FFNet/FFNet_Matrix.h

@@ -29,12 +29,12 @@
 
 /* The Matrix organization is as follows:									*/
 /*																			*/
-/* nx = numberOfUnitsInLayer[layer]												*/
-/* ny = numberOfUnitsInLayer[layer-1]+1											*/
-/* xmin = 1 xmax = nx														*/
-/* ymin = 1 ymax = ny														*/
-/* dx = dy = 1																*/
-/* x1 = y1 = 1																*/
+/* nx == numberOfUnitsInLayer[layer]										*/
+/* ny == numberOfUnitsInLayer[layer-1]+1									*/
+/* xmin == 1, xmax == nx													*/
+/* ymin == 1, ymax == ny													*/
+/* dx == dy == 1															*/
+/* x1 == y1 == 1															*/
 /*																			*/
 
 autoMatrix FFNet_weightsToMatrix (FFNet me, integer layer, bool deltaWeights);

FFNet/FFNet_PatternList_ActivationList.cpp

 /* FFNet_PatternList_ActivationList.cpp
  *
- * Copyright (C) 1994-2018 David Weenink, 2015,2017 Paul Boersma
+ * Copyright (C) 1994-2019 David Weenink, 2015,2017 Paul Boersma
  *
  * This code is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
@@ -27,7 +27,7 @@
 #include "Graphics.h"
 #include "FFNet_PatternList_ActivationList.h"
 
-static double func (Daata object, VEC p) {
+static double func (Daata object, VEC const& p) {
 	FFNet me = (FFNet) object;
 	Minimizer thee = my minimizer.get();
 	longdouble fp = 0.0;
@@ -45,11 +45,11 @@ static double func (Daata object, VEC p) {
 		for (integer k = 1; k <= my numberOfWeights; k ++)
 			my dw [k] += my dwi [k];
 	}
-	thy funcCalls ++;
+	thy numberOfFunctionCalls ++;
 	return (double) fp;
 }
 
-static void dfunc_optimized (Daata object, VEC /* p */, VEC dp) {
+static void dfunc_optimized (Daata object, VEC const& /* p */, VEC const& dp) {
 	FFNet me = (FFNet) object;
 
 	integer j = 1;

LPC/Cepstrum.h

@@ -63,12 +63,12 @@ autoPowerCepstrum PowerCepstrum_create (double qmax, integer nq);
 /* Preconditions:
 		nq >= 2;
 	Postconditions:
-		my xmin = 0;				my ymin = 1;
-		my xmax = qmax;				my ymax = 1;
-		my nx = nq;					my ny = 1;
-		my dx = qmax / (nq -1);			my dy = 1;
-		my x1 = 0;			my y1 = 1;
-		my z [1..ny] [1..nx] = 0.0;
+		my xmin == 0;				my ymin == 1;
+		my xmax == qmax;				my ymax == 1;
+		my nx == nq;					my ny == 1;
+		my dx == qmax / (nq -1);			my dy == 1;
+		my x1 == 0.0;			my y1 == 1.0;
+		my z [1..ny] [1..nx] == 0.0;
 */
 
 void PowerCepstrum_draw (PowerCepstrum me, Graphics g, double qmin, double qmax, double dBminimum, double dBmaximum, int garnish);

debian/What_s_new_.html

@@ -7,6 +7,36 @@ What's new?
 <p>
 Latest changes in Praat.</p>
 <p>
+<b>6.0.56</b> (20 June 2019)</p>
+<ul>
+<li>
+&nbsp;Windows: file dropping on the Praat icon works for higher-Unicode file names.
+<li>
+&nbsp;SpellingChecker: Unicode support.
+</ul>
+<p>
+<b>6.0.55</b> (13 June 2019)</p>
+<ul>
+<li>
+&nbsp;Unicode normalization in file names.
+</ul>
+<p>
+<b>6.0.54</b> (6 June 2019)</p>
+<ul>
+<li>
+&nbsp;Removed a bug introduced in 6.0.51 that could cause incorrect axes in Demo window.
+</ul>
+<p>
+<b>6.0.53</b> (26 May 2019)</p>
+<ul>
+<li>
+&nbsp;Much faster playing of short parts of long sounds that need resampling.
+<li>
+&nbsp;Better handling of broken CSV files.
+<li>
+&nbsp;64-bit floating-point WAV files.
+</ul>
+<p>
 <b>6.0.52</b> (2 May 2019)</p>
 <ul>
 <li>
@@ -680,7 +710,7 @@ What used to be new?</h3>
 </ul>
 <hr>
 <address>
-	<p>&copy; ppgb, May 2, 2019</p>
+	<p>&copy; ppgb, June 20, 2019</p>
 </address>
 </body>
 </html>

debian/changelog

+praat (6.0.56-1) unstable; urgency=medium
+
+  * New upstream version 6.0.56
+  * Upload to unstable
+  * d/control: Bump Standards-Version to 4.4.0 (no changes needed)
+  * Add lacking file for unit test test/kar/unicode.praat.
+    See https://github.com/praat/praat/pull/1018 for details.
+
+ -- Rafael Laboissiere <rafael@debian.org>  Wed, 10 Jul 2019 07:41:08 -0300
+
 praat (6.0.52-2) experimental; urgency=medium
 
