 Timestamp:
 04/12/11 20:28:53 (9 years ago)
 File:

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trunk/projects/curvefitter/src/main/java/loci/curvefitter/SLIMCurveFitter.java
r7692 r7694 46 46 47 47 /** 48 * T ODO48 * This class is a Java wrapper around the SLIMCurve fitting C code. 49 49 * 50 50 * <dl><dt><b>Source code:</b></dt> … … 55 55 */ 56 56 public class SLIMCurveFitter extends AbstractCurveFitter { 57 //TODO old style:58 static CLibrary s_library;59 boolean m_oldStyle = false;60 //TODO61 static boolean s_loaded = false;62 57 public enum AlgorithmType { RLD, LMA, RLD_LMA }; 58 59 private static boolean s_libraryLoaded = false; 60 private static boolean s_libraryOnPath = false; 61 private static CLibrary s_library; 62 63 63 private AlgorithmType m_algorithmType; 64 64 65 //TODO old style 65 /** 66 * This interface supports loading the library using JNA. 67 */ 66 68 public interface CLibrary extends Library { 67 69 … … 102 104 double chiSquareTarget 103 105 ); 104 105 106 /* public int nr_GCI_triple_integral_fitting_engine(float xincr, float y[], int fitStart, int fitEnd, 107 float instr[], int nInstr, int noise, float sig[], 108 FloatByReference z, FloatByReference a, FloatByReference tau, 109 float fitted[], float residuals[], 110 FloatByReference chiSq, float chiSqTarget); 111 112 public int nr_GCI_marquardt_fitting_engine(float xincr, float y[], int nData, int fitStart, int fitEnd, 113 float instr[], int nInstr, int noise, float sig[], 114 float param[], int paramFree[], int nParam, 115 int restrainType, int fitType, 116 float fitted[], float residuals[], 117 FloatByReference chiSq);*/ 118 //, 119 // float covar[], float alpha[], float errAxes[], 120 // float chiSqTarget, int chiSqPercent); 121 122 123 /*public int nr_GCI_marquardt_fitting_engine(float xincr, float *trans, int ndata, int fit_start, int fit_end, 124 float prompt[], int nprompt, //TODO ARG is this actually instr[] & ninstr? 125 noise_type noise, float sig[], 126 float param[], int paramfree[], 127 int nparam, restrain_type restrain, 128 fit_type fit, //TODO ARG void (*fitfunc)(float, float [], float *, float [], int), 129 float *fitted, float *residuals, float *chisq, 130 float **covar, float **alpha, float **erraxes, 131 float chisq_target, int chisq_percent) {*/ 132 } 133 //TODO 106 } 107 108 109 /** 110 * This supports calling the libray using JNI. 111 * 112 * @param xInc 113 * @param y 114 * @param fitStart 115 * @param fitEnd 116 * @param instr 117 * @param nInstr 118 * @param sig 119 * @param z 120 * @param a 121 * @param tau 122 * @param fitted 123 * @param chiSquare 124 * @param chiSquareTarget 125 * @return 126 */ 134 127 135 128 … … 152 145 ); 153 146 147 /** 148 * This supports calling the library using JNI. 149 * 150 * @param xInc 151 * @param y 152 * @param fitStart 153 * @param fitEnd 154 * @param instr 155 * @param n_instr 156 * @param sig 157 * @param param 158 * @param paramFree 159 * @param nParam 160 * @param fitted 161 * @param chiSquare 162 * @param chiSquareTarget 163 * @return 164 */ 154 165 //TODO I'm omitted noise, see above and restrainType and fitType, for now 155 166 //TODO also covar, alpha, errAxes and chiSqPercent … … 171 182 ); 172 183 184 /** 185 * Create a curve fitter for a given algorithm type. 186 * 187 * @param algorithmType 188 */ 173 189 public SLIMCurveFitter(AlgorithmType algorithmType) { 