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Contents of /trunk/escript/src/Data.cpp

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Revision 1859 - (show annotations)
Wed Oct 8 03:03:37 2008 UTC (10 years, 11 months ago) by gross
File size: 75845 byte(s)
first version of testing for transport solver.
1
2 /*******************************************************
3 *
4 * Copyright (c) 2003-2008 by University of Queensland
5 * Earth Systems Science Computational Center (ESSCC)
6 * http://www.uq.edu.au/esscc
7 *
8 * Primary Business: Queensland, Australia
9 * Licensed under the Open Software License version 3.0
10 * http://www.opensource.org/licenses/osl-3.0.php
11 *
12 *******************************************************/
13
14
15 #include "Data.h"
16
17 #include "DataExpanded.h"
18 #include "DataConstant.h"
19 #include "DataTagged.h"
20 #include "DataEmpty.h"
21 #include "FunctionSpaceFactory.h"
22 #include "AbstractContinuousDomain.h"
23 #include "UnaryFuncs.h"
24 #include "FunctionSpaceException.h"
25
26 extern "C" {
27 #include "escript/blocktimer.h"
28 }
29
30 #include <fstream>
31 #include <algorithm>
32 #include <vector>
33 #include <functional>
34
35 #include <boost/python/dict.hpp>
36 #include <boost/python/extract.hpp>
37 #include <boost/python/long.hpp>
38
39 using namespace std;
40 using namespace boost::python;
41 using namespace boost;
42 using namespace escript;
43
44 Data::Data()
45 {
46 //
47 // Default data is type DataEmpty
48 DataAbstract* temp=new DataEmpty();
49 shared_ptr<DataAbstract> temp_data(temp);
50 m_data=temp_data;
51 m_protected=false;
52 }
53
54 Data::Data(double value,
55 const tuple& shape,
56 const FunctionSpace& what,
57 bool expanded)
58 {
59 DataTypes::ShapeType dataPointShape;
60 for (int i = 0; i < shape.attr("__len__")(); ++i) {
61 dataPointShape.push_back(extract<const int>(shape[i]));
62 }
63
64 int len = DataTypes::noValues(dataPointShape);
65 DataVector temp_data(len,value,len);
66 // DataArrayView temp_dataView(temp_data, dataPointShape);
67
68 // initialise(temp_dataView, what, expanded);
69 initialise(temp_data, dataPointShape, what, expanded);
70
71 m_protected=false;
72 }
73
74 Data::Data(double value,
75 const DataTypes::ShapeType& dataPointShape,
76 const FunctionSpace& what,
77 bool expanded)
78 {
79 int len = DataTypes::noValues(dataPointShape);
80
81 DataVector temp_data(len,value,len);
82 // DataArrayView temp_dataView(temp_data, dataPointShape);
83
84 // initialise(temp_dataView, what, expanded);
85 initialise(temp_data, dataPointShape, what, expanded);
86
87 m_protected=false;
88 }
89
90 Data::Data(const Data& inData)
91 {
92 m_data=inData.m_data;
93 m_protected=inData.isProtected();
94 }
95
96
97 Data::Data(const Data& inData,
98 const DataTypes::RegionType& region)
99 {
100 //
101 // Create Data which is a slice of another Data
102 DataAbstract* tmp = inData.m_data->getSlice(region);
103 shared_ptr<DataAbstract> temp_data(tmp);
104 m_data=temp_data;
105 m_protected=false;
106 }
107
108 Data::Data(const Data& inData,
109 const FunctionSpace& functionspace)
110 {
111 if (inData.isEmpty())
112 {
113 throw DataException("Error - will not interpolate for instances of DataEmpty.");
114 }
115 if (inData.getFunctionSpace()==functionspace) {
116 m_data=inData.m_data;
117 } else if (inData.isConstant()) { // for a constant function, we just need to use the new function space
118 if (!inData.probeInterpolation(functionspace))
119 { // Even though this is constant, we still need to check whether interpolation is allowed
120 throw FunctionSpaceException("Call to probeInterpolation returned false for DataConstant.");
121 }
122 DataConstant* dc=new DataConstant(functionspace,inData.m_data->getShape(),inData.m_data->getVector());
123 m_data=shared_ptr<DataAbstract>(dc);
124 } else {
125 Data tmp(0,inData.getDataPointShape(),functionspace,true);
126 // Note: Must use a reference or pointer to a derived object
127 // in order to get polymorphic behaviour. Shouldn't really
128 // be able to create an instance of AbstractDomain but that was done
129 // as a boost:python work around which may no longer be required.
130 const AbstractDomain& inDataDomain=inData.getDomain();
131 if (inDataDomain==functionspace.getDomain()) {
132 inDataDomain.interpolateOnDomain(tmp,inData);
133 } else {
134 inDataDomain.interpolateACross(tmp,inData);
135 }
136 m_data=tmp.m_data;
137 }
138 m_protected=false;
139 }
140
141 // Data::Data(const DataTagged::TagListType& tagKeys,
142 // const DataTagged::ValueListType & values,
143 // const DataArrayView& defaultValue,
144 // const FunctionSpace& what,
145 // bool expanded)
146 // {
147 // DataAbstract* temp=new DataTagged(tagKeys,values,defaultValue,what);
148 // shared_ptr<DataAbstract> temp_data(temp);
149 // m_data=temp_data;
150 // m_protected=false;
151 // if (expanded) {
152 // expand();
153 // }
154 // }
155
156
157
158 Data::Data(DataAbstract* underlyingdata)
159 {
160 m_data=shared_ptr<DataAbstract>(underlyingdata);
161 m_protected=false;
162 }
163
164 Data::Data(const numeric::array& value,
165 const FunctionSpace& what,
166 bool expanded)
167 {
168 initialise(value,what,expanded);
169 m_protected=false;
170 }
171 /*
172 Data::Data(const DataArrayView& value,
173 const FunctionSpace& what,
174 bool expanded)
175 {
176 initialise(value,what,expanded);
177 m_protected=false;
178 }*/
179
180 Data::Data(const DataTypes::ValueType& value,
181 const DataTypes::ShapeType& shape,
182 const FunctionSpace& what,
183 bool expanded)
184 {
185 initialise(value,shape,what,expanded);
186 m_protected=false;
187 }
188
189
190 Data::Data(const object& value,
191 const FunctionSpace& what,
192 bool expanded)
193 {
194 numeric::array asNumArray(value);
195 initialise(asNumArray,what,expanded);
196 m_protected=false;
197 }
198
199
200 Data::Data(const object& value,
201 const Data& other)
202 {
203 numeric::array asNumArray(value);
204
205 // extract the shape of the numarray
206 DataTypes::ShapeType tempShape=DataTypes::shapeFromNumArray(asNumArray);
207 // /* for (int i=0; i < asNumArray.getrank(); i++) {
208 // tempShape.push_back(extract<int>(asNumArray.getshape()[i]));
209 // }*/
210 // // get the space for the data vector
211 // int len = DataTypes::noValues(tempShape);
212 // DataVector temp_data(len, 0.0, len);
213 // /* DataArrayView temp_dataView(temp_data, tempShape);
214 // temp_dataView.copy(asNumArray);*/
215 // temp_data.copyFromNumArray(asNumArray);
216
217 //
218 // Create DataConstant using the given value and all other parameters
219 // copied from other. If value is a rank 0 object this Data
220 // will assume the point data shape of other.
221
222 if (DataTypes::getRank(tempShape)/*temp_dataView.getRank()*/==0) {
223
224
225 // get the space for the data vector
226 int len1 = DataTypes::noValues(tempShape);
227 DataVector temp_data(len1, 0.0, len1);
228 temp_data.copyFromNumArray(asNumArray);
229
230 int len = DataTypes::noValues(other.getDataPointShape());
231
232 DataVector temp2_data(len, temp_data[0]/*temp_dataView()*/, len);
233 //DataArrayView temp2_dataView(temp2_data, other.getPointDataView().getShape());
234 // initialise(temp2_dataView, other.getFunctionSpace(), false);
235
236 DataConstant* t=new DataConstant(other.getFunctionSpace(),other.getDataPointShape(),temp2_data);
237 boost::shared_ptr<DataAbstract> sp(t);
238 m_data=sp;
239
240
241 } else {
242 //
243 // Create a DataConstant with the same sample shape as other
244 // initialise(temp_dataView, other.getFunctionSpace(), false);
245 DataConstant* t=new DataConstant(asNumArray,other.getFunctionSpace());
246 boost::shared_ptr<DataAbstract> sp(t);
247 m_data=sp;
248 }
249 m_protected=false;
250 }
251
252 Data::~Data()
253 {
254
255 }
256
257
258
259 void
260 Data::initialise(const boost::python::numeric::array& value,
261 const FunctionSpace& what,
262 bool expanded)
263 {
264 //
265 // Construct a Data object of the appropriate type.
266 // Construct the object first as there seems to be a bug which causes
267 // undefined behaviour if an exception is thrown during construction
268 // within the shared_ptr constructor.
269 if (expanded) {
270 DataAbstract* temp=new DataExpanded(value, what);
271 boost::shared_ptr<DataAbstract> temp_data(temp);
272 m_data=temp_data;
273 } else {
274 DataAbstract* temp=new DataConstant(value, what);
275 boost::shared_ptr<DataAbstract> temp_data(temp);
276 m_data=temp_data;
277 }
278 }
279
280
281 void
282 Data::initialise(const DataTypes::ValueType& value,
283 const DataTypes::ShapeType& shape,
284 const FunctionSpace& what,
285 bool expanded)
286 {
287 //
288 // Construct a Data object of the appropriate type.
289 // Construct the object first as there seems to be a bug which causes
290 // undefined behaviour if an exception is thrown during construction
291 // within the shared_ptr constructor.
292 if (expanded) {
293 DataAbstract* temp=new DataExpanded(what, shape, value);
294 boost::shared_ptr<DataAbstract> temp_data(temp);
295 m_data=temp_data;
296 } else {
297 DataAbstract* temp=new DataConstant(what, shape, value);
298 boost::shared_ptr<DataAbstract> temp_data(temp);
299 m_data=temp_data;
300 }
301 }
302
303
304 // void
305 // Data::CompareDebug(const Data& rd)
306 // {
307 // using namespace std;
308 // bool mismatch=false;
309 // std::cout << "Comparing left and right" << endl;
310 // const DataTagged* left=dynamic_cast<DataTagged*>(m_data.get());
311 // const DataTagged* right=dynamic_cast<DataTagged*>(rd.m_data.get());
312 //
313 // if (left==0)
314 // {
315 // cout << "left arg is not a DataTagged\n";
316 // return;
317 // }
318 //
319 // if (right==0)
320 // {
321 // cout << "right arg is not a DataTagged\n";
322 // return;
323 // }
324 // cout << "Num elements=" << left->getVector().size() << ":" << right->getVector().size() << std::endl;
325 // cout << "Shapes ";
326 // if (left->getShape()==right->getShape())
327 // {
328 // cout << "ok\n";
329 // }
330 // else
331 // {
332 // cout << "Problem: shapes do not match\n";
333 // mismatch=true;
334 // }
335 // int lim=left->getVector().size();
336 // if (right->getVector().size()) lim=right->getVector().size();
337 // for (int i=0;i<lim;++i)
338 // {
339 // if (left->getVector()[i]!=right->getVector()[i])
340 // {
341 // cout << "[" << i << "] value mismatch " << left->getVector()[i] << ":" << right->getVector()[i] << endl;
342 // mismatch=true;
343 // }
344 // }
345 //
346 // // still need to check the tag map
347 // // also need to watch what is happening to function spaces, are they copied or what?
