MRI_shindss * GRINCOR_read_input( char *fname )
{
NI_element *nel=NULL ;
char *dfname=NULL , *atr ;
NI_float_array *facar ; NI_int_array *nvar, *nnode=NULL, *ninmask=NULL;
MRI_shindss *shd ;
long long nbytes_needed , nbytes_dfname=0 ; int fdes ;
void *var ; int ids , nvmax , nvtot ;
int datum , datum_size ;
char *geometry_string=NULL ;
THD_3dim_dataset *tdset=NULL; int nvox;
int no_ivec=0 , *ivec=NULL , *nvals=NULL , nvec,ndset ; float *fac=NULL ;
NI_str_array *slabar=NULL ;
if( fname == NULL || *fname == '\0' ) GQUIT(NULL) ;
/* get data element */
if (!THD_is_ondisk(fname))
GQUIT("not on disk") ;
nelshd = nel = NI_read_element_fromfile(fname) ;
if( nel == NULL || nel->type != NI_ELEMENT_TYPE )
GQUIT("not properly formatted") ;
if( strcmp(nel->name,"3dGroupInCorr") != 0 )
GQUIT("data element name is not '3dGroupInCorr'") ;
/* no data vector ==> using all voxels */
no_ivec = ( nel->vec_num < 1 ||
nel->vec_len < 1 || nel->vec_typ[0] != NI_INT ) ;
/* number of vectors in each dataset */
atr = NI_get_attribute(nel,"nvec");
if( atr == NULL ) GQUIT("nvec attribute missing?") ;
nvec = (int)strtod(atr,NULL) ;
if( nvec < 2 || (!no_ivec && nel->vec_len != nvec) )
GQUIT("nvec attribute has illegal value") ;
/* number of datasets */
atr = NI_get_attribute(nel,"ndset");
if( atr == NULL ) GQUIT("ndset attribute missing") ;
ndset = (int)strtod(atr,NULL) ;
if( ndset < 1 ) GQUIT("ndset attribute has illegal value") ;
/* number of time points in each dataset (varies with dataset) */
atr = NI_get_attribute(nel,"nvals");
if( atr == NULL ) GQUIT("nvals attribute missing") ;
nvar = NI_decode_int_list(atr,",") ;
if( nvar == NULL || nvar->num < ndset )
GQUIT("nvals attribute doesn't match ndset") ;
nvals = nvar->ar ; nvar->ar = NULL ; NI_delete_int_array(nvar) ;
nvmax = nvtot = nvals[0] ;
for( ids=1 ; ids < ndset ; ids++ ){ /* Feb 2011 */
nvtot += nvals[ids] ;
if( nvals[ids] > nvmax ) nvmax = nvals[ids] ;
}
/* dataset labels [23 May 2010] */
atr = NI_get_attribute(nel,"dset_labels") ;
if( atr != NULL ){
slabar = NI_decode_string_list(atr,";,") ;
if( slabar == NULL || slabar->num < ndset )
GQUIT("dset_labels attribute invalid") ;
}
/* datum of datasets */
atr = NI_get_attribute(nel,"datum") ;
if( atr != NULL && strcasecmp(atr,"byte") == 0 ){
datum = 1 ; datum_size = sizeof(sbyte) ;
} else {
datum = 2 ; datum_size = sizeof(short) ;
}
/* number of bytes needed:
sizeof(datum) * number of vectors per dataset
* number of datasets
* sum of per dataset vector lengths */
nbytes_needed = 0 ;
for( ids=0 ; ids < ndset ; ids++ ) nbytes_needed += nvals[ids] ;
nbytes_needed *= ((long long)nvec) * datum_size ;
if( nbytes_needed >= twogig &&
( sizeof(void *) < 8 || sizeof(size_t) < 8 ) ) /* too much for 32-bit */
GQUIT("datafile size exceeds 2 GB -- you need a 64-bit computer!") ;
/* scale factor for each dataset */
atr = NI_get_attribute(nel,"fac") ;
if( atr == NULL ) GQUIT("fac attribute missing") ;
