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Changeset 36064


Ignore:
Timestamp:
Aug 30, 2013, 4:36:08 PM (13 years ago)
Author:
eugene
Message:

ongoing attempts to get a reasonable sersic index fit : after non-LMM fit to a coarse grid of Reff/Sindex values, use the best Sindex value and the grid neighbors to define a search region; use quadratic fit to find min chisq, after each fit to the shape

File:
1 edited

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  • branches/eam_branches/ipp-20130711/psphot/src/psphotSourceFits.c

    r36033 r36064  
    559559
    560560# define TIMING 0
     561# define EXTRA_VERBOSE 0
    561562
    562563bool psphotSersicModelGuessPCM (pmPCMdata *pcm, pmSource *source, psImageMaskType maskVal, float psfSize);
     564bool psphotFitSersicShapeAndIndex (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize);
     565bool psphotFitSersicShapeAndIndexGrid (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize);
     566bool psphotFitSersicShapeAndIndexGridAuto (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize);
    563567
    564568pmModel *psphotFitPCM (pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, pmModelType modelType, psImageMaskType maskVal, psImageMaskType markVal, int psfSize) {
    … …  
    586590
    587591    if (modelType == pmModelClassGetType("PS_MODEL_SERSIC")) {
    588         options.mode = PM_SOURCE_FIT_NO_INDEX;
     592        options.mode = PM_SOURCE_FIT_NO_INDEX; // XXX note that there may be a conflict with psphotExtendedSourceFits.c:133
     593        options.mode = PM_SOURCE_FIT_EXT_AND_SKY;
     594        // if we are ever (in a given psphot implementation) going to fit a parameter, we must set the options here to include
     595        // that parameter (otherwise pmPCMupdate will fail to allocate the dmodelFlux image)
     596        // thus, if the sersic analysis below uses an index fit, need to use this EXT_AND_SKY mode for init
    589597    } else {
    590         options.mode = PM_SOURCE_FIT_EXT_AND_SKY; // XXX note that there may be a conflict with psphotExtendedSourceFits.c:133
     598        options.mode = PM_SOURCE_FIT_EXT_AND_SKY;
    591599    }
    592600
    … …  
    608616            return model;
    609617        }
     618        if (TIMING) { t2 = psTimerMark ("psphotFitPCM"); }
     619
     620        // psphotFitSersicShapeAndIndex (pcm, readout, source, fitOptions, maskVal, markVal, psfSize);
     621        options.mode = PM_SOURCE_FIT_NO_INDEX;
     622        if (!psphotFitSersicShapeAndIndexGridAuto (pcm, readout, source, &options, maskVal, markVal, psfSize)) {
     623            psFree (pcm);
     624            model->flags |= PM_MODEL_STATUS_BADARGS;
     625            psError(PS_ERR_UNKNOWN, true, "Failed to find a index & shape");
     626            psErrorClear (); // clear the polynomial error
     627            return model;
     628        }
    610629    } else {
    611630        // use the source moments, etc to guess basic model parameters
    … …  
    615634            return model;
    616635        }
    617         // XXX TEST:
    618         // pcm->modelConv->params->data.F32[4] = 30.0;
    619         // pcm->modelConv->params->data.F32[5] = 30.0;
    620     }
    621 
    622     if (TIMING) { t2 = psTimerMark ("psphotFitPCM"); }
    623 
    624     // update the pcm elements if we have changed the circumstance (options.mode or source->pixels)
    625     // pmPCMupdate(pcm, source, &options, model);
    626     // if (TIMING) { t4 = psTimerMark ("psphotFitPCM"); }
     636    }
     637
     638    if (TIMING) { t4 = psTimerMark ("psphotFitPCM"); }
    627639
    628640    // psTraceSetLevel("psLib.math.psMinimizeLMChi2", 5);
    … …  
    635647        int nPixBig = source->pixels->numCols * source->pixels->numRows;
    636648        fprintf (stderr, "psphotFitPCM : nIter: %2d, radius: %6.1f, npix: %5d of %5d, t1: %6.4f, t2: %6.4f, t4: %6.4f, t5: %6.4f\n", model->nIter, model->fitRadius, model->nPix, nPixBig, t1, t2, t4, t5);
     649    }
     650    if (EXTRA_VERBOSE && !TIMING) {
     651        int nPixBig = source->pixels->numCols * source->pixels->numRows;
    637652        float *PAR = model->params->data.F32;
    638         fprintf (stderr, "%f - %f %f %f - %f\n", PAR[7], PAR[4], PAR[5], PAR[6], PAR[1]);
    639     }
    640 
    641     // psTraceSetLevel("psLib.math.psMinimizeLMChi2", 0);
     653        fprintf (stderr, "source %d : %f - %f %f - %f %f %f - %f | nIter: %2d, radius: %6.1f, npix: %5d of %5d, chisq %f\n", source->id, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1], model->nIter, model->fitRadius, model->nPix, nPixBig, model->chisqNorm);
     654    }
     655
    642656    psFree (pcm);
    643657
    … …  
    762776# define N_INDEX_GUESS_INV 7
    763777
    764 float reffGuess[] = {3.0, 10.0, 20.0, 30.0, 40.0};
     778// float reffGuess[] = {3.0, 10.0, 20.0, 30.0, 40.0};
     779float reffGuess[] = {0.5, 0.75, 1.0, 1.4, 2.0};
    765780# define N_REFF_GUESS 5
    766781
    … …  
    827842
    828843        psEllipseAxes guessAxes;
    829         guessAxes.major = reffGuess[j];
    830         guessAxes.minor = (momentAxes.minor / momentAxes.minor) * guessAxes.major;
     844        guessAxes.major = reffGuess[j] * source->moments->Mrf;
     845        guessAxes.minor = guessAxes.major * (momentAxes.minor / momentAxes.major);
    831846        guessAxes.theta = momentAxes.theta;
    832847
    … …  
    889904    {
    890905        psEllipseAxes guessAxes;
    891         guessAxes.major = rMin;
    892         guessAxes.minor = (momentAxes.minor / momentAxes.minor) * guessAxes.major;
     906        guessAxes.major = rMin * source->moments->Mrf;
     907        guessAxes.minor = guessAxes.major * (momentAxes.minor / momentAxes.major);
    893908        guessAxes.theta = momentAxes.theta;
    894909
    … …  
    906921    return true;
    907922}
     923
     924// we have a set of guess parameters, do a small number of iterations fitting only SHAPE then only INDEX
     925bool psphotFitSersicShapeAndIndex (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize) {
     926
     927    pmModel *model = pcm->modelConv;
     928
     929    assert (model->type == pmModelClassGetType("PS_MODEL_SERSIC"));
     930
     931    pmSourceFitOptions options = *fitOptions;
     932   
     933    for (int i = 0; i < 3; i++) {
     934      // fit EXT (not PSF) model (set/unset the pixel mask)
     935      options.mode = PM_SOURCE_FIT_SHAPE;
     936      options.nIter = 2;
     937
     938      // update the pcm elements if we have changed the circumstance (here, options.mode)
     939      pmPCMupdate(pcm, source, &options, model);
     940     
     941      pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     942      if (EXTRA_VERBOSE) {
     943        float *PAR = model->params->data.F32;
     944        fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     945      }
     946     
     947      // fit EXT (not PSF) model (set/unset the pixel mask)
     948      options.mode = PM_SOURCE_FIT_INDEX;
     949      // options.mode = PM_SOURCE_FIT_EXT_AND_SKY;
     950      options.nIter = 30;
     951     
     952      // update the pcm elements if we have changed the circumstance (here, options.mode)
     953      pmPCMupdate(pcm, source, &options, model);
     954     
     955      pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     956      if (EXTRA_VERBOSE) {
     957        float *PAR = model->params->data.F32;
     958        fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     959      }
     960    }
     961
     962    // update the pcm elements if we have changed the circumstance (here, options.mode)
     963    pmPCMupdate(pcm, source, fitOptions, model);
     964
     965    return true;
     966}
     967
     968// we have a set of guess parameters, do a small number of iterations fitting only SHAPE then only INDEX
     969bool psphotFitSersicShapeAndIndexGridAuto (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize) {
     970
     971    pmModel *model = pcm->modelConv;
     972
     973    assert (model->type == pmModelClassGetType("PS_MODEL_SERSIC"));
     974
     975    pmSourceFitOptions options = *fitOptions;
     976   
     977    psF32 *PAR = pcm->modelConv->params->data.F32;
     978
     979    options.mode = PM_SOURCE_FIT_SHAPE;
     980    options.nIter = 7;
     981   
     982    // update the pcm elements if we have changed the circumstance (here, options.mode)
     983    pmPCMupdate(pcm, source, &options, model);
     984   
     985    // we have been provided a guess at the index (P[7]) from the list of indexGuessInv
     986
     987    // find the matching indexGuessInv
     988    int nStart = -1;
     989    for (int i = 0; i < N_INDEX_GUESS_INV; i++) {
     990        if (fabs(PAR[PM_PAR_7] - indexGuessInv[i]) < 0.01) {
     991            nStart = i;
     992            break;
     993        }
     994    }
     995    if (nStart == -1) {
     996        fprintf (stderr, "WARNING: could not find start guess %f\n", PAR[PM_PAR_7]);
     997        return false;
     998    }
     999
     1000    psVector *chi2 = psVectorAllocEmpty (16, PS_TYPE_F32);
     1001    psVector *Sidx = psVectorAllocEmpty (16, PS_TYPE_F32);
     1002
     1003    PAR[PM_PAR_7] = indexGuessInv[nStart];
     1004    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1005    if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1006    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1007    psVectorAppend (chi2, model->chisqNorm);
     1008
     1009    PAR[PM_PAR_7] = (nStart < N_INDEX_GUESS_INV - 1) ? 0.5*(indexGuessInv[nStart + 1] + indexGuessInv[nStart]) : 0.1;
     1010    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1011    if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1012    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1013    psVectorAppend (chi2, model->chisqNorm);
     1014
     1015    PAR[PM_PAR_7] = (nStart > 0) ? 0.5*(indexGuessInv[nStart - 1] + indexGuessInv[nStart]) : 0.55;
     1016    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1017    if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1018    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1019    psVectorAppend (chi2, model->chisqNorm);
     1020
     1021    if (chi2->data.F32[1] < chi2->data.F32[2]) {
     1022      if (nStart == N_INDEX_GUESS_INV - 1) {
     1023        PAR[PM_PAR_7] = 0.11;
     1024      } else {
     1025        PAR[PM_PAR_7] = indexGuessInv[nStart + 1];
     1026      }
     1027    } else {
     1028      if (nStart == 0) {
     1029        PAR[PM_PAR_7] = 0.52;
     1030      } else {
     1031        PAR[PM_PAR_7] = indexGuessInv[nStart - 1];
     1032      }
     1033    }
     1034
     1035    psPolynomial1D *poly = psPolynomial1DAlloc (PS_POLYNOMIAL_ORD, 2);
     1036    if (!psVectorFitPolynomial1D (poly, NULL, 0, chi2, NULL, Sidx)) {
     1037        psError(PS_ERR_UNKNOWN, true, "Failed to find a good chisq parabola");
     1038        psFree (chi2);
     1039        psFree (Sidx);
     1040        psFree (poly);
     1041        return false;
     1042    }
     1043
     1044    // where is the minimum of this polynomial fit?
     1045    float Smin = -0.5 * poly->coeff[1] / poly->coeff[2] / 100.0;
     1046
     1047    // constrain Smin to be in a valid range (1.0 - 0.1, corresponding to 0.5 (Gauss) to 5.0 (slightly peakier than Dev)
     1048    Smin = PS_MAX(PS_MIN(Smin, 1.0), 0.1);
     1049    PAR[PM_PAR_7] = Smin;
     1050
     1051    // return to the original fitting mode (fitOptions)
     1052    pmPCMupdate(pcm, source, fitOptions, model);
     1053
     1054    psFree (chi2);
     1055    psFree (Sidx);
     1056    psFree (poly);
     1057
     1058    return true;
     1059}
     1060
     1061 
     1062// we have a set of guess parameters, do a small number of iterations fitting only SHAPE then only INDEX
     1063bool psphotFitSersicShapeAndIndexGridAutoScaled (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize) {
     1064
     1065    pmModel *model = pcm->modelConv;
     1066
     1067    assert (model->type == pmModelClassGetType("PS_MODEL_SERSIC"));
     1068
     1069    pmSourceFitOptions options = *fitOptions;
     1070   
     1071    psF32 *PAR = pcm->modelConv->params->data.F32;
     1072
     1073    options.mode = PM_SOURCE_FIT_SHAPE;
     1074    options.nIter = 5;
     1075   
     1076    // update the pcm elements if we have changed the circumstance (here, options.mode)
     1077    pmPCMupdate(pcm, source, &options, model);
     1078   
     1079    float parStart[8];
     1080    for (int i = 0; i < 8; i++) parStart[i] = PAR[i];
     1081
     1082    // we start with a guess at the index (P[7])
     1083
     1084    // get chisq for P[7], P[7]*1.1, P[7]*1.25 (or *0.75 depending on the result of 1.1)
     1085
     1086    psVector *chi2 = psVectorAllocEmpty (16, PS_TYPE_F32);
     1087    psVector *Sidx = psVectorAllocEmpty (16, PS_TYPE_F32);
     1088
     1089    PAR[PM_PAR_7] = parStart[7];
     1090    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1091      if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1092    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1093    psVectorAppend (chi2, model->chisqNorm);
     1094
     1095    float fI = 1.1;
     1096    PAR[PM_PAR_7] = parStart[7]*fI;
     1097    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1098      if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1099    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1100    psVectorAppend (chi2, model->chisqNorm);
     1101
     1102    if (chi2->data.F32[1] < chi2->data.F32[0]) {
     1103      fI = 1.3;
     1104    } else {
     1105      fI = 1.0 / 1.3;
     1106    }
     1107   
     1108    PAR[PM_PAR_7] = parStart[7]*fI;
     1109    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1110      if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1111    psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1112    psVectorAppend (chi2, model->chisqNorm);
     1113
     1114    // can we fit the 3 pts with a parabola?
     1115    int nTry = 0;
     1116    psPolynomial1D *poly = psPolynomial1DAlloc (PS_POLYNOMIAL_ORD, 2);
     1117    while (!psVectorFitPolynomial1D (poly, NULL, 0, chi2, NULL, Sidx)) {
     1118      psErrorClear (); // clear the polynomial error
     1119      if (nTry > 4) {
     1120        psError(PS_ERR_UNKNOWN, true, "Failed to find a good chisq parabola");
     1121        psFree (chi2);
     1122        psFree (Sidx);
     1123        psFree (poly);
     1124        return false;
     1125      }
     1126      fI = (fI < 1.0) ? fI / 1.3 : fI * 1.3;
     1127      PAR[PM_PAR_7] = parStart[7]*fI;
     1128      pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1129      if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1130      psVectorAppend (Sidx, 100*PAR[PM_PAR_7]);
     1131      psVectorAppend (chi2, model->chisqNorm);
     1132      nTry ++;
     1133    }
     1134
     1135    // where is the minimum of this polynomial fit?
     1136    float Smin = -0.5 * poly->coeff[1] / poly->coeff[2] / 100.0;
     1137
     1138    // constrain Smin to be in a valid range (1.0 - 0.1, corresponding to 0.5 (Gauss) to 5.0 (slightly peakier than Dev)
     1139    Smin = PS_MAX(PS_MIN(Smin, 1.0), 0.1);
     1140    PAR[PM_PAR_7] = Smin;
     1141
     1142    // pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1143    // if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1144   
     1145    //// for (int i = 0; i < 8; i++) PAR[i] = parStart[i];
     1146    ////
     1147    //// for (float fI = 0.0; fI < 0.15; fI += 0.01) {
     1148    ////   PAR[PM_PAR_7] = parStart[7] - fI;
     1149    ////
     1150    ////   // fit EXT (not PSF) model (set/unset the pixel mask)
     1151    ////   
     1152    ////   pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1153    ////   if (TIMING) {
     1154    ////        float *PAR = model->params->data.F32;
     1155    ////        fprintf (stderr, "%d %f : %f - %f %f %f - %f\n", model->nIter, model->chisqNorm, PAR[7], PAR[4], PAR[5], PAR[6], PAR[1]);
     1156    ////   }
     1157    //// }
     1158
     1159    // return to the original fitting mode (fitOptions)
     1160    pmPCMupdate(pcm, source, fitOptions, model);
     1161
     1162    psFree (chi2);
     1163    psFree (Sidx);
     1164    psFree (poly);
     1165
     1166    return true;
     1167}
     1168
     1169 
     1170// we have a set of guess parameters, do a small number of iterations fitting only SHAPE then only INDEX
     1171bool psphotFitSersicShapeAndIndexGrid (pmPCMdata *pcm, pmReadout *readout, pmSource *source, pmSourceFitOptions *fitOptions, psImageMaskType maskVal, psImageMaskType markVal, int psfSize) {
     1172
     1173    pmModel *model = pcm->modelConv;
     1174
     1175    assert (model->type == pmModelClassGetType("PS_MODEL_SERSIC"));
     1176
     1177    pmSourceFitOptions options = *fitOptions;
     1178   
     1179    psF32 *PAR = pcm->modelConv->params->data.F32;
     1180
     1181    options.mode = PM_SOURCE_FIT_SHAPE;
     1182    options.nIter = 10;
     1183   
     1184    // update the pcm elements if we have changed the circumstance (here, options.mode)
     1185    pmPCMupdate(pcm, source, &options, model);
     1186   
     1187    psVector *chi2 = psVectorAllocEmpty (16, PS_TYPE_F32);
     1188    psVector *Sidx = psVectorAllocEmpty (16, PS_TYPE_F32);
     1189
     1190    float par7[] = {0.100, 0.125, 0.150, 0.175, 0.200, 0.225, 0.250};
     1191    for (int i = 0; i < 7; i++) {
     1192      PAR[PM_PAR_7] = par7[i];
     1193      pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1194      if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1195      psVectorAppend (Sidx, PAR[PM_PAR_7]);
     1196      psVectorAppend (chi2, model->chisqNorm);
     1197    }
     1198
     1199    psPolynomial1D *poly = psPolynomial1DAlloc (PS_POLYNOMIAL_ORD, 2);
     1200    if (!psVectorFitPolynomial1D (poly, NULL, 0, chi2, NULL, Sidx)) {
     1201      psError(PS_ERR_UNKNOWN, true, "Failed to find a good chisq parabola");
     1202      psFree (chi2);
     1203      psFree (Sidx);
     1204      psFree (poly);
     1205      return false;
     1206    }
     1207
     1208    // where is the minimum of this polynomial fit?
     1209    fprintf (stderr, "fit1d: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]);
     1210    float Smin = -0.5 * poly->coeff[1] / poly->coeff[2];
     1211
     1212    // constrain Smin to be in a valid range (1.0 - 0.1, corresponding to 0.5 (Gauss) to 5.0 (slightly peakier than Dev)
     1213    Smin = PS_MAX(PS_MIN(Smin, 1.0), 0.1);
     1214    PAR[PM_PAR_7] = Smin;
     1215    pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1216    if (EXTRA_VERBOSE) fprintf (stderr, "%d >>> %d %f : %f - %f %f - %f %f %f - %f\n", source->id, model->nIter, model->chisqNorm, PAR[7], PAR[2], PAR[3], PAR[4], PAR[5], PAR[6], PAR[1]);
     1217   
     1218    //// for (int i = 0; i < 8; i++) PAR[i] = parStart[i];
     1219    ////
     1220    //// for (float fI = 0.0; fI < 0.15; fI += 0.01) {
     1221    ////   PAR[PM_PAR_7] = parStart[7] - fI;
     1222    ////
     1223    ////   // fit EXT (not PSF) model (set/unset the pixel mask)
     1224    ////   
     1225    ////   pmSourceFitPCM (pcm, source, &options, maskVal, markVal, psfSize);
     1226    ////   if (TIMING) {
     1227    ////        float *PAR = model->params->data.F32;
     1228    ////        fprintf (stderr, "%d %f : %f - %f %f %f - %f\n", model->nIter, model->chisqNorm, PAR[7], PAR[4], PAR[5], PAR[6], PAR[1]);
     1229    ////   }
     1230    //// }
     1231
     1232    // return to the original fitting mode (fitOptions)
     1233    pmPCMupdate(pcm, source, fitOptions, model);
     1234
     1235    psFree (chi2);
     1236    psFree (Sidx);
     1237    psFree (poly);
     1238
     1239    return true;
     1240}
     1241
     1242 
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