void DataSet::loadstim2(){
name = "fMRI_stim";
type = FMRI_STIM;
dims[3] = 220;
dims[0] = 3;
dims[1] = 1;
dims[2] = 1;
train = gsl_matrix_float_calloc(dims[3], dims[0]*dims[1]);
std::cout << "Loading stimulus" << std::endl;
for (int i = 0; i < 220; ++i) {
if (i%55 < 15)
gsl_matrix_float_set(train, i, 0, 1);
else if ((i - 20)%55 < 15 && i >= 20)
gsl_matrix_float_set(train, i, 1, 1);
else
gsl_matrix_float_set(train, i, 2, 1);
}
}
VAttrList
VNCM(VAttrList list,VImage mask,VShort minval,VShort first,VShort length,VFloat threshold)
{
VAttrList out_list=NULL;
VAttrListPosn posn;
VImage src[NSLICES],map=NULL;
VImage dest=NULL;
size_t b,r,c,i,j,i1,n,m,nt,last,ntimesteps,nrows,ncols,nslices;
gsl_matrix_float *mat=NULL;
float *A=NULL,*ev=NULL;
/*
** get image dimensions
*/
i = ntimesteps = nrows = ncols = 0;
for (VFirstAttr (list, & posn); VAttrExists (& posn); VNextAttr (& posn)) {
if (VGetAttrRepn (& posn) != VImageRepn) continue;
VGetAttrValue (& posn, NULL,VImageRepn, & src[i]);
if (VPixelRepn(src[i]) != VShortRepn) continue;
if (VImageNBands(src[i]) > ntimesteps) ntimesteps = VImageNBands(src[i]);
if (VImageNRows(src[i]) > nrows) nrows = VImageNRows(src[i]);
if (VImageNColumns(src[i]) > ncols) ncols = VImageNColumns(src[i]);
i++;
if (i >= NSLICES) VError(" too many slices");
}
nslices = i;
/* get time steps to include */
if (length < 1) length = ntimesteps-2;
last = first + length -1;
if (last >= ntimesteps) last = ntimesteps-1;
if (first < 0) first = 1;
nt = last - first + 1;
i1 = first+1;
if (nt < 2) VError(" not enough timesteps, nt= %d",nt);
fprintf(stderr,"# ntimesteps: %d, first= %d, last= %d, nt= %d\n",
(int)ntimesteps,(int)first,(int)last,(int)nt);
/* count number of voxels */
n = 0;
for (b=0; b<nslices; b++) {
if (VImageNRows(src[b]) < 2) continue;
for (r=0; r<nrows; r++) {
for (c=0; c<ncols; c++) {
if (VPixel(src[b],i1,r,c,VShort) < minval) continue;
if (VGetPixel(mask,b,r,c) < 0.5) continue;
n++;
}
}
}
fprintf(stderr," nvoxels: %ld\n",(long)n);
/*
** voxel addresses
*/
map = VCreateImage(1,5,n,VFloatRepn);
if (map == NULL) VError(" error allocating addr map");
VFillImage(map,VAllBands,0);
VPixel(map,0,3,0,VFloat) = nslices;
VPixel(map,0,3,1,VFloat) = nrows;
VPixel(map,0,3,2,VFloat) = ncols;
i = 0;
for (b=0; b<nslices; b++) {
if (VImageNRows(src[b]) < 2) continue;
for (r=0; r<nrows; r++) {
for (c=0; c<ncols; c++) {
if (VPixel(src[b],i1,r,c,VShort) < minval) continue;
if (VGetPixel(mask,b,r,c) < 0.5) continue;
VPixel(map,0,0,i,VFloat) = b;
VPixel(map,0,1,i,VFloat) = r;
VPixel(map,0,2,i,VFloat) = c;
i++;
}
}
}
/*
** avoid casting to float, copy data to matrix
*/
mat = gsl_matrix_float_calloc(n,nt);
for (i=0; i<n; i++) {
b = VPixel(map,0,0,i,VFloat);
r = VPixel(map,0,1,i,VFloat);
c = VPixel(map,0,2,i,VFloat);
float *ptr = gsl_matrix_float_ptr(mat,i,0);
int k;
j = 0;
for (k=first; k<=last; k++) {
if (j >= mat->size2) VError(" j= %d %d",j,mat->size2);
if (k >= VImageNBands(src[b])) VError(" k= %d %d",k, VImageNBands(src[b]));
*ptr++ = (float) VPixel(src[b],k,r,c,VShort);
j++;
}
}
/*
** compute similarity matrix
*/
m = (n*(n+1))/2;
fprintf(stderr," matrix computation, n= %ld...\n",(long)n);
A = (float *) calloc(m,sizeof(float));
if (!A) VError(" err allocating correlation matrix");
memset(A,0,m*sizeof(float));
size_t progress=0;