348 //
349 // const DataTagged::DataMapType& mapleft=left->getTagLookup();
350 // const DataTagged::DataMapType& mapright=right->getTagLookup();
351 //
352 // if (mapleft.size()!=mapright.size())
353 // {
354 // cout << "Maps are different sizes " << mapleft.size() << ":" << mapright.size() << endl;
355 // mismatch=true;
356 // cout << "Left map\n";
357 // DataTagged::DataMapType::const_iterator i,j;
358 // for (i=mapleft.begin();i!=mapleft.end();++i) {
359 // cout << "(" << i->first << "=>" << i->second << ")\n";
360 // }
361 // cout << "Right map\n";
362 // for (i=mapright.begin();i!=mapright.end();++i) {
363 // cout << "(" << i->first << "=>" << i->second << ")\n";
364 // }
365 // cout << "End map\n";
366 //
367 // }
368 //
369 // DataTagged::DataMapType::const_iterator i,j;
370 // for (i=mapleft.begin(),j=mapright.begin();i!=mapleft.end() && j!=mapright.end();++i,++j) {
371 // if ((i->first!=j->first) || (i->second!=j->second))
372 // {
373 // cout << "(" << i->first << "=>" << i->second << ")";
374 // cout << ":(" << j->first << "=>" << j->second << ") ";
375 // mismatch=true;
376 // }
377 // }
378 // if (mismatch)
379 // {
380 // cout << "#Mismatch\n";
381 // }
382 // }
383
384 escriptDataC
385 Data::getDataC()
386 {
387 escriptDataC temp;
388 temp.m_dataPtr=(void*)this;
389 return temp;
390 }
391
392 escriptDataC
393 Data::getDataC() const
394 {
395 escriptDataC temp;
396 temp.m_dataPtr=(void*)this;
397 return temp;
398 }
399
400 const boost::python::tuple
401 Data::getShapeTuple() const
402 {
403 const DataTypes::ShapeType& shape=getDataPointShape();
404 switch(getDataPointRank()) {
405 case 0:
406 return make_tuple();
407 case 1:
408 return make_tuple(long_(shape[0]));
409 case 2:
410 return make_tuple(long_(shape[0]),long_(shape[1]));
411 case 3:
412 return make_tuple(long_(shape[0]),long_(shape[1]),long_(shape[2]));
413 case 4:
414 return make_tuple(long_(shape[0]),long_(shape[1]),long_(shape[2]),long_(shape[3]));
415 default:
416 throw DataException("Error - illegal Data rank.");
417 }
418 }
419
420
421 // The different name is needed because boost has trouble with overloaded functions.
422 // It can't work out what type the function is based soley on its name.
423 // There are ways to fix this involving creating function pointer variables for each form
424 // but there doesn't seem to be a need given that the methods have the same name from the python point of view
425 Data*
426 Data::copySelf()
427 {
428 DataAbstract* temp=m_data->deepCopy();
429 return new Data(temp);
430 }
431
432 void
433 Data::copy(const Data& other)
434 {
435 DataAbstract* temp=other.m_data->deepCopy();
436 shared_ptr<DataAbstract> temp_data(temp);
437 m_data=temp_data;
438 }
439
440
441 void
442 Data::setToZero()
443 {
444 if (isEmpty())
445 {
446 throw DataException("Error - Operations not permitted on instances of DataEmpty.");
447 }
448 {
449 DataExpanded* temp=dynamic_cast<DataExpanded*>(m_data.get());
450 if (temp!=0) {
451 temp->setToZero();
452 return;
453 }
454 }
455 {
456 DataTagged* temp=dynamic_cast<DataTagged*>(m_data.get());
457 if (temp!=0) {
458 temp->setToZero();
459 return;
460 }
461 }
462 {
463 DataConstant* temp=dynamic_cast<DataConstant*>(m_data.get());
464 if (temp!=0) {
465 temp->setToZero();
466 return;
467 }
468 }
469 throw DataException("Error - Data can not be set to zero.");
470 }
471
472 // void
473 // Data::copyWithMask(const Data& other,
474 // const Data& mask)
475 // {
476 // if (other.isEmpty() || mask.isEmpty())
477 // {
478 // throw DataException("Error - copyWithMask not permitted using instances of DataEmpty.");
479 // }
480 // Data mask1;
481 // Data mask2;
482 // mask1 = mask.wherePositive();
483 //
484 // mask2.copy(mask1);
485 // mask1 *= other;
486 //
487 // mask2 *= *this;
488 // mask2 = *this - mask2;
489 // *this = mask1 + mask2;
490 // }
491
492 void
493 Data::copyWithMask(const Data& other,
494 const Data& mask)
495 {
496 // 1. Interpolate if required so all Datas use the same FS as this
497 // 2. Tag or Expand so that all Data's are the same type
498 // 3. Iterate over the data vectors copying values where mask is >0
499 if (other.isEmpty() || mask.isEmpty())
500 {
501 throw DataException("Error - copyWithMask not permitted using instances of DataEmpty.");
502 }
503 Data other2(other);
504 Data mask2(mask);
505 FunctionSpace myFS=getFunctionSpace();
506 FunctionSpace oFS=other2.getFunctionSpace();
507 FunctionSpace mFS=mask2.getFunctionSpace();
508 if (oFS!=myFS)
509 {
510 if (other2.probeInterpolation(myFS))
511 {
512 other2=other2.interpolate(myFS);
513 }
514 else
515 {
516 throw DataException("Error - copyWithMask: other FunctionSpace is not compatible with this one.");
517 }
518 }
519 if (mFS!=myFS)
520 {
521 if (mask2.probeInterpolation(myFS))
522 {
523 mask2=mask2.interpolate(myFS);
524 }
525 else
526 {
527 throw DataException("Error - copyWithMask: mask FunctionSpace is not compatible with this one.");
528 }
529 }
530 // Ensure that all args have the same type
531 if (this->isExpanded() || mask2.isExpanded() || other2.isExpanded())
532 {
533 this->expand();
534 other2.expand();
535 mask2.expand();
536 }
537 else if (this->isTagged() || mask2.isTagged() || other2.isTagged())
538 {
539 this->tag();
540 other2.tag();
541 mask2.tag();
542 }
543 else if (this->isConstant() && mask2.isConstant() && other2.isConstant())
544 {
545 }
546 else
547 {
548 throw DataException("Error - Unknown DataAbstract passed to copyWithMask.");
549 }
550 // Now we iterate over the elements
551 DataVector& self=m_data->getVector();
552 const DataVector& ovec=other2.m_data->getVector();
553 const DataVector& mvec=mask2.m_data->getVector();
554 if ((self.size()!=ovec.size()) || (self.size()!=mvec.size()))
555 {
556 throw DataException("Error - size mismatch in arguments to copyWithMask.");
557 }
558 size_t num_points=self.size();
559 long i;
560 #pragma omp parallel for private(i) schedule(static)
561 for (i=0;i<num_points;++i)
562 {
563 if (mvec[i]>0)
564 {
565 self[i]=ovec[i];
566 }
567 }
568 }
569
570
571
572 bool
573 Data::isExpanded() const
574 {
575 DataExpanded* temp=dynamic_cast<DataExpanded*>(m_data.get());
576 return (temp!=0);
577 }
578
579 bool
580 Data::isTagged() const
581 {
582 DataTagged* temp=dynamic_cast<DataTagged*>(m_data.get());
583 return (temp!=0);
584 }
585
586 bool
587 Data::isEmpty() const
588 {
589 DataEmpty* temp=dynamic_cast<DataEmpty*>(m_data.get());
590 return (temp!=0);
591 }
592
593 bool
594 Data::isConstant() const
595 {
596 DataConstant* temp=dynamic_cast<DataConstant*>(m_data.get());
597 return (temp!=0);
598 }
599
600 void
601 Data::setProtection()
602 {
603 m_protected=true;
604 }
605
606 bool
607 Data::isProtected() const
608 {
609 return m_protected;
610 }
611
612
613
614 void
615 Data::expand()
616 {
617 if (isConstant()) {
618 DataConstant* tempDataConst=dynamic_cast<DataConstant*>(m_data.get());
619 DataAbstract* temp=new DataExpanded(*tempDataConst);
620 shared_ptr<DataAbstract> temp_data(temp);
621 m_data=temp_data;
622 } else if (isTagged()) {
623 DataTagged* tempDataTag=dynamic_cast<DataTagged*>(m_data.get());
624 DataAbstract* temp=new DataExpanded(*tempDataTag);
625 shared_ptr<DataAbstract> temp_data(temp);
626 m_data=temp_data;
627 } else if (isExpanded()) {
628 //
629 // do nothing
630 } else if (isEmpty()) {
631 throw DataException("Error - Expansion of DataEmpty not possible.");
632 } else {
633 throw DataException("Error - Expansion not implemented for this Data type.");
634 }
635 }
636
637 void
638 Data::tag()
639 {
640 if (isConstant()) {
641 DataConstant* tempDataConst=dynamic_cast<DataConstant*>(m_data.get());
642 DataAbstract* temp=new DataTagged(*tempDataConst);
643 shared_ptr<DataAbstract> temp_data(temp);
644 m_data=temp_data;
645 } else if (isTagged()) {
646 // do nothing
647 } else if (isExpanded()) {
648 throw DataException("Error - Creating tag data from DataExpanded not possible.");
649 } else if (isEmpty()) {
650 throw DataException("Error - Creating tag data from DataEmpty not possible.");
651 } else {
652 throw DataException("Error - Tagging not implemented for this Data type.");
653 }
654 }
655
656 Data
657 Data::oneOver() const
658 {
659 return C_TensorUnaryOperation(*this, bind1st(divides<double>(),1.));
660 }
661
662 Data
663 Data::wherePositive() const
664 {
665 return C_TensorUnaryOperation(*this, bind2nd(greater<double>(),0.0));
666 }
667
668 Data
669 Data::whereNegative() const
670 {
671 return C_TensorUnaryOperation(*this, bind2nd(less<double>(),0.0));
672 }
673
674 Data
675 Data::whereNonNegative() const
676 {
