Skip to content
Snippets Groups Projects
Commit 480a0d9b authored by Matthew Webster's avatar Matthew Webster
Browse files

In progress Newimage conversion, DO NOT TAG STABLE

parent d4b72baa
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
AutoCorrEstimator.cc
+ 37
− 78
View file @ 480a0d9b
......@@ -29,7 +29,6 @@ namespace FILM {
void AutoCorrEstimator::setDesignMatrix(const Matrix& dm) {
Tracer tr("AutoCorrEstimator::setDesignMatrix");
int sizeTS = xdata.getNumVolumes();
int numPars = dm.Ncols();
dminFFTReal.ReSize(zeropad, numPars);
......@@ -62,17 +61,15 @@ namespace FILM {
void AutoCorrEstimator::preWhiten(ColumnVector& in, ColumnVector& ret, int i, Matrix& dmret, bool highfreqremovalonly) {
Tracer tr("AutoCorrEstimator::preWhiten");
int sizeTS = xdata.getNumVolumes();
int numPars = dminFFTReal.getNumSeries();
ret.ReSize(sizeTS);
dmret.ReSize(sizeTS, numPars);
// FFT auto corr estimate
dummy = 0;
vrow = 0;
vrow.Rows(1,sizeTS/2) = acEst.getSeries(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.getSeries(i).Rows(2, sizeTS/2).Reverse();
vrow.Rows(1,sizeTS/2) = acEst.Column(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.Column(i).Rows(2, sizeTS/2).Reverse();
FFT(vrow, dummy, ac_fft_real, ac_fft_im);
......@@ -151,9 +148,6 @@ namespace FILM {
cerr << "Prewhitening... ";
int sizeTS = xdata.getNumVolumes();
int numTS = xdata.getNumSeries();
ret.ReSize(sizeTS, numTS);
// make sure p_vrow is cyclic (even function)
......@@ -172,8 +166,8 @@ namespace FILM {
// FFT auto corr estimate
dummy = 0;
vrow = 0;
vrow.Rows(1,sizeTS/2) = acEst.getSeries(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.getSeries(i).Rows(2, sizeTS/2).Reverse();
vrow.Rows(1,sizeTS/2) = acEst.Column(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.Column(i).Rows(2, sizeTS/2).Reverse();
FFT(vrow, dummy, ac_fft_real, ac_fft_im);
// FFT x data
......@@ -213,7 +207,7 @@ namespace FILM {
cerr << " Completed" << endl;
}
void AutoCorrEstimator::fitAutoRegressiveModel()
Matrix AutoCorrEstimator::fitAutoRegressiveModel()
{
Tracer trace("AutoCorrEstimator::fitAutoRegressiveModel");
......@@ -221,9 +215,6 @@ namespace FILM {
const int maxorder = 15;
const int minorder = 1;
int sizeTS = xdata.getNumVolumes();
int numTS = xdata.getNumSeries();
// setup temp variables
ColumnVector x(sizeTS);
......@@ -236,7 +227,7 @@ namespace FILM {
for(int i = 1; i <= numTS; i++)
{
x = xdata.getSeries(i).AsColumn();
x = xdata.Column(i);
ColumnVector betastmp;
order(i) = pacf(x, minorder, maxorder, betastmp);
......@@ -269,8 +260,6 @@ namespace FILM {
else
Kinv.SubMatrix(j,j,1,j) = Krow.Rows(sizeTS-j+1,sizeTS).t();
}
//MISCMATHS::write_ascii_matrix(Kinv,"Kinv");
// Kinv now becomes V:
if(order(i)!=1)
......@@ -292,21 +281,15 @@ namespace FILM {
// output betas:
write_ascii_matrix(LogSingleton::getInstance().appendDir("betas"), betas);
VolumeInfo vinfo = xdata.getInfo();
vinfo.v = maxorder;
betas.unthresholdSeries(vinfo,xdata.getPreThresholdPositions());
betas.writeAsFloat(LogSingleton::getInstance().getDir() + "/betas");
countLargeE = 0;
cerr << " Completed" << endl;
return(betas);
}
