int fill_mask(options_t * opts)
{
THD_3dim_dataset * mset;
int nvox;
ENTRY("fill_mask");
if( opts->automask ) {
if( opts->verb ) INFO_message("creating automask...");
opts->mask = THD_automask(opts->inset);
if( ! opts->mask ) {
ERROR_message("failed to apply -automask");
RETURN(1);
}
RETURN(0);
}
if( opts->mask_name ) {
if( opts->verb )
INFO_message("reading mask dset from %s...", opts->mask_name);
mset = THD_open_dataset( opts->mask_name );
if( ! mset ) ERROR_exit("cannot open mask dset '%s'", opts->mask_name);
nvox = DSET_NVOX(opts->inset);
if( DSET_NVOX(mset) != nvox ) {
ERROR_message("mask does not have the same voxel count as input");
RETURN(1);
}
/* fill mask array and mask_nxyz, remove mask dset */
DSET_load(mset); CHECK_LOAD_ERROR(mset);
opts->mask = THD_makemask(mset, 0, 1, 0);
DSET_delete(mset);
if( ! opts->mask ) {
ERROR_message("cannot make mask from '%s'", opts->mask_name);
RETURN(1);
}
if( opts->verb > 1 )
INFO_message("have mask with %d voxels", nvox);
}
RETURN(0);
}
int main( int argc , char * argv[] )
{
int do_norm=0 , qdet=2 , have_freq=0 , do_automask=0 ;
float dt=0.0f , fbot=0.0f,ftop=999999.9f , blur=0.0f ;
MRI_IMARR *ortar=NULL ; MRI_IMAGE *ortim=NULL ;
THD_3dim_dataset **ortset=NULL ; int nortset=0 ;
THD_3dim_dataset *inset=NULL , *outset=NULL;
char *prefix="RSFC" ;
byte *mask=NULL ;
int mask_nx=0,mask_ny=0,mask_nz=0,nmask , verb=1 ,
nx,ny,nz,nvox , nfft=0 , kk ;
float **vec , **ort=NULL ; int nort=0 , vv , nopt , ntime ;
MRI_vectim *mrv ;
float pvrad=0.0f ; int nosat=0 ;
int do_despike=0 ;
// @@ non-BP variables
float fbotALL=0.0f, ftopALL=999999.9f; // do full range version
int NumDen = 0; // switch for doing numerator or denom
THD_3dim_dataset *outsetALL=NULL ;
int m, mm;
float delf; // harmonics
int ind_low,ind_high,N_ny, ctr;
float sqnt,nt_fac;
gsl_fft_real_wavetable *real1, *real2; // GSL stuff
gsl_fft_real_workspace *work;
double *series1, *series2;
double *xx1,*xx2;
float numer,denom,val;
float *alff=NULL,*malff=NULL,*falff=NULL,
*rsfa=NULL,*mrsfa=NULL,*frsfa=NULL; // values
float meanALFF=0.0f,meanRSFA=0.0f; // will be for mean in brain region
THD_3dim_dataset *outsetALFF=NULL;
THD_3dim_dataset *outsetmALFF=NULL;
THD_3dim_dataset *outsetfALFF=NULL;
THD_3dim_dataset *outsetRSFA=NULL;
THD_3dim_dataset *outsetmRSFA=NULL;
THD_3dim_dataset *outsetfRSFA=NULL;
char out_lff[300];
char out_alff[300];
char out_malff[300];
char out_falff[300];
char out_rsfa[300];
char out_mrsfa[300];
char out_frsfa[300];
char out_unBP[300];
int SERIES_OUT = 1;
int UNBP_OUT = 0;
int DO_RSFA = 1;
int BP_LAST = 0; // option for only doing filter to LFFs at very end of proc
float de_rsfa=0.0f,nu_rsfa=0.0f;
double pow1=0.0,pow2=0.0;
/*-- help? --*/
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"\n Program to calculate common resting state functional connectivity (RSFC)\n"
" parameters (ALFF, mALFF, fALFF, RSFA, etc.) for resting state time\n"
" series. This program is **heavily** based on the existing\n"
" 3dBandPass by RW Cox, with the amendments to calculate RSFC\n"
" parameters written by PA Taylor (July, 2012).\n"
" This program is part of FATCAT (Taylor & Saad, 2013) in AFNI. Importantly,\n"
" its functionality can be included in the `afni_proc.py' processing-script \n"
" generator; see that program's help file for an example including RSFC\n"
" and spectral parameter calculation via the `-regress_RSFC' option.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" All options of 3dBandPass may be used here (with a couple other\n"
" parameter options, as well): essentially, the motivation of this\n"
" program is to produce ALFF, etc. values of the actual RSFC time\n"
" series that you calculate. Therefore, all the 3dBandPass processing\n"
" you normally do en route to making your final `resting state time\n"
" series' is done here to generate your LFFs, from which the\n"
" amplitudes in the LFF band are calculated at the end. In order to\n"
" calculate fALFF, the same initial time series are put through the\n"
" same processing steps which you have chosen but *without* the\n"
" bandpass part; the spectrum of this second time series is used to\n"
" calculate the fALFF denominator.\n"
" \n"
" For more information about each RSFC parameter, see, e.g.: \n"
" ALFF/mALFF -- Zang et al. (2007),\n"
" fALFF -- Zou et al. (2008),\n"
" RSFA -- Kannurpatti & Biswal (2008).\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" + USAGE: 3dRSFC [options] fbot ftop dataset\n"
"\n"
"* One function of this program is to prepare datasets for input\n"
" to 3dSetupGroupInCorr. Other uses are left to your imagination.\n"
"\n"
"* 'dataset' is a 3D+time sequence of volumes\n"
" ++ This must be a single imaging run -- that is, no discontinuities\n"
" in time from 3dTcat-ing multiple datasets together.\n"
"\n"
"* fbot = lowest frequency in the passband, in Hz\n"
" ++ fbot can be 0 if you want to do a lowpass filter only;\n"
" HOWEVER, the mean and Nyquist freq are always removed.\n"
"\n"
"* ftop = highest frequency in the passband (must be > fbot)\n"
" ++ if ftop > Nyquist freq, then it's a highpass filter only.\n"
"\n"
"* Set fbot=0 and ftop=99999 to do an 'allpass' filter.\n"
" ++ Except for removal of the 0 and Nyquist frequencies, that is.\n"
"\n"
"* You cannot construct a 'notch' filter with this program!\n"
" ++ You could use 3dRSFC followed by 3dcalc to get the same effect.\n"
" ++ If you are understand what you are doing, that is.\n"
" ++ Of course, that is the AFNI way -- if you don't want to\n"
" understand what you are doing, use Some other PrograM, and\n"
" you can still get Fine StatisticaL maps.\n"
"\n"
"* 3dRSFC will fail if fbot and ftop are too close for comfort.\n"
" ++ Which means closer than one frequency grid step df,\n"
" where df = 1 / (nfft * dt) [of course]\n"
"\n"
"* The actual FFT length used will be printed, and may be larger\n"
" than the input time series length for the sake of efficiency.\n"
" ++ The program will use a power-of-2, possibly multiplied by\n"
" a power of 3 and/or 5 (up to and including the 3rd power of\n"
" each of these: 3, 9, 27, and 5, 25, 125).\n"
"\n"
"* Note that the results of combining 3dDetrend and 3dRSFC will\n"
" depend on the order in which you run these programs. That's why\n"
" 3dRSFC has the '-ort' and '-dsort' options, so that the\n"
" time series filtering can be done properly, in one place.\n"
"\n"
"* The output dataset is stored in float format.\n"
"\n"
"* The order of processing steps is the following (most are optional), and\n"
" for the LFFs, the bandpass is done between the specified fbot and ftop,\n"
" while for the `whole spectrum' (i.e., fALFF denominator) the bandpass is:\n"
" done only to exclude the time series mean and the Nyquist frequency:\n"
" (0) Check time series for initial transients [does not alter data]\n"
" (1) Despiking of each time series\n"
" (2) Removal of a constant+linear+quadratic trend in each time series\n"
" (3) Bandpass of data time series\n"
" (4) Bandpass of -ort time series, then detrending of data\n"
" with respect to the -ort time series\n"
" (5) Bandpass and de-orting of the -dsort dataset,\n"
" then detrending of the data with respect to -dsort\n"
" (6) Blurring inside the mask [might be slow]\n"
" (7) Local PV calculation [WILL be slow!]\n"
" (8) L2 normalization [will be fast.]\n"
" (9) Calculate spectrum and amplitudes, for RSFC parameters.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"--------\n"
"OPTIONS:\n"
"--------\n"
" -despike = Despike each time series before other processing.\n"
" ++ Hopefully, you don't actually need to do this,\n"
" which is why it is optional.\n"
" -ort f.1D = Also orthogonalize input to columns in f.1D\n"
" ++ Multiple '-ort' options are allowed.\n"
" -dsort fset = Orthogonalize each voxel to the corresponding\n"
" voxel time series in dataset 'fset', which must\n"
" have the same spatial and temporal grid structure\n"
" as the main input dataset.\n"
" ++ At present, only one '-dsort' option is allowed.\n"
" -nodetrend = Skip the quadratic detrending of the input that\n"
" occurs before the FFT-based bandpassing.\n"
" ++ You would only want to do this if the dataset\n"
" had been detrended already in some other program.\n"
" -dt dd = set time step to 'dd' sec [default=from dataset header]\n"
" -nfft N = set the FFT length to 'N' [must be a legal value]\n"
" -norm = Make all output time series have L2 norm = 1\n"
" ++ i.e., sum of squares = 1\n"
" -mask mset = Mask dataset\n"
" -automask = Create a mask from the input dataset\n"
" -blur fff = Blur (inside the mask only) with a filter\n"
" width (FWHM) of 'fff' millimeters.\n"
" -localPV rrr = Replace each vector by the local Principal Vector\n"
" (AKA first singular vector) from a neighborhood\n"
" of radius 'rrr' millimiters.\n"
" ++ Note that the PV time series is L2 normalized.\n"
" ++ This option is mostly for Bob Cox to have fun with.\n"
"\n"
" -input dataset = Alternative way to specify input dataset.\n"
" -band fbot ftop = Alternative way to specify passband frequencies.\n"
"\n"
" -prefix ppp = Set prefix name of output dataset. Name of filtered time\n"
" series would be, e.g., ppp_LFF+orig.*, and the parameter\n"
" outputs are named with obvious suffices.\n"
" -quiet = Turn off the fun and informative messages. (Why?)\n"
" -no_rs_out = Don't output processed time series-- just output\n"
" parameters (not recommended, since the point of\n"
" calculating RSFC params here is to have them be quite\n"
" related to the time series themselves which are used for\n"
" further analysis)."
" -un_bp_out = Output the un-bandpassed series as well (default is not \n"
" to). Name would be, e.g., ppp_unBP+orig.* .\n"
" with suffix `_unBP'.\n"
" -no_rsfa = If you don't want RSFA output (default is to do so).\n"
" -bp_at_end = A (probably unnecessary) switch to have bandpassing be \n"
" the very last processing step that is done in the\n"
" sequence of steps listed above; at Step 3 above, only \n"
" the time series mean and nyquist are BP'ed out, and then\n"
" the LFF series is created only after Step 9. NB: this \n"
" probably makes only very small changes for most\n"
" processing sequences (but maybe not, depending usage).\n"
"\n"
" -notrans = Don't check for initial positive transients in the data:\n"
" *OR* ++ The test is a little slow, so skipping it is OK,\n"
" -nosat if you KNOW the data time series are transient-free.\n"
" ++ Or set AFNI_SKIP_SATCHECK to YES.\n"
" ++ Initial transients won't be handled well by the\n"
" bandpassing algorithm, and in addition may seriously\n"
" contaminate any further processing, such as inter-\n"
" voxel correlations via InstaCorr.\n"
" ++ No other tests are made [yet] for non-stationary \n"
" behavior in the time series data.\n"
"\n"
"* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n"
"\n"
" If you use this program, please reference the introductory/description\n"
" paper for the FATCAT toolbox:\n"
" Taylor PA, Saad ZS (2013). FATCAT: (An Efficient) Functional\n"
" And Tractographic Connectivity Analysis Toolbox. Brain \n"
" Connectivity 3(5):523-535.\n"
"____________________________________________________________________________\n"
);
PRINT_AFNI_OMP_USAGE(
" 3dRSFC" ,
" * At present, the only part of 3dRSFC that is parallelized is the\n"
" '-blur' option, which processes each sub-brick independently.\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*-- startup --*/
mainENTRY("3dRSFC"); machdep();
AFNI_logger("3dRSFC",argc,argv);
PRINT_VERSION("3dRSFC (from 3dBandpass by RW Cox): version THETA");
AUTHOR("PA Taylor");
nosat = AFNI_yesenv("AFNI_SKIP_SATCHECK") ;
nopt = 1 ;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-despike") == 0 ){ /* 08 Oct 2010 */
do_despike++ ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-nfft") == 0 ){
int nnup ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -nfft!") ;
nfft = (int)strtod(argv[nopt],NULL) ;
nnup = csfft_nextup_even(nfft) ;
if( nfft < 16 || nfft != nnup )
ERROR_exit("value %d after -nfft is illegal! Next legal value = %d",nfft,nnup) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-blur") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -blur!") ;
blur = strtod(argv[nopt],NULL) ;
if( blur <= 0.0f ) WARNING_message("non-positive blur?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-localPV") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -localpv!") ;
pvrad = strtod(argv[nopt],NULL) ;
if( pvrad <= 0.0f ) WARNING_message("non-positive -localpv?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-prefix") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -prefix!") ;
prefix = strdup(argv[nopt]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("bad -prefix option!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't use -mask AND -automask!") ;
do_automask = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
THD_3dim_dataset *mset ;
if( ++nopt >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || do_automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[nopt] ) ;
CHECK_OPEN_ERROR(mset,argv[nopt]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[nopt]) ;
nmask = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
if( verb ) INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < 1 ) ERROR_exit("Mask is too small to process") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-norm") == 0 ){
do_norm = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-quiet") == 0 ){
verb = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-no_rs_out") == 0 ){ // @@
SERIES_OUT = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-un_bp_out") == 0 ){ // @@
UNBP_OUT = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-no_rsfa") == 0 ){ // @@
DO_RSFA = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-bp_at_end") == 0 ){ // @@
BP_LAST = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-notrans") == 0 || strcmp(argv[nopt],"-nosat") == 0 ){
nosat = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-ort") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -ort!") ;
if( ortar == NULL ) INIT_IMARR(ortar) ;
ortim = mri_read_1D( argv[nopt] ) ;
if( ortim == NULL ) ERROR_exit("can't read from -ort '%s'",argv[nopt]) ;
mri_add_name(argv[nopt],ortim) ;
ADDTO_IMARR(ortar,ortim) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dsort") == 0 ){
THD_3dim_dataset *qset ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -dsort!") ;
if( nortset > 0 ) ERROR_exit("only 1 -dsort option is allowed!") ;
qset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(qset,argv[nopt]) ;
ortset = (THD_3dim_dataset **)realloc(ortset,
sizeof(THD_3dim_dataset *)*(nortset+1)) ;
ortset[nortset++] = qset ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-nodetrend",6) == 0 ){
qdet = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dt") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -dt!") ;
dt = (float)strtod(argv[nopt],NULL) ;
if( dt <= 0.0f ) WARNING_message("value after -dt illegal!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have 2 -input options!") ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -input!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-band",5) == 0 ){
if( ++nopt >= argc-1 ) ERROR_exit("need 2 arguments after -band!") ;
if( have_freq ) WARNING_message("second -band option replaces first one!") ;
fbot = strtod(argv[nopt++],NULL) ;
ftop = strtod(argv[nopt++],NULL) ;
have_freq = 1 ; continue ;
}
ERROR_exit("Unknown option: '%s'",argv[nopt]) ;
}
/** check inputs for reasonablositiness **/
if( !have_freq ){
if( nopt+1 >= argc )
ERROR_exit("Need frequencies on command line after options!") ;
fbot = (float)strtod(argv[nopt++],NULL) ;
ftop = (float)strtod(argv[nopt++],NULL) ;
}
if( inset == NULL ){
if( nopt >= argc )
ERROR_exit("Need input dataset name on command line after options!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
nopt++ ;
}
DSET_UNMSEC(inset) ;
if( fbot < 0.0f ) ERROR_exit("fbot value can't be negative!") ;
if( ftop <= fbot ) ERROR_exit("ftop value %g must be greater than fbot value %g!",ftop,fbot) ;
ntime = DSET_NVALS(inset) ;
if( ntime < 9 ) ERROR_exit("Input dataset is too short!") ;
if( nfft <= 0 ){
nfft = csfft_nextup_even(ntime) ;
if( verb ) INFO_message("Data length = %d FFT length = %d",ntime,nfft) ;
(void)THD_bandpass_set_nfft(nfft) ;
} else if( nfft < ntime ){
ERROR_exit("-nfft %d is less than data length = %d",nfft,ntime) ;
} else {
kk = THD_bandpass_set_nfft(nfft) ;
if( kk != nfft && verb )
INFO_message("Data length = %d FFT length = %d",ntime,kk) ;
}
if( dt <= 0.0f ){
dt = DSET_TR(inset) ;
if( dt <= 0.0f ){
WARNING_message("Setting dt=1.0 since input dataset lacks a time axis!") ;
dt = 1.0f ;
}
}
ftopALL = 1./dt ;// Aug,2016: should solve problem of a too-large
// value for THD_bandpass_vectors(), while still
// being >f_{Nyquist}
if( !THD_bandpass_OK(ntime,dt,fbot,ftop,1) ) ERROR_exit("Can't continue!") ;
nx = DSET_NX(inset); ny = DSET_NY(inset); nz = DSET_NZ(inset); nvox = nx*ny*nz;
/* check mask, or create it */
if( verb ) INFO_message("Loading input dataset time series" ) ;
DSET_load(inset) ;
if( mask != NULL ){
if( mask_nx != nx || mask_ny != ny || mask_nz != nz )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( do_automask ){
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
nmask = THD_countmask( DSET_NVOX(inset) , mask ) ;
if( verb ) INFO_message("Number of voxels in automask = %d",nmask);
if( nmask < 1 ) ERROR_exit("Automask is too small to process") ;
} else {
mask = (byte *)malloc(sizeof(byte)*nvox) ; nmask = nvox ;
memset(mask,1,sizeof(byte)*nvox) ;
// if( verb ) // @@ alert if aaaalllllll vox are going to be analyzed!
INFO_message("No mask ==> processing all %d voxels",nvox);
}
/* A simple check of dataset quality [08 Feb 2010] */
if( !nosat ){
float val ;
INFO_message(
"Checking dataset for initial transients [use '-notrans' to skip this test]") ;
val = THD_saturation_check(inset,mask,0,0) ; kk = (int)(val+0.54321f) ;
if( kk > 0 )
ININFO_message(
"Looks like there %s %d non-steady-state initial time point%s :-(" ,
((kk==1) ? "is" : "are") , kk , ((kk==1) ? " " : "s") ) ;
else if( val > 0.3210f ) /* don't ask where this threshold comes from! */
ININFO_message(
"MAYBE there's an initial positive transient of 1 point, but it's hard to tell\n") ;
else
ININFO_message("No widespread initial positive transient detected :-)") ;
}
/* check -dsort inputs for match to inset */
for( kk=0 ; kk < nortset ; kk++ ){
if( DSET_NX(ortset[kk]) != nx ||
DSET_NY(ortset[kk]) != ny ||
DSET_NZ(ortset[kk]) != nz ||
DSET_NVALS(ortset[kk]) != ntime )
ERROR_exit("-dsort %s doesn't match input dataset grid" ,
DSET_BRIKNAME(ortset[kk]) ) ;
}
/* convert input dataset to a vectim, which is more fun */
// @@ convert BP'ing ftop/bot into indices for the DFT (below)
delf = 1.0/(ntime*dt);
ind_low = (int) rint(fbot/delf);
ind_high = (int) rint(ftop/delf);
if( ntime % 2 ) // nyquist number
N_ny = (ntime-1)/2;
else
N_ny = ntime/2;
sqnt = sqrt(ntime);
nt_fac = sqrt(ntime*(ntime-1));
// @@ if BP_LAST==0:
// now we go through twice, doing LFF bandpass for NumDen==0 and
// `full spectrum' processing for NumDen==1.
// if BP_LAST==1:
// now we go through once, doing only `full spectrum' processing
for( NumDen=0 ; NumDen<2 ; NumDen++) {
//if( NumDen==1 ){ // full spectrum
// fbot = fbotALL;
// ftop = ftopALL;
//}
// essentially, just doesn't BP here, and the perfect filtering at end
// is used for both still; this makes the final output spectrum
// contain only frequencies in range of 0.01-0.08
if( BP_LAST==1 )
INFO_message("Only doing filtering to LFFs at end!");
mrv = THD_dset_to_vectim( inset , mask , 0 ) ;
if( mrv == NULL ) ERROR_exit("Can't load time series data!?") ;
if( NumDen==1 )
DSET_unload(inset) ; // @@ only unload on 2nd pass
/* similarly for the ort vectors */
if( ortar != NULL ){
for( kk=0 ; kk < IMARR_COUNT(ortar) ; kk++ ){
ortim = IMARR_SUBIM(ortar,kk) ;
if( ortim->nx < ntime )
ERROR_exit("-ort file %s is shorter than input dataset time series",
ortim->name ) ;
ort = (float **)realloc( ort , sizeof(float *)*(nort+ortim->ny) ) ;
for( vv=0 ; vv < ortim->ny ; vv++ )
ort[nort++] = MRI_FLOAT_PTR(ortim) + ortim->nx * vv ;
}
}
/* all the real work now */
if( do_despike ){
int_pair nsp ;
if( verb ) INFO_message("Testing data time series for spikes") ;
nsp = THD_vectim_despike9( mrv ) ;
if( verb ) ININFO_message(" -- Squashed %d spikes from %d voxels",nsp.j,nsp.i) ;
}
if( verb ) INFO_message("Bandpassing data time series") ;
if( (BP_LAST==0) && (NumDen==0) )
(void)THD_bandpass_vectim( mrv , dt,fbot,ftop , qdet , nort,ort ) ;
else
(void)THD_bandpass_vectim( mrv , dt,fbotALL,ftopALL, qdet,nort,ort ) ;
/* OK, maybe a little more work */
if( nortset == 1 ){
MRI_vectim *orv ;
orv = THD_dset_to_vectim( ortset[0] , mask , 0 ) ;
if( orv == NULL ){
ERROR_message("Can't load -dsort %s",DSET_BRIKNAME(ortset[0])) ;
} else {
float *dp , *mvv , *ovv , ff ;
if( verb ) INFO_message("Orthogonalizing to bandpassed -dsort") ;
//(void)THD_bandpass_vectim( orv , dt,fbot,ftop , qdet , nort,ort ) ; //@@
if( (BP_LAST==0) && (NumDen==0) )
(void)THD_bandpass_vectim(orv,dt,fbot,ftop,qdet,nort,ort);
else
(void)THD_bandpass_vectim(orv,dt,fbotALL,ftopALL,qdet,nort,ort);
THD_vectim_normalize( orv ) ;
dp = malloc(sizeof(float)*mrv->nvec) ;
THD_vectim_vectim_dot( mrv , orv , dp ) ;
for( vv=0 ; vv < mrv->nvec ; vv++ ){
ff = dp[vv] ;
if( ff != 0.0f ){
mvv = VECTIM_PTR(mrv,vv) ; ovv = VECTIM_PTR(orv,vv) ;
for( kk=0 ; kk < ntime ; kk++ ) mvv[kk] -= ff*ovv[kk] ;
}
}
VECTIM_destroy(orv) ; free(dp) ;
}
}
if( blur > 0.0f ){
if( verb )
INFO_message("Blurring time series data spatially; FWHM=%.2f",blur) ;
mri_blur3D_vectim( mrv , blur ) ;
}
if( pvrad > 0.0f ){
if( verb )
INFO_message("Local PV-ing time series data spatially; radius=%.2f",pvrad) ;
THD_vectim_normalize( mrv ) ;
THD_vectim_localpv( mrv , pvrad ) ;
}
if( do_norm && pvrad <= 0.0f ){
if( verb ) INFO_message("L2 normalizing time series data") ;
THD_vectim_normalize( mrv ) ;
}
/* create output dataset, populate it, write it, then quit */
if( (NumDen==0) ) { // @@ BP'ed version; will do filt if BP_LAST
if(BP_LAST) // do bandpass here for BP_LAST
(void)THD_bandpass_vectim(mrv,dt,fbot,ftop,qdet,0,NULL);
if( verb ) INFO_message("Creating output dataset in memory, then writing it") ;
outset = EDIT_empty_copy(inset) ;
if(SERIES_OUT){
sprintf(out_lff,"%s_LFF",prefix);
EDIT_dset_items( outset , ADN_prefix,out_lff , ADN_none ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dBandpass" , argc,argv , outset ) ;
}
for( vv=0 ; vv < ntime ; vv++ )
EDIT_substitute_brick( outset , vv , MRI_float , NULL ) ;
#if 1
THD_vectim_to_dset( mrv , outset ) ;
#else
AFNI_OMP_START ;
#pragma omp parallel
{ float *far , *var ; int *ivec=mrv->ivec ; int vv,kk ;
#pragma omp for
for( vv=0 ; vv < ntime ; vv++ ){
far = DSET_BRICK_ARRAY(outset,vv) ; var = mrv->fvec + vv ;
for( kk=0 ; kk < nmask ; kk++ ) far[ivec[kk]] = var[kk*ntime] ;
}
}
AFNI_OMP_END ;
#endif
VECTIM_destroy(mrv) ;
if(SERIES_OUT){ // @@
DSET_write(outset) ; if( verb ) WROTE_DSET(outset) ;
}
}
else{ // @@ non-BP'ed version
if( verb ) INFO_message("Creating output dataset 2 in memory") ;
// do this here because LFF version was also BP'ed at end.
