/[escript]/branches/arrexp_2137_win_merge/escript/src/DataVector.cpp
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Revision 1796 - (show annotations)
Wed Sep 17 01:45:46 2008 UTC (11 years ago) by jfenwick
Original Path: trunk/escript/src/DataVector.cpp
File size: 5421 byte(s)
Merged noarrayview branch onto trunk.


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 "DataVector.h"
17
18 #include "Taipan.h"
19 #include "DataException.h"
20 #include <boost/python/extract.hpp>
21 #include "DataTypes.h"
22
23 #include <cassert>
24
25 using namespace std;
26 using namespace escript;
27 using namespace boost::python;
28
29 namespace escript {
30
31 Taipan arrayManager;
32
33 void releaseUnusedMemory()
34 {
35 arrayManager.release_unused_arrays();
36 }
37
38
39 DataVector::DataVector() :
40 m_array_data(0),
41 m_size(0),
42 m_dim(0),
43 m_N(0)
44 {
45 }
46
47 DataVector::DataVector(const DataVector& other) :
48 m_array_data(0),
49 m_size(other.m_size),
50 m_dim(other.m_dim),
51 m_N(other.m_N)
52 {
53 m_array_data = arrayManager.new_array(m_dim,m_N);
54 int i;
55 #pragma omp parallel for private(i) schedule(static)
56 for (i=0; i<m_size; i++) {
57 m_array_data[i] = other.m_array_data[i];
58 }
59 }
60
61 DataVector::DataVector(const DataVector::size_type size,
62 const DataVector::value_type val,
63 const DataVector::size_type blockSize) :
64 m_array_data(0),
65 m_size(size),
66 m_dim(blockSize)
67 {
68 resize(size, val, blockSize);
69 }
70
71 DataVector::~DataVector()
72 {
73 // dispose of data array
74 if (m_array_data!=0) {
75 arrayManager.delete_array(m_array_data);
76 }
77
78 // clear data members
79 m_size = -1;
80 m_dim = -1;
81 m_N = -1;
82 m_array_data = 0;
83 }
84
85 void
86 DataVector::resize(const DataVector::size_type newSize,
87 const DataVector::value_type newValue,
88 const DataVector::size_type newBlockSize)
89 {
90 assert(m_size >= 0);
91
92 if ( newBlockSize == 0) {
93 throw DataException("DataVector: invalid blockSize specified (newBlockSize)");
94 }
95
96 if ( (newSize % newBlockSize) != 0) {
97 throw DataException("DataVector: invalid blockSize specified");
98 }
99
100 if (m_array_data!=0) {
101 arrayManager.delete_array(m_array_data);
102 }
103
104 m_size = newSize;
105 m_dim = newBlockSize;
106 m_N = newSize / newBlockSize;
107 m_array_data = arrayManager.new_array(m_dim,m_N);
108
109 int i;
110 #pragma omp parallel for private(i) schedule(static)
111 for (i=0; i<m_size; i++) {
112 m_array_data[i] = newValue;
113 }
114 }
115
116 DataVector&
117 DataVector::operator=(const DataVector& other)
118 {
119 assert(m_size >= 0);
120
121 if (m_array_data!=0) {
122 arrayManager.delete_array(m_array_data);
123 }
124
125 m_size = other.m_size;
126 m_dim = other.m_dim;
127 m_N = other.m_N;
128
129 m_array_data = arrayManager.new_array(m_dim,m_N);
130 int i;
131 #pragma omp parallel for private(i) schedule(static)
132 for (i=0; i<m_size; i++) {
133 m_array_data[i] = other.m_array_data[i];
134 }
135
136 return *this;
137 }
138
139 bool
140 DataVector::operator==(const DataVector& other) const
141 {
142 assert(m_size >= 0);
143
144 if (m_size!=other.m_size) {
145 return false;
146 }
147 if (m_dim!=other.m_dim) {
148 return false;
149 }
150 if (m_N!=other.m_N) {
151 return false;
152 }
153 for (int i=0; i<m_size; i++) {
154 if (m_array_data[i] != other.m_array_data[i]) {
155 return false;
156 }
157 }
158 return true;
159 }
160
161 bool
162 DataVector::operator!=(const DataVector& other) const
163 {
164 return !(*this==other);
165 }
166
167
168 void
169 DataVector::copyFromNumArray(const boost::python::numeric::array& value)
170 {
171 using DataTypes::ValueType;
172 if (m_array_data!=0) {
173 arrayManager.delete_array(m_array_data);
174 }
175
176
177 m_array_data = arrayManager.new_array(1,value.nelements());
178
179 int si=0,sj=0,sk=0,sl=0; // bounds for each dimension of the shape
180 DataTypes::ShapeType tempShape;
181 for (int i=0; i<value.getrank(); i++) {
182 tempShape.push_back(extract<int>(value.getshape()[i]));
183 }
184
185 if (value.getrank()==0) {
186 m_array_data[0]=extract<double>(value[value.getshape()]);
187 } else if (value.getrank()==1) {
188 si=tempShape[0];
189 for (ValueType::size_type i=0;i<si;i++) {
190 m_array_data[i]=extract<double>(value[i]);
191 }
192 } else if (value.getrank()==2) {
193 si=tempShape[0];
194 sj=tempShape[1];
195 for (ValueType::size_type i=0;i<si;i++) {
196 for (ValueType::size_type j=0;j<sj;j++) {
197 m_array_data[DataTypes::getRelIndex(tempShape,i,j)]=extract<double>(value[i][j]);
198 }
199 }
200 } else if (value.getrank()==3) {
201 si=tempShape[0];
202 sj=tempShape[1];
203 sk=tempShape[2];
204 for (ValueType::size_type i=0;i<si;i++) {
205 for (ValueType::size_type j=0;j<sj;j++) {
206 for (ValueType::size_type k=0;k<sk;k++) {
207 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k)]=extract<double>(value[i][j][k]);
208 }
209 }
210 }
211 } else if (value.getrank()==4) {
212 si=tempShape[0];
213 sj=tempShape[1];
214 sk=tempShape[2];
215 sl=tempShape[3];
216 for (ValueType::size_type i=0;i<si;i++) {
217 for (ValueType::size_type j=0;j<sj;j++) {
218 for (ValueType::size_type k=0;k<sk;k++) {
219 for (ValueType::size_type l=0;l<sl;l++) {
220 m_array_data[DataTypes::getRelIndex(tempShape,i,j,k,l)]=extract<double>(value[i][j][k][l]);
221 }
222 }
223 }
224 }
225 }
226 m_size=value.nelements(); // total amount of elements
227 m_dim=m_size; // elements per sample
228 m_N=1; // number of samples
229 }
230
231
232
233 } // end of namespace

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