677 return C_TensorUnaryOperation(*this, bind2nd(greater_equal<double>(),0.0));
678 }
679
680 Data
681 Data::whereNonPositive() const
682 {
683 return C_TensorUnaryOperation(*this, bind2nd(less_equal<double>(),0.0));
684 }
685
686 Data
687 Data::whereZero(double tol) const
688 {
689 Data dataAbs=abs();
690 return C_TensorUnaryOperation(dataAbs, bind2nd(less_equal<double>(),tol));
691 }
692
693 Data
694 Data::whereNonZero(double tol) const
695 {
696 Data dataAbs=abs();
697 return C_TensorUnaryOperation(dataAbs, bind2nd(greater<double>(),tol));
698 }
699
700 Data
701 Data::interpolate(const FunctionSpace& functionspace) const
702 {
703 return Data(*this,functionspace);
704 }
705
706 bool
707 Data::probeInterpolation(const FunctionSpace& functionspace) const
708 {
709 if (getFunctionSpace()==functionspace) {
710 return true;
711 } else {
712 const AbstractDomain& domain=getDomain();
713 if (domain==functionspace.getDomain()) {
714 return domain.probeInterpolationOnDomain(getFunctionSpace().getTypeCode(),functionspace.getTypeCode());
715 } else {
716 return domain.probeInterpolationACross(getFunctionSpace().getTypeCode(),functionspace.getDomain(),functionspace.getTypeCode());
717 }
718 }
719 }
720
721 Data
722 Data::gradOn(const FunctionSpace& functionspace) const
723 {
724 if (isEmpty())
725 {
726 throw DataException("Error - operation not permitted on instances of DataEmpty.");
727 }
728 double blocktimer_start = blocktimer_time();
729 if (functionspace.getDomain()!=getDomain())
730 throw DataException("Error - gradient cannot be calculated on different domains.");
731 DataTypes::ShapeType grad_shape=getDataPointShape();
732 grad_shape.push_back(functionspace.getDim());
733 Data out(0.0,grad_shape,functionspace,true);
734 getDomain().setToGradient(out,*this);
735 blocktimer_increment("grad()", blocktimer_start);
736 return out;
737 }
738
739 Data
740 Data::grad() const
741 {
742 if (isEmpty())
743 {
744 throw DataException("Error - operation not permitted on instances of DataEmpty.");
745 }
746 return gradOn(escript::function(getDomain()));
747 }
748
749 int
750 Data::getDataPointSize() const
751 {
752 return m_data->getNoValues();
753 }
754
755 DataTypes::ValueType::size_type
756 Data::getLength() const
757 {
758 return m_data->getLength();
759 }
760
761 const
762 boost::python::numeric::array
763 Data:: getValueOfDataPoint(int dataPointNo)
764 {
765 size_t length=0;
766 int i, j, k, l;
767 //
768 // determine the rank and shape of each data point
769 int dataPointRank = getDataPointRank();
770 const DataTypes::ShapeType& dataPointShape = getDataPointShape();
771
772 //
773 // create the numeric array to be returned
774 boost::python::numeric::array numArray(0.0);
775
776 //
777 // the shape of the returned numeric array will be the same
778 // as that of the data point
779 int arrayRank = dataPointRank;
780 const DataTypes::ShapeType& arrayShape = dataPointShape;
781
782 //
783 // resize the numeric array to the shape just calculated
784 if (arrayRank==0) {
785 numArray.resize(1);
786 }
787 if (arrayRank==1) {
788 numArray.resize(arrayShape[0]);
789 }
790 if (arrayRank==2) {
791 numArray.resize(arrayShape[0],arrayShape[1]);
792 }
793 if (arrayRank==3) {
794 numArray.resize(arrayShape[0],arrayShape[1],arrayShape[2]);
795 }
796 if (arrayRank==4) {
797 numArray.resize(arrayShape[0],arrayShape[1],arrayShape[2],arrayShape[3]);
798 }
799
800 if (getNumDataPointsPerSample()>0) {
801 int sampleNo = dataPointNo/getNumDataPointsPerSample();
802 int dataPointNoInSample = dataPointNo - sampleNo * getNumDataPointsPerSample();
803 //
804 // Check a valid sample number has been supplied
805 if ((sampleNo >= getNumSamples()) || (sampleNo < 0 )) {
806 throw DataException("Error - Data::convertToNumArray: invalid sampleNo.");
807 }
808
809 //
810 // Check a valid data point number has been supplied
811 if ((dataPointNoInSample >= getNumDataPointsPerSample()) || (dataPointNoInSample < 0)) {
812 throw DataException("Error - Data::convertToNumArray: invalid dataPointNoInSample.");
813 }
814 // TODO: global error handling
815 // create a view of the data if it is stored locally
816 // DataArrayView dataPointView = getDataPoint(sampleNo, dataPointNoInSample);
817 DataTypes::ValueType::size_type offset=getDataOffset(sampleNo, dataPointNoInSample);
818
819
820 switch( dataPointRank ){
821 case 0 :
822 numArray[0] = getDataAtOffset(offset);
823 break;
824 case 1 :
825 for( i=0; i<dataPointShape[0]; i++ )
826 numArray[i]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i));
827 break;
828 case 2 :
829 for( i=0; i<dataPointShape[0]; i++ )
830 for( j=0; j<dataPointShape[1]; j++)
831 numArray[make_tuple(i,j)]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j));
832 break;
833 case 3 :
834 for( i=0; i<dataPointShape[0]; i++ )
835 for( j=0; j<dataPointShape[1]; j++ )
836 for( k=0; k<dataPointShape[2]; k++)
837 numArray[make_tuple(i,j,k)]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j,k));
838 break;
839 case 4 :
840 for( i=0; i<dataPointShape[0]; i++ )
841 for( j=0; j<dataPointShape[1]; j++ )
842 for( k=0; k<dataPointShape[2]; k++ )
843 for( l=0; l<dataPointShape[3]; l++)
844 numArray[make_tuple(i,j,k,l)]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j,k,l));
845 break;
846 }
847 }
848 //
849 // return the array
850 return numArray;
851
852 }
853
854 void
855 Data::setValueOfDataPointToPyObject(int dataPointNo, const boost::python::object& py_object)
856 {
857 // this will throw if the value cannot be represented
858 boost::python::numeric::array num_array(py_object);
859 setValueOfDataPointToArray(dataPointNo,num_array);
860 }
861
862 void
863 Data::setValueOfDataPointToArray(int dataPointNo, const boost::python::numeric::array& num_array)
864 {
865 if (isProtected()) {
866 throw DataException("Error - attempt to update protected Data object.");
867 }
868 //
869 // check rank
870 if (num_array.getrank()<getDataPointRank())
871 throw DataException("Rank of numarray does not match Data object rank");
872
873 //
874 // check shape of num_array
875 for (int i=0; i<getDataPointRank(); i++) {
876 if (extract<int>(num_array.getshape()[i])!=getDataPointShape()[i])
877 throw DataException("Shape of numarray does not match Data object rank");
878 }
879 //
880 // make sure data is expanded:
881 //
882 if (!isExpanded()) {
883 expand();
884 }
885 if (getNumDataPointsPerSample()>0) {
886 int sampleNo = dataPointNo/getNumDataPointsPerSample();
887 int dataPointNoInSample = dataPointNo - sampleNo * getNumDataPointsPerSample();
888 m_data->copyToDataPoint(sampleNo, dataPointNoInSample,num_array);
889 } else {
890 m_data->copyToDataPoint(-1, 0,num_array);
891 }
892 }
893
894 void
895 Data::setValueOfDataPoint(int dataPointNo, const double value)
896 {
897 if (isProtected()) {
898 throw DataException("Error - attempt to update protected Data object.");
899 }
900 //
901 // make sure data is expanded:
902 if (!isExpanded()) {
903 expand();
904 }
905 if (getNumDataPointsPerSample()>0) {
906 int sampleNo = dataPointNo/getNumDataPointsPerSample();
907 int dataPointNoInSample = dataPointNo - sampleNo * getNumDataPointsPerSample();
908 m_data->copyToDataPoint(sampleNo, dataPointNoInSample,value);
909 } else {
910 m_data->copyToDataPoint(-1, 0,value);
911 }
912 }
913
914 const
915 boost::python::numeric::array
916 Data::getValueOfGlobalDataPoint(int procNo, int dataPointNo)
917 {
918 size_t length=0;
919 int i, j, k, l, pos;
920 //
921 // determine the rank and shape of each data point
922 int dataPointRank = getDataPointRank();
923 const DataTypes::ShapeType& dataPointShape = getDataPointShape();
924
925 //
926 // create the numeric array to be returned
927 boost::python::numeric::array numArray(0.0);
928
929 //
930 // the shape of the returned numeric array will be the same
931 // as that of the data point
932 int arrayRank = dataPointRank;
933 const DataTypes::ShapeType& arrayShape = dataPointShape;
934
935 //
936 // resize the numeric array to the shape just calculated
937 if (arrayRank==0) {
938 numArray.resize(1);
939 }
940 if (arrayRank==1) {
941 numArray.resize(arrayShape[0]);
942 }
943 if (arrayRank==2) {
944 numArray.resize(arrayShape[0],arrayShape[1]);
945 }
946 if (arrayRank==3) {
947 numArray.resize(arrayShape[0],arrayShape[1],arrayShape[2]);
948 }
949 if (arrayRank==4) {
950 numArray.resize(arrayShape[0],arrayShape[1],arrayShape[2],arrayShape[3]);
951 }
952
953 // added for the MPI communication
954 length=1;
955 for( i=0; i<arrayRank; i++ ) length *= arrayShape[i];
956 double *tmpData = new double[length];
957
958 //
959 // load the values for the data point into the numeric array.
