl;)  
   if (m_readytype!='E')     // if the whole sub-expression is Constant or Tagged, then evaluate it normally  
1836    {    {
1837      collapse();      collapse();
1838    }    }
1839    if (m_op==IDENTITY)       // So a lazy expression of Constant or Tagged data will be returned here.    if (m_op==IDENTITY)           // So a lazy expression of Constant or Tagged data will be returned here.
1840    {    {
1841      return m_id;      return m_id;
1842    }    }
1843      // 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'
1844    size_t threadbuffersize=m_maxsamplesize*(max(1,m_buffsRequired)); // Each thread needs to have enough    DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  RealVectorType(getNoValues()));
1845      // storage to evaluate its expression    RealVectorType& resvec=result->getVectorRW();
   int numthreads=1;  
 #ifdef _OPENMP  
   numthreads=omp_get_max_threads();  
 #endif  
   ValueType v(numthreads*threadbuffersize);  
 LAZYDEBUG(cout << "Buffer created with size=" << v.size() << endl;)  
   DataExpanded* result=new DataExpanded(getFunctionSpace(),getShape(),  ValueType(getNoValues()));  
   ValueType& resvec=result->getVectorRW();  
1846    DataReady_ptr resptr=DataReady_ptr(result);    DataReady_ptr resptr=DataReady_ptr(result);
1847    
1848    int sample;    int sample;
   size_t outoffset;     // offset in the output data  
1849    int totalsamples=getNumSamples();    int totalsamples=getNumSamples();
1850    const ValueType* res=0;   // Vector storing the answer    const RealVectorType* res=0;       // Storage for answer
   size_t resoffset=0;       // where in the vector to find the answer  
1851  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)  LAZYDEBUG(cout << "Total number of samples=" <<totalsamples << endl;)
1852    #pragma omp parallel for private(sample,resoffset,outoffset,res) schedule(static)    #pragma omp parallel private(sample,res)
   for (sample=0;sample<totalsamples;++sample)  
1853    {    {
1854  LAZYDEBUG(cout << "################################# " << sample << endl;)          size_t roffset=0;
1855    #ifdef LAZY_STACK_PROF
1856            stackstart[omp_get_thread_num()]=&roffset;
1857            stackend[omp_get_thread_num()]=&roffset;
1858    #endif
1859            #pragma omp for schedule(static)
1860            for (sample=0;sample<totalsamples;++sample)
1861            {
1862                    roffset=0;
1863  #ifdef _OPENMP  #ifdef _OPENMP
1864      res=resolveSample(v,threadbuffersize*omp_get_thread_num(),sample,resoffset);                  res=resolveNodeSample(omp_get_thread_num(),sample,roffset);
1865  #else  #else
1866      res=resolveSample(v,0,sample,resoffset);   // res would normally be v, but not if its a single IDENTITY op.                  res=resolveNodeSample(0,sample,roffset);
1867  #endif  #endif
1868  LAZYDEBUG(cerr << "-------------------------------- " << endl;)  LAZYDEBUG(cout << "Sample #" << sample << endl;)
1869  LAZYDEBUG(cerr<< "Copying sample#" << sample << endl;)  LAZYDEBUG(cout << "Final res[" << roffset<< "]=" << (*res)[roffset] << (*res)[roffset]<< endl; )
1870      outoffset=result->getPointOffset(sample,0);                  RealVectorType::size_type outoffset=result->getPointOffset(sample,0);
1871  LAZYDEBUG(cerr << "offset=" << outoffset << " from offset=" << resoffset << " " << m_samplesize << " doubles" << endl;)                  memcpy(&(resvec[outoffset]),&((*res)[roffset]),m_samplesize*sizeof(RealVectorType::ElementType));
1872      for (unsigned int i=0;i<m_samplesize;++i,++outoffset,++resoffset)   // copy values into the output vector          }
1873      {    }
1874  LAZYDEBUG(cerr << "outoffset=" << outoffset << " resoffset=" << resoffset << " " << (*res)[resoffset]<< endl;)  #ifdef LAZY_STACK_PROF
1875      resvec[outoffset]=(*res)[resoffset];    for (int i=0;i<getNumberOfThreads();++i)
1876      }    {
1877  LAZYDEBUG(cerr << DataTypes::pointToString(resvec,getShape(),outoffset-m_samplesize+DataTypes::noValues(getShape()),"Final result:") << endl;)          size_t r=((size_t)stackstart[i] - (size_t)stackend[i]);