if(BP_LAST) // do bandpass here for BP_LAST
(void)THD_bandpass_vectim(mrv,dt,fbotALL,ftopALL,qdet,0,NULL);
outsetALL = EDIT_empty_copy(inset) ;
if(UNBP_OUT){
sprintf(out_unBP,"%s_unBP",prefix);
EDIT_dset_items( outsetALL, ADN_prefix, out_unBP, ADN_none );
tross_Copy_History( inset , outsetALL ) ;
tross_Make_History( "3dRSFC" , argc,argv , outsetALL ) ;
}
for( vv=0 ; vv < ntime ; vv++ )
EDIT_substitute_brick( outsetALL , vv , MRI_float , NULL ) ;
#if 1
THD_vectim_to_dset( mrv , outsetALL ) ;
#else
AFNI_OMP_START ;
#pragma omp parallel
{ float *far , *var ; int *ivec=mrv->ivec ; int vv,kk ;
#pragma omp for
for( vv=0 ; vv < ntime ; vv++ ){
far = DSET_BRICK_ARRAY(outsetALL,vv) ; var = mrv->fvec + vv ;
for( kk=0 ; kk < nmask ; kk++ ) far[ivec[kk]] = var[kk*ntime] ;
}
}
AFNI_OMP_END ;
#endif
VECTIM_destroy(mrv) ;
if(UNBP_OUT){
DSET_write(outsetALL) ; if( verb ) WROTE_DSET(outsetALL) ;
}
}
}// end of NumDen loop
// @@
INFO_message("Starting the (f)ALaFFel calcs") ;
// allocations
series1 = (double *)calloc(ntime,sizeof(double));
series2 = (double *)calloc(ntime,sizeof(double));
xx1 = (double *)calloc(2*ntime,sizeof(double));
xx2 = (double *)calloc(2*ntime,sizeof(double));
alff = (float *)calloc(nvox,sizeof(float));
malff = (float *)calloc(nvox,sizeof(float));
falff = (float *)calloc(nvox,sizeof(float));
if( (series1 == NULL) || (series2 == NULL)
|| (xx1 == NULL) || (xx2 == NULL)
|| (alff == NULL) || (malff == NULL) || (falff == NULL)) {
fprintf(stderr, "\n\n MemAlloc failure.\n\n");
exit(122);
}
if(DO_RSFA) {
rsfa = (float *)calloc(nvox,sizeof(float));
mrsfa = (float *)calloc(nvox,sizeof(float));
frsfa = (float *)calloc(nvox,sizeof(float));
if( (rsfa == NULL) || (mrsfa == NULL) || (frsfa == NULL)) {
fprintf(stderr, "\n\n MemAlloc failure.\n\n");
exit(123);
}
}
work = gsl_fft_real_workspace_alloc (ntime);
real1 = gsl_fft_real_wavetable_alloc (ntime);
real2 = gsl_fft_real_wavetable_alloc (ntime);
gsl_complex_packed_array compl_freqs1 = xx1;
gsl_complex_packed_array compl_freqs2 = xx2;
// *********************************************************************
// *********************************************************************
// ************** Falafelling = ALFF/fALFF calcs *****************
// *********************************************************************
// *********************************************************************
// Be now have the BP'ed data set (outset) and the non-BP'ed one
// (outsetALL). now we'll FFT both, get amplitudes in appropriate
// ranges, and calculate: ALFF, mALFF, fALFF,
ctr = 0;
for( kk=0; kk<nvox ; kk++) {
if(mask[kk]) {
// BP one, and unBP one, either for BP_LAST or !BP_LAST
for( m=0 ; m<ntime ; m++ ) {
series1[m] = THD_get_voxel(outset,kk,m);
series2[m] = THD_get_voxel(outsetALL,kk,m);
}
mm = gsl_fft_real_transform(series1, 1, ntime, real1, work);
mm = gsl_fft_halfcomplex_unpack(series1, compl_freqs1, 1, ntime);
mm = gsl_fft_real_transform(series2, 1, ntime, real2, work);
mm = gsl_fft_halfcomplex_unpack(series2, compl_freqs2, 1, ntime);
numer = 0.0f;
denom = 0.0f;
de_rsfa = 0.0f;
nu_rsfa = 0.0f;
for( m=1 ; m<N_ny ; m++ ) {
mm = 2*m;
pow2 = compl_freqs2[mm]*compl_freqs2[mm] +
compl_freqs2[mm+1]*compl_freqs2[mm+1]; // power
//pow2*=2;// factor of 2 since ampls are even funcs
denom+= (float) sqrt(pow2); // amplitude
de_rsfa+= (float) pow2;
if( ( m>=ind_low ) && ( m<=ind_high ) ){
pow1 = compl_freqs1[mm]*compl_freqs1[mm]+
compl_freqs1[mm+1]*compl_freqs1[mm+1];
//pow1*=2;
numer+= (float) sqrt(pow1);
nu_rsfa+= (float) pow1;
}
}
if( denom>0.000001 )
falff[kk] = numer/denom;
else
falff[kk] = 0.;
alff[kk] = 2*numer/sqnt;// factor of 2 since ampl is even funct
meanALFF+= alff[kk];
if(DO_RSFA){
nu_rsfa = sqrt(2*nu_rsfa); // factor of 2 since ampls
de_rsfa = sqrt(2*de_rsfa); // are even funcs
if( de_rsfa>0.000001 )
frsfa[kk] = nu_rsfa/de_rsfa;
else
frsfa[kk]=0.;
rsfa[kk] = nu_rsfa/nt_fac;
meanRSFA+= rsfa[kk];
}
ctr+=1;
}
}
meanALFF/= ctr;
meanRSFA/= ctr;
gsl_fft_real_wavetable_free(real1);
gsl_fft_real_wavetable_free(real2);
gsl_fft_real_workspace_free(work);
// ALFFs divided by mean of brain value
for( kk=0 ; kk<nvox ; kk++ )
if(mask[kk]){
malff[kk] = alff[kk]/meanALFF;
if(DO_RSFA)
mrsfa[kk] = rsfa[kk]/meanRSFA;
}
// **************************************************************
// **************************************************************
// Store and output
// **************************************************************
// **************************************************************
outsetALFF = EDIT_empty_copy( inset ) ;
sprintf(out_alff,"%s_ALFF",prefix);
EDIT_dset_items( outsetALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_alff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetALFF));
EDIT_substitute_brick(outsetALFF, 0, MRI_float, alff);
alff=NULL;
THD_load_statistics(outsetALFF);
tross_Make_History("3dRSFC", argc, argv, outsetALFF);
THD_write_3dim_dataset(NULL, NULL, outsetALFF, True);
outsetfALFF = EDIT_empty_copy( inset ) ;
sprintf(out_falff,"%s_fALFF",prefix);
EDIT_dset_items( outsetfALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_falff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetfALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetfALFF));
EDIT_substitute_brick(outsetfALFF, 0, MRI_float, falff);
falff=NULL;
THD_load_statistics(outsetfALFF);
tross_Make_History("3dRSFC", argc, argv, outsetfALFF);
THD_write_3dim_dataset(NULL, NULL, outsetfALFF, True);
outsetmALFF = EDIT_empty_copy( inset ) ;
sprintf(out_malff,"%s_mALFF",prefix);
EDIT_dset_items( outsetmALFF,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_malff,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetmALFF)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetmALFF));
EDIT_substitute_brick(outsetmALFF, 0, MRI_float, malff);
malff=NULL;
THD_load_statistics(outsetmALFF);
tross_Make_History("3dRSFC", argc, argv, outsetmALFF);
THD_write_3dim_dataset(NULL, NULL, outsetmALFF, True);
if(DO_RSFA){
outsetRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_rsfa,"%s_RSFA",prefix);
EDIT_dset_items( outsetRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_rsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetRSFA));
EDIT_substitute_brick(outsetRSFA, 0, MRI_float, rsfa);
rsfa=NULL;
THD_load_statistics(outsetRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetRSFA, True);
outsetfRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_frsfa,"%s_fRSFA",prefix);
EDIT_dset_items( outsetfRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_frsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetfRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetfRSFA));
EDIT_substitute_brick(outsetfRSFA, 0, MRI_float, frsfa);
frsfa=NULL;
THD_load_statistics(outsetfRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetfRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetfRSFA, True);
outsetmRSFA = EDIT_empty_copy( inset ) ;
sprintf(out_mrsfa,"%s_mRSFA",prefix);
EDIT_dset_items( outsetmRSFA,
ADN_nvals, 1,
ADN_datum_all , MRI_float ,
ADN_prefix , out_mrsfa,
ADN_none ) ;
if( !THD_ok_overwrite() && THD_is_ondisk(DSET_HEADNAME(outsetmRSFA)) )
ERROR_exit("Can't overwrite existing dataset '%s'",
DSET_HEADNAME(outsetmRSFA));
EDIT_substitute_brick(outsetmRSFA, 0, MRI_float, mrsfa);
mrsfa=NULL;
THD_load_statistics(outsetmRSFA);
tross_Make_History("3dRSFC", argc, argv, outsetmRSFA);
THD_write_3dim_dataset(NULL, NULL, outsetmRSFA, True);
}
// ************************************************************
// ************************************************************
// Freeing
// ************************************************************
// ************************************************************
DSET_delete(inset);
DSET_delete(outsetALL);
DSET_delete(outset);
DSET_delete(outsetALFF);
DSET_delete(outsetmALFF);
DSET_delete(outsetfALFF);
DSET_delete(outsetRSFA);
DSET_delete(outsetmRSFA);
DSET_delete(outsetfRSFA);
free(inset);
free(outsetALL);
free(outset);
free(outsetALFF);
free(outsetmALFF);
free(outsetfALFF);
free(outsetRSFA);
free(outsetmRSFA);
free(outsetfRSFA);
free(rsfa);
free(mrsfa);
free(frsfa);
free(alff);
free(malff);
free(falff);
free(mask);
free(series1);
free(series2);
free(xx1);
free(xx2);
exit(0) ;
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *xset , *cset, *mset=NULL ;
int nopt=1 , method=PEARSON , do_autoclip=0 ;
int nvox , nvals , ii, jj, kout, kin, polort=1 ;
int ix1,jy1,kz1, ix2, jy2, kz2 ;
char *prefix = "degree_centrality" ;
byte *mask=NULL;
int nmask , abuc=1 ;
int all_source=0; /* output all source voxels 25 Jun 2010 [rickr] */
char str[32] , *cpt ;
int *imap = NULL ; MRI_vectim *xvectim ;
float (*corfun)(int,float *,float*) = NULL ;
/* djc - add 1d file output for similarity matrix */
FILE *fout1D=NULL;
/* CC - we will have two subbricks: binary and weighted centrality */
int nsubbriks = 2;
int subbrik = 0;
float * bodset;
float * wodset;
int nb_ctr = 0;
/* CC - added flags for thresholding correlations */
double thresh = 0.0;
double othresh = 0.0;
int dothresh = 0;
double sparsity = 0.0;
int dosparsity = 0;
/* variables for calculating degree centrality */
long * binaryDC = NULL;
double * weightedDC = NULL;
/* variables for histogram */
hist_node_head* histogram=NULL;
hist_node* hptr=NULL;
hist_node* pptr=NULL;
int bottom_node_idx = 0;
int totNumCor = 0;
long totPosCor = 0;
int ngoal = 0;
int nretain = 0;
float binwidth = 0.0;
int nhistnodes = 50;
/*----*/
AFNI_SETUP_OMP(0) ; /* 24 Jun 2013 */
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"Usage: 3dDegreeCentrality [options] dset\n"
" Computes voxelwise weighted and binary degree centrality and\n"
" stores the result in a new 3D bucket dataset as floats to\n"
" preserve their values. Degree centrality reflects the strength and\n"
" extent of the correlation of a voxel with every other voxel in\n"
" the brain.\n\n"
" Conceptually the process involves: \n"
" 1. Calculating the correlation between voxel time series for\n"
" every pair of voxels in the brain (as determined by masking)\n"
" 2. Applying a threshold to the resulting correlations to exclude\n"
" those that might have arisen by chance, or to sparsify the\n"
" connectivity graph.\n"
" 3. At each voxel, summarizing its correlation with other voxels\n"
" in the brain, by either counting the number of voxels correlated\n"
" with the seed voxel (binary) or by summing the correlation \n"
" coefficients (weighted).\n"
" Practically the algorithm is ordered differently to optimize for\n"
" computational time and memory usage.\n\n"
" The threshold can be supplied as a correlation coefficient, \n"
" or a sparsity threshold. The sparsity threshold reflects the fraction\n"
" of connections that should be retained after the threshold has been\n"
" applied. To minimize resource consumption, using a sparsity threshold\n"
" involves a two-step procedure. In the first step, a correlation\n"
" coefficient threshold is applied to substantially reduce the number\n"
" of correlations. Next, the remaining correlations are sorted and a\n"
" threshold is calculated so that only the specified fraction of \n"
" possible correlations are above threshold. Due to ties between\n"
" correlations, the fraction of correlations that pass the sparsity\n"
" threshold might be slightly more than the number specified.\n\n"
" Regardless of the thresholding procedure employed, negative \n"
" correlations are excluded from the calculations.\n"
"\n"
"Options:\n"
" -pearson = Correlation is the normal Pearson (product moment)\n"
" correlation coefficient [default].\n"
#if 0
" -spearman = Correlation is the Spearman (rank) correlation\n"
" coefficient.\n"
" -quadrant = Correlation is the quadrant correlation coefficient.\n"
#else
" -spearman AND -quadrant are disabled at this time :-(\n"
#endif
"\n"
" -thresh r = exclude correlations <= r from calculations\n"
" -sparsity s = only use top s percent of correlations in calculations\n"
" s should be an integer between 0 and 100. Uses an\n"
" an adaptive thresholding procedure to reduce memory.\n"
" The speed of determining the adaptive threshold can\n"
" be improved by specifying an initial threshold with\n"
" the -thresh flag.\n"
"\n"
" -polort m = Remove polynomical trend of order 'm', for m=-1..3.\n"
" [default is m=1; removal is by least squares].\n"
" Using m=-1 means no detrending; this is only useful\n"
" for data/information that has been pre-processed.\n"
"\n"
" -autoclip = Clip off low-intensity regions in the dataset,\n"
" -automask = so that the correlation is only computed between\n"
" high-intensity (presumably brain) voxels. The\n"
" mask is determined the same way that 3dAutomask works.\n"
"\n"
" -mask mmm = Mask to define 'in-brain' voxels. Reducing the number\n"
" the number of voxels included in the calculation will\n"
" significantly speedup the calculation. Consider using\n"
" a mask to constrain the calculations to the grey matter\n"
" rather than the whole brain. This is also preferrable\n"
" to using -autoclip or -automask.\n"
"\n"
" -prefix p = Save output into dataset with prefix 'p', this file will\n"
" contain bricks for both 'weighted' or 'degree' centrality\n"
" [default prefix is 'deg_centrality'].\n"
"\n"
" -out1D f = Save information about the above threshold correlations to\n"
" 1D file 'f'. Each row of this file will contain:\n"
" Voxel1 Voxel2 i1 j1 k1 i2 j2 k2 Corr\n"
" Where voxel1 and voxel2 are the 1D indices of the pair of\n"
" voxels, i j k correspond to their 3D coordinates, and Corr\n"
" is the value of the correlation between the voxel time courses.\n"
"\n"
"Notes:\n"
" * The output dataset is a bucket type of floats.\n"
" * The program prints out an estimate of its memory used\n"
" when it ends. It also prints out a progress 'meter'\n"
" to keep you pacified.\n"
"\n"
"-- RWCox - 31 Jan 2002 and 16 Jul 2010\n"
"-- Cameron Craddock - 26 Sept 2015 \n"
) ;
PRINT_AFNI_OMP_USAGE("3dDegreeCentrality",NULL) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
mainENTRY("3dDegreeCentrality main"); machdep(); PRINT_VERSION("3dDegreeCentrality");
AFNI_logger("3dDegreeCentrality",argc,argv);
/*-- option processing --*/
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-time") == 0 ){
abuc = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-autoclip") == 0 ||
strcmp(argv[nopt],"-automask") == 0 ){
do_autoclip = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
mset = THD_open_dataset(argv[++nopt]);
CHECK_OPEN_ERROR(mset,argv[nopt]);
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-pearson") == 0 ){
method = PEARSON ; nopt++ ; continue ;
}
#if 0
if( strcmp(argv[nopt],"-spearman") == 0 ){
method = SPEARMAN ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-quadrant") == 0 ){
method = QUADRANT ; nopt++ ; continue ;
}
#endif
if( strcmp(argv[nopt],"-eta2") == 0 ){
method = ETA2 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-prefix") == 0 ){
prefix = strdup(argv[++nopt]) ;
if( !THD_filename_ok(prefix) ){
ERROR_exit("Illegal value after -prefix!") ;
}
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-thresh") == 0 ){
double val = (double)strtod(argv[++nopt],&cpt) ;
if( *cpt != '\0' || val >= 1.0 || val < 0.0 ){
ERROR_exit("Illegal value (%f) after -thresh!", val) ;
}
dothresh = 1;
thresh = val ; othresh = val ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-sparsity") == 0 ){
double val = (double)strtod(argv[++nopt],&cpt) ;
if( *cpt != '\0' || val > 100 || val <= 0 ){
ERROR_exit("Illegal value (%f) after -sparsity!", val) ;
}
if( val > 5.0 )
{
WARNING_message("Sparsity %3.2f%% is large and will require alot of memory and time, consider using a smaller value. ", val);
}
dosparsity = 1 ;
sparsity = val ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-polort") == 0 ){
int val = (int)strtod(argv[++nopt],&cpt) ;
if( *cpt != '\0' || val < -1 || val > 3 ){
ERROR_exit("Illegal value after -polort!") ;
}
polort = val ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mem_stat") == 0 ){
MEM_STAT = 1 ; nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-mem_profile",8) == 0 ){
MEM_PROF = 1 ; nopt++ ; continue ;
}
/* check for 1d argument */
if ( strcmp(argv[nopt],"-out1D") == 0 ){
if (!(fout1D = fopen(argv[++nopt], "w"))) {
ERROR_message("Failed to open %s for writing", argv[nopt]);
exit(1);
}
nopt++ ; continue ;
}
ERROR_exit("Illegal option: %s",argv[nopt]) ;
}
/*-- open dataset, check for legality --*/
if( nopt >= argc ) ERROR_exit("Need a dataset on command line!?") ;
xset = THD_open_dataset(argv[nopt]); CHECK_OPEN_ERROR(xset,argv[nopt]);
if( DSET_NVALS(xset) < 3 )
ERROR_exit("Input dataset %s does not have 3 or more sub-bricks!",argv[nopt]) ;
DSET_load(xset) ; CHECK_LOAD_ERROR(xset) ;
/*-- compute mask array, if desired --*/
nvox = DSET_NVOX(xset) ; nvals = DSET_NVALS(xset) ;
INC_MEM_STATS((nvox * nvals * sizeof(double)), "input dset");
PRINT_MEM_STATS("inset");
/* if a mask was specified make sure it is appropriate */
if( mset ){
if( DSET_NVOX(mset) != nvox )
ERROR_exit("Input and mask dataset differ in number of voxels!") ;
mask = THD_makemask(mset, 0, 1.0, 0.0) ;
/* update running memory statistics to reflect loading the image */
INC_MEM_STATS( mset->dblk->total_bytes, "mask dset" );
PRINT_MEM_STATS( "mset load" );
nmask = THD_countmask( nvox , mask ) ;
INC_MEM_STATS( nmask * sizeof(byte), "mask array" );
PRINT_MEM_STATS( "mask" );
INFO_message("%d voxels in -mask dataset",nmask) ;
if( nmask < 2 ) ERROR_exit("Only %d voxels in -mask, exiting...",nmask);
/* update running memory statistics to reflect loading the image */
DEC_MEM_STATS( mset->dblk->total_bytes, "mask dset" );
DSET_unload(mset) ;
PRINT_MEM_STATS( "mset unload" );
}
/* if automasking is requested, handle that now */
else if( do_autoclip ){
mask = THD_automask( xset ) ;
nmask = THD_countmask( nvox , mask ) ;
INFO_message("%d voxels survive -autoclip",nmask) ;
if( nmask < 2 ) ERROR_exit("Only %d voxels in -automask!",nmask);
}
/* otherwise we use all of the voxels in the image */
else {
nmask = nvox ;
INFO_message("computing for all %d voxels",nmask) ;
}
if( method == ETA2 && polort >= 0 )
WARNING_message("Polort for -eta2 should probably be -1...");
/* djc - 1d file out init */
if (fout1D != NULL) {
/* define affine matrix */
mat44 affine_mat = xset->daxes->ijk_to_dicom;
/* print command line statement */
fprintf(fout1D,"#Similarity matrix from command:\n#");
for(ii=0; ii<argc; ++ii) fprintf(fout1D,"%s ", argv[ii]);
/* Print affine matrix */
fprintf(fout1D,"\n");
fprintf(fout1D,"#[ ");
int mi, mj;
for(mi = 0; mi < 4; mi++) {
for(mj = 0; mj < 4; mj++) {
fprintf(fout1D, "%.6f ", affine_mat.m[mi][mj]);
}
}
fprintf(fout1D, "]\n");
/* Print image extents*/
THD_dataxes *xset_daxes = xset->daxes;
fprintf(fout1D, "#Image dimensions:\n");
fprintf(fout1D, "#[%d, %d, %d]\n",
xset_daxes->nxx, xset_daxes->nyy, xset_daxes->nzz);
/* Similarity matrix headers */
fprintf(fout1D,"#Voxel1 Voxel2 i1 j1 k1 i2 j2 k2 Corr\n");
}
/* CC calculate the total number of possible correlations, will be
usefule down the road */
totPosCor = (.5*((float)nmask))*((float)(nmask-1));
/** For the case of Pearson correlation, we make sure the **/
/** data time series have their mean removed (polort >= 0) **/
/** and are normalized, so that correlation = dot product, **/
/** and we can use function zm_THD_pearson_corr for speed. **/
switch( method ){
default:
case PEARSON: corfun = zm_THD_pearson_corr ; break ;
case ETA2: corfun = my_THD_eta_squared ; break ;
}
/*-- create vectim from input dataset --*/
INFO_message("vectim-izing input dataset") ;
/*-- CC added in mask to reduce the size of xvectim -- */
xvectim = THD_dset_to_vectim( xset , mask , 0 ) ;
if( xvectim == NULL ) ERROR_exit("Can't create vectim?!") ;
/*-- CC update our memory stats to reflect vectim -- */
INC_MEM_STATS((xvectim->nvec*sizeof(int)) +
((xvectim->nvec)*(xvectim->nvals))*sizeof(float) +
sizeof(MRI_vectim), "vectim");
PRINT_MEM_STATS( "vectim" );
/*--- CC the vectim contains a mapping between voxel index and mask index,
tap into that here to avoid duplicating memory usage ---*/
if( mask != NULL )
{
imap = xvectim->ivec;
/* --- CC free the mask */
DEC_MEM_STATS( nmask*sizeof(byte), "mask array" );
free(mask); mask=NULL;
PRINT_MEM_STATS( "mask unload" );
}
/* -- CC unloading the dataset to reduce memory usage ?? -- */
DEC_MEM_STATS((DSET_NVOX(xset) * DSET_NVALS(xset) * sizeof(double)), "input dset");
DSET_unload(xset) ;
PRINT_MEM_STATS("inset unload");
/* -- CC configure detrending --*/
if( polort < 0 && method == PEARSON ){
polort = 0; WARNING_message("Pearson correlation always uses polort >= 0");
}
if( polort >= 0 ){
for( ii=0 ; ii < xvectim->nvec ; ii++ ){ /* remove polynomial trend */
DETREND_polort(polort,nvals,VECTIM_PTR(xvectim,ii)) ;
}
}
/* -- this procedure does not change time series that have zero variance -- */
if( method == PEARSON ) THD_vectim_normalize(xvectim) ; /* L2 norm = 1 */
/* -- CC create arrays to hold degree and weighted centrality while
they are being calculated -- */
if( dosparsity == 0 )
{
if( ( binaryDC = (long*)calloc( nmask, sizeof(long) )) == NULL )
{
ERROR_message( "Could not allocate %d byte array for binary DC calculation\n",
nmask*sizeof(long));
}
/* -- update running memory estimate to reflect memory allocation */
INC_MEM_STATS( nmask*sizeof(long), "binary DC array" );
PRINT_MEM_STATS( "binaryDC" );
if( ( weightedDC = (double*)calloc( nmask, sizeof(double) )) == NULL )
{
if (binaryDC){ free(binaryDC); binaryDC = NULL; }
ERROR_message( "Could not allocate %d byte array for weighted DC calculation\n",
nmask*sizeof(double));
}
/* -- update running memory estimate to reflect memory allocation */
INC_MEM_STATS( nmask*sizeof(double), "weighted DC array" );
PRINT_MEM_STATS( "weightedDC" );
}
/* -- CC if we are using a sparsity threshold, build a histogram to calculate the
threshold */
if (dosparsity == 1)
{
/* make sure that there is a bin for correlation values that == 1.0 */
binwidth = (1.005-thresh)/nhistnodes;
/* calculate the number of correlations we wish to retain */
ngoal = nretain = (int)(((double)totPosCor)*((double)sparsity) / 100.0);
/* allocate memory for the histogram bins */
if(( histogram = (hist_node_head*)malloc(nhistnodes*sizeof(hist_node_head))) == NULL )
{
/* if the allocation fails, free all memory and exit */
if (binaryDC){ free(binaryDC); binaryDC = NULL; }
if (weightedDC){ free(weightedDC); weightedDC = NULL; }
ERROR_message( "Could not allocate %d byte array for histogram\n",
nhistnodes*sizeof(hist_node_head));
}
else {
/* -- update running memory estimate to reflect memory allocation */
INC_MEM_STATS( nhistnodes*sizeof(hist_node_head), "hist bins" );
PRINT_MEM_STATS( "hist1" );
}
/* initialize history bins */
for( kout = 0; kout < nhistnodes; kout++ )
{
histogram[ kout ].bin_low = thresh+kout*binwidth;
histogram[ kout ].bin_high = histogram[ kout ].bin_low+binwidth;
histogram[ kout ].nbin = 0;
histogram[ kout ].nodes = NULL;
/*INFO_message("Hist bin %d [%3.3f, %3.3f) [%d, %p]\n",
kout, histogram[ kout ].bin_low, histogram[ kout ].bin_high,
histogram[ kout ].nbin, histogram[ kout ].nodes );*/
}
}
/*-- tell the user what we are about to do --*/
if (dosparsity == 0 )
{
INFO_message( "Calculating degree centrality with threshold = %f.\n", thresh);
}
else
{
INFO_message( "Calculating degree centrality with threshold = %f and sparsity = %3.2f%% (%d)\n",
thresh, sparsity, nretain);
}
/*---------- loop over mask voxels, correlate ----------*/
AFNI_OMP_START ;
#pragma omp parallel if( nmask > 999 )
{
int lii,ljj,lin,lout,ithr,nthr,vstep,vii ;
float *xsar , *ysar ;
hist_node* new_node = NULL ;
hist_node* tptr = NULL ;
hist_node* rptr = NULL ;
int new_node_idx = 0;
double car = 0.0 ;
/*-- get information about who we are --*/
#ifdef USE_OMP
ithr = omp_get_thread_num() ;
nthr = omp_get_num_threads() ;
if( ithr == 0 ) INFO_message("%d OpenMP threads started",nthr) ;
#else
ithr = 0 ; nthr = 1 ;
#endif
/*-- For the progress tracker, we want to print out 50 numbers,
figure out a number of loop iterations that will make this easy */
vstep = (int)( nmask / (nthr*50.0f) + 0.901f ) ; vii = 0 ;
if((MEM_STAT==0) && (ithr == 0 )) fprintf(stderr,"Looping:") ;
#pragma omp for schedule(static, 1)
for( lout=0 ; lout < xvectim->nvec ; lout++ ){ /*----- outer voxel loop -----*/
if( ithr == 0 && vstep > 2 ) /* allow small dsets 16 Jun 2011 [rickr] */
{ vii++ ; if( vii%vstep == vstep/2 && MEM_STAT == 0 ) vstep_print(); }
/* get ref time series from this voxel */
xsar = VECTIM_PTR(xvectim,lout) ;
/* try to make calculation more efficient by only calculating the unique
correlations */
for( lin=(lout+1) ; lin < xvectim->nvec ; lin++ ){ /*----- inner loop over voxels -----*/
/* extract the voxel time series */
ysar = VECTIM_PTR(xvectim,lin) ;
/* now correlate the time series */
car = (double)(corfun(nvals,xsar,ysar)) ;
if ( car <= thresh )
{
continue ;
}
/* update degree centrality values, hopefully the pragma
will handle mutual exclusion */
#pragma omp critical(dataupdate)
{
/* if the correlation is less than threshold, ignore it */
if ( car > thresh )
{
totNumCor += 1;
if ( dosparsity == 0 )
{
binaryDC[lout] += 1; binaryDC[lin] += 1;
weightedDC[lout] += car; weightedDC[lin] += car;
/* print correlation out to the 1D file */
if ( fout1D != NULL )
{
/* determine the i,j,k coords */
ix1 = DSET_index_to_ix(xset,lii) ;
jy1 = DSET_index_to_jy(xset,lii) ;
kz1 = DSET_index_to_kz(xset,lii) ;
ix2 = DSET_index_to_ix(xset,ljj) ;
jy2 = DSET_index_to_jy(xset,ljj) ;
kz2 = DSET_index_to_kz(xset,ljj) ;
/* add source, dest, correlation to 1D file */
fprintf(fout1D, "%d %d %d %d %d %d %d %d %.6f\n",
lii, ljj, ix1, jy1, kz1, ix2, jy2, kz2, car);
}
}
else
{
/* determine the index in the histogram to add the node */
new_node_idx = (int)floor((double)(car-othresh)/(double)binwidth);
if ((new_node_idx > nhistnodes) || (new_node_idx < bottom_node_idx))
{
/* this error should indicate a programming error and should not happen */
WARNING_message("Node index %d is out of range [%d,%d)!",new_node_idx,
bottom_node_idx, nhistnodes);
}
else
{
/* create a node to add to the histogram */
new_node = (hist_node*)calloc(1,sizeof(hist_node));
if( new_node == NULL )
{
/* allocate memory for this node, rather than fiddling with
error handling here, lets just move on */
WARNING_message("Could not allocate a new node!");
}
else
{
/* populate histogram node */
new_node->i = lout;
new_node->j = lin;
new_node->corr = car;
new_node->next = NULL;
/* -- update running memory estimate to reflect memory allocation */
INC_MEM_STATS( sizeof(hist_node), "hist nodes" );
if ((totNumCor % (1024*1024)) == 0) PRINT_MEM_STATS( "hist nodes" );
/* populate histogram */
new_node->next = histogram[new_node_idx].nodes;
histogram[new_node_idx].nodes = new_node;
histogram[new_node_idx].nbin++;
/* see if there are enough correlations in the histogram
for the sparsity */
if ((totNumCor - histogram[bottom_node_idx].nbin) > nretain)
{
/* delete the list of nodes */
