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) ;
}
void NWC_help(void)
{
printf(
"Usage: 3dNwarpCat [options] warp1 warp2 ...\n"
"------\n"
" * This program catenates (composes) 3D warps defined on a grid,\n"
" OR via a matrix.\n"
" ++ All transformations are from DICOM xyz (in mm) to DICOM xyz.\n"
"\n"
" * Matrix warps are in files that end in '.1D' or in '.txt'. A matrix\n"
" warp file should have 12 numbers in it, as output (for example), by\n"
" '3dAllineate -1Dmatrix_save'.\n"
" ++ The matrix (affine) warp can have either 12 numbers on one row,\n"
" or be in the 3x4 format.\n"
" ++ The 12-numbers-on-one-row format is preferred, and is the format\n"
" output by the '-1Dmatrix_save' option in 3dvolreg and 3dAllineate.\n"
" ++ The matrix warp is a transformation of coordinates, not voxels,\n"
" and its use presumes the correctness of the voxel-to-coordinate\n"
" transformation stored in the header of the datasets involved.\n"
"\n"
" * Nonlinear warps are in dataset files (AFNI .HEAD/.BRIK or NIfTI .nii)\n"
" with 3 sub-bricks giving the DICOM order xyz grid displacements in mm.\n"
" ++ Note that it is not required that the xyz order of voxel storage be in\n"
" DICOM order, just that the displacements be in DICOM order (and sign).\n"
" ++ However, it is important that the warp dataset coordinate order be\n"
" properly specified in the dataset header, since warps are applied\n"
" based on coordinates, not on voxels.\n"
" ++ Also note again that displacements are in mm, NOT in voxel.\n"
" ++ You can 'edit' the warp on the command line by using the 'FAC:'\n"
" scaling prefix, described later. This input editing could be used\n"
" to change the sign of the xyz displacments, if needed.\n"
"\n"
" * If all the input warps are matrices, then the output is a matrix\n"
" and will be written to the file 'prefix.aff12.1D'.\n"
" ++ Unless the prefix already contains the string '.1D', in which case\n"
" the filename is just the prefix.\n"
" ++ If 'prefix' is just 'stdout', then the output matrix is written\n"
" to standard output.\n"
" ++ In any of these cases, the output format is 12 numbers in one row.\n"
"\n"
" * If any of the input warps are datasets, they must all be defined on\n"
" the same 3D grid!\n"
" ++ And of course, then the output will be a dataset on the same grid.\n"
" ++ However, you can expand the grid using the '-expad' option.\n"
"\n"
" * The order of operations in the final (output) warp is, for the\n"
" case of 3 input warps:\n"
"\n"
" OUTPUT(x) = warp3( warp2( warp1(x) ) )\n"
"\n"
" That is, warp1 is applied first, then warp2, et cetera.\n"
" The 3D x coordinates are taken from each grid location in the\n"
" first dataset defined on a grid.\n"
"\n"
" * For example, if you aligned a dataset to a template with @auto_tlrc,\n"
" then further refined the alignment with 3dQwarp, you would do something\n"
" like this:\n"
" warp1 is the output of 3dQwarp\n"
" warp2 is the matrix from @auto_tlrc\n"
" This is the proper order, since the desired warp takes template xyz\n"
" to original dataset xyz, and we have\n"
" 3dQwarp warp: takes template xyz to affinely aligned xyz, and\n"
" @auto_tlrc matrix: takes affinely aligned xyz to original xyz\n"
"\n"
" 3dNwarpCat -prefix Fred_total_WARP -warp1 Fred_WARP+tlrc.HEAD -warp2 Fred.Xat.1D \n"
"\n"
" The dataset Fred_total_WARP+tlrc.HEAD could then be used to transform original\n"
" datasets directly to the final template space, as in\n"
"\n"
" 3dNwarpApply -prefix Wilma_warped \\\n"
" -nwarp Fred_total_WARP+tlrc \\\n"
" -source Wilma+orig \\\n"
" -master Fred_total_WARP+tlrc\n"
"\n"
