| 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 | } |