facar = NI_decode_float_list(atr,",") ;
if( facar == NULL || facar->num < ndset )
GQUIT("can't decode fac attribute") ;
fac = facar->ar ; facar->ar = NULL ; NI_delete_float_array(facar) ;
for( ids=0 ; ids < ndset ; ids++ ) if( fac[ids] <= 0.0f ) fac[ids] = 1.0f ;
/* grid definition */
atr = NI_get_attribute(nel,"geometry") ;
if( atr == NULL ) GQUIT("geometry attribute missing") ;
geometry_string = strdup(atr) ;
tdset = EDIT_geometry_constructor( geometry_string , "GrpInCorr" ) ;
if( tdset == NULL ) GQUIT("can't decode geometry attribute") ;
nvox = DSET_NVOX(tdset) ;
if( no_ivec && nvox != nvec )
GQUIT("geometry attribute doesn't match nvec attribute") ;
if( !no_ivec && nvox < nvec )
GQUIT("geometry attribute specifies too few voxels") ;
/* name of data file: check its size against what's needed */
#if 0
atr = NI_get_attribute(nel,"datafile") ;
if( atr != NULL ){
dfname = strdup(atr) ; nbytes_dfname = THD_filesize(dfname) ;
if( nbytes_dfname <= 0 && strstr(dfname,"/") != NULL ){
char *tnam = THD_trailname(atr,0) ;
nbytes_dfname = THD_filesize(tnam) ;
if( nbytes_dfname > 0 ){ free(dfname); dfname = strdup(tnam); }
}
}
#endif
if( nbytes_dfname <= 0 && strstr(fname,".niml") != NULL ){
if( dfname != NULL ) free(dfname) ;
dfname = strdup(fname) ; strcpy(dfname+strlen(dfname)-5,".data") ;
nbytes_dfname = THD_filesize(dfname) ;
}
if( nbytes_dfname <= 0 ){
char mess[THD_MAX_NAME+256] ;
sprintf(mess,"datafile is missing (%s)",dfname) ; GQUIT(mess) ;
} else if( nbytes_dfname < nbytes_needed ){
char mess[THD_MAX_NAME+1024] ;
sprintf(mess,"datafile %s has %s bytes but needs at least %s",
dfname ,
commaized_integer_string(nbytes_dfname) ,
commaized_integer_string(nbytes_needed) ) ;
GQUIT(mess) ;
} else {
INFO_message("EIC: data file %s found with %s bytes of data",
dfname , commaized_integer_string(nbytes_dfname) ) ;
}
fdes = open( dfname , O_RDWR ) ;
if( fdes < 0 ){
char mess[THD_MAX_NAME+256] ;
sprintf(mess,"can't open datafile (%s)",dfname) ; GQUIT(mess) ;
}
NI_set_attribute( nelshd , "datafile" , dfname ) ;
/* ivec[i] is the voxel spatial index of the i-th vector */
if( no_ivec ){
ivec = NULL ; /* means all voxels: ivec[i] == i */
} else {
ivec = (int *)nel->vec[0] ; /* copy pointer */
nel->vec[0] = NULL ; /* NULL out in element so won't be free-ed */
}
/* And stuff for LR surface pairs ZSS Jan 09*/
if ((atr=NI_get_attribute(nel,"LRpair_nnode"))) {
nnode = NI_decode_int_list(atr,",") ;
}
if ((atr=NI_get_attribute(nel,"LRpair_ninmask"))) {
ninmask = NI_decode_int_list(atr,",") ;
}
/* create output struct */
shd = (MRI_shindss *)malloc(sizeof(MRI_shindss)) ;
shd->nvals = nvals ; shd->nvals_max = nvmax ; shd->nvals_tot = nvtot ;
shd->nvec = nvec ;
shd->ndset = ndset ;
shd->geometry_string = geometry_string ;
shd->tdset = tdset ;
shd->dfname = dfname ;
shd->nvox = nvox ;
shd->nx = DSET_NX(tdset); shd->ny = DSET_NY(tdset); shd->nz = DSET_NZ(tdset);
shd->ivec = ivec ;
shd->fac = fac ;
/* and surface fields... ZSS Jan 09 */
