| 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 | < | data_.clear(); | 
| 53 | > | CubicSpline::CubicSpline() : isUniform(true), generated(false) { | 
| 54 | > | x_.clear(); | 
| 55 | > | y_.clear(); | 
| 56 |  | } | 
| 57 |  |  | 
| 58 |  | void CubicSpline::addPoint(const RealType xp, const RealType yp) { | 
| 59 | < | data_.push_back(make_pair(xp, yp)); | 
| 59 | > | x_.push_back(xp); | 
| 60 | > | y_.push_back(yp); | 
| 61 |  | } | 
| 62 |  |  | 
| 63 |  | void CubicSpline::addPoints(const vector<RealType>& xps, | 
| 65 |  |  | 
| 66 |  | assert(xps.size() == yps.size()); | 
| 67 |  |  | 
| 68 | < | for (unsigned int i = 0; i < xps.size(); i++) | 
| 69 | < | data_.push_back(make_pair(xps[i], yps[i])); | 
| 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() { | 
| 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). | 
| 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 = data_.size(); | 
| 90 | > | n = x_.size(); | 
| 91 |  | b.resize(n); | 
| 92 |  | c.resize(n); | 
| 93 |  | d.resize(n); | 
| 97 |  | bool sorted = true; | 
| 98 |  |  | 
| 99 |  | for (int i = 1; i < n; i++) { | 
| 100 | < | if ( (data_[i].first - data_[i-1].first ) <= 0.0 ) sorted = false; | 
| 100 | > | if ( (x_[i] - x_[i-1] ) <= 0.0 ) sorted = false; | 
| 101 |  | } | 
| 102 |  |  | 
| 103 |  | // sort if necessary | 
| 104 |  |  | 
| 105 | < | if (!sorted) sort(data_.begin(), data_.end()); | 
| 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] = data_[1].first - data_[0].first; | 
| 116 | < | c[0] = (data_[1].second - data_[0].second) / b[0]; | 
| 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 | < | // (data_[1].second - data_[0].second) / (data_[1].first - data_[0].first) | 
| 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((data_[1].second - data_[0].second) / | 
| 127 | < | (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); | 
| 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 / (data_[1].first - data_[0].first); | 
| 131 | > | dx = 1.0 / (x_[1] - x_[0]); | 
| 132 |  | isUniform = true; | 
| 133 |  | generated = true; | 
| 134 |  | return; | 
| 137 |  | d[0] = 2.0 * b[0]; | 
| 138 |  |  | 
| 139 |  | for (int i = 1; i < n-1; i++) { | 
| 140 | < | b[i] = data_[i+1].first - data_[i].first; | 
| 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] = (data_[i+1].second - data_[i].second) / b[i]; | 
| 142 | > | c[i] = (y_[i+1] - y_[i]) / b[i]; | 
| 143 |  | d[i] = 2.0 * (b[i] + b[i-1]); | 
| 144 |  | } | 
| 145 |  |  | 
| 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]) / (data_[3].first - data_[0].first); | 
| 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 | < | (data_[n-1].first - data_[n-4].first); | 
| 161 | > | (x_[n-1] - x_[n-4]); | 
| 162 |  |  | 
| 163 |  | // Calculate the right hand side and store it in C. | 
| 164 |  |  | 
| 183 |  | // Calculate the coefficients defining the spline. | 
| 184 |  |  | 
| 185 |  | for (int i = 0; i < n-1; i++) { | 
| 186 | < | h = data_[i+1].first - data_[i].first; | 
| 186 | > | h = x_[i+1] - x_[i]; | 
| 187 |  | d[i] = (c[i+1] - c[i]) / (3.0 * h); | 
| 188 | < | b[i] = (data_[i+1].second - data_[i].second)/h - h * (c[i] + h * d[i]); | 
| 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 / (data_[1].first - data_[0].first); | 
| 193 | > | if (isUniform) dx = 1.0 / (x_[1] - x_[0]); | 
| 194 |  |  | 
| 195 |  | generated = true; | 
| 196 |  | return; | 
| 206 |  |  | 
| 207 |  | if (!generated) generate(); | 
| 208 |  |  | 
| 209 | < | assert(t >= data_.front().first); | 
| 210 | < | assert(t <= data_.back().first); | 
| 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 - data_[0].first) * dx))); | 
| 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 < data_[i].first ) { | 
| 224 | > | if ( t < x_[i] ) { | 
| 225 |  | j = i-1; | 
| 226 |  | break; | 
| 227 |  | } | 
| 230 |  |  | 
| 231 |  | //  Evaluate the cubic polynomial. | 
| 232 |  |  | 
| 233 | < | dt = t - data_[j].first; | 
| 234 | < | return data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j])); | 
| 233 | > | dt = t - x_[j]; | 
| 234 | > | return y_[j] + dt*(b[j] + dt*(c[j] + dt*d[j])); | 
| 235 |  | } | 
| 236 |  |  | 
| 237 |  |  | 
| 242 |  | //   t = point where spline is to be evaluated. | 
| 243 |  | // Output: | 
| 244 |  | //   value of spline 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 | – | } | 
| 245 |  |  | 
| 262 | – | //  Evaluate the cubic polynomial. | 
| 263 | – |  | 
| 264 | – | dt = t - data_[j].first; | 
| 265 | – | v = data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j])); | 
| 266 | – | } | 
| 267 | – |  | 
| 268 | – |  | 
| 269 | – | pair<RealType, RealType> CubicSpline::getValueAndDerivativeAt(const RealType& t){ | 
| 270 | – | // Evaluate the spline and first derivative at t using coefficients | 
| 271 | – | // | 
| 272 | – | // Input parameters | 
| 273 | – | //   t = point where spline is to be evaluated. | 
| 274 | – | // Output: | 
| 275 | – | //   pair containing value of spline at t and first derivative at t | 
| 276 | – |  | 
| 246 |  | if (!generated) generate(); | 
| 247 |  |  | 
| 248 | < | assert(t >= data_.front().first); | 
| 249 | < | assert(t <= data_.back().first); | 
| 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 - data_[0].first) * dx))); | 
| 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 < data_[i].first ) { | 
| 263 | > | if ( t < x_[i] ) { | 
| 264 |  | j = i-1; | 
| 265 |  | break; | 
| 266 |  | } | 
| 269 |  |  | 
| 270 |  | //  Evaluate the cubic polynomial. | 
| 271 |  |  | 
| 272 | < | dt = t - data_[j].first; | 
| 273 | < |  | 
| 305 | < | yval = data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j])); | 
| 306 | < | dydx = b[j] + dt*(2.0 * c[j] + 3.0 * dt * d[j]); | 
| 307 | < |  | 
| 308 | < | return make_pair(yval, dydx); | 
| 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( data_.front().first, data_.back().first ); | 
| 278 | > | return make_pair( x_.front(), x_.back() ); | 
| 279 |  | } | 
| 280 |  |  | 
| 281 |  | void CubicSpline::getValueAndDerivativeAt(const RealType& t, RealType& v, | 
| 287 |  |  | 
| 288 |  | if (!generated) generate(); | 
| 289 |  |  | 
| 290 | < | assert(t >= data_.front().first); | 
| 291 | < | assert(t <= data_.back().first); | 
| 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 - data_[0].first) * dx))); | 
| 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 < data_[i].first ) { | 
| 305 | > | if ( t < x_[i] ) { | 
| 306 |  | j = i-1; | 
| 307 |  | break; | 
| 308 |  | } | 
| 311 |  |  | 
| 312 |  | //  Evaluate the cubic polynomial. | 
| 313 |  |  | 
| 314 | < | dt = t - data_[j].first; | 
| 314 | > | dt = t - x_[j]; | 
| 315 |  |  | 
| 316 | < | v = data_[j].second + dt*(b[j] + dt*(c[j] + dt*d[j])); | 
| 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 | + | } |