rptr = histogram[bottom_node_idx].nodes;
while(rptr != NULL)
{
tptr = rptr;
rptr = rptr->next;
/* check that the ptr is not null before freeing it*/
if(tptr!= NULL)
{
DEC_MEM_STATS( sizeof(hist_node), "hist nodes" );
free(tptr);
}
}
PRINT_MEM_STATS( "unloaded hist nodes - thresh increase" );
histogram[bottom_node_idx].nodes = NULL;
totNumCor -= histogram[bottom_node_idx].nbin;
histogram[bottom_node_idx].nbin=0;
/* get the new threshold */
thresh = (double)histogram[++bottom_node_idx].bin_low;
if(MEM_STAT == 1) INFO_message("Increasing threshold to %3.2f (%d)\n",
thresh,bottom_node_idx);
}
} /* else, newptr != NULL */
} /* else, new_node_idx in range */
} /* else, do_sparsity == 1 */
} /* car > thresh */
} /* this is the end of the critical section */
} /* end of inner loop over voxels */
} /* end of outer loop over ref voxels */
if( ithr == 0 ) fprintf(stderr,".\n") ;
} /* end OpenMP */
AFNI_OMP_END ;
/* update the user so that they know what we are up to */
INFO_message ("AFNI_OMP finished\n");
INFO_message ("Found %d (%3.2f%%) correlations above threshold (%f)\n",
totNumCor, 100.0*((float)totNumCor)/((float)totPosCor), thresh);
/*---------- Finish up ---------*/
/*if( dosparsity == 1 )
{
for( kout = 0; kout < nhistnodes; kout++ )
{
INFO_message("Hist bin %d [%3.3f, %3.3f) [%d, %p]\n",
kout, histogram[ kout ].bin_low, histogram[ kout ].bin_high,
histogram[ kout ].nbin, histogram[ kout ].nodes );
}
}*/
/*-- create output dataset --*/
cset = EDIT_empty_copy( xset ) ;
/*-- configure the output dataset */
if( abuc ){
EDIT_dset_items( cset ,
ADN_prefix , prefix ,
ADN_nvals , nsubbriks , /* 2 subbricks, degree and weighted centrality */
ADN_ntt , 0 , /* no time axis */
ADN_type , HEAD_ANAT_TYPE ,
ADN_func_type , ANAT_BUCK_TYPE ,
ADN_datum_all , MRI_float ,
ADN_none ) ;
} else {
EDIT_dset_items( cset ,
ADN_prefix , prefix ,
ADN_nvals , nsubbriks , /* 2 subbricks, degree and weighted centrality */
ADN_ntt , nsubbriks , /* num times */
ADN_ttdel , 1.0 , /* fake TR */
ADN_nsl , 0 , /* no slice offsets */
ADN_type , HEAD_ANAT_TYPE ,
ADN_func_type , ANAT_EPI_TYPE ,
ADN_datum_all , MRI_float ,
ADN_none ) ;
}
/* add history information to the hearder */
tross_Make_History( "3dDegreeCentrality" , argc,argv , cset ) ;
ININFO_message("creating output dataset in memory") ;
/* -- Configure the subbriks: Binary Degree Centrality */
subbrik = 0;
EDIT_BRICK_TO_NOSTAT(cset,subbrik) ; /* stat params */
/* CC this sets the subbrik scaling factor, which we will probably want
to do again after we calculate the voxel values */
EDIT_BRICK_FACTOR(cset,subbrik,1.0) ; /* scale factor */
sprintf(str,"Binary Degree Centrality") ;
EDIT_BRICK_LABEL(cset,subbrik,str) ;
EDIT_substitute_brick(cset,subbrik,MRI_float,NULL) ; /* make array */
/* copy measure data into the subbrik */
bodset = DSET_ARRAY(cset,subbrik);
/* -- Configure the subbriks: Weighted Degree Centrality */
subbrik = 1;
EDIT_BRICK_TO_NOSTAT(cset,subbrik) ; /* stat params */
/* CC this sets the subbrik scaling factor, which we will probably want
to do again after we calculate the voxel values */
EDIT_BRICK_FACTOR(cset,subbrik,1.0) ; /* scale factor */
sprintf(str,"Weighted Degree Centrality") ;
EDIT_BRICK_LABEL(cset,subbrik,str) ;
EDIT_substitute_brick(cset,subbrik,MRI_float,NULL) ; /* make array */
/* copy measure data into the subbrik */
wodset = DSET_ARRAY(cset,subbrik);
/* increment memory stats */
INC_MEM_STATS( (DSET_NVOX(cset)*DSET_NVALS(cset)*sizeof(float)), "output dset");
PRINT_MEM_STATS( "outset" );
/* pull the values out of the histogram */
if( dosparsity == 0 )
{
for( kout = 0; kout < nmask; kout++ )
{
if ( imap != NULL )
{
ii = imap[kout] ; /* ii= source voxel (we know that ii is in the mask) */
}
else
{
ii = kout ;
}
if( ii >= DSET_NVOX(cset) )
{
WARNING_message("Avoiding bodset, wodset overflow %d > %d (%s,%d)\n",
ii,DSET_NVOX(cset),__FILE__,__LINE__ );
}
else
{
bodset[ ii ] = (float)(binaryDC[kout]);
wodset[ ii ] = (float)(weightedDC[kout]);
}
}
/* we are done with this memory, and can kill it now*/
if(binaryDC)
{
free(binaryDC);
binaryDC=NULL;
/* -- update running memory estimate to reflect memory allocation */
DEC_MEM_STATS( nmask*sizeof(long), "binary DC array" );
PRINT_MEM_STATS( "binaryDC" );
}
if(weightedDC)
{
free(weightedDC);
weightedDC=NULL;
/* -- update running memory estimate to reflect memory allocation */
DEC_MEM_STATS( nmask*sizeof(double), "weighted DC array" );
PRINT_MEM_STATS( "weightedDC" );
}
}
else
{
/* add in the values from the histogram, this is a two stage procedure:
at first we add in values a whole bin at the time until we get to a point
where we need to add in a partial bin, then we create a new histogram
to sort the values in the bin and then add those bins at a time */
kout = nhistnodes - 1;
while (( histogram[kout].nbin < nretain ) && ( kout >= 0 ))
{
hptr = pptr = histogram[kout].nodes;
while( hptr != NULL )
{
/* determine the indices corresponding to this node */
if ( imap != NULL )
{
ii = imap[hptr->i] ; /* ii= source voxel (we know that ii is in the mask) */
}
else
{
ii = hptr->i ;
}
if ( imap != NULL )
{
jj = imap[hptr->j] ; /* ii= source voxel (we know that ii is in the mask) */
}
else
{
jj = hptr->j ;
}
/* add in the values */
if(( ii >= DSET_NVOX(cset) ) || ( jj >= DSET_NVOX(cset)))
{
if( ii >= DSET_NVOX(cset))
{
WARNING_message("Avoiding bodset, wodset overflow (ii) %d > %d\n (%s,%d)\n",
ii,DSET_NVOX(cset),__FILE__,__LINE__ );
}
if( jj >= DSET_NVOX(cset))
{
WARNING_message("Avoiding bodset, wodset overflow (jj) %d > %d\n (%s,%d)\n",
jj,DSET_NVOX(cset),__FILE__,__LINE__ );
}
}
else
{
bodset[ ii ] += 1.0 ;
wodset[ ii ] += (float)(hptr->corr);
bodset[ jj ] += 1.0 ;
wodset[ jj ] += (float)(hptr->corr);
}
if( fout1D != NULL )
{
/* add source, dest, correlation to 1D file */
ix1 = DSET_index_to_ix(cset,ii) ;
jy1 = DSET_index_to_jy(cset,ii) ;
kz1 = DSET_index_to_kz(cset,ii) ;
ix2 = DSET_index_to_ix(cset,jj) ;
jy2 = DSET_index_to_jy(cset,jj) ;
kz2 = DSET_index_to_kz(cset,jj) ;
fprintf(fout1D, "%d %d %d %d %d %d %d %d %.6f\n",
ii, jj, ix1, jy1, kz1, ix2, jy2, kz2, (float)(hptr->corr));
}
/* increment node pointers */
pptr = hptr;
hptr = hptr->next;
/* delete the node */
if(pptr)
{
/* -- update running memory estimate to reflect memory allocation */
DEC_MEM_STATS(sizeof( hist_node ), "hist nodes" );
/* free the mem */
free(pptr);
pptr=NULL;
}
}
/* decrement the number of correlations we wish to retain */
nretain -= histogram[kout].nbin;
histogram[kout].nodes = NULL;
/* go on to the next bin */
kout--;
}
PRINT_MEM_STATS( "hist1 bins free - inc into output" );
/* if we haven't used all of the correlations that are available, go through and
add a subset of the voxels from the remaining bin */
if(( nretain > 0 ) && (kout >= 0))
{
hist_node_head* histogram2 = NULL;
hist_node_head* histogram2_save = NULL;
int h2nbins = 100;
float h2binwidth = 0.0;
int h2ndx=0;
h2binwidth = (((1.0+binwidth/((float)h2nbins))*histogram[kout].bin_high) - histogram[kout].bin_low) /
((float)h2nbins);
/* allocate the bins */
if(( histogram2 = (hist_node_head*)malloc(h2nbins*sizeof(hist_node_head))) == NULL )
{
if (binaryDC){ free(binaryDC); binaryDC = NULL; }
if (weightedDC){ free(weightedDC); weightedDC = NULL; }
if (histogram){ histogram = free_histogram(histogram, nhistnodes); }
ERROR_message( "Could not allocate %d byte array for histogram2\n",
h2nbins*sizeof(hist_node_head));
}
else {
/* -- update running memory estimate to reflect memory allocation */
histogram2_save = histogram2;
INC_MEM_STATS(( h2nbins*sizeof(hist_node_head )), "hist bins");
PRINT_MEM_STATS( "hist2" );
}
/* initiatize the bins */
for( kin = 0; kin < h2nbins; kin++ )
{
histogram2[ kin ].bin_low = histogram[kout].bin_low + kin*h2binwidth;
histogram2[ kin ].bin_high = histogram2[ kin ].bin_low + h2binwidth;
histogram2[ kin ].nbin = 0;
histogram2[ kin ].nodes = NULL;
/*INFO_message("Hist2 bin %d [%3.3f, %3.3f) [%d, %p]\n",
kin, histogram2[ kin ].bin_low, histogram2[ kin ].bin_high,
histogram2[ kin ].nbin, histogram2[ kin ].nodes );*/
}
/* move correlations from histogram to histgram2 */
INFO_message ("Adding %d nodes from histogram to histogram2",histogram[kout].nbin);
while ( histogram[kout].nodes != NULL )
{
hptr = histogram[kout].nodes;
h2ndx = (int)floor((double)(hptr->corr - histogram[kout].bin_low)/(double)h2binwidth);
if(( h2ndx < h2nbins ) && ( h2ndx >= 0 ))
{
histogram[kout].nodes = hptr->next;
hptr->next = histogram2[h2ndx].nodes;
histogram2[h2ndx].nodes = hptr;
histogram2[h2ndx].nbin++;
histogram[kout].nbin--;
}
else
{
WARNING_message("h2ndx %d is not in range [0,%d) :: %.10f,%.10f\n",h2ndx,h2nbins,hptr->corr, histogram[kout].bin_low);
}
}
/* free the remainder of histogram */
{
int nbins_rem = 0;
for(ii = 0; ii < nhistnodes; ii++) nbins_rem+=histogram[ii].nbin;
histogram = free_histogram(histogram, nhistnodes);
PRINT_MEM_STATS( "free remainder of histogram1" );
}
kin = h2nbins - 1;
while (( nretain > 0 ) && ( kin >= 0 ))
{
hptr = pptr = histogram2[kin].nodes;
while( hptr != NULL )
{
/* determine the indices corresponding to this node */
if ( imap != NULL )
{
ii = imap[hptr->i] ;
}
else
{
ii = hptr->i ;
}
if ( imap != NULL )
{
jj = imap[hptr->j] ;
}
else
{
jj = hptr->j ;
}
/* add in the values */
if(( ii >= DSET_NVOX(cset) ) || ( jj >= DSET_NVOX(cset)))
{
if( ii >= DSET_NVOX(cset))
{
WARNING_message("Avoiding bodset, wodset overflow (ii) %d > %d\n (%s,%d)\n",
ii,DSET_NVOX(cset),__FILE__,__LINE__ );
}
if( jj >= DSET_NVOX(cset))
{
WARNING_message("Avoiding bodset, wodset overflow (jj) %d > %d\n (%s,%d)\n",
jj,DSET_NVOX(cset),__FILE__,__LINE__ );
}
}
else
{
bodset[ ii ] += 1.0 ;
wodset[ ii ] += (float)(hptr->corr);
bodset[ jj ] += 1.0 ;
wodset[ jj ] += (float)(hptr->corr);
}
if( fout1D != NULL )
{
/* add source, dest, correlation to 1D file */
ix1 = DSET_index_to_ix(cset,ii) ;
jy1 = DSET_index_to_jy(cset,ii) ;
kz1 = DSET_index_to_kz(cset,ii) ;
ix2 = DSET_index_to_ix(cset,jj) ;
jy2 = DSET_index_to_jy(cset,jj) ;
kz2 = DSET_index_to_kz(cset,jj) ;
fprintf(fout1D, "%d %d %d %d %d %d %d %d %.6f\n",
ii, jj, ix1, jy1, kz1, ix2, jy2, kz2, (float)(hptr->corr));
}
/* increment node pointers */
pptr = hptr;
hptr = hptr->next;
/* delete the node */
if(pptr)
{
free(pptr);
DEC_MEM_STATS(( sizeof(hist_node) ), "hist nodes");
pptr=NULL;
}
}
/* decrement the number of correlations we wish to retain */
nretain -= histogram2[kin].nbin;
histogram2[kin].nodes = NULL;
/* go on to the next bin */
kin--;
}
PRINT_MEM_STATS("hist2 nodes free - incorporated into output");
/* we are finished with histogram2 */
{
histogram2 = free_histogram(histogram2, h2nbins);
/* -- update running memory estimate to reflect memory allocation */
PRINT_MEM_STATS( "free hist2" );
}
if (nretain < 0 )
{
WARNING_message( "Went over sparsity goal %d by %d, with a resolution of %f",
ngoal, -1*nretain, h2binwidth);
}
}
if (nretain > 0 )
{
WARNING_message( "Was not able to meet goal of %d (%3.2f%%) correlations, %d (%3.2f%%) correlations passed the threshold of %3.2f, maybe you need to change the threshold or the desired sparsity?",
ngoal, 100.0*((float)ngoal)/((float)totPosCor), totNumCor, 100.0*((float)totNumCor)/((float)totPosCor), thresh);
}
}
INFO_message("Done..\n") ;
/* update running memory statistics to reflect freeing the vectim */
DEC_MEM_STATS(((xvectim->nvec*sizeof(int)) +
((xvectim->nvec)*(xvectim->nvals))*sizeof(float) +
sizeof(MRI_vectim)), "vectim");
/* toss some trash */
VECTIM_destroy(xvectim) ;
DSET_delete(xset) ;
if(fout1D!=NULL)fclose(fout1D);
PRINT_MEM_STATS( "vectim unload" );
if (weightedDC) free(weightedDC) ; weightedDC = NULL;
if (binaryDC) free(binaryDC) ; binaryDC = NULL;
/* finito */
INFO_message("Writing output dataset to disk [%s bytes]",
commaized_integer_string(cset->dblk->total_bytes)) ;
/* write the dataset */
DSET_write(cset) ;
WROTE_DSET(cset) ;
/* increment our memory stats, since we are relying on the header for this
information, we update the stats before actually freeing the memory */
DEC_MEM_STATS( (DSET_NVOX(cset)*DSET_NVALS(cset)*sizeof(float)), "output dset");
/* free up the output dataset memory */
DSET_unload(cset) ;
DSET_delete(cset) ;
/* force a print */
MEM_STAT = 1;
PRINT_MEM_STATS( "Fin" );
exit(0) ;
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *inset=NULL , *outset=NULL ;
MCW_cluster *nbhd=NULL ;
byte *mask=NULL ; int mask_nx,mask_ny,mask_nz , automask=0 ;
char *prefix="./LocalCormat" ;
int iarg=1 , verb=1 , ntype=0 , kk,nx,ny,nz,nxy,nxyz,nt , xx,yy,zz, vstep ;
float na,nb,nc , dx,dy,dz ;
MRI_IMARR *imar=NULL ; MRI_IMAGE *pim=NULL ;
int mmlag=10 , ii,jj , do_arma=0 , nvout ;
MRI_IMAGE *concim=NULL ; float *concar=NULL ;
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"Usage: 3dLocalCORMAT [options] inputdataset\n"
"\n"
"Compute the correlation matrix (in time) of the input dataset,\n"
"up to lag given by -maxlag. The matrix is averaged over the\n"
"neighborhood specified by the -nbhd option, and then the entries\n"
"are output at each voxel in a new dataset.\n"
"\n"
"Normally, the input to this program would be the -errts output\n"
"from 3dDeconvolve, or the equivalent residuals from some other\n"
"analysis. If you input a non-residual time series file, you at\n"
"least should use an appropriate -polort level for detrending!\n"
"\n"
"Options:\n"
" -input inputdataset\n"
" -prefix ppp\n"
" -mask mset {these 2 options are}\n"
" -automask {mutually exclusive.}\n"
" -nbhd nnn [e.g., 'SPHERE(9)' for 9 mm radius]\n"
" -polort ppp [default = 0, which is reasonable for -errts output]\n"
" -concat ccc [as in 3dDeconvolve]\n"
" -maxlag mmm [default = 10]\n"
" -ARMA [estimate ARMA(1,1) parameters into last 2 sub-bricks]\n"
"\n"
"A quick hack for my own benignant purposes -- RWCox -- June 2008\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*---- official startup ---*/
PRINT_VERSION("3dLocalCormat"); mainENTRY("3dLocalCormat main"); machdep();
AFNI_logger("3dLocalCormat",argc,argv); AUTHOR("Zhark the Toeplitzer");
/*---- loop over options ----*/
while( iarg < argc && argv[iarg][0] == '-' ){
#if 0
fprintf(stderr,"argv[%d] = %s\n",iarg,argv[iarg]) ;
#endif
if( strcmp(argv[iarg],"-ARMA") == 0 ){
do_arma = 1 ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-polort") == 0 ){
char *cpt ;
if( ++iarg >= argc )
ERROR_exit("Need argument after option %s",argv[iarg-1]) ;
pport = (int)strtod(argv[iarg],&cpt) ;
if( *cpt != '\0' )
WARNING_message("Illegal non-numeric value after -polort") ;
if( pport > 3 ){
pport = 3 ; WARNING_message("-polort set to 3 == max implemented") ;
} else if( pport < 0 ){
pport = 0 ; WARNING_message("-polort set to 0 == min implemented") ;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have two -input options") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-input'") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-prefix") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '-prefix'") ;
prefix = strdup(argv[iarg]) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-mask") == 0 ){
THD_3dim_dataset *mset ; int mmm ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[iarg]) ;
mmm = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
INFO_message("Number of voxels in mask = %d",mmm) ;
if( mmm < 2 ) ERROR_exit("Mask is too small to process") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't have -automask and -mask") ;
automask = 1 ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-nbhd") == 0 ){
char *cpt ;
if( ntype > 0 ) ERROR_exit("Can't have 2 '-nbhd' options") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-nbhd'") ;
cpt = argv[iarg] ;
if( strncasecmp(cpt,"SPHERE",6) == 0 ){
sscanf( cpt+7 , "%f" , &na ) ;
if( na == 0.0f ) ERROR_exit("Can't have a SPHERE of radius 0") ;
ntype = NTYPE_SPHERE ;
} else if( strncasecmp(cpt,"RECT",4) == 0 ){
sscanf( cpt+5 , "%f,%f,%f" , &na,&nb,&nc ) ;
if( na == 0.0f && nb == 0.0f && nc == 0.0f )
ERROR_exit("'RECT(0,0,0)' is not a legal neighborhood") ;
ntype = NTYPE_RECT ;
} else if( strncasecmp(cpt,"RHDD",4) == 0 ){
sscanf( cpt+5 , "%f" , &na ) ;
if( na == 0.0f ) ERROR_exit("Can't have a RHDD of radius 0") ;
ntype = NTYPE_RHDD ;
} else {
ERROR_exit("Unknown -nbhd shape: '%s'",cpt) ;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-maxlag") == 0 ){
if( ++iarg >= argc )
ERROR_exit("Need argument after option %s",argv[iarg-1]) ;
mmlag = (int)strtod(argv[iarg],NULL) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-concat") == 0 ){
if( concim != NULL )
ERROR_exit("Can't have two %s options!",argv[iarg]) ;
if( ++iarg >= argc )
ERROR_exit("Need argument after option %s",argv[iarg-1]) ;
concim = mri_read_1D( argv[iarg] ) ;
if( concim == NULL )
ERROR_exit("Can't read -concat file '%s'",argv[iarg]) ;
if( concim->nx < 2 )
ERROR_exit("-concat file '%s' must have at least 2 entries!",
argv[iarg]) ;
concar = MRI_FLOAT_PTR(concim) ;
for( ii=1 ; ii < concim->nx ; ii++ )
if( (int)concar[ii-1] >= (int)concar[ii] )
ERROR_exit("-concat file '%s' is not ordered increasingly!",
argv[iarg]) ;
iarg++ ; continue ;
}
ERROR_exit("Unknown option '%s'",argv[iarg]) ;
} /*--- end of loop over options ---*/
if( do_arma && mmlag > 0 && mmlag < 5 )
ERROR_exit("Can't do -ARMA with -maxlag %d",mmlag) ;
/*---- deal with input dataset ----*/
if( inset == NULL ){
if( iarg >= argc ) ERROR_exit("No input dataset on command line?") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
}
ntime = DSET_NVALS(inset) ;
if( ntime < 9 )
ERROR_exit("Must have at least 9 values per voxel") ;
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
if( mask != NULL ){
if( mask_nx != DSET_NX(inset) ||
mask_ny != DSET_NY(inset) ||
mask_nz != DSET_NZ(inset) )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( automask ){
int mmm ;
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
mmm = THD_countmask( DSET_NVOX(inset) , mask ) ;
INFO_message("Number of voxels in automask = %d",mmm) ;
if( mmm < 2 ) ERROR_exit("Automask is too small to process") ;
}
/*-- set up blocks of continuous time data --*/
if( DSET_IS_TCAT(inset) ){
if( concim != NULL ){
WARNING_message("Ignoring -concat, since dataset is auto-catenated") ;
mri_free(concim) ;
}
concim = mri_new(inset->tcat_num,1,MRI_float) ;
concar = MRI_FLOAT_PTR(concim) ;
concar[0] = 0.0 ;
for( ii=0 ; ii < inset->tcat_num-1 ; ii++ )
concar[ii+1] = concar[ii] + inset->tcat_len[ii] ;
} else if( concim == NULL ){
concim = mri_new(1,1,MRI_float) ;
concar = MRI_FLOAT_PTR(concim) ; concar[0] = 0 ;
}
nbk = concim->nx ;
bk = (int *)malloc(sizeof(int)*(nbk+1)) ;
for( ii=0 ; ii < nbk ; ii++ ) bk[ii] = (int)concar[ii] ;
bk[nbk] = ntime ;
mri_free(concim) ;
mlag = DSET_NVALS(inset) ;
for( ii=0 ; ii < nbk ; ii++ ){
jj = bk[ii+1]-bk[ii] ; if( jj < mlag ) mlag = jj ;
if( bk[ii] < 0 || jj < 9 )
ERROR_exit("something is rotten in the dataset run lengths") ;
}
mlag-- ;
if( mmlag > 0 && mlag > mmlag ) mlag = mmlag ;
else INFO_message("Max lag set to %d",mlag) ;
if( do_arma && mlag < 5 )
ERROR_exit("Can't do -ARMA with maxlag=%d",mlag) ;
/*---- create neighborhood (as a cluster) -----*/
if( ntype <= 0 ){ /* default neighborhood */
ntype = NTYPE_SPHERE ; na = -1.01f ;
INFO_message("Using default neighborhood = self + 6 neighbors") ;
}
switch( ntype ){
default:
ERROR_exit("WTF? ntype=%d",ntype) ;
case NTYPE_SPHERE:{
if( na < 0.0f ){ dx = dy = dz = 1.0f ; na = -na ; }
else { dx = fabsf(DSET_DX(inset)) ;
dy = fabsf(DSET_DY(inset)) ;
dz = fabsf(DSET_DZ(inset)) ; }
nbhd = MCW_spheremask( dx,dy,dz , na ) ;
}
break ;
case NTYPE_RECT:{
if( na < 0.0f ){ dx = 1.0f; na = -na; } else dx = fabsf(DSET_DX(inset));
if( nb < 0.0f ){ dy = 1.0f; nb = -nb; } else dy = fabsf(DSET_DY(inset));
if( nc < 0.0f ){ dz = 1.0f; nc = -nc; } else dz = fabsf(DSET_DZ(inset));
nbhd = MCW_rectmask( dx,dy,dz , na,nb,nc ) ;
}
break ;
case NTYPE_RHDD:{
if( na < 0.0f ){ dx = dy = dz = 1.0f ; na = -na ; }
else { dx = fabsf(DSET_DX(inset)) ;
dy = fabsf(DSET_DY(inset)) ;
dz = fabsf(DSET_DZ(inset)) ; }
nbhd = MCW_rhddmask( dx,dy,dz , na ) ;
}
break ;
}
MCW_radsort_cluster( nbhd , dx,dy,dz ) ; /* 26 Feb 2008 */
INFO_message("Neighborhood comprises %d voxels",nbhd->num_pt) ;
/** create output dataset **/
outset = EDIT_empty_copy(inset) ;
nvout = mlag ; if( do_arma ) nvout += 2 ;
EDIT_dset_items( outset,
ADN_prefix , prefix,
ADN_brick_fac, NULL ,
ADN_nvals , nvout ,
ADN_ntt , nvout ,
ADN_none );
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dLocalCormat" , argc,argv , outset ) ;
for( kk=0 ; kk < nvout ; kk++ )
EDIT_substitute_brick( outset , kk , MRI_float , NULL ) ;
nx = DSET_NX(outset) ;
ny = DSET_NY(outset) ; nxy = nx*ny ;
nz = DSET_NZ(outset) ; nxyz = nxy*nz ;
vstep = (verb && nxyz > 999) ? nxyz/50 : 0 ;
if( vstep ) fprintf(stderr,"++ voxel loop: ") ;
/** actually do the long long slog through all voxels **/
for( kk=0 ; kk < nxyz ; kk++ ){
if( vstep && kk%vstep==vstep-1 ) vstep_print() ;
if( !INMASK(kk) ) continue ;
IJK_TO_THREE( kk , xx,yy,zz , nx,nxy ) ;
imar = THD_get_dset_nbhd_array( inset , mask , xx,yy,zz , nbhd ) ;
if( imar == NULL ) continue ;
pim = mri_cormat_vector(imar) ; DESTROY_IMARR(imar) ;
if( pim == NULL ) continue ;
THD_insert_series( kk, outset, pim->nx, MRI_float, MRI_FLOAT_PTR(pim), 0 ) ;
if( do_arma ){ /* estimate ARMA(1,1) params and store those, too */
float_pair ab ;
float *aa=DSET_ARRAY(outset,mlag), *bb=DSET_ARRAY(outset,mlag+1) ;
ab = estimate_arma11( pim->nx , MRI_FLOAT_PTR(pim) ) ;
aa[kk] = ab.a ; bb[kk] = ab.b ;
}
mri_free(pim) ;
}
if( vstep ) fprintf(stderr,"\n") ;
DSET_delete(inset) ;
DSET_write(outset) ;
WROTE_DSET(outset) ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *inset=NULL ;
byte *mask=NULL ; int mask_nx=0,mask_ny=0,mask_nz=0 , automask=0 , masknum=0 ;
int iarg=1 , verb=1 , ntype=0 , nev,kk,ii,nxyz,nt ;
float na,nb,nc , dx,dy,dz ;
MRI_IMARR *imar=NULL ; int *ivox ; MRI_IMAGE *pim ;
int do_vmean=0 , do_vnorm=0 , sval_itop=0 ;
int polort=-1 ; float *ev ;
MRI_IMARR *ortar ; MRI_IMAGE *ortim ; int nyort=0 ;
float bpass_L=0.0f , bpass_H=0.0f , dtime ; int do_bpass=0 ;
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"Usage: 3dmaskSVD [options] inputdataset\n"
"Author: Zhark the Gloriously Singular\n"
"\n"
"* Computes the principal singular vector of the time series\n"
" vectors extracted from the input dataset over the input mask.\n"
" ++ You can use the '-sval' option to change which singular\n"
" vectors are output.\n"
"* The sign of the output vector is chosen so that the average\n"
" of arctanh(correlation coefficient) over all input data\n"
" vectors (from the mask) is positive.\n"
"* The output vector is normalized: the sum of its components\n"
" squared is 1.\n"
"* You probably want to use 3dDetrend (or something similar) first,\n"
" to get rid of annoying artifacts, such as motion, breathing,\n"
" dark matter interactions with the brain, etc.\n"
" ++ If you are lazy scum like Zhark, you might be able to get\n"
" away with using the '-polort' option.\n"
" ++ In particular, if your data time series has a nonzero mean,\n"
" then you probably want at least '-polort 0' to remove the\n"
" mean, otherwise you'll pretty much just get a constant\n"
" time series as the principal singular vector!\n"
"* An alternative to this program would be 3dmaskdump followed\n"
" by 1dsvd, which could give you all the singular vectors you\n"
" could ever want, and much more -- enough to confuse you for days.\n"
" ++ In particular, although you COULD input a 1D file into\n"
" 3dmaskSVD, the 1dsvd program would make much more sense.\n"
"* This program will be pretty slow if there are over about 2000\n"
" voxels in the mask. It could be made more efficient for\n"
" such cases, but you'll have to give Zhark some 'incentive'.\n"
"* Result vector goes to stdout. Redirect per your pleasures and needs.\n"
"* Also see program 3dLocalSVD if you want to compute the principal\n"
" singular time series vector from a neighborhood of EACH voxel.\n"
" ++ (Which is a pretty slow operation!)\n"
"* http://en.wikipedia.org/wiki/Singular_value_decomposition\n"
"\n"
"-------\n"
"Options:\n"
"-------\n"
" -vnorm = L2 normalize all time series before SVD [recommended!]\n"
" -sval a = output singular vectors 0 .. a [default a=0 = first one only]\n"
" -mask mset = define the mask [default is entire dataset == slow!]\n"
" -automask = you'll have to guess what this option does\n"
" -polort p = if you are lazy and didn't run 3dDetrend (like Zhark)\n"
" -bpass L H = bandpass [mutually exclusive with -polort]\n"
" -ort xx.1D = time series to remove from the data before SVD-ization\n"
" ++ You can give more than 1 '-ort' option\n"
" ++ 'xx.1D' can contain more than 1 column\n"
" -input ddd = alternative way to give the input dataset name\n"
"\n"
"-------\n"
"Example:\n"
"-------\n"
" You have a mask dataset with discrete values 1, 2, ... 77 indicating\n"
" some ROIs; you want to get the SVD from each ROI's time series separately,\n"
" and then put these into 1 big 77 column .1D file. You can do this using\n"
" a csh shell script like the one below:\n"
"\n"
" # Compute the individual SVD vectors\n"
" foreach mm ( `count 1 77` )\n"
" 3dmaskSVD -vnorm -mask mymask+orig\"<${mm}..${mm}>\" epi+orig > qvec${mm}.1D\n"
" end\n"
" # Glue them together into 1 big file, then delete the individual files\n"
" 1dcat qvec*.1D > allvec.1D\n"
" /bin/rm -f qvec*.1D\n"
" # Plot the results to a JPEG file, then compute their correlation matrix\n"
" 1dplot -one -nopush -jpg allvec.jpg allvec.1D\n"
" 1ddot -terse allvec.1D > allvec_COR.1D\n"
"\n"
" [[ If you use the bash shell, you'll have to figure out the syntax ]]\n"
" [[ yourself. Zhark has no sympathy for you bash shell infidels, and ]]\n"
" [[ considers you only slightly better than those lowly Emacs users. ]]\n"
" [[ And do NOT ever even mention 'nedit' in Zhark's august presence! ]]\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*---- official startup ---*/
PRINT_VERSION("3dmaskSVD"); mainENTRY("3dmaskSVD main"); machdep();
AFNI_logger("3dmaskSVD",argc,argv); AUTHOR("Zhark the Singular");
/*---- loop over options ----*/
INIT_IMARR(ortar) ;
mpv_sign_meth = AFNI_yesenv("AFNI_3dmaskSVD_meansign") ;
while( iarg < argc && argv[iarg][0] == '-' ){
if( strcasecmp(argv[iarg],"-bpass") == 0 ){
if( iarg+2 >= argc ) ERROR_exit("need 2 args after -bpass") ;
bpass_L = (float)strtod(argv[++iarg],NULL) ;
bpass_H = (float)strtod(argv[++iarg],NULL) ;
if( bpass_L < 0.0f || bpass_H <= bpass_L )
ERROR_exit("Illegal values after -bpass: %g %g",bpass_L,bpass_H) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-ort") == 0 ){ /* 01 Oct 2009 */
int nx,ny ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-ort'") ;
ortim = mri_read_1D( argv[iarg] ) ;
if( ortim == NULL ) ERROR_exit("-ort '%s': Can't read 1D file",argv[iarg]) ;
nx = ortim->nx ; ny = ortim->ny ;
if( nx == 1 && ny > 1 ){
MRI_IMAGE *tim=mri_transpose(ortim); mri_free(ortim); ortim = tim; ny = 1;
}
mri_add_name(argv[iarg],ortim) ; ADDTO_IMARR(ortar,ortim) ; nyort += ny ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-polort") == 0 ){