#pragma omp parallel for shared(progress) private(j) schedule(guided) firstprivate(mat,A)
for (i=0; i<n; i++) {
if (i%100 == 0) fprintf(stderr," %d00\r",(int)(++progress));
const float *arr1 = gsl_matrix_float_const_ptr(mat,i,0);
for (j=0; j<i; j++) {
const float *arr2 = gsl_matrix_float_const_ptr(mat,j,0);
const double v = Correlation(arr1,arr2,nt);
const size_t k=j+i*(i+1)/2;
if (k >= m) VError(" illegal addr k= %d, m= %d",k,m);
A[k] = (float)v;
}
}
fprintf(stderr," matrix done.\n");
gsl_matrix_float_free(mat);
/*
** eigenvector centrality
*/
ev = (float *) VCalloc(n,sizeof(float));
DegreeCentrality(A,ev,n,threshold);
dest = WriteOutput(src[0],map,nslices,nrows,ncols,ev,n);
VSetAttr(VImageAttrList(dest),"name",NULL,VStringRepn,"DegreeCM");
out_list = VCreateAttrList();
VAppendAttr(out_list,"image",NULL,VImageRepn,dest);
return out_list;
}
float *
VCorrMatrix(VAttrList list,VImage map,VShort first,VShort length)
{
VAttrListPosn posn;
VImage src[NSLICES];
size_t b,r,c,i,j,i1,n,m,nt,last,ntimesteps,nrows,ncols,nslices;
gsl_matrix_float *mat=NULL;
float *A=NULL;
/*
** get image dimensions
*/
i = ntimesteps = nrows = ncols = 0;
for (VFirstAttr (list, & posn); VAttrExists (& posn); VNextAttr (& posn)) {
if (VGetAttrRepn (& posn) != VImageRepn) continue;
VGetAttrValue (& posn, NULL,VImageRepn, & src[i]);
if (VPixelRepn(src[i]) != VShortRepn) continue;
if (VImageNBands(src[i]) > ntimesteps) ntimesteps = VImageNBands(src[i]);
if (VImageNRows(src[i]) > nrows) nrows = VImageNRows(src[i]);
if (VImageNColumns(src[i]) > ncols) ncols = VImageNColumns(src[i]);
i++;
if (i >= NSLICES) VError(" too many slices");
}
nslices = i;
/* get time steps to include */
if (length < 1) length = ntimesteps-2;
last = first + length -1;
if (last >= ntimesteps) last = ntimesteps-1;
if (first < 0) first = 1;
nt = last - first + 1;
i1 = first+1;
if (nt < 2) VError(" not enough timesteps, nt= %d",nt);
/* number of voxels */
n = VImageNColumns(map);
fprintf(stderr," ntimesteps: %d, first= %d, last= %d, nt= %d, nvoxels: %ld\n",
(int)ntimesteps,(int)first,(int)last,(int)nt,(long)n);
/*
** avoid casting to float, copy data to matrix
*/
mat = gsl_matrix_float_calloc(n,nt);
if (!mat) VError(" err allocating mat");
for (i=0; i<n; i++) {
b = VPixel(map,0,0,i,VShort);
r = VPixel(map,0,1,i,VShort);
c = VPixel(map,0,2,i,VShort);
float *ptr = gsl_matrix_float_ptr(mat,i,0);
int k;
j = 0;
for (k=first; k<=last; k++) {
if (j >= mat->size2) VError(" j= %d %d",j,mat->size2);
if (k >= VImageNBands(src[b])) VError(" k= %d %d",k, VImageNBands(src[b]));
*ptr++ = (float) VPixel(src[b],k,r,c,VShort);
j++;
}
}
/*
** compute similarity matrix
*/
m = (n*(n+1))/2;
fprintf(stderr," compute correlation matrix...\n");
A = (float *) VCalloc(m,sizeof(float));
if (!A) VError(" err allocating correlation matrix");
memset(A,0,m*sizeof(float));
size_t progress=0;
#pragma omp parallel for shared(progress) private(j) schedule(guided) firstprivate(mat,A)
for (i=0; i<n; i++) {
if (i%100 == 0) fprintf(stderr," %d00\r",(int)(++progress));
const float *arr1 = gsl_matrix_float_const_ptr(mat,i,0);
for (j=0; j<i; j++) {
const float *arr2 = gsl_matrix_float_const_ptr(mat,j,0);
const double v = Correlation(arr1,arr2,nt);
const size_t k=j+i*(i+1)/2;
if (k >= m) VError(" illegal addr k= %d, m= %d",k,m);
A[k] = v;
}
}
fprintf(stderr," matrix done.\n");
gsl_matrix_float_free(mat);
return A;
