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root/OpenMD/trunk/src/math/CubicSpline.cpp
Revision: 2058
Committed: Tue Mar 3 15:35:45 2015 UTC (10 years, 1 month ago) by gezelter
File size: 9343 byte(s)
Log Message:
Replaced std::iota with explicit code

File Contents

# Content
1 /*
2 * Copyright (c) 2005 The University of Notre Dame. All Rights Reserved.
3 *
4 * The University of Notre Dame grants you ("Licensee") a
5 * non-exclusive, royalty free, license to use, modify and
6 * redistribute this software in source and binary code form, provided
7 * that the following conditions are met:
8 *
9 * 1. Redistributions of source code must retain the above copyright
10 * notice, this list of conditions and the following disclaimer.
11 *
12 * 2. Redistributions in binary form must reproduce the above copyright
13 * notice, this list of conditions and the following disclaimer in the
14 * documentation and/or other materials provided with the
15 * distribution.
16 *
17 * This software is provided "AS IS," without a warranty of any
18 * kind. All express or implied conditions, representations and
19 * warranties, including any implied warranty of merchantability,
20 * fitness for a particular purpose or non-infringement, are hereby
21 * excluded. The University of Notre Dame and its licensors shall not
22 * be liable for any damages suffered by licensee as a result of
23 * using, modifying or distributing the software or its
24 * derivatives. In no event will the University of Notre Dame or its
25 * licensors be liable for any lost revenue, profit or data, or for
26 * direct, indirect, special, consequential, incidental or punitive
27 * damages, however caused and regardless of the theory of liability,
28 * arising out of the use of or inability to use software, even if the
29 * University of Notre Dame has been advised of the possibility of
30 * such damages.
31 *
32 * SUPPORT OPEN SCIENCE! If you use OpenMD or its source code in your
33 * research, please cite the appropriate papers when you publish your
34 * work. Good starting points are:
35 *
36 * [1] Meineke, et al., J. Comp. Chem. 26, 252-271 (2005).
37 * [2] Fennell & Gezelter, J. Chem. Phys. 124, 234104 (2006).
38 * [3] Sun, Lin & Gezelter, J. Chem. Phys. 128, 234107 (2008).
39 * [4] Kuang & Gezelter, J. Chem. Phys. 133, 164101 (2010).
40 * [5] Vardeman, Stocker & Gezelter, J. Chem. Theory Comput. 7, 834 (2011).
41 */
42
43 #include "math/CubicSpline.hpp"
44 #include <cmath>
45 #include <cassert>
46 #include <cstdio>
47 #include <algorithm>
48 #include <numeric>
49
50 using namespace OpenMD;
51 using namespace std;
52
53 CubicSpline::CubicSpline() : generated(false), isUniform(true) {
54 x_.clear();
55 y_.clear();
56 }
57
58 void CubicSpline::addPoint(const RealType xp, const RealType yp) {
59 x_.push_back(xp);
60 y_.push_back(yp);
61 }
62
63 void CubicSpline::addPoints(const vector<RealType>& xps,
64 const vector<RealType>& yps) {
65
66 assert(xps.size() == yps.size());
67
68 for (unsigned int i = 0; i < xps.size(); i++){
69 x_.push_back(xps[i]);
70 y_.push_back(yps[i]);
71 }
72 }
73
74 void CubicSpline::generate() {
75 // Calculate coefficients defining a smooth cubic interpolatory spline.
76 //
77 // class values constructed:
78 // n = number of data_ points.
79 // x_ = vector of independent variable values
80 // y_ = vector of dependent variable values
81 // b = vector of S'(x_[i]) values.
82 // c = vector of S"(x_[i])/2 values.
83 // d = vector of S'''(x_[i]+)/6 values (i < n).
