Index: trunk/psLib/src/math/psStats.c =================================================================== --- trunk/psLib/src/math/psStats.c (revision 15335) +++ trunk/psLib/src/math/psStats.c (revision 15370) @@ -13,6 +13,6 @@ * use ->min and ->max (PS_STAT_USE_RANGE) * - * @version $Revision: 1.218 $ $Name: not supported by cvs2svn $ - * @date $Date: 2007-09-29 23:58:55 $ + * @version $Revision: 1.219 $ $Name: not supported by cvs2svn $ + * @date $Date: 2007-10-24 19:08:24 $ * * Copyright 2006 IfA, University of Hawaii @@ -152,5 +152,6 @@ // static prototypes: static psF32 minimizeLMChi2Gauss1D(psVector *deriv, const psVector *params, const psVector *coords); -static psF32 fitQuadraticSearchForYThenReturnX(const psVector *xVec, psVector *yVec, psS32 binNum, psF32 yVal); +static psF32 fitQuadraticSearchForYThenReturnX(const psVector *xVec, psVector *yVec, psS32 binNum, + psF32 yVal); /****************************************************************************** @@ -309,5 +310,5 @@ // Allocate temporary vectors for the data. - psVector *outVector = psVectorAllocEmpty(inVector->n, PS_TYPE_F32); // Vector containing the unmasked values + psVector *outVector = psVectorAllocEmpty(inVector->n, PS_TYPE_F32); // Vector containing unmasked values psF32 *output = outVector->data.F32; // Dereference the vector @@ -401,5 +402,6 @@ // If the mean is NAN, then generate a warning and set the stdev to NAN. if (isnan(stats->sampleMean)) { - psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): sample mean is NAN. Setting stats->sampleStdev = NAN.\n"); + psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): sample mean is NAN. " + "Setting stats->sampleStdev = NAN.\n"); stats->sampleStdev = NAN; return true; @@ -443,10 +445,12 @@ // Assume that the user knows what he's doing when he masks out everything --> no error. stats->sampleStdev = NAN; - psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): no valid psVector elements (%ld). Setting stats->sampleStdev = NAN.\n", count); + psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): no valid psVector elements (%ld). " + "Setting stats->sampleStdev = NAN.\n", count); return true; } if (count == 1) { stats->sampleStdev = 0.0; - psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): only one valid psVector elements (%ld). Setting stats->sampleStdev = 0.0.\n", count); + psTrace(TRACE, 5, "WARNING: vectorSampleStdev(): only one valid psVector elements (%ld). " + "Setting stats->sampleStdev = 0.0.\n", count); return true; } @@ -623,5 +627,6 @@ // a) Exclude all values x_i for which |x_i - x| > K * stdev if (errors) { - // XXXX if we convert errors to variance, this should square the other terms (A*A faster than sqrt(A)) + // XXXX if we convert errors to variance, this should square the other terms (A*A faster than + // sqrt(A)) for (long j = 0; j < myVector->n; j++) { if (!tmpMask->data.U8[j] && @@ -749,5 +754,7 @@ // Iterate to get the best bin size for (int iterate = 1; iterate > 0; iterate++) { - psTrace(TRACE, 6, "-------------------- Iterating on Bin size. Iteration number %d --------------------\n", iterate); + psTrace(TRACE, 6, + "-------------------- Iterating on Bin size. Iteration number %d --------------------\n", + iterate); // Get the minimum and maximum values @@ -832,11 +839,12 @@ binMedian, totalDataPoints/2.0); if (isnan(stats->robustMedian)) { - psError(PS_ERR_UNKNOWN, false, "Failed to fit a quadratic and calculate the 50-percent position.\n"); + psError(PS_ERR_UNKNOWN, false, + "Failed to fit a quadratic and calculate the 50-percent position.