   * d/t/run-tests: Exclude unit test test_Discriminant (fails on i386)

debian/clean

@@ -17,3 +17,4 @@ praat.1
 praat_nogui.1
 sendpraat.1
 praat-open-files.1
+test/kar/éééürtüéŋəü.wav

debian/control

@@ -12,7 +12,7 @@ Build-Depends: debhelper-compat (= 12),
                xauth,
                xvfb,
                xmlto
-Standards-Version: 4.3.0
+Standards-Version: 4.4.0
 Vcs-Browser: https://salsa.debian.org/med-team/praat
 Vcs-Git: https://salsa.debian.org/med-team/praat.git
 Homepage: http://www.praat.org

debian/copyright

@@ -149,7 +149,7 @@ Copyright: 1996-2004, 2006-2007 Brian Gough
 License: GPL-3+
 
 Files: debian/*
-Copyright: 2005-2009, 2012-2018 Rafael Laboissière <rafael@debian.org>
+Copyright: 2005-2009, 2012-2019 Rafael Laboissière <rafael@debian.org>
            2009-2011 Andreas Tille <tille@debian.org>
 License: GPL-3+
 

debian/rules

@@ -69,6 +69,8 @@ sendpraat: sys/sendpraat.c
 		$(shell $(PKG_CONFIG) --cflags --libs gtk+-2.0)
 
 override_dh_auto_build: sendpraat praat.1 praat_nogui.1 sendpraat.1 praat-open-files.1
+	# Copy lacking file for unit test unicode.praat
+	cp debian/éééürtüéŋəü.wav test/kar
 	dh_auto_build
 	sed 's/^LIBS = /& -Wl,--as-needed /'	\
 		makefiles/makefile.defs.linux.nogui > makefile.defs

debian/source/include-binaries

+debian/éééürtüéŋəü.wav

dwsys/NUM2.cpp

@@ -1536,14 +1536,14 @@ double NUMcubicSplineInterpolation (constVEC x, constVEC y, constVEC y2, double
 	integer klo = 1, khi = x.size;
 	while (khi - klo > 1) {
 		integer k = (khi + klo) >> 1;
-		if (x [k] > xin) {
+		if (x [k] > xin)
 			khi = k;
-		} else {
+		else
 			klo = k;
 	}
-	}
 	double h = x [khi] - x [klo];
-	Melder_require (h != 0.0, U"NUMcubicSplineInterpolation: bad input value.");
+	Melder_require (h != 0.0,
+		U"NUMcubicSplineInterpolation: bad input value.");
 	
 	double a = (x [khi] - xin) / h;
 	double b = (xin - x [klo]) / h;

dwsys/NUM2.h

@@ -1196,7 +1196,7 @@ inline double NUMmean_weighted (constVEC x, constVEC w) {
 	return inproduct / wsum;
 }
 
-inline void VECchainRows_preallocated (VEC v, MAT m) {
+inline void VECchainRows_preallocated (VECVU const& v, constMATVU const& m) {
 	Melder_assert (m.nrow * m.ncol == v.size);
 	integer k = 1;
 	for (integer irow = 1; irow <= m.nrow; irow ++)
@@ -1204,13 +1204,13 @@ inline void VECchainRows_preallocated (VEC v, MAT m) {
 			v [k ++] = m [irow] [icol];
 }
 
-inline autoVEC VECchainRows (MAT m) {
+inline autoVEC VECchainRows (constMATVU const& m) {
 	autoVEC result = newVECraw (m.nrow * m.ncol);
 	VECchainRows_preallocated (result.get(), m);
 	return result;
 }
 
-inline void VECchainColumns_preallocated (VEC v, MAT m) {
+inline void VECchainColumns_preallocated (VEC const& v, constMATVU const& m) {
 	Melder_assert (m.nrow * m.ncol == v.size);
 	integer k = 1;
 	for (integer icol = 1; icol <= m.ncol; icol ++)
@@ -1218,7 +1218,7 @@ inline void VECchainColumns_preallocated (VEC v, MAT m) {
 			v [k ++] = m [irow] [icol];
 }
 
-inline autoVEC VECchainColumns (MAT m) {
+inline autoVEC VECchainColumns (constMATVU const& m) {
 	autoVEC result = newVECraw (m.nrow * m.ncol);
 	VECchainColumns_preallocated (result.get(), m);
 	return result;

dwtest/test_MDS.praat

@@ -164,7 +164,7 @@ procedure testDissimilarityInterface
 	.mdsCommand$ [5] = "To Configuration (absolute mds): "
 	.extraParameters$ [5] = ""
 	.mdsCommand$ [6] = "To Configuration (kruskal): "
-	.extraParameters$ [6] = """Primary approach"", ""Formula1"", "
+	.extraParameters$ [6] = """Primary approach"", ""Kruskal's stress-1"", "
 	
 	# Create a random configuration
 	.command$ = .mdsCommand$ [1] + .numberOfDimensions$ [1] + .extraParameters$ [1] + .minimizationParameters$
@@ -196,8 +196,10 @@ procedure testDissimilarityInterface
 	.stressMeasure$ [4] = "Raw"
 	.tiesHandling$ [1] = "Primary approach"
 	.tiesHandling$ [2] = "Secondary approach"
-	.stressCalculation$ [1] = "Formula1"
-	.stressCalculation$ [2] = "Formula2"
+	;.stressCalculation$ [1] = "Formula1"
+	;.stressCalculation$ [2] = "Formula2"
+	.stressCalculation$ [1] = "Kruskal's stress-1"
+	.stressCalculation$ [2] = "Kruskal's stress-1"
 