" * If you wish to invert a warp before it is used here, supply its\n"
" input name in the form of\n"
" INV(warpfilename)\n"
" To produce the inverse of the warp in the example above:\n"
"\n"
" 3dNwarpCat -prefix Fred_total_WARPINV \\\n"
" -warp2 'INV(Fred_WARP+tlrc.HEAD)' \\\n"
" -warp1 'INV(Fred.Xat.1D)' \n"
"\n"
" Note the order of the warps is reversed, in addition to the use of 'INV()'.\n"
"\n"
" * The final warp may also be inverted simply by adding the '-iwarp' option, as in\n"
"\n"
" 3dNwarpCat -prefix Fred_total_WARPINV -iwarp -warp1 Fred_WARP+tlrc.HEAD -warp2 Fred.Xat.1D \n"
"\n"
" * Other functions you can apply to modify a 3D dataset warp are:\n"
" SQRT(datasetname) to get the square root of a warp\n"
" SQRTINV(datasetname) to get the inverse square root of a warp\n"
" However, you can't do more complex expressions, such as 'SQRT(SQRT(warp))'.\n"
" If you think you need something so rococo, use 3dNwarpCalc. Or think again.\n"
"\n"
" * You can also manufacture a 3D warp from a 1-brick dataset with displacments\n"
" in a single direction. For example:\n"
" AP:0.44:disp+tlrc.HEAD (note there are no blanks here!)\n"
" means to take the 1-brick dataset disp+tlrc.HEAD, scale the values inside\n"
" by 0.44, then load them into the y-direction displacements of a 3-brick 3D\n"
" warp, and fill the other 2 directions with zeros. The prefixes you can use\n"
" here for the 1-brick to 3-brick displacment trick are\n"
" RL: for x-displacements (Right-to-Left)\n"
" AP: for y-displacements (Anterior-to-Posterior)\n"
" IS: for z-displacements (Inferior-to-Superior)\n"
" VEC:a,b,c: for displacements in the vector direction (a,b,c),\n"
" which vector will be scaled to be unit length.\n"
" Following the prefix's colon, you can put in a scale factor followed\n"
" by another colon (as in '0.44:' in the example above). Then the name\n"
" of the dataset with the 1D displacments follows.\n"
" * You might reasonably ask of what possible value is this peculiar format?\n"
" This was implemented to use Bz fieldmaps for correction of EPI datasets,\n"
" which are distorted only along the phase-encoding direction. This format\n"
" for specifying the input dataset (the fieldmap) is built to make the\n"
" scripting a little easier. Its principal use is in the program 3dNwarpApply.\n"
"\n"
" * You can scale the displacements in a 3D warp file via the 'FAC:' prefix, as in\n"
" FAC:0.6,0.4,-0.2:fred_WARP.nii\n"
" which will scale the x-displacements by 0.6, the y-displacements by 0.4, and\n"
" the z-displacments by -0.2.\n"
"\n"
" * Finally, you can input a warp catenation string directly as in the '-nwarp'\n"
" option of 3dNwarpApply, as in\n"
"\n"
" 3dNwarpCat -prefix Fred_total_WARP 'Fred_WARP+tlrc.HEAD Fred.Xat.1D' \n"
"\n"
"\n"
"OPTIONS\n"
"-------\n"
" -interp iii == 'iii' is the interpolation mode:\n"
" ++ Modes allowed are a subset of those in 3dAllineate:\n"
" linear quintic wsinc5\n"
" ++ The default interpolation mode is 'wsinc5'.\n"
" ++ 'linear' is much faster but less accurate.\n"
" ++ 'quintic' is between 'linear' and 'wsinc5',\n"
" in both accuracy and speed.\n"
"\n"
" -verb == print (to stderr) various fun messages along the road.\n"
"\n"
" -prefix ppp == prefix name for the output dataset that holds the warp.\n"
" -space sss == attach string 'sss' to the output dataset as its atlas\n"
" space marker.\n"
"\n"
" -warp1 ww1 == alternative way to specify warp#1\n"
" -warp2 ww2 == alternative way to specify warp#2 (etc.)\n"
" ++ If you use any '-warpX' option for X=1..99, then\n"
" any addition warps specified after all command\n"
" line options appear AFTER these enumerated warps.\n"