\n"); goto escape; } psTrace(TRACE, 6, "Current robust median is %f\n", stats->robustMedian); - // ADD step 4: Find the bins which contains the 15.8655% (-1 sigma) and 84.1345% (+1 sigma) data points - // also find the 30.8538% (-0.5 sigma) and 69.1462% (+0.5 sigma) points + // ADD step 4: Find the bins which contains the 15.8655% (-1 sigma) and 84.1345% (+1 sigma) data + // points also find the 30.8538% (-0.5 sigma) and 69.1462% (+0.5 sigma) points PS_BIN_FOR_VALUE(binLo, cumulative->nums, totalDataPoints * 0.158655f, 0); PS_BIN_FOR_VALUE(binHi, cumulative->nums, totalDataPoints * 0.841345f, 0); @@ -858,5 +866,6 @@ } - // ADD step 4b: Interpolate Sigma (linearly) to find these two positions exactly: these are the 1sigma positions. + // ADD step 4b: Interpolate Sigma (linearly) to find these two positions exactly: these are the 1sigma + // positions. psTrace(TRACE, 6, "binLo is %ld. Nums at that bin and the next are (%.2f, %.2f)\n", binLo, cumulative->nums->data.F32[binLo], cumulative->nums->data.F32[binLo+1]); @@ -868,10 +877,16 @@ // (extrapolation should not be needed and will result in errors) float binLoF32, binHiF32, binL2F32, binH2F32, binL4F32, binH4F32; - PS_BIN_INTERPOLATE (binLoF32, cumulative->nums, cumulative->bounds, binLo, totalDataPoints * 0.158655f); - PS_BIN_INTERPOLATE (binHiF32, cumulative->nums, cumulative->bounds, binHi, totalDataPoints * 0.841345f); - PS_BIN_INTERPOLATE (binL2F32, cumulative->nums, cumulative->bounds, binL2, totalDataPoints * 0.308538f); - PS_BIN_INTERPOLATE (binH2F32, cumulative->nums, cumulative->bounds, binH2, totalDataPoints * 0.691462f); - PS_BIN_INTERPOLATE (binL4F32, cumulative->nums, cumulative->bounds, binL4, totalDataPoints * 0.022481f); - PS_BIN_INTERPOLATE (binH4F32, cumulative->nums, cumulative->bounds, binH4, totalDataPoints * 0.977519f); + PS_BIN_INTERPOLATE (binLoF32, cumulative->nums, cumulative->bounds, binLo, + totalDataPoints * 0.158655f); + PS_BIN_INTERPOLATE (binHiF32, cumulative->nums, cumulative->bounds, binHi, + totalDataPoints * 0.841345f); + PS_BIN_INTERPOLATE (binL2F32, cumulative->nums, cumulative->bounds, binL2, + totalDataPoints * 0.308538f); + PS_BIN_INTERPOLATE (binH2F32, cumulative->nums, cumulative->bounds, binH2, + totalDataPoints * 0.691462f); + PS_BIN_INTERPOLATE (binL4F32, cumulative->nums, cumulative->bounds, binL4, + totalDataPoints * 0.022481f); + PS_BIN_INTERPOLATE (binH4F32, cumulative->nums, cumulative->bounds, binH4, + totalDataPoints * 0.977519f); // report +/- 1 sigma points @@ -891,8 +906,8 @@ float sigma4 = (binH4F32 - binL4F32) / 4.0; - // take the smallest of the three: if we have a clump with wide outliers, sigma2 and - // sigma4 will be biased high; if we have a bi-modal distribution, sigma1 and sigma2 - // will be biased high. - sigma = PS_MIN (sigma1, PS_MIN (sigma2, sigma4)); + // take the smallest of the three: if we have a clump with wide outliers, sigma2 and + // sigma4 will be biased high; if we have a bi-modal distribution, sigma1 and sigma2 + // will be biased high. + sigma = PS_MIN (sigma1, PS_MIN (sigma2, sigma4)); psTrace(TRACE, 6, "The 2x sigma is %f.\n", sigma1); @@ -1328,5 +1343,6 @@ if (poly->coeff[2] >= 0.0) { - psTrace(TRACE, 6, "Parabolic fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Parabolic fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psError(PS_ERR_UNKNOWN, false, "fit did not converge\n"); psFree(x); @@ -1344,5 +1360,6 @@ done = true; } else { - psTrace(TRACE, 6, "Parabolic fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Parabolic fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psTrace(TRACE, 6, "The new guess mean is %f.\n", guessMean); psTrace(TRACE, 6, "The new guess stdev is %f.\n", guessStdev); @@ -1490,14 +1507,17 @@ valPeak = histogram->nums->data.F32[binPeak]; } - psTrace (TRACE, 6, "(%f = %.0f) ", histogram->bounds->data.F32[i], histogram->nums->data.F32[i]); - } - psTrace (TRACE, 6, "\n"); + psTrace (TRACE, 6, "(%f = %.0f) ", histogram->bounds->data.F32[i], histogram->nums->data.F32[i]); + } + psTrace (TRACE, 6, "\n"); // assume a reasonably well-defined gaussian-like population; run from peak out until val < 0.25*peak psTrace(TRACE, 6, "The clipped numBins is %ld\n", binMax - binMin); - psTrace(TRACE, 6, "The clipped min is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binMin), binMin); - psTrace(TRACE, 6, "The clipped max is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binMax - 1), binMax - 