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Revision 1440 - (show annotations)
Thu Feb 28 06:27:48 2008 UTC (11 years, 2 months ago) by trankine
File size: 75892 byte(s)
Pretty much settled on a coding pattern for the exception classes.
Other changes are me eliminating unused variables and signed/unsigned comparisons as I go.

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

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