" That is, '-warp1 A+tlrc -warp2 B+tlrc C+tlrc'\n"
" is like using '-warp3 C+tlrc'.\n"
" ++ At most 99 warps can be used. If you need more,\n"
" PLEASE back away from the computer slowly, and\n"
" get professional counseling.\n"
"\n"
" -iwarp == Invert the final warp before output.\n"
"\n"
" -expad PP == Pad the nonlinear warps by 'PP' voxels in all directions.\n"
" The warp displacements are extended by linear extrapolation\n"
" from the faces of the input grid.\n"
) ;
printf(
"\n"
"AUTHOR -- RWCox -- March 2013\n"
) ;
PRINT_AFNI_OMP_USAGE("3dNwarpCat",NULL) ; PRINT_COMPILE_DATE ;
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[] )
{
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) ;
}
void NWC_help(void)
{
printf(
"Usage: 3dNwarpCalc [options] expression\n"
"------\n"
" * This program performs calculations on 3D warps defined on a grid.\n"
"\n"
" * The fundamental idea is a 'stack' of warps, with operators being\n"
" applied to the top element(s) of the stack.\n"
" ++ If you don't know what a computer science 'stack' is, see\n"
" http://en.wikipedia.org/wiki/Stack_(data_structure)\n"
" ++ Also see 1dmatcalc for a similar implementation of a stack\n"
" of matrix operations.\n"
" ++ In the explanations below, the stack will be denoted as\n"
" [ A B C ... ]\n"
" where A is the top element, B the next element, etc.\n"
" Operations take place using the top one or two elements.\n"
"\n"
" * The expression is a single string enclosed in quotes ' or \",\n"
" with operators separated by spaces. See EXAMPLES below.\n"
" ++ The expression is the last thing on the command line!\n"
" ++ For scripting convenience, you can actually break the\n"
" expression into multiple strings (after all the '-' options),\n"
" and they will be re-assembled into one big string for\n"
" parsing and processing.\n"
"\n"
"** Note that to get any output, you will have to use the '&write'\n"
" operator at least once. Otherwise, the program computes stuff\n"
" and then just throws it away. (Fun perhaps, but useless.)\n"
"\n"
" * To actually use a 3D warp to transform a dataset, you must run the\n"
" program 3dNwarpApply:\n"
" ++ Note that the warp used in 3dNwarpApply does not need to be on the\n"
" same grid as the dataset being transformed. You can define a warp\n"
" on a high-resolution anatomical grid and apply it to a low-resolution\n"
" functional dataset, for example -- 3dNwarpApply will figure it out.\n"
" ++ On the other hand, all the warps in 3dNwarpCalc must be defined on\n"
" the same spatial grid!\n"
" ++ (LATER) You can use the &apply command to transform a 3D dataset\n"
" from within 3dNwarpCalc, if you don't need the special capabilities\n"
" of 3dNwarpApply.\n"
"\n"
" * Operations such as &invert and &sqrt may produce artifacts and be\n"
" inaccurate near the edges of the 3D grid, since they might require\n"
" extrapolating the warp to the outside of the grid in places, where\n"
" there is no information.\n"
"\n"
" * For convenience, you can break the expression up into multiple strings\n"
" (after all the options), and they will be re-assembled into the single\n"
" expression string the controlling C function needs to work.\n"
" ++ But note that since the '&' and '()' characters are special to the shell\n"
" you have to put the expression string(s) inside single quote ' or double\n"
" quote \" pairs, or else ugly things will happen.\n"
" (Not as ugly as having a hippopotamus step on your head, but almost.)\n"
"\n"
"OPTIONS\n"
"-------\n"
" -interp iii == 'iii' is the interpolation mode:\n"
" ++ Modes allowed are a subset of those in 3dAllineate:\n"