 	# test kruskal's stress-1 and stress-2
 

dwtest/test_NMF.praat

@@ -21,7 +21,9 @@ procedure test_simple
 		.dist = Get Euclidean distance
 		Improve factorization (m.u.): 10, 1e-09, 1e-09, "yes"
 		.dist2 = Get Euclidean distance
-		assert .dist2 <= .dist
+		# if we are very near the minimum assert .dist2 <= .dist  is not always true
+		# abs (.dist2 - .dist) <= 1e-09 makes sure that we are within tolerace to the minimum.
+		assert .dist2 <= .dist || abs (.dist2 - .dist) <= 1e-09
 		appendInfoLine: tab$, tab$, .nrow, "x", .ncol, ", aprox = ", .nfeatures, " 2-norm=", .dist2
 		removeObject: .mat, .nmf
 	endfor

dwtools/CCA_and_Correlation.cpp

@@ -30,7 +30,7 @@ autoTableOfReal CCA_Correlation_factorLoadings (CCA me, Correlation thee) {
 	try {
 		integer ny = my y -> dimension, nx = my x -> dimension;
 		Melder_require (ny + nx == thy numberOfColumns,
-			U"The number of columns in the Correlation should equal the sum of the dimensions in the CCA object");
+			U"The number of columns in the Correlation should equal the sum of the dimensions in the CCA object.");
 		
 		autoTableOfReal him = TableOfReal_create (2 * my numberOfCoefficients, thy numberOfColumns);
 		his columnLabels.all() <<= thy columnLabels.all();
@@ -53,7 +53,7 @@ autoTableOfReal CCA_Correlation_factorLoadings (CCA me, Correlation thee) {
 
 static void _CCA_Correlation_check (CCA me, Correlation thee, integer canonicalVariate_from, integer canonicalVariate_to) {
 	Melder_require (my y -> dimension + my x -> dimension == thy numberOfColumns, 
-		U"The number of columns in the Correlation object should equal the sum of the dimensions in the CCA object");
+		U"The number of columns in the Correlation object should equal the sum of the dimensions in the CCA object.");
 	Melder_require (canonicalVariate_to >= canonicalVariate_from,
 		U"The second value in the \"Canonical variate range\" should be equal or larger than the first.");
 	Melder_require (canonicalVariate_from > 0 && canonicalVariate_to <= my numberOfCoefficients, 

dwtools/ComplexSpectrogram.cpp

@@ -42,7 +42,8 @@ Thing_implement (ComplexSpectrogram, Matrix, 2);
 
 
 autoComplexSpectrogram ComplexSpectrogram_create (double tmin, double tmax, integer nt, double dt,
-	double t1, double fmin, double fmax, integer nf, double df, double f1) {
+	double t1, double fmin, double fmax, integer nf, double df, double f1)
+{
 	try {
 		autoComplexSpectrogram me = Thing_new (ComplexSpectrogram);
 		Matrix_init (me.get(), tmin, tmax, nt, dt, t1, fmin, fmax, nf, df, f1);
@@ -55,34 +56,35 @@ autoComplexSpectrogram ComplexSpectrogram_create (double tmin, double tmax, inte
 
 autoComplexSpectrogram Sound_to_ComplexSpectrogram (Sound me, double windowLength, double timeStep) {
 	try {
-		double samplingFrequency = 1.0 / my dx, myDuration = my xmax - my xmin, t1;
+		const double samplingFrequency = 1.0 / my dx, myDuration = my xmax - my xmin;
 		Melder_require (windowLength <= myDuration,
-			U"Your sound is too short:\nit should be at least as long as one window length.");
+			U"Your sound is too short: it should be at least as long as one window length.");
 		
 		integer nsamp_window = Melder_ifloor (windowLength / my dx);
-		integer halfnsamp_window = nsamp_window / 2 - 1;
+		const integer halfnsamp_window = nsamp_window / 2 - 1;
 		nsamp_window = halfnsamp_window * 2;
 		
 		Melder_require (nsamp_window > 1,
 			U"There should be at least two samples in the window.");
 		
 		integer numberOfFrames;
+		double t1;
 		Sampled_shortTermAnalysis (me, windowLength, timeStep, & numberOfFrames, & t1);
 
 		// Compute sampling of the spectrum
 
-		integer numberOfFrequencies = halfnsamp_window + 1;
-		double df = samplingFrequency / (numberOfFrequencies - 1);
+		const integer numberOfFrequencies = halfnsamp_window + 1;
+		const double df = samplingFrequency / (numberOfFrequencies - 1);
 		
 		autoComplexSpectrogram thee = ComplexSpectrogram_create (my xmin, my xmax, numberOfFrames, timeStep, t1, 0.0, 0.5 * samplingFrequency, numberOfFrequencies, df, 0.0);
 		// 
 		autoSound analysisWindow = Sound_create (1, 0.0, nsamp_window * my dx, nsamp_window, my dx, 0.5 * my dx);
 		
 		for (integer iframe = 1; iframe <= numberOfFrames; iframe ++) {
-			double t = Sampled_indexToX (thee.get(), iframe);
-			integer leftSample = Sampled_xToLowIndex (me, t), rightSample = leftSample + 1;
-			integer startSample = rightSample - halfnsamp_window;
-			integer endSample = leftSample + halfnsamp_window;
+			const double t = Sampled_indexToX (thee.get(), iframe);
+			const integer leftSample = Sampled_xToLowIndex (me, t), rightSample = leftSample + 1;
+			const integer startSample = rightSample - halfnsamp_window;
+			const integer endSample = leftSample + halfnsamp_window;
 			Melder_assert (startSample >= 1);
 			Melder_assert (endSample <= my nx);
 			