960
961 // updated for the MPI case
962 if( get_MPIRank()==procNo ){
963 if (getNumDataPointsPerSample()>0) {
964 int sampleNo = dataPointNo/getNumDataPointsPerSample();
965 int dataPointNoInSample = dataPointNo - sampleNo * getNumDataPointsPerSample();
966 //
967 // Check a valid sample number has been supplied
968 if ((sampleNo >= getNumSamples()) || (sampleNo < 0 )) {
969 throw DataException("Error - Data::convertToNumArray: invalid sampleNo.");
970 }
971
972 //
973 // Check a valid data point number has been supplied
974 if ((dataPointNoInSample >= getNumDataPointsPerSample()) || (dataPointNoInSample < 0)) {
975 throw DataException("Error - Data::convertToNumArray: invalid dataPointNoInSample.");
976 }
977 // TODO: global error handling
978 // create a view of the data if it is stored locally
979 //DataArrayView dataPointView = getDataPoint(sampleNo, dataPointNoInSample);
980 DataTypes::ValueType::size_type offset=getDataOffset(sampleNo, dataPointNoInSample);
981
982 // pack the data from the view into tmpData for MPI communication
983 pos=0;
984 switch( dataPointRank ){
985 case 0 :
986 tmpData[0] = getDataAtOffset(offset);
987 break;
988 case 1 :
989 for( i=0; i<dataPointShape[0]; i++ )
990 tmpData[i]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i));
991 break;
992 case 2 :
993 for( i=0; i<dataPointShape[0]; i++ )
994 for( j=0; j<dataPointShape[1]; j++, pos++ )
995 tmpData[pos]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j));
996 break;
997 case 3 :
998 for( i=0; i<dataPointShape[0]; i++ )
999 for( j=0; j<dataPointShape[1]; j++ )
1000 for( k=0; k<dataPointShape[2]; k++, pos++ )
1001 tmpData[pos]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j,k));
1002 break;
1003 case 4 :
1004 for( i=0; i<dataPointShape[0]; i++ )
1005 for( j=0; j<dataPointShape[1]; j++ )
1006 for( k=0; k<dataPointShape[2]; k++ )
1007 for( l=0; l<dataPointShape[3]; l++, pos++ )
1008 tmpData[pos]=getDataAtOffset(offset+DataTypes::getRelIndex(dataPointShape, i,j,k,l));
1009 break;
1010 }
1011 }
1012 }
1013 #ifdef PASO_MPI
1014 // broadcast the data to all other processes
1015 MPI_Bcast( tmpData, length, MPI_DOUBLE, procNo, get_MPIComm() );
1016 #endif
1017
1018 // unpack the data
1019 switch( dataPointRank ){
1020 case 0 :
1021 numArray[0]=tmpData[0];
1022 break;
1023 case 1 :
1024 for( i=0; i<dataPointShape[0]; i++ )
1025 numArray[i]=tmpData[i];
1026 break;
1027 case 2 :
1028 for( i=0; i<dataPointShape[0]; i++ )
1029 for( j=0; j<dataPointShape[1]; j++ )
1030 numArray[make_tuple(i,j)]=tmpData[i+j*dataPointShape[0]];
1031 break;
1032 case 3 :
1033 for( i=0; i<dataPointShape[0]; i++ )
1034 for( j=0; j<dataPointShape[1]; j++ )
1035 for( k=0; k<dataPointShape[2]; k++ )
1036 numArray[make_tuple(i,j,k)]=tmpData[i+dataPointShape[0]*(j*+k*dataPointShape[1])];
1037 break;
1038 case 4 :
1039 for( i=0; i<dataPointShape[0]; i++ )
1040 for( j=0; j<dataPointShape[1]; j++ )
1041 for( k=0; k<dataPointShape[2]; k++ )
1042 for( l=0; l<dataPointShape[3]; l++ )
1043 numArray[make_tuple(i,j,k,l)]=tmpData[i+dataPointShape[0]*(j*+dataPointShape[1]*(k+l*dataPointShape[2]))];
1044 break;
1045 }
1046
1047 delete [] tmpData;
1048 //
1049 // return the loaded array
1050 return numArray;
1051 }
1052
1053
1054
1055 boost::python::numeric::array
1056 Data::integrate() const
1057 {
1058 int index;
1059 int rank = getDataPointRank();
1060 DataTypes::ShapeType shape = getDataPointShape();
1061 int dataPointSize = getDataPointSize();
1062
1063 //
1064 // calculate the integral values
1065 vector<double> integrals(dataPointSize);
1066 vector<double> integrals_local(dataPointSize);
1067 #ifdef PASO_MPI
1068 AbstractContinuousDomain::asAbstractContinuousDomain(getDomain()).setToIntegrals(integrals_local,*this);
1069 // Global sum: use an array instead of a vector because elements of array are guaranteed to be contiguous in memory
1070 double *tmp = new double[dataPointSize];
1071 double *tmp_local = new double[dataPointSize];
1072 for (int i=0; i<dataPointSize; i++) { tmp_local[i] = integrals_local[i]; }
1073 MPI_Allreduce( &tmp_local[0], &tmp[0], dataPointSize, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD );
1074 for (int i=0; i<dataPointSize; i++) { integrals[i] = tmp[i]; }
1075 delete[] tmp;
1076 delete[] tmp_local;
1077 #else
1078 AbstractContinuousDomain::asAbstractContinuousDomain(getDomain()).setToIntegrals(integrals,*this);
1079 #endif
1080
1081 //
1082 // create the numeric array to be returned
1083 // and load the array with the integral values
1084 boost::python::numeric::array bp_array(1.0);
1085 if (rank==0) {
1086 bp_array.resize(1);
1087 index = 0;
1088 bp_array[0] = integrals[index];
1089 }
1090 if (rank==1) {
1091 bp_array.resize(shape[0]);
1092 for (int i=0; i<shape[0]; i++) {
1093 index = i;
1094 bp_array[i] = integrals[index];
1095 }
1096 }
1097 if (rank==2) {
1098 bp_array.resize(shape[0],shape[1]);
1099 for (int i=0; i<shape[0]; i++) {
1100 for (int j=0; j<shape[1]; j++) {
1101 index = i + shape[0] * j;
1102 bp_array[make_tuple(i,j)] = integrals[index];
1103 }
1104 }
1105 }
1106 if (rank==3) {
1107 bp_array.resize(shape[0],shape[1],shape[2]);
1108 for (int i=0; i<shape[0]; i++) {
1109 for (int j=0; j<shape[1]; j++) {
1110 for (int k=0; k<shape[2]; k++) {
1111 index = i + shape[0] * ( j + shape[1] * k );
1112 bp_array[make_tuple(i,j,k)] = integrals[index];
1113 }
1114 }
1115 }
1116 }
1117 if (rank==4) {
1118 bp_array.resize(shape[0],shape[1],shape[2],shape[3]);
1119 for (int i=0; i<shape[0]; i++) {
1120 for (int j=0; j<shape[1]; j++) {
1121 for (int k=0; k<shape[2]; k++) {
1122 for (int l=0; l<shape[3]; l++) {
1123 index = i + shape[0] * ( j + shape[1] * ( k + shape[2] * l ) );
1124 bp_array[make_tuple(i,j,k,l)] = integrals[index];
1125 }
1126 }
1127 }
1128 }
1129 }
1130
1131 //
1132 // return the loaded array
1133 return bp_array;
1134 }
1135
1136 Data
1137 Data::sin() const
1138 {
1139 return C_TensorUnaryOperation<double (*)(double)>(*this, ::sin);
1140 }
1141
1142 Data
1143 Data::cos() const
1144 {
1145 return C_TensorUnaryOperation<double (*)(double)>(*this, ::cos);
1146 }
1147
1148 Data
1149 Data::tan() const
1150 {
1151 return C_TensorUnaryOperation<double (*)(double)>(*this, ::tan);
1152 }
1153
1154 Data
1155 Data::asin() const
1156 {
1157 return C_TensorUnaryOperation<double (*)(double)>(*this, ::asin);
1158 }
1159
1160 Data
1161 Data::acos() const
1162 {
1163 return C_TensorUnaryOperation<double (*)(double)>(*this, ::acos);
1164 }
1165
1166
1167 Data
1168 Data::atan() const
1169 {
1170 return C_TensorUnaryOperation<double (*)(double)>(*this, ::atan);
1171 }
1172
1173 Data
1174 Data::sinh() const
1175 {
1176 return C_TensorUnaryOperation<double (*)(double)>(*this, ::sinh);
1177
1178 }
1179
1180 Data
1181 Data::cosh() const
1182 {
1183 return C_TensorUnaryOperation<double (*)(double)>(*this, ::cosh);
1184 }
1185
1186 Data
1187 Data::tanh() const
1188 {
1189 return C_TensorUnaryOperation<double (*)(double)>(*this, ::tanh);
1190 }
1191
1192
1193 Data
1194 Data::erf() const
1195 {
1196 #ifdef _WIN32
1197 throw DataException("Error - Data:: erf function is not supported on _WIN32 platforms.");
1198 #else
1199 return C_TensorUnaryOperation(*this, ::erf);
1200 #endif
1201 }
1202
1203 Data
1204 Data::asinh() const
1205 {
1206 #ifdef _WIN32
1207 return C_TensorUnaryOperation(*this, escript::asinh_substitute);
1208 #else
1209 return C_TensorUnaryOperation(*this, ::asinh);
1210 #endif
1211 }
1212
1213 Data
1214 Data::acosh() const
1215 {
1216 #ifdef _WIN32
1217 return C_TensorUnaryOperation(*this, escript::acosh_substitute);
1218 #else
1219 return C_TensorUnaryOperation(*this, ::acosh);
1220 #endif
1221 }
1222
1223 Data
1224 Data::atanh() const
1225 {
1226 #ifdef _WIN32
1227 return C_TensorUnaryOperation(*this, escript::atanh_substitute);
1228 #else
1229 return C_TensorUnaryOperation(*this, ::atanh);
1230 #endif
1231 }
1232
1233 Data
1234 Data::log10() const
1235 {
1236 return C_TensorUnaryOperation<double (*)(double)>(*this, ::log10);
1237 }
1238
1239 Data
1240 Data::log() const
1241 {
1242 return C_TensorUnaryOperation<double (*)(double)>(*this, ::log);
1243 }
1244
1245 Data
1246 Data::sign() const
1247 {
1248 return C_TensorUnaryOperation(*this, escript::fsign);
1249 }
1250
1251 Data
1252 Data::abs() const
1253 {
1254 return C_TensorUnaryOperation<double (*)(double)>(*this, ::fabs);
1255 }
1256
1257 Data
1258 Data::neg() const
1259 {
1260 return C_TensorUnaryOperation(*this, negate<double>());
1261 }
1262
1263 Data
1264 Data::pos() const
1265 {
1266 Data result;
1267 // perform a deep copy
1268 result.copy(*this);
1269 return result;
1270 }
1271
1272 Data
1273 Data::exp() const
1274 {
1275 return C_TensorUnaryOperation<double (*)(double)>(*this, ::exp);
1276 }
1277
1278 Data
1279 Data::sqrt() const
1280 {
1281 return C_TensorUnaryOperation<double (*)(double)>(*this, ::sqrt);
1282 }
1283
1284 double
1285 Data::Lsup() const
1286 {
1287 double localValue;
1288 //
1289 // set the initial absolute maximum value to zero
1290
1291 AbsMax abs_max_func;
1292 localValue = algorithm(abs_max_func,0);
1293 #ifdef PASO_MPI
1294 double globalValue;
1295 MPI_Allreduce( &localValue, &globalValue, 1, MPI_DOUBLE, MPI_MAX, MPI_COMM_WORLD );
1296 return globalValue;
1297 #else
1298 return localValue;
1299 #endif
1300 }
1301
1302 double
1303 Data::sup() const
1304 {
1305 double localValue;
1306 //
1307 // set the initial maximum value to min possible double
1308 FMax fmax_func;
1309 localValue = algorithm(fmax_func,numeric_limits<double>::max()*-1);
1310 #ifdef PASO_MPI
1311 double globalValue;
1312 MPI_Allreduce( &localValue, &globalValue, 1, MPI_DOUBLE, MPI_MAX, MPI_COMM_WORLD );
1313 return globalValue;
1314 #else
1315 return localValue;
1316 #endif
1317 }
1318
1319 double
1320 Data::inf() const
1321 {
1322 double localValue;
1323 //
1324 // set the initial minimum value to max possible double
1325 FMin fmin_func;
1326 localValue = algorithm(fmin_func,numeric_limits<double>::max());
1327 #ifdef PASO_MPI
1328 double globalValue;
1329 MPI_Allreduce( &localValue, &globalValue, 1, MPI_DOUBLE, MPI_MIN, MPI_COMM_WORLD );
1330 return globalValue;
1331 #else
1332 return localValue;
1333 #endif
1334 }
1335
1336 /* TODO */
1337 /* global reduction */
1338 Data
1339 Data::maxval() const
1340 {
1341 //
1342 // set the initial maximum value to min possible double
1343 FMax fmax_func;
1344 return dp_algorithm(fmax_func,numeric_limits<double>::max()*-1);
1345 }
1346
1347 Data
1348 Data::minval() const
1349 {
1350 //
1351 // set the initial minimum value to max possible double
1352 FMin fmin_func;
1353 return dp_algorithm(fmin_func,numeric_limits<double>::max());
1354 }
1355
1356 Data