int AutoCorrEstimator::pacf(const ColumnVector& x, int minorder, int maxorder, ColumnVector& betas)
{
Tracer ts("pacf");
int order = -1;
int sizeTS = x.Nrows();
// Set c
Matrix c(1,1);
......@@ -342,11 +325,10 @@ namespace FILM {
return order;
}
int AutoCorrEstimator::establishUsanThresh(const Volume& epivol)
int AutoCorrEstimator::establishUsanThresh(const ColumnVector& epivol)
{
int usanthresh = 100;
int num = epivol.getVolumeSize();
int num = epivol.Nrows();
Histogram hist(epivol, num/200);
hist.generate();
float mode = hist.mode();
......@@ -358,7 +340,7 @@ namespace FILM {
// Work out standard deviation from mode for values greater than mode:
for(int i = 1; i <= num; i++) {
if(epivol(i) > mode) {
sum = sum + (epivol(i) - mode)*(epivol(i) - mode);
sum += (epivol(i) - mode)*(epivol(i) - mode);
count++;
}
}
......@@ -371,54 +353,39 @@ namespace FILM {
return usanthresh;
}
void AutoCorrEstimator::spatiallySmooth(const string& usanfname, const Volume& epivol, int masksize, const string& epifname, const string& susanpath, int usan_thresh, int lag) {
void AutoCorrEstimator::spatiallySmooth(const string& usanfname, const ColumnVector& epivol, int masksize, const string& epifname, int usan_thresh, int lag) {
Tracer trace("AutoCorrEstimator::spatiallySmooth");
if(xdata.getNumSeries()<=1)
if(numTS<=1)
{
cerr << "Warning: Number of voxels = " << xdata.getNumSeries() << ". Spatial smoothing of autocorrelation estimates is not carried out" << endl;
cerr << "Warning: Number of voxels = " << numTS << ". Spatial smoothing of autocorrelation estimates is not carried out" << endl;
}
else
{
if(lag==0)
lag = MISCMATHS::Min(40,int(xdata.getNumVolumes()/4));
if(usan_thresh == 0)
{
// Establish epi thresh to use:
usan_thresh = establishUsanThresh(epivol);
}
lag = MISCMATHS::Min(40,int(sizeTS/4));
volume<float> susan_vol(xdata.getInfo().x,xdata.getInfo().y,xdata.getInfo().z);
volume<float> usan_area(xdata.getInfo().x,xdata.getInfo().y,xdata.getInfo().z);
if(usan_thresh == 0) usan_thresh = establishUsanThresh(epivol); // Establish epi thresh to use:
volume4D<float> susan_vol(mask.xsize(),mask.ysize(),mask.zsize(),1);
volume<float> usan_area(mask.xsize(),mask.ysize(),mask.zsize());
volume<float> usan_vol;
read_volume(usan_vol,usanfname);
volume<float> kernel;
kernel = gaussian_kernel3D(masksize,xdata.getInfo().vx,xdata.getInfo().vy,xdata.getInfo().vz,2.0);
// Setup volume for reading and writing volumes:
Volume vol(acEst.getNumSeries(), xdata.getInfo(), xdata.getPreThresholdPositions());
int i = 2;
kernel = gaussian_kernel3D(masksize,mask.xdim(),mask.ydim(),mask.zdim(),2.0);
int factor = 10000;
cerr << "Spatially smoothing auto corr estimates" << endl;
for(; i <= lag; i++)
for(int i=2 ; i <= lag; i++)
{
// setup susan input
vol = acEst.getVolume(i).AsColumn()*factor;
vol.unthreshold();
susan_vol.insert_vec(vol);
// call susan
susan_vol=susan_convolve(susan_vol,kernel,1,0,1,&usan_area,usan_vol,usan_thresh*usan_thresh);
susan_vol.setmatrix(acEst.Row(i),mask);
susan_vol*=factor;
susan_vol[0]=susan_convolve(susan_vol[0],kernel,1,0,1,&usan_area,usan_vol,usan_thresh*usan_thresh);
// insert output back into acEst