@@ -95,7 +97,7 @@ autoComplexSpectrogram Sound_to_ComplexSpectrogram (Sound me, double windowLengt
 			thy z [1] [iframe] = spec -> z [1] [1] * spec -> z [1] [1];
 			thy phase [1] [iframe] = 0.0;
 			for (integer ifreq = 2; ifreq <= numberOfFrequencies - 1; ifreq ++) {
-				double x = spec -> z [1] [ifreq], y = spec -> z [2] [ifreq];
+				const double x = spec -> z [1] [ifreq], y = spec -> z [2] [ifreq];
 				thy z [ifreq] [iframe] = x * x + y * y; // power
 				thy phase [ifreq] [iframe] = atan2 (y, x); // phase [-pi,+pi]
 			}
@@ -114,62 +116,59 @@ autoSound ComplexSpectrogram_to_Sound (ComplexSpectrogram me, double stretchFact
 		/* original number of samples is odd: imaginary part of last spectral value is zero -> 
 		 * phase is either zero or +/-pi
 		 */
-		double pi = atan2 (0.0, - 0.5);
-		double samplingFrequency = 2.0 * my ymax;
-		double lastFrequency = my y1 + (my ny - 1) * my dy, lastPhase = my phase [my ny] [1];
-		int originalNumberOfSamplesProbablyOdd = (lastPhase != 0.0 && lastPhase != pi && lastPhase != -pi) || 
-			my ymax - lastFrequency > 0.25 * my dx;
+		const double pi = atan2 (0.0, - 0.5);
+		const double samplingFrequency = 2.0 * my ymax;
+		const double lastFrequency = my y1 + (my ny - 1) * my dy, lastPhase = my phase [my ny] [1];
+		const bool originalNumberOfSamplesProbablyOdd = ( lastPhase != 0.0 && lastPhase != pi && lastPhase != -pi ||
+				my ymax - lastFrequency > 0.25 * my dx );
 		Melder_require (my y1 == 0.0, 
 			U"A Fourier-transformable ComplexSpectrogram should have a first frequency of 0 Hz, not ", my y1, U" Hz.");
 		
-		integer nsamp_window = 2 * my ny - ( originalNumberOfSamplesProbablyOdd ? 1 : 2 );
-		integer halfnsamp_window = nsamp_window / 2;
-		double synthesisWindowDuration = nsamp_window / samplingFrequency;
+		const integer nsamp_window = 2 * my ny - ( originalNumberOfSamplesProbablyOdd ? 1 : 2 );
+		const integer halfnsamp_window = nsamp_window / 2;
+		const double synthesisWindowDuration = nsamp_window / samplingFrequency;
 		autoSpectrum spectrum = Spectrum_create (my ymax, my ny);
 		autoSound synthesisWindow = Sound_createSimple (1, synthesisWindowDuration, samplingFrequency);
-		double newDuration = (my xmax - my xmin) * stretchFactor;
+		const double newDuration = (my xmax - my xmin) * stretchFactor;
 		autoSound thee = Sound_createSimple (1, newDuration, samplingFrequency); //TODO
 		double thyStartTime;
 		for (integer iframe = 1; iframe <= my nx; iframe ++) {
 			// "original" sound :
-			double tmid = Sampled_indexToX (me, iframe);
-			integer leftSample = Sampled_xToLowIndex (thee.get(), tmid);
-			integer rightSample = leftSample + 1;
-			integer startSample = rightSample - halfnsamp_window;
-			double startTime = Sampled_indexToX (thee.get(), startSample);
-			if (iframe == 1) {
+			const double tmid = Sampled_indexToX (me, iframe);
+			const integer leftSample = Sampled_xToLowIndex (thee.get(), tmid);
+			const integer rightSample = leftSample + 1;
+			const integer startSample = rightSample - halfnsamp_window;
+			const double startTime = Sampled_indexToX (thee.get(), startSample);
+			if (iframe == 1)
 				thyStartTime = Sampled_indexToX (thee.get(), startSample);
-			}
 			//integer endSample = leftSample + halfnsamp_window;
 			// New Sound with stretch
-			integer thyStartSample = Sampled_xToLowIndex (thee.get(),thyStartTime);
-			double thyEndTime = thyStartTime + my dx * stretchFactor;
-			integer thyEndSample = Sampled_xToLowIndex (thee.get(), thyEndTime);
-			integer stretchedStepSizeSamples = thyEndSample - thyStartSample + 1;
+			const integer thyStartSample = Sampled_xToLowIndex (thee.get(), thyStartTime);
+			const double thyEndTime = thyStartTime + my dx * stretchFactor;
+			const integer thyEndSample = Sampled_xToLowIndex (thee.get(), thyEndTime);
+			const integer stretchedStepSizeSamples = thyEndSample - thyStartSample + 1;
 			//double extraTime = (thyStartSample - startSample + 1) * thy dx;
-			double extraTime = (thyStartTime - startTime);
+			const double extraTime = thyStartTime - startTime;
 			spectrum -> z [1] [1] = sqrt (my z [1] [iframe]);
 			for (integer ifreq = 2; ifreq <= my ny; ifreq ++) {
-				double f = my y1 + (ifreq - 1) * my dy;
-				double a = sqrt (my z [ifreq] [iframe]);
-				double phi = my phase [ifreq] [iframe], intPart;
-				double extraPhase = 2.0 * pi * modf (extraTime * f, & intPart); // fractional part
-				phi += extraPhase;
+				const double f = my y1 + (ifreq - 1) * my dy;
+				const double a = sqrt (my z [ifreq] [iframe]);
+				double intPart;
+				const double extraPhase = 2.0 * pi * modf (extraTime * f, & intPart); // fractional part
+				const double phi = my phase [ifreq] [iframe] + extraPhase;
 				spectrum -> z [1] [ifreq] = a * cos (phi);
 				spectrum -> z [2] [ifreq] = a * sin (phi);
 			}
-
 			autoSound synthesis = Spectrum_to_Sound (spectrum.get());
 
 			// Where should the sound be placed?
 
