inline
void
reaccum_moments(accum &A, int *t, int n)
{
clear_accum(A);
for(int i=0; i<n; i++)
accum_moment(A, RAPID_moment[t[i]]);
}
void nemo_main(void)
{
char line[80];
stream instr = stropen(getparam("in"),"r");
int mom = getiparam("moment");
int maxsize = getiparam("maxsize");
real x;
Moment m;
bool Qminmax = getbparam("minmax");
bool Qmedian = getbparam("median");
ini_moment(&m,ABS(mom),maxsize);
while (fgets(line,80,instr) != NULL) {
x = atof(line);
accum_moment(&m,x,1.0);
if (maxsize > 0) {
debug_moment(1,&m);
printf("%d ",n_moment(&m));
if (Qminmax)
printf("%g %g\n",min_moment(&m), max_moment(&m));
else if (Qmedian)
printf("%g\n",median_moment(&m));
else
printf("%g\n",show_moment(&m,mom));
}
}
if (maxsize == 0) {
printf("%d ",n_moment(&m));
if (Qminmax)
printf("%g %g\n",min_moment(&m), max_moment(&m));
else if (Qmedian)
printf("%g\n",median_moment(&m));
else
printf("%g\n",show_moment(&m,mom));
}
}
nemo_main()
{
stream instr, tabstr;
real tsnap, ekin, etot, dr, r, rv, v, vr, vt, aux;
real varmin[MAXOPT], varmax[MAXOPT];
real var0[MAXOPT], var1[MAXOPT], var2[MAXOPT];
Moment var[MAXOPT];
string headline=NULL, options, times, mnmxmode;
Body *btab = NULL, *bp, *bq;
bool Qmin, Qmax, Qmean, Qsig, Qtime, scanopt();
int i, n, nbody, bits, nsep, isep, nopt, ParticlesBit;
char fmt[20],*pfmt;
string *burststring(), *opt;
rproc btrtrans(), fopt[MAXOPT], faux;
ParticlesBit = (MassBit | PhaseSpaceBit | PotentialBit | AccelerationBit |
AuxBit | KeyBit);
instr = stropen(getparam("in"), "r"); /* open input file */
mnmxmode= getparam("mode");
opt = burststring(getparam("var"),", ");
nopt = 0; /* count options */
while (opt[nopt]) { /* scan through options */
fopt[nopt] = btrtrans(opt[nopt]);
nopt++;
if (nopt==MAXOPT) {
dprintf(0,"\n\nMaximum number of var's = %d exhausted\n",MAXOPT);
break;
}
}
dprintf(0,"var: \n");
for (i=0; i<nopt; i++)
dprintf(0,"%s ",opt[i]);
dprintf(0,"\n");
dprintf(0,"mode: %s\n",mnmxmode);
Qmin = scanopt(mnmxmode,"min");
Qmax = scanopt(mnmxmode,"max");
Qmean = scanopt(mnmxmode,"mean");
Qsig = scanopt(mnmxmode,"sigma");
Qtime = scanopt(mnmxmode,"time");
if (!Qmin && !Qmax && !Qmean && !Qsig && !Qtime)
error("No mode selected");
#if 0
pfmt = getparam("tab");
if (pfmt!=NULL && *pfmt!=NULL) {
dprintf(0,"Saving table in %s\n",pfmt);
tabstr = stropen(pfmt,"w");
} else
#endif
tabstr = stdout;
times = getparam("times");
pfmