/* MELODIC - Multivariate exploratory linear optimized decomposition into independent components melreport.cc - report generation Christian F. Beckmann, FMRIB Image Analysis Group Copyright (C) 1999-2008 University of Oxford */ /* Part of FSL - FMRIB's Software Library http://www.fmrib.ox.ac.uk/fsl fsl@fmrib.ox.ac.uk Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance Imaging of the Brain), Department of Clinical Neurology, Oxford University, Oxford, UK LICENCE FMRIB Software Library, Release 5.0 (c) 2012, The University of Oxford (the "Software") The Software remains the property of the University of Oxford ("the University"). The Software is distributed "AS IS" under this Licence solely for non-commercial use in the hope that it will be useful, but in order that the University as a charitable foundation protects its assets for the benefit of its educational and research purposes, the University makes clear that no condition is made or to be implied, nor is any warranty given or to be implied, as to the accuracy of the Software, or that it will be suitable for any particular purpose or for use under any specific conditions. Furthermore, the University disclaims all responsibility for the use which is made of the Software. It further disclaims any liability for the outcomes arising from using the Software. The Licensee agrees to indemnify the University and hold the University harmless from and against any and all claims, damages and liabilities asserted by third parties (including claims for negligence) which arise directly or indirectly from the use of the Software or the sale of any products based on the Software. No part of the Software may be reproduced, modified, transmitted or transferred in any form or by any means, electronic or mechanical, without the express permission of the University. The permission of the University is not required if the said reproduction, modification, transmission or transference is done without financial return, the conditions of this Licence are imposed upon the receiver of the product, and all original and amended source code is included in any transmitted product. You may be held legally responsible for any copyright infringement that is caused or encouraged by your failure to abide by these terms and conditions. You are not permitted under this Licence to use this Software commercially. Use for which any financial return is received shall be defined as commercial use, and includes (1) integration of all or part of the source code or the Software into a product for sale or license by or on behalf of Licensee to third parties or (2) use of the Software or any derivative of it for research with the final aim of developing software products for sale or license to a third party or (3) use of the Software or any derivative of it for research with the final aim of developing non-software products for sale or license to a third party, or (4) use of the Software to provide any service to an external organisation for which payment is received. If you are interested in using the Software commercially, please contact Isis Innovation Limited ("Isis"), the technology transfer company of the University, to negotiate a licence. Contact details are: innovation@isis.ox.ac.uk quoting reference DE/9564. */ #include "newimage/newimageall.h" #include "utils/log.h" #include "melreport.h" #include "melhlprfns.h" #include "miscmaths/miscprob.h" namespace Melodic{ void MelodicReport::IC_rep(MelGMix &mmodel, int cnum, int dim, Matrix ICstats){ if( bool(opts.genreport.value()) ){ addlink(mmodel.get_prefix()+".html",num2str(cnum)); IChtml.setDir(report.getDir(),mmodel.get_prefix()+".html"); {//start IC page IChtml << "" << endl << "" << "FSL" << endl << "
"<< endl << "
" << endl; if(cnum>1) IChtml << "< - "; else IChtml << "< - "; if(cnum>"; else IChtml << ">"; IChtml << "

MELODIC Component " << num2str(cnum) << "

" << endl; } {//output IC stats if(ICstats.Storage()>0&&ICstats.Nrows()>=cnum){ IChtml << fixed << setprecision(2) << std::abs(ICstats(cnum,1)) << " % of explained variance"; if(ICstats.Ncols()>1) IChtml << ";     " << std::abs(ICstats(cnum,2)) << " % of total variance"; if(ICstats.Ncols()>2&&opts.addsigchng.value()){ IChtml << "

" <

" << endl; } } volume4D tempVol; if(mmodel.get_threshmaps().Storage()>0&& (mmodel.get_threshmaps().Ncols() == mmodel.get_data().Ncols())) {//Output thresholded IC map tempVol.setmatrix(mmodel.get_threshmaps().Row(1),melodat.get_mask()); volume map1; volume map2; map1 = threshold(tempVol[0],float(0.0), tempVol[0].max()); map2 = threshold(tempVol[0],tempVol[0].min(), float(0.0)); volume newvol; miscpic newpic; float map1min = std::max((map1 + binarise(tempVol[0],tempVol[0].min(), float(0.0)) * map1.max()).min(),float(0.001)); float map1max = std::max(map1.max(),float(0.001)); float map2min = std::min(map2.min(),float(-0.001)); float map2max = std::min((map2 + binarise(tempVol[0],float(0.0), tempVol[0].max()) * map2.min()).max(),float(-0.001)); newpic.overlay(newvol, melodat.get_bg(), map1, map2, melodat.get_bg().percentile(0.02), melodat.get_bg().percentile(0.98), map1min, map1max, map2max, map2min, 0, 0); char instr[10000]; sprintf(instr," "); strcat(instr,axials_instr.c_str()); strcat(instr,string(report.appendDir(mmodel.get_prefix()+ "_thresh.png")).c_str()); newpic.set_title(string("Component No. "+num2str(cnum)+ " - thresholded IC map ") + mmodel.get_infstr(1)); newpic.set_cbar(string("ysb")); if((std::abs(map1.max()-map1.min())>0.01) || (std::abs(map2.max()-map2.min())>0.01)) newpic.slicer(newvol, instr); else newpic.slicer(newvol, instr); IChtml << ""; IChtml << "\"MMfit\"

" << endl; } {//plot time course IChtml << "

Temporal mode

" << endl <0 && melodat.glmT.get_beta().Nrows() == melodat.Tdes.Ncols()){ tmptc &= melodat.glmT.get_beta().Column(cnum).t() * melodat.Tdes.t(); newplot.add_label("full model fit"); } //add deviation around time course if(melodat.get_Tmodes(cnum-1).Ncols()>1 && opts.varplots.value()){ Matrix tmp = stdev(melodat.get_Tmodes(cnum-1).Columns(2,melodat.get_Tmodes(cnum-1).Ncols()).t(),1); tmptc &= melodat.get_Tmodes(cnum-1).Column(1).t()+tmp; tmptc &= melodat.get_Tmodes(cnum-1).Column(1).t()-tmp; newplot.add_label("std error across subjects"); newplot.col_replace(tmptc.Nrows()-1,0x808080); newplot.col_replace(tmptc.Nrows()-2,0x808080); } if(opts.tr.value()>0.0) newplot.add_xlabel(string("Time (seconds); TR = ")+ float2str(opts.tr.value(),0,2,0)+" s"); else newplot.add_xlabel(string("Time (TRs)")); newplot.add_ylabel("Normalised Response"); newplot.set_yrange(tmptc.Row(1).Minimum()-0.05*(tmptc.Row(1).Maximum() - tmptc.Row(1).Minimum()),tmptc.Row(1).Maximum()+ 0.05*(tmptc.Row(1).Maximum()-tmptc.Row(1).Minimum())); newplot.grid_swapdefault(); newplot.timeseries(tmptc, report.appendDir(string("t")+num2str(cnum)+".png"), string("Timecourse No. ")+num2str(cnum), opts.tr.value(),150,12,1,false); if(melodat.get_Tmodes(cnum-1).Ncols()>1) tmptc &= melodat.get_Tmodes(cnum-1).Columns(2,melodat.get_Tmodes(cnum-1).Ncols()).t(); write_ascii_matrix(report.appendDir(string("t") +num2str(cnum)+".txt"),tmptc.t()); IChtml << " "; IChtml << "

" << endl; if(melodat.get_numfiles()>1 && melodat.explained_var.Storage()>0 && melodat.explained_var.Ncols()>=cnum && opts.varvals.value()) IChtml << "Rank-1 approximation of individual time courses explains " << std::abs(melodat.explained_var(cnum)) << "% of variance.