1878  LAZYDEBUG(cerr << "*********************************" << endl;)  //      cout << i << " " << stackstart[i] << " .. " << stackend[i] << " = " <<  r << endl;
1879            if (r>maxstackuse)
1880            {
1881                    maxstackuse=r;
1882            }
1883    }    }
1884      cout << "Max resolve Stack use=" << maxstackuse << endl;
1885    #endif
1886    return resptr;    return resptr;
1887  }  }
1888    
# Line 2335  std::string Line 1890  std::string
1890  DataLazy::toString() const  DataLazy::toString() const
1891  {  {
1892    ostringstream oss;    ostringstream oss;
1893    oss << "Lazy Data:";    oss << "Lazy Data: [depth=" << m_height<< "] ";
1894    intoString(oss);    switch (escriptParams.getLAZY_STR_FMT())
1895      {
1896      case 1:       // tree format
1897            oss << endl;
1898            intoTreeString(oss,"");
1899            break;
1900      case 2:       // just the depth
1901            break;
1902      default:
1903            intoString(oss);
1904            break;
1905      }
1906    return oss.str();    return oss.str();
1907  }  }
1908    
# Line 2348  DataLazy::intoString(ostringstream& oss) Line 1914  DataLazy::intoString(ostringstream& oss)
1914    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
1915    {    {
1916    case G_IDENTITY:    case G_IDENTITY:
1917      if (m_id->isExpanded())          if (m_id->isExpanded())
1918      {          {
1919         oss << "E";             oss << "E";
1920      }          }
1921      else if (m_id->isTagged())          else if (m_id->isTagged())
1922      {          {
1923        oss << "T";            oss << "T";
1924      }          }
1925      else if (m_id->isConstant())          else if (m_id->isConstant())
1926      {          {
1927        oss << "C";            oss << "C";
1928      }          }
1929      else          else
1930      {          {
1931        oss << "?";            oss << "?";
1932      }          }
1933      oss << '@' << m_id.get();          oss << '@' << m_id.get();
1934      break;          break;
1935    case G_BINARY:    case G_BINARY:
1936      oss << '(';          oss << '(';
1937      m_left->intoString(oss);          m_left->intoString(oss);
1938      oss << ' ' << opToString(m_op) << ' ';          oss << ' ' << opToString(m_op) << ' ';
1939      m_right->intoString(oss);          m_right->intoString(oss);
1940      oss << ')';          oss << ')';
1941      break;          break;
1942    case G_UNARY:    case G_UNARY:
1943    case G_UNARY_P:    case G_UNARY_P:
1944    case G_NP1OUT:    case G_NP1OUT:
1945    case G_NP1OUT_P:    case G_NP1OUT_P:
1946      oss << opToString(m_op) << '(';    case G_REDUCTION:
1947      m_left->intoString(oss);          oss << opToString(m_op) << '(';
1948      oss << ')';          m_left->intoString(oss);
1949      break;          oss << ')';
1950            break;
1951    case G_TENSORPROD:    case G_TENSORPROD:
1952      oss << opToString(m_op) << '(';          oss << opToString(m_op) << '(';
1953      m_left->intoString(oss);          m_left->intoString(oss);
1954      oss << ", ";          oss << ", ";
1955      m_right->intoString(oss);          m_right->intoString(oss);
1956      oss << ')';          oss << ')';
1957      break;          break;
1958    case G_NP1OUT_2P:    case G_NP1OUT_2P:
1959      oss << opToString(m_op) << '(';          oss << opToString(m_op) << '(';
1960      m_left->intoString(oss);          m_left->intoString(oss);
1961      oss << ", " << m_axis_offset << ", " << m_transpose;          oss << ", " << m_axis_offset << ", " << m_transpose;
1962      oss << ')';          oss << ')';
1963      break;          break;
1964      case G_CONDEVAL:
1965            oss << opToString(m_op)<< '(' ;
1966            m_mask->intoString(oss);
1967            oss << " ? ";
1968            m_left->intoString(oss);
1969            oss << " : ";
1970            m_right->intoString(oss);
1971            oss << ')';
1972            break;
1973    default:    default:
1974      oss << "UNKNOWN";          oss << "UNKNOWN";
1975    }    }
1976  }  }
1977    
1978    
1979    void
