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root/OpenMD/branches/development/src/math/CubicSpline.cpp
Revision: 1855
Committed: Tue Apr 2 18:31:51 2013 UTC (12 years ago) by gezelter
File size: 8186 byte(s)
Log Message:
Fixed a bunch of bugs in CubicSpline debug sections, ForceMatrix Decomp 
computing costhetas for non-existent rotation matrices, Hidden accumulator counts, and SMIPD non-coupling

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
49 using namespace OpenMD;
50 using namespace std;
51
52 CubicSpline::CubicSpline() : generated(false), isUniform(true) {
53 data_.clear();
54 }
55
56 void CubicSpline::addPoint(const RealType xp, const RealType yp) {
57 data_.push_back(make_pair(xp, yp));
58 }
59
60 void CubicSpline::addPoints(const vector<RealType>& xps,
61 const vector<RealType>& yps) {
62
63 assert(xps.size() == yps.size());
64
65 for (unsigned int i = 0; i < xps.size(); i++)
66 data_.push_back(make_pair(xps[i], yps[i]));
67 }
68
69 void CubicSpline::generate() {
70 // Calculate coefficients defining a smooth cubic interpolatory spline.
71 //
72 // class values constructed:
73 // n = number of data_ points.
74 // x = vector of independent variable values
75 // y = vector of dependent variable values
76 // b = vector of S'(x[i]) values.
77 // c = vector of S"(x[i])/2 values.
78 // d = vector of S'''(x[i]+)/6 values (i < n).
79 // Local variables:
80
81 RealType fp1, fpn, h, p;
82
83 // make sure the sizes match
84
85 n = data_.size();
86 b.resize(n);
87 c.resize(n);
88 d.resize(n);
89
90 // make sure we are monotonically increasing in x:
91
92 bool sorted = true;
93
94 for (int i = 1; i < n; i++) {
95 if ( (data_[i].first - data_[i-1].first ) <= 0.0 ) sorted = false;
96 }
97
98 // sort if necessary
99
100 if (!sorted) sort(data_.begin(), data_.end());
101
102 // Calculate coefficients for the tridiagonal system: store
103 // sub-diagonal in B, diagonal in D, difference quotient in C.
104
105 b[0] = data_[1].first - data_[0].first;
106 c[0] = (data_[1].second - data_[0].second) / b[0];
107
108 if (n == 2) {
109
110 // Assume the derivatives at both endpoints are zero. Another
111 // assumption could be made to have a linear interpolant between
112 // the two points. In that case, the b coefficients below would be
113 // (data_[1].second - data_[0].second) / (data_[1].first - data_[0].first)
114 // and the c and d coefficients would both be zero.
115 b[0] = 0.0;
116 c[0] = -3.0 * pow((data_[1].second - data_[0].second) /
117 (data_[1].first-data_[0].first), 2);
118 d[0] = -2.0 * pow((data_[1].second - data_[0].second) /
119 (data_[1].first-data_[0].first), 3);
120 b[1] = b[0];
121 c[1] = 0.0;
122 d[1] = 0.0;
123 dx = 1.0 / (data_[1].first - data_[0].first);
124 isUniform = true;
125 generated = true;
126 return;
127 }
128
129 d[0] = 2.0 * b[0];
130
131 for (int i = 1; i < n-1; i++) {
132 b[i] = data_[i+1].first - data_[i].first;
133 if ( fabs( b[i] - b[0] ) / b[0] > 1.0e-5) isUniform = false;
134 c[i] = (data_[i+1].second - data_[i].second) / b[i];
135 d[i] = 2.0 * (b[i] + b[i-1]);
136 }
137
138 d[n-1] = 2.0 * b[n-2];
139
140 // Calculate estimates for the end slopes using polynomials
141 // that interpolate the data_ nearest the end.