1357 Data::swapaxes(const int axis0, const int axis1) const
1358 {
1359 int axis0_tmp,axis1_tmp;
1360 DataTypes::ShapeType s=getDataPointShape();
1361 DataTypes::ShapeType ev_shape;
1362 // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
1363 // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
1364 int rank=getDataPointRank();
1365 if (rank<2) {
1366 throw DataException("Error - Data::swapaxes argument must have at least rank 2.");
1367 }
1368 if (axis0<0 || axis0>rank-1) {
1369 throw DataException("Error - Data::swapaxes: axis0 must be between 0 and rank-1=" + rank-1);
1370 }
1371 if (axis1<0 || axis1>rank-1) {
1372 throw DataException("Error - Data::swapaxes: axis1 must be between 0 and rank-1=" + rank-1);
1373 }
1374 if (axis0 == axis1) {
1375 throw DataException("Error - Data::swapaxes: axis indices must be different.");
1376 }
1377 if (axis0 > axis1) {
1378 axis0_tmp=axis1;
1379 axis1_tmp=axis0;
1380 } else {
1381 axis0_tmp=axis0;
1382 axis1_tmp=axis1;
1383 }
1384 for (int i=0; i<rank; i++) {
1385 if (i == axis0_tmp) {
1386 ev_shape.push_back(s[axis1_tmp]);
1387 } else if (i == axis1_tmp) {
1388 ev_shape.push_back(s[axis0_tmp]);
1389 } else {
1390 ev_shape.push_back(s[i]);
1391 }
1392 }
1393 Data ev(0.,ev_shape,getFunctionSpace());
1394 ev.typeMatchRight(*this);
1395 m_data->swapaxes(ev.m_data.get(), axis0_tmp, axis1_tmp);
1396 return ev;
1397
1398 }
1399
1400 Data
1401 Data::symmetric() const
1402 {
1403 // check input
1404 DataTypes::ShapeType s=getDataPointShape();
1405 if (getDataPointRank()==2) {
1406 if(s[0] != s[1])
1407 throw DataException("Error - Data::symmetric can only be calculated for rank 2 object with equal first and second dimension.");
1408 }
1409 else if (getDataPointRank()==4) {
1410 if(!(s[0] == s[2] && s[1] == s[3]))
1411 throw DataException("Error - Data::symmetric can only be calculated for rank 4 object with dim0==dim2 and dim1==dim3.");
1412 }
1413 else {
1414 throw DataException("Error - Data::symmetric can only be calculated for rank 2 or 4 object.");
1415 }
1416 Data ev(0.,getDataPointShape(),getFunctionSpace());
1417 ev.typeMatchRight(*this);
1418 m_data->symmetric(ev.m_data.get());
1419 return ev;
1420 }
1421
1422 Data
1423 Data::nonsymmetric() const
1424 {
1425 // check input
1426 DataTypes::ShapeType s=getDataPointShape();
1427 if (getDataPointRank()==2) {
1428 if(s[0] != s[1])
1429 throw DataException("Error - Data::nonsymmetric can only be calculated for rank 2 object with equal first and second dimension.");
1430 DataTypes::ShapeType ev_shape;
1431 ev_shape.push_back(s[0]);
1432 ev_shape.push_back(s[1]);
1433 Data ev(0.,ev_shape,getFunctionSpace());
1434 ev.typeMatchRight(*this);
1435 m_data->nonsymmetric(ev.m_data.get());
1436 return ev;
1437 }
1438 else if (getDataPointRank()==4) {
1439 if(!(s[0] == s[2] && s[1] == s[3]))
1440 throw DataException("Error - Data::nonsymmetric can only be calculated for rank 4 object with dim0==dim2 and dim1==dim3.");
1441 DataTypes::ShapeType ev_shape;
1442 ev_shape.push_back(s[0]);
1443 ev_shape.push_back(s[1]);
1444 ev_shape.push_back(s[2]);
1445 ev_shape.push_back(s[3]);
1446 Data ev(0.,ev_shape,getFunctionSpace());
1447 ev.typeMatchRight(*this);
1448 m_data->nonsymmetric(ev.m_data.get());
1449 return ev;
1450 }
1451 else {
1452 throw DataException("Error - Data::nonsymmetric can only be calculated for rank 2 or 4 object.");
1453 }
1454 }
1455
1456 Data
1457 Data::trace(int axis_offset) const
1458 {
1459 DataTypes::ShapeType s=getDataPointShape();
1460 if (getDataPointRank()==2) {
1461 DataTypes::ShapeType ev_shape;
1462 Data ev(0.,ev_shape,getFunctionSpace());
1463 ev.typeMatchRight(*this);
1464 m_data->trace(ev.m_data.get(), axis_offset);
1465 return ev;
1466 }
1467 if (getDataPointRank()==3) {
1468 DataTypes::ShapeType ev_shape;
1469 if (axis_offset==0) {
1470 int s2=s[2];
1471 ev_shape.push_back(s2);
1472 }
1473 else if (axis_offset==1) {
1474 int s0=s[0];
1475 ev_shape.push_back(s0);
1476 }
1477 Data ev(0.,ev_shape,getFunctionSpace());
1478 ev.typeMatchRight(*this);
1479 m_data->trace(ev.m_data.get(), axis_offset);
1480 return ev;
1481 }
1482 if (getDataPointRank()==4) {
1483 DataTypes::ShapeType ev_shape;
1484 if (axis_offset==0) {
1485 ev_shape.push_back(s[2]);
1486 ev_shape.push_back(s[3]);
1487 }
1488 else if (axis_offset==1) {
1489 ev_shape.push_back(s[0]);
1490 ev_shape.push_back(s[3]);
1491 }
1492 else if (axis_offset==2) {
1493 ev_shape.push_back(s[0]);
1494 ev_shape.push_back(s[1]);
1495 }
1496 Data ev(0.,ev_shape,getFunctionSpace());
1497 ev.typeMatchRight(*this);
1498 m_data->trace(ev.m_data.get(), axis_offset);
1499 return ev;
1500 }
1501 else {
1502 throw DataException("Error - Data::trace can only be calculated for rank 2, 3 or 4 object.");
1503 }
1504 }
1505
1506 Data
1507 Data::transpose(int axis_offset) const
1508 {
1509 DataTypes::ShapeType s=getDataPointShape();
1510 DataTypes::ShapeType ev_shape;
1511 // Here's the equivalent of python s_out=s[axis_offset:]+s[:axis_offset]
1512 // which goes thru all shape vector elements starting with axis_offset (at index=rank wrap around to 0)
1513 int rank=getDataPointRank();
1514 if (axis_offset<0 || axis_offset>rank) {
1515 throw DataException("Error - Data::transpose must have 0 <= axis_offset <= rank=" + rank);
1516 }
1517 for (int i=0; i<rank; i++) {
1518 int index = (axis_offset+i)%rank;
1519 ev_shape.push_back(s[index]); // Append to new shape
1520 }
1521 Data ev(0.,ev_shape,getFunctionSpace());
1522 ev.typeMatchRight(*this);
1523 m_data->transpose(ev.m_data.get(), axis_offset);
1524 return ev;
1525 }
1526
1527 Data
1528 Data::eigenvalues() const
1529 {
1530 // check input
1531 DataTypes::ShapeType s=getDataPointShape();
1532 if (getDataPointRank()!=2)
1533 throw DataException("Error - Data::eigenvalues can only be calculated for rank 2 object.");
1534 if(s[0] != s[1])
1535 throw DataException("Error - Data::eigenvalues can only be calculated for object with equal first and second dimension.");
1536 // create return
1537 DataTypes::ShapeType ev_shape(1,s[0]);
1538 Data ev(0.,ev_shape,getFunctionSpace());
1539 ev.typeMatchRight(*this);
1540 m_data->eigenvalues(ev.m_data.get());
1541 return ev;
1542 }
1543
1544 const boost::python::tuple
1545 Data::eigenvalues_and_eigenvectors(const double tol) const
1546 {
1547 DataTypes::ShapeType s=getDataPointShape();
1548 if (getDataPointRank()!=2)
1549 throw DataException("Error - Data::eigenvalues and eigenvectors can only be calculated for rank 2 object.");
1550 if(s[0] != s[1])
1551 throw DataException("Error - Data::eigenvalues and eigenvectors can only be calculated for object with equal first and second dimension.");
1552 // create return
1553 DataTypes::ShapeType ev_shape(1,s[0]);
1554 Data ev(0.,ev_shape,getFunctionSpace());
1555 ev.typeMatchRight(*this);
1556 DataTypes::ShapeType V_shape(2,s[0]);
1557 Data V(0.,V_shape,getFunctionSpace());
1558 V.typeMatchRight(*this);
1559 m_data->eigenvalues_and_eigenvectors(ev.m_data.get(),V.m_data.get(),tol);
1560 return make_tuple(boost::python::object(ev),boost::python::object(V));
1561 }
1562
1563 const boost::python::tuple
1564 Data::minGlobalDataPoint() const
1565 {
1566 // NB: calc_minGlobalDataPoint( had to be split off from minGlobalDataPoint( as boost::make_tuple causes an
1567 // abort (for unknown reasons) if there are openmp directives with it in the
1568 // surrounding function
1569
1570 int DataPointNo;
1571 int ProcNo;
1572 calc_minGlobalDataPoint(ProcNo,DataPointNo);
1573 return make_tuple(ProcNo,DataPointNo);
1574 }
1575
1576 void
1577 Data::calc_minGlobalDataPoint(int& ProcNo,
1578 int& DataPointNo) const
1579 {
1580 int i,j;
1581 int lowi=0,lowj=0;
1582 double min=numeric_limits<double>::max();
1583
1584 Data temp=minval();
1585
1586 int numSamples=temp.getNumSamples();
1587 int numDPPSample=temp.getNumDataPointsPerSample();
1588
1589 double next,local_min;
1590 int local_lowi,local_lowj;
1591
1592 #pragma omp parallel private(next,local_min,local_lowi,local_lowj)
1593 {
1594 local_min=min;
1595 #pragma omp for private(i,j) schedule(static)
1596 for (i=0; i<numSamples; i++) {
1597 for (j=0; j<numDPPSample; j++) {
1598 next=temp.getDataAtOffset(temp.getDataOffset(i,j));
1599 if (next<local_min) {
1600 local_min=next;
1601 local_lowi=i;
1602 local_lowj=j;
1603 }
1604 }
1605 }
1606 #pragma omp critical
1607 if (local_min<min) {
1608 min=local_min;
1609 lowi=local_lowi;
1610 lowj=local_lowj;
1611 }
1612 }
1613
1614 #ifdef PASO_MPI
1615 // determine the processor on which the minimum occurs
1616 next = temp.getDataPoint(lowi,lowj);
1617 int lowProc = 0;
1618 double *globalMins = new double[get_MPISize()+1];
1619 int error = MPI_Gather ( &next, 1, MPI_DOUBLE, globalMins, 1, MPI_DOUBLE, 0, get_MPIComm() );
1620
1621 if( get_MPIRank()==0 ){
1622 next = globalMins[lowProc];
1623 for( i=1; i<get_MPISize(); i++ )
1624 if( next>globalMins[i] ){
1625 lowProc = i;
1626 next = globalMins[i];
1627 }
1628 }
1629 MPI_Bcast( &lowProc, 1, MPI_DOUBLE, 0, get_MPIComm() );
1630
1631 delete [] globalMins;
1632 ProcNo = lowProc;
1633 #else
1634 ProcNo = 0;
1635 #endif
1636 DataPointNo = lowj + lowi * numDPPSample;
1637 }
1638
1639 void
1640 Data::saveDX(std::string fileName) const
1641 {
1642 if (isEmpty())
1643 {
1644 throw DataException("Error - Operations not permitted on instances of DataEmpty.");
1645 }
1646 boost::python::dict args;
1647 args["data"]=boost::python::object(this);
1648 getDomain().saveDX(fileName,args);
1649 return;
1650 }
1651
1652 void
1653 Data::saveVTK(std::string fileName) const
1654 {
1655 if (isEmpty())
1656 {
1657 throw DataException("Error - Operations not permitted on instances of DataEmpty.");
1658 }
1659 boost::python::dict args;
1660 args["data"]=boost::python::object(this);
1661 getDomain().saveVTK(fileName,args);
1662 return;
1663 }
1664
1665 Data&
1666 Data::operator+=(const Data& right)
1667 {
1668 if (isProtected()) {
1669 throw DataException("Error - attempt to update protected Data object.");
1670 }
1671 binaryOp(right,plus<double>());
1672 return (*this);
1673 }
1674
1675 Data&
1676 Data::operator+=(const boost::python::object& right)
1677 {
1678 Data tmp(right,getFunctionSpace(),false);
1679 binaryOp(tmp,plus<double>());
1680 return (*this);
1681 }
1682 Data&
1683 Data::operator=(const Data& other)
1684 {
1685 copy(other);
1686 return (*this);
1687 }
1688
1689 Data&
1690 Data::operator-=(const Data& right)
1691 {
1692 if (isProtected()) {
1693 throw DataException("Error - attempt to update protected Data object.");
1694 }
1695 binaryOp(right,minus<double>());
1696 return (*this);
1697 }
1698
1699 Data&
1700 Data::operator-=(const boost::python::object& right)
1701 {