" << endl; }//time series plot if(!opts.pspec.value()) {//plot frequency miscplot newplot; RowVector empty(1); empty = 0.0; int fact = int(std::pow(10.0,int(std::log10(float(melodat.get_Tmodes(0).Nrows()))))); if(opts.logPower.value()) newplot.add_ylabel(string("log-Power")); else newplot.add_ylabel(string("Power")); Matrix fmixtc = calc_FFT(melodat.get_Tmodes(cnum-1).Column(1), opts.logPower.value()); newplot.set_Ylabel_fmt("%.0f"); newplot.set_yrange(0.0,1.02*fmixtc.Maximum()); newplot.grid_swapdefault(); if(opts.tr.value()>0.0){ newplot.add_xlabel(string("Frequency (in Hz / ")+num2str(fact)+ " )"); newplot.timeseries(empty | fmixtc.t(), report.appendDir(string("f")+ num2str(cnum)+".png"), string("Powerspectrum of timecourse"), fact/(opts.tr.value()*melodat.get_Tmodes(0).Nrows()), 150,0,2); }else{ newplot.add_xlabel(string("Frequency (in cycles); ") +"frequency(Hz)=cycles/(" +num2str(melodat.get_Tmodes(0).Nrows()) +"* TR); period(s)=(" +num2str(melodat.get_Tmodes(0).Nrows()) +"* TR)/cycles" ); newplot.timeseries(fmixtc.t(), report.appendDir(string("f")+num2str(cnum)+".png"), string("Powerspectrum of timecourse")); } write_ascii_matrix(report.appendDir(string("f") +num2str(cnum)+".txt"), fmixtc); IChtml << " "; IChtml << "

" << endl; }//frequency plot {//add T-mode GLM F-stats for full model fit & contrasts if(melodat.Tdes.Storage() > 0 && melodat.glmT.get_beta().Nrows() == melodat.Tdes.Ncols()){ IChtml << " " << "" << endl << "
GLM (OLS) on time series
GLM β's F-test on
full model fit "; if(melodat.Tcon.Storage() > 0) IChtml << "		Contrasts"<" << endl; else IChtml << fixed << setprecision(5) << " p < " << melodat.glmT.get_pf_fmf().Column(cnum) << "
(uncorrected for #comp.)" << endl; if(melodat.Tcon.Storage() > 0 && melodat.Tdes.Ncols() == melodat.Tcon.Ncols()){ IChtml << fixed << setprecision(2) << "" << endl; } } IChtml << "
" << endl; for(int ctr=1;ctr <= melodat.Tdes.Ncols();ctr++) IChtml << " PE(" <" << endl; IChtml << "" << endl; for(int ctr=1;ctr <= melodat.Tdes.Ncols();ctr++) IChtml << melodat.glmT.get_beta().Column(cnum).Row(ctr) << "
" <" << " 		F = "<< melodat.glmT.get_f_fmf().Column(cnum) << "
dof1 = " << melodat.Tdes.Ncols() << "; dof2 = " << melodat.glmT.get_dof() << "
" < p < " << melodat.glmT.get_pf_fmf().Column(cnum) << "
(uncorrected for #comp.)
" <" << endl; IChtml << "" << endl; for(int ctr=1; ctr <= melodat.Tcon.Nrows() ; ctr++) IChtml <<" z =
" <" << endl; for(int ctr=1; ctr <= melodat.Tcon.Nrows() ; ctr++) IChtml << melodat.glmT.get_z().Column(cnum).Row(ctr) <<";
" <" << endl; for(int ctr=1; ctr <= melodat.Tcon.Nrows() ; ctr++) if(melodat.glmT.get_p().Column(cnum).Row(ctr).AsScalar() < 0.05) IChtml << fixed << setprecision(5) << " p < " << melodat.glmT.get_p().Column(cnum).Row(ctr) << "
" << endl; else IChtml << fixed << setprecision(5) <<" p < " << melodat.glmT.get_p().Column(cnum).Row(ctr) << "
" << endl; IChtml << "

" << endl; } if(cnum <= (int)melodat.get_Smodes().size()) {//plot subject mode Matrix smode; smode = melodat.get_Smodes(cnum-1); if(smode.Nrows() > 1){ IChtml << "