-			integer thyEndSampleP = Melder_ifloor (fmin (thyStartSample + synthesis -> nx - 1, thyStartSample + stretchedStepSizeSamples - 1)); // guard against extreme stretches
-			if (iframe == my nx) {
-				thyEndSampleP = Melder_ifloor (fmin (thy nx, thyStartSample + synthesis -> nx - 1));   // ppgb: waarom naar beneden afgerond?
-			}
-			for (integer j = thyStartSample; j <= thyEndSampleP; j++) {
+			integer thyEndSampleP = ( iframe == my nx ?
+				std::min (thy nx, thyStartSample + synthesis -> nx - 1) :
+				std::min (thyStartSample + synthesis -> nx - 1, thyStartSample + stretchedStepSizeSamples - 1)
+			);   // guard against extreme stretches
+			for (integer j = thyStartSample; j <= thyEndSampleP; j ++)
 				thy z [1] [j] = synthesis -> z [1] [j - thyStartSample + 1];
-			}
 			thyStartTime += my dx * stretchFactor;
 		}
 		return thee;
@@ -202,19 +201,18 @@ static autoSound ComplexSpectrogram_to_Sound2 (ComplexSpectrogram me, double str
 		for (integer iframe = 1; iframe <= my nx; iframe++) {
 			spectrum -> z [1] [1] = sqrt (my z [1] [iframe]);
 			for (integer ifreq = 2; ifreq <= my ny; ifreq ++) {
-				double f = my y1 + (ifreq - 1) * my dy;
-				double a = sqrt (my z[ifreq][iframe]);
-				double phi = my phase[ifreq][iframe];
-				double extraPhase = 2.0 * pi * (stretchFactor - 1.0) * my dx * f;
-				phi += extraPhase;
+				const double f = my y1 + (ifreq - 1) * my dy;
+				const double a = sqrt (my z [ifreq] [iframe]);
+				const double extraPhase = 2.0 * pi * (stretchFactor - 1.0) * my dx * f;
+				const double phi = my phase [ifreq] [iframe] + extraPhase;
 				spectrum -> z [1] [ifreq] = a * cos (phi);
 				spectrum -> z [2] [ifreq] = a * sin (phi);
 			}
 			autoSound synthesis = Spectrum_to_Sound (spectrum.get());
-			for (integer j = istart; j <= iend; j++) {
+			for (integer j = istart; j <= iend; j ++)
 				thy z [1] [j] = synthesis -> z [1] [j - istart + 1];
-			}
-			istart = iend + 1; iend = istart + stepSizeSamples - 1;
+			istart = iend + 1;
+			iend = istart + stepSizeSamples - 1;
 		}
 		return thee;
 	} catch (MelderError) {
@@ -234,18 +232,22 @@ autoSpectrogram ComplexSpectrogram_to_Spectrogram (ComplexSpectrogram me) {
 }
 
 void ComplexSpectrogram_Spectrogram_replaceAmplitudes (ComplexSpectrogram me, Spectrogram thee) {
-	Melder_require (my nx == thy nx && my ny == thy ny, U"The number of cells must be equal.");
+	Melder_require (my nx == thy nx && my ny == thy ny,
+		U"The numbers of cells in the ComplexSpectrogram and Spectrogram should be equal.");
 	my z.all() <<= thy z.all();
 }
 
 autoSpectrum ComplexSpectrogram_to_Spectrum (ComplexSpectrogram me, double time) {
 	try {
-		integer iframe = Sampled_xToLowIndex (me, time);   // ppgb: geen Sampled_xToIndex gebruiken voor integers (afrondingen altijd expliciet maken)
-		iframe = ( iframe < 1 ? 1 : ( iframe > my nx ? my nx : iframe ) );
+		integer iframe = Sampled_xToLowIndex (me, time);
+		if (iframe < 1)
+			iframe = 1;
+		if (iframe > my nx)
+			iframe = my nx;
 		autoSpectrum thee = Spectrum_create (my ymax, my ny);
 		for (integer ifreq = 1; ifreq <= my ny; ifreq ++) {
-			double a = sqrt (my z [ifreq] [iframe]);
-			double phi = my phase [ifreq] [iframe];
+			const double a = sqrt (my z [ifreq] [iframe]);
+			const double phi = my phase [ifreq] [iframe];
 			thy z [1] [ifreq] = a * cos (phi);
 			thy z [2] [ifreq] = a * sin (phi);
 		}
@@ -254,4 +256,5 @@ autoSpectrum ComplexSpectrogram_to_Spectrum (ComplexSpectrogram me, double time)
 		Melder_throw (me, U": no Spectrum created.");
 	}
 }
+
 /* End of file ComplexSpectrogram.cpp */