if (nnode) {
if (nnode->num != 2) GQUIT("LRpair_nnode must have 2 values");
shd->nnode[0] = nnode->ar[0];
shd->nnode[1] = nnode->ar[1];
NI_delete_int_array(nnode); nnode=NULL;
} else {
shd->nnode[0] = shd->nnode[1] = -1 ;
}
if (ninmask) {
if (ninmask->num != 2) GQUIT("LRpair_ninmask must have 2 values");
shd->ninmask[0] = ninmask->ar[0];
shd->ninmask[1] = ninmask->ar[1];
NI_delete_int_array(ninmask); ninmask=NULL;
} else {
shd->ninmask[0] = shd->ninmask[1] = -1 ;
}
/*--- 07 Apr 2010: setup default use list (all of them) ---*/
shd->nuse = ndset ;
shd->use = (int *)malloc(sizeof(int)*ndset) ;
for( ids=0 ; ids < ndset ; ids++ ) shd->use[ids] = ids ;
shd->dslab = (slabar != NULL) ? slabar->str : NULL ; /* 23 May 2010 */
/*--- now have to map data from disk ---*/
var = mmap( 0 , (size_t)nbytes_needed ,
PROT_WRITE , THD_MMAP_FLAG , fdes , 0 ) ;
close(fdes) ; /* close file descriptor does not unmap data */
if( var == (void *)(-1) ){ /* this is bad */
ERROR_message(
"EIC: file %s: can't mmap() datafile -- memory space exhausted?" , dfname ) ;
free(shd) ; return NULL ;
}
/*-- create array of pointers to each dataset's data array --*/
shd->datum = datum ;
if( datum == 2 ){ /* shorts */
shd->sv = (short **)malloc(sizeof(short *)*ndset) ;
shd->bv = NULL ;
shd->sv[0] = (short *)var ;
for( ids=1 ; ids < ndset ; ids++ )
shd->sv[ids] = shd->sv[ids-1] + nvals[ids-1]*nvec ;
} else { /* sbytes */
shd->sv = NULL ;
shd->bv = (sbyte **)malloc(sizeof(sbyte *)*ndset) ;
shd->bv[0] = (sbyte *)var ;
for( ids=1 ; ids < ndset ; ids++ )
shd->bv[ids] = shd->bv[ids-1] + nvals[ids-1]*nvec ;
}
shd->nbytes = nbytes_needed ;
return shd ;
}
char * THD_dataset_info( THD_3dim_dataset *dset , int verbose )
{
THD_dataxes *daxes ;
THD_fvec3 fv1 , fv2 , fv3 ;
int ival , ntimes , nval_per , n1,n2,n3 , kv,npar ;
float tf, angle=0.0;
long long tb ;
static char *RR="[R]" , *LL="[L]" ,
*PP="[P]" , *AA="[A]" ,
*SS="[S]" , *II="[I]" , *ZZ=" " ;
char *xlbot , *xltop , *ylbot , *yltop , *zlbot , *zltop , *cpt ;
char str[1024], soblq[1024] ;
int nstr , obliquity;
char *outbuf = NULL ; /* output buffer */
ENTRY("THD_dataset_info") ;
if( ! ISVALID_3DIM_DATASET(dset) ) RETURN(NULL) ;
daxes = dset->daxes ;
if( DSET_IS_BRIK(dset) )
outbuf = THD_zzprintf(outbuf,"Dataset File: %s\n" , DSET_FILECODE(dset) ) ;
else
outbuf = THD_zzprintf(outbuf,"Dataset File: %s\n" , DSET_BRIKNAME(dset) ) ;
outbuf = THD_zzprintf(outbuf,"Identifier Code: %s Creation Date: %s\n" ,
dset->idcode.str , dset->idcode.date ) ;
outbuf = THD_zzprintf(outbuf, "Template Space: %s\n", dset->atlas_space);
if( ISANAT(dset) ){
outbuf = THD_zzprintf(outbuf,"Dataset Type: %s (-%s)\n",
ANAT_typestr[dset->func_type] , ANAT_prefixstr[dset->func_type] ) ;
} else {
outbuf = THD_zzprintf(outbuf,"Dataset Type: %s (-%s)\n",
FUNC_typestr[dset->func_type] , FUNC_prefixstr[dset->func_type] ) ;
}
/* 25 April 1998: do byte order stuff */
switch( DSET_BYTEORDER(dset) ){
case LSB_FIRST:
outbuf = THD_zzprintf(outbuf,"Byte Order: %s" , LSB_FIRST_STRING) ;
break ;
case MSB_FIRST:
outbuf = THD_zzprintf(outbuf,"Byte Order: %s" , MSB_FIRST_STRING) ;
break ;
}
if( THD_find_string_atr(dset->dblk,ATRNAME_BYTEORDER) == NULL ) /* 19 Sep 1999 */
outbuf = THD_zzprintf(outbuf," {assumed}") ;
kv = mri_short_order() ;
switch( kv ){
case LSB_FIRST:
outbuf = THD_zzprintf(outbuf," [this CPU native = %s]\n" , LSB_FIRST_STRING) ;
break ;
case MSB_FIRST:
outbuf = THD_zzprintf(outbuf," [this CPU native = %s]\n" , MSB_FIRST_STRING) ;
break ;
}
/*-- 21 Jun 2002: print storage mode --*/
if( dset->dblk->diskptr != NULL ){
outbuf = THD_zzprintf(outbuf,"Storage Mode: %s\n",
storage_mode_str(dset->dblk->diskptr->storage_mode));
}
tb = dset->dblk->total_bytes ;
if( tb > 0 )
outbuf = THD_zzprintf(outbuf,"Storage Space: %s (%s) bytes\n",
commaized_integer_string(dset->dblk->total_bytes) ,
approximate_number_string(dset->dblk->total_bytes) ) ;
/*-- keywords --*/
if( verbose >= 0 ){
cpt = DSET_KEYWORDS(dset) ;
if( cpt != NULL && cpt[0] != '\0' ){
int j = strlen(cpt) ;
if( j < 99 ){
outbuf = THD_zzprintf(outbuf,"Keywords: %s\n" , cpt ) ;
} else {
int k ;
outbuf = THD_zzprintf(outbuf,"\n----- KEYWORDS -----\n") ;
for( k=0 ; k < j ; k += ZMAX )
outbuf = THD_zzprintf(outbuf,SZMAX,cpt+k) ;
outbuf = THD_zzprintf(outbuf,"\n") ;
}
}
}
/*-- idcodes --*/
if( verbose >= 0 ){
if( ! ISZERO_IDCODE(dset->anat_parent_idcode) )
outbuf = THD_zzprintf(outbuf,"Anatomy Parent: %s [%s]\n" ,
dset->anat_parent_name , dset->anat_parent_idcode.str ) ;
else if( strlen(dset->anat_parent_name) > 0 )
outbuf = THD_zzprintf(outbuf,"Anatomy Parent: %s\n" , dset->anat_parent_name ) ;
if( ! ISZERO_IDCODE(dset->warp_parent_idcode) )
outbuf = THD_zzprintf(outbuf,"Warp Parent: %s [%s]\n" ,
dset->warp_parent_name , dset->warp_parent_idcode.str) ;
else if( strlen(dset->warp_parent_name) > 0 )
outbuf = THD_zzprintf(outbuf,"Warp Parent: %s\n" , dset->warp_parent_name ) ;
}
/*-- tagset --*/
if( verbose > 0 && dset->tagset != NULL && dset->tagset->num > 0 ){
int ii , ns=0 ;
for( ii=0 ; ii < dset->tagset->num ; ii++ )
if( dset->tagset->tag[ii].set ) ns++ ;
outbuf = THD_zzprintf(outbuf,"Tagset: %d set [out of %d total]\n",
ns , dset->tagset->num ) ;
}
/* are we oblique ? */
if((obliquity = dset_obliquity(dset, &angle)) >= 0) {
if(angle>0.0) {
sprintf (soblq,
"Data Axes Tilt: Oblique (%.3f deg. from plumb)\n"
"Data Axes Approximate Orientation:",
angle);
} else {
sprintf (soblq,
"Data Axes Tilt: Plumb\n"
"Data Axes Orientation:");
}
{ char *gstr = EDIT_get_geometry_string(dset) ;
if( gstr != NULL && *gstr != '\0' )
outbuf = THD_zzprintf(outbuf,"Geometry String: \"%s\"\n",gstr) ;
}
} else {
sprintf (soblq,
"Data Axes Tilt: Unspecified, assumed plumb\n"
"Data Axes Orientation:");
}
outbuf = THD_zzprintf(outbuf,
"%s\n"
" first (x) = %s\n"
" second (y) = %s\n"
" third (z) = %s [-orient %c%c%c]\n" ,
soblq,
ORIENT_typestr[daxes->xxorient] ,
ORIENT_typestr[daxes->yyorient] ,
ORIENT_typestr[daxes->zzorient] ,
ORIENT_typestr[daxes->xxorient][0] ,
ORIENT_typestr[daxes->yyorient][0] ,
ORIENT_typestr[daxes->zzorient][0] ) ;
LOAD_FVEC3(fv1 , daxes->xxorg , daxes->yyorg , daxes->zzorg) ;
fv1 = THD_3dmm_to_dicomm( dset , fv1 ) ;
LOAD_FVEC3(fv2 , daxes->xxorg + (daxes->nxx-1)*daxes->xxdel ,
daxes->yyorg + (daxes->nyy-1)*daxes->yydel ,
daxes->zzorg + (daxes->nzz-1)*daxes->zzdel ) ;
fv2 = THD_3dmm_to_dicomm( dset , fv2 ) ;
if( fv1.xyz[0] > fv2.xyz[0] ) FSWAP( fv1.xyz[0] , fv2.xyz[0] ) ;
if( fv1.xyz[1] > fv2.xyz[1] ) FSWAP( fv1.xyz[1] , fv2.xyz[1] ) ;
if( fv1.xyz[2] > fv2.xyz[2] ) FSWAP( fv1.xyz[2] , fv2.xyz[2] ) ;
LOAD_FVEC3(fv3 , daxes->xxdel , daxes->yydel , daxes->zzdel) ;
fv3 = THD_3dmm_to_dicomm( dset , fv3 ) ;
XLAB(xlbot,fv1.xyz[0]) ; YLAB(ylbot,fv1.xyz[1]) ; ZLAB(zlbot,fv1.xyz[2]) ;
XLAB(xltop,fv2.xyz[0]) ; YLAB(yltop,fv2.xyz[1]) ; ZLAB(zltop,fv2.xyz[2]) ;
n1 = DAXES_NUM(daxes,ORI_R2L_TYPE) ;
n2 = DAXES_NUM(daxes,ORI_A2P_TYPE) ;
n3 = DAXES_NUM(daxes,ORI_I2S_TYPE) ;
outbuf = THD_zzprintf(outbuf,
"R-to-L extent: %9.3f %s -to- %9.3f %s -step- %9.3f mm [%3d voxels]\n"
"A-to-P extent: %9.3f %s -to- %9.3f %s -step- %9.3f mm [%3d voxels]\n"
"I-to-S extent: %9.3f %s -to- %9.3f %s -step- %9.3f mm [%3d voxels]\n" ,
fv1.xyz[0],xlbot , fv2.xyz[0],xltop , fabs(fv3.xyz[0]) , n1 ,
fv1.xyz[1],ylbot , fv2.xyz[1],yltop , fabs(fv3.xyz[1]) , n2 ,
fv1.xyz[2],zlbot , fv2.xyz[2],zltop , fabs(fv3.xyz[2]) , n3 ) ;
/*-- 01 Feb 2001: print the center of the dataset as well --*/
if( verbose > 0 ){
fv1.xyz[0] = 0.5*(fv1.xyz[0]+fv2.xyz[0]) ; XLAB(xlbot,fv1.xyz[0]) ;
fv1.xyz[1] = 0.5*(fv1.xyz[1]+fv2.xyz[1]) ; YLAB(ylbot,fv1.xyz[1]) ;
fv1.xyz[2] = 0.5*(fv1.xyz[2]+fv2.xyz[2]) ; ZLAB(zlbot,fv1.xyz[2]) ;
outbuf = THD_zzprintf(outbuf,
"R-to-L center: %9.3f %s\n"
"A-to-P center: %9.3f %s\n"
"I-to-S center: %9.3f %s\n" ,
fv1.xyz[0],xlbot ,
fv1.xyz[1],ylbot ,
fv1.xyz[2],zlbot ) ;
}
ntimes = DSET_NUM_TIMES(dset) ;
nval_per = DSET_NVALS_PER_TIME(dset) ;
if( ntimes > 1 ){
outbuf = THD_zzprintf(outbuf,
"Number of time steps = %d" , ntimes ) ;
STATUS("timestep") ;
outbuf = THD_zzprintf(outbuf, " Time step = %.5f%s Origin = %.5f%s" ,
dset->taxis->ttdel ,
UNITS_TYPE_LABEL(dset->taxis->units_type) ,
dset->taxis->ttorg ,
UNITS_TYPE_LABEL(dset->taxis->units_type) ) ;
if( dset->taxis->nsl > 0 )
outbuf = THD_zzprintf(outbuf," Number time-offset slices = %d Thickness = %.3f",
dset->taxis->nsl , fabs(dset->taxis->dz_sl) ) ;
outbuf = THD_zzprintf(outbuf,"\n") ;
STATUS("nsl done") ;
if( verbose > 0 && dset->taxis->nsl > 0 && dset->taxis->toff_sl != NULL ){
outbuf = THD_zzprintf(outbuf,"Time-offsets per slice:") ;
for( ival=0 ; ival < dset->taxis->nsl ; ival++ )
outbuf = THD_zzprintf(outbuf, " %.3f" , dset->taxis->toff_sl[ival] ) ;
outbuf = THD_zzprintf(outbuf,"\n") ;
}
} else {
outbuf = THD_zzprintf(outbuf,
"Number of values stored at each pixel = %d\n" , nval_per ) ;
}
#if 0