char *qpt ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-polort'") ;
polort = (int)strtod(argv[iarg],&qpt) ;
if( *qpt != '\0' ) WARNING_message("Illegal non-numeric value after -polort") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-vnorm") == 0 ){
do_vnorm = 1 ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have two -input options") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-input'") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-sval") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '-sval'") ;
sval_itop = (int)strtod(argv[iarg],NULL) ;
if( sval_itop < 0 ){ sval_itop = 0 ; WARNING_message("'-sval' reset to 0") ; }
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-mask") == 0 ){
THD_3dim_dataset *mset ; int mmm ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[iarg]) ;
masknum = mmm = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
INFO_message("Number of voxels in mask = %d",mmm) ;
if( mmm < 2 ) ERROR_exit("Mask is too small to process") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't have two mask inputs!") ;
automask = 1 ; iarg++ ; continue ;
}
ERROR_exit("Unknown option '%s'",argv[iarg]) ;
} /*--- end of loop over options ---*/
/*---- deal with input dataset ----*/
if( inset == NULL ){
if( iarg >= argc ) ERROR_exit("No input dataset on command line?") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
}
nt = DSET_NVALS(inset) ; /* vector lengths */
if( nt < 9 )
ERROR_exit("Must have at least 9 values per voxel") ;
if( polort+1 >= nt )
ERROR_exit("'-polort %d' too big for time series length = %d",polort,nt) ;
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
nxyz = DSET_NVOX(inset) ;
DSET_UNMSEC(inset) ;
dtime = DSET_TR(inset) ;
if( dtime <= 0.0f ) dtime = 1.0f ;
do_bpass = (bpass_L < bpass_H) ;
if( do_bpass ){
kk = THD_bandpass_OK( nt , dtime , bpass_L , bpass_H , 1 ) ;
if( kk <= 0 ) ERROR_exit("Can't continue since -bpass setup is illegal") ;
polort = -1 ;
}
/*--- deal with the masking ---*/
if( mask != NULL ){
if( mask_nx != DSET_NX(inset) ||
mask_ny != DSET_NY(inset) ||
mask_nz != DSET_NZ(inset) )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( automask ){
int mmm ;
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
masknum = mmm = THD_countmask( DSET_NVOX(inset) , mask ) ;
INFO_message("Number of voxels in automask = %d",mmm) ;
if( mmm < 9 ) ERROR_exit("Automask is too small to process") ;
} else {
mask = (byte *)malloc(sizeof(byte)*nxyz) ; masknum = nxyz ;
memset( mask , 1 , sizeof(byte)*nxyz ) ;
INFO_message("Using all %d voxels in dataset",nxyz) ;
}
nev = MIN(nt,masknum) ; /* max possible number of eigenvalues */
if( sval_itop >= nev ){
sval_itop = nev-1 ;
WARNING_message("'-sval' reset to '%d'",sval_itop) ;
}
mri_principal_vector_params( 0 , do_vnorm , sval_itop ) ;
mri_principal_setev(nev) ;
/*-- get data vectors --*/
ivox = (int *)malloc(sizeof(int)*masknum) ;
for( kk=ii=0 ; ii < nxyz ; ii++ ) if( mask[ii] ) ivox[kk++] = ii ;
INFO_message("Extracting data vectors") ;
imar = THD_extract_many_series( masknum, ivox, inset ) ; DSET_unload(inset) ;
if( imar == NULL ) ERROR_exit("Can't get data vector?!") ;
/*-- detrending --*/
if( polort >= 0 || nyort > 0 || do_bpass ){
float **polref=NULL ; float *tsar ;
int nort=IMARR_COUNT(ortar) , nref=0 ;
if( polort >= 0 ){ /* polynomials */
nref = polort+1 ; polref = THD_build_polyref(nref,nt) ;
}
if( nort > 0 ){ /* other orts */
float *oar , *par ; int nx,ny , qq,tt ;
for( kk=0 ; kk < nort ; kk++ ){ /* loop over input -ort files */
ortim = IMARR_SUBIM(ortar,kk) ;
nx = ortim->nx ; ny = ortim->ny ;
if( nx < nt )
ERROR_exit("-ort '%s' length %d shorter than dataset length %d" ,
ortim->name , nx , nt ) ;
polref = (float **)realloc(polref,(nref+ny)*sizeof(float *)) ;
oar = MRI_FLOAT_PTR(ortim) ;
for( qq=0 ; qq < ny ; qq++,oar+=nx ){
par = polref[nref+qq] = (float *)malloc(sizeof(float)*nt) ;
for( tt=0 ; tt < nt ; tt++ ) par[tt] = oar[tt] ;
if( polort == 0 ) THD_const_detrend (nt,par,NULL) ;
else if( polort > 0 ) THD_linear_detrend(nt,par,NULL,NULL) ;
}
nref += ny ;
}
DESTROY_IMARR(ortar) ;
}
if( !do_bpass ){ /* old style ort-ification */
MRI_IMAGE *imq , *imp ; float *qar ;
INFO_message("Detrending data vectors") ;
#if 1
imq = mri_new( nt , nref , MRI_float) ; qar = MRI_FLOAT_PTR(imq) ;
for( kk=0 ; kk < nref ; kk++ )
memcpy( qar+kk*nt , polref[kk] , sizeof(float)*nt ) ;
imp = mri_matrix_psinv( imq , NULL , 1.e-8 ) ;
for( kk=0 ; kk < IMARR_COUNT(imar) ; kk++ ){
mri_matrix_detrend( IMARR_SUBIM(imar,kk) , imq , imp ) ;
}
mri_free(imp) ; mri_free(imq) ;
#else
for( kk=0 ; kk < IMARR_COUNT(imar) ; kk++ ){
tsar = MRI_FLOAT_PTR(IMARR_SUBIM(imar,kk)) ;
THD_generic_detrend_LSQ( nt , tsar , -1 , nref , polref , NULL ) ;
}
#endif
} else { /* bandpass plus (maybe) orts */
float **vec = (float **)malloc(sizeof(float *)*IMARR_COUNT(imar)) ;
INFO_message("Bandpassing data vectors") ;
for( kk=0 ; kk < IMARR_COUNT(imar) ; kk++ )
vec[kk] = MRI_FLOAT_PTR(IMARR_SUBIM(imar,kk)) ;
(void)THD_bandpass_vectors( nt , IMARR_COUNT(imar) , vec ,
dtime , bpass_L , bpass_H ,
2 , nref , polref ) ;
free(vec) ;
}
for( kk=0 ; kk < nref; kk++ ) free(polref[kk]) ;
free(polref) ;
} /* end of detrendization */
/*--- the actual work ---*/
INFO_message("Computing SVD") ;
pim = mri_principal_vector( imar ) ; DESTROY_IMARR(imar) ;
if( pim == NULL ) ERROR_exit("SVD failure!?!") ;
ev = mri_principal_getev() ;
switch(sval_itop+1){
case 1:
INFO_message("First singular value: %g",ev[0]) ; break ;
case 2:
INFO_message("First 2 singular values: %g %g",ev[0],ev[1]) ; break ;
case 3:
INFO_message("First 3 singular values: %g %g %g",ev[0],ev[1],ev[2]) ; break ;
case 4:
INFO_message("First 4 singular values: %g %g %g %g",ev[0],ev[1],ev[2],ev[3]) ; break ;
default:
case 5:
INFO_message("First 5 singular values: %g %g %g %g %g",ev[0],ev[1],ev[2],ev[3],ev[4]) ; break ;
}
mri_write_1D(NULL,pim) ;
exit(0) ;
}
int main( int argc , char * argv[] )
{
int do_norm=0 , qdet=2 , have_freq=0 , do_automask=0 ;
float dt=0.0f , fbot=0.0f,ftop=999999.9f , blur=0.0f ;
MRI_IMARR *ortar=NULL ; MRI_IMAGE *ortim=NULL ;
THD_3dim_dataset **ortset=NULL ; int nortset=0 ;
THD_3dim_dataset *inset=NULL , *outset ;
char *prefix="bandpass" ;
byte *mask=NULL ;
int mask_nx=0,mask_ny=0,mask_nz=0,nmask , verb=1 ,
nx,ny,nz,nvox , nfft=0 , kk ;
float **vec , **ort=NULL ; int nort=0 , vv , nopt , ntime ;
MRI_vectim *mrv ;
float pvrad=0.0f ; int nosat=0 ;
int do_despike=0 ;
/*-- help? --*/
AFNI_SETUP_OMP(0) ; /* 24 Jun 2013 */
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"\n"
"** NOTA BENE: For the purpose of preparing resting-state FMRI datasets **\n"
"** for analysis (e.g., with 3dGroupInCorr), this program is now mostly **\n"
"** superseded by the afni_proc.py script. See the 'afni_proc.py -help' **\n"
"** section 'Resting state analysis (modern)' to get our current rs-FMRI **\n"
"** pre-processing recommended sequence of steps. -- RW Cox, et alii. **\n"
"\n"
"Usage: 3dBandpass [options] fbot ftop dataset\n"
"\n"
"* One function of this program is to prepare datasets for input\n"
" to 3dSetupGroupInCorr. Other uses are left to your imagination.\n"
"\n"
"* 'dataset' is a 3D+time sequence of volumes\n"
" ++ This must be a single imaging run -- that is, no discontinuities\n"
" in time from 3dTcat-ing multiple datasets together.\n"
"\n"
"* fbot = lowest frequency in the passband, in Hz\n"
" ++ fbot can be 0 if you want to do a lowpass filter only;\n"
" HOWEVER, the mean and Nyquist freq are always removed.\n"
"\n"
"* ftop = highest frequency in the passband (must be > fbot)\n"
" ++ if ftop > Nyquist freq, then it's a highpass filter only.\n"
"\n"
"* Set fbot=0 and ftop=99999 to do an 'allpass' filter.\n"
" ++ Except for removal of the 0 and Nyquist frequencies, that is.\n"
"\n"
"* You cannot construct a 'notch' filter with this program!\n"
" ++ You could use 3dBandpass followed by 3dcalc to get the same effect.\n"
" ++ If you are understand what you are doing, that is.\n"
" ++ Of course, that is the AFNI way -- if you don't want to\n"
" understand what you are doing, use Some other PrograM, and\n"
" you can still get Fine StatisticaL maps.\n"
"\n"
"* 3dBandpass will fail if fbot and ftop are too close for comfort.\n"
" ++ Which means closer than one frequency grid step df,\n"
" where df = 1 / (nfft * dt) [of course]\n"
"\n"
"* The actual FFT length used will be printed, and may be larger\n"
" than the input time series length for the sake of efficiency.\n"
" ++ The program will use a power-of-2, possibly multiplied by\n"
" a power of 3 and/or 5 (up to and including the 3rd power of\n"
" each of these: 3, 9, 27, and 5, 25, 125).\n"
"\n"
"* Note that the results of combining 3dDetrend and 3dBandpass will\n"
" depend on the order in which you run these programs. That's why\n"
" 3dBandpass has the '-ort' and '-dsort' options, so that the\n"
" time series filtering can be done properly, in one place.\n"
"\n"
"* The output dataset is stored in float format.\n"
"\n"
"* The order of processing steps is the following (most are optional):\n"
" (0) Check time series for initial transients [does not alter data]\n"
" (1) Despiking of each time series\n"
" (2) Removal of a constant+linear+quadratic trend in each time series\n"
" (3) Bandpass of data time series\n"
" (4) Bandpass of -ort time series, then detrending of data\n"
" with respect to the -ort time series\n"
" (5) Bandpass and de-orting of the -dsort dataset,\n"
" then detrending of the data with respect to -dsort\n"
" (6) Blurring inside the mask [might be slow]\n"
" (7) Local PV calculation [WILL be slow!]\n"
" (8) L2 normalization [will be fast.]\n"
"\n"
"--------\n"
"OPTIONS:\n"
"--------\n"
" -despike = Despike each time series before other processing.\n"
" ++ Hopefully, you don't actually need to do this,\n"
" which is why it is optional.\n"
" -ort f.1D = Also orthogonalize input to columns in f.1D\n"
" ++ Multiple '-ort' options are allowed.\n"
" -dsort fset = Orthogonalize each voxel to the corresponding\n"
" voxel time series in dataset 'fset', which must\n"
" have the same spatial and temporal grid structure\n"
" as the main input dataset.\n"
" ++ At present, only one '-dsort' option is allowed.\n"
" -nodetrend = Skip the quadratic detrending of the input that\n"
" occurs before the FFT-based bandpassing.\n"
" ++ You would only want to do this if the dataset\n"
" had been detrended already in some other program.\n"
" -dt dd = set time step to 'dd' sec [default=from dataset header]\n"
" -nfft N = set the FFT length to 'N' [must be a legal value]\n"
" -norm = Make all output time series have L2 norm = 1\n"
" ++ i.e., sum of squares = 1\n"
" -mask mset = Mask dataset\n"
" -automask = Create a mask from the input dataset\n"
" -blur fff = Blur (inside the mask only) with a filter\n"
" width (FWHM) of 'fff' millimeters.\n"
" -localPV rrr = Replace each vector by the local Principal Vector\n"
" (AKA first singular vector) from a neighborhood\n"
" of radius 'rrr' millimiters.\n"
" ++ Note that the PV time series is L2 normalized.\n"
" ++ This option is mostly for Bob Cox to have fun with.\n"
"\n"
" -input dataset = Alternative way to specify input dataset.\n"
" -band fbot ftop = Alternative way to specify passband frequencies.\n"
"\n"
" -prefix ppp = Set prefix name of output dataset.\n"
" -quiet = Turn off the fun and informative messages. (Why?)\n"
"\n"
" -notrans = Don't check for initial positive transients in the data:\n"
" *OR* ++ The test is a little slow, so skipping it is OK,\n"
" -nosat if you KNOW the data time series are transient-free.\n"
" ++ Or set AFNI_SKIP_SATCHECK to YES.\n"
" ++ Initial transients won't be handled well by the\n"
" bandpassing algorithm, and in addition may seriously\n"
" contaminate any further processing, such as inter-voxel\n"
" correlations via InstaCorr.\n"
" ++ No other tests are made [yet] for non-stationary behavior\n"
" in the time series data.\n"
) ;
PRINT_AFNI_OMP_USAGE(
"3dBandpass" ,
"* At present, the only part of 3dBandpass that is parallelized is the\n"
" '-blur' option, which processes each sub-brick independently.\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*-- startup --*/
mainENTRY("3dBandpass"); machdep();
AFNI_logger("3dBandpass",argc,argv);
PRINT_VERSION("3dBandpass"); AUTHOR("RW Cox");
nosat = AFNI_yesenv("AFNI_SKIP_SATCHECK") ;
nopt = 1 ;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-despike") == 0 ){ /* 08 Oct 2010 */
do_despike++ ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-nfft") == 0 ){
int nnup ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -nfft!") ;
nfft = (int)strtod(argv[nopt],NULL) ;
nnup = csfft_nextup_even(nfft) ;
if( nfft < 16 || nfft != nnup )
ERROR_exit("value %d after -nfft is illegal! Next legal value = %d",nfft,nnup) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-blur") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -blur!") ;
blur = strtod(argv[nopt],NULL) ;
if( blur <= 0.0f ) WARNING_message("non-positive blur?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-localPV") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -localpv!") ;
pvrad = strtod(argv[nopt],NULL) ;
if( pvrad <= 0.0f ) WARNING_message("non-positive -localpv?!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-prefix") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -prefix!") ;
prefix = strdup(argv[nopt]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("bad -prefix option!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't use -mask AND -automask!") ;
do_automask = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
THD_3dim_dataset *mset ;
if( ++nopt >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || do_automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[nopt] ) ;
CHECK_OPEN_ERROR(mset,argv[nopt]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[nopt]) ;
nmask = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
if( verb ) INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < 1 ) ERROR_exit("Mask is too small to process") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-norm") == 0 ){
do_norm = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-quiet") == 0 ){
verb = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-notrans") == 0 || strcmp(argv[nopt],"-nosat") == 0 ){
nosat = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-ort") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -ort!") ;
if( ortar == NULL ) INIT_IMARR(ortar) ;
ortim = mri_read_1D( argv[nopt] ) ;
if( ortim == NULL ) ERROR_exit("can't read from -ort '%s'",argv[nopt]) ;
mri_add_name(argv[nopt],ortim) ;
ADDTO_IMARR(ortar,ortim) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dsort") == 0 ){
THD_3dim_dataset *qset ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -dsort!") ;
if( nortset > 0 ) ERROR_exit("only 1 -dsort option is allowed!") ;
qset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(qset,argv[nopt]) ;
ortset = (THD_3dim_dataset **)realloc(ortset,
sizeof(THD_3dim_dataset *)*(nortset+1)) ;
ortset[nortset++] = qset ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-nodetrend",6) == 0 ){
qdet = 0 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-dt") == 0 ){
if( ++nopt >= argc ) ERROR_exit("need an argument after -dt!") ;
dt = (float)strtod(argv[nopt],NULL) ;
if( dt <= 0.0f ) WARNING_message("value after -dt illegal!") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have 2 -input options!") ;
if( ++nopt >= argc ) ERROR_exit("need an argument after -input!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
nopt++ ; continue ;
}
if( strncmp(argv[nopt],"-band",5) == 0 ){
if( ++nopt >= argc-1 ) ERROR_exit("need 2 arguments after -band!") ;
if( have_freq ) WARNING_message("second -band option replaces first one!") ;
fbot = strtod(argv[nopt++],NULL) ;
ftop = strtod(argv[nopt++],NULL) ;
have_freq = 1 ; continue ;
}
ERROR_exit("Unknown option: '%s'",argv[nopt]) ;
}
/** check inputs for reasonablositiness **/
if( !have_freq ){
if( nopt+1 >= argc )
ERROR_exit("Need frequencies on command line after options!") ;
fbot = (float)strtod(argv[nopt++],NULL) ;
ftop = (float)strtod(argv[nopt++],NULL) ;
}
if( inset == NULL ){
if( nopt >= argc )
ERROR_exit("Need input dataset name on command line after options!") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ; nopt++ ;
}
DSET_UNMSEC(inset) ;
if( fbot < 0.0f ) ERROR_exit("fbot value can't be negative!") ;
if( ftop <= fbot ) ERROR_exit("ftop value %g must be greater than fbot value %g!",ftop,fbot) ;
ntime = DSET_NVALS(inset) ;
if( ntime < 9 ) ERROR_exit("Input dataset is too short!") ;
if( nfft <= 0 ){
nfft = csfft_nextup_even(ntime) ;
if( verb ) INFO_message("Data length = %d FFT length = %d",ntime,nfft) ;
(void)THD_bandpass_set_nfft(nfft) ;
} else if( nfft < ntime ){
ERROR_exit("-nfft %d is less than data length = %d",nfft,ntime) ;
} else {
kk = THD_bandpass_set_nfft(nfft) ;
if( kk != nfft && verb )
INFO_message("Data length = %d FFT length = %d",ntime,kk) ;
}
if( dt <= 0.0f ){
dt = DSET_TR(inset) ;
if( dt <= 0.0f ){
WARNING_message("Setting dt=1.0 since input dataset lacks a time axis!") ;
dt = 1.0f ;
}
}
if( !THD_bandpass_OK(ntime,dt,fbot,ftop,1) ) ERROR_exit("Can't continue!") ;
nx = DSET_NX(inset); ny = DSET_NY(inset); nz = DSET_NZ(inset); nvox = nx*ny*nz;
/* check mask, or create it */
if( verb ) INFO_message("Loading input dataset time series" ) ;
DSET_load(inset) ;
if( mask != NULL ){
if( mask_nx != nx || mask_ny != ny || mask_nz != nz )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( do_automask ){
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
nmask = THD_countmask( DSET_NVOX(inset) , mask ) ;
if( verb ) INFO_message("Number of voxels in automask = %d",nmask);
if( nmask < 1 ) ERROR_exit("Automask is too small to process") ;
} else {
mask = (byte *)malloc(sizeof(byte)*nvox) ; nmask = nvox ;
memset(mask,1,sizeof(byte)*nvox) ;
if( verb ) INFO_message("No mask ==> processing all %d voxels",nvox);
}
/* A simple check of dataset quality [08 Feb 2010] */
if( !nosat ){
float val ;
INFO_message(
"Checking dataset for initial transients [use '-notrans' to skip this test]") ;
val = THD_saturation_check(inset,mask,0,0) ; kk = (int)(val+0.54321f) ;
if( kk > 0 )
ININFO_message(
"Looks like there %s %d non-steady-state initial time point%s :-(" ,
((kk==1) ? "is" : "are") , kk , ((kk==1) ? " " : "s") ) ;
else if( val > 0.3210f ) /* don't ask where this threshold comes from! */
ININFO_message(
"MAYBE there's an initial positive transient of 1 point, but it's hard to tell\n") ;
else
ININFO_message("No widespread initial positive transient detected :-)") ;
}
/* check -dsort inputs for match to inset */
for( kk=0 ; kk < nortset ; kk++ ){
if( DSET_NX(ortset[kk]) != nx ||
DSET_NY(ortset[kk]) != ny ||
DSET_NZ(ortset[kk]) != nz ||
DSET_NVALS(ortset[kk]) != ntime )
ERROR_exit("-dsort %s doesn't match input dataset grid" ,
DSET_BRIKNAME(ortset[kk]) ) ;
}
/* convert input dataset to a vectim, which is more fun */
mrv = THD_dset_to_vectim( inset , mask , 0 ) ;
if( mrv == NULL ) ERROR_exit("Can't load time series data!?") ;
DSET_unload(inset) ;
/* similarly for the ort vectors */
if( ortar != NULL ){
for( kk=0 ; kk < IMARR_COUNT(ortar) ; kk++ ){
ortim = IMARR_SUBIM(ortar,kk) ;
if( ortim->nx < ntime )
ERROR_exit("-ort file %s is shorter than input dataset time series",
ortim->name ) ;
ort = (float **)realloc( ort , sizeof(float *)*(nort+ortim->ny) ) ;
for( vv=0 ; vv < ortim->ny ; vv++ )
ort[nort++] = MRI_FLOAT_PTR(ortim) + ortim->nx * vv ;
}
}
/* check whether processing leaves any DoF remaining 18 Mar 2015 [rickr] */
{
int nbprem = THD_bandpass_remain_dim(ntime, dt, fbot, ftop, 1);
int bpused, nremain;
int wlimit; /* warning limit */
bpused = ntime - nbprem; /* #dim lost in bandpass step */
nremain = nbprem - nort; /* #dim left in output */
if( nortset == 1 ) nremain--;
nremain -= (qdet+1);
if( verb ) INFO_message("%d dimensional data reduced to %d by:\n"
" %d (bandpass), %d (-ort), %d (-dsort), %d (detrend)",
ntime, nremain, bpused, nort, nortset?1:0, qdet+1);
/* possibly warn (if 95% lost) user or fail */
wlimit = ntime/20;
if( wlimit < 3 ) wlimit = 3;
if( nremain < wlimit && nremain > 0 )
WARNING_message("dimensionality reduced from %d to %d, be careful!",
ntime, nremain);
if( nremain <= 0 ) /* FAILURE */
ERROR_exit("dimensionality reduced from %d to %d, failing!",
ntime, nremain);
}
/* all the real work now */
if( do_despike ){
int_pair nsp ;
if( verb ) INFO_message("Testing data time series for spikes") ;
nsp = THD_vectim_despike9( mrv ) ;
if( verb ) ININFO_message(" -- Squashed %d spikes from %d voxels",nsp.j,nsp.i) ;
}
if( verb ) INFO_message("Bandpassing data time series") ;
(void)THD_bandpass_vectim( mrv , dt,fbot,ftop , qdet , nort,ort ) ;
/* OK, maybe a little more work */
if( nortset == 1 ){
MRI_vectim *orv ;
orv = THD_dset_to_vectim( ortset[0] , mask , 0 ) ;
if( orv == NULL ){
ERROR_message("Can't load -dsort %s",DSET_BRIKNAME(ortset[0])) ;
} else {
float *dp , *mvv , *ovv , ff ;
if( verb ) INFO_message("Orthogonalizing to bandpassed -dsort") ;
(void)THD_bandpass_vectim( orv , dt,fbot,ftop , qdet , nort,ort ) ;
THD_vectim_normalize( orv ) ;
dp = malloc(sizeof(float)*mrv->nvec) ;
THD_vectim_vectim_dot( mrv , orv , dp ) ;
for( vv=0 ; vv < mrv->nvec ; vv++ ){
ff = dp[vv] ;
if( ff != 0.0f ){
mvv = VECTIM_PTR(mrv,vv) ; ovv = VECTIM_PTR(orv,vv) ;
for( kk=0 ; kk < ntime ; kk++ ) mvv[kk] -= ff*ovv[kk] ;
}
}
VECTIM_destroy(orv) ; free(dp) ;
}
}
if( blur > 0.0f ){
if( verb )
INFO_message("Blurring time series data spatially; FWHM=%.2f",blur) ;
mri_blur3D_vectim( mrv , blur ) ;
}
if( pvrad > 0.0f ){
if( verb )
INFO_message("Local PV-ing time series data spatially; radius=%.2f",pvrad) ;
THD_vectim_normalize( mrv ) ;
THD_vectim_localpv( mrv , pvrad ) ;
}
if( do_norm && pvrad <= 0.0f ){
if( verb ) INFO_message("L2 normalizing time series data") ;
THD_vectim_normalize( mrv ) ;
}
/* create output dataset, populate it, write it, then quit */
if( verb ) INFO_message("Creating output dataset in memory, then writing it") ;
outset = EDIT_empty_copy(inset) ;
/* do not copy scalars 11 Sep 2015 [rickr] */
EDIT_dset_items( outset , ADN_prefix,prefix ,
ADN_brick_fac,NULL ,
ADN_none ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dBandpass" , argc,argv , outset ) ;
for( vv=0 ; vv < ntime ; vv++ )
EDIT_substitute_brick( outset , vv , MRI_float , NULL ) ;
#if 1
THD_vectim_to_dset( mrv , outset ) ;
#else
AFNI_OMP_START ;
#pragma omp parallel
{ float *far , *var ; int *ivec=mrv->ivec ; int vv,kk ;
#pragma omp for
for( vv=0 ; vv < ntime ; vv++ ){
far = DSET_BRICK_ARRAY(outset,vv) ; var = mrv->fvec + vv ;
for( kk=0 ; kk < nmask ; kk++ ) far[ivec[kk]] = var[kk*ntime] ;
}
}
AFNI_OMP_END ;
#endif
VECTIM_destroy(mrv) ;
DSET_write(outset) ; if( verb ) WROTE_DSET(outset) ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
int iarg , nerr=0 , nvals,nvox , nx,ny,nz , ii,jj,kk ;
char *prefix="LSSout" , *save1D=NULL , nbuf[256] ;
THD_3dim_dataset *inset=NULL , *outset ;
MRI_vectim *inset_mrv=NULL ;
byte *mask=NULL ; int mask_nx=0,mask_ny=0,mask_nz=0, automask=0, nmask=0 ;
NI_element *nelmat=NULL ; char *matname=NULL ;
char *cgl ;
int Ngoodlist,*goodlist=NULL , nfull , ncmat,ntime ;
NI_int_array *giar ; NI_str_array *gsar ; NI_float_array *gfar ;
MRI_IMAGE *imX, *imA, *imC, *imS ; float *Xar, *Sar ; MRI_IMARR *imar ;
int nS ; float *ss , *oo , *fv , sum ; int nvec , iv ;
int nbstim , nst=0 , jst_bot,jst_top ; char *stlab="LSS" ;
int nodata=1 ;
/*--- help me if you can ---*/
if( argc < 2 || strcasecmp(argv[1],"-HELP") == 0 ) LSS_help() ;
/*--- bureaucratic startup ---*/
PRINT_VERSION("3dLSS"); mainENTRY("3dLSS main"); machdep();
AFNI_logger("3dLSS",argc,argv); AUTHOR("RWCox");
(void)COX_clock_time() ;
/**------- scan command line --------**/
iarg = 1 ;
while( iarg < argc ){
if( strcmp(argv[iarg],"-verb") == 0 ){ verb++ ; iarg++ ; continue ; }
if( strcmp(argv[iarg],"-VERB") == 0 ){ verb+=2 ; iarg++ ; continue ; }
/**========== -mask ==========**/
if( strcasecmp(argv[iarg],"-mask") == 0 ){
THD_3dim_dataset *mset ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%.33s'",argv[iarg]) ;
nmask = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
if( verb || nmask < 1 ) INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < 1 ) ERROR_exit("Mask is too small to process") ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't have -automask and -mask") ;
automask = 1 ; iarg++ ; continue ;
}
/**========== -matrix ==========**/
if( strcasecmp(argv[iarg],"-matrix") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]) ;
if( nelmat != NULL ) ERROR_exit("More than 1 -matrix option!?");
nelmat = NI_read_element_fromfile( argv[iarg] ) ; /* read NIML file */
matname = argv[iarg];
if( nelmat == NULL || nelmat->type != NI_ELEMENT_TYPE )
ERROR_exit("Can't process -matrix file '%s'!?",matname) ;
iarg++ ; continue ;
}
/**========== -nodata ===========**/
if( strcasecmp(argv[iarg],"-nodata") == 0 ){
nodata = 1 ; iarg++ ; continue ;
}
/**========== -input ==========**/
if( strcasecmp(argv[iarg],"-input") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have two -input options!?") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]) ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
nodata = 0 ; iarg++ ; continue ;
}
/**========== -prefix =========**/
if( strcasecmp(argv[iarg],"-prefix") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]) ;
prefix = strdup(argv[iarg]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("Illegal string after %s",argv[iarg-1]) ;
if( verb && strcmp(prefix,"NULL") == 0 )
INFO_message("-prefix NULL ==> no dataset will be written") ;
iarg++ ; continue ;
}
/**========== -save1D =========**/
if( strcasecmp(argv[iarg],"-save1D") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]) ;
save1D = strdup(argv[iarg]) ;
if( !THD_filename_ok(save1D) ) ERROR_exit("Illegal string after %s",argv[iarg-1]) ;
iarg++ ; continue ;
}
/***** Loser User *****/
ERROR_message("Unknown option: %s",argv[iarg]) ;
suggest_best_prog_option(argv[0], argv[iarg]);
exit(1);
} /* end of loop over options */
/*----- check for errors -----*/
if( nelmat == NULL ){ ERROR_message("No -matrix option!?") ; nerr++ ; }
if( nerr > 0 ) ERROR_exit("Can't continue without these inputs!") ;
if( inset != NULL ){
nvals = DSET_NVALS(inset) ; nvox = DSET_NVOX(inset) ;
nx = DSET_NX(inset) ; ny = DSET_NY(inset) ; nz = DSET_NZ(inset) ;
} else {
automask = nvals = 0 ;
nvox = nx = ny = nz = nodata = 1 ; /* nodata */
mask = NULL ;
}
/*----- masque -----*/
if( mask != NULL ){ /* check -mask option for compatibility */
if( mask_nx != nx || mask_ny != ny || mask_nz != nz )
ERROR_exit("-mask dataset grid doesn't match input dataset :-(") ;
} else if( automask ){ /* create a mask from input dataset */
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset :-(") ;
nmask = THD_countmask( nvox , mask ) ;
if( verb || nmask < 1 )
INFO_message("Number of voxels in automask = %d (out of %d = %.1f%%)",