1); - psTrace(TRACE, 6, "The clipped peak is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binPeak), binPeak); + psTrace(TRACE, 6, "The clipped min is %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binMin), binMin); + psTrace(TRACE, 6, "The clipped max is %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binMax - 1), binMax - 1); + psTrace(TRACE, 6, "The clipped peak is %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binPeak), binPeak); psTrace(TRACE, 6, "The clipped peak value is %f\n", histogram->nums->data.F32[binPeak]); @@ -1513,6 +1533,8 @@ } } - psTrace(TRACE, 6, "Lower bound for lower half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binS), binS); - psTrace(TRACE, 6, "Upper bound for lower half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binE), binE); + psTrace(TRACE, 6, "Lower bound for lower half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binS), binS); + psTrace(TRACE, 6, "Upper bound for lower half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binE), binE); psVector *y = psVectorAllocEmpty(binE - binS, PS_TYPE_F32); // Vector of coordinates @@ -1523,5 +1545,6 @@ continue; x->data.F32[j] = PS_BIN_MIDPOINT(histogram, i); - y->data.F32[j] = log(histogram->nums->data.F32[i]); // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + y->data.F32[j] = log(histogram->nums->data.F32[i]); j++; } @@ -1544,11 +1567,13 @@ if (poly->coeff[2] >= 0.0) { - psTrace(TRACE, 6, "Failed parabolic fit: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Failed parabolic fit: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psFree(poly); psFree(histogram); psFree(statsMinMax); - // sometimes, the guessStdev is much too large. in this case, the entire real population tends to be found - // in a single bin. make one attempt to recover by dropping the guessStdev down by a jump and trying again + // sometimes, the guessStdev is much too large. in this case, the entire real population + // tends to be found in a single bin. make one attempt to recover by dropping the guessStdev + // down by a jump and trying again if (iteration == 0) { guessStdev = 0.25*guessStdev; @@ -1568,5 +1593,6 @@ done = true; } - psTrace(TRACE, 6, "Parabolic Lower fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Parabolic Lower fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psTrace(TRACE, 6, "The lower mean is %f.\n", guessMean); psTrace(TRACE, 6, "The lower stdev is %f.\n", guessStdev); @@ -1575,5 +1601,5 @@ } - // for test, measure the same result for the upper section + // for test, measure the same result for the upper section { // fit the upper half of the distribution @@ -1587,6 +1613,8 @@ } } - psTrace(TRACE, 6, "Lower bound for upper half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binS), binS); - psTrace(TRACE, 6, "Upper bound for upper half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binE), binE); + psTrace(TRACE, 6, "Lower bound for upper half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binS), binS); + psTrace(TRACE, 6, "Upper bound for upper half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binE), binE); psVector *y = psVectorAllocEmpty(binE - binS, PS_TYPE_F32); // Vector of coordinates @@ -1597,5 +1625,6 @@ continue; x->data.F32[j] = PS_BIN_MIDPOINT(histogram, i); - y->data.F32[j] = log(histogram->nums->data.F32[i]); // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + y->data.F32[j] = log(histogram->nums->data.F32[i]); j++; } @@ -1618,17 +1647,18 @@ // calculate upper mean & stdev from parabolic fit -- ignore this value -#ifndef PS_NO_TRACE float upperStdev = sqrt(-0.5/poly->coeff[2]); float upperMean = poly->coeff[1]*PS_SQR(upperStdev); - psTrace(TRACE, 6, "Parabolic Upper fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); +#ifndef PS_NO_TRACE + psTrace(TRACE, 6, "Parabolic Upper fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psTrace(TRACE, 6, "The upper mean is %f.\n", upperMean); psTrace(TRACE, 6, "The upper stdev is %f.