1980    DataLazy::intoTreeString(ostringstream& oss, string indent) const
1981    {
1982      oss << '[' << m_rank << ':' << setw(3) << m_samplesize << "] " << indent;
1983      switch (getOpgroup(m_op))
1984      {
1985      case G_IDENTITY:
1986            if (m_id->isExpanded())
1987            {
1988               oss << "E";
1989            }
1990            else if (m_id->isTagged())
1991            {
1992              oss << "T";
1993            }
1994            else if (m_id->isConstant())
1995            {
1996              oss << "C";
1997            }
1998            else
1999            {
2000              oss << "?";
2001            }
2002            oss << '@' << m_id.get() << endl;
2003            break;
2004      case G_BINARY:
2005            oss << opToString(m_op) << endl;
2006            indent+='.';
2007            m_left->intoTreeString(oss, indent);
2008            m_right->intoTreeString(oss, indent);
2009            break;
2010      case G_UNARY:
2011      case G_UNARY_P:
2012      case G_NP1OUT:
2013      case G_NP1OUT_P:
2014      case G_REDUCTION:
2015            oss << opToString(m_op) << endl;
2016            indent+='.';
2017            m_left->intoTreeString(oss, indent);
2018            break;
2019      case G_TENSORPROD:
2020            oss << opToString(m_op) << endl;
2021            indent+='.';
2022            m_left->intoTreeString(oss, indent);
2023            m_right->intoTreeString(oss, indent);
2024            break;
2025      case G_NP1OUT_2P:
2026            oss << opToString(m_op) << ", " << m_axis_offset << ", " << m_transpose<< endl;
2027            indent+='.';
2028            m_left->intoTreeString(oss, indent);
2029            break;
2030      default:
2031            oss << "UNKNOWN";
2032      }
2033    }
2034    
2035    
2036  DataAbstract*  DataAbstract*
2037  DataLazy::deepCopy()  DataLazy::deepCopy() const
2038  {  {
2039    switch (getOpgroup(m_op))    switch (getOpgroup(m_op))
2040    {    {
2041    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());    case G_IDENTITY:  return new DataLazy(m_id->deepCopy()->getPtr());
2042    case G_UNARY: return new DataLazy(m_left->deepCopy()->getPtr(),m_op);    case G_UNARY:
2043    case G_BINARY:    return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);    case G_REDUCTION:      return new DataLazy(m_left->deepCopy()->getPtr(),m_op);
2044      case G_UNARY_P:       return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_tol);
2045      case G_BINARY:        return new DataLazy(m_left->deepCopy()->getPtr(),m_right->deepCopy()->getPtr(),m_op);
2046    case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);    case G_NP1OUT: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(),m_op);
2047    case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);    case G_TENSORPROD: return new DataLazy(m_left->deepCopy()->getPtr(), m_right->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2048      case G_NP1OUT_P:   return new DataLazy(m_left->deepCopy()->getPtr(),m_op,  m_axis_offset);
2049      case G_NP1OUT_2P:  return new DataLazy(m_left->deepCopy()->getPtr(), m_op, m_axis_offset, m_transpose);
2050    default:    default:
2051      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)+".");
2052    }    }
2053  }  }
2054    
2055    
2056    
2057  // There is no single, natural interpretation of getLength on DataLazy.  // There is no single, natural interpretation of getLength on DataLazy.
2058  // Instances of DataReady can look at the size of their vectors.  // Instances of DataReady can look at the size of their vectors.
2059  // For lazy though, it could be the size the data would be if it were resolved;  // For lazy though, it could be the size the data would be if it were resolved;
2060  // or it could be some function of the lengths of the DataReady instances which  // or it could be some function of the lengths of the DataReady instances which
2061  // form part of the expression.  // form part of the expression.
2062  // Rather than have people making assumptions, I have disabled the method.  // Rather than have people making assumptions, I have disabled the method.