if( verbose > 0 && ntimes > 1 ) nval_per = dset->dblk->nvals ;
else nval_per = 1 ; /* 12 Feb 2002 */
#else
nval_per = dset->dblk->nvals ;
if( verbose < 0 && nval_per > 5 ) nval_per = 3 ;
#endif
/* print out stuff for each sub-brick */
for( ival=0 ; ival < nval_per ; ival++ ){
STATUS("ival a") ;
sprintf( str ,
" -- At sub-brick #%d '%s' datum type is %s" ,
ival , DSET_BRICK_LAB(dset,ival) ,
MRI_TYPE_name[DSET_BRICK_TYPE(dset,ival)] ) ;
nstr = strlen(str) ;
tf = DSET_BRICK_FACTOR(dset,ival) ;
if( ISVALID_STATISTIC(dset->stats) ){
if( tf != 0.0 ){
sprintf( str+nstr ,
":%13.6g to %13.6g [internal]\n"
"%*s[*%13.6g] %13.6g to %13.6g [scaled]\n" ,
dset->stats->bstat[ival].min/tf ,
dset->stats->bstat[ival].max/tf ,
nstr-16," " , tf ,
dset->stats->bstat[ival].min , dset->stats->bstat[ival].max ) ;
} else {
sprintf( str+nstr , ":%13.6g to %13.6g\n" ,
dset->stats->bstat[ival].min , dset->stats->bstat[ival].max ) ;
}
} else if( tf != 0.0 ){
sprintf( str+nstr , " [*%g]\n",tf) ;
} else {
sprintf( str+nstr , "\n") ;
}
STATUS("ival b") ;
outbuf = THD_zzprintf(outbuf,"%s",str) ;
/** 30 Nov 1997: print sub-brick stat params **/
kv = DSET_BRICK_STATCODE(dset,ival) ;
if( FUNC_IS_STAT(kv) ){
STATUS("ival c") ;
outbuf = THD_zzprintf(outbuf," statcode = %s",FUNC_prefixstr[kv] ) ;
npar = FUNC_need_stat_aux[kv] ;
if( npar > 0 ){
outbuf = THD_zzprintf(outbuf,"; statpar =") ;
for( kv=0 ; kv < npar ; kv++ )
outbuf = THD_zzprintf(outbuf," %g",DSET_BRICK_STATPAR(dset,ival,kv)) ;
}
outbuf = THD_zzprintf(outbuf,"\n") ;
STATUS("ival d") ;
}
cpt = DSET_BRICK_KEYWORDS(dset,ival) ;
if( cpt != NULL && cpt[0] != '\0' ){
outbuf = THD_zzprintf(outbuf," keywords = %.66s\n",cpt) ;
}
STATUS("ival z") ;
}
if( verbose < 0 && nval_per < dset->dblk->nvals ) /* 21 Sep 2007 */
outbuf = THD_zzprintf(outbuf,
"** For info on all %d sub-bricks, use '3dinfo -verb' **\n",
dset->dblk->nvals) ;
/** print out dataset global statistical parameters **/
if( ISFUNC(dset) && FUNC_need_stat_aux[dset->func_type] > 0 ){
outbuf = THD_zzprintf(outbuf,"Auxiliary functional statistical parameters:\n %s\n",
FUNC_label_stat_aux[dset->func_type] ) ;
for( ival=0 ; ival < FUNC_need_stat_aux[dset->func_type] ; ival++ )
outbuf = THD_zzprintf(outbuf," %g",dset->stat_aux[ival]) ;
outbuf = THD_zzprintf(outbuf,"\n") ;
}
/** If present, print out History **/
{ char *chn ; int j,k ;
chn = tross_Get_History(dset) ;
if( chn != NULL ){
j = strlen(chn) ;
outbuf = THD_zzprintf(outbuf,"\n----- HISTORY -----\n") ;
for( k=0 ; k < j ; k += ZMAX )
outbuf = THD_zzprintf(outbuf,SZMAX,chn+k) ;
free(chn) ;
outbuf = THD_zzprintf(outbuf,"\n") ;
}
}
/** If present, print out Notes **/
if( verbose >= 0 ){
ATR_int *notecount;
int num_notes, i, j, mmm ;
char *chn , *chd ;
notecount = THD_find_int_atr(dset->dblk, "NOTES_COUNT");
if( notecount != NULL ){
num_notes = notecount->in[0] ;
if( verbose == 0 && num_notes > 5 ) num_notes = 5 ;
mmm = (verbose > 0) ? ZMAX : 1200 ; /* 400 it was!