" linear quintic wsinc5\n"
" ++ The default interpolation mode is 'quintic'.\n"
" ++ 'linear' is much faster but less accurate.\n"
" ++ 'wsinc5' is much slower but more accurate.\n"
"\n"
" -ainterp jjj == 'jjj' is the interpolation mode for the '&apply' operation.\n"
" ++ Modes allowed here are\n"
" NN linear cubic quintic wsinc5\n"
" ++ If this option isn't given, then the value from '-interp'\n"
" is used (which should be good enough for government work).\n"
"\n"
" -verb == print (to stderr) various fun messages along the road\n"
" ++ A second '-verb' gives you even more fun!\n"
"\n"
"BUT WHERE DO WARPS COME FROM, MOMMY?\n"
"------------------------------------\n"
" * The program 3dAllineate with the -nwarp_save option will save a\n"
" displacement representation of a nonlinear warp to a 3D dataset\n"
" with 3 sub-bricks (1 for each of x, y, and z).\n"
"\n"
" * The contents of these sub-bricks are the displacments of each voxel in mm.\n"
" ++ The identity warp would be all zero, for example.\n"
"\n"
" * An input warp dataset can contain extra sub-bricks -- only the first 3\n"
" are used.\n"
"\n"
#if 0
" * Warp datasets output by this program have a 4th sub-brick, labeled\n"
" 'hexvol', which contains the volume of each distorted hexahedron in\n"
" the grid. This sub-brick is NOT used in any application of the\n"
" warp, such as 3dNwarpApply or further runs of 3dNwarpCalc, but can\n"
" help you understand how much distortion is present in the warp.\n"
" ++ If there are any negative values in the hexvol sub-brick, this\n"
" indicates that something bad happened in the warp calculation.\n"
#else
" * If you want the volume distortion at each voxel, use the program\n"
" 3dNwarpFuncs.\n"
#endif
"\n"
"OPERATORS\n"
"---------\n"
" * In the explanations below, the single character 'x' represents a 3D\n"
" coordinate vector, and a capital letter such as 'A' represents a\n"
" whole 3D warp function, whose output at a particular location is 'A(x)'.\n"
" * You can replace the '&' character that starts a command with '%%' or '@',\n"
" if that is more convenient for you.\n"
" * Operator names are not case sensitive: &INVERT is the same as &invert.\n"
"\n"
"&readnwarp(FF) == Read a 3D warp from a file and place it on top of the stack.\n"
" *OR* The input file should be a 3D dataset with 3 sub-bricks\n"
"&readwarp(FF) (volumes) storing the xyz displacments of each grid point.\n"
"\n"
"&identwarp(FF) == Create an identity warp (all displacements 0) on the grid\n"
" of a 3D dataset specified by the filename 'FF'.\n"
" ++ This operation is to be used to create a starting point\n"
" for calculations that otherwise do not involve a warp\n"
" defined on a grid, such a polynomial warps.\n"
" ++ The actual data in 'FF' is ignored by '&identwarp'; only the\n"
" 3D grid definition in the header is actually needed.\n"
" ----**==>> ++ Either '&identwarp' or '&readnwarp' should be the first\n"
" operation, in order to define the grid for all subsequent\n"
" calculations.\n"
"\n"
"&readpoly(FF) == The input is a text file with one line of numbers\n"
" specifying a warp as a polynomial, as output from\n"
" '3dAllineate -1Dparam_save'.\n"
" ++ The count of values determines the type of warp:\n"
" 12 ==> affine (shifts+angles+scales+shears)\n"
" 64 ==> cubic (3rd order) polyomial\n"
" 172 ==> quintic (5th order) polynomial\n"
" 364 ==> heptic (7th order) polynomial\n"
" 664 ==> nonic (9th order) polynomial\n"
" ++ Any other count of values on the single input line is\n"
" illegal, unconstitutional, against the laws of God,\n"
" fattening, and will make you get red pimples on your nose.\n"