nmask, nvox, (100.0f*nmask)/nvox ) ;
if( nmask < 1 ) ERROR_exit("Automask is too small to process") ;
} else if( !nodata ) { /* create a 'mask' for all voxels */
if( verb )
INFO_message("No mask ==> computing for all %d voxels",nvox) ;
mask = (byte *)malloc(sizeof(byte)*nvox) ; nmask = nvox ;
memset( mask , 1 , sizeof(byte)*nvox ) ;
}
/*----- get matrix info from the NIML element -----*/
if( verb ) INFO_message("extracting matrix info") ;
ncmat = nelmat->vec_num ; /* number of columns */
ntime = nelmat->vec_len ; /* number of rows */
if( ntime < ncmat+2 )
ERROR_exit("Matrix has too many columns (%d) for number of rows (%d)",ncmat,ntime) ;
/*--- number of rows in the full matrix (without censoring) ---*/
cgl = NI_get_attribute( nelmat , "NRowFull" ) ;
if( cgl == NULL ) ERROR_exit("Matrix is missing 'NRowFull' attribute!") ;
nfull = (int)strtod(cgl,NULL) ;
if( nodata ){
nvals = nfull ;
} else if( nvals != nfull ){
ERROR_exit("-input dataset has %d time points, but matrix indicates %d",
nvals , nfull ) ;
}
/*--- the goodlist = mapping from matrix row index to time index
(which allows for possible time point censoring) ---*/
cgl = NI_get_attribute( nelmat , "GoodList" ) ;
if( cgl == NULL ) ERROR_exit("Matrix is missing 'GoodList' attribute!") ;
giar = NI_decode_int_list( cgl , ";," ) ;
if( giar == NULL || giar->num < ntime )
ERROR_exit("Matrix 'GoodList' badly formatted?!") ;
Ngoodlist = giar->num ; goodlist = giar->ar ;
if( Ngoodlist != ntime )
ERROR_exit("Matrix 'GoodList' incorrect length?!") ;
else if( verb > 1 && Ngoodlist < nfull )
ININFO_message("censoring reduces time series length from %d to %d",nfull,Ngoodlist) ;
/*--- extract the matrix from the NIML element ---*/
imX = mri_new( ntime , ncmat , MRI_float ) ;
Xar = MRI_FLOAT_PTR(imX) ;
if( nelmat->vec_typ[0] == NI_FLOAT ){ /* from 3dDeconvolve_f */
float *cd ;
for( jj=0 ; jj < ncmat ; jj++ ){
cd = (float *)nelmat->vec[jj] ;
for( ii=0 ; ii < ntime ; ii++ ) Xar[ii+jj*ntime] = cd[ii] ;
}
} else if( nelmat->vec_typ[0] == NI_DOUBLE ){ /* from 3dDeconvolve */
double *cd ;
for( jj=0 ; jj < ncmat ; jj++ ){
cd = (double *)nelmat->vec[jj] ;
for( ii=0 ; ii < ntime ; ii++ ) Xar[ii+jj*ntime] = cd[ii] ;
}
} else {
ERROR_exit("Matrix file stored with illegal data type!?") ;
}
/*--- find the stim_times_IM option ---*/
cgl = NI_get_attribute( nelmat , "BasisNstim") ;
if( cgl == NULL ) ERROR_exit("Matrix doesn't have 'BasisNstim' attribute!") ;
nbstim = (int)strtod(cgl,NULL) ;
if( nbstim <= 0 ) ERROR_exit("Matrix 'BasisNstim' attribute is %d",nbstim) ;
for( jj=1 ; jj <= nbstim ; jj++ ){
sprintf(nbuf,"BasisOption_%06d",jj) ;
cgl = NI_get_attribute( nelmat , nbuf ) ;
if( cgl == NULL || strcmp(cgl,"-stim_times_IM") != 0 ) continue ;
if( nst > 0 )
ERROR_exit("More than one -stim_times_IM option was found in the matrix") ;
nst = jj ;
sprintf(nbuf,"BasisColumns_%06d",jj) ;
cgl = NI_get_attribute( nelmat , nbuf ) ;
if( cgl == NULL )
ERROR_exit("Matrix doesn't have %s attribute!",nbuf) ;
jst_bot = jst_top = -1 ;
sscanf(cgl,"%d:%d",&jst_bot,&jst_top) ;
if( jst_bot < 0 || jst_top < 0 )
ERROR_exit("Can't decode matrix attribute %s",nbuf) ;
if( jst_bot == jst_top )
ERROR_exit("Matrix attribute %s shows only 1 column for -stim_time_IM:\n"
" -->> 3dLSS is meant to be used when more than one stimulus\n"
" time was given, and then it computes the response beta\n"
" for each stim time separately. If you have only one\n"
" stim time with -stim_times_IM, you can use the output\n"
" dataset from 3dDeconvolve (or 3dREMLfit) to get that\n"
" single beta directly.\n" , nbuf ) ;
if( jst_bot >= jst_top || jst_top >= ncmat )
ERROR_exit("Matrix attribute %s has illegal value: %d:%d (ncmat=%d)",nbuf,jst_bot,jst_top,ncmat) ;
sprintf(nbuf,"BasisName_%06d",jj) ;
cgl = NI_get_attribute( nelmat , nbuf ) ;
if( cgl != NULL ) stlab = strdup(cgl) ;
if( verb > 1 )
ININFO_message("-stim_times_IM at stim #%d; cols %d..%d",jj,jst_bot,jst_top) ;
}
if( nst == 0 )
ERROR_exit("Matrix doesn't have any -stim_times_IM options inside :-(") ;
/*--- mangle matrix to segregate IM regressors from the rest ---*/
if( verb ) INFO_message("setting up LSS vectors") ;
imar = LSS_mangle_matrix( imX , jst_bot , jst_top ) ;
if( imar == NULL )
ERROR_exit("Can't compute LSS 'mangled' matrix :-(") ;
/*--- setup for LSS computations ---*/
imA = IMARR_SUBIM(imar,0) ;
imC = IMARR_SUBIM(imar,1) ;
imS = LSS_setup( imA , imC ) ; DESTROY_IMARR(imar) ;
if( imS == NULL )
ERROR_exit("Can't complete LSS setup :-((") ;
nS = imS->ny ; Sar = MRI_FLOAT_PTR(imS) ;
if( save1D != NULL ){
mri_write_1D( save1D , imS ) ;
if( verb ) ININFO_message("saved LSS vectors into file %s",save1D) ;
} else if( nodata ){
WARNING_message("-nodata used but -save1D not used ==> you get no output!") ;
}
if( nodata || strcmp(prefix,"NULL") == 0 ){
INFO_message("3dLSS ends since prefix is 'NULL' or -nodata was used") ;
exit(0) ;
}
/*----- create output dataset -----*/
if( verb ) INFO_message("creating output datset in memory") ;
outset = EDIT_empty_copy(inset) ;
EDIT_dset_items( outset ,
ADN_prefix , prefix ,
ADN_datum_all , MRI_float ,
ADN_brick_fac , NULL ,
ADN_nvals , nS ,
ADN_ntt , nS ,
ADN_none ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dLSS" , argc,argv , outset ) ;
for( kk=0 ; kk < nS ; kk++ ){
EDIT_substitute_brick( outset , kk , MRI_float , NULL ) ;
sprintf(nbuf,"%s#%03d",stlab,kk) ;
EDIT_BRICK_LABEL( outset , kk , nbuf ) ;
}
/*----- convert input dataset to vectim -----*/
if( verb ) INFO_message("loading input dataset into memory") ;
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
inset_mrv = THD_dset_to_vectim( inset , mask , 0 ) ;
DSET_unload(inset) ;
/*----- compute dot products, store results -----*/
if( verb ) INFO_message("computing away, me buckos!") ;
nvec = inset_mrv->nvec ;
for( kk=0 ; kk < nS ; kk++ ){
ss = Sar + kk*ntime ;
oo = DSET_ARRAY(outset,kk) ;
for( iv=0 ; iv < nvec ; iv++ ){
fv = VECTIM_PTR(inset_mrv,iv) ;
for( sum=0.0f,ii=0 ; ii < ntime ; ii++ )
sum += ss[ii] * fv[goodlist[ii]] ;
oo[inset_mrv->ivec[iv]] = sum ;
}
}
DSET_write(outset) ; WROTE_DSET(outset) ;
/*-------- Hasta la vista, baby --------*/
if( verb )
INFO_message("3dLSS finished: total CPU=%.2f Elapsed=%.2f",
COX_cpu_time() , COX_clock_time() ) ;
exit(0) ;
}
int main( int argc , char * argv[] )
{
float mrad=0.0f , fwhm=0.0f ;
int nrep=1 ;
char *prefix = "Polyfit" ;
char *resid = NULL ;
char *cfnam = NULL ;
int iarg , verb=0 , do_automask=0 , nord=3 , meth=2 , do_mclip=0 ;
THD_3dim_dataset *inset ;
MRI_IMAGE *imout , *imin ;
byte *mask=NULL ; int nvmask=0 , nmask=0 , do_mone=0 , do_byslice=0 ;
MRI_IMARR *exar=NULL ;
floatvec *fvit=NULL ; /* 26 Feb 2019 */
if( argc < 2 || strcasecmp(argv[1],"-help") == 0 ){
printf("\n"
"Usage: 3dPolyfit [options] dataset ~1~\n"
"\n"
"* Fits a polynomial in space to the input dataset and outputs that fitted dataset.\n"
"\n"
"* You can also add your own basis datasets to the fitting mix, using the\n"
" '-base' option.\n"
"\n"
"* You can get the fit coefficients using the '-1Dcoef' option.\n"
"\n"
"--------\n"
"Options: ~1~\n"
"--------\n"
"\n"
" -nord n = Maximum polynomial order (0..9) [default order=3]\n"
" [n=0 is the constant 1]\n"
" [n=-1 means only use volumes from '-base']\n"
"\n"
" -blur f = Gaussian blur input dataset (inside mask) with FWHM='f' (mm)\n"
"\n"
" -mrad r = Radius (voxels) of preliminary median filter of input\n"
" [default is no blurring of either type; you can]\n"
" [do both types (Gaussian and median), but why??]\n"
" [N.B.: median blur is slower than Gaussian]\n"
"\n"
" -prefix pp = Use 'pp' for prefix of output dataset (the fit).\n"
" [default prefix is 'Polyfit'; use NULL to skip this output]\n"
"\n"
" -resid rr = Use 'rr' for the prefix of the residual dataset.\n"
" [default is not to output residuals]\n"
"\n"
" -1Dcoef cc = Save coefficients of fit into text file cc.1D.\n"
" [default is not to save these coefficients]\n"
"\n"
" -automask = Create a mask (a la 3dAutomask)\n"
" -mask mset = Create a mask from nonzero voxels in 'mset'.\n"
" [default is not to use a mask, which is probably a bad idea]\n"
"\n"
" -mone = Scale the mean value of the fit (inside the mask) to 1.\n"
" [probably this option is not useful for anything]\n"
"\n"
" -mclip = Clip fit values outside the rectilinear box containing the\n"
" mask to the edge of that box, to avoid weird artifacts.\n"
"\n"
" -meth mm = Set 'mm' to 2 for least squares fit;\n"
" set it to 1 for L1 fit [default method=2]\n"
" [Note that L1 fitting is slower than L2 fitting!]\n"
"\n"
" -base bb = In addition to the polynomial fit, also use\n"
" the volumes in dataset 'bb' as extra basis functions.\n"
" [If you use a base dataset, then you can set nord]\n"
" [to -1, to skip using any spatial polynomial fit.]\n"
"\n"
" -verb = Print fun and useful progress reports :-)\n"
"\n"
"------\n"
"Notes: ~1~\n"
"------\n"
"* Output dataset is always stored in float format.\n"
"\n"
"* If the input dataset has more than 1 sub-brick, only sub-brick #0\n"
" is processed. To fit more than one volume, you'll have to use a script\n"
" to loop over the input sub-bricks, and then glue (3dTcat) the results\n"
" together to get a final result. A simple example:\n"
" #!/bin/tcsh\n"
" set base = model.nii\n"
" set dset = errts.nii\n"
" set nval = `3dnvals $dset`\n"
" @ vtop = $nval - 1\n"
" foreach vv ( `count 0 $vtop` )\n"
" 3dPolyfit -base \"$base\" -nord 0 -mask \"$base\" -1Dcoef QQ.$vv -prefix QQ.$vv.nii $dset\"[$vv]\"\n"
" end\n"
" 3dTcat -prefix QQall.nii QQ.0*.nii\n"
" 1dcat QQ.0*.1D > QQall.1D\n"
" \rm QQ.0*\n"
" exit 0\n"
"\n"
"* If the '-base' dataset has multiple sub-bricks, all of them are used.\n"
"\n"
"* You can use the '-base' option more than once, if desired or needed.\n"
"\n"
"* The original motivation for this program was to fit a spatial model\n"
" to a field map MRI, but that didn't turn out to be useful. Nevertheless,\n"
" I make this program available to someone who might find it beguiling.\n"
"\n"
"* If you really want, I could allow you to put sign constraints on the\n"
" fit coefficients (e.g., say that the coefficient for a given base volume\n"
" should be non-negative). But you'll have to beg for this.\n"
"\n"
"-- Emitted by RWCox\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*-- startup paperwork --*/
mainENTRY("3dPolyfit main"); machdep(); AFNI_logger("3dPolyfit",argc,argv);
PRINT_VERSION("3dPolyfit") ;
/*-- scan command line --*/
iarg = 1 ;
while( iarg < argc && argv[iarg][0] == '-' ){
if( strcasecmp(argv[iarg],"-base") == 0 ){
THD_3dim_dataset *bset ; int kk ; MRI_IMAGE *bim ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-base'") ;
bset = THD_open_dataset(argv[iarg]) ;
CHECK_OPEN_ERROR(bset,argv[iarg]) ;
DSET_load(bset) ; CHECK_LOAD_ERROR(bset) ;
if( exar == NULL ) INIT_IMARR(exar) ;
for( kk=0 ; kk < DSET_NVALS(bset) ; kk++ ){
bim = THD_extract_float_brick(kk,bset) ;
if( bim != NULL ) ADDTO_IMARR(exar,bim) ;
DSET_unload_one(bset,kk) ;
}
DSET_delete(bset) ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-verb") == 0 ){
verb++ ; iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-hermite") == 0 ){ /* 25 Mar 2013 [New Year's Day] */
mri_polyfit_set_basis("hermite") ; /* HIDDEN */
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-byslice") == 0 ){ /* 25 Mar 2013 [New Year's Day] */
do_byslice++ ; iarg++ ; continue ; /* HIDDEN */
}
if( strcasecmp(argv[iarg],"-mask") == 0 ){
THD_3dim_dataset *mset ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || do_automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset(argv[iarg]) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
nvmask = DSET_NVOX(mset) ;
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[iarg]) ;
nmask = THD_countmask( nvmask , mask ) ;
if( nmask < 99 ) ERROR_exit("Too few voxels in mask (%d)",nmask) ;
if( verb ) INFO_message("Number of voxels in mask = %d",nmask) ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-nord") == 0 ){
nord = (int)strtol( argv[++iarg], NULL , 10 ) ;
if( nord < -1 || nord > 9 )
ERROR_exit("Illegal value after -nord :(") ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-meth") == 0 ){
meth = (int)strtol( argv[++iarg], NULL , 10 ) ;
if( meth < 1 || meth > 2 )
ERROR_exit("Illegal value after -meth :(") ;
iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-automask",5) == 0 ){
if( mask != NULL ) ERROR_exit("Can't use -mask and -automask together!") ;
do_automask++ ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-mclip",5) == 0 ){
do_mclip++ ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-mone",5) == 0 ){
do_mone++ ; iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-mrad") == 0 ){
mrad = strtod( argv[++iarg] , NULL ) ; iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-blur") == 0 ){
fwhm = strtod( argv[++iarg] , NULL ) ; iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-prefix") == 0 ){
prefix = argv[++iarg] ;
if( !THD_filename_ok(prefix) )
ERROR_exit("Illegal value after -prefix :(");
if( strcasecmp(prefix,"NULL") == 0 ) prefix = NULL ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-resid") == 0 ){
resid = argv[++iarg] ;
if( !THD_filename_ok(resid) )
ERROR_exit("Illegal value after -resid :(");
if( strcasecmp(resid,"NULL") == 0 ) resid = NULL ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-1Dcoef") == 0 ){ /* 26 Feb 2019 */
cfnam = argv[++iarg] ;
if( !THD_filename_ok(cfnam) )
ERROR_exit("Illegal value after -1Dcoef :(");
if( strcasecmp(cfnam,"NULL") == 0 ) cfnam = NULL ;
iarg++ ; continue ;
}
ERROR_exit("Unknown option: %s\n",argv[iarg]);
}
/*--- check for blatant errors ---*/
if( iarg >= argc )
ERROR_exit("No input dataset name on command line?");
if( prefix == NULL && resid == NULL && cfnam == NULL )
ERROR_exit("-prefix and -resid and -1Dcoef are all NULL?!") ;
if( do_byslice && cfnam != NULL ){
WARNING_message("-byslice does not work with -1Dcoef option :(") ;
cfnam = NULL ;
}
if( nord < 0 && exar == NULL )
ERROR_exit("no polynomial fit AND no -base option ==> nothing to compute :(") ;
/*-- read input --*/
if( verb ) INFO_message("Load input dataset") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
if( DSET_NVALS(inset) > 1 )
WARNING_message( "Only processing sub-brick #0 (out of %d)" , DSET_NVALS(inset) );
/* check input mask or create automask */
if( mask != NULL ){
if( nvmask != DSET_NVOX(inset) )
ERROR_exit("-mask and input datasets don't match in voxel counts :-(") ;
} else if( do_automask ){
THD_automask_verbose( (verb > 1) ) ;
THD_automask_extclip( 1 ) ;
mask = THD_automask( inset ) ; nvmask = DSET_NVOX(inset) ;
nmask = THD_countmask( nvmask , mask ) ;
if( nmask < 99 ) ERROR_exit("Too few voxels in automask (%d)",nmask) ;
if( verb ) ININFO_message("Number of voxels in automask = %d",nmask) ;
} else {
WARNING_message("3dPolyfit is running without a mask") ;
}
#undef GOOD
#define GOOD(i) (mask == NULL || mask[i])
/* check -base input datasets */
if( exar != NULL ){
int ii,kk , nvbad=0 , nvox=DSET_NVOX(inset),nm ; float *ex , exb ;
for( kk=0 ; kk < IMARR_COUNT(exar) ; kk++ ){
if( nvox != IMARR_SUBIM(exar,kk)->nvox ){
if( IMARR_SUBIM(exar,kk)->nvox != nvbad ){
ERROR_message("-base volume (%d voxels) doesn't match input dataset grid size (%d voxels)",
IMARR_SUBIM(exar,kk)->nvox , nvox ) ;
nvbad = IMARR_SUBIM(exar,kk)->nvox ;
}
}
}
if( nvbad != 0 ) ERROR_exit("Cannot continue :-(") ;
/* subtract mean from each base input, if is a constant polynomial in the fit */
if( nord >= 0 ){
if( verb ) INFO_message("subtracting spatial mean from '-base'") ;
for( kk=0 ; kk < IMARR_COUNT(exar) ; kk++ ){
exb = 0.0f ; ex = MRI_FLOAT_PTR(IMARR_SUBIM(exar,kk)) ;
for( nm=ii=0 ; ii < nvox ; ii++ ){ if( GOOD(ii) ){ exb += ex[ii]; nm++; } }
exb /= nm ;
for( ii=0 ; ii < nvox ; ii++ ) ex[ii] -= exb ;
}
}
}
/* if blurring, edit mask a little */
if( mask != NULL && (fwhm > 0.0f || mrad > 0.0f) ){
int ii ;
ii = THD_mask_remove_isolas( DSET_NX(inset),DSET_NY(inset),DSET_NZ(inset),mask ) ;
if( ii > 0 ){
nmask = THD_countmask( nvmask , mask ) ;
if( verb )
ININFO_message("Removed %d isola%s from mask, leaving %d voxels" ,
ii,(ii==1)?"\0":"s" , nmask ) ;
if( nmask < 99 )
ERROR_exit("Too few voxels left in mask after isola removal :-(") ;
}
}
/* convert input to float, which is simpler to deal with */
imin = THD_extract_float_brick(0,inset) ;
if( imin == NULL ) ERROR_exit("Can't extract input dataset brick?! :-(") ;
DSET_unload(inset) ;
if( verb ) INFO_message("Start fitting process") ;
/* do the Gaussian blurring */
if( fwhm > 0.0f ){
if( verb ) ININFO_message("Gaussian blur: FWHM=%g mm",fwhm) ;
imin->dx = fabsf(DSET_DX(inset)) ;
imin->dy = fabsf(DSET_DY(inset)) ;
imin->dz = fabsf(DSET_DZ(inset)) ;
mri_blur3D_addfwhm( imin , mask , fwhm ) ;
}
/* do the fitting */
mri_polyfit_verb(verb) ;
if( do_byslice )
imout = mri_polyfit_byslice( imin , nord , exar , mask , mrad , meth ) ;
else
imout = mri_polyfit ( imin , nord , exar , mask , mrad , meth ) ;
/* WTF? */
if( imout == NULL )
ERROR_exit("Can't compute polynomial fit :-( !?") ;
if( resid == NULL ) mri_free(imin) ;
if( ! do_byslice )
fvit = mri_polyfit_get_fitvec() ; /* get coefficients of fit [26 Feb 2019] */
/* scale the fit dataset? */
if( do_mone ){
float sum=0.0f ; int nsum=0 , ii,nvox ; float *par=MRI_FLOAT_PTR(imout) ;
nvox = imout->nvox ;
for( ii=0 ; ii < nvox ; ii++ ){
if( mask != NULL && mask[ii] == 0 ) continue ;
sum += par[ii] ; nsum++ ;
}
if( nsum > 0 && sum != 0.0f ){
sum = nsum / sum ;
if( verb ) ININFO_message("-mone: scaling fit by %g",sum) ;
for( ii=0 ; ii < nvox ; ii++ ) par[ii] *= sum ;
}
}
/* if there's a mask, clip values outside of its box */
#undef PF
#define PF(i,j,k) par[(i)+(j)*nx+(k)*nxy]
if( mask != NULL && do_mclip ){
int xm,xp,ym,yp,zm,zp , ii,jj,kk , nx,ny,nz,nxy ; float *par ;
MRI_IMAGE *bim = mri_empty_conforming( imout , MRI_byte ) ;
mri_fix_data_pointer(mask,bim) ;
if( verb ) ININFO_message("-mclip: polynomial fit to autobox of mask") ;
MRI_autobbox( bim , &xm,&xp , &ym,&yp , &zm,&zp ) ;
mri_clear_data_pointer(bim) ; mri_free(bim) ;
nx = imout->nx ; ny = imout->ny ; nz = imout->nz ; nxy = nx*ny ;
par = MRI_FLOAT_PTR(imout) ;
for( ii=0 ; ii < xm ; ii++ )
for( kk=0 ; kk < nz ; kk++ )
for( jj=0 ; jj < ny ; jj++ ) PF(ii,jj,kk) = PF(xm,jj,kk) ;
for( ii=xp+1 ; ii < nx ; ii++ )
for( kk=0 ; kk < nz ; kk++ )
for( jj=0 ; jj < ny ; jj++ ) PF(ii,jj,kk) = PF(xp,jj,kk) ;
for( jj=0 ; jj < ym ; jj++ )
for( kk=0 ; kk < nz ; kk++ )
for( ii=0 ; ii < nx ; ii++ ) PF(ii,jj,kk) = PF(ii,ym,kk) ;
for( jj=yp+1 ; jj < ny ; jj++ )
for( kk=0 ; kk < nz ; kk++ )
for( ii=0 ; ii < nx ; ii++ ) PF(ii,jj,kk) = PF(ii,yp,kk) ;
for( kk=0 ; kk < zm ; kk++ )
for( jj=0 ; jj < ny ; jj++ )
for( ii=0 ; ii < nx ; ii++ ) PF(ii,jj,kk) = PF(ii,jj,zm) ;
for( kk=zp+1 ; kk < nz ; kk++ )
for( jj=0 ; jj < ny ; jj++ )
for( ii=0 ; ii < nx ; ii++ ) PF(ii,jj,kk) = PF(ii,jj,zp) ;
}
if( mask != NULL ) free(mask) ;
/* write outputs */
if( prefix != NULL ){
THD_3dim_dataset *outset = EDIT_empty_copy( inset ) ;
EDIT_dset_items( outset ,
ADN_prefix , prefix ,
ADN_nvals , 1 ,
ADN_ntt , 0 ,
ADN_none ) ;
EDIT_substitute_brick( outset , 0 , MRI_float , MRI_FLOAT_PTR(imout) ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dPolyfit" , argc,argv , outset ) ;
DSET_write(outset) ;
WROTE_DSET(outset) ;
}
if( resid != NULL ){
THD_3dim_dataset *outset = EDIT_empty_copy( inset ) ;
float *inar=MRI_FLOAT_PTR(imin) , *outar=MRI_FLOAT_PTR(imout) ;
int nx,ny,nz , nxyz , kk ;
nx = imout->nx ; ny = imout->ny ; nz = imout->nz ; nxyz = nx*ny*nz ;
for( kk=0 ; kk < nxyz ; kk++ ) outar[kk] = inar[kk] - outar[kk] ;
mri_free(imin) ;
EDIT_dset_items( outset ,
ADN_prefix , resid ,
ADN_nvals , 1 ,
ADN_ntt , 0 ,
ADN_none ) ;
EDIT_substitute_brick( outset , 0 , MRI_float , MRI_FLOAT_PTR(imout) ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dPolyfit" , argc,argv , outset ) ;
DSET_write(outset) ;
WROTE_DSET(outset) ;
}
if( cfnam != NULL && fvit != NULL ){ /* won't work with '-byslice' */
char *qn ;
qn = STRING_HAS_SUFFIX(cfnam,".1D") ? cfnam : modify_afni_prefix(cfnam,NULL,".1D") ;
mri_write_floatvec( qn , fvit ) ;
}
exit(0) ;
}
int main(int argc, char **argv)
{
static char FuncName[]={"3dSeg"};
SEG_OPTS *Opt=NULL;
char *atr=NULL;
float *mixfrac= NULL;
int i=0;
double ff;
SUMA_SEND_2AFNI SS2A;
SUMA_Boolean LocalHead = NOPE;
SUMA_STANDALONE_INIT;
SUMA_mainENTRY;
SUMAg_DOv = SUMA_Alloc_DisplayObject_Struct (SUMA_MAX_DISPLAYABLE_OBJECTS);
Opt = Seg_Default(argv, argc);
Opt = Seg_ParseInput (Opt,argv, argc);
Opt->hist = tross_commandline( FuncName , argc , argv ) ;
/* load the input data */
if (!(Opt->aset = Seg_load_dset( Opt->aset_name ))) {
SUMA_RETURN(1);
}
if (!Seg_CheckOpts(Opt)) {
SUMA_S_Err("Failed on option check");
SUMA_RETURN(1);
}
/* Load mask dataset */
if (Opt->mset_name) {
if (!strncasecmp(Opt->mset_name,"auto", 4)) {
byte *mm=NULL;
int j;
short *sb=NULL;
if (!(mm = THD_automask(Opt->aset))) {
SUMA_RETURN(1);
}
NEW_SHORTY(Opt->aset, DSET_NVALS(Opt->aset),
"automask.cp", Opt->mset);
sb = (short *)DSET_ARRAY(Opt->mset,0);
for (j=0; j<DSET_NVOX(Opt->mset); ++j) {
sb[j] = (short)mm[j];
}
free(mm); mm=NULL;
} else if (!(Opt->mset = Seg_load_dset( Opt->mset_name ))) {
SUMA_RETURN(1);
}
}
/* reference classes */
if (Opt->gold_name) {
if (!(Opt->gold = Seg_load_dset( Opt->gold_name ))) {
SUMA_RETURN(1);
}
}
if (Opt->gold_bias_name) {
if (!(Opt->gold_bias = Seg_load_dset( Opt->gold_bias_name ))) {
SUMA_RETURN(1);
}
}
if (!Opt->clss) {
SUMA_S_Err("Need -classes option");
SUMA_RETURN(1);
}
/* Talk ? */
if (Opt->ps->cs->talk_suma) {
Opt->ps->cs->istream = SUMA_BRAINWRAP_LINE;
Opt->ps->cs->afni_istream = SUMA_AFNI_STREAM_INDEX2;
if (!SUMA_SendToAfni (Opt->ps->cs, NULL, 0)) {
SUMA_SL_Err("Failed to initialize SUMA_SendToAfni");
Opt->ps->cs->afni_Send = NOPE;
Opt->ps->cs->Send = NOPE;
} else {
/* send in_vol to afni */
SUMA_SL_Note("Sending anat volume to AFNI");
SS2A.dset = Opt->aset; SS2A.at_sb=-1;
if (!SUMA_SendToAfni(Opt->ps->cs, &SS2A, 1)) {
SUMA_SL_Err("Failed to send volume to AFNI");
Opt->ps->cs->afni_Send = NOPE;
}
}
}
/* classified set ? */
if (Opt->this_cset_name) { /* user supplied initializer */
if (!(Opt->cset = Seg_load_dset( Opt->this_cset_name ))) {
SUMA_RETURN(1);
}
}
/* labeltable? */
if (Opt->labeltable_name) {
Dtable *vl_dtable=NULL;
char *labeltable_str=NULL;
int kk=0;
/* read the table */
if (!(labeltable_str = AFNI_suck_file( Opt->labeltable_name))) {
ERROR_exit("Failed to read %s", Opt->labeltable_name);
}
if (!(vl_dtable = Dtable_from_nimlstring(labeltable_str))) {
ERROR_exit("Could not parse labeltable");
}
CLASS_KEYS_FROM_LT(vl_dtable);
destroy_Dtable(vl_dtable); vl_dtable=NULL;
}
if (!Opt->keys) {
Dtable *vl_dtable=NULL;
if (Opt->cset && (vl_dtable = DSET_Label_Dtable(Opt->cset))) {
if (Opt->debug) SUMA_S_Note("Getting keys from -cset dataset");
/* try getting keys from cset */
CLASS_KEYS_FROM_LT(vl_dtable);
/* Do not delete vl_dtable, it is the same pointer in Opt->cset */
} else {
/* add default keys */
if (Opt->debug) SUMA_S_Note("Keys not available, assuming defaults");
Opt->keys = (int *)calloc(Opt->clss->num, sizeof(int));
for (i=0; i<Opt->clss->num; ++i) {
Opt->keys[i] = i+1;
}
}
}
/* Make sure you have no negative values and requesting bias field correction.
The implementation uses log() for this so the negative values would be
ill advised */
{
float amin, amax;
THD_subbrick_minmax(Opt->aset, 0, 1,&amin, &amax);
if (amin < 0 && Opt->bias_param > 0) {
SUMA_S_Err("Cannot use field bias correction on volumes with negative\n"
"values. Either turn off bias field estimation with -bias_fwhm 0.0\n"
"or shift the values of the input by something like:\n"
" 3dcalc -a %s -expr 'a+bool(a)*%d' -prefix SHIFTED\n"
"and rerun the segmentation on SHIFTED. Note the suggested shift\n"
"leaves zero values unchanged.",
DSET_HEADNAME(Opt->aset), (int)ceil(-amin+1.0));
exit(1);
}
}
/* Show the match between keys and classes */
if (Opt->debug > 1) {
SUMA_S_Note("Class-->key map");
SUMA_ShowClssKeys(Opt->clss->str, Opt->clss->num, Opt->keys);
}
if (Opt->clss->num < 2) {
if (Opt->debug <= 1) {
SUMA_S_Note("Class-->key map");
SUMA_ShowClssKeys(Opt->clss->str, Opt->clss->num, Opt->keys);
}
SUMA_S_Err("Less than 2 classes? I am out of here");
SUMA_RETURN(0);
}
/* Mask setup */
if (Opt->debug > 1) {
SUMA_S_Note("MaskSetup");
}
Opt->cmask = MaskSetup(Opt, Opt->aset, 1,
&(Opt->mset), &(Opt->cmask), Opt->dimcmask,
Opt->mask_bot, Opt->mask_top, &(Opt->cmask_count));
if (Opt->VoxDbg >= 0) {
SUMA_S_Note("DBG setup");
fprintf(Opt->VoxDbgOut, "Command:");
for (i=0; i<argc; ++i) {
fprintf(Opt->VoxDbgOut, "%s ", argv[i]);
}
fprintf(Opt->VoxDbgOut, "\nDebug info for voxel %d\n", Opt->VoxDbg);
}
Opt->cs = SUMA_New_Class_Stat(Opt->clss->str, Opt->clss->num,
Opt->keys, 3, NULL);
/* Load prob. of class given features */
if (Opt->priCgAname && strcmp(Opt->priCgAname, "INIT_MIXFRAC")) {
if (!(Opt->priCgA = Seg_load_dset(Opt->priCgAname))) {
SUMA_S_Errv("Failed to read priCgA %s\n", Opt->priCgAname);
SUMA_RETURN(1);
}
if (GRID_MISMATCH(Opt->priCgA, Opt->aset)) {
SUMA_S_Err("All input data must have same grid (-priCgA != -aset)");
SUMA_RETURN(1);
}
/* Make sure dset is properly formatted */
if (!SUMA_ShortizeProbDset(&Opt->priCgA,
Opt->cs,
Opt->cmask, Opt->cmask_count,
Opt, &Opt->priCgA)) {
SUMA_S_Errv("Failed to shortize priCgA %s\n", Opt->priCgAname);
SUMA_RETURN(1);
}
/* set the floor of the input dset */
if (0) {
SUMA_S_Note("Setting probability floor, USEFULNESS NOT TESTED...");
if (!set_p_floor(Opt->priCgA, 0.1, Opt->cmask)) {
SUMA_S_Errv("Failed to set p floor for priCgA %s\n",
Opt->priCgAname);
SUMA_RETURN(1);
}
}
} else {
/* uniform probability */
}
/* Load prob. of class given location */
if (Opt->priCgLname && strcmp(Opt->priCgLname, "INIT_MIXFRAC")) {
if (!(Opt->priCgL = Seg_load_dset(Opt->priCgLname))) {
SUMA_S_Errv("Failed to read priCgL %s\n", Opt->priCgLname);
SUMA_RETURN(1);
}
if (GRID_MISMATCH(Opt->priCgL, Opt->aset)) {
SUMA_S_Err("All input data must have same grid (-priCgL != -aset)");
SUMA_RETURN(1);
}
/* Make sure dset is properly formatted */
if (!SUMA_ShortizeProbDset(&Opt->priCgL,
Opt->cs,
Opt->cmask, Opt->cmask_count,
Opt, &Opt->priCgL)) {
SUMA_S_Errv("Failed to shortize priCgL %s\n", Opt->priCgLname);
SUMA_RETURN(1);
}
} else {
/* uniform probability */
}
/* check on weights of priors */
if (Opt->wA >= 0.0 && Opt->wL < 0.0) {
Opt->wL = 1.0 - Opt->wA;
} else if (Opt->wL >= 0.0 && Opt->wA < 0.0) {
Opt->wA = 1.0 - Opt->wL;
} else if (Opt->wA < 0.0 && Opt->wL < 0.0) { /* defaults */
Opt->wA = 0.5; Opt->wL = 0.5;
}
ff = Opt->wA+Opt->wL;
Opt->wA = Opt->wA/ff; Opt->wL = Opt->wL/ff;
if (!Opt->cset) {
if (!SUMA_SegInitCset(Opt->aset,
&Opt->cset,
Opt->cmask, Opt->cmask_count,
Opt->mixopt,
Opt->cs,
Opt)) {
SUMA_S_Err("Failed to get initializer");
SUMA_RETURN(1);
}
}
if (Opt->debug > 1) {
SUMA_Seg_Write_Dset(Opt->proot, "classes_init", Opt->cset,
-1, Opt->hist);
}
/* Now add the 'OTHER' class if needed */
if (Opt->Other) {
if (!SUMA_AddOther( Opt->clss, &Opt->keys,
Opt->cmask, Opt->cmask_count,