vol=susan_vol.vec();
vol.threshold();
acEst.setVolume(static_cast<RowVector>((vol/factor).AsRow()), i);
susan_vol/=factor;
acEst.Row(i)=susan_vol.matrix(mask);
cerr << ".";
}
......@@ -453,14 +420,13 @@ namespace FILM {
ColumnVector fft_im;
ColumnVector dummy(zeropad);
ColumnVector realifft(zeropad);
int sizeTS = xdata.getNumVolumes();
for(int i = 1; i <= xdata.getNumSeries(); i++)
for(int i = 1; i <= numTS; i++)
{
dummy = 0;
vrow = 0;
vrow.Rows(1,sizeTS/2) = acEst.getSeries(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.getSeries(i).Rows(2, sizeTS/2).Reverse();
vrow.Rows(1,sizeTS/2) = acEst.Column(i).Rows(1,sizeTS/2);
vrow.Rows(zeropad - sizeTS/2 + 2, zeropad) = acEst.Column(i).Rows(2, sizeTS/2).Reverse();
FFT(vrow, dummy, fft_real, fft_im);
......@@ -478,8 +444,6 @@ namespace FILM {
Tracer tr("AutoCorrEstimator::multitaper");
cerr << "Multitapering... ";
int sizeTS = xdata.getNumVolumes();
int numTS = xdata.getNumSeries();
Matrix slepians;
getSlepians(M, sizeTS, slepians);
......@@ -504,10 +468,8 @@ namespace FILM {
// Compute FFT for each slepian taper
for(int k = 1; k <= slepians.Ncols(); k++)
{
x.Rows(1,sizeTS) = SP(slepians.Column(k), xdata.getSeries(i));
//LogSingleton::getInstance().out("x", xdata.getSeries(i), false);
x.Rows(1,sizeTS) = SP(slepians.Column(k), xdata.Column(i));
FFT(x, dummy, fft_real, fft_im);
for(int j = 1; j <= zeropad; j++)
{
......@@ -532,7 +494,7 @@ namespace FILM {
//LogSingleton::getInstance().out("fftreal", fft_real);
float varx = MISCMATHS::var(ColumnVector(x.Rows(1,sizeTS))).AsScalar();
acEst.setSeries(realifft.Rows(1,sizeTS)/varx, i);
acEst.Column(i)=realifft.Rows(1,sizeTS)/varx;
}
countLargeE = 0;
cerr << "Completed" << endl;
......@@ -571,7 +533,6 @@ namespace FILM {
cerr << "Tukey M = " << M << endl;
cerr << "Tukey estimates... ";
int sizeTS = xdata.getNumVolumes();
ColumnVector window(M);
......@@ -580,7 +541,7 @@ namespace FILM {
window(j) = 0.5*(1+cos(M_PI*j/(float(M))));
}
for(int i = 1; i <= xdata.getNumSeries(); i++) {
for(int i = 1; i <= xdata.Ncols(); i++) {
acEst.SubMatrix(1,M,i,i) = SP(acEst.SubMatrix(1,M,i,i),window);
acEst.SubMatrix(M+1,sizeTS,i,i) = 0;
......@@ -596,8 +557,7 @@ namespace FILM {
cerr << "Using New PAVA on AutoCorr estimates... ";
for(int i = 1; i <= xdata.getNumSeries(); i++) {
int sizeTS = xdata.getNumVolumes();
for(int i = 1; i <= numTS; i++) {
int stopat = (int)sizeTS/2;
// 5% point of distribution of autocorr about zero
......@@ -683,9 +643,8 @@ namespace FILM {
cerr << "Applying constraints to AutoCorr estimates... ";
for(int i = 1; i <= xdata.getNumSeries(); i++)
for(int i = 1; i <= numTS; i++)
{
int sizeTS = xdata.getNumVolumes();
int j = 3;
int stopat = (int)sizeTS/4;
......@@ -740,11 +699,11 @@ namespace FILM {
{
Tracer tr("AutoCorrEstimator::getMeanEstimate");
ret.ReSize(acEst.getNumVolumes());
ret.ReSize(acEst.Nrows());
// Calc global Vrow:
for(int i = 1; i <= acEst.getNumVolumes(); i++)
for(int i = 1; i <= acEst.Nrows(); i++)
{
ret(i) = MISCMATHS::mean(ColumnVector(acEst.getVolume(i).AsColumn())).AsScalar();
ret(i) = MISCMATHS::mean(ColumnVector(acEst.Row(i).AsColumn())).AsScalar();
}
}
}
......