1702 Data tmp(right,getFunctionSpace(),false);
1703 binaryOp(tmp,minus<double>());
1704 return (*this);
1705 }
1706
1707 Data&
1708 Data::operator*=(const Data& right)
1709 {
1710 if (isProtected()) {
1711 throw DataException("Error - attempt to update protected Data object.");
1712 }
1713 binaryOp(right,multiplies<double>());
1714 return (*this);
1715 }
1716
1717 Data&
1718 Data::operator*=(const boost::python::object& right)
1719 {
1720 Data tmp(right,getFunctionSpace(),false);
1721 binaryOp(tmp,multiplies<double>());
1722 return (*this);
1723 }
1724
1725 Data&
1726 Data::operator/=(const Data& right)
1727 {
1728 if (isProtected()) {
1729 throw DataException("Error - attempt to update protected Data object.");
1730 }
1731 binaryOp(right,divides<double>());
1732 return (*this);
1733 }
1734
1735 Data&
1736 Data::operator/=(const boost::python::object& right)
1737 {
1738 Data tmp(right,getFunctionSpace(),false);
1739 binaryOp(tmp,divides<double>());
1740 return (*this);
1741 }
1742
1743 Data
1744 Data::rpowO(const boost::python::object& left) const
1745 {
1746 Data left_d(left,*this);
1747 return left_d.powD(*this);
1748 }
1749
1750 Data
1751 Data::powO(const boost::python::object& right) const
1752 {
1753 Data tmp(right,getFunctionSpace(),false);
1754 return powD(tmp);
1755 }
1756
1757 Data
1758 Data::powD(const Data& right) const
1759 {
1760 return C_TensorBinaryOperation<double (*)(double, double)>(*this, right, ::pow);
1761 }
1762
1763 //
1764 // NOTE: It is essential to specify the namespace this operator belongs to
1765 Data
1766 escript::operator+(const Data& left, const Data& right)
1767 {
1768 return C_TensorBinaryOperation(left, right, plus<double>());
1769 }
1770
1771 //
1772 // NOTE: It is essential to specify the namespace this operator belongs to
1773 Data
1774 escript::operator-(const Data& left, const Data& right)
1775 {
1776 return C_TensorBinaryOperation(left, right, minus<double>());
1777 }
1778
1779 //
1780 // NOTE: It is essential to specify the namespace this operator belongs to
1781 Data
1782 escript::operator*(const Data& left, const Data& right)
1783 {
1784 return C_TensorBinaryOperation(left, right, multiplies<double>());
1785 }
1786
1787 //
1788 // NOTE: It is essential to specify the namespace this operator belongs to
1789 Data
1790 escript::operator/(const Data& left, const Data& right)
1791 {
1792 return C_TensorBinaryOperation(left, right, divides<double>());
1793 }
1794
1795 //
1796 // NOTE: It is essential to specify the namespace this operator belongs to
1797 Data
1798 escript::operator+(const Data& left, const boost::python::object& right)
1799 {
1800 return left+Data(right,left.getFunctionSpace(),false);
1801 }
1802
1803 //
1804 // NOTE: It is essential to specify the namespace this operator belongs to
1805 Data
1806 escript::operator-(const Data& left, const boost::python::object& right)
1807 {
1808 return left-Data(right,left.getFunctionSpace(),false);
1809 }
1810
1811 //
1812 // NOTE: It is essential to specify the namespace this operator belongs to
1813 Data
1814 escript::operator*(const Data& left, const boost::python::object& right)
1815 {
1816 return left*Data(right,left.getFunctionSpace(),false);
1817 }
1818
1819 //
1820 // NOTE: It is essential to specify the namespace this operator belongs to
1821 Data
1822 escript::operator/(const Data& left, const boost::python::object& right)
1823 {
1824 return left/Data(right,left.getFunctionSpace(),false);
1825 }
1826
1827 //
1828 // NOTE: It is essential to specify the namespace this operator belongs to
1829 Data
1830 escript::operator+(const boost::python::object& left, const Data& right)
1831 {
1832 return Data(left,right.getFunctionSpace(),false)+right;
1833 }
1834
1835 //
1836 // NOTE: It is essential to specify the namespace this operator belongs to
1837 Data
1838 escript::operator-(const boost::python::object& left, const Data& right)
1839 {
1840 return Data(left,right.getFunctionSpace(),false)-right;
1841 }
1842
1843 //
1844 // NOTE: It is essential to specify the namespace this operator belongs to
1845 Data
1846 escript::operator*(const boost::python::object& left, const Data& right)
1847 {
1848 return Data(left,right.getFunctionSpace(),false)*right;
1849 }
1850
1851 //
1852 // NOTE: It is essential to specify the namespace this operator belongs to
1853 Data
1854 escript::operator/(const boost::python::object& left, const Data& right)
1855 {
1856 return Data(left,right.getFunctionSpace(),false)/right;
1857 }
1858
1859 //
1860 //bool escript::operator==(const Data& left, const Data& right)
1861 //{
1862 // /*
1863 // NB: this operator does very little at this point, and isn't to
1864 // be relied on. Requires further implementation.
1865 // */
1866 //
1867 // bool ret;
1868 //
1869 // if (left.isEmpty()) {
1870 // if(!right.isEmpty()) {
1871 // ret = false;
1872 // } else {
1873 // ret = true;
1874 // }
1875 // }
1876 //
1877 // if (left.isConstant()) {
1878 // if(!right.isConstant()) {
1879 // ret = false;
1880 // } else {
1881 // ret = true;
1882 // }
1883 // }
1884 //
1885 // if (left.isTagged()) {
1886 // if(!right.isTagged()) {
1887 // ret = false;
1888 // } else {
1889 // ret = true;
1890 // }
1891 // }
1892 //
1893 // if (left.isExpanded()) {
1894 // if(!right.isExpanded()) {
1895 // ret = false;
1896 // } else {
1897 // ret = true;
1898 // }
1899 // }
1900 //
1901 // return ret;
1902 //}
1903
1904 /* TODO */
1905 /* global reduction */
1906 Data
1907 Data::getItem(const boost::python::object& key) const
1908 {
1909 // const DataArrayView& view=getPointDataView();
1910
1911 DataTypes::RegionType slice_region=DataTypes::getSliceRegion(getDataPointShape(),key);
1912
1913 if (slice_region.size()!=getDataPointRank()) {
1914 throw DataException("Error - slice size does not match Data rank.");
1915 }
1916
1917 return getSlice(slice_region);
1918 }
1919
1920 /* TODO */
1921 /* global reduction */
1922 Data
1923 Data::getSlice(const DataTypes::RegionType& region) const
1924 {
1925 return Data(*this,region);
1926 }
1927
1928 /* TODO */
1929 /* global reduction */
1930 void
1931 Data::setItemO(const boost::python::object& key,
1932 const boost::python::object& value)
1933 {
1934 Data tempData(value,getFunctionSpace());
1935 setItemD(key,tempData);
1936 }
1937
1938 void
1939 Data::setItemD(const boost::python::object& key,
1940 const Data& value)
1941 {
1942 // const DataArrayView& view=getPointDataView();
1943
1944 DataTypes::RegionType slice_region=DataTypes::getSliceRegion(getDataPointShape(),key);
1945 if (slice_region.size()!=getDataPointRank()) {
1946 throw DataException("Error - slice size does not match Data rank.");
1947 }
1948 if (getFunctionSpace()!=value.getFunctionSpace()) {
1949 setSlice(Data(value,getFunctionSpace()),slice_region);
1950 } else {
1951 setSlice(value,slice_region);
1952 }
1953 }
1954
1955 void
1956 Data::setSlice(const Data& value,
1957 const DataTypes::RegionType& region)
1958 {
1959 if (isProtected()) {
1960 throw DataException("Error - attempt to update protected Data object.");
1961 }
1962 Data tempValue(value);
1963 typeMatchLeft(tempValue);
1964 typeMatchRight(tempValue);
1965 m_data->setSlice(tempValue.m_data.get(),region);
1966 }
1967
1968 void
1969 Data::typeMatchLeft(Data& right) const
1970 {
1971 if (isExpanded()){
1972 right.expand();
1973 } else if (isTagged()) {
1974 if (right.isConstant()) {
1975 right.tag();
1976 }
1977 }
1978 }
1979
1980 void
1981 Data::typeMatchRight(const Data& right)
1982 {
1983 if (isTagged()) {
1984 if (right.isExpanded()) {
1985 expand();
1986 }
1987 } else if (isConstant()) {
1988 if (right.isExpanded()) {
1989 expand();
1990 } else if (right.isTagged()) {
1991 tag();
1992 }
1993 }
1994 }
1995
1996 void
1997 Data::setTaggedValueByName(std::string name,
1998 const boost::python::object& value)
1999 {
2000 if (getFunctionSpace().getDomain().isValidTagName(name)) {
2001 int tagKey=getFunctionSpace().getDomain().getTag(name);
2002 setTaggedValue(tagKey,value);
2003 }
2004 }
2005 void
2006 Data::setTaggedValue(int tagKey,
2007 const boost::python::object& value)
2008 {
2009 if (isProtected()) {
2010 throw DataException("Error - attempt to update protected Data object.");
2011 }
2012 //
2013 // Ensure underlying data object is of type DataTagged
2014 if (isConstant()) tag();
2015
2016 numeric::array asNumArray(value);
2017
2018
2019 // extract the shape of the numarray
2020 DataTypes::ShapeType tempShape;
2021 for (int i=0; i < asNumArray.getrank(); i++) {
2022 tempShape.push_back(extract<int>(asNumArray.getshape()[i]));
2023 }
2024
2025 // get the space for the data vector
2026 // int len = DataTypes::noValues(tempShape);
2027 // DataVector temp_data(len, 0.0, len);
2028 // DataArrayView temp_dataView(temp_data, tempShape);
2029 // temp_dataView.copy(asNumArray);
2030
2031 DataVector temp_data2;
2032 temp_data2.copyFromNumArray(asNumArray);
2033
2034 //
2035 // Call DataAbstract::setTaggedValue
2036 //m_data->setTaggedValue(tagKey,temp_dataView);
2037
2038 m_data->setTaggedValue(tagKey,tempShape, temp_data2);
2039 }
2040
2041 // void
2042 // Data::setTaggedValueFromCPP(int tagKey,
2043 // const DataArrayView& value)
2044 // {
2045 // if (isProtected()) {
2046 // throw DataException("Error - attempt to update protected Data object.");
2047 // }
2048 // //
2049 // // Ensure underlying data object is of type DataTagged
2050 // if (isConstant()) tag();
2051 //
2052 // //
2053 // // Call DataAbstract::setTaggedValue
2054 // m_data->setTaggedValue(tagKey,value);
2055 // }
2056
2057 void
2058 Data::setTaggedValueFromCPP(int tagKey,
2059 const DataTypes::ShapeType& pointshape,
2060 const DataTypes::ValueType& value,
2061 int dataOffset)
2062 {
2063 if (isProtected()) {
2064 throw DataException("Error - attempt to update protected Data object.");
2065 }
2066 //
2067 // Ensure underlying data object is of type DataTagged
2068 if (isConstant()) tag();
2069
2070 //
2071 // Call DataAbstract::setTaggedValue
2072 m_data->setTaggedValue(tagKey,pointshape, value, dataOffset);
2073 }
2074
2075 int
2076 Data::getTagNumber(int dpno)
2077 {
2078 if (isEmpty())
2079 {
2080 throw DataException("Error - operation not permitted on instances of DataEmpty.");
2081 }
2082 return getFunctionSpace().getTagFromDataPointNo(dpno);
2083 }
2084
2085
2086 ostream& escript::operator<<(ostream& o, const Data& data)
2087 {
2088 o << data.toString();
2089 return o;
2090 }
2091
2092 Data
2093 escript::C_GeneralTensorProduct(Data& arg_0,
2094 Data& arg_1,
2095 int axis_offset,
2096 int transpose)
2097 {
2098 // General tensor product: res(SL x SR) = arg_0(SL x SM) * arg_1(SM x SR)
2099 // SM is the product of the last axis_offset entries in arg_0.getShape().