}
VImage
SimilarityMatrix(VAttrList list, VImage mask, VShort minval, VShort fisher) {
VAttrListPosn posn;
VImage src[NSLICES], dest = NULL, map = NULL;
int b, r, c, i, j, n, ntimesteps, nrows, ncols, nslices;
double u, x, y, smin, smax, tiny = 1.0e-6;
gsl_matrix_float *mat = NULL;
/*
** get image dimensions
*/
i = ntimesteps = nrows = ncols = 0;
for(VFirstAttr(list, & posn); VAttrExists(& posn); VNextAttr(& posn)) {
if(VGetAttrRepn(& posn) != VImageRepn)
continue;
VGetAttrValue(& posn, NULL, VImageRepn, & src[i]);
if(VPixelRepn(src[i]) != VShortRepn)
continue;
if(VImageNBands(src[i]) > ntimesteps)
ntimesteps = VImageNBands(src[i]);
if(VImageNRows(src[i]) > nrows)
nrows = VImageNRows(src[i]);
if(VImageNColumns(src[i]) > ncols)
ncols = VImageNColumns(src[i]);
i++;
if(i >= NSLICES)
VError(" too many slices");
}
nslices = i;
n = 0;
for(b = 0; b < nslices; b++) {
if(VImageNRows(src[b]) < 2)
continue;
for(r = 0; r < nrows; r++) {
for(c = 0; c < ncols; c++) {
if(VPixel(src[b], 0, r, c, VShort) < minval)
continue;
if(VGetPixel(mask, b, r, c) < 0.1)
continue;
n++;
}
}
}
fprintf(stderr, " n: %d\n", n);
/*
** voxel addresses
*/
map = VCreateImage(1, 5, n, VFloatRepn);
VFillImage(map, VAllBands, 0);
VPixel(map, 0, 3, 0, VFloat) = nslices;
VPixel(map, 0, 3, 1, VFloat) = nrows;
VPixel(map, 0, 3, 2, VFloat) = ncols;
i = 0;
for(b = 0; b < nslices; b++) {
if(VImageNRows(src[b]) < 2)
continue;
for(r = 0; r < nrows; r++) {
for(c = 0; c < ncols; c++) {
if(VPixel(src[b], 0, r, c, VShort) < minval)
continue;
if(VGetPixel(mask, b, r, c) < 0.1)
continue;
VPixel(map, 0, 0, i, VFloat) = b;
VPixel(map, 0, 1, i, VFloat) = r;
VPixel(map, 0, 2, i, VFloat) = c;
i++;
}
}
}
/* ini output image */
if(n < 8000)
dest = VCreateImage(1, n, n, VFloatRepn);
else if(n >= 8000 && n < 10000)
dest = VCreateImage(1, n, n, VShortRepn);
else
dest = VCreateImage(1, n, n, VSByteRepn);
VFillImage(dest, VAllBands, 0);
smin = VPixelMinValue(dest);
smax = VPixelMaxValue(dest);
/*
** avoid casting to float, copy data to matrix
*/
mat = gsl_matrix_float_calloc(n, ntimesteps);
for(i = 0; i < n; i++) {
b = VPixel(map, 0, 0, i, VFloat);
r = VPixel(map, 0, 1, i, VFloat);
c = VPixel(map, 0, 2, i, VFloat);
float *ptr = gsl_matrix_float_ptr(mat, i, 0);
int k;
j = 0;
for(k = 0; k < ntimesteps; k++) {
if(j >= mat->size2)
VError(" j= %d %d", j, mat->size2);
if(k >= VImageNBands(src[b]))
VError(" k= %d %d", k, VImageNBands(src[b]));
*ptr++ = (float) VPixel(src[b], k, r, c, VShort);
j++;
}
}
/*
** main process loop
*/
for(i = 0; i < n; i++) {
if(i % 50 == 0)
fprintf(stderr, " i: %4d\r", i);
const float *arr1 = gsl_matrix_float_const_ptr(mat, i, 0);
for(j = 0; j <= i; j++) {
const float *arr2 = gsl_matrix_float_const_ptr(mat, j, 0);
u = Correlation(arr1, arr2, ntimesteps);
/* Fisher r-to-z transform */
if(fisher) {
x = 1 + u;
y = 1 - u;
if(x < tiny)
x = tiny;
if(y < tiny)
y = tiny;
u = 0.5 * log(x / y);
}
if(VPixelRepn(dest) != VFloatRepn) {
u *= smax;
if(u < smin)
u = smin;
if(u > smax)
u = smax;
}
VSetPixel(dest, 0, i, j, u);
VSetPixel(dest, 0, j, i, u);
}
}
VCopyImageAttrs(src[0], dest);
VSetAttr(VImageAttrList(dest), "similarity_metric", NULL, VStringRepn, "corr_pearson");
VSetAttr(VImageAttrList(dest), "name", NULL, VStringRepn, "similarity_matrix");
VSetAttr(VImageAttrList(dest), "map", NULL, VImageRepn, map);
return dest;
}