Sessions/Subjects mode

" << endl < 0&& melodat.glmS.get_beta().Nrows() == melodat.Sdes.Ncols()){ smode |= melodat.Sdes * melodat.glmS.get_beta().Column(cnum); newplot.add_label(string("IC ")+num2str(cnum)+" subject/session-mode"); newplot.add_label("full model fit"); } newplot.grid_swapdefault(); newplot.set_Ylabel_fmt("%.2f"); newplot.add_xlabel(" Subject Number"); // newplot.set_xysize(smode.Nrows()*80,150); newplot.timeseries(smode.t(), report.appendDir(string("s")+num2str(cnum)+".png"), string("Subject/Session mode No. ") + num2str(cnum)); newplot.clear_xlabel(); newplot.clear_labels(); newplot.set_xysize(120,200); newplot.set_minmaxscale(1.1); newplot.boxplot((Matrix)smode.Column(1), report.appendDir(string("b")+num2str(cnum)+".png"), string("Subject/Session mode")); write_ascii_matrix(report.appendDir(string("s") +num2str(cnum)+".txt"), smode); IChtml << " "; IChtml << "" << endl; IChtml << " "; IChtml << "

" << endl; } {//add S-mode GLM F-stats for full model fit & contrasts if(melodat.Sdes.Storage() > 0 && melodat.glmS.get_beta().Nrows() == melodat.Sdes.Ncols()){ IChtml << " " << "" << endl << "
GLM (OLS) on subject/session-mode
GLM β's F-test on
full model fit "; if(melodat.Scon.Storage() > 0) IChtml << "		Contrasts"<" << endl; else IChtml << fixed << setprecision(5) << " p < " << melodat.glmS.get_pf_fmf().Column(cnum) << "
(uncorrected for #comp.)" << endl; if(melodat.Scon.Storage() > 0 && melodat.Sdes.Storage() > 0 && melodat.Sdes.Ncols() == melodat.Scon.Ncols()){ IChtml << fixed << setprecision(2) << "" << endl; } } IChtml << "
" << endl; for(int ctr=1;ctr <= melodat.Sdes.Ncols();ctr++) IChtml << " PE(" <" << endl; IChtml << "" << endl; for(int ctr=1;ctr <= melodat.Sdes.Ncols();ctr++) IChtml << melodat.glmS.get_beta().Column(cnum).Row(ctr) << "
" <" << " 		F = "<< melodat.glmS.get_f_fmf().Column(cnum) << "
dof1 = " << melodat.Sdes.Ncols() << "; dof2 = " << melodat.glmS.get_dof() << "
" < p < " << melodat.glmS.get_pf_fmf().Column(cnum) << "
(uncorrected for #comp.)
" <" << endl; IChtml << "" << endl; for(int ctr=1; ctr <= melodat.Scon.Nrows() ; ctr++) IChtml <<" z =
" <" << endl; for(int ctr=1; ctr <= melodat.Scon.Nrows() ; ctr++) IChtml << melodat.glmS.get_z().Column(cnum).Row(ctr) <<";
" <" << endl; for(int ctr=1; ctr <= melodat.Scon.Nrows() ; ctr++) if(melodat.glmS.get_p().Column(cnum).Row(ctr).AsScalar() < 0.05) IChtml << fixed << setprecision(5) << " p < " << melodat.glmS.get_p().Column(cnum).Row(ctr) << "
" << endl; else IChtml << fixed << setprecision(5) <<" p < " << melodat.glmS.get_p().Column(cnum).Row(ctr) << "
" << endl; IChtml << "