\n", upperStdev); #endif - // if the resulting value is outside of the range binMin - binMax, use the upper value - if (done && (guessMean > PS_BIN_MIDPOINT(histogram, binMax - 1))) { - guessMean = upperMean; - guessStdev = upperStdev; - } + // if the resulting value is outside of the range binMin - binMax, use the upper value + if (done && (guessMean > PS_BIN_MIDPOINT(histogram, binMax - 1))) { + guessMean = upperMean; + guessStdev = upperStdev; + } psFree (poly); @@ -1718,7 +1748,7 @@ // Calculate the number of bins. // XXX can we calculate the binMin, binMax **before** building this histogram? - // if the range is too absurd, adjust numBins & binSize + // if the range is too absurd, adjust numBins & binSize long numBins = PS_MAX (50, PS_MIN (10000, (max - min) / binSize)); - binSize = (max - min) / (float) numBins; + binSize = (max - min) / (float) numBins; psTrace(TRACE, 6, "The new min/max values are (%f, %f).\n", min, max); psTrace(TRACE, 6, "The new bin size is %f.\n", binSize); @@ -1774,7 +1804,7 @@ valPeak = histogram->nums->data.F32[binPeak]; } - psTrace (TRACE, 6, "(%f = %.0f) ", histogram->bounds->data.F32[i], histogram->nums->data.F32[i]); - } - psTrace (TRACE, 6, "\n"); + psTrace (TRACE, 6, "(%f = %.0f) ", histogram->bounds->data.F32[i], histogram->nums->data.F32[i]); + } + psTrace (TRACE, 6, "\n"); // assume a reasonably well-defined gaussian-like population; run from peak out until val < 0.25*peak @@ -1782,5 +1812,6 @@ psTrace(TRACE, 6, "The clipped numBins is %ld\n", binMax - binMin); psTrace(TRACE, 6, "The clipped min is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binMin), binMin); - psTrace(TRACE, 6, "The clipped max is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binMax - 1), binMax - 1); + psTrace(TRACE, 6, "The clipped max is %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binMax - 1), binMax - 1); psTrace(TRACE, 6, "The clipped peak is %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binPeak), binPeak); psTrace(TRACE, 6, "The clipped peak value is %f\n", histogram->nums->data.F32[binPeak]); @@ -1804,6 +1835,8 @@ } } - psTrace(TRACE, 6, "Lower bound for lower half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binS), binS); - psTrace(TRACE, 6, "Upper bound for lower half: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binE), binE); + psTrace(TRACE, 6, "Lower bound for lower half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binS), binS); + psTrace(TRACE, 6, "Upper bound for lower half: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binE), binE); psVector *y = psVectorAllocEmpty(binE - binS, PS_TYPE_F32); // Vector of coordinates @@ -1814,5 +1847,6 @@ continue; x->data.F32[j] = PS_BIN_MIDPOINT(histogram, i); - y->data.F32[j] = log(histogram->nums->data.F32[i]); // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + y->data.F32[j] = log(histogram->nums->data.F32[i]); j++; } @@ -1835,11 +1869,13 @@ if (poly->coeff[2] >= 0.0) { - psTrace(TRACE, 6, "Failed parabolic fit: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Failed parabolic fit: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psFree(poly); psFree(histogram); psFree(statsMinMax); - // sometimes, the guessStdev is much too large. in this case, the entire real population tends to be found - // in a single bin. make one attempt to recover by dropping the guessStdev down by a jump and trying again + // sometimes, the guessStdev is much too large. in this case, the entire real population + // tends to be found in a single bin. make one attempt to recover by dropping the guessStdev + // down by a jump and trying again if (iteration == 0) { guessStdev = 0.25*guessStdev; @@ -1859,5 +1895,6 @@ done = true; } - psTrace(TRACE, 6, "Parabolic Lower fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); + psTrace(TRACE, 6, "Parabolic Lower fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psTrace(TRACE, 6, "The lower mean is %f.\n", guessMean); psTrace(TRACE, 6, "The lower stdev is %f.