dwtools/Discriminant.cpp

@@ -115,7 +115,8 @@ integer Discriminant_getNumberOfObservations (Discriminant me, integer group) {
 void Discriminant_setAprioriProbability (Discriminant me, integer group, double p) {
 	Melder_require (group > 0 && group <= my numberOfGroups,
 		U"The group number (", group, U") should be in the interval [1, ", my numberOfGroups, U"]; the supplied value (", group, U") falls outside it.");
-	Melder_require (p >= 0.0 && p <= 1.0, U"The probability should be in the interval [0, 1]");
+	Melder_require (p >= 0.0 && p <= 1.0,
+		U"The probability should be in the interval [0, 1]");
 
 	my aprioriProbabilities [group] = p;
 }
@@ -204,7 +205,7 @@ double Discriminant_getWilksLambda (Discriminant me, integer from) {
 	unstandardized u [j]: sqrt(N-g) * r [j]
 	standardized s [j]: u [j] sqrt (w [i] [i] / (N-g))
 */
-autoTableOfReal Discriminant_extractCoefficients (Discriminant me, int choice) {
+autoTableOfReal Discriminant_extractCoefficients (Discriminant me, integer choice) {
 	try {
 		bool raw = choice == 0, standardized = choice == 2;
 		integer nx = my eigen -> dimension, ny = my eigen -> numberOfEigenvalues;
@@ -215,24 +216,22 @@ autoTableOfReal Discriminant_extractCoefficients (Discriminant me, int choice) {
 		// The elements in my groups always have my eigen -> dimension columns
 
 		autoSSCP within;
-		if (standardized) {
+		if (standardized)
 			within = Discriminant_extractPooledWithinGroupsSSCP (me);
-		}
 
 		TableOfReal_setColumnLabel (thee.get(), nx + 1, U"constant");
 		TableOfReal_setSequentialRowLabels (thee.get(), 1, ny, U"function_", 1, 1);
 
 		double scale = sqrt (total -> numberOfObservations - my numberOfGroups);
-		double *centroid = my total -> centroid.at;
+		//double *centroid = my total -> centroid.at;
 		for (integer i = 1; i <= ny; i ++) {
 			longdouble u0 = 0.0;
 			for (integer j = 1; j <= nx; j ++) {
-				if (standardized) {
+				if (standardized)
 					scale = sqrt (within -> data [j] [j]);
-				}
 				double ui = scale * my eigen -> eigenvectors [i] [j];
 				thy data [i] [j] = ui;
-				u0 += ui * centroid [j];
+				u0 += ui * my total -> centroid [j];
 			}
 			thy data [i] [nx + 1] = ( raw ? 0.0 : - (double) u0 );
 		}
@@ -488,25 +487,30 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable (Discrim
 		integer numberOfGroups = Discriminant_getNumberOfGroups (me);
 		integer dimension = Eigen_getDimensionOfComponents (my eigen.get());
 
-		Melder_require (dimension == thy numberOfColumns, U"The number of columns should agree with the dimension of the discriminant.");
+		Melder_require (dimension == thy numberOfColumns,
+			U"The number of columns should agree with the dimension of the discriminant.");
 		
-		autoVEC log_p (numberOfGroups, kTensorInitializationType::RAW);
-		autoVEC log_apriori (numberOfGroups, kTensorInitializationType::RAW);
-		autoVEC ln_determinant (numberOfGroups, kTensorInitializationType::RAW);
-		autoVEC buf (dimension, kTensorInitializationType::RAW);
+		autoVEC log_p = newVECraw (numberOfGroups);
+		autoVEC log_apriori = newVECraw (numberOfGroups);
+		autoVEC ln_determinant = newVECraw (numberOfGroups);
+		autoVEC buf = newVECraw (dimension);
 		
 		autoNUMvector<SSCP> sscpvec (1, numberOfGroups);
 		autoSSCP pool = SSCPList_to_SSCP_pool (my groups.get());
 		autoClassificationTable him = ClassificationTable_create (thy numberOfRows, numberOfGroups);
 		his rowLabels.all() <<= thy rowLabels.all();
 
-		// Scale the sscp to become a covariance matrix
+		/*
+			Scale the sscp to become a covariance matrix.
+		*/
 		
 		pool -> data.get()  *=  1.0 / (pool -> numberOfObservations - numberOfGroups);
 		
 		double lnd;
-		autoSSCPList agroups; SSCPList groups;   // ppgb FIXME dit kan niet goed izjn
+		autoSSCPList agroups;
+		SSCPList groups;   // ppgb FIXME dit kan niet goed izjn
 		if (poolCovarianceMatrices) {
+			
 			/*
 				Covariance matrix S can be decomposed as S = L.L'. Calculate L^-1.
 				L^-1 will be used later in the Mahalanobis distance calculation:
@@ -520,8 +524,11 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable (Discrim
 			}
 			groups = my groups.get();
 		} else {
-			// Calculate the inverses of all group covariance matrices.
-			// In case of a singular matrix, substitute inverse of pooled.
+			
+			/*
+				Calculate the inverses of all group covariance matrices.
+				In case of a singular matrix, substitute inverse of pooled.
+			*/
 
 			agroups = Data_copy (my groups.get());
 			groups = agroups.get();
@@ -535,8 +542,11 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable (Discrim
 				try {
 					MATlowerCholeskyInverse_inplace (t -> data.get(), & ln_determinant [j]);
 				} catch (MelderError) {
-					// Try the alternative: the pooled covariance matrix.
-					// Clear the error.
+					
+					/*
+						Clear the error.
+						Try the alternative: the pooled covariance matrix.
+					*/
 