84 // Local variables:
85
86 RealType fp1, fpn, h, p;
87
88 // make sure the sizes match
89
90 n = x_.size();
91 b.resize(n);
92 c.resize(n);
93 d.resize(n);
94
95 // make sure we are monotonically increasing in x:
96
97 bool sorted = true;
98
99 for (int i = 1; i < n; i++) {
100 if ( (x_[i] - x_[i-1] ) <= 0.0 ) sorted = false;
101 }
102
103 // sort if necessary
104
105 if (!sorted) {
106 vector<int> p = sort_permutation(x_);
107 x_ = apply_permutation(x_, p);
108 y_ = apply_permutation(y_, p);
109 }
110
111
112 // Calculate coefficients for the tridiagonal system: store
113 // sub-diagonal in B, diagonal in D, difference quotient in C.
114
115 b[0] = x_[1] - x_[0];
116 c[0] = (y_[1] - y_[0]) / b[0];
117
118 if (n == 2) {
119
120 // Assume the derivatives at both endpoints are zero. Another
121 // assumption could be made to have a linear interpolant between
122 // the two points. In that case, the b coefficients below would be
123 // (y_[1] - y_[0]) / (x_[1] - x_[0])
124 // and the c and d coefficients would both be zero.
125 b[0] = 0.0;
126 c[0] = -3.0 * pow((y_[1] - y_[0]) / (x_[1] - x_[0]), 2);
127 d[0] = -2.0 * pow((y_[1] - y_[0]) / (x_[1] - x_[0]), 3);
128 b[1] = b[0];
129 c[1] = 0.0;
130 d[1] = 0.0;
131 dx = 1.0 / (x_[1] - x_[0]);
132 isUniform = true;
133 generated = true;
134 return;
135 }
136
137 d[0] = 2.0 * b[0];
138
139 for (int i = 1; i < n-1; i++) {
140 b[i] = x_[i+1] - x_[i];
141 if ( fabs( b[i] - b[0] ) / b[0] > 1.0e-5) isUniform = false;
142 c[i] = (y_[i+1] - y_[i]) / b[i];
143 d[i] = 2.0 * (b[i] + b[i-1]);
144 }
145
146 d[n-1] = 2.0 * b[n-2];
147
148 // Calculate estimates for the end slopes using polynomials
149 // that interpolate the data_ nearest the end.
150
151 fp1 = c[0] - b[0]*(c[1] - c[0])/(b[0] + b[1]);
152 if (n > 3) fp1 = fp1 + b[0]*((b[0] + b[1]) * (c[2] - c[1]) /
153 (b[1] + b[2]) -
154 c[1] + c[0]) / (x_[3] - x_[0]);
155
156 fpn = c[n-2] + b[n-2]*(c[n-2] - c[n-3])/(b[n-3] + b[n-2]);
157
158 if (n > 3) fpn = fpn + b[n-2] *
159 (c[n-2] - c[n-3] - (b[n-3] + b[n-2]) *
160 (c[n-3] - c[n-4])/(b[n-3] + b[n-4])) /
161 (x_[n-1] - x_[n-4]);
162
163 // Calculate the right hand side and store it in C.
164
165 c[n-1] = 3.0 * (fpn - c[n-2]);
166 for (int i = n-2; i > 0; i--)
167 c[i] = 3.0 * (c[i] - c[i-1]);
168 c[0] = 3.0 * (c[0] - fp1);
169
170 // Solve the tridiagonal system.
171
172 for (int k = 1; k < n; k++) {
173 p = b[k-1] / d[k-1];
174 d[k] = d[k] - p*b[k-1];
175 c[k] = c[k] - p*c[k-1];
176 }
177
178 c[n-1] = c[n-1] / d[n-1];
179
180 for (int k = n-2; k >= 0; k--)
181 c[k] = (c[k] - b[k] * c[k+1]) / d[k];
182
183 // Calculate the coefficients defining the spline.
184
185 for (int i = 0; i < n-1; i++) {
186 h = x_[i+1] - x_[i];
187 d[i] = (c[i+1] - c[i]) / (3.0 * h);
188 b[i] = (y_[i+1] - y_[i])/h - h * (c[i] + h * d[i]);
189 }
190
191 b[n-1] = b[n-2] + h * (2.0 * c[n-2] + h * 3.0 * d[n-2]);
192
193 if (isUniform) dx = 1.0 / (x_[1] - x_[0]);
194
195 generated = true;
196 return;
197 }
198
199 RealType CubicSpline::getValueAt(const RealType& t) {
200 // Evaluate the spline at t using coefficients
201 //
202 // Input parameters
203 // t = point where spline is to be evaluated.