Come on Bob, have a heart! -ZSS */
for (i=1; i<= num_notes; i++) {
chn = tross_Get_Note( dset , i ) ;
if( chn != NULL ){
j = strlen(chn) ; if( j > mmm ) chn[mmm] = '\0' ;
chd = tross_Get_Notedate(dset,i) ;
if( chd == NULL ){ chd = AFMALL(char,16) ; strcpy(chd,"no date") ; }
outbuf = THD_zzprintf(outbuf,"\n----- NOTE %d [%s] -----\n%s\n",i,chd,chn) ;
free(chn) ; free(chd) ;
}
int main( int argc , char *argv[] )
{
MRI_shindss *shd ;
int ids , nopt , kk ;
char *prefix = "EIC" ;
char *fname=NULL , *buf ;
MRI_vectim *mv ; THD_3dim_dataset *dset ;
/*--- official AFNI startup stuff ---*/
mainENTRY("3dExtractGroupInCorr"); machdep();
AFNI_logger("3dExtractGroupInCorr",argc,argv);
PRINT_VERSION("3dExtractGroupInCorr"); AUTHOR("RW Cox");
/*-- process options --*/
nopt = 1 ;
while( nopt < argc && argv[nopt][0] == '-' ){
if( strcasecmp(argv[nopt],"-prefix") == 0 ){
nopt++ ;
if( strcasecmp(argv[nopt],"NULL") == 0 ) prefix = NULL ;
else prefix = strdup(argv[nopt]) ;
nopt++ ; continue ;
}
ERROR_message("Unknown option: '%s'",argv[nopt]) ;
suggest_best_prog_option(argv[0], argv[nopt]);
exit(1);
}
if( argc < 2 ){ usage_3dExtractGroupInCorr(2) ; exit(0) ; }
/* check for errors */
if( nopt >= argc ) ERROR_exit("No input filename on command line?!") ;
/*-- read input file --*/
fname = strdup(argv[nopt]) ;
if( STRING_HAS_SUFFIX(fname,".data") ){
strcpy(fname+strlen(fname)-5,".niml") ;
WARNING_message("EIC: Replaced '.data' with '.niml' in filename") ;
} else if( STRING_HAS_SUFFIX(fname,".grpincorr") ){
fname = (char *)realloc(fname,strlen(fname)+16) ;
strcat(fname,".niml") ;
INFO_message("EIC: Added '.niml' to end of filename") ;
} else if( STRING_HAS_SUFFIX(fname,".grpincorr.") ){
fname = (char *)realloc(fname,strlen(fname)+16) ;
strcat(fname,"niml") ;
INFO_message("EIC: Added 'niml' to end of filename") ;
}
shd = GRINCOR_read_input( fname ) ;
if( shd == NULL ) ERROR_exit("EIC: Cannot continue after input error") ;
INFO_message("EIC: file opened, contains %d datasets, %d time series, %s bytes",
shd->ndset , shd->nvec , commaized_integer_string(shd->nbytes) ) ;
/*-- process input file --*/
fprintf(stderr,"++ %d datasets: ",shd->ndset) ;
for( ids=0 ; ids < shd->ndset ; ids++ ){
fprintf(stderr,"%d",ids+1) ;
dset = GRINCOR_extract_dataset( shd, ids, prefix ) ; fprintf(stderr,".") ;
DSET_write(dset) ;
DSET_delete(dset) ;
}
fprintf(stderr,"\n") ; 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) ;
}