\n", guessStdev); @@ -1866,9 +1903,9 @@ } - // if we converge on a solution outside the range binMin - binMax, use a more conservative range - float minValue = PS_BIN_MIDPOINT(histogram, binMin); - float maxValue = PS_BIN_MIDPOINT(histogram, binMax - 1); - - if (done && ((guessMean < minValue) || (guessMean > maxValue))) { + // if we converge on a solution outside the range binMin - binMax, use a more conservative range + float minValue = PS_BIN_MIDPOINT(histogram, binMin); + float maxValue = PS_BIN_MIDPOINT(histogram, binMax - 1); + + if (done && ((guessMean < minValue) || (guessMean > maxValue))) { psTrace(TRACE, 6, "Inconsistent result, re-trying the fit\n"); @@ -1890,6 +1927,8 @@ } } - psTrace(TRACE, 6, "Lower bound for symmetric range: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binS), binS); - psTrace(TRACE, 6, "Upper bound for symmetric range: %f (%ld)\n", PS_BIN_MIDPOINT(histogram, binE), binE); + psTrace(TRACE, 6, "Lower bound for symmetric range: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binS), binS); + psTrace(TRACE, 6, "Upper bound for symmetric range: %f (%ld)\n", + PS_BIN_MIDPOINT(histogram, binE), binE); psVector *y = psVectorAllocEmpty(binE - binS, PS_TYPE_F32); // Vector of coordinates @@ -1900,5 +1939,6 @@ continue; x->data.F32[j] = PS_BIN_MIDPOINT(histogram, i); - y->data.F32[j] = log(histogram->nums->data.F32[i]); // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + // note this is the natural log: expected distribution is A exp(-(x-xo)^2/2sigma^2) + y->data.F32[j] = log(histogram->nums->data.F32[i]); j++; } @@ -1921,27 +1961,28 @@ // calculate upper mean & stdev from parabolic fit -- ignore this value -#ifndef PS_NO_TRACE guessStdev = sqrt(-0.5/poly->coeff[2]); guessMean = poly->coeff[1]*PS_SQR(guessStdev); - psTrace(TRACE, 6, "Parabolic Symmetric fit results: %f + %f x + %f x^2\n", poly->coeff[0], poly->coeff[1], poly->coeff[2]); +#ifndef PS_NO_TRACE + psTrace(TRACE, 6, "Parabolic Symmetric fit results: %f + %f x + %f x^2\n", + poly->coeff[0], poly->coeff[1], poly->coeff[2]); psTrace(TRACE, 6, "The symmetric mean is %f.\n", guessMean); psTrace(TRACE, 6, "The symmetric stdev is %f.\n", guessStdev); #endif - // if we converge on a solution outside the range binMin - binMax, use a more conservative range - float minValueSym = PS_BIN_MIDPOINT(histogram, binS); - float maxValueSym = PS_BIN_MIDPOINT(histogram, binE - 1); - - // saturate on min or max value - if (guessMean < minValueSym) { - guessMean = minValueSym; - psTrace(TRACE, 6, "The symmetric mean is out of bounds, saturating to %f.\n", guessMean); - } - - // saturate on min or max value - if (guessMean > maxValueSym) { - guessMean = maxValueSym; - psTrace(TRACE, 6, "The symmetric mean is out of bounds, saturating to %f.\n", guessMean); - } + // if we converge on a solution outside the range binMin - binMax, use a more conservative range + float minValueSym = PS_BIN_MIDPOINT(histogram, binS); + float maxValueSym = PS_BIN_MIDPOINT(histogram, binE - 1); + + // saturate on min or max value + if (guessMean < minValueSym) { + guessMean = minValueSym; + psTrace(TRACE, 6, "The symmetric mean is out of bounds, saturating to %f.\n", guessMean); + } + + // saturate on min or max value + if (guessMean > maxValueSym) { + guessMean = maxValueSym; + psTrace(TRACE, 6, "The symmetric mean is out of bounds, saturating to %f.\n", guessMean); + } psFree (poly); @@ -1996,5 +2037,6 @@ // However, it is also not used anywhere, yet. // - psWarning("WARNING: vectorSmoothHistGaussian() on non-uniform histograms has not been tested or used.\n"); + psWarning("WARNING: vectorSmoothHistGaussian() on non-uniform " + "histograms has not been tested or used.\n"); for (long i = 0; i < numBins; i++) { @@ -2607,5 +2649,6 @@ if (!psVectorFitPolynomial1D(myPoly, NULL, 0, y, NULL, x)) { psError(PS_ERR_UNEXPECTED_NULL, false, - _("Failed to fit a 1-dimensional polynomial to the three specified data points. Returning NAN.")); + _("Failed to fit a 1-dimensional polynomial to the three specified data points. " + "Returning NAN.")); psFree(x); psFree(y);