" << endl; } }//subject mode plot if(mmodel.get_threshmaps().Storage()>0&& (mmodel.get_threshmaps().Ncols() == mmodel.get_data().Ncols())&& (mmodel.get_threshmaps().Nrows()>1)) {//Output other thresholded IC map for(int tctr=2; tctr<=mmodel.get_threshmaps().Nrows(); tctr++){ tempVol.setmatrix(mmodel.get_threshmaps().Row(tctr),melodat.get_mask()); volume map1; volume map2; map1 = threshold(tempVol[0],float(0.0), tempVol[0].max()); map2 = threshold(tempVol[0],tempVol[0].min(), float(0.0)); volume newvol; miscpic newpic; float map1min = (map1 + binarise(tempVol[0],tempVol[0].min(), float(0.0)) * map1.max()).min(); float map1max = map1.max(); float map2min = map2.min(); float map2max = (map2 + binarise(tempVol[0],float(0.0), tempVol[0].max()) * map2.min()).max(); //cerr << endl << map1min << " " << map1max << endl // << map2min << " " << map2max << endl; // if(map1.max()-map1.min()>0.01) newpic.overlay(newvol, melodat.get_bg(), map1, map2, melodat.get_bg().percentile(0.02), melodat.get_bg().percentile(0.98), map1min, map1max, map2max, map2min, 0, 0); char instr[10000]; sprintf(instr," "); strcat(instr,axials_instr.c_str()); strcat(instr,string(report.appendDir(mmodel.get_prefix()+"_thresh"+ num2str(tctr)+".png")).c_str()); newpic.set_title(string("Component No. "+num2str(cnum)+ " - thresholded IC map ("+ num2str(tctr)+") ")+ mmodel.get_infstr(tctr)); newpic.set_cbar(string("ysb")); //cerr << instr << endl; newpic.slicer(newvol, instr); IC_rep_det(mmodel, cnum, dim); IChtml << ""; IChtml << "\"MMfit\"

" << endl; } } { //finish IC page IChtml<< "

This page produced automatically by " << " MELODIC Version " << version << " - a part of FSL - " << "FMRIB Software Library." << endl << "" << endl; } //finish IC page IC_rep_det(mmodel, cnum, dim); } } void MelodicReport::IC_rep_det(MelGMix &mmodel, int cnum, int dim){ if( bool(opts.genreport.value()) ){ {//start IC2 page IChtml2.setDir(report.getDir(),mmodel.get_prefix()+"_MM.html"); IChtml2 << "" << endl << "" << "FSL" << endl << "

"<< endl << "

"; if(cnum>1) IChtml2 << "< - "; else IChtml2 << "< - "; // IChtml << " index " ; if(cnum>"; else IChtml2 << ">"; IChtml2 << "

Component " << num2str(cnum) << " Mixture Model fit

" << endl; } volume4D tempVol; if(melodat.get_IC().Storage()>0) {//Output raw IC map // tempVol.setmatrix(melodat.get_IC().Row(cnum), // melodat.get_mask()); tempVol.setmatrix(mmodel.get_data(), melodat.get_mask()); volume map1; volume map2; map1 = threshold(tempVol[0],float(0.0), tempVol[0].max()); map2 = threshold(tempVol[0],tempVol[0].min(), float(-0.0)); volume newvol; miscpic newpic; // float map1min = (map1 + binarise(tempVol[0],tempVol[0].min(), // float(0.0)) * map1.max()).robustmin(); float map1max = map1.percentile(0.99); float map2min = map2.percentile(0.01); //float map2max = (map2 + binarise(tempVol[0],float(0.0), // tempVol[0].max()) * map2.min()).robustmax(); newpic.overlay(newvol, melodat.get_bg(), map1, map2, float(0.0), float(0.0), float(0.01), map1max, float(-0.01), map2min, 0, 0); char instr[10000]; sprintf(instr," "); strcat(instr,axials_instr.c_str()); strcat(instr,string(report.appendDir(mmodel.get_prefix()+ ".png")).c_str()); newpic.set_title(string("Component No. "+num2str(cnum)+ " - raw Z transformed IC map (1 - 99 percentile)")); newpic.set_cbar(string("ysb")); newpic.slicer(newvol, instr); } IChtml2 << ""; IChtml2 << "

" << endl; if(mmodel.get_probmap().Storage()>0&& (mmodel.get_probmap().Ncols() == mmodel.get_data().Ncols())&& (mmodel.get_probmap().Nrows() == mmodel.get_data().Nrows())) {//Output probmap tempVol.setmatrix(mmodel.get_probmap(),melodat.get_mask()); volume map; map = tempVol[0]; volume newvol; miscpic newpic; newpic.overlay(newvol, melodat.get_bg(), map, map, melodat.get_bg().percentile(0.02), melodat.get_bg().percentile(0.98), float(0.1), float(1.0), float(0.0), float(0.0), 0, 0); char instr[10000]; sprintf(instr," "); strcat(instr,"-l render1 "); strcat(instr,axials_instr.c_str()); strcat(instr,string(report.appendDir(mmodel.get_prefix()+ "_prob.png")).c_str()); newpic.set_title(string("Component No. "+num2str(cnum)+ " - Mixture Model probability map")); newpic.set_cbar(string("y")); newpic.slicer(newvol, instr); IChtml2 << ""; IChtml2 << "" << endl; IChtml2 << "