2063  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2064  DataLazy::getLength() const  DataLazy::getLength() const
2065  {  {
2066    throw DataException("getLength() does not make sense for lazy data.");    throw DataException("getLength() does not make sense for lazy data.");
# Line 2436  DataLazy::getSlice(const DataTypes::Regi Line 2075  DataLazy::getSlice(const DataTypes::Regi
2075    
2076    
2077  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2078  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2079  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2080                   int dataPointNo)                   int dataPointNo)
2081  {  {
2082    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2083    {    {
2084      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2085    }    }
2086    if (m_readytype!='E')    if (m_readytype!='E')
2087    {    {
2088      collapse();          collapse();
2089      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2090    }    }
2091    // at this point we do not have an identity node and the expression will be Expanded    // at this point we do not have an identity node and the expression will be Expanded
2092    // so we only need to know which child to ask    // so we only need to know which child to ask
2093    if (m_left->m_readytype=='E')    if (m_left->m_readytype=='E')
2094    {    {
2095      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2096    }    }
2097    else    else
2098    {    {
2099      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2100    }    }
2101  }  }
2102    
2103  // To do this we need to rely on our child nodes  // To do this we need to rely on our child nodes
2104  DataTypes::ValueType::size_type  DataTypes::RealVectorType::size_type
2105  DataLazy::getPointOffset(int sampleNo,  DataLazy::getPointOffset(int sampleNo,
2106                   int dataPointNo) const                   int dataPointNo) const
2107  {  {
2108    if (m_op==IDENTITY)    if (m_op==IDENTITY)
2109    {    {
2110      return m_id->getPointOffset(sampleNo,dataPointNo);          return m_id->getPointOffset(sampleNo,dataPointNo);
2111    }    }
2112    if (m_readytype=='E')    if (m_readytype=='E')
2113    {    {
# Line 2476  DataLazy::getPointOffset(int sampleNo, Line 2115  DataLazy::getPointOffset(int sampleNo,
2115      // so we only need to know which child to ask      // so we only need to know which child to ask
2116      if (m_left->m_readytype=='E')      if (m_left->m_readytype=='E')
2117      {      {
2118      return m_left->getPointOffset(sampleNo,dataPointNo);          return m_left->getPointOffset(sampleNo,dataPointNo);
2119      }      }
2120      else      else
2121      {      {
2122      return m_right->getPointOffset(sampleNo,dataPointNo);          return m_right->getPointOffset(sampleNo,dataPointNo);
2123      }      }
2124    }    }
2125    if (m_readytype=='C')    if (m_readytype=='C')
2126    {    {
2127      return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter          return m_left->getPointOffset(sampleNo,dataPointNo); // which child doesn't matter
2128    }    }
2129    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).");
2130  }  }
# Line 2495  DataLazy::getPointOffset(int sampleNo, Line 2134  DataLazy::getPointOffset(int sampleNo,
2134  void  void
2135  DataLazy::setToZero()  DataLazy::setToZero()
2136  {  {
2137  //   DataTypes::ValueType v(getNoValues(),0);  //   DataTypes::RealVectorType v(getNoValues(),0);
2138  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));  //   m_id=DataReady_ptr(new DataConstant(getFunctionSpace(),getShape(),v));
2139  //   m_op=IDENTITY;  //   m_op=IDENTITY;
2140  //   m_right.reset();    //   m_right.reset();  
# Line 2503  DataLazy::setToZero() Line 2142  DataLazy::setToZero()
2142  //   m_readytype='C';  //   m_readytype='C';
2143  //   m_buffsRequired=1;  //   m_buffsRequired=1;
2144    
2145    privdebug=privdebug;  // to stop the compiler complaining about unused privdebug    (void)privdebug;  // to stop the compiler complaining about unused privdebug
2146    throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");    throw DataException("Programmer error - setToZero not supported for DataLazy (DataLazy objects should be read only).");
2147  }  }
2148    
2149  bool  bool
2150  DataLazy::actsExpanded() const  DataLazy::actsExpanded() const
2151  {  {
2152      return (m_readytype=='E');          return (m_readytype=='E');
2153  }  }
2154    
2155  }   // end namespace  } // end namespace
2156    

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