" ++ The parameters could come, most probably, from using\n"
" 3dAllineate with the '-1Dparam_save' and '-nwarp' options.\n"
"\n"
"&read4x4(FF) == Read an affine 4x4 transform matrix directly; the input\n"
" file should contain 12 numbers in the order:\n"
" r11 r12 r13 r14 r21 r22 r23 r24 r31 r32 r33 r34\n"
" which will be organized into the 3D transformation matrix:\n"
" r11 r12 r13 r14\n"
" r21 r22 r23 r24\n"
" r31 r32 r33 r34\n"
" 0.0 0.0 0.0 1.0\n"
" ++ This matrix defines the transformation from input spatial\n"
" DICOM coordinates (x,y,z) to output coordinates, in mm.\n"
" ++ One way to get this matrix is via '3dAllineate -1Dmatrix_save'.\n"
" ++ This matrix should have non-zero determinant!\n"
"\n"
"&write(FF) == Write the 3D warp on the top of the stack to a file.\n"
" The output file is always in a 3D nwarp (dataset) format\n"
" -- NEVER a matrix or polynomial.\n"
"\n"
"&dup == Push the duplicate of the top of the stack onto the stack:\n"
" [ A B C ... ] goes to [ A A B C ... ] after &dup.\n"
"\n"
"&swap == Interchange the top two elements of the stack:\n"
" [ A B C ... ] goes to [ B A C ... ] after &swap\n"
" ++ You can swap other elements of the stack by using\n"
" indexes in the form '&swap(p,q)' where 'p' and 'q'\n"
" are distinct non-negative integers indicating depth\n"
" into the stack; '&swap' is equivalent to '&swap(0,1)'.\n"
"\n"
"&pop == Remove (and delete) the top element from the stack:\n"
" [ A B C ... ] goes to [ B C ... ] after &pop\n"
"\n"
"&compose == If the stack is [ A(x) B(x) C(x) ... ], compute the warp\n"
" *OR* B(A(x)) and replace these top 2 elements with the result:\n"
"&mult [ A B C ... ] goes to [ B(A(x)) C(x) ... ] after &compose\n"
" ++ If you wanted to compute A(B(x)), then you would use the\n"
" operator combination '&swap &compose'.\n"
"\n"
"&invert == Replace top element of the stack with its inverse:\n"
" the warp J(x) such that A(J(x)) = x.\n"
" ++ Inversion is done via a functional iteration:\n"
" Jnew(x) = Jold( 2*x - A(Jold(x)) )\n"
" which requires 1 warp composition and 1 warp interpolation\n"
" for each step.\n"
" ++ &invert and &invsqrt (and thus &sqrt) are slow operations\n"
" due to the iterative nature of the calculations.\n"
#ifdef USE_OMP
" ++ Multiple CPUS (via OpenMP) are used to help speed up\n"
" these functions.\n"
#else
" ++ On a system with OpenMP enabled, multiple CPUs would\n"
" be used to speed up these functions. However, this\n"
" binary copy of 3dNwarpCalc has not been compiled with\n"
" OpenMP (alas).\n"
#endif
" ++ The '-verb' option to 3dNwarpCalc will show you the\n"
" progress of the iterations for &invert and &invsqrt.\n"
"\n"
"&sqrt == Replace top element of the stack with its 'square root':\n"
" the warp Q(x) such that Q(Q(x)) = A(x).\n"
" ++ NOTE: not all warps have square roots, so this operation\n"
" is not guaranteed to work. Be careful out there.\n"
" ++ Nor is the square root of a nonlinear operator guaranteed\n"
" to be unique!\n"
#ifndef USE_SQRTPAIR
" ++ The basic algorithm used computes the inverse of Q(x),\n"
" as in &invsqrt, so an extra warp inversion is required\n"
" at the end here, so this operation is slower than &invsqrt.\n"
#endif
"\n"
"&invsqrt == Replace the top element of the stack with the inverse of\n"
" its square root: the warp R(x) such that A(R(R(x)) = x.\n"
" ++ '&sqrtinv' is a synonym for this operation, since I always\n"
" have trouble remembering which one is correct-imundo-ific.\n"
#ifndef USE_SQRTPAIR
" ++ This operation is based on the functional iteration\n"
" Rnew(x) = Rold( 1.5*x - 0.5*A(Rold(Rold(x))) )\n"
" which is adapted from the Schulz iteration for matrix\n"