Opt->cset, Opt->pstCgALL,
Opt->priCgA, Opt->priCgL,
Opt->cs)) {
SUMA_S_Err("Failed to add other");
SUMA_RETURN(0);
}
}
/* store mixfrac in class stats */
for (i=0;i<Opt->cs->N_label; ++i) {
if ((ff = SUMA_mixopt_2_mixfrac(Opt->mixopt, Opt->cs->label[i],
Opt->cs->keys[i], Opt->cs->N_label,
Opt->cmask, Opt->cset))<0.0) {
SUMA_S_Errv("Can't get mixfrac for %s\n", Opt->mixopt);
SUMA_RETURN(1);
}
SUMA_set_Stat(Opt->cs, Opt->cs->label[i], "init.mix", ff);
SUMA_set_Stat(Opt->cs, Opt->cs->label[i], "mix", ff);
}
/* Now call the workhorse */
if (!SUMA_SegEngine(Opt)) {
SUMA_S_Err("Failed in SUMA_SegEngine");
exit(1);
}
/* write output */
if (Opt->Bset && !Opt->this_fset_name) {
tross_Append_History(Opt->Bset, Opt->hist);
SUMA_Seg_Write_Dset(Opt->proot, "BiasField", /* DSET_PREFIX(Opt->Bset) */
Opt->Bset, -1, Opt->hist);
}
if (Opt->xset && !Opt->this_xset_name) {
AFNI_FEED(Opt->ps->cs, "BiasCorrect", -1, Opt->xset);
SUMA_Seg_Write_Dset(Opt->proot, "AnatUB", /* DSET_PREFIX(Opt->xset)*/
Opt->xset, -1, Opt->hist);
}
if (Opt->cset) {
SUMA_Seg_Write_Dset(Opt->proot, "Classes", /* Opt->crefix */
Opt->cset, -1, Opt->hist);
AFNI_FEED(Opt->ps->cs, "FinalClasses", -1, Opt->cset);
}
if (Opt->pstCgALL) {
SUMA_Seg_Write_Dset(Opt->proot, "Posterior", /* Opt->prefix */
Opt->pstCgALL, -1, Opt->hist);
AFNI_FEED(Opt->ps->cs, "pstCgALL-final", -1, Opt->pstCgALL);
}
if (Opt->aset) {
SUMA_Seg_Write_Dset(Opt->proot, "Anat",
Opt->aset, -1, Opt->hist);
}
if (Opt->debug) SUMA_S_Note("Writing Unmodulated output");
if (!(SUMA_pst_C_giv_ALL(Opt->xset,
Opt->cmask, Opt->cmask_count,
Opt->cs,
NULL, NULL,
Opt->B, Opt->T, 0,
&Opt->pstCgALL))) {
SUMA_S_Err("Failed in SUMA_pst_C_giv_ALL unmodulated");
SUMA_RETURN(1);
}
SUMA_Seg_Write_Dset(Opt->proot, "Unmodulated.p",
Opt->pstCgALL, -1, Opt->hist);
if (!(SUMA_assign_classes( Opt->pstCgALL, Opt->cs,
Opt->cmask, &Opt->cset))) {
SUMA_S_Err("Failed in assign_classes");
SUMA_RETURN(1);
}
SUMA_Seg_Write_Dset(Opt->proot, "Unmodulated.c",
Opt->cset, -1, Opt->hist);
/* all done, free */
Opt = free_SegOpts(Opt);
PRINT_COMPILE_DATE ;
SUMA_RETURN(0);
}
/*! compute the overall minimum and maximum voxel values for a dataset */
int main( int argc , char * argv[] )
{
THD_3dim_dataset * old_dset , * new_dset ; /* input and output datasets */
int nopt, nbriks;
int slow_flag, quick_flag, min_flag, max_flag, mean_flag,
automask,count_flag, sum_flag, var_flag, absolute_flag;
int positive_flag, negative_flag, zero_flag, nan_flag, perc_flag, vol_flag;
byte * mmm=NULL ;
int mmvox=0 ;
int nxyz, i;
float *dvec = NULL, mmin=0.0, mmax=0.0;
int N_mp;
double *mpv=NULL, *perc = NULL;
double mp =0.0f, mp0 = 0.0f, mps = 0.0f, mp1 = 0.0f, di =0.0f ;
byte *mmf = NULL;
MRI_IMAGE *anat_im = NULL;
char *mask_dset_name=NULL;
/*----- Read command line -----*/
mainENTRY("3dBrickStat main"); machdep(); AFNI_logger("3dBrickStat",argc,argv);
nopt = 1 ;
min_flag = 0;
max_flag = -1;
mean_flag = 0;
sum_flag = 0;
var_flag = 0;
slow_flag = 0;
quick_flag = -1;
automask = 0;
count_flag = 0;
vol_flag = 0;
positive_flag = -1;
negative_flag = -1;
absolute_flag = 0;
zero_flag = -1;
nan_flag = -1;
perc_flag = 0;
mmin = 1.0;
mmax = -1.0;
mask_dset_name = NULL;
datum = MRI_float;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-help") == 0 ||
strcmp(argv[nopt],"-h") == 0){
usage_3dBrickStat(strlen(argv[nopt])> 3 ? 2:1);
exit(0);
}
if( strcmp(argv[nopt],"-ver") == 0 ){
PRINT_VERSION("3dBrickStat"); AUTHOR("Daniel Glen");
nopt++; continue;
}
if( strcmp(argv[nopt],"-quick") == 0 ){
quick_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-percentile") == 0 ){
perc_flag = 1;
++nopt;
if (nopt + 2 >= argc) {
ERROR_exit( "** Error: Need 3 parameter after -percentile\n");
}
mp0 = atof(argv[nopt])/100.0f; ++nopt;
mps = atof(argv[nopt])/100.0f; ++nopt;
mp1 = atof(argv[nopt])/100.0f;
if (mps == 0.0f) {
ERROR_exit( "** Error: step cannot be 0" );
}
if (mp0 < 0 || mp0 > 100 || mp1 < 0 || mp1 > 100) {
ERROR_exit( "** Error: p0 and p1 must be >=0 and <= 100" );
}
nopt++; continue;
}
if( strcmp(argv[nopt],"-median") == 0 ){
perc_flag = 1;
mp0 = 0.50f;
mps = 0.01f;
mp1 = 0.50f;
nopt++; continue;
}
if( strcmp(argv[nopt],"-slow") == 0 ){
slow_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-min") == 0 ){
min_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-max") == 0 ){
max_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-sum") == 0 ){
sum_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-mean") == 0 ){
mean_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-var") == 0 ){
if (var_flag) {
ERROR_message("Looks like -stdev is already used.\n"
"-var and -stdev are mutually exclusive");
exit (1);
}
var_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-stdev") == 0 ){
if (var_flag) {
ERROR_message("Looks like -var is already used.\n"
"-var and -stdev are mutually exclusive");
exit (1);
}
var_flag = 2;
nopt++; continue;
}
if( strcmp(argv[nopt],"-count") == 0 ){
count_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-volume") == 0 ){
vol_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-positive") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 1;
negative_flag = 0;
zero_flag = 0;
nopt++; continue;
}
if( strcmp(argv[nopt],"-negative") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 0;
negative_flag = 1;
zero_flag = 0;
nopt++; continue;
}
if( strcmp(argv[nopt],"-zero") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 0;
negative_flag = 0;
zero_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-non-positive") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 0;
negative_flag = 1;
zero_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-non-negative") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 1;
negative_flag = 0;
zero_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-non-zero") == 0 ){
if(positive_flag!=-1) {
ERROR_exit( "Can not use multiple +/-/0 options");
}
positive_flag = 1;
negative_flag = 1;
zero_flag = 0;
nopt++; continue;
}
if( strcmp(argv[nopt],"-absolute") == 0 ){
absolute_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-nan") == 0 ){
if(nan_flag!=-1) {
ERROR_exit( "Can not use both -nan -nonan options");
}
nan_flag = 1;
nopt++; continue;
}
if( strcmp(argv[nopt],"-nonan") == 0 ){
if(nan_flag!=-1) {
ERROR_exit( "Can not use both -nan -nonan options");
}
nan_flag = 0;
nopt++; continue;
}
if( strcmp(argv[nopt],"-autoclip") == 0 ||
strcmp(argv[nopt],"-automask") == 0 ){
if( mmm != NULL ){
ERROR_exit(" ERROR: can't use -autoclip/mask with -mask!");
}
automask = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mrange") == 0 ){
if (nopt+2 >= argc) {
ERROR_exit(" ERROR: Need two values after -mrange");
}
mmin = atof(argv[++nopt]);
mmax = atof(argv[++nopt]);
if (mmax < mmin) {
ERROR_exit(
"1st value in -mrange %s %s should be the smallest one",
argv[nopt-1], argv[nopt]);
}
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mvalue") == 0 ){
if (nopt+1 >= argc) {
ERROR_exit(" ERROR: Need 1 value after -mvalue");
}
mmin = atof(argv[++nopt]);
mmax = mmin ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
if( mask_dset_name != NULL )
ERROR_exit(" ERROR: can't have 2 -mask options!");
mask_dset_name = argv[++nopt];
nopt++ ; continue ;
}
ERROR_message( " Error - unknown option %s", argv[nopt]);
suggest_best_prog_option(argv[0], argv[nopt]);
exit(1);
}
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
ERROR_message("Too few options");
usage_3dBrickStat(0);
exit(1) ;
}
if (mask_dset_name) {
int ninmask = 0;
THD_3dim_dataset * mask_dset ;
if( automask ){
ERROR_exit(" ERROR: can't use -mask with -automask!");
}
mask_dset = THD_open_dataset(mask_dset_name) ;
CHECK_OPEN_ERROR(mask_dset,mask_dset_name) ;
mmm = THD_makemask( mask_dset , 0 , mmin, mmax ) ;
mmvox = DSET_NVOX( mask_dset ) ;
ninmask = THD_countmask (mmvox, mmm);
if (!ninmask) {
ERROR_exit(" No voxels in mask !");
}
/* text output program, so avoid extras 26 Dec 2013 [rickr] */
/* INFO_message("%d voxels in mask\n", ninmask); */
DSET_delete(mask_dset) ;
}
if(((mmm!=NULL) && (quick_flag))||(automask &&quick_flag)) {
if(quick_flag==1)
WARNING_message( "+++ Warning - can't have quick option with mask");
quick_flag = 0;
slow_flag = 1;
}
/* if max_flag is not set by user, check if other user options set */
if(max_flag==-1) {
if(min_flag || mean_flag || count_flag || vol_flag || sum_flag
|| perc_flag || var_flag)
max_flag = 0;
else
max_flag = 1; /* otherwise check only for max */
}
if((var_flag==1)||(mean_flag==1)||(count_flag==1)||
(vol_flag==1)||(absolute_flag==1) ||
(positive_flag!=-1)||(nan_flag!=-1)||
(sum_flag == 1)||(perc_flag == 1) || (var_flag==2)) {
/* mean flag or count_flag implies slow */
slow_flag = 1;
}
/* check slow and quick options */
if((slow_flag)&&(quick_flag!=1)) /* if user asked for slow give it to him */
quick_flag = 0;
else
quick_flag = 1;
if((max_flag==0)&&(min_flag==0)) /* if the user only asked for mean */
quick_flag = 0; /* no need to do quick way */
if((quick_flag) &&
((absolute_flag==1)||(positive_flag==1)||(negative_flag==1)||(zero_flag==1)))
WARNING_message( " Warning - ignoring +/-/0/abs flags for quick computations");
if(positive_flag==-1) { /* if no +/-/0 options set, allow all voxels */
positive_flag = 1;
negative_flag = 1;
zero_flag = 1;
}
/*----- read input dataset -----*/
if( nopt >= argc ){
ERROR_exit(" No input dataset!?");
}
old_dset = THD_open_dataset( argv[nopt] ) ;
CHECK_OPEN_ERROR(old_dset,argv[nopt]) ;
nxyz = DSET_NVOX(old_dset) ;
if( mmm != NULL && mmvox != nxyz ){
ERROR_exit(" Mask and input datasets not the same size!") ;
}
if(automask && mmm == NULL ){
mmm = THD_automask( old_dset ) ;
for(i=0;i<nxyz;i++) {
if(mmm[i]!=0) ++mmvox;
}
}
if(quick_flag)
Print_Header_MinMax(min_flag, max_flag, old_dset);
if(slow_flag!=1)
exit(0);
/* ZSS do some diddlyiddly sorting - DO not affect Daniel's function later on*/
if (perc_flag == 1) {
DSET_mallocize (old_dset);
DSET_load (old_dset);
if (DSET_NVALS(old_dset) != 1) {
ERROR_exit( "-percentile can only be used on one sub-brick only.\n"
"Use sub-brick selectors '[.]' to specify sub-brick of interest.\n");
}
/* prep for input and output of percentiles */
if (mp0 > mp1) {
N_mp = 1;
} else {
/* allocate one above ceiling to prevent truncation error (and crash),
N_mp is recomputed anyway 16 Mar 2009 [rickr] */
N_mp = (int)((double)(mp1-mp0)/(double)mps) + 2;
}
mpv = (double *)malloc(sizeof(double)*N_mp);
perc = (double *)malloc(sizeof(double)*N_mp);
if (!mpv || !perc) {
ERROR_message("Failed to allocate for mpv or perc");
exit(1);
}
N_mp = 0;
mp = mp0;
do {
mpv[N_mp] = mp; ++N_mp; mp += mps;
} while (mp <= mp1+.00000001);
if (!Percentate (DSET_ARRAY(old_dset, 0), mmm, nxyz,
DSET_BRICK_TYPE(old_dset,0), mpv, N_mp,
0, perc,
zero_flag, positive_flag, negative_flag )) {
ERROR_message("Failed to compute percentiles.");
exit(1);
}
/* take care of brick factor */
if (DSET_BRICK_FACTOR(old_dset,0)) {
for (i=0; i<N_mp; ++i) {
perc[i] = perc[i]*DSET_BRICK_FACTOR(old_dset,0);
}
}
for (i=0; i<N_mp; ++i) {
fprintf(stdout,"%.1f %f ", mpv[i]*100.0f, perc[i]);
}
free(mpv); mpv = NULL;
free(perc); perc = NULL;
}
Max_func(min_flag, max_flag, mean_flag,count_flag,
positive_flag, negative_flag, zero_flag, absolute_flag,
nan_flag, sum_flag, var_flag, vol_flag,old_dset, mmm, mmvox);
if(mmm!=NULL)
free(mmm);
exit(0);
/* unused code time series method for extracting data */
#if 0
EDIT_dset_items( old_dset ,
ADN_ntt , DSET_NVALS(old_dset) ,
ADN_ttorg , 0.0 ,
ADN_ttdel , 1.0 ,
ADN_tunits , UNITS_SEC_TYPE ,
NULL ) ;
nbriks = 1;
/*------------- ready to compute new min, max -----------*/
new_dset = MAKER_4D_to_typed_fbuc(
old_dset , /* input dataset */
"temp" , /* output prefix */
datum , /* output datum */
0 , /* ignore count */
0 , /* can't detrend in maker function KRH 12/02*/
nbriks , /* number of briks */
Max_tsfunc , /* timeseries processor */
NULL, /* data for tsfunc */
NULL, /* mask */
0 /* Allow auto scaling of output */
) ;
if(min_flag)
printf("%-13.6g ", minvalue);
if(max_flag)
printf("%-13.6g", maxvalue);
printf("\n");
exit(0) ;
#endif
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *inset=NULL , *jnset=NULL , *outset , *wset=NULL;
int ncode=0 , code[MAX_NCODE] , iarg=1 , ii ;
MCW_cluster *nbhd=NULL ;
byte *mask=NULL ; int mask_nx=0,mask_ny=0,mask_nz=0 , automask=0 ;
char *prefix="./localstat" ;
int ntype=0 ; float na=0.0f,nb=0.0f,nc=0.0f ;
double hist_pow=0.3333333 ; int hist_nbin=0 ;
float hbot1=1.0f , htop1=-1.0f ; int hbot1_perc=0, htop1_perc=0 ;
float hbot2=1.0f , htop2=-1.0f ; int hbot2_perc=0, htop2_perc=0 ;
/*---- for the clueless who wish to become clued-in ----*/
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"Usage: 3dLocalBistat [options] dataset1 dataset2\n"
"\n"
"This program computes statistics between 2 datasets,\n"
"at each voxel, based on a local neighborhood of that voxel.\n"
" - The neighborhood is defined by the '-nbhd' option.\n"
" - Statistics to be calculated are defined by the '-stat' option(s).\n"
" - The 2 input datasets should have the same number of sub-bricks.\n"
" - OR dataset1 should have 1 sub-brick and dataset2 can have more than 1:\n"
" - In which case, the statistics of dataset2 against dataset1 are\n"
" calculated for the #0 sub-brick of dataset1 against each sub-brick\n"
" of dataset2.\n"
"\n"
"OPTIONS\n"
"-------\n"
" -nbhd 'nnn' = The string 'nnn' defines the region around each\n"
" voxel that will be extracted for the statistics\n"
" calculation. The format of the 'nnn' string are:\n"
" * 'SPHERE(r)' where 'r' is the radius in mm;\n"
" the neighborhood is all voxels whose center-to-\n"
" center distance is less than or equal to 'r'.\n"
" ** A negative value for 'r' means that the region\n"
" is calculated using voxel indexes rather than\n"
" voxel dimensions; that is, the neighborhood\n"
" region is a \"sphere\" in voxel indexes of\n"
" \"radius\" abs(r).\n"
" * 'RECT(a,b,c)' is a rectangular block which\n"
" proceeds plus-or-minus 'a' mm in the x-direction,\n"
" 'b' mm in the y-direction, and 'c' mm in the\n"
" z-direction. The correspondence between the\n"
" dataset xyz axes and the actual spatial orientation\n"
" can be determined by using program 3dinfo.\n"
" ** A negative value for 'a' means that the region\n"
" extends plus-and-minus abs(a) voxels in the\n"
" x-direction, rather than plus-and-minus a mm.\n"
" Mutatis mutandum for negative 'b' and/or 'c'.\n"
" * 'RHDD(r)' is a rhombic dodecahedron of 'radius' r.\n"
" * 'TOHD(r)' is a truncated octahedron of 'radius' r.\n"
"\n"
" -stat sss = Compute the statistic named 'sss' on the values\n"
" extracted from the region around each voxel:\n"
" * pearson = Pearson correlation coefficient\n"
" * spearman = Spearman correlation coefficient\n"
" * quadrant = Quadrant correlation coefficient\n"
" * mutinfo = Mutual Information\n"
" * normuti = Normalized Mutual Information\n"
" * jointent = Joint entropy\n"
" * hellinger= Hellinger metric\n"
" * crU = Correlation ratio (Unsymmetric)\n"
" * crM = Correlation ratio (symmetrized by Multiplication)\n"
" * crA = Correlation ratio (symmetrized by Addition)\n"
" * L2slope = slope of least-squares (L2) linear regression of\n"
" the data from dataset1 vs. the dataset2\n"
" (i.e., d2 = a + b*d1 ==> this is 'b')\n"
" * L1slope = slope of least-absolute-sum (L1) linear regression\n"
" of the data from dataset1 vs. the dataset2\n"
" * num = number of the values in the region:\n"
" with the use of -mask or -automask,\n"
" the size of the region around any given\n"
" voxel will vary; this option lets you\n"
" map that size.\n"
#if 0
" * ncd = Normalized Compression Distance (zlib; very slow)\n"
#endif
#if 0 /* activate after testing */
" * euclidian = Euclidian distance.\n"
" * cityblock = City Block distance.\n"
#endif
" * ALL = all of the above, in that order\n"
" More than one '-stat' option can be used.\n"
"\n"
" -mask mset = Read in dataset 'mset' and use the nonzero voxels\n"
" therein as a mask. Voxels NOT in the mask will\n"
" not be used in the neighborhood of any voxel. Also,\n"
" a voxel NOT in the mask will have its statistic(s)\n"
" computed as zero (0).\n"
" -automask = Compute the mask as in program 3dAutomask.\n"
" -mask and -automask are mutually exclusive: that is,\n"
" you can only specify one mask.\n"
" -weight ws = Use dataset 'ws' as a weight. Only applies to 'pearson'.\n"
"\n"
" -prefix ppp = Use string 'ppp' as the prefix for the output dataset.\n"
" The output dataset is always stored as floats.\n"
"\n"
"ADVANCED OPTIONS\n"
"----------------\n"
" -histpow pp = By default, the number of bins in the histogram used\n"
" for calculating the Hellinger, Mutual Information,\n"
" and Correlation Ratio statistics is n^(1/3), where n\n"
" is the number of data points in the -nbhd mask. You\n"
" can change that exponent to 'pp' with this option.\n"
" -histbin nn = Or you can just set the number of bins directly to 'nn'.\n"
" -hclip1 a b = Clip dataset1 to lie between values 'a' and 'b'. If 'a'\n"
" and 'b' end in '%%', then these values are percentage\n"
" points on the cumulative histogram.\n"
" -hclip2 a b = Similar to '-hclip1' for dataset2.\n"
"\n"
"-----------------------------\n"
"Author: RWCox - October 2006.\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*---- official startup ---*/
PRINT_VERSION("3dLocalBistat"); mainENTRY("3dLocalBistat main"); machdep();
AFNI_logger("3dLocalBistat",argc,argv) ;
/*---- loop over options ----*/
while( iarg < argc && argv[iarg][0] == '-' ){
if( strcmp(argv[iarg],"-hclip1") == 0 ){
char *cpt1, *cpt2 ;
if( ++iarg >= argc-1 ) ERROR_exit("need 2 arguments after -hclip1") ;
hbot1 = (float)strtod(argv[iarg],&cpt1) ; iarg++ ;
htop1 = (float)strtod(argv[iarg],&cpt2) ;
if( hbot1 >= htop1 ) ERROR_exit("illegal values after -hclip1") ;
if( *cpt1 == '%' ){
hbot1_perc = 1 ; hbot1 = (int)rint((double)hbot1) ;
if( hbot1 < 0 || hbot1 > 99 ) ERROR_exit("illegal bot percentage after -hclip1") ;
}
if( *cpt2 == '%' ){
htop1_perc = 1 ; htop1 = (int)rint((double)htop1) ;
if( htop1 < 1 || htop1 > 100 ) ERROR_exit("illegal top percentage after -hclip1") ;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-hclip2") == 0 ){
char *cpt1, *cpt2 ;
if( ++iarg >= argc-1 ) ERROR_exit("need 2 arguments after -hclip2") ;
hbot2 = (float)strtod(argv[iarg],&cpt1) ; iarg++ ;
htop2 = (float)strtod(argv[iarg],&cpt2) ;
if( hbot2 >= htop2 ) ERROR_exit("illegal values after -hclip2") ;
if( *cpt1 == '%' ){
hbot2_perc = 1 ; hbot2 = (int)rint((double)hbot2) ;
if( hbot2 < 0 || hbot2 > 99 ) ERROR_exit("illegal bot percentage after -hclip2") ;
}
if( *cpt2 == '%' ){
htop2_perc = 1 ; htop2 = (int)rint((double)htop2) ;
if( htop2 < 1 || htop2 > 100 ) ERROR_exit("illegal top percentage after -hclip2") ;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-histpow") == 0 ){
if( ++iarg >= argc ) ERROR_exit("no argument after '%s'!",argv[iarg-1]) ;
hist_pow = strtod(argv[iarg],NULL) ;
if( hist_pow <= 0.0 || hist_pow > 0.5 ){
WARNING_message("Illegal value after -histpow"); hist_pow = 0.33333;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-histbin") == 0 ){
if( ++iarg >= argc ) ERROR_exit("no argument after '%s'!",argv[iarg-1]) ;
hist_nbin = (int)strtod(argv[iarg],NULL) ;
if( hist_nbin <= 1 ) WARNING_message("Illegal value after -histbin");
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-prefix") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '-prefix'") ;
prefix = strdup(argv[iarg]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("Bad name after '-prefix'") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-mask") == 0 ){
THD_3dim_dataset *mset ; int mmm ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_delete(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[iarg]) ;
mmm = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
INFO_message("Number of voxels in mask = %d",mmm) ;
if( mmm < 2 ) ERROR_exit("Mask is too small to process") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-weight") == 0 ){ /* 14 Aug 2007 */
if( ++iarg >= argc ) ERROR_exit("Need argument after '-weight'") ;
if( wset != NULL ) ERROR_exit("Can't have two weight inputs") ;
wset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(wset,argv[iarg]) ;
DSET_load(wset) ; CHECK_LOAD_ERROR(wset) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-automask") == 0 ){
if( mask != NULL ) ERROR_exit("Can't have -automask and -mask") ;
automask = 1 ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-stat") == 0 ){
char *cpt ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-stat'") ;
cpt = argv[iarg] ; if( *cpt == '-' ) cpt++ ;
if( strcasecmp(cpt,"pearson") == 0 ) code[ncode++] = NBISTAT_PEARSON_CORR ;
else if( strcasecmp(cpt,"spearman") == 0 ) code[ncode++] = NBISTAT_SPEARMAN_CORR;
else if( strcasecmp(cpt,"quadrant") == 0 ) code[ncode++] = NBISTAT_QUADRANT_CORR;
else if( strcasecmp(cpt,"mutinfo") == 0 ) code[ncode++] = NBISTAT_MUTUAL_INFO ;
else if( strcasecmp(cpt,"normuti") == 0 ) code[ncode++] = NBISTAT_NORMUT_INFO ;
else if( strcasecmp(cpt,"jointent") == 0 ) code[ncode++] = NBISTAT_JOINT_ENTROPY;
else if( strcasecmp(cpt,"hellinger")== 0 ) code[ncode++] = NBISTAT_HELLINGER ;
else if( strcasecmp(cpt,"crU") == 0 ) code[ncode++] = NBISTAT_CORR_RATIO_U ;
else if( strcasecmp(cpt,"crM") == 0 ) code[ncode++] = NBISTAT_CORR_RATIO_M ;
else if( strcasecmp(cpt,"crA") == 0 ) code[ncode++] = NBISTAT_CORR_RATIO_A ;
else if( strcasecmp(cpt,"L2slope") == 0 ) code[ncode++] = NBISTAT_L2SLOPE ;
else if( strcasecmp(cpt,"L1slope") == 0 ) code[ncode++] = NBISTAT_L1SLOPE ;
else if( strcasecmp(cpt,"num") == 0 ) code[ncode++] = NBISTAT_NUM ;
#if 0
else if( strcasecmp(cpt,"ncd") == 0 ) code[ncode++] = NBISTAT_NCD ;
#endif
#if 0 /* activate after testing */
else if( strcasecmp(cpt,"euclidian") == 0 )
code[ncode++] = NBISTAT_EUCLIDIAN_DIST ;
else if( strcasecmp(cpt,"cityblock") == 0 )
code[ncode++] = NBISTAT_CITYBLOCK_DIST ;
#endif
else if( strcasecmp(cpt,"ALL") == 0 ){
code[ncode++] = NBISTAT_PEARSON_CORR ; code[ncode++] = NBISTAT_SPEARMAN_CORR;
code[ncode++] = NBISTAT_QUADRANT_CORR; code[ncode++] = NBISTAT_MUTUAL_INFO ;
code[ncode++] = NBISTAT_NORMUT_INFO ; code[ncode++] = NBISTAT_JOINT_ENTROPY;
code[ncode++] = NBISTAT_HELLINGER ; code[ncode++] = NBISTAT_CORR_RATIO_U ;
code[ncode++] = NBISTAT_CORR_RATIO_M ; code[ncode++] = NBISTAT_CORR_RATIO_A ;
code[ncode++] = NBISTAT_L2SLOPE ; code[ncode++] = NBISTAT_L1SLOPE ;
code[ncode++] = NBISTAT_NUM ;
#if 0
code[ncode++] = NBISTAT_NCD ;
#endif
#if 0 /* activate after testing */
code[ncode++] = NBISTAT_EUCLIDIAN_DIST;
code[ncode++] = NBISTAT_CITYBLOCK_DIST;
#endif
}
else
ERROR_exit("-stat '%s' is an unknown statistic type",argv[iarg]) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-nbhd") == 0 ){
char *cpt ;
if( ntype > 0 ) ERROR_exit("Can't have 2 '-nbhd' options") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-nbhd'") ;
cpt = argv[iarg] ;
if( strncasecmp(cpt,"SPHERE",6) == 0 ){
sscanf( cpt+7 , "%f" , &na ) ;
if( na == 0.0f ) ERROR_exit("Can't have a SPHERE of radius 0") ;
ntype = NTYPE_SPHERE ;
} else if( strncasecmp(cpt,"RHDD",4) == 0 ){
sscanf( cpt+5 , "%f" , &na ) ;
if( na == 0.0f ) ERROR_exit("Can't have a RHDD of radius 0") ;
ntype = NTYPE_RHDD ;
} else if( strncasecmp(cpt,"TOHD",4) == 0 ){
sscanf( cpt+5 , "%f" , &na ) ;
if( na == 0.0f ) ERROR_exit("Can't have a TOHD of radius 0") ;
ntype = NTYPE_TOHD ;
} else if( strncasecmp(cpt,"RECT",4) == 0 ){
sscanf( cpt+5 , "%f,%f,%f" , &na,&nb,&nc ) ;
if( na == 0.0f && nb == 0.0f && nc == 0.0f )
ERROR_exit("'RECT(0,0,0)' is not a legal neighborhood") ;
ntype = NTYPE_RECT ;
} else {
ERROR_exit("Unknown -nbhd shape: '%s'",cpt) ;
}
iarg++ ; continue ;
}
ERROR_exit("Unknown option '%s'",argv[iarg]) ;
} /*--- end of loop over options ---*/
/*---- check for stupid user inputs ----*/
if( ncode <= 0 ) ERROR_exit("No '-stat' options given?") ;
if( ntype <= 0 ) ERROR_exit("No '-nbhd' option given?!") ;
/*---- deal with input datasets ----*/
if( iarg > argc-1 ) ERROR_exit("No first input dataset on command line?") ;
inset = THD_open_dataset( argv[iarg] ) ; CHECK_OPEN_ERROR(inset,argv[iarg]) ;
iarg++ ;
if( iarg > argc-1 ) ERROR_exit("No second input dataset on command line?") ;
jnset = THD_open_dataset( argv[iarg] ) ; CHECK_OPEN_ERROR(jnset,argv[iarg]) ;
if( jnset == NULL ) ERROR_exit("Can't open dataset '%s'",argv[iarg]) ;
if( DSET_NVOX(jnset) != DSET_NVOX(inset) )
ERROR_exit("Input datasets have different numbers of voxels!?");
if( DSET_NVALS(jnset) != DSET_NVALS(inset) ){
if( DSET_NVALS(inset) > 1 ){
ERROR_exit("Input datasets have different numbers of sub-bricks!?");
} else {
INFO_message("first input dataset has 1 sub-brick, second has %d",DSET_NVALS(jnset)) ;
}
}
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
DSET_load(jnset) ; CHECK_LOAD_ERROR(jnset) ;
if( mask != NULL ){
if( mask_nx != DSET_NX(inset) ||
mask_ny != DSET_NY(inset) ||
mask_nz != DSET_NZ(inset) )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( automask ){
int mmm , nvox ; byte *jask ;
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset #1?") ;
jask = THD_automask( jnset ) ;
if( jask == NULL )
ERROR_message("Can't create -automask from input dataset #2?") ;
nvox = DSET_NVOX(inset) ;
for( ii=0 ; ii < nvox ; ii++ ) mask[ii] = (mask[ii] || jask[ii]) ;
free(jask) ;
mmm = THD_countmask( nvox , mask ) ;
INFO_message("Number of voxels in dual automask = %d",mmm) ;
if( mmm < 11 ) ERROR_exit("Automask is too small to process") ;
}
if( wset != NULL ){ /* 14 Aug 2007 */
MRI_IMAGE *wim ; float *war ;
if( DSET_NVOX(wset) != DSET_NVOX(inset) )
ERROR_exit("-weight dataset mismatch with input datasets!") ;
wim = mri_scale_to_float( DSET_BRICK_FACTOR(wset,0), DSET_BRICK(wset,0) );
war = MRI_FLOAT_PTR(wim) ;
DSET_delete(wset) ;
if( mask != NULL ){
int nvox = DSET_NVOX(inset) ;
for( ii=0 ; ii < nvox ; ii++ ) if( !mask[ii] ) war[ii] = 0.0f ;
}
mri_bistat_setweight( wim ) ; mri_free(wim) ;
}
/*---- create neighborhood -----*/
switch( ntype ){
default:
ERROR_exit("WTF? ntype=%d",ntype) ;
case NTYPE_SPHERE:{
float dx , dy , dz ;
if( na < 0.0f ){ dx = dy = dz = 1.0f ; na = -na ; }
else { dx = fabsf(DSET_DX(inset)) ;
dy = fabsf(DSET_DY(inset)) ;
dz = fabsf(DSET_DZ(inset)) ; }
nbhd = MCW_spheremask( dx,dy,dz , na ) ;
}
break ;
case NTYPE_RHDD:{
float dx , dy , dz ;
if( na < 0.0f ){ dx = dy = dz = 1.0f ; na = -na ; }
else { dx = fabsf(DSET_DX(inset)) ;
dy = fabsf(DSET_DY(inset)) ;
dz = fabsf(DSET_DZ(inset)) ; }
nbhd = MCW_rhddmask( dx,dy,dz , na ) ;
}
break ;
case NTYPE_TOHD:{
float dx , dy , dz ;
if( na < 0.0f ){ dx = dy = dz = 1.0f ; na = -na ; }
else { dx = fabsf(DSET_DX(inset)) ;
dy = fabsf(DSET_DY(inset)) ;
dz = fabsf(DSET_DZ(inset)) ; }
nbhd = MCW_tohdmask( dx,dy,dz , na ) ;
}
break ;
case NTYPE_RECT:{
float dx , dy , dz ;
if( na < 0.0f ){ dx = 1.0f; na = -na; } else dx = fabsf(DSET_DX(inset));
if( nb < 0.0f ){ dy = 1.0f; nb = -nb; } else dy = fabsf(DSET_DY(inset));
if( nc < 0.0f ){ dz = 1.0f; nc = -nc; } else dz = fabsf(DSET_DZ(inset));
nbhd = MCW_rectmask( dx,dy,dz , na,nb,nc ) ;