AutoCorrEstimator.h
+ 17
− 9
View file @ 480a0d9b
......@@ -14,6 +14,7 @@
#define WANT_STREAM
#define WANT_MATH
#include "newimage/newimageall.h"
#include "miscmaths/volumeseries.h"
#include "miscmaths/volume.h"
#include "newmatap.h"
......@@ -22,15 +23,18 @@
using namespace NEWMAT;
using namespace MISCMATHS;
using namespace NEWIMAGE;
namespace FILM {
class AutoCorrEstimator
{
public:
AutoCorrEstimator(const VolumeSeries& pxdata) :
AutoCorrEstimator(const Matrix& pxdata) :
sizeTS(pxdata.Nrows()),
numTS(pxdata.Ncols()),
xdata(pxdata),
acEst(pxdata.getNumVolumes(), pxdata.getNumSeries(), pxdata.getInfo(), pxdata.getPreThresholdPositions()),
acEst(pxdata.Nrows(), pxdata.Ncols()),
vrow(),
xrow(),
dummy(),
......@@ -38,7 +42,7 @@ namespace FILM {
dm_mn(),
zeropad(0)
{
zeropad = MISCMATHS::nextpow2(pxdata.getNumVolumes());
zeropad = MISCMATHS::nextpow2(pxdata.Nrows());
vrow.ReSize(zeropad);
xrow.ReSize(zeropad);
dummy.ReSize(zeropad);
......@@ -50,19 +54,19 @@ namespace FILM {
}
void calcRaw(int lag = 0);
void spatiallySmooth(const string& usanfname, const Volume& epivol, int masksize, const string& epifname, const string& susanpath, int usanthresh, int lag=0);
void spatiallySmooth(const string& usanfname, const ColumnVector& epivol, int masksize, const string& epifname, int usanthresh, int lag=0);
void applyConstraints();
void filter(const ColumnVector& filterFFT);
void fitAutoRegressiveModel();
Matrix fitAutoRegressiveModel();
void pava();
void preWhiten(VolumeSeries& in, VolumeSeries& ret);
void preWhiten(ColumnVector& in, ColumnVector& ret, int i, Matrix& dmret, bool highfreqremovalonly=false);
void setDesignMatrix(const Matrix& dm);
int establishUsanThresh(const Volume& epivol);
int establishUsanThresh(const ColumnVector& epivol);
void getMeanEstimate(ColumnVector& ret);
VolumeSeries& getEstimates() { return acEst; }
Matrix& getEstimates() { return acEst; }
VolumeSeries& getE() { return E; }
ColumnVector& getCountLargeE(){ return countLargeE; }
......@@ -70,15 +74,19 @@ namespace FILM {
void tukey(int M);
void multitaper(int M);
int pacf(const ColumnVector& x, int minorder, int maxorder, ColumnVector& betas);
volume<float> mask;
private:
const int sizeTS;
const int numTS;
AutoCorrEstimator();
const AutoCorrEstimator& operator=(AutoCorrEstimator&);
AutoCorrEstimator(AutoCorrEstimator&);
void getSlepians(int M, int sizeTS, Matrix& slepians);
const VolumeSeries& xdata;
VolumeSeries acEst;
const Matrix& xdata;
Matrix acEst;
VolumeSeries E;
ColumnVector countLargeE;
......