" square roots, and requires 2 warp compositions and 1 warp\n"
" interpolation for each step.\n"
#else
" ++ This operation is based on a functional iteration\n"
" adapted from the Denman-Beavers method for computing\n"
" the square root of a matrix:\n"
" initialize Y(x) = A(x) and Z(x) = x; then iterate\n"
" Ynew(x) = 0.5*(Yold(x)+inv(Zold(x)))\n"
" Znew(x) = 0.5*(Zold(x)+inv(Yold(x)))\n"
" which converges to Y=sqrt(A) and Z=invsqrt(A).\n"
" ++ For speed, these square root iterations are always done\n"
" with linear interpolation, no matter what '-interp' is.\n"
#endif
"\n"
"&sqrtpair == Compute both &sqrtinv and &sqrt, and leave both of them\n"
" on the stack -- &sqrt on top, &sqrtinv 'below' it.\n"
"\n"
"&sqr == Replace the top element of the stack with its 'square':\n"
" the warp S(x) = A(A(x)). Equivalent to '&dup &compose'.\n"
" ++ To compute the fourth power of a warp: '&sqr &sqr'\n"
" ++ To compute the third power of a warp: '&dup &sqr &compose'\n"
" ++ '&square' is a synonym for this operation.\n"
"\n"
"&scale(a) == Scale the top-of-stack warp displacements by numerical\n"
" factor 'a' in all 3 dimensions.\n"
" ++ NOTE: this might make the warp non-invertible, (e.g., give\n"
" negative results in 'hexvol') for large enough 'a'.\n"
" Proceed at your own risk!\n"
" ++ If a=0, then the result is the identity warp, since\n"
" all the displacements are now 0.\n"
" ++ The case a=-1 is NOT the inverse warp!\n"
"\n"
"&sum == Add the displacements of the two warps on the stack,\n"
" then replace BOTH of them with the result.\n"
" ++ You can do something like '&sum(0.5,0.5)' to average\n"
" the displacements, or '&sum(1,-1)' to difference them.\n"
" In this case, the first value scales the displacements\n"
" of the stack's top warp, and the second value scales the\n"
" displacements of the stack's second warp.\n"
" ++ NOTE: you can produce a non-invertible warp this way!\n"
"\n"
"&apply(DD,PP) == Apply the 3D warp at the top of the stack to a dataset\n"
" whose name is given by the 'DD' argument, to produce\n"
" a dataset whose prefix is given by the 'PP' argument.\n"
" ++ This operation does not affect the stack of warps.\n"
" ++ &apply is provided to make your life simpler and happier :-)\n"
" ++ &apply is like 3dNwarpApply with the output dataset PP\n"
" always being on the same grid as the input dataset DD.\n"
" ++ The grid of dataset DD does NOT have to be the same as the\n"
" grid defining the warp defined on the stack. If needed,\n"
" the warp will be interpolated to be used with DD.\n"
" ++ Program 3dNwarpApply provides more options to control\n"
" the way that a warp is applied to a dataset; for example,\n"
" to control the output grid spacing.\n"
"\n"
"EXAMPLES\n"
"--------\n"
"** Read a warp from a dataset, invert it, save the inverse.\n"
"\n"
" 3dNwarpCalc '&readnwarp(Warp+tlrc.HEAD) &invert &write(WarpInv)'\n"
"\n"
"** Do the same, but also compute the composition of the warp with the inverse,\n"
" and save that -- ideally, the output warp displacements would be identically\n"
" zero (i.e., the identity warp), and the 'hexvol' entries would be constant\n"
" and equal to the voxel volume.\n"
"\n"
" 3dNwarpCalc -verb '&readnwarp(Warp+tlrc.HEAD) &dup &invert' \\\n"
" '&write(WarpInv) &compose &write(WarpOut)'\n"
"\n"
"** Read in a warp, compute its inverse square root, then square that, and compose\n"
" the result with the original warp -- the result should be the identity warp\n"
" (i.e., all zero displacments) -- except for numerical errors, of course.\n"
"\n"
" 3dNwarpCalc '&readnwarp(Warp+tlrc.HEAD) &dup &invsqrt &sqr &compose &write(WarpOut)'\n"