204 // Output:
205 // value of spline at t.
206
207 if (!generated) generate();
208
209 assert(t >= x_.front());
210 assert(t <= x_.back());
211
212 // Find the interval ( x[j], x[j+1] ) that contains or is nearest
213 // to t.
214
215 if (isUniform) {
216
217 j = max(0, min(n-1, int((t - x_[0]) * dx)));
218
219 } else {
220
221 j = n-1;
222
223 for (int i = 0; i < n; i++) {
224 if ( t < x_[i] ) {
225 j = i-1;
226 break;
227 }
228 }
229 }
230
231 // Evaluate the cubic polynomial.
232
233 dt = t - x_[j];
234 return y_[j] + dt*(b[j] + dt*(c[j] + dt*d[j]));
235 }
236
237
238 void CubicSpline::getValueAt(const RealType& t, RealType& v) {
239 // Evaluate the spline at t using coefficients
240 //
241 // Input parameters
242 // t = point where spline is to be evaluated.
243 // Output:
244 // value of spline at t.
245
246 if (!generated) generate();
247
248 assert(t >= x_.front());
249 assert(t <= x_.back());
250
251 // Find the interval ( x[j], x[j+1] ) that contains or is nearest
252 // to t.
253
254 if (isUniform) {
255
256 j = max(0, min(n-1, int((t - x_[0]) * dx)));
257
258 } else {
259
260 j = n-1;
261
262 for (int i = 0; i < n; i++) {
263 if ( t < x_[i] ) {
264 j = i-1;
265 break;
266 }
267 }
268 }
269
270 // Evaluate the cubic polynomial.
271
272 dt = t - x_[j];
273 v = y_[j] + dt*(b[j] + dt*(c[j] + dt*d[j]));
274 }
275
276 pair<RealType, RealType> CubicSpline::getLimits(){
277 if (!generated) generate();
278 return make_pair( x_.front(), x_.back() );
279 }
280
281 void CubicSpline::getValueAndDerivativeAt(const RealType& t, RealType& v,
282 RealType &dv) {
283 // Evaluate the spline and first derivative at t using coefficients
284 //
285 // Input parameters
286 // t = point where spline is to be evaluated.
287
288 if (!generated) generate();
289
290 assert(t >= x_.front());
291 assert(t <= x_.back());
292
293 // Find the interval ( x[j], x[j+1] ) that contains or is nearest
294 // to t.
295
296 if (isUniform) {
297
298 j = max(0, min(n-1, int((t - x_[0]) * dx)));
299
300 } else {
301
302 j = n-1;
303
304 for (int i = 0; i < n; i++) {
305 if ( t < x_[i] ) {
306 j = i-1;
307 break;
308 }
309 }
310 }
311
312 // Evaluate the cubic polynomial.
313
314 dt = t - x_[j];
315
316 v = y_[j] + dt*(b[j] + dt*(c[j] + dt*d[j]));
317 dv = b[j] + dt*(2.0 * c[j] + 3.0 * dt * d[j]);
318 }
319
320 std::vector<int> CubicSpline::sort_permutation(std::vector<RealType>& v) {
321 std::vector<int> p(v.size());
322
323 // 6 lines to replace std::iota(p.begin(), p.end(), 0);
324 int value = 0;
325 std::vector<int>::iterator i;
326 for (i = p.begin(); i != p.end(); ++i) {
327 (*i) = value;
328 ++value;
329 }
330
331 std::sort(p.begin(), p.end(), OpenMD::Comparator(v) );
332 return p;
333 }
334
335 std::vector<RealType> CubicSpline::apply_permutation(std::vector<RealType> const& v,
336 std::vector<int> const& p) {
337 int n = p.size();
338 std::vector<double> sorted_vec(n);
339 for (int i = 0; i < n; ++i) {
340 sorted_vec[i] = v[p[i]];
341 }
342 return sorted_vec;
343 }

Properties

Name Value
svn:eol-style native