2100
2101 // Interpolate if necessary and find an appropriate function space
2102 Data arg_0_Z, arg_1_Z;
2103 if (arg_0.getFunctionSpace()!=arg_1.getFunctionSpace()) {
2104 if (arg_0.probeInterpolation(arg_1.getFunctionSpace())) {
2105 arg_0_Z = arg_0.interpolate(arg_1.getFunctionSpace());
2106 arg_1_Z = Data(arg_1);
2107 }
2108 else if (arg_1.probeInterpolation(arg_0.getFunctionSpace())) {
2109 arg_1_Z=arg_1.interpolate(arg_0.getFunctionSpace());
2110 arg_0_Z =Data(arg_0);
2111 }
2112 else {
2113 throw DataException("Error - C_GeneralTensorProduct: arguments have incompatible function spaces.");
2114 }
2115 } else {
2116 arg_0_Z = Data(arg_0);
2117 arg_1_Z = Data(arg_1);
2118 }
2119 // Get rank and shape of inputs
2120 int rank0 = arg_0_Z.getDataPointRank();
2121 int rank1 = arg_1_Z.getDataPointRank();
2122 const DataTypes::ShapeType& shape0 = arg_0_Z.getDataPointShape();
2123 const DataTypes::ShapeType& shape1 = arg_1_Z.getDataPointShape();
2124
2125 // Prepare for the loops of the product and verify compatibility of shapes
2126 int start0=0, start1=0;
2127 if (transpose == 0) {}
2128 else if (transpose == 1) { start0 = axis_offset; }
2129 else if (transpose == 2) { start1 = rank1-axis_offset; }
2130 else { throw DataException("C_GeneralTensorProduct: Error - transpose should be 0, 1 or 2"); }
2131
2132
2133 // Adjust the shapes for transpose
2134 DataTypes::ShapeType tmpShape0(rank0); // pre-sizing the vectors rather
2135 DataTypes::ShapeType tmpShape1(rank1); // than using push_back
2136 for (int i=0; i<rank0; i++) { tmpShape0[i]=shape0[(i+start0)%rank0]; }
2137 for (int i=0; i<rank1; i++) { tmpShape1[i]=shape1[(i+start1)%rank1]; }
2138
2139 #if 0
2140 // For debugging: show shape after transpose
2141 char tmp[100];
2142 std::string shapeStr;
2143 shapeStr = "(";
2144 for (int i=0; i<rank0; i++) { sprintf(tmp, "%d,", tmpShape0[i]); shapeStr += tmp; }
2145 shapeStr += ")";
2146 cout << "C_GeneralTensorProduct: Shape of arg0 is " << shapeStr << endl;
2147 shapeStr = "(";
2148 for (int i=0; i<rank1; i++) { sprintf(tmp, "%d,", tmpShape1[i]); shapeStr += tmp; }
2149 shapeStr += ")";
2150 cout << "C_GeneralTensorProduct: Shape of arg1 is " << shapeStr << endl;
2151 #endif
2152
2153 // Prepare for the loops of the product
2154 int SL=1, SM=1, SR=1;
2155 for (int i=0; i<rank0-axis_offset; i++) {
2156 SL *= tmpShape0[i];
2157 }
2158 for (int i=rank0-axis_offset; i<rank0; i++) {
2159 if (tmpShape0[i] != tmpShape1[i-(rank0-axis_offset)]) {
2160 throw DataException("C_GeneralTensorProduct: Error - incompatible shapes");
2161 }
2162 SM *= tmpShape0[i];
2163 }
2164 for (int i=axis_offset; i<rank1; i++) {
2165 SR *= tmpShape1[i];
2166 }
2167
2168 // Define the shape of the output (rank of shape is the sum of the loop ranges below)
2169 DataTypes::ShapeType shape2(rank0+rank1-2*axis_offset);
2170 { // block to limit the scope of out_index
2171 int out_index=0;
2172 for (int i=0; i<rank0-axis_offset; i++, ++out_index) { shape2[out_index]=tmpShape0[i]; } // First part of arg_0_Z
2173 for (int i=axis_offset; i<rank1; i++, ++out_index) { shape2[out_index]=tmpShape1[i]; } // Last part of arg_1_Z
2174 }
2175
2176 // Declare output Data object
2177 Data res;
2178
2179 if (arg_0_Z.isConstant() && arg_1_Z.isConstant()) {
2180 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace()); // DataConstant output
2181 double *ptr_0 = &(arg_0_Z.getDataAtOffset(0));
2182 double *ptr_1 = &(arg_1_Z.getDataAtOffset(0));
2183 double *ptr_2 = &(res.getDataAtOffset(0));
2184 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2185 }
2186 else if (arg_0_Z.isConstant() && arg_1_Z.isTagged()) {
2187
2188 // Prepare the DataConstant input
2189 DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2190 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataConstant."); }
2191
2192 // Borrow DataTagged input from Data object
2193 DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2194 if (tmp_1==0) { throw DataException("GTP_1 Programming error - casting to DataTagged."); }
2195
2196 // Prepare a DataTagged output 2
2197 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace()); // DataTagged output
2198 res.tag();
2199 DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2200 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2201
2202 // Prepare offset into DataConstant
2203 int offset_0 = tmp_0->getPointOffset(0,0);
2204 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2205 // Get the views
2206 // DataArrayView view_1 = tmp_1->getDefaultValue();
2207 // DataArrayView view_2 = tmp_2->getDefaultValue();
2208 // // Get the pointers to the actual data
2209 // double *ptr_1 = &((view_1.getData())[0]);
2210 // double *ptr_2 = &((view_2.getData())[0]);
2211
2212 double *ptr_1 = &(tmp_1->getDefaultValue(0));
2213 double *ptr_2 = &(tmp_2->getDefaultValue(0));
2214
2215
2216 // Compute an MVP for the default
2217 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2218 // Compute an MVP for each tag
2219 const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2220 DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2221 for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2222 tmp_2->addTag(i->first);
2223 // DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);
2224 // DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);
2225 // double *ptr_1 = &view_1.getData(0);
2226 // double *ptr_2 = &view_2.getData(0);
2227
2228 double *ptr_1 = &(tmp_1->getDataByTag(i->first,0));
2229 double *ptr_2 = &(tmp_2->getDataByTag(i->first,0));
2230
2231 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2232 }
2233
2234 }
2235 else if (arg_0_Z.isConstant() && arg_1_Z.isExpanded()) {
2236
2237 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2238 DataConstant* tmp_0=dynamic_cast<DataConstant*>(arg_0_Z.borrowData());
2239 DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2240 DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2241 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataConstant."); }
2242 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2243 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2244 int sampleNo_1,dataPointNo_1;
2245 int numSamples_1 = arg_1_Z.getNumSamples();
2246 int numDataPointsPerSample_1 = arg_1_Z.getNumDataPointsPerSample();
2247 int offset_0 = tmp_0->getPointOffset(0,0);
2248 #pragma omp parallel for private(sampleNo_1,dataPointNo_1) schedule(static)
2249 for (sampleNo_1 = 0; sampleNo_1 < numSamples_1; sampleNo_1++) {
2250 for (dataPointNo_1 = 0; dataPointNo_1 < numDataPointsPerSample_1; dataPointNo_1++) {
2251 int offset_1 = tmp_1->getPointOffset(sampleNo_1,dataPointNo_1);
2252 int offset_2 = tmp_2->getPointOffset(sampleNo_1,dataPointNo_1);
2253 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2254 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2255 double *ptr_2 = &(res.getDataAtOffset(offset_2));
2256 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2257 }
2258 }
2259
2260 }
2261 else if (arg_0_Z.isTagged() && arg_1_Z.isConstant()) {
2262
2263 // Borrow DataTagged input from Data object
2264 DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2265 if (tmp_0==0) { throw DataException("GTP_0 Programming error - casting to DataTagged."); }
2266
2267 // Prepare the DataConstant input
2268 DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2269 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataConstant."); }
2270
2271 // Prepare a DataTagged output 2
2272 res = Data(0.0, shape2, arg_0_Z.getFunctionSpace()); // DataTagged output
2273 res.tag();
2274 DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2275 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2276
2277 // Prepare offset into DataConstant
2278 int offset_1 = tmp_1->getPointOffset(0,0);
2279 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2280 // Get the views
2281 // DataArrayView view_0 = tmp_0->getDefaultValue();
2282 // DataArrayView view_2 = tmp_2->getDefaultValue();
2283 // // Get the pointers to the actual data
2284 // double *ptr_0 = &((view_0.getData())[0]);
2285 // double *ptr_2 = &((view_2.getData())[0]);
2286
2287 double *ptr_0 = &(tmp_0->getDefaultValue(0));
2288 double *ptr_2 = &(tmp_2->getDefaultValue(0));
2289
2290 // Compute an MVP for the default
2291 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2292 // Compute an MVP for each tag
2293 const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2294 DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2295 for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2296 // tmp_2->addTaggedValue(i->first,tmp_2->getDefaultValue());
2297 // DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);
2298 // DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);
2299 // double *ptr_0 = &view_0.getData(0);
2300 // double *ptr_2 = &view_2.getData(0);
2301
2302 tmp_2->addTag(i->first);
2303 double *ptr_0 = &(tmp_0->getDataByTag(i->first,0));
2304 double *ptr_2 = &(tmp_2->getDataByTag(i->first,0));
2305 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2306 }
2307
2308 }
2309 else if (arg_0_Z.isTagged() && arg_1_Z.isTagged()) {
2310
2311 // Borrow DataTagged input from Data object
2312 DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2313 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2314
2315 // Borrow DataTagged input from Data object
2316 DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2317 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2318
2319 // Prepare a DataTagged output 2
2320 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace());
2321 res.tag(); // DataTagged output
2322 DataTagged* tmp_2=dynamic_cast<DataTagged*>(res.borrowData());
2323 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2324
2325 // // Get the views
2326 // DataArrayView view_0 = tmp_0->getDefaultValue();
2327 // DataArrayView view_1 = tmp_1->getDefaultValue();
2328 // DataArrayView view_2 = tmp_2->getDefaultValue();
2329 // // Get the pointers to the actual data