) ;
printf(
"\n"
"AUTHOR -- RWCox -- August 2011\n"
) ;
PRINT_AFNI_OMP_USAGE("3dNwarpCalc",NULL) ; PRINT_COMPILE_DATE ;
exit(0) ;
}
int main( int argc , char *argv[] )
{
THD_3dim_dataset *xset=NULL , *cset ;
int nopt=1, datum=MRI_float, nvals, ii;
MRI_IMAGE *ysim=NULL ;
char *prefix = "Tcorr1D", *smethod="pearson";
char *xnam=NULL , *ynam=NULL ;
byte *mask=NULL ; int mask_nx,mask_ny,mask_nz , nmask=0 ;
int do_atanh = 0 ; /* 12 Jan 2018 */
/*----*/
AFNI_SETUP_OMP(0) ; /* 24 Jun 2013 */
if( argc < 2 || strcmp(argv[1],"-help") == 0 ){
printf("Usage: 3dTcorr1D [options] xset y1D\n"
"Computes the correlation coefficient between each voxel time series\n"
"in the input 3D+time dataset 'xset' and each column in the 1D time\n"
"series file 'y1D', and stores the output values in a new dataset.\n"
"\n"
"OPTIONS:\n"
" -pearson = Correlation is the normal Pearson (product moment)\n"
" correlation coefficient [this is the default method].\n"
" -spearman = Correlation is the Spearman (rank) correlation\n"
" coefficient.\n"
" -quadrant = Correlation is the quadrant correlation coefficient.\n"
" -ktaub = Correlation is Kendall's tau_b coefficient.\n"
" ++ For 'continuous' or finely-discretized data, tau_b and\n"
" rank correlation are nearly equivalent (but not equal).\n"
" -dot = Doesn't actually compute a correlation coefficient; just\n"
" calculates the dot product between the y1D vector(s)\n"
" and the dataset time series.\n"
"\n"
" -Fisher = Apply the 'Fisher' (inverse hyperbolic tangent) transformation\n"
" to the results.\n"
" ++ It does not make sense to use this with '-ktaub', but if\n"
" you want to do it, the program will not stop you.\n"
" ++ Cannot be used with '-dot'!\n"
"\n"
" -prefix p = Save output into dataset with prefix 'p'\n"
" [default prefix is 'Tcorr1D'].\n"
"\n"
" -mask mmm = Only process voxels from 'xset' that are nonzero\n"
" in the 3D mask dataset 'mmm'.\n"
" ++ Other voxels in the output will be set to zero.\n"
"\n"
" -float = Save results in float format [the default format].\n"
" -short = Save results in scaled short format [to save disk space].\n"
" ++ Cannot be used with '-dot'!\n"
"\n"
"NOTES:\n"
"* The output dataset is functional bucket type, with one sub-brick\n"
" per column of the input y1D file.\n"
"* No detrending, blurring, or other pre-processing options are available;\n"
" if you want these things, see 3dDetrend or 3dTproject or 3dcalc.\n"
" [In other words, this program presumes you know what you are doing!]\n"
"* Also see 3dTcorrelate to do voxel-by-voxel correlation of TWO\n"
" 3D+time datasets' time series, with similar options.\n"
"* You can extract the time series from a single voxel with given\n"
" spatial indexes using 3dmaskave, and then run it with 3dTcorr1D:\n"
" 3dmaskave -quiet -ibox 40 30 20 epi_r1+orig > r1_40_30_20.1D\n"
" 3dTcorr1D -pearson -Fisher -prefix c_40_30_20 epi_r1+orig r1_40_30_20.1D\n"
"* http://en.wikipedia.org/wiki/Correlation\n"
"* http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient\n"
"* http://en.wikipedia.org/wiki/Spearman%%27s_rank_correlation_coefficient\n"
"* http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient\n"
"\n"
"-- RWCox - Apr 2010\n"
" - Jun 2010: Multiple y1D columns; OpenMP; -short; -mask.\n"
) ;
PRINT_AFNI_OMP_USAGE("3dTcorr1D",NULL) ;
PRINT_COMPILE_DATE ; exit(0) ;
}
mainENTRY("3dTcorr1D main"); machdep(); AFNI_logger("3dTcorr1D",argc,argv);
PRINT_VERSION("3dTcorr1D") ; THD_check_AFNI_version("3dTcorr1D") ;
/*-- option processing --*/
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcmp(argv[nopt],"-mask") == 0 ){ /* 28 Jun 2010 */