 					Melder_clearError ();
 					if (npool == 0)
@@ -550,7 +560,9 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable (Discrim
 				Melder_warning (npool, U" groups use pooled covariance matrix.");
 		}
 
-		// Labels for columns in ClassificationTable
+		/*
+			Labels for columns in ClassificationTable
+		*/
 
 		for (integer j = 1; j <= numberOfGroups; j ++) {
 			conststring32 name = Thing_getName (my groups->at [j]);
@@ -559,37 +571,37 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable (Discrim
 			TableOfReal_setColumnLabel (him.get(), j, name);
 		}
 
-		// Normalize the sum of the apriori probabilities to 1.
-		// Next take ln (p) because otherwise probabilities might be too small to represent.
+		/*
+			Normalize the sum of the apriori probabilities to 1.
+			Next take ln (p) because otherwise probabilities might be too small to represent.
+		*/
 
 		VECnormalize_inplace (my aprioriProbabilities.get(), 1.0, 1.0);
 		double logg = log (numberOfGroups);
-		for (integer j = 1; j <= numberOfGroups; j ++) {
+		for (integer j = 1; j <= numberOfGroups; j ++)
 			log_apriori [j] = ( useAprioriProbabilities ? log (my aprioriProbabilities [j]) : - logg );
-		}
 
-		// Generalized squared distance function:
-		// D^2(x) = (x - mu)' S^-1 (x - mu) + ln (determinant(S)) - 2 ln (apriori)
+		/*
+			Generalized squared distance function:
+			D^2(x) = (x - mu)' S^-1 (x - mu) + ln (determinant(S)) - 2 ln (apriori)
+		*/
 
 		for (integer i = 1; i <= thy numberOfRows; i ++) {
 			double norm = 0.0, pt_max = -1e308;
 			for (integer j = 1; j <= numberOfGroups; j ++) {
 				SSCP t = groups->at [j];
 				double md = NUMmahalanobisDistance (sscpvec [j] -> data.get(), thy data.row (i), t -> centroid.get());
-				//double md = mahalanobisDistanceSq (sscpvec [j] -> data.at, dimension, thy data [i], t -> centroid, buf.at);
 				double pt = log_apriori [j] - 0.5 * (ln_determinant [j] + md);
-				if (pt > pt_max) {
+				if (pt > pt_max)
 					pt_max = pt;
-				}
 				log_p [j] = pt;
 			}
-			for (integer j = 1; j <= numberOfGroups; j ++) {
+			for (integer j = 1; j <= numberOfGroups; j ++)
 				norm += log_p [j] = exp (log_p [j] - pt_max);
-			}
-			for (integer j = 1; j <= numberOfGroups; j ++) {
+
+			for (integer j = 1; j <= numberOfGroups; j ++)
 				his data [i] [j] = log_p [j] / norm;
 		}
-		}
 		return him;
 	} catch (MelderError) {
 		Melder_throw (U"ClassificationTable from Discriminant & TableOfReal not created.");
@@ -602,13 +614,14 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 		integer p = Eigen_getDimensionOfComponents (my eigen.get());
 		integer m = thy numberOfRows;
 
-		Melder_require (p == thy numberOfColumns, U"The number of columns does not agree with the dimension of the discriminant.");
+		Melder_require (p == thy numberOfColumns,
+			U"The number of columns does not agree with the dimension of the discriminant.");
 
-		autoNUMvector<double> log_p (1, g);
-		autoNUMvector<double> log_apriori (1, g);
-		autoNUMvector<double> ln_determinant (1, g);
-		autoNUMvector<double> buf (1, p);
-		autoNUMvector<double> displacement (1, p);
+		autoVEC log_p = newVECraw (g);
+		autoVEC log_apriori = newVECraw (g);
+		autoVEC ln_determinant = newVECraw (g);
+		autoVEC buf = newVECraw (p);
+		autoVEC displacement = newVECraw (p);
 		autoVEC x = newVECzero (p);
 		autoNUMvector<SSCP> sscpvec (1, g);
 		autoSSCP pool = SSCPList_to_SSCP_pool (my groups.get());
@@ -616,16 +629,23 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 		his rowLabels.all() <<= thy rowLabels.all();
 		autoTableOfReal adisplacements = Data_copy (thee);
 
-		// Scale the sscp to become a covariance matrix.
+		/*
+			Scale the sscp to become a covariance matrix.
+		*/
+		
 		pool -> data.get()  *=  1.0 / (pool -> numberOfObservations - g);
 
 		double lnd;
 		autoSSCPList agroups;
 		SSCPList groups;
 		if (poolCovarianceMatrices) {
-			// Covariance matrix S can be decomposed as S = L.L'. Calculate L^-1.
-			// L^-1 will be used later in the Mahalanobis distance calculation:
-			// v'.S^-1.v == v'.L^-1'.L^-1.v == (L^-1.v)'.(L^-1.v).
+			
+			/*
+				Covariance matrix S can be Cholesky decomposed as S = L.L'. 
+				Calculate L^-1.
+				L^-1 will be used later in the Mahalanobis distance calculation:
+				v'.S^-1.v = v'.L^-1'.L^-1.v = (L^-1.v)'.(L^-1.v).
+			*/
 