film_gls.cc
+ 40
− 50
View file @ 480a0d9b
......@@ -44,16 +44,24 @@ int main(int argc, char *argv[])
globalopts.parse_command_line(argc, argv, logger);
// load data
volume4D<double> input_data;
read_volume4D(input_data,globalopts.inputfname);
volume4D<float> input_data;
volumeinfo vinfo;
read_volume4D(input_data,globalopts.inputfname,vinfo);
int sizeTS = input_data.tsize();
Matrix datam;
VolumeSeries x,y;
y.read(globalopts.inputfname);
x.setInfo(y.getInfo());
x=input_data.matrix();
x.thresholdSeries(globalopts.thresh, true);
//save_volume_dtype(input_data[int(sizeTS/2)-1],logger.getDir() + "/" + globalopts.epifname,DT_SIGNED_INT );
save_volume(input_data[int(sizeTS/2)-1],logger.getDir() + "/" + globalopts.epifname);
volume<double> mask(input_data[0]);
volume<float> mask(input_data[0]);
volume4D<float> epivol2(mask.xsize(),mask.ysize(),mask.zsize(),1);
epivol2[0]=input_data[int(sizeTS/2)-1];
mask=0;
for(int t=0;t<input_data.tsize();t++) mask+=input_data[t];
mask/=input_data.tsize();
......@@ -64,32 +72,9 @@ int main(int argc, char *argv[])
for(int t=0;t<input_data.tsize();t++) input_data(x,y,z,t)-= mask(x,y,z);
mask.binarise(globalopts.thresh,mask.max()+1,exclusive);
input_data*=mask;
save_volume4D(input_data,"foo2");
datam=input_data.matrix(mask);
int sizeTS = input_data.tsize();
int numTS = mask.sum();
cerr << datam.Nrows() << " " << datam.Ncols() << endl;
// if needed for SUSAN spatial smoothing, output the halfway volume for use later
Volume epivol;
if(globalopts.smoothACEst)
{
epivol = x.getVolume(int(sizeTS/2)).AsColumn();
epivol.setInfo(x.getInfo());
epivol.writeAsInt(logger.getDir() + "/" + globalopts.epifname);
}
// This also removes the mean from each of the time series:
x.thresholdSeries(globalopts.thresh, true);
x.writeThresholdedSeriesAsFloat(x.getInfo(),x.getPreThresholdPositions(),"foo3");
// if needed for SUSAN spatial smoothing, also threshold the epi volume
if(globalopts.smoothACEst)
{
epivol.setPreThresholdPositions(x.getPreThresholdPositions());
epivol.threshold();
}
// Load paradigm:
Paradigm parad;
......@@ -122,16 +107,18 @@ int main(int argc, char *argv[])
// Residuals container:
VolumeSeries residuals(sizeTS, numTS, x.getInfo(), x.getPreThresholdPositions());
Matrix residualsm = datam;
// Setup autocorrelation estimator:
AutoCorrEstimator acEst(residuals);
acEst.mask=mask;
if(!globalopts.noest)
{
cout << "Calculating residuals..." << endl;
for(int i = 1; i <= numTS; i++)
{
//glimGls.setData(x.getSeries(i), parad.getDesignMatrix(), i);
glimGls.setData(datam.Column(i), parad.getDesignMatrix(), i);
residuals.setSeries(glimGls.getResiduals(),i);
}
......@@ -141,7 +128,11 @@ int main(int argc, char *argv[])
if(globalopts.fitAutoRegressiveModel)
{
acEst.fitAutoRegressiveModel();
volume4D<float> beta;
beta.setmatrix(acEst.fitAutoRegressiveModel(),mask);
copybasicproperties(input_data,beta);
FslSetCalMinMax(&vinfo,beta.min(),beta.max());
save_volume4D(beta,LogSingleton::getInstance().getDir() + "/betas",vinfo);
}
else if(globalopts.tukey)
{
......@@ -153,7 +144,8 @@ int main(int argc, char *argv[])
// SUSAN smooth raw estimates:
if(globalopts.smoothACEst)
{
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.susanpath, globalopts.epith, globalopts.tukeysize);
ColumnVector epivol = epivol2.matrix(mask).t();
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.epith, globalopts.tukeysize);
}
acEst.tukey(globalopts.tukeysize);