" << endl; } RowVector dat = mmodel.get_data().Row(1); if(dat.Maximum()>dat.Minimum()) {//Output GGM/GMM fit miscplot newplot; if(mmodel.get_type()=="GGM"){ newplot.add_label("IC map histogram"); newplot.add_label("full GGM fit"); newplot.add_label("background Gaussian"); newplot.add_label("Gamma distributions"); newplot.gmmfit(mmodel.get_data().Row(1), mmodel.get_means(), mmodel.get_vars(), mmodel.get_pi(), report.appendDir(mmodel.get_prefix()+"_MMfit.png"), string(mmodel.get_prefix() + " Gaussian/Gamma Mixture Model("+num2str(mmodel.mixtures())+") fit"), true, float(0.0), float(0.0)); } else{ newplot.add_label("IC map histogram"); newplot.add_label("full GMM fit"); newplot.add_label("individual Gaussians"); newplot.gmmfit(mmodel.get_data().Row(1), mmodel.get_means(), mmodel.get_vars(), mmodel.get_pi(), report.appendDir(mmodel.get_prefix()+"_MMfit.png"), string(mmodel.get_prefix() + " Gaussian Mixture Model("+num2str(mmodel.mixtures())+") fit"), false, float(0.0), float(2.0)); } // IChtml2 << " "; IChtml2 << "

" << endl; } //GGM/GMM plot {//MM parameters IChtml2 << "
 " << mmodel.get_prefix() << " Mixture Model fit
" << endl << "
  Means : " << mmodel.get_means() << endl << "
  Vars : " << mmodel.get_vars() << endl << "
  Prop. : " << mmodel.get_pi() << endl; } { //finish IC2 page IChtml2<< "

This page produced automatically by " << " MELODIC Version " << version << " - a part of FSL - " << "FMRIB Software Library.
" << endl << "" << endl; } //finish IC2 page } } void MelodicReport::IC_simplerep(string prefix, int cnum, int dim){ if( bool(opts.genreport.value()) ){ addlink(prefix+".html",num2str(cnum)); IChtml.setDir(report.getDir(),prefix+".html"); {//start IC page IChtml << " " << endl << "MELODIC Component " << num2str(cnum) << "" << endl << "" << endl << "

MELODIC Component " << num2str(cnum) << "

"<< endl; if(cnum>1) IChtml << "previous - "; IChtml << " index " ; if(cnumnext

"; IChtml << "

" << endl; } volume4D tempVol; if(melodat.get_IC().Storage()>0) {//Output raw IC map tempVol.setmatrix(melodat.get_IC().Row(cnum), melodat.get_mask()); volume map1; volume map2; map1 = threshold(tempVol[0],float(0.0), tempVol[0].max()); map2 = threshold(tempVol[0],tempVol[0].min(), float(-0.0)); volume newvol; miscpic newpic; // float map1min = (map1 + binarise(tempVol[0],tempVol[0].min(), // float(0.0)) * map1.max()).robustmin(); float map1max = map1.percentile(0.99); float map2min = map2.percentile(0.01); //float map2max = (map2 + binarise(tempVol[0],float(0.0), // tempVol[0].max()) * map2.min()).robustmax(); newpic.overlay(newvol, melodat.get_bg(), map1, map2, float(0.0), float(0.0), float(0.01), map1max, float(-0.01), map2min, 0, 0); char instr[10000]; sprintf(instr," "); strcat(instr,axials_instr.c_str()); strcat(instr,string(report.appendDir(prefix+ ".png")).c_str()); newpic.set_title(string("Component No. "+num2str(cnum)+ " - raw Z transformed IC map (1 - 99 percentile)")); newpic.set_cbar(string("ysb")); newpic.slicer(newvol, instr); } IChtml << "