THD_3dim_dataset *mset ;
if( ++nopt >= argc ) ERROR_exit("Need argument after '-mask'") ;
if( mask != NULL ) 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 ) ;
INFO_message("Number of voxels in mask = %d",nmask) ;
if( nmask < 2 ) ERROR_exit("Mask is too small to process") ;
nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-float") == 0 ){ /* 27 Jun 2010 */
datum = MRI_float ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-short") == 0 ){
datum = MRI_short ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-pearson") == 0 ){
smethod = "pearson" ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-dot") == 0 ){
smethod = "dot" ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-spearman") == 0 || strcasecmp(argv[nopt],"-rank") == 0 ){
smethod = "spearman" ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-quadrant") == 0 ){
smethod = "quadrant" ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-ktaub") == 0 || strcasecmp(argv[nopt],"-taub") == 0 ){
smethod = "ktaub" ; nopt++ ; continue ;
}
if( strcasecmp(argv[nopt],"-fisher") == 0 ){ /* 12 Jan 2018 */
do_atanh = 1 ; nopt++ ; continue ;
}
if( strcmp(argv[nopt],"-prefix") == 0 ){
prefix = argv[++nopt] ;
if( !THD_filename_ok(prefix) ) ERROR_exit("Illegal value after -prefix!") ;
nopt++ ; continue ;
}
ERROR_exit("Unknown option: %s",argv[nopt]) ;
}
/*------------ open datasets, check for legality ------------*/
if( nopt+1 >= argc )
ERROR_exit("Need 2 non-option arguments on command line!?") ;
/* despite what the help says, if the 1D file is first, that's OK */
if( STRING_HAS_SUFFIX(argv[nopt],"1D") ){
ININFO_message("reading 1D file %s",argv[nopt]) ;
ysim = mri_read_1D( argv[nopt] ) ; ynam = argv[nopt] ;
if( ysim == NULL ) ERROR_exit("Can't read 1D file %s",argv[nopt]) ;
} else {
ININFO_message("reading dataset file %s",argv[nopt]) ;
xset = THD_open_dataset( argv[nopt] ) ; xnam = argv[nopt] ;
if( xset == NULL ) ERROR_exit("Can't open dataset %s",argv[nopt]) ;
}
/* read whatever type of file (3D or 1D) we don't already have */
nopt++ ;
if( xset != NULL ){
ININFO_message("reading 1D file %s",argv[nopt]) ;
ysim = mri_read_1D( argv[nopt] ) ; ynam = argv[nopt] ;
if( ysim == NULL ) ERROR_exit("Can't read 1D file %s",argv[nopt]) ;
} else {
ININFO_message("reading dataset file %s",argv[nopt]) ;
xset = THD_open_dataset( argv[nopt] ) ; xnam = argv[nopt] ;
if( xset == NULL ) ERROR_exit("Can't open dataset %s",argv[nopt]) ;
}
nvals = DSET_NVALS(xset) ; /* number of time points */
ii = (strcmp(smethod,"dot")==0) ? 2 : 3 ;
if( nvals < ii )
ERROR_exit("Input dataset %s length is less than ii?!",xnam,ii) ;
if( ysim->nx < nvals )
ERROR_exit("1D file %s has %d time points, but dataset has %d values",
ynam,ysim->nx,nvals) ;
else if( ysim->nx > nvals )
WARNING_message("1D file %s has %d time points, dataset has %d",
ynam,ysim->nx,nvals) ;
if( mri_allzero(ysim) )
ERROR_exit("1D file %s is all zero!",ynam) ;
if( ysim->ny > 1 )
INFO_message("1D file %s has %d columns: correlating with ALL of them!",
ynam,ysim->ny) ;
if( strcmp(smethod,"dot") == 0 && do_atanh ){
WARNING_message("'-dot' turns off '-Fisher'") ; do_atanh = 0 ;
}
if( strcmp(smethod,"dot") == 0 && datum == MRI_short ){
WARNING_message("'-dot' turns off '-short'") ; datum = MRI_float ;
}
cset = THD_Tcorr1D( xset, mask, nmask, ysim, smethod,
prefix, (datum==MRI_short) , do_atanh );
tross_Make_History( "3dTcorr1D" , argc,argv , cset ) ;
DSET_unload(xset) ; /* no longer needful */
/* finito */
DSET_write(cset) ;
INFO_message("Wrote dataset: %s\n",DSET_BRIKNAME(cset)) ;
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() ) ;
}