 			MATlowerCholeskyInverse_inplace (pool -> data.get(), & lnd);
 			for (integer j = 1; j <= g; j ++) {
@@ -634,8 +654,11 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 			}
 			groups = my groups.get();
 		} else {
-			//Calculate the inverses of all group covariance matrices.
-			// In case of a singular matrix, substitute inverse of pooled.
+			
+			/*
+				Calculate the inverses of all group covariance matrices.
+				In case of a singular matrix, substitute inverse of pooled.
+			*/
 
 			agroups = Data_copy (my groups.get()); 
 			groups = agroups.get();
@@ -649,24 +672,27 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 				try {
 					MATlowerCholeskyInverse_inplace (t -> data.get(), & ln_determinant [j]);
 				} catch (MelderError) {
-					// Try the alternative: the pooled covariance matrix.
-					// Clear the error.
+					
+					/*
+						Clear the error.
+						Try the alternative: the pooled covariance matrix.
+					*/
 
 					Melder_clearError ();
-					if (npool == 0) {
+					if (npool == 0)
 						MATlowerCholeskyInverse_inplace (pool -> data.get(), & lnd);
-					}
 					npool ++;
 					sscpvec [j] = pool.get();
 					ln_determinant [j] = lnd;
 				}
 			}
-			if (npool > 0) {
+			if (npool > 0)
 				Melder_warning (npool, U" groups use pooled covariance matrix.");
 		}
-		}
 
-		// Labels for columns in ClassificationTable
+		/*
+			Labels for columns in ClassificationTable
+		*/
 
 		for (integer j = 1; j <= g; j ++) {
 			conststring32 name = Thing_getName (my groups->at [j]);
@@ -675,8 +701,10 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 			TableOfReal_setColumnLabel (him.get(), j, name);
 		}
 
-		// Normalize the sum of the apriori probabilities to 1.
-		// Next take ln (p) because otherwise probabilities might be too small to represent.
+		/*
+			Normalize the sum of the apriori probabilities to 1.
+			Next take ln (p) because otherwise probabilities might be too small to represent.
+		*/
 
 		double logg = log (g);
 		VECnormalize_inplace (my aprioriProbabilities.get(), 1.0, 1.0);
@@ -684,20 +712,20 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 			log_apriori [j] = ( useAprioriProbabilities ? log (my aprioriProbabilities [j]) : - logg );
 		}
 
-		// Generalized squared distance function:
-		// D^2(x) = (x - mu)' S^-1 (x - mu) + ln (determinant(S)) - 2 ln (apriori)
+		/*
+			Generalized squared distance function:
+			D^2(x) = (x - mu)' S^-1 (x - mu) + ln (determinant(S)) - 2 ln (apriori)
+		*/
 		
 		for (integer i = 1; i <= m; i ++) {
 			SSCP winner;
 			double norm = 0, pt_max = -1e308;
 			integer iwinner = 1;
-			for (integer k = 1; k <= p; k ++) {
+			for (integer k = 1; k <= p; k ++)
 				x [k] = thy data [i] [k] + displacement [k];
-			}
 			for (integer j = 1; j <= g; j ++) {
 				SSCP t = groups->at [j];
 				double md = NUMmahalanobisDistance (sscpvec [j] -> data.get(), x.get(), t -> centroid.get());
-//				double md = mahalanobisDistanceSq (sscpvec [j] -> data.at, p, x.peek(), t -> centroid, buf.peek());
 				double pt = log_apriori [j] - 0.5 * (ln_determinant [j] + md);
 				if (pt > pt_max) {
 					pt_max = pt;
@@ -705,15 +733,15 @@ autoClassificationTable Discriminant_TableOfReal_to_ClassificationTable_dw (Disc
 				}
 				log_p [j] = pt;
 			}
-			for (integer j = 1; j <= g; j ++) {
+			for (integer j = 1; j <= g; j ++)
 				norm += log_p [j] = exp (log_p [j] - pt_max);
-			}
 
-			for (integer j = 1; j <= g; j ++) {
+			for (integer j = 1; j <= g; j ++)
 				his data [i] [j] = log_p [j] / norm;
-			}
 
-			// Save old displacement, calculate new displacement
+			/*
+				Save old displacement, calculate new displacement
+			*/
 
 			winner = groups->at [iwinner];
 			for (integer k = 1; k <= p; k ++) {

dwtools/Discriminant.h

@@ -2,7 +2,7 @@
 #define _Discriminant_h_
 /* Discriminant.h
  *
- * Copyright (C) 1993-2017 David Weenink
+ * Copyright (C) 1993-2019 David Weenink
  *
  * This code is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
@@ -59,7 +59,7 @@ void Discriminant_drawConcentrationEllipses (Discriminant me, Graphics g,
 	integer d1, integer d2, double xmin, double xmax, double ymin, double ymax,
 	double fontSize, bool garnish);
 
-autoTableOfReal Discriminant_extractCoefficients (Discriminant me, int choice);
+autoTableOfReal Discriminant_extractCoefficients (Discriminant me, integer choice);
 
 autoTableOfReal Discriminant_extractGroupCentroids (Discriminant me);
 

dwtools/Distance.cpp

@@ -26,6 +26,8 @@
 
 Thing_implement (Distance, Proximity, 0);
 
+Thing_implement (DistanceList, ProximityList, 0);
+
 autoDistance Distance_create (integer numberOfPoints) {
 	try {
 		autoDistance me = Thing_new (Distance);
@@ -75,7 +77,6 @@ autoDistance Configuration_to_Distance (Configuration me) {
 	}
 }
 
-
 void Distance_drawDendogram (Distance me, Graphics g, int method) {
 	(void) me;
 	(void) g;