142
143 fp1 = c[0] - b[0]*(c[1] - c[0])/(b[0] + b[1]);
144 if (n > 3) fp1 = fp1 + b[0]*((b[0] + b[1]) * (c[2] - c[1]) /
145 (b[1] + b[2]) -
146 c[1] + c[0]) / (data_[3].first - data_[0].first);
147
148 fpn = c[n-2] + b[n-2]*(c[n-2] - c[n-3])/(b[n-3] + b[n-2]);
149
150 if (n > 3) fpn = fpn + b[n-2] *
151 (c[n-2] - c[n-3] - (b[n-3] + b[n-2]) *
152 (c[n-3] - c[n-4])/(b[n-3] + b[n-4])) /
153 (data_[n-1].first - data_[n-4].first);
154
155 // Calculate the right hand side and store it in C.
156
157 c[n-1] = 3.0 * (fpn - c[n-2]);
158 for (int i = n-2; i > 0; i--)
159 c[i] = 3.0 * (c[i] - c[i-1]);
160 c[0] = 3.0 * (c[0] - fp1);
161
162 // Solve the tridiagonal system.
163
164 for (int k = 1; k < n; k++) {
165 p = b[k-1] / d[k-1];
166 d[k] = d[k] - p*b[k-1];
167 c[k] = c[k] - p*c[k-1];
168 }
169
170 c[n-1] = c[n-1] / d[n-1];
171
172 for (int k = n-2; k >= 0; k--)
173 c[k] = (c[k] - b[k] * c[k+1]) / d[k];
174
175 // Calculate the coefficients defining the spline.
176
177 for (int i = 0; i < n-1; i++) {
178 h = data_[i+1].first - data_[i].first;
179 d[i] = (c[i+1] - c[i]) / (3.0 * h);
180 b[i] = (data_[i+1].second - data_[i].second)/h - h * (c[i] + h * d[i]);
181 }
182
183 b[n-1] = b[n-2] + h * (2.0 * c[n-2] + h * 3.0 * d[n-2]);
184
185 if (isUniform) dx = 1.0 / (data_[1].first - data_[0].first);
186
187 generated = true;
188 return;
189 }
190
191 RealType CubicSpline::getValueAt(RealType t) {
192 // Evaluate the spline at t using coefficients
193 //
194 // Input parameters
195 // t = point where spline is to be evaluated.
196 // Output:
197 // value of spline at t.
198
199 if (!generated) generate();
200
201 assert(t > data_.front().first);
202 assert(t < data_.back().first);
203
204 // Find the interval ( x[j], x[j+1] ) that contains or is nearest
205 // to t.
206
207 if (isUniform) {
208
209 j = max(0, min(n-1, int((t - data_[0].first) * dx)));
210
211 } else {
212
213 j = n-1;
214
215 for (int i = 0; i < n; i++) {
216 if ( t < data_[i].first ) {
217 j = i-1;
218 break;
219 }
220 }
221 }
222
223 // Evaluate the cubic polynomial.
224
225 dt = t - data_[j].first;
226 return data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j]));
227 }
228
229
230 pair<RealType, RealType> CubicSpline::getValueAndDerivativeAt(RealType t) {
231 // Evaluate the spline and first derivative at t using coefficients
232 //
233 // Input parameters
234 // t = point where spline is to be evaluated.
235 // Output:
236 // pair containing value of spline at t and first derivative at t
237
238 if (!generated) generate();
239
240 assert(t > data_.front().first);
241 assert(t < data_.back().first);
242
243 // Find the interval ( x[j], x[j+1] ) that contains or is nearest
244 // to t.
245
246 if (isUniform) {
247
248 j = max(0, min(n-1, int((t - data_[0].first) * dx)));
249
250 } else {
251
252 j = n-1;
253
254 for (int i = 0; i < n; i++) {
255 if ( t < data_[i].first ) {
256 j = i-1;
257 break;
258 }
259 }
260 }
261
262 // Evaluate the cubic polynomial.
263
264 dt = t - data_[j].first;
265
266 yval = data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j]));
267 dydx = b[j] + dt*(2.0 * c[j] + 3.0 * dt * d[j]);
268
269 return make_pair(yval, dydx);
270 }

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