2330 // double *ptr_0 = &((view_0.getData())[0]);
2331 // double *ptr_1 = &((view_1.getData())[0]);
2332 // double *ptr_2 = &((view_2.getData())[0]);
2333
2334 double *ptr_0 = &(tmp_0->getDefaultValue(0));
2335 double *ptr_1 = &(tmp_1->getDefaultValue(0));
2336 double *ptr_2 = &(tmp_2->getDefaultValue(0));
2337
2338
2339 // Compute an MVP for the default
2340 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2341 // Merge the tags
2342 DataTagged::DataMapType::const_iterator i; // i->first is a tag, i->second is an offset into memory
2343 const DataTagged::DataMapType& lookup_0=tmp_0->getTagLookup();
2344 const DataTagged::DataMapType& lookup_1=tmp_1->getTagLookup();
2345 for (i=lookup_0.begin();i!=lookup_0.end();i++) {
2346 tmp_2->addTag(i->first); // use tmp_2 to get correct shape
2347 }
2348 for (i=lookup_1.begin();i!=lookup_1.end();i++) {
2349 tmp_2->addTag(i->first);
2350 }
2351 // Compute an MVP for each tag
2352 const DataTagged::DataMapType& lookup_2=tmp_2->getTagLookup();
2353 for (i=lookup_2.begin();i!=lookup_2.end();i++) {
2354 // DataArrayView view_0 = tmp_0->getDataPointByTag(i->first);
2355 // DataArrayView view_1 = tmp_1->getDataPointByTag(i->first);
2356 // DataArrayView view_2 = tmp_2->getDataPointByTag(i->first);
2357 // double *ptr_0 = &view_0.getData(0);
2358 // double *ptr_1 = &view_1.getData(0);
2359 // double *ptr_2 = &view_2.getData(0);
2360
2361 double *ptr_0 = &(tmp_0->getDataByTag(i->first,0));
2362 double *ptr_1 = &(tmp_1->getDataByTag(i->first,0));
2363 double *ptr_2 = &(tmp_2->getDataByTag(i->first,0));
2364
2365 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2366 }
2367
2368 }
2369 else if (arg_0_Z.isTagged() && arg_1_Z.isExpanded()) {
2370
2371 // After finding a common function space above the two inputs have the same numSamples and num DPPS
2372 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2373 DataTagged* tmp_0=dynamic_cast<DataTagged*>(arg_0_Z.borrowData());
2374 DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2375 DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2376 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2377 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2378 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2379 int sampleNo_0,dataPointNo_0;
2380 int numSamples_0 = arg_0_Z.getNumSamples();
2381 int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2382 #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2383 for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2384 int offset_0 = tmp_0->getPointOffset(sampleNo_0,0); // They're all the same, so just use #0
2385 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2386 for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2387 int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2388 int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2389 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2390 double *ptr_2 = &(res.getDataAtOffset(offset_2));
2391 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2392 }
2393 }
2394
2395 }
2396 else if (arg_0_Z.isExpanded() && arg_1_Z.isConstant()) {
2397
2398 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2399 DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2400 DataConstant* tmp_1=dynamic_cast<DataConstant*>(arg_1_Z.borrowData());
2401 DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2402 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2403 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataConstant."); }
2404 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2405 int sampleNo_0,dataPointNo_0;
2406 int numSamples_0 = arg_0_Z.getNumSamples();
2407 int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2408 int offset_1 = tmp_1->getPointOffset(0,0);
2409 #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2410 for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2411 for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2412 int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2413 int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2414 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2415 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2416 double *ptr_2 = &(res.getDataAtOffset(offset_2));
2417 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2418 }
2419 }
2420
2421
2422 }
2423 else if (arg_0_Z.isExpanded() && arg_1_Z.isTagged()) {
2424
2425 // After finding a common function space above the two inputs have the same numSamples and num DPPS
2426 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2427 DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2428 DataTagged* tmp_1=dynamic_cast<DataTagged*>(arg_1_Z.borrowData());
2429 DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2430 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2431 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataTagged."); }
2432 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2433 int sampleNo_0,dataPointNo_0;
2434 int numSamples_0 = arg_0_Z.getNumSamples();
2435 int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2436 #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2437 for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2438 int offset_1 = tmp_1->getPointOffset(sampleNo_0,0);
2439 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2440 for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2441 int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2442 int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2443 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2444 double *ptr_2 = &(res.getDataAtOffset(offset_2));
2445 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2446 }
2447 }
2448
2449 }
2450 else if (arg_0_Z.isExpanded() && arg_1_Z.isExpanded()) {
2451
2452 // After finding a common function space above the two inputs have the same numSamples and num DPPS
2453 res = Data(0.0, shape2, arg_1_Z.getFunctionSpace(),true); // DataExpanded output
2454 DataExpanded* tmp_0=dynamic_cast<DataExpanded*>(arg_0_Z.borrowData());
2455 DataExpanded* tmp_1=dynamic_cast<DataExpanded*>(arg_1_Z.borrowData());
2456 DataExpanded* tmp_2=dynamic_cast<DataExpanded*>(res.borrowData());
2457 if (tmp_0==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2458 if (tmp_1==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2459 if (tmp_2==0) { throw DataException("GTP Programming error - casting to DataExpanded."); }
2460 int sampleNo_0,dataPointNo_0;
2461 int numSamples_0 = arg_0_Z.getNumSamples();
2462 int numDataPointsPerSample_0 = arg_0_Z.getNumDataPointsPerSample();
2463 #pragma omp parallel for private(sampleNo_0,dataPointNo_0) schedule(static)
2464 for (sampleNo_0 = 0; sampleNo_0 < numSamples_0; sampleNo_0++) {
2465 for (dataPointNo_0 = 0; dataPointNo_0 < numDataPointsPerSample_0; dataPointNo_0++) {
2466 int offset_0 = tmp_0->getPointOffset(sampleNo_0,dataPointNo_0);
2467 int offset_1 = tmp_1->getPointOffset(sampleNo_0,dataPointNo_0);
2468 int offset_2 = tmp_2->getPointOffset(sampleNo_0,dataPointNo_0);
2469 double *ptr_0 = &(arg_0_Z.getDataAtOffset(offset_0));
2470 double *ptr_1 = &(arg_1_Z.getDataAtOffset(offset_1));
2471 double *ptr_2 = &(res.getDataAtOffset(offset_2));
2472 matrix_matrix_product(SL, SM, SR, ptr_0, ptr_1, ptr_2, transpose);
2473 }
2474 }
2475
2476 }
2477 else {
2478 throw DataException("Error - C_GeneralTensorProduct: unknown combination of inputs");
2479 }
2480
2481 return res;
2482 }
2483
2484 DataAbstract*
2485 Data::borrowData() const
2486 {
2487 return m_data.get();
2488 }
2489
2490
2491 std::string
2492 Data::toString() const
2493 {
2494 static const DataTypes::ValueType::size_type TOO_MANY_POINTS=80;
2495 if (getNumDataPoints()*getDataPointSize()>TOO_MANY_POINTS)
2496 {
2497 stringstream temp;
2498 temp << "Summary: inf="<< inf() << " sup=" << sup() << " data points=" << getNumDataPoints();
2499 return temp.str();
2500 }
2501 return m_data->toString();
2502 }
2503
2504
2505
2506 DataTypes::ValueType::const_reference
2507 Data::getDataAtOffset(DataTypes::ValueType::size_type i) const
2508 {
2509 return m_data->getDataAtOffset(i);
2510 }
2511
2512
2513 DataTypes::ValueType::reference
2514 Data::getDataAtOffset(DataTypes::ValueType::size_type i)
2515 {
2516 return m_data->getDataAtOffset(i);
2517 }
2518
2519 DataTypes::ValueType::const_reference
2520 Data::getDataPoint(int sampleNo, int dataPointNo) const
2521 {
2522 return m_data->getDataAtOffset(m_data->getPointOffset(sampleNo, dataPointNo));
2523 }
2524
2525
2526 DataTypes::ValueType::reference
2527 Data::getDataPoint(int sampleNo, int dataPointNo)
2528 {
2529 return m_data->getDataAtOffset(m_data->getPointOffset(sampleNo, dataPointNo));
2530 }
2531
2532
2533 /* Member functions specific to the MPI implementation */
2534
2535 void
2536 Data::print()
2537 {
2538 int i,j;
2539
2540 printf( "Data is %dX%d\n", getNumSamples(), getNumDataPointsPerSample() );
2541 for( i=0; i<getNumSamples(); i++ )
2542 {
2543 printf( "[%6d]", i );
2544 for( j=0; j<getNumDataPointsPerSample(); j++ )
2545 printf( "\t%10.7g", (getSampleData(i))[j] );
2546 printf( "\n" );
2547 }
2548 }
2549 void
2550 Data::dump(const std::string fileName) const
2551 {
2552 try
2553 {
2554 return m_data->dump(fileName);
2555 }
2556 catch (exception& e)
2557 {
2558 cout << e.what() << endl;
2559 }
2560 }
2561
2562 int
2563 Data::get_MPISize() const
2564 {
2565 int size;
2566 #ifdef PASO_MPI
2567 int error;
2568 error = MPI_Comm_size( get_MPIComm(), &size );
2569 #else
2570 size = 1;
2571 #endif
2572 return size;
2573 }
2574
2575 int
2576 Data::get_MPIRank() const
2577 {
2578 int rank;
2579 #ifdef PASO_MPI
2580 int error;
2581 error = MPI_Comm_rank( get_MPIComm(), &rank );
2582 #else
2583 rank = 0;
2584 #endif
2585 return rank;
2586 }
2587
2588 MPI_Comm
2589 Data::get_MPIComm() const
2590 {
2591 #ifdef PASO_MPI
2592 return MPI_COMM_WORLD;
2593 #else
2594 return -1;
2595 #endif
2596 }
2597
2598

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