}
break ;
}
INFO_message("Neighborhood comprises %d voxels",nbhd->num_pt) ;
if( hist_nbin <= 1 ) hist_nbin = (int)pow((double)nbhd->num_pt,hist_pow) ;
if( hist_nbin <= 1 ) hist_nbin = 2 ;
INFO_message("2D histogram size = %d",hist_nbin) ;
set_2Dhist_hbin( hist_nbin ) ;
if( hbot1_perc || htop1_perc ){
MRI_IMAGE *fim ; float perc[101] ;
fim = THD_median_brick(inset) ;
mri_percents(fim,100,perc) ; mri_free(fim) ;
if( hbot1_perc ) hbot1 = perc[(int)hbot1] ;
if( htop1_perc ) htop1 = perc[(int)htop1] ;
INFO_message("Clipping dataset '%s' between %g and %g" ,
DSET_BRIKNAME(inset),hbot1,htop1 ) ;
}
if( hbot2_perc || htop2_perc ){
MRI_IMAGE *fim ; float perc[101] ;
fim = THD_median_brick(jnset) ;
mri_percents(fim,100,perc) ; mri_free(fim) ;
if( hbot2_perc ) hbot2 = perc[(int)hbot2] ;
if( htop2_perc ) htop2 = perc[(int)htop2] ;
INFO_message("Clipping dataset '%s' between %g and %g" ,
DSET_BRIKNAME(jnset),hbot2,htop2 ) ;
}
mri_nbistat_setclip( hbot1,htop1 , hbot2,htop2 ) ;
/*---- actually do some work for a change ----*/
THD_localbistat_verb(1) ;
outset = THD_localbistat( inset,jnset , mask , nbhd , ncode , code ) ;
DSET_unload(inset) ; DSET_unload(jnset) ;
if( outset == NULL ) ERROR_exit("Function THD_localbistat() fails?!") ;
EDIT_dset_items( outset , ADN_prefix,prefix , ADN_none ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dLocalBistat" , argc,argv , outset ) ;
#if 1
{ char *lcode[66] , lll[66] ;
lcode[0] = "Rank cor" ;
lcode[1] = "Quad cor" ;
lcode[2] = "Pear cor" ;
lcode[3] = "MI" ;
lcode[4] = "NMI" ;
lcode[5] = "Jnt Entropy" ;
lcode[6] = "Hlngr metric" ;
lcode[7] = "CorRat Sym*" ;
lcode[8] = "CorRat Sym+" ;
lcode[9] = "CorRatUnsym" ;
lcode[10]= "Number" ;
lcode[11]= "NCD" ;
lcode[12]= "Euclidian Dist";
lcode[13]= "CityBlock Dist";
if( DSET_NVALS(inset) == 1 ){
for( ii=0 ; ii < DSET_NVALS(outset) ; ii++ )
EDIT_dset_items( outset ,
ADN_brick_label_one+ii, lcode[code[ii%ncode]-NBISTAT_BASE],
ADN_none ) ;
} else {
for( ii=0 ; ii < DSET_NVALS(outset) ; ii++ ){
sprintf(lll,"%s[%d]",lcode[code[ii%ncode]-NBISTAT_BASE],(ii/ncode)) ;
EDIT_dset_items( outset , ADN_brick_label_one+ii,lll, ADN_none ) ;
}
}
}
#endif
DSET_write( outset ) ;
WROTE_DSET( outset ) ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
char *prefix = "PVmap" , *uvpref=NULL , *cpt ;
THD_3dim_dataset *inset=NULL , *outset=NULL , *mset=NULL ;
char *maskname=NULL ; byte *mask=NULL ; int nmask ;
int nopt ;
MRI_IMAGE *pvim ;
MRI_IMAGE *uvim ; float_pair uvlam ; float ulam , vlam , scon ;
if( argc < 2 || ! strncmp(argv[1],"-h",2) ){
printf("\n"
"3dPVmap [-prefix XXX] [-mask MMM] [-automask] inputdataset\n"
"\n"
"Computes the first 2 principal component vectors of a\n"
"time series datasets, then outputs the R-squared coefficient\n"
"of each voxel time series with these first 2 components.\n"
"\n"
"Each voxel times series from the input dataset is minimally pre-processed\n"
"before the PCA is computed:\n"
" Despiking\n"
" Legendre polynomial detrending\n"
" L2 normalizing (sum-of-squares = 1)\n"
"If you want more impressive pre-processing, you'll have to do that\n"
"before running 3dPVmap (e.g., use the errts dataset from afni_proc.py).\n"
"\n"
"Program also outputs the first 2 principal component time series\n"
"vectors into a 1D file, for fun and profit.\n"
"\n"
"The fractions of total-sum-of-squares allocable to the first 2\n"
"principal components are written to stdout at the end of the program.\n"
"along with a 3rd number that is a measure of the spatial concentration\n"
"or dispersion of the PVmap.\n"
"\n"
"These values can be captured into a file by Unix shell redirection\n"
"or into a shell variable by assigment:\n"
" 3dPVmap -mask AUTO Fred.nii > Fred.sval.1D\n"
" set sval = ( `3dPVmap -mask AUTO Fred.nii` ) # csh syntax\n"
"If the first value is very large, for example, this might indicate\n"
"the widespread presence of some artifact in the dataset.\n"
"\n"
"If the 3rd number is bigger than 1, it indicates that the PVmap\n"
"is more concentrated in space; if it is less than one, it indicates\n"
"that it is more dispersed in space (relative to a uniform density).\n"
" 3dPVmap -mask AUTO Zork.nii\n"
" ++ mask has 21300 voxels\n"
" ++ Output dataset ./PVmap+orig.BRIK\n"
" 0.095960 0.074847 1.356635\n"
"The first principal component accounted for 9.6%% of the total sum-of-squares,\n"
"the second component for 7.5%%, and the PVmap is fairly concentrated in space.\n"
"These %% values are not very unusual, but the concentration is fairly high\n"
"and the dataset should be further investigated.\n"
"\n"
"A concentration value below 1 indicates the PVmap is fairly dispersed; this\n"
"often means the larger PVmap values are found near the edges of the brain\n"
"and can be caused by motion or respiration artifacts.\n"
"\n"
"The goal is to visualize any widespread time series artifacts.\n"
"For example, if a 'significant' part of the brain shows R-squared > 0.25,\n"
"that could be a subject for concern -- look at your data!\n"
"\n"
"Author: Zhark the Unprincipaled\n\n"
) ;
exit(0) ;
}
nopt = 1 ;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-prefix") == 0 ){
if( ++nopt >= argc ) ERROR_exit("-prefix needs an argument!");
prefix = strdup(argv[nopt]) ;
if( !THD_filename_ok(prefix) )
ERROR_exit("%s is not a valid prefix!",prefix);
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-mask") == 0 ){
if( ++nopt >= argc ) ERROR_exit("-mask needs an argument!");
maskname = strdup(argv[nopt]) ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-automask") == 0 ){ /* 18 Apr 2019 */
maskname = strdup("AUTO") ;
nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-") == 0 ){ nopt++ ; continue ; }
ERROR_exit("Unknown option %s",argv[nopt]) ;
}
if( nopt >= argc ) ERROR_exit("No input dataset name?") ;
inset = THD_open_dataset(argv[nopt]) ;
CHECK_OPEN_ERROR(inset,argv[nopt]) ;
if( DSET_NVALS(inset) < 9 ) ERROR_exit("input dataset too short") ;
if( maskname != NULL ){
if( strncasecmp(maskname,"AUTO",4) != 0 ){
mset = THD_open_dataset(maskname) ;
CHECK_OPEN_ERROR(mset,maskname) ;
if( !EQUIV_GRIDXYZ(inset,mset) )
ERROR_exit("-mask and input dataset don't match") ;
mask = THD_makemask( mset , 0 , 1.0f,0.0f ) ;
nmask = THD_countmask( DSET_NVOX(mset) , mask ) ;
} else {
mask = THD_automask(inset) ;
if( mask == NULL ) ERROR_exit("Can't make automask :(") ;
nmask = THD_countmask( DSET_NVOX(inset) , mask ) ;
}
INFO_message("mask has %d voxels",nmask) ;
if( nmask < 9 ) ERROR_exit("mask is too small") ;
} else {
nmask = DSET_NVOX(inset) ;
INFO_message("No mask == using all %d voxels",nmask) ;
}
pvim = THD_dataset_to_pvmap( inset , mask ) ;
if( pvim == NULL ) ERROR_exit("Can't compute PVmap :(") ;
DSET_unload(inset) ;
outset = EDIT_empty_copy(inset) ;
EDIT_dset_items( outset ,
ADN_prefix , prefix ,
ADN_datum_all , MRI_float ,
ADN_nvals , 1 ,
ADN_ntt , 0 ,
ADN_type , HEAD_FUNC_TYPE ,
ADN_func_type , FUNC_FIM_TYPE ,
ADN_none ) ;
EDIT_substitute_brick( outset , 0 , MRI_float , MRI_FLOAT_PTR(pvim) ) ;
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dPVmap", argc,argv, outset ) ;
DSET_write(outset) ;
WROTE_DSET(outset) ;
uvim = mri_pvmap_get_vecpair() ;
uvlam = mri_pvmap_get_lampair() ;
uvpref = (char *)malloc(sizeof(char)*(strlen(prefix)+32)) ;
strcpy(uvpref,prefix) ;
cpt = strstr(uvpref,".nii" ) ; if( cpt != NULL ) *cpt = '\0' ;
cpt = strstr(uvpref,".HEAD") ; if( cpt != NULL ) *cpt = '\0' ;
cpt = strrchr(uvpref,'+' ) ; if( cpt != NULL ) *cpt = '\0' ;
strcat(uvpref,".1D") ;
mri_write_1D( uvpref , uvim ) ;
ulam = uvlam.a*uvlam.a / nmask ;
vlam = uvlam.b*uvlam.b / nmask ;
scon = mri_spatial_concentration(pvim) ;
printf("%.6f %.6f %.6f\n",ulam,vlam,scon) ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
int iarg=1 , ii,nvox , nvals ;
THD_3dim_dataset *inset=NULL, *outset=NULL , *mset=NULL ;
char *prefix="./blurinmask" ;
float fwhm_goal=0.0f ; int fwhm_2D=0 ;
byte *mask=NULL ; int mask_nx=0,mask_ny=0,mask_nz=0 , automask=0 , nmask=0 ;
float dx,dy,dz=0.0f , *bar , val ;
int floatize=0 ; /* 18 May 2009 */
MRI_IMAGE *immask=NULL ; /* 07 Oct 2009 */
short *mmask=NULL ;
short *unval_mmask=NULL ; int nuniq_mmask=0 ;
int do_preserve=0 , use_qsar ; /* 19 Oct 2009 */
THD_3dim_dataset *fwhmset=NULL ;
MRI_IMAGE *fxim=NULL, *fyim=NULL, *fzim=NULL ; /* 13 Jun 2016 */
int niter_fxyz=0 ; float dmax=0.0f , dmin=0.0f ;
/*------- help the pitifully ignorant luser? -------*/
AFNI_SETUP_OMP(0) ; /* 24 Jun 2013 */
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf(
"Usage: ~1~\n"
"3dBlurInMask [options]\n"
"Blurs a dataset spatially inside a mask. That's all. Experimental.\n"
"\n"
"OPTIONS ~1~\n"
"-------\n"
" -input ddd = This required 'option' specifies the dataset\n"
" that will be smoothed and output.\n"
" -FWHM f = Add 'f' amount of smoothness to the dataset (in mm).\n"
" **N.B.: This is also a required 'option'.\n"
" -FWHMdset d = Read in dataset 'd' and add the amount of smoothness\n"
" given at each voxel -- spatially variable blurring.\n"
" ** EXPERIMENTAL EXPERIMENTAL EXPERIMENTAL **\n"
" -mask mmm = Mask dataset, if desired. Blurring will\n"
" occur only within the mask. Voxels NOT in\n"
" the mask will be set to zero in the output.\n"
" -Mmask mmm = Multi-mask dataset -- each distinct nonzero\n"
" value in dataset 'mmm' will be treated as\n"
" a separate mask for blurring purposes.\n"
" **N.B.: 'mmm' must be byte- or short-valued!\n"
" -automask = Create an automask from the input dataset.\n"
" **N.B.: only 1 masking option can be used!\n"
" -preserve = Normally, voxels not in the mask will be\n"
" set to zero in the output. If you want the\n"
" original values in the dataset to be preserved\n"
" in the output, use this option.\n"
" -prefix ppp = Prefix for output dataset will be 'ppp'.\n"
" **N.B.: Output dataset is always in float format.\n"
" -quiet = Don't be verbose with the progress reports.\n"
" -float = Save dataset as floats, no matter what the\n"
" input data type is.\n"
" **N.B.: If the input dataset is unscaled shorts, then\n"
" the default is to save the output in short\n"
" format as well. In EVERY other case, the\n"
" program saves the output as floats. Thus,\n"
" the ONLY purpose of the '-float' option is to\n"
" force an all-shorts input dataset to be saved\n"
" as all-floats after blurring.\n"
"\n"
"NOTES ~1~\n"
"-----\n"
" * If you don't provide a mask, then all voxels will be included\n"
" in the blurring. (But then why are you using this program?)\n"
" * Note that voxels inside the mask that are not contiguous with\n"
" any other voxels inside the mask will not be modified at all!\n"
" * Works iteratively, similarly to 3dBlurToFWHM, but without\n"
" the extensive overhead of monitoring the smoothness.\n"
" * But this program will be faster than 3dBlurToFWHM, and probably\n"
" slower than 3dmerge.\n"
" * Since the blurring is done iteratively, rather than all-at-once as\n"
" in 3dmerge, the results will be slightly different than 3dmerge's,\n"
" even if no mask is used here (3dmerge, of course, doesn't take a mask).\n"
" * If the original FWHM of the dataset was 'S' and you input a value\n"
" 'F' with the '-FWHM' option, then the output dataset's smoothness\n"
" will be about sqrt(S*S+F*F). The number of iterations will be\n"
" about (F*F/d*d) where d=grid spacing; this means that a large value\n"
" of F might take a lot of CPU time!\n"
" * The spatial smoothness of a 3D+time dataset can be estimated with a\n"
" command similar to the following:\n"
" 3dFWHMx -detrend -mask mmm+orig -input ddd+orig\n"
) ;
printf(
" * The minimum number of voxels in the mask is %d\n",MASK_MIN) ;
printf(
" * Isolated voxels will be removed from the mask!\n") ;
PRINT_AFNI_OMP_USAGE("3dBlurInMask",NULL) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/*---- official startup ---*/
PRINT_VERSION("3dBlurInMask"); mainENTRY("3dBlurInMask main"); machdep();
AFNI_logger("3dBlurInMask",argc,argv); AUTHOR("RW Cox") ;
/*---- loop over options ----*/
while( iarg < argc && argv[iarg][0] == '-' ){
if( strncmp(argv[iarg],"-preserve",5) == 0 ){ /* 19 Oct 2009 */
do_preserve = 1 ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-qui",4) == 0 ){
verb = 0 ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-ver",4) == 0 ){
verb++ ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-input") == 0 || strcmp(argv[iarg],"-dset") == 0 ){
if( inset != NULL ) ERROR_exit("Can't have two -input options") ;
if( ++iarg >= argc ) ERROR_exit("Need argument after '-input'") ;
inset = THD_open_dataset( argv[iarg] );
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-prefix") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '-prefix'") ;
prefix = strdup(argv[iarg]) ;
if( !THD_filename_ok(prefix) ) ERROR_exit("Bad name after '-prefix'") ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-Mmask") == 0 ){ /* 07 Oct 2009 */
if( ++iarg >= argc ) ERROR_exit("Need argument after '-Mmask'") ;
if( mmask != NULL || mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
#if 0
if( !MRI_IS_INT_TYPE(DSET_BRICK_TYPE(mset,0)) )
ERROR_exit("-Mmask dataset is not integer type!") ;
#endif
immask = mri_to_short( 1.0 , DSET_BRICK(mset,0) ) ;
mmask = MRI_SHORT_PTR(immask) ;
unval_mmask = UniqueShort( mmask, mask_nx*mask_ny*mask_nz, &nuniq_mmask, 0 ) ;
if( unval_mmask == NULL || nuniq_mmask == 0 )
ERROR_exit("-Mmask dataset cannot be processed!?") ;
if( nuniq_mmask == 1 && unval_mmask[0] == 0 )
ERROR_exit("-Mmask dataset is all zeros!?") ;
if( verb ){
int qq , ww ;
for( ii=qq=0 ; ii < nuniq_mmask ; ii++ ) if( unval_mmask[ii] != 0 ) qq++ ;
for( ii=ww=0 ; ii < immask->nvox ; ii++ ) if( mmask[ii] != 0 ) ww++ ;
INFO_message("%d unique nonzero values in -Mmask; %d nonzero voxels",qq,ww) ;
}
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-mask") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mmask != NULL || mask != NULL || automask ) ERROR_exit("Can't have two mask inputs") ;
mset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(mset,argv[iarg]) ;
DSET_load(mset) ; CHECK_LOAD_ERROR(mset) ;
mask_nx = DSET_NX(mset); mask_ny = DSET_NY(mset); mask_nz = DSET_NZ(mset);
mask = THD_makemask( mset , 0 , 0.5f, 0.0f ) ; DSET_unload(mset) ;
if( mask == NULL ) ERROR_exit("Can't make mask from dataset '%s'",argv[iarg]) ;
ii = THD_mask_remove_isolas( mask_nx,mask_ny,mask_nz , mask ) ;
if( verb && ii > 0 ) INFO_message("Removed %d isola%s from mask dataset",ii,(ii==1)?"\0":"s") ;
nmask = THD_countmask( mask_nx*mask_ny*mask_nz , mask ) ;
if( verb ) INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < MASK_MIN ) ERROR_exit("Mask is too small to process") ;
iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-automask") == 0 ){
if( mmask != NULL || mask != NULL ) ERROR_exit("Can't have 2 mask inputs") ;
automask = 1 ; iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-FWHM") == 0 || strcasecmp(argv[iarg],"-FHWM") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]);
val = (float)strtod(argv[iarg],NULL) ;
if( val <= 0.0f ) ERROR_exit("Illegal value after '%s': '%s'",
argv[iarg-1],argv[iarg]) ;
fwhm_goal = val ; fwhm_2D = 0 ;
iarg++ ; continue ;
}
if( strcasecmp(argv[iarg],"-FWHMdset") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]);
if( fwhmset != NULL ) ERROR_exit("You can't use option '-FWHMdset' twice :(") ;
fwhmset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(fwhmset,argv[iarg]) ;
do_preserve = 1 ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-float",6) == 0 ){ /* 18 May 2009 */
floatize = 1 ; iarg++ ; continue ;
}
#if 0
if( strcmp(argv[iarg],"-FWHMxy") == 0 || strcmp(argv[iarg],"-FHWMxy") == 0 ){
if( ++iarg >= argc ) ERROR_exit("Need argument after '%s'",argv[iarg-1]);
val = (float)strtod(argv[iarg],NULL) ;
if( val <= 0.0f ) ERROR_exit("Illegal value after '%s': '%s'",
argv[iarg-1],argv[iarg]) ;
fwhm_goal = val ; fwhm_2D = 1 ;
iarg++ ; continue ;
}
#endif
ERROR_exit("Uknown option '%s'",argv[iarg]) ;
} /*--- end of loop over options ---*/
/*----- check for stupid inputs, load datasets, et cetera -----*/
if( fwhmset == NULL && fwhm_goal == 0.0f )
ERROR_exit("No -FWHM option given! What do you want?") ;
if( fwhmset != NULL && fwhm_goal > 0.0f ){
WARNING_message("-FWHMdset option replaces -FWHM value") ;
fwhm_goal = 0.0f ;
}
if( fwhmset != NULL && mmask != NULL )
ERROR_exit("Sorry: -FWHMdset and -Mmask don't work together (yet)") ;
if( inset == NULL ){
if( iarg >= argc ) ERROR_exit("No input dataset on command line?") ;
inset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(inset,argv[iarg]) ;
}
nvox = DSET_NVOX(inset) ;
dx = fabs(DSET_DX(inset)) ; if( dx == 0.0f ) dx = 1.0f ;
dy = fabs(DSET_DY(inset)) ; if( dy == 0.0f ) dy = 1.0f ;
dz = fabs(DSET_DZ(inset)) ; if( dz == 0.0f ) dz = 1.0f ;
dmax = MAX(dx,dy) ; if( dmax < dz ) dmax = dz ; /* 13 Jun 2016 */
dmin = MIN(dx,dy) ; if( dmin > dz ) dmin = dz ;
if( !floatize ){ /* 18 May 2009 */
if( !THD_datum_constant(inset->dblk) ||
THD_need_brick_factor(inset) ||
DSET_BRICK_TYPE(inset,0) != MRI_short ){
if( verb ) INFO_message("forcing output to be stored in float format") ;
floatize = 1 ;
} else {
if( verb ) INFO_message("output dataset will be stored as shorts") ;
}
} else {
if( verb ) INFO_message("output dataset will be stored as floats") ;
}
#if 0
if( DSET_NZ(inset) == 1 && !fwhm_2D ){
WARNING_message("Dataset is 2D ==> switching from -FWHM to -FWHMxy") ;
fwhm_2D = 1 ;
}
#endif
/*--- deal with mask or automask ---*/
if( mask != NULL ){
if( mask_nx != DSET_NX(inset) ||
mask_ny != DSET_NY(inset) ||
mask_nz != DSET_NZ(inset) )
ERROR_exit("-mask dataset grid doesn't match input dataset") ;
} else if( automask ){
mask = THD_automask( inset ) ;
if( mask == NULL )
ERROR_message("Can't create -automask from input dataset?") ;
nmask = THD_countmask( DSET_NVOX(inset) , mask ) ;
if( verb ) INFO_message("Number of voxels in automask = %d",nmask);
if( nmask < MASK_MIN ) ERROR_exit("Automask is too small to process") ;
} else if( mmask != NULL ){
if( mask_nx != DSET_NX(inset) ||
mask_ny != DSET_NY(inset) ||
mask_nz != DSET_NZ(inset) )
ERROR_exit("-Mmask dataset grid doesn't match input dataset") ;
} else {
mask = (byte *)malloc(sizeof(byte)*nvox) ; nmask = nvox ;
memset(mask,1,sizeof(byte)*nvox) ;
if( verb ) INFO_message("No mask ==> processing all %d voxels",nvox);
}
/*--- process FWHMdset [13 Jun 2016] ---*/
if( fwhmset != NULL ){
float *fxar,*fyar,*fzar , *fwar ; MRI_IMAGE *fwim ;
float fwmax=0.0f , fsx,fsy,fsz ; int ii, nfpos=0 ;
if( DSET_NX(inset) != DSET_NX(fwhmset) ||
DSET_NY(inset) != DSET_NY(fwhmset) ||
DSET_NZ(inset) != DSET_NZ(fwhmset) )
ERROR_exit("grid dimensions for FWHMdset and input dataset do not match :(") ;
STATUS("get fwim") ;
DSET_load(fwhmset) ;
fwim = mri_scale_to_float(DSET_BRICK_FACTOR(fwhmset,0),DSET_BRICK(fwhmset,0));
fwar = MRI_FLOAT_PTR(fwim);
DSET_unload(fwhmset) ;
STATUS("process fwar") ;
for( ii=0 ; ii < nvox ; ii++ ){
if( mask[ii] && fwar[ii] > 0.0f ){
nfpos++ ;
if( fwar[ii] > fwmax ) fwmax = fwar[ii] ;
} else {
fwar[ii] = 0.0f ; mask[ii] = 0 ;
}
}
if( nfpos < 100 )
ERROR_exit("Cannot proceed: too few (%d) voxels are positive in -FWHMdset!",nfpos) ;
niter_fxyz = (int)rintf(2.0f*fwmax*fwmax*FFAC/(0.05f*dmin*dmin)) + 1 ;
if( verb ) INFO_message("-FWHMdset: niter=%d npos=%d",niter_fxyz,nfpos) ;
STATUS("create fxim etc.") ;
fxim = mri_new_conforming(fwim,MRI_float); fxar = MRI_FLOAT_PTR(fxim);
fyim = mri_new_conforming(fwim,MRI_float); fyar = MRI_FLOAT_PTR(fyim);
fzim = mri_new_conforming(fwim,MRI_float); fzar = MRI_FLOAT_PTR(fzim);
fsx = FFAC/(dx*dx*niter_fxyz) ;
fsy = FFAC/(dy*dy*niter_fxyz) ;
fsz = FFAC/(dz*dz*niter_fxyz) ;
/** INFO_message("fsx=%g fsy=%g fsz=%g",fsx,fsy,fsz) ; **/
for( ii=0 ; ii < nvox ; ii++ ){
if( fwar[ii] > 0.0f ){
fxar[ii] = fwar[ii]*fwar[ii] * fsx ;
fyar[ii] = fwar[ii]*fwar[ii] * fsy ;
fzar[ii] = fwar[ii]*fwar[ii] * fsz ;
} else {
fxar[ii] = fyar[ii] = fzar[ii] = 0.0f ;
}
}
STATUS("free(fwim)") ;
mri_free(fwim) ;
}
/*--- process input dataset ---*/
STATUS("load input") ;
DSET_load(inset) ; CHECK_LOAD_ERROR(inset) ;
outset = EDIT_empty_copy( inset ) ; /* moved here 04 Jun 2007 */
EDIT_dset_items( outset , ADN_prefix , prefix , ADN_none ) ;
EDIT_dset_items( outset , ADN_brick_fac , NULL , ADN_none ) ; /* 11 Sep 2007 */
tross_Copy_History( inset , outset ) ;
tross_Make_History( "3dBlurInMask" , argc,argv , outset ) ;
nvals = DSET_NVALS(inset) ;
use_qsar = (do_preserve || mmask != NULL) ; /* 19 Oct 20090 */
AFNI_OMP_START ;
#pragma omp parallel if( nvals > 1 )
{
MRI_IMAGE *dsim ; int ids,qit ; byte *qmask=NULL ; register int vv ;
MRI_IMAGE *qim=NULL, *qsim=NULL; float *qar=NULL, *dsar, *qsar=NULL;
#pragma omp critical (MALLOC)
{ if( use_qsar ){
qsim = mri_new_conforming(DSET_BRICK(inset,0),MRI_float); qsar = MRI_FLOAT_PTR(qsim);
}
if( mmask != NULL ){
qmask = (byte *)malloc(sizeof(byte)*nvox) ;
qim = mri_new_conforming(immask,MRI_float); qar = MRI_FLOAT_PTR(qim);
qim->dx = dx ; qim->dy = dy ; qim->dz = dz ;
}
}
#pragma omp for
for( ids=0 ; ids < nvals ; ids++ ){
#pragma omp critical (MALLOC)
{ dsim = mri_scale_to_float(DSET_BRICK_FACTOR(inset,ids),DSET_BRICK(inset,ids));
DSET_unload_one(inset,ids) ;
}
dsim->dx = dx ; dsim->dy = dy ; dsim->dz = dz ; dsar = MRI_FLOAT_PTR(dsim) ;
/* if needed, initialize qsar with data to be preserved in output */
if( do_preserve ){
for( vv=0 ; vv < nvox ; vv++ ) qsar[vv] = dsar[vv] ;
} else if( mmask != NULL ){
for( vv=0 ; vv < nvox ; vv++ ) qsar[vv] = 0.0f ;
}
if( fwhmset != NULL ){ /* 13 Jun 2016: spatially variable blurring */
for( qit=0 ; qit < niter_fxyz ; qit++ ){
mri_blur3D_variable( dsim , mask , fxim,fyim,fzim ) ;
}
if( do_preserve ){
for( vv=0 ; vv < nvox ; vv++ ) if( mask[vv] ) qsar[vv] = dsar[vv] ;
}
} else if( mmask != NULL ){ /* 07 Oct 2009: multiple masks */
int qq ; register short uval ;
for( qq=0 ; qq < nuniq_mmask ; qq++ ){
uval = unval_mmask[qq] ; if( uval == 0 ) continue ;
for( vv=0 ; vv < nvox ; vv++ ) qmask[vv] = (mmask[vv]==uval) ; /* make mask */
(void)THD_mask_remove_isolas( mask_nx,mask_ny,mask_nz , qmask ) ;
nmask = THD_countmask( nvox , qmask ) ;
if( verb && ids==0 ) ININFO_message("voxels in Mmask[%d] = %d",uval,nmask) ;
if( nmask >= MASK_MIN ){
/* copy data from dataset to qar */
for( vv=0 ; vv < nvox ; vv++ ) if( qmask[vv] ) qar[vv] = dsar[vv] ;
/* blur qar (output will be zero where qmask==0) */
mri_blur3D_addfwhm( qim , qmask , fwhm_goal ) ; /** the real work **/
/* copy results back to qsar */
for( vv=0 ; vv < nvox ; vv++ ) if( qmask[vv] ) qsar[vv] = qar[vv] ;
}
}
} else { /* the olden way: 1 mask */
mri_blur3D_addfwhm( dsim , mask , fwhm_goal ) ; /** all the work **/
/* dsim will be zero where mask==0;
if we want to preserve the input values, copy dsar into qsar now
at all mask!=0 voxels, since qsar contains the original data values */
if( do_preserve ){
for( vv=0 ; vv < nvox ; vv++ ) if( mask[vv] ) qsar[vv] = dsar[vv] ;
}
}
/* if necessary, copy combined results in qsar to dsar for output */
if( use_qsar ){
for( vv=0 ; vv < nvox ; vv++ ) dsar[vv] = qsar[vv] ;
}
if( floatize ){
EDIT_substitute_brick( outset , ids , MRI_float , dsar ) ;
} else {
#pragma omp critical (MALLOC)
{ EDIT_substscale_brick( outset , ids , MRI_float , dsar ,
MRI_short , 1.0f ) ;
mri_free(dsim) ;
}
}
} /* end of loop over sub-bricks */
#pragma omp critical (MALLOC)
{ if( qsim != NULL ) mri_free(qsim);
if( immask != NULL ){ free(qmask); mri_free(qim); }
}
} /* end OpenMP */
AFNI_OMP_END ;
if( mask != NULL ) free( mask) ;
if( immask != NULL ) mri_free(immask) ;
DSET_unload(inset) ;
DSET_write(outset) ;
WROTE_DSET(outset) ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *dset , *oset=NULL , *tset=NULL ;
int nvals , iv , nxyz , ii,jj,kk , iarg , kz,kzold ;
float cut1=2.5,cut2=4.0 , sq2p,sfac , fq ;
MRI_IMAGE *flim ;
char *prefix="despike" , *tprefix=NULL ;
int corder=-1 , nref , ignore=0 , polort=2 , nuse , nomask=0 ;
int nspike, nbig, nproc ;
float **ref ;
float c21,ic21 , pspike,pbig ;
short *sar , *qar ;
byte *tar , *mask=NULL ;
float *zar , *yar ;
int datum ;
int localedit=0 ; /* 04 Apr 2007 */
int verb=1 ;
int do_NEW = 0 ; /* 29 Nov 2013 */
MRI_IMAGE *NEW_psinv=NULL ;
int dilate = 4 ; /* 04 Dec 2013 */
int ctim = 0 ;
/*----- Read command line -----*/
AFNI_SETUP_OMP(0) ; /* 24 Jun 2013 */
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf("Usage: 3dDespike [options] dataset\n"
"Removes 'spikes' from the 3D+time input dataset and writes\n"
"a new dataset with the spike values replaced by something\n"
"more pleasing to the eye.\n"
"\n"
"Method:\n"
" * L1 fit a smooth-ish curve to each voxel time series\n"
" [see -corder option for description of the curve]\n"
" [see -NEW option for a different & faster fitting method]\n"
" * Compute the MAD of the difference between the curve and\n"
" the data time series (the residuals).\n"
" * Estimate the standard deviation 'sigma' of the residuals\n"
" as sqrt(PI/2)*MAD.\n"
" * For each voxel value, define s = (value-curve)/sigma.\n"
" * Values with s > c1 are replaced with a value that yields\n"
" a modified s' = c1+(c2-c1)*tanh((s-c1)/(c2-c1)).\n"
" * c1 is the threshold value of s for a 'spike' [default c1=2.5].\n"
" * c2 is the upper range of the allowed deviation from the curve:\n"
" s=[c1..infinity) is mapped to s'=[c1..c2) [default c2=4].\n"
"\n"
"Options:\n"
" -ignore I = Ignore the first I points in the time series:\n"
" these values will just be copied to the\n"
" output dataset [default I=0].\n"
" -corder L = Set the curve fit order to L:\n"
" the curve that is fit to voxel data v(t) is\n"
"\n"
" k=L [ (2*PI*k*t) (2*PI*k*t) ]\n"
" f(t) = a+b*t+c*t*t + SUM [ d * sin(--------) + e * cos(--------) ]\n"
" k=1 [ k ( T ) k ( T ) ]\n"
"\n"
" where T = duration of time series;\n"
" the a,b,c,d,e parameters are chosen to minimize\n"
" the sum over t of |v(t)-f(t)| (L1 regression);\n"
" this type of fitting is is insensitive to large\n"
" spikes in the data. The default value of L is\n"
" NT/30, where NT = number of time points.\n"
"\n"
" -cut c1 c2 = Alter default values for the spike cut values\n"
" [default c1=2.5, c2=4.0].\n"
" -prefix pp = Save de-spiked dataset with prefix 'pp'\n"
" [default pp='despike']\n"
" -ssave ttt = Save 'spikiness' measure s for each voxel into a\n"
" 3D+time dataset with prefix 'ttt' [default=no save]\n"
" -nomask = Process all voxels\n"
" [default=use a mask of high-intensity voxels, ]\n"
" [as created via '3dAutomask -dilate 4 dataset'].\n"
" -dilate nd = Dilate 'nd' times (as in 3dAutomask). The default\n"
" value of 'nd' is 4.\n"
" -q[uiet] = Don't print '++' informational messages.\n"
"\n"
" -localedit = Change the editing process to the following:\n"
" If a voxel |s| value is >= c2, then replace\n"
" the voxel value with the average of the two\n"
" nearest non-spike (|s| < c2) values; the first\n"
" one previous and the first one after.\n"
" Note that the c1 cut value is not used here.\n"
"\n"
" -NEW = Use the 'new' method for computing the fit, which\n"
" should be faster than the L1 method for long time\n"
" series (200+ time points); however, the results\n"
" are similar but NOT identical. [29 Nov 2013]\n"
" * You can also make the program use the 'new'\n"
" method by setting the environment variable\n"
" AFNI_3dDespike_NEW\n"
" to the value YES; as in\n"
" setenv AFNI_3dDespike_NEW YES (csh)\n"
" export AFNI_3dDespike_NEW=YES (bash)\n"
" * If this variable is set to YES, you can turn off\n"
" the '-NEW' processing by using the '-OLD' option.\n"
" -->>* For time series more than 500 points long, the\n"
" '-OLD' algorithm is tremendously slow. You should\n"
" use the '-NEW' algorith in such cases.\n"
" ** At some indeterminate point in the future, the '-NEW'\n"
" method will become the default!\n"
" -->>* As of 29 Sep 2016, '-NEW' is the default if there\n"
" is more than 500 points in the time series dataset.\n"
"\n"
" -NEW25 = A slightly more aggressive despiking approach than\n"
" the '-NEW' method.\n"
"\n"
"Caveats:\n"
"* Despiking may interfere with image registration, since head\n"
" movement may produce 'spikes' at the edge of the brain, and\n"
" this information would be used in the registration process.\n"
" This possibility has not been explored or calibrated.\n"
"* [LATER] Actually, it seems like the registration problem\n"
" does NOT happen, and in fact, despiking seems to help!\n"
"* Check your data visually before and after despiking and\n"
" registration!\n"
" [Hint: open 2 AFNI controllers, and turn Time Lock on.]\n"
) ;
PRINT_AFNI_OMP_USAGE("3dDespike",NULL) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
/** AFNI package setup and logging **/
mainENTRY("3dDespike main"); machdep(); AFNI_logger("3dDespike",argc,argv);
PRINT_VERSION("3dDespike") ; AUTHOR("RW Cox") ;
/** parse options **/
if( AFNI_yesenv("AFNI_3dDespike_NEW") ) do_NEW = 1 ; /* 29 Nov 2013 */
iarg = 1 ;
while( iarg < argc && argv[iarg][0] == '-' ){
if( strncmp(argv[iarg],"-q",2) == 0 ){ /* 04 Apr 2007 */
verb = 0 ; iarg++ ; continue ;
}
if( strncmp(argv[iarg],"-v",2) == 0 ){
verb++ ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-NEW") == 0 ){ /* 29 Nov 2013 */
do_NEW = 1 ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-NEW25") == 0 ){ /* 29 Sep 2016 */
do_NEW = 1 ; use_des25 = 1 ; cut1 = 2.5f ; cut2 = 3.2f ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-OLD") == 0 ){
do_NEW = 0 ; iarg++ ; continue ;
}
/** -localedit **/
if( strcmp(argv[iarg],"-localedit") == 0 ){ /* 04 Apr 2007 */
localedit = 1 ; iarg++ ; continue ;
}
/** don't use masking **/
if( strcmp(argv[iarg],"-nomask") == 0 ){
nomask = 1 ; iarg++ ; continue ;
}
/** dilation count [04 Dec 2013] **/
if( strcmp(argv[iarg],"-dilate") == 0 ){
dilate = (int)strtod(argv[++iarg],NULL) ;
if( dilate <= 0 ) dilate = 1 ;
else if( dilate > 99 ) dilate = 99 ;
iarg++ ; continue ;
}
/** output dataset prefix **/
if( strcmp(argv[iarg],"-prefix") == 0 ){
prefix = argv[++iarg] ;
if( !THD_filename_ok(prefix) ) ERROR_exit("-prefix is not good");
iarg++ ; continue ;
}
/** ratio dataset prefix **/
if( strcmp(argv[iarg],"-ssave") == 0 ){
tprefix = argv[++iarg] ;
if( !THD_filename_ok(tprefix) ) ERROR_exit("-ssave prefix is not good");
iarg++ ; continue ;
}
/** trigonometric polynomial order **/
if( strcmp(argv[iarg],"-corder") == 0 ){
corder = strtol( argv[++iarg] , NULL , 10 ) ;
if( corder < 0 ) ERROR_exit("Illegal value of -corder");
iarg++ ; continue ;
}
/** how much to ignore at start **/
if( strcmp(argv[iarg],"-ignore") == 0 ){
ignore = strtol( argv[++iarg] , NULL , 10 ) ;
if( ignore < 0 ) ERROR_exit("Illegal value of -ignore");
iarg++ ; continue ;
}
/** thresholds for s ratio **/
if( strcmp(argv[iarg],"-cut") == 0 ){
cut1 = strtod( argv[++iarg] , NULL ) ;
cut2 = strtod( argv[++iarg] , NULL ) ;
if( cut1 < 1.0 || cut2 < cut1+0.5 )
ERROR_exit("Illegal values after -cut");
iarg++ ; continue ;
}
ERROR_exit("Unknown option: %s",argv[iarg]) ;
}
c21 = cut2-cut1 ; ic21 = 1.0/c21 ;
/*----- read input dataset -----*/
if( iarg >= argc ) ERROR_exit("No input dataset!!??");
dset = THD_open_dataset( argv[iarg] ) ;
CHECK_OPEN_ERROR(dset,argv[iarg]) ;
datum = DSET_BRICK_TYPE(dset,0) ;
if( (datum != MRI_short && datum != MRI_float) || !DSET_datum_constant(dset) )
ERROR_exit("Can't process non-short, non-float dataset!") ;
if( verb ) INFO_message("Input data type = %s\n",MRI_TYPE_name[datum]) ;
nvals = DSET_NUM_TIMES(dset) ; nuse = nvals - ignore ;
if( nuse < 15 )
ERROR_exit("Can't use dataset with < 15 time points per voxel!") ;
if( nuse > 500 && !do_NEW ){
INFO_message("Switching to '-NEW' method since number of time points = %d > 500",nuse) ;
do_NEW = 1 ;
}
if( use_des25 && nuse < 99 ) use_des25 = 0 ;
if( verb ) INFO_message("ignoring first %d time points, using last %d",ignore,nuse);
if( corder > 0 && 4*corder+2 > nuse ){
ERROR_exit("-corder %d is too big for NT=%d",corder,nvals) ;
} else if( corder < 0 ){
corder = rint(nuse/30.0) ; if( corder > 50 && !do_NEW ) corder = 50 ;
if( verb ) INFO_message("using %d time points => -corder %d",nuse,corder) ;
} else {
if( verb ) INFO_message("-corder %d set from command line",corder) ;
}
nxyz = DSET_NVOX(dset) ;
if( verb ) INFO_message("Loading dataset %s",argv[iarg]) ;
DSET_load(dset) ; CHECK_LOAD_ERROR(dset) ;
/*-- create automask --*/
if( !nomask ){
mask = THD_automask( dset ) ;
if( verb ){
ii = THD_countmask( DSET_NVOX(dset) , mask ) ;
INFO_message("%d voxels in the automask [out of %d in dataset]",ii,DSET_NVOX(dset)) ;
}
for( ii=0 ; ii < dilate ; ii++ )
THD_mask_dilate( DSET_NX(dset), DSET_NY(dset), DSET_NZ(dset), mask, 3 ) ;
if( verb ){
ii = THD_countmask( DSET_NVOX(dset) , mask ) ;
INFO_message("%d voxels in the dilated automask [out of %d in dataset]",ii,DSET_NVOX(dset)) ;
}
} else {
if( verb ) INFO_message("processing all %d voxels in dataset",DSET_NVOX(dset)) ;
}
/*-- create empty despiked dataset --*/
oset = EDIT_empty_copy( dset ) ;
EDIT_dset_items( oset ,
ADN_prefix , prefix ,
ADN_brick_fac , NULL ,
ADN_datum_all , datum ,
ADN_none ) ;
if( THD_deathcon() && THD_is_file(DSET_HEADNAME(oset)) )
ERROR_exit("output dataset already exists: %s",DSET_HEADNAME(oset));
tross_Copy_History( oset , dset ) ;
tross_Make_History( "3dDespike" , argc , argv , oset ) ;
/* create bricks (will be filled with zeros) */
for( iv=0 ; iv < nvals ; iv++ )
EDIT_substitute_brick( oset , iv , datum , NULL ) ;
/* copy the ignored bricks */
switch( datum ){
case MRI_short:
for( iv=0 ; iv < ignore ; iv++ ){
sar = DSET_ARRAY(oset,iv) ;
qar = DSET_ARRAY(dset,iv) ;
memcpy( sar , qar , DSET_BRICK_BYTES(dset,iv) ) ;
DSET_unload_one(dset,iv) ;
}
break ;
case MRI_float:
for( iv=0 ; iv < ignore ; iv++ ){
zar = DSET_ARRAY(oset,iv) ;
yar = DSET_ARRAY(dset,iv) ;
memcpy( zar , yar , DSET_BRICK_BYTES(dset,iv) ) ;
DSET_unload_one(dset,iv) ;
}
break ;
}
/*-- setup to save a threshold statistic dataset, if desired --*/
if( tprefix != NULL ){
float *fac ;
tset = EDIT_empty_copy( dset ) ;
fac = (float *) malloc( sizeof(float) * nvals ) ;
for( ii=0 ; ii < nvals ; ii++ ) fac[ii] = TFAC ;
EDIT_dset_items( tset ,
ADN_prefix , tprefix ,
ADN_brick_fac , fac ,
ADN_datum_all , MRI_byte ,
ADN_func_type , FUNC_FIM_TYPE ,
ADN_none ) ;
free(fac) ;
tross_Copy_History( tset , dset ) ;
tross_Make_History( "3dDespike" , argc , argv , tset ) ;
#if 0
if( THD_is_file(DSET_HEADNAME(tset)) )
ERROR_exit("-ssave dataset already exists");
#endif
tross_Copy_History( tset , dset ) ;
tross_Make_History( "3dDespike" , argc , argv , tset ) ;
for( iv=0 ; iv < nvals ; iv++ )
EDIT_substitute_brick( tset , iv , MRI_byte , NULL ) ;
}
/*-- setup to find spikes --*/
sq2p = sqrt(0.5*PI) ;
sfac = sq2p / 1.4826f ;
/* make ref functions */
nref = 2*corder+3 ;
ref = (float **) malloc( sizeof(float *) * nref ) ;
for( jj=0 ; jj < nref ; jj++ )
ref[jj] = (float *) malloc( sizeof(float) * nuse ) ;
/* r(t) = 1 */
for( iv=0 ; iv < nuse ; iv++ ) ref[0][iv] = 1.0 ;
jj = 1 ;
/* r(t) = t - tmid */
{ float tm = 0.5 * (nuse-1.0) ; float fac = 2.0 / nuse ;
for( iv=0 ; iv < nuse ; iv++ ) ref[1][iv] = (iv-tm)*fac ;
jj = 2 ;
/* r(t) = (t-tmid)**jj */
for( ; jj <= polort ; jj++ )
for( iv=0 ; iv < nuse ; iv++ )
ref[jj][iv] = pow( (iv-tm)*fac , (double)jj ) ;
}
for( kk=1 ; kk <= corder ; kk++ ){
fq = (2.0*PI*kk)/nuse ;
/* r(t) = sin(2*PI*k*t/N) */
for( iv=0 ; iv < nuse ; iv++ )
ref[jj][iv] = sin(fq*iv) ;
jj++ ;
/* r(t) = cos(2*PI*k*t/N) */
for( iv=0 ; iv < nuse ; iv++ )
ref[jj][iv] = cos(fq*iv) ;
jj++ ;
}
/****** setup for the NEW solution method [29 Nov 2013] ******/
if( do_NEW ){
NEW_psinv = DES_get_psinv(nuse,nref,ref) ;
INFO_message("Procesing time series with NEW model fit algorithm") ;
} else {
INFO_message("Procesing time series with OLD model fit algorithm") ;
}
/*--- loop over voxels and do work ---*/
#define Laplace_t2p(val) ( 1.0 - nifti_stat2cdf( (val), 15, 0.0, 1.4427 , 0.0 ) )
if( verb ){
if( !localedit ){
INFO_message("smash edit thresholds: %.1f .. %.1f MADs",cut1*sq2p,cut2*sq2p) ;
ININFO_message(" [ %.3f%% .. %.3f%% of normal distribution]",
200.0*qg(cut1*sfac) , 200.0*qg(cut2*sfac) ) ;
ININFO_message(" [ %.3f%% .. %.3f%% of Laplace distribution]" ,
100.0*Laplace_t2p(cut1) , 100.0*Laplace_t2p(cut2) ) ;
} else {
INFO_message("local edit threshold: %.1f MADS",cut2*sq2p) ;
ININFO_message(" [ %.3f%% of normal distribution]",
200.0*qg(cut2*sfac) ) ;
ININFO_message(" [ %.3f%% of Laplace distribution]",
100.0*Laplace_t2p(cut1) ) ;
}
INFO_message("%d slices to process",DSET_NZ(dset)) ;
}
kzold = -1 ;
nspike = 0 ; nbig = 0 ; nproc = 0 ; ctim = NI_clock_time() ;
AFNI_OMP_START ;
#pragma omp parallel if( nxyz > 6666 )
{ int ii , iv , iu , id , jj ;
float *far , *dar , *var , *fitar , *ssp , *fit , *zar ;
short *sar , *qar ; byte *tar ;
float fsig , fq , cls , snew , val ;
float *NEW_wks=NULL ;
#pragma omp critical (DESPIKE_malloc)
{ far = (float *) malloc( sizeof(float) * nvals ) ;
dar = (float *) malloc( sizeof(float) * nvals ) ;
var = (float *) malloc( sizeof(float) * nvals ) ;
fitar = (float *) malloc( sizeof(float) * nvals ) ;
ssp = (float *) malloc( sizeof(float) * nvals ) ;
fit = (float *) malloc( sizeof(float) * nref ) ;
if( do_NEW ) NEW_wks = (float *)malloc(sizeof(float)*DES_workspace_size(nuse,nref)) ;
}
#ifdef USE_OMP
INFO_message("start OpenMP thread #%d",omp_get_thread_num()) ;
#endif
#pragma omp for
for( ii=0 ; ii < nxyz ; ii++ ){ /* ii = voxel index */
if( mask != NULL && mask[ii] == 0 ) continue ; /* skip this voxel */
#ifndef USE_OMP
kz = DSET_index_to_kz(dset,ii) ; /* starting a new slice */
if( kz != kzold ){
if( verb ){
fprintf(stderr, "++ start slice %2d",kz ) ;
if( nproc > 0 ){
pspike = (100.0*nspike)/nproc ;
pbig = (100.0*nbig )/nproc ;
fprintf(stderr,
"; so far %d data points, %d edits [%.3f%%], %d big edits [%.3f%%]",
nproc,nspike,pspike,nbig,pbig ) ;
}
fprintf(stderr,"\n") ;
}
kzold = kz ;
}
#else
if( verb && ii % 2345 == 1234 ) fprintf(stderr,".") ;
#endif
/*** extract ii-th time series into far[] ***/
switch( datum ){
case MRI_short:
for( iv=0 ; iv < nuse ; iv++ ){
qar = DSET_ARRAY(dset,iv+ignore) ; /* skip ignored data */
far[iv] = (float)qar[ii] ;
}
break ;
case MRI_float:
for( iv=0 ; iv < nuse ; iv++ ){
zar = DSET_ARRAY(dset,iv+ignore) ;
far[iv] = zar[ii] ;
}
break ;
}
AAmemcpy(dar,far,sizeof(float)*nuse) ; /* copy time series into dar[] */
/*** solve for L1 fit ***/
if( do_NEW )
cls = DES_solve( NEW_psinv , far , fit , NEW_wks ) ; /* 29 Nov 2013 */
else
cls = cl1_solve( nuse , nref , far , ref , fit,0 ) ; /* the slow part */
if( cls < 0.0f ){ /* fit failed! */
#if 0
fprintf(stderr,"curve fit fails at voxel %d %d %d\n",
DSET_index_to_ix(dset,ii) ,
DSET_index_to_jy(dset,ii) ,
DSET_index_to_kz(dset,ii) ) ;
#endif
continue ; /* skip this voxel */
}
for( iv=0 ; iv < nuse ; iv++ ){ /* detrend */
val = fit[0]
+ fit[1]*ref[1][iv] /* quadratic part of curve fit */
+ fit[2]*ref[2][iv] ;
for( jj=3 ; jj < nref ; jj++ ) /* rest of curve fit */
val += fit[jj] * ref[jj][iv] ;
fitar[iv] = val ; /* save curve fit value */
var[iv] = dar[iv]-val ; /* remove fitted value = resid */
far[iv] = fabsf(var[iv]) ; /* abs value of resid */
}
/*** compute estimate standard deviation of detrended data ***/
fsig = sq2p * qmed_float(nuse,far) ; /* also mangles far array */
/*** process time series for spikes, editing data in dar[] ***/
if( fsig > 0.0f ){ /* data wasn't fit perfectly */
/* find spikiness for each point in time */
fq = 1.0f / fsig ;
for( iv=0 ; iv < nuse ; iv++ ){
ssp[iv] = fq * var[iv] ; /* spikiness s = how many sigma out */
}
/* save spikiness in -ssave datset */
if( tset != NULL ){
for( iv=0 ; iv < nuse ; iv++ ){
tar = DSET_ARRAY(tset,iv+ignore) ;
snew = ITFAC*fabsf(ssp[iv]) ; /* scale for byte storage */
tar[ii] = BYTEIZE(snew) ; /* cf. mrilib.h */
}
}
/* process values of |s| > cut1, editing dar[] */
for( iv=0 ; iv < nuse ; iv++ ){ /* loop over time points */
if( !localedit ){ /** classic 'smash' edit **/
if( ssp[iv] > cut1 ){
snew = cut1 + c21*mytanh((ssp[iv]-cut1)*ic21) ; /* edit s down */
dar[iv] = fitar[iv] + snew*fsig ;
#pragma omp critical (DESPIKE_counter)
{ nspike++ ; if( ssp[iv] > cut2 ) nbig++ ; }
} else if( ssp[iv] < -cut1 ){
snew = -cut1 + c21*mytanh((ssp[iv]+cut1)*ic21) ; /* edit s up */
dar[iv] = fitar[iv] + snew*fsig ;
#pragma omp critical (DESPIKE_counter)
{ nspike++ ; if( ssp[iv] < -cut2 ) nbig++ ; }
}
} else { /** local edit: 04 Apr 2007 **/
if( ssp[iv] >= cut2 || ssp[iv] <= -cut2 ){
for( iu=iv+1 ; iu < nuse ; iu++ ) /* find non-spike above */
if( ssp[iu] < cut2 && ssp[iu] > -cut2 ) break ;
for( id=iv-1 ; id >= 0 ; id-- ) /* find non-spike below */
if( ssp[id] < cut2 && ssp[id] > -cut2 ) break ;
switch( (id>=0) + 2*(iu<nuse) ){ /* compute replacement val */
case 3: val = 0.5*(dar[iu]+dar[id]); break; /* iu and id OK */
case 2: val = dar[iu] ; break; /* only iu OK */
case 1: val = dar[id] ; break; /* only id OK */
default: val = fitar[iv] ; break; /* shouldn't be */
}
dar[iv] = val ;
#pragma omp critical (DESPIKE_counter)
{ nspike++ ; nbig++ ; }
}
}
} /* end of loop over time points */
#pragma omp atomic
nproc += nuse ; /* number data points processed */
} /* end of processing time series when fsig is positive */
/* put dar[] time series (possibly edited above) into output bricks */
switch( datum ){
case MRI_short:
for( iv=0 ; iv < nuse ; iv++ ){
sar = DSET_ARRAY(oset,iv+ignore) ; /* output brick */
sar[ii] = (short)dar[iv] ; /* original or mutated data */
}
break ;
case MRI_float:
for( iv=0 ; iv < nuse ; iv++ ){
zar = DSET_ARRAY(oset,iv+ignore) ; /* output brick */
zar[ii] = dar[iv] ; /* original or mutated data */
}
break ;
}
} /* end of loop over voxels #ii */
#pragma omp critical (DESPIKE_malloc)
{ free(fit); free(ssp); free(fitar); free(var); free(dar); free(far);
if( do_NEW ) free(NEW_wks) ; }
} /* end OpenMP */
AFNI_OMP_END ;
#ifdef USE_OMP
if( verb ) fprintf(stderr,"\n") ;
#endif
ctim = NI_clock_time() - ctim ;
INFO_message( "Elapsed despike time = %s" , nice_time_string(ctim) ) ;
if( ctim > 345678 && !do_NEW )
ININFO_message("That was SLOW -- try the '-NEW' option for a speedup") ;
#ifdef USE_OMP
if( verb ) fprintf(stderr,"\n") ;
#endif
/*--- finish up ---*/
if( do_NEW ) mri_free(NEW_psinv) ;
DSET_delete(dset) ; /* delete input dataset */
if( verb ){
if( nproc > 0 ){
pspike = (100.0*nspike)/nproc ;
pbig = (100.0*nbig )/nproc ;
INFO_message("FINAL: %d data points, %d edits [%.3f%%], %d big edits [%.3f%%]",
nproc,nspike,pspike,nbig,pbig ) ;
} else {
INFO_message("FINAL: no good voxels found to process!!??") ;
}
}
/* write results */
DSET_write(oset) ;
if( verb ) WROTE_DSET(oset) ;
DSET_delete(oset) ;
if( tset != NULL ){
DSET_write(tset) ;
if( verb ) WROTE_DSET(tset) ;
DSET_delete(tset) ;
}
exit( THD_get_write_error_count() ) ;
}
int main( int argc , char * argv[] )
{
int iarg , nvox=0 , iv,ii,cnum , verb=1 ;
int automask=1 ; /* allow masks as input 14 Jul 2010 [rickr] */
THD_3dim_dataset *aset , *bset ;
byte *amm , *bmm ; int naa , nbb , nabu,nabi , naout , nbout ;
float paout , pbout , xrat,yrat,zrat ;
float_triple axyz , bxyz ;
/*-- read command line arguments --*/
if( argc < 2 || strncmp(argv[1],"-help",5) == 0 ){
printf(
"Usage: 3dABoverlap [options] A B\n"
"Output (to screen) is a count of various things about how\n"
"the automasks of datasets A and B overlap or don't overlap.\n"
"\n"
"* Dataset B will be resampled to match dataset A, if necessary,\n"
" which will be slow if A is high resolution. In such a case,\n"
" you should only use one sub-brick from dataset B.\n"
" ++ The resampling of B is done before the automask is generated.\n"
"* The values output are labeled thusly:\n"
" #A = number of voxels in the A mask\n"
" #B = number of voxels in the B mask\n"
" #(A uni B) = number of voxels in the either or both masks (set union)\n"
" #(A int B) = number of voxels present in BOTH masks (set intesection)\n"
" #(A \\ B) = number of voxels in A mask that aren't in B mask\n"
" #(B \\ A) = number of voxels in B mask that arent' in A mask\n"
" %%(A \\ B) = percentage of voxels from A mask that aren't in B mask\n"
" %%(B \\ A) = percentage of voxels from B mask that aren't in A mask\n"
" Rx(B/A) = radius of gyration of B mask / A mask, in x direction\n"
" Ry(B/A) = radius of gyration of B mask / A mask, in y direction\n"
" Rz(B/A) = radius of gyration of B mask / A mask, in z direction\n"
"* If B is an EPI dataset sub-brick, and A is a skull stripped anatomical\n"
" dataset, then %%(B \\ A) might be useful for assessing if the EPI\n"
" brick B is grossly misaligned with respect to the anatomical brick A.\n"
"* The radius of gyration ratios might be useful for determining if one\n"
" dataset is grossly larger or smaller than the other.\n"
"\n"
"OPTIONS\n"
"-------\n"
" -no_automask = consider input datasets as masks\n"
" (automask does not work on mask datasets)\n"
" -quiet = be as quiet as possible (without being entirely mute)\n"
" -verb = print out some progress reports (to stderr)\n"
"\n"
"NOTES\n"
"-----\n"
" * If an input dataset is comprised of bytes and contains only one\n"
" sub-brick, then this program assumes it is already an automask-\n"
" generated dataset and the automask operation will be skipped.\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
iarg = 1 ;
/*-- 20 Apr 2001: addto the arglist, if user wants to [RWCox] --*/
/* check options */
while( iarg < argc && argv[iarg][0] == '-' ){
if( strcmp(argv[iarg],"-verb") == 0 ){
verb++ ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-quiet") == 0 ){
verb = 0 ; iarg++ ; continue ;
}
if( strcmp(argv[iarg],"-no_automask") == 0 ){
automask = 0 ; iarg++ ; continue ;
}
ERROR_exit("Illegal option: %s",argv[iarg]) ;
}
mainENTRY("3dOverlap main") ; machdep() ;
if( verb ) PRINT_VERSION("3dOverlap") ;
AFNI_logger("3dOverlap",argc,argv) ;
/* input datasets */
if( iarg+1 >= argc ) ERROR_exit("Need 2 input datasets on command line") ;
aset = THD_open_dataset(argv[iarg]) ; CHECK_OPEN_ERROR(aset,argv[iarg]) ; iarg++ ;
bset = THD_open_dataset(argv[iarg]) ; CHECK_OPEN_ERROR(bset,argv[iarg]) ; iarg++ ;
nvox = DSET_NVOX(aset) ;
if( !EQUIV_GRIDS(aset,bset) ){ /** must resample **/
THD_3dim_dataset *cset ;
if( verb ) INFO_message("resampling dataset B to match dataset A") ;
cset = r_new_resam_dset( bset, aset, 0.0,0.0,0.0,NULL,
MRI_BILINEAR, NULL, 1, 0 ) ;
DSET_delete(bset) ; bset = cset ;
}
if( iarg < argc ) WARNING_message("Extra arguments?") ;
DSET_load(aset); CHECK_LOAD_ERROR(aset);
DSET_load(bset); CHECK_LOAD_ERROR(bset);
/* 10 Aug 2009: keep input datasets without automask, if appropriate */
if( DSET_NVALS(aset) > 1 || DSET_BRICK_TYPE(aset,0) != MRI_byte ){
/* allow masks as input (via -no_automask) 14 Jul 2010 [rickr] */
if( automask ) amm = THD_automask(aset);
else amm = THD_makemask(aset, 0, 1, 0); /* use any non-zero */
DSET_unload(aset);
} else {
amm = DSET_BRICK_ARRAY(aset,0) ;
}
if( DSET_NVALS(bset) > 1 || DSET_BRICK_TYPE(bset,0) != MRI_byte ){
/* allow masks as input (via -no_automask) 14 Jul 2010 [rickr] */
if( automask ) bmm = THD_automask(bset);
else bmm = THD_makemask(bset, 0, 1, 0); /* use any non-zero */
DSET_unload(bset);
} else {
bmm = DSET_BRICK_ARRAY(bset,0) ;
}
naa = mask_count ( nvox , amm ) ;
nbb = mask_count ( nvox , bmm ) ;
nabi = mask_intersect_count( nvox , amm , bmm ) ;
nabu = mask_union_count ( nvox , amm , bmm ) ;
naout = naa - nabi ;
nbout = nbb - nabi ;
paout = (naa > 0) ? naout/(float)naa : 0.0f ;
pbout = (nbb > 0) ? nbout/(float)nbb : 0.0f ;
axyz = mask_rgyrate( DSET_NX(aset),DSET_NY(aset),DSET_NZ(aset) , amm ) ;
bxyz = mask_rgyrate( DSET_NX(bset),DSET_NY(bset),DSET_NZ(bset) , bmm ) ;
xrat = (axyz.a > 0.0f && bxyz.a > 0.0f) ? bxyz.a / axyz.a : 0.0f ;
yrat = (axyz.b > 0.0f && bxyz.b > 0.0f) ? bxyz.b / axyz.b : 0.0f ;
zrat = (axyz.c > 0.0f && bxyz.c > 0.0f) ? bxyz.c / axyz.c : 0.0f ;
if( verb )
printf("#A=%s B=%s\n",DSET_BRIKNAME(aset),DSET_BRIKNAME(bset)) ;
if( verb )
printf("#A #B #(A uni B) #(A int B) "
"#(A \\ B) #(B \\ A) %%(A \\ B) %%(B \\ A) "
"Rx(B/A) Ry(B/A) Rz(B/A)\n") ;
printf("%-12d %-12d %-12d %-12d %-12d %-12d %7.4f %7.4f %7.4f %7.4f %7.4f\n",
naa , nbb , nabu, nabi, naout,nbout,100.0f*paout,100.0f*pbout,
xrat,yrat,zrat ) ;
exit(0) ;
}
int main( int argc , char * argv[] )
{
int narg=1, do_automask=0 , iv , nxyz , do_set=0 ,
*rois=NULL, N_rois=0, all_rois = 0;
THD_3dim_dataset *xset ;
byte *mmm=NULL ; int nmask=0 , nvox_mask=0 ;
THD_fvec3 cmv , setv ;
/*-- read command line arguments --*/
if( argc < 2 || strncmp(argv[1],"-help",5) == 0 ){
printf("Usage: 3dCM [options] dset\n"
"Output = center of mass of dataset, to stdout.\n"
" -mask mset Means to use the dataset 'mset' as a mask:\n"
" Only voxels with nonzero values in 'mset'\n"
" will be averaged from 'dataset'. Note\n"
" that the mask dataset and the input dataset\n"
" must have the same number of voxels.\n"
" -automask Generate the mask automatically.\n"
" -set x y z After computing the CM of the dataset, set the\n"
" origin fields in the header so that the CM\n"
" will be at (x,y,z) in DICOM coords.\n"
" -roi_vals v0 v1 v2 ... : Compute center of mass for each blob\n"
" with voxel value of v0, v1, v2, etc.\n"
" This option is handy for getting ROI \n"
" centers of mass.\n"
" -all_rois Don't bother listing the values of ROIs you want\n"
" the program will find all of them and produce a \n"
" full list.\n"
" NOTE: Masking options are ignored with -roi_vals and -all_rois\n"
) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
LOAD_FVEC3(setv,0,0,0) ; /* ZSS: To quiet init. warnings */
narg = 1 ;
while( narg < argc && argv[narg][0] == '-' ){
if( strcmp(argv[narg],"-set") == 0 ){
float xset,yset,zset ;
if( narg+3 >= argc ){
fprintf(stderr,"*** -set need 3 args following!\n") ; exit(1) ;
}
xset = strtod( argv[++narg] , NULL ) ;
yset = strtod( argv[++narg] , NULL ) ;
zset = strtod( argv[++narg] , NULL ) ;
LOAD_FVEC3(setv,xset,yset,zset) ; do_set = 1 ;
THD_set_write_compression(COMPRESS_NONE); /* do not alter compression*/
narg++ ; continue ;
}
if( strncmp(argv[narg],"-mask",5) == 0 ){
THD_3dim_dataset *mask_dset ;
if( mmm != NULL ){
fprintf(stderr,"*** Cannot have two -mask options!\n") ; exit(1) ;
}
if( do_automask ){
fprintf(stderr,"*** Can't have -mask and -automask!\n") ; exit(1) ;
}
if( narg+1 >= argc ){
fprintf(stderr,"*** -mask option requires a following argument!\n");
exit(1) ;
}
mask_dset = THD_open_dataset( argv[++narg] ) ;
CHECK_OPEN_ERROR(mask_dset,argv[narg]) ;
if( DSET_BRICK_TYPE(mask_dset,0) == MRI_complex ){
fprintf(stderr,"*** Cannot deal with complex-valued mask dataset!\n");
exit(1) ;
}
mmm = THD_makemask( mask_dset , 0 , 1.0,0.0 ) ;
nvox_mask = DSET_NVOX(mask_dset) ;
nmask = THD_countmask( nvox_mask , mmm ) ;
if( mmm == NULL || nmask <= 0 ){
fprintf(stderr,"*** Can't make mask from dataset %s\n",argv[narg-1]);
exit(1) ;
}
DSET_delete( mask_dset ) ;
narg++ ; continue ;
}
if( strncmp(argv[narg],"-roi_vals",5) == 0 ){
if( narg+1 >= argc ){
fprintf(stderr,"*** -mask option requires a following argument(s)!\n");
exit(1) ;
}
rois = (int *)calloc(argc, sizeof(int));
N_rois = 0;
++narg;
while (narg < argc-1 && argv[narg][0] != '-') {
rois[N_rois++] = atoi(argv[narg]);
++narg;
}
continue ;
}
if( strncmp(argv[narg],"-all_rois",5) == 0 ){
all_rois = 1;
narg++ ; continue ;
}
if( strcmp(argv[narg],"-automask") == 0 ){
if( mmm != NULL ){
fprintf(stderr,"*** Can't have -mask and -automask!\n") ; exit(1) ;
}
do_automask = 1 ; narg++ ; continue ;
}
fprintf(stderr,"*** Unknown option: %s\n",argv[narg]) ; exit(1) ;
}
/* should have at least 1 more argument */
if( argc <= narg ){
fprintf(stderr,"*** No input dataset!?\n") ; exit(1) ;
}
for( ; narg < argc ; narg++ ){
xset = THD_open_dataset( argv[narg] ) ;
if( xset == NULL ){
fprintf(stderr,"+++ Can't open dataset %s -- skipping\n",argv[narg]);
continue ;
}
DSET_load(xset) ;
if( !DSET_LOADED(xset) ){
fprintf(stderr,"+++ Can't load dataset %s -- skipping\n",argv[narg]);
DSET_delete(xset) ; continue ;
}
if( do_automask ){
if( mmm != NULL ){ free(mmm); mmm = NULL; }
mmm = THD_automask(xset) ;
nvox_mask = DSET_NVOX(xset) ;
nmask = THD_countmask( nvox_mask , mmm ) ;
if( mmm == NULL || nmask <= 0 ){
fprintf( stderr,
"+++ Can't make automask from dataset %s "
"-- skipping\n",argv[narg]) ;
DSET_delete(xset) ; continue ;
}
}
nxyz = DSET_NVOX(xset) ;
if( mmm != NULL && nxyz != nvox_mask ){
fprintf(stderr,"+++ Mask/Dataset grid size mismatch at %s\n -- skipping\n",argv[narg]) ;
DSET_delete(xset) ; continue ;
}
if (all_rois) {
if (!(rois = THD_unique_vals(xset, 0, &N_rois, NULL)) || N_rois == 0) {
ERROR_message("No rois or error in THD_unique_vals"); continue;
}
fprintf(stderr,"#%d distinct ROIs", N_rois);
}
if (!N_rois) {
cmv = THD_cmass( xset , 0 , mmm ) ;
printf("%g %g %g\n",cmv.xyz[0],cmv.xyz[1],cmv.xyz[2]) ;
DSET_unload(xset) ;
if( do_set ){
THD_fvec3 dv , ov ;
if( DSET_IS_MASTERED(xset) ){
fprintf(stderr,"+++ Can't modify CM of dataset %s\n",argv[narg]) ;
} else {
/* lose obliquity */
/* recompute Tc(Cardinal transformation matrix for new grid output */
THD_make_cardinal(xset);
LOAD_FVEC3(ov,DSET_XORG(xset),DSET_YORG(xset),DSET_ZORG(xset)) ;
ov = THD_3dmm_to_dicomm( xset , ov ) ;
dv = SUB_FVEC3(setv,cmv) ;
ov = ADD_FVEC3(dv,ov) ;
ov = THD_dicomm_to_3dmm( xset , ov ) ;
xset->daxes->xxorg = ov.xyz[0] ;
xset->daxes->yyorg = ov.xyz[1] ;
xset->daxes->zzorg = ov.xyz[2] ;
/* allow overwriting header for all types of output data */
putenv("AFNI_DECONFLICT=OVERWRITE") ;
tross_Make_History( "3dCM" , argc,argv , xset );/* ZSS Dec. 09 08 */
if(DSET_IS_BRIK(xset)) {
INFO_message("Rewriting header %s",DSET_HEADNAME(xset)) ;
DSET_overwrite_header( xset ) ;
}
else { /* for other dataset types like NIFTI, rewrite whole dset */
DSET_load( xset ) ;
DSET_overwrite(xset) ;
INFO_message("Wrote new dataset: %s",DSET_BRIKNAME(xset)) ;
}
}
}
} else {
float *xyz;
if ((xyz = THD_roi_cmass(xset , 0 , rois, N_rois))) {
printf("#Dset %s\n",DSET_BRIKNAME(xset));
for (iv=0; iv<N_rois; ++iv) {
printf("#ROI %d\n", rois[iv]);
printf("%g %g %g\n",xyz[3*iv],xyz[3*iv+1],xyz[3*iv+2]) ;
}
free(xyz); free(rois);
} else {
ERROR_message("Failed in THD_roi_cmass"); continue;
}
}
DSET_delete(xset) ;
}
exit(0);
}