" << endl; {//plot time course miscplot newplot; if(opts.tr.value()>0.0) newplot.timeseries(melodat.get_Tmodes(cnum-1).t(), report.appendDir(string("t")+ num2str(cnum)+".png"), string("Timecourse (in seconds); TR = ")+ float2str(opts.tr.value(),0,2,0)+" s", opts.tr.value(),150,4,1); else newplot.timeseries(melodat.get_Tmodes(cnum-1).t(), report.appendDir(string("t")+ num2str(cnum)+".png"), string("Timecourse (in TRs)")); write_ascii_matrix(report.appendDir(string("t") +num2str(cnum)+".txt"), melodat.get_Tmodes(cnum-1)); IChtml << " "; IChtml << "

" << endl; }//time series plot {//plot frequency miscplot newplot; int fact = int(std::pow(10.0, int(std::log10(float(melodat.get_Tmodes(0).Nrows()))))); if(opts.tr.value()>0.0) newplot.timeseries(melodat.get_fmix().Column(cnum).t(), report.appendDir(string("f")+ num2str(cnum)+".png"), string("FFT of timecourse (in Hz / ") + num2str(fact)+")", fact/(opts.tr.value()*melodat.get_Tmodes(0).Nrows()), 150,0,2); else newplot.timeseries(melodat.get_fmix().Column(cnum).t(), report.appendDir(string("f")+ num2str(cnum)+".png"), string(string("FFT of timecourse (in cycles); ") +"frequency(Hz)=cycles/(" +num2str(melodat.get_Tmodes(0).Nrows()) +"* TR); period(s)=(" +num2str(melodat.get_Tmodes(0).Nrows()) +"* TR)/cycles")); write_ascii_matrix(report.appendDir(string("f") +num2str(cnum)+".txt"), melodat.get_Tmodes(cnum-1)); IChtml << " "; IChtml << "

" << endl; }//frequency plot { //finish IC page IChtml<< "

This page produced automatically by " << " MELODIC Version " << version << " - a part of FSL - " << "FMRIB Software Library." << endl << "" << endl; } //finish IC page } } void MelodicReport::PPCA_rep(){ {//plot time course report << "

PCA estimates

" << endl; Matrix what; miscplot newplot; what = melodat.get_EV(); what &= melodat.get_EVP(); newplot.add_label("ordered Eigenvalues"); newplot.add_label("% of expl. variance"); if(melodat.get_PPCA().Storage()>0){ what = what.Columns(1,melodat.get_PPCA().Nrows()); if(opts.allPPCA.value()&&melodat.get_PPCA().Ncols()==7){ what &= melodat.get_PPCA().Columns(3,7).t(); newplot.add_label("Laplace"); newplot.add_label("BIC"); newplot.add_label("MDL"); newplot.add_label("RRN"); newplot.add_label("AIC"); }else{ what &= melodat.get_PPCA().Column(1).t(); newplot.add_label("dim. estimate"); } } newplot.set_Ylabel_fmt("%.2f"); newplot.add_xlabel("Number of included components"); newplot.set_yrange(0.0,1.02); newplot.grid_swapdefault(); newplot.timeseries(what, report.appendDir("EVplot.png"), string("Eigenspectrum Analysis"), 0,450,4,0); report << "

" << endl; }//time series plot } void MelodicReport::Smode_rep(){ if(melodat.get_Smodes().size()>0){ report << "

TICA Subject/Session modes
" << endl; miscplot newplot; report << "Boxplots show the relative response amplitudes across the " << "session/subject domain (" << melodat.get_numfiles() << " input files). Components have been sorted in decreasing order of " << " the median response per component.

"; outMsize("Smode.at(0)", melodat.get_Smodes().at(0)); Matrix allmodes = melodat.get_Smodes().at(0); for(int ctr = 1; ctr < (int)melodat.get_Smodes().size();++ctr) allmodes |= melodat.get_Smodes().at(ctr); outMsize("allmodes", allmodes); newplot.add_xlabel("Component No."); newplot.add_ylabel(""); newplot.set_xysize(100+30*allmodes.Ncols(),300); newplot.boxplot(allmodes,report.appendDir(string("bp_Smodes.png")), string("Subject/Session modes")); report << "

" << endl; } } }