174 190 m_algorithmType = algorithmType; 175 191 } 176 192 193 /** 194 * Create the default curve fitter, which uses RLD. 195 * 196 */ 177 197 public SLIMCurveFitter() { 178 198 m_algorithmType = AlgorithmType.RLD; … … 183 203 public int fitData(ICurveFitData[] dataArray, int start, int stop) { 184 204 int returnValue = 0; 185 IJ.log("SLIMCurveFitter.fitData " + m_algorithmType + " s_loaded " + s_l oaded + " dataArray.length " + dataArray.length);186 187 // TODO old style:188 if ( m_oldStyle) {189 190 if (null == s_library) {205 IJ.log("SLIMCurveFitter.fitData " + m_algorithmType + " s_loaded " + s_libraryLoaded + " dataArray.length " + dataArray.length); 206 207 // load the native library, if not already loaded 208 if (!s_libraryLoaded) { 209 210 // look for library on path 191 211 try { 192 // extract to library path 193 //TODO sort out the nameSystem.out.println("extract native library returns " + NativeLibraryUtil.extractNativeLibraryToPath(this.getClass(), "SLIMCurve2.0SNAPSHOT")); 194 //System.out.println("extract native library returns " + NativeLibraryUtil.extractNativeLibraryToPath(this.getClass(), "slimcurve1.0SNAPSHOT")); 195 System.out.println("loadNativeLibrary returns " + NativeLibraryUtil.loadNativeLibrary(this.getClass(), "slimcurve")); 196 197 IJ.log("before System load library"); 198 ////// System.loadLibrary("slimcurve1.0SNAPSHOT"); 199 IJ.log("after System load library"); 200 201 // load once, ondemand 202 //TODO sort out the name s_library = (CLibrary) Native.loadLibrary("SLIMCurve", CLibrary.class); 203 //TODO test with old code instead: s_library = (CLibrary) Native.loadLibrary("slimcurve1.0SNAPSHOT", CLibrary.class); 204 //TODO this was yet another version s_library = (CLibrary) Native.loadLibrary("SLIMCurve_trimmed_down", CLibrary.class); 212 // use JNA 205 213 s_library = (CLibrary) Native.loadLibrary("slimcurve1.0SNAPSHOT", CLibrary.class); 206 207 System.out.println("s_library is " + s_library); 214 s_libraryLoaded = true; 215 s_libraryOnPath = true; 216 217 IJ.log("load library from library path, use JNA"); 208 218 } 209 219 catch (UnsatisfiedLinkError e) { 210 IJ.log("unable to load dynamic library " + e.getMessage()); 211 System.out.println("unable to load dynamic library " + e.getMessage()); 220 System.out.println("Library not on path " + e.getMessage()); 221 } 222 223 if (!s_libraryLoaded) { 224 // look for library in jar, using JNI 225 s_libraryLoaded = NativeLibraryUtil.loadNativeLibrary(this.getClass(), "slimcurve"); 226 227 if (s_libraryLoaded) { 228 IJ.log("load library from jar, use JNI"); 229 } 230 } 231 232 if (!s_libraryLoaded) { 233 IJ.log("Unable to do fit."); 212 234 return 0; 213 235 } … … 230 252 } 231 253 232 DoubleByReference chiSquare = new DoubleByReference(); 233 double chiSquareTarget = 1.0; //TODO s/b specified incoming 234 235 if (AlgorithmType.RLD.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) { 236 // RLD or triple integral fit 237 DoubleByReference z = new DoubleByReference(); 238 DoubleByReference a = new DoubleByReference(); 239 DoubleByReference tau = new DoubleByReference(); 240 241 for (ICurveFitData data: dataArray) { 242 // grab incoming parameters 243 a.setValue( data.getParams()[2]); 244 tau.setValue(data.getParams()[3]); 245 z.setValue( data.getParams()[1]); 246 247 // get IRF curve, if any 248 double[] instrumentResponse = getInstrumentResponse(data.getPixels()); 249 int nInstrumentResponse = 0; 250 if (null != instrumentResponse) { 251 nInstrumentResponse = instrumentResponse.length; 254 if (s_libraryOnPath) { 255 // JNA version 256 DoubleByReference chiSquare = new DoubleByReference(); 257 double chiSquareTarget = 1.0; //TODO s/b specified incoming 258 259 if (AlgorithmType.RLD.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) { 260 // RLD or triple integral fit 261 DoubleByReference z = new DoubleByReference(); 262 DoubleByReference a = new DoubleByReference(); 263 DoubleByReference tau = new DoubleByReference(); 264 265 for (ICurveFitData data: dataArray) { 266 // grab incoming parameters 267 a.setValue( data.getParams()[2]); 268 tau.setValue(data.getParams()[3]); 269 z.setValue( data.getParams()[1]); 270 271 // get IRF curve, if any 272 double[] instrumentResponse = getInstrumentResponse(data.getPixels()); 273 int nInstrumentResponse = 0; 274 if (null != instrumentResponse) { 275 nInstrumentResponse = instrumentResponse.length; 276 } 277 278 returnValue = s_library.RLD_fit( 279 m_xInc, 280 data.getYCount(), 281 start, 282 stop, 283 instrumentResponse, 284 nInstrumentResponse, 285 data.getSig(), 286 z, 287 a, 288 tau, 289 data.getYFitted(), 290 chiSquare, 291 chiSquareTarget 292 ); 293 // set outgoing parameters, unless they are fixed 294 data.getParams()[0] = chiSquare.getValue(); 295 if (free[0]) { 296 data.getParams()[1] = z.getValue(); 297 } 298 if (free[1]) { 299 data.getParams()[2] = a.getValue(); 300 } 301 if (free[2]) { 302 data.getParams()[3] = tau.getValue(); 303 } 252 304 } 253 254 returnValue = s_library.RLD_fit( 255 m_xInc, 256 data.getYCount(), 257 start, 258 stop, 259 instrumentResponse, 260 nInstrumentResponse, 261 data.getSig(), 262 z, 263 a, 264 tau, 265 data.getYFitted(), 266 chiSquare, 267 chiSquareTarget 268 ); 269 // set outgoing parameters, unless they are fixed 270 data.getParams()[0] = chiSquare.getValue(); 271 if (free[0]) { 272 data.getParams()[1] = z.getValue(); 305 } 306 307 if (AlgorithmType.LMA.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) { 308 // LMA fit 309 for (ICurveFitData data: dataArray) { 310 int nInstrumentResponse = 0; 311 if (null != m_instrumentResponse) { 312 nInstrumentResponse = m_instrumentResponse.length; 313 } 314 returnValue = s_library.LMA_fit( 315 m_xInc, 316 data.getYCount(), 317 start, 318 stop, 319 m_instrumentResponse, 320 nInstrumentResponse, 321 data.getSig(), 322 data.getParams(), 323 toIntArray(m_free), 324 data.getParams().length  1, 325 data.getYFitted(), 326 chiSquare, 327 chiSquareTarget 328 ); 273 329 } 274 if (free[1]) { 275 data.getParams()[2] = a.getValue(); 330 } 331 } 332 else { 333 // JNI version 334 335 // use array to pass double by reference 336 double[] chiSquare = new double[1]; 337 double chiSquareTarget = 1.0; //TODO s/b specified incoming 338 339 if (AlgorithmType.RLD.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) { 340 // RLD or triple integral fit 341 342 // use arrays to pass double by reference 343 double[] z = new double[1]; 344 double[] a = new double[1]; 345 double[] tau = new double[1]; 346 347 for (ICurveFitData data: dataArray) { 348 // grab incoming parameters 349 a[0] = data.getParams()[2]; 350 tau[0] = data.getParams()[3]; 351 z[0] = data.getParams()[1]; 352 353 // get IRF curve, if any 354 double[] instrumentResponse = getInstrumentResponse(data.getPixels()); 355 int nInstrumentResponse = 0; 356 if (null != instrumentResponse) { 357 nInstrumentResponse = instrumentResponse.length; 358 } 359 360 returnValue = RLD_fit(m_xInc, 361 data.getYCount(), 362 start, 363 stop, 364 instrumentResponse, 365 nInstrumentResponse, 366 data.getSig(), 367 z, 368 a, 369 tau, 370 data.getYFitted(), 371 chiSquare, 372 chiSquareTarget 373 ); 374 375 // set outgoing parameters, unless they are fixed 376 data.getParams()[0] = chiSquare[0]; 377 if (free[0]) { 378 data.getParams()[1] = z[0]; 379 } 380 if (free[1]) { 381 data.getParams()[2] = a[0]; 382 } 383 if (free[2]) { 384 data.getParams()[3] = tau[0]; 385 } 276 386 } 277 if (free[2]) { 278 data.getParams()[3] = tau.getValue(); 387 } 388 389 if (AlgorithmType.LMA.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) { 390 // LMA fit 391 for (ICurveFitData data: dataArray) { 392 int nInstrumentResponse = 0; 393 if (null != m_instrumentResponse) { 394 nInstrumentResponse = m_instrumentResponse.length; 395 } 396 returnValue = LMA_fit( 397 m_xInc, 398 data.getYCount(), 399 start, 400 stop, 401 m_instrumentResponse, 402 nInstrumentResponse, 403 data.getSig(), 404 data.getParams(), 405 toIntArray(m_free), 406 data.getParams().length  1, 407 data.getYFitted(), 408 chiSquare, 409 chiSquareTarget 410 ); 279 411 } 280 412 } 281 413 } 282 414 283 if (AlgorithmType.LMA.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) {284 // LMA fit285 for (ICurveFitData data: dataArray) {286 int nInstrumentResponse = 0;287 if (null != m_instrumentResponse) {288 nInstrumentResponse = m_instrumentResponse.length;289 }290 returnValue = s_library.LMA_fit(291 m_xInc,292 data.getYCount(),293 start,294 stop,295 m_instrumentResponse,296 nInstrumentResponse,297 data.getSig(),298 data.getParams(),299 toIntArray(m_free),300 data.getParams().length  1,301 data.getYFitted(),302 chiSquare,303 chiSquareTarget304 );305 }306 }307 415 //TODO error return deserves much more thought!! Just returning the last value here!! 308 416 return returnValue; 309 417 310 }311 else {312 //TODO313 if (!s_loaded) {314 try {315 // extract to library path316 //TODO sort out the nameSystem.out.println("extract native library returns " + NativeLibraryUtil.extractNativeLibraryToPath(this.getClass(), "SLIMCurve2.0SNAPSHOT"));317 //System.out.println("extract native library returns " + NativeLibraryUtil.extractNativeLibraryToPath(this.getClass(), "slimcurve1.0SNAPSHOT"));318 319 //System.out.println("loadNativeLibrary returns " + NativeLibraryUtil.loadNativeLibrary(this.getClass(), "slimcurve"));320 321 boolean inNetBeans = false; //TODO useful for debugging when running NetBeans, requires dylib to be in slimplugin directory // true;322 if (inNetBeans) {323 System.loadLibrary("slimcurve1.0SNAPSHOT");324 s_loaded = true;325 }326 else {327 s_loaded = NativeLibraryUtil.loadNativeLibrary(this.getClass(), "slimcurve");328 }329 330 //IJ.log("before System load library");331 ////// System.loadLibrary("slimcurve1.0SNAPSHOT");332 //IJ.log("after System load library");333 334 // load once, ondemand335 //TODO sort out the name s_library = (CLibrary) Native.loadLibrary("SLIMCurve", CLibrary.class);336 //TODO test with old code instead: s_library = (CLibrary) Native.loadLibrary("slimcurve1.0SNAPSHOT", CLibrary.class);337 //TODO this was yet another version s_library = (CLibrary) Native.loadLibrary("SLIMCurve_trimmed_down", CLibrary.class);338 //s_library = (CLibrary) Native.loadLibrary("slimcurve1.0SNAPSHOT", CLibrary.class);339 340 //System.out.println("s_library is " + s_library);341 System.out.println("s_loaded is " + s_loaded);342 IJ.log("s_loaded is " + s_loaded);343 }344 catch (UnsatisfiedLinkError e) {345 IJ.log("unable to load dynamic library " + e.getMessage());346 System.out.println("unable to load dynamic library " + e.getMessage());347 return 0;348 }349 }350 IJ.log("test");351 352 //TODO ARG 9/3/10 these issues still need to be addressed:353 354 //TODO ARG since initial x = fit_start * xincr we have to supply the unused portion of y[] before fit_start.355 // if this data were already premassaged it might be better to get rid of fit_start & _end, just give the356 // portion to be fitted and specify an initial x.357 //TODO ARG August use initial X of 0.358 359 boolean[] free = m_free.clone();360 if (AlgorithmType.RLD.equals(m_algorithmType)) {361 // pure RLD (versus RLD followed by LMA) has no way to fix362 // parameters363 for (int i = 0; i < free.length; ++i) {364 free[i] = true;365 }366 }367 368 // use array to pass double by reference369 double[] chiSquare = new double[1];370 double chiSquareTarget = 1.0; //TODO s/b specified incoming371 372 if (AlgorithmType.RLD.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) {373 // RLD or triple integral fit374 375 // use arrays to pass double by reference376 double[] z = new double[1];377 double[] a = new double[1];378 double[] tau = new double[1];379 380 for (ICurveFitData data: dataArray) {381 // grab incoming parameters382 a[0] = data.getParams()[2];383 tau[0] = data.getParams()[3];384 z[0] = data.getParams()[1];385 386 // get IRF curve, if any387 double[] instrumentResponse = getInstrumentResponse(data.getPixels());388 int nInstrumentResponse = 0;389 if (null != instrumentResponse) {390 nInstrumentResponse = instrumentResponse.length;391 }392 393 IJ.log("about to do RLD_fit");394 returnValue = RLD_fit(m_xInc,395 data.getYCount(),396 start,397 stop,398 instrumentResponse,399 nInstrumentResponse,400 data.getSig(),401 z,402 a,403 tau,404 data.getYFitted(),405 chiSquare,406 chiSquareTarget407 );408 IJ.log("did RLD_fit");409 // set outgoing parameters, unless they are fixed410 data.getParams()[0] = chiSquare[0];411 if (free[0]) {412 data.getParams()[1] = z[0];413 }414 if (free[1]) {415 data.getParams()[2] = a[0];416 }417 if (free[2]) {418 data.getParams()[3] = tau[0];419 }420 }421 }422 423 if (AlgorithmType.LMA.equals(m_algorithmType)  AlgorithmType.RLD_LMA.equals(m_algorithmType)) {424 // LMA fit425 for (ICurveFitData data: dataArray) {426 int nInstrumentResponse = 0;427 if (null != m_instrumentResponse) {428 nInstrumentResponse = m_instrumentResponse.length;429 }430 returnValue = LMA_fit(431 m_xInc,432 data.getYCount(),433 start,434 stop,435 m_instrumentResponse,436 nInstrumentResponse,437 data.getSig(),438 data.getParams(),439 toIntArray(m_free),440 data.getParams().length  1,441 data.getYFitted(),442 chiSquare,443 chiSquareTarget444 );445 }446 }447 //TODO error return deserves much more thought!! Just returning the last value here!!448 IJ.log("SLIMCurveFitter.fitData returns " + returnValue);449 return returnValue;450 //TODO old style:451 }452 //TODO453 418 } 454 419
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