......@@ -170,7 +162,8 @@ int main(int argc, char *argv[])
// Smooth raw estimates:
if(globalopts.smoothACEst)
{
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.susanpath, globalopts.epith);
ColumnVector epivol = epivol2.matrix(mask).t();
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.epith);
}
acEst.pava();
......@@ -196,7 +189,7 @@ int main(int argc, char *argv[])
acEst.setDesignMatrix(parad.getDesignMatrix());
// Use autocorr estimate to prewhiten data:
xprew = x.getSeries(i);
xprew = datam.Column(i);
Matrix designmattw;
acEst.preWhiten(xprew, xw, i, designmattw);
......@@ -207,9 +200,8 @@ int main(int argc, char *argv[])
co++;
}
x.setSeries(xw,i);
glimGls.setData(x.getSeries(i), designmattw, i);
datam.Column(i)=xw;
glimGls.setData(datam.Column(i), designmattw, i);
if(globalopts.output_pwdata || globalopts.verbose)
{
......@@ -224,8 +216,7 @@ int main(int argc, char *argv[])
cout.flush();
co++;
}
glimGls.setData(x.getSeries(i), parad.getDesignMatrix(), i);
glimGls.setData(datam.Column(i), parad.getDesignMatrix(), i);
residuals.setSeries(glimGls.getResiduals(),i);
if(globalopts.output_pwdata || globalopts.verbose)
......@@ -245,16 +236,15 @@ int main(int argc, char *argv[])
cerr << "Saving results... " << endl;
// write residuals
//residuals.unthresholdSeries(x.getInfo(),x.getPreThresholdPositions());
//residuals.writeAsFloat(logger.getDir() + "/res4d");
residuals.writeThresholdedSeriesAsFloat(x.getInfo(),x.getPreThresholdPositions(),logger.getDir() + "/res4d");
residuals.CleanUp();
if(globalopts.output_pwdata || globalopts.verbose)
{
// Write out whitened data
x.writeThresholdedSeriesAsFloat(x.getInfo(),x.getPreThresholdPositions(),logger.getDir() + "/prewhitened_data");
input_data.setmatrix(datam,mask);
FslSetCalMinMax(&vinfo,input_data.min(),input_data.max());
save_volume4D(input_data,logger.getDir() + "/prewhitened_data",vinfo);
// Write out whitened design matrix
write_vest(logger.appendDir("mean_prewhitened_dm.mat"), mean_prewhitened_dm);
......@@ -263,19 +253,19 @@ int main(int argc, char *argv[])
x.CleanUp();
// Write out threshac:
VolumeSeries& threshac = acEst.getEstimates();
Matrix& threshacm = acEst.getEstimates();
int cutoff = sizeTS/2;
if(globalopts.tukey)
cutoff = globalopts.tukeysize;
cutoff = MISCMATHS::Max(1,cutoff);
threshac = threshac.Rows(1,cutoff);
VolumeInfo volinfo = x.getInfo();
volinfo.v = cutoff;
volinfo.intent_code = NIFTI_INTENT_ESTIMATE;
threshac.unthresholdSeries(volinfo,x.getPreThresholdPositions());
threshac.writeAsFloat(logger.getDir() + "/threshac1");
threshac.thresholdSeries();
threshac.CleanUp();
threshacm = threshacm.Rows(1,cutoff);
volume4D<float> threshac;
threshac.setmatrix(threshacm,mask);
copybasicproperties(input_data,threshac);
threshac.set_intent(NIFTI_INTENT_ESTIMATE,0,0,0);
FslSetCalMinMax(&vinfo,threshac.min(),threshac.max());
save_volume4D(threshac,logger.getDir() + "/threshac1",vinfo);
threshacm.CleanUp();
// save gls results:
glimGls.Save(x.getInfo(), x.getPreThresholdPositions());
......
film_ols.cc
+ 1
− 1
View file @ 480a0d9b
......@@ -132,7 +132,7 @@ int main(int argc, char *argv[])
// Smooth raw estimates:
if(globalopts.smoothACEst)
{
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.susanpath, 0);
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, 0);
}
// Apply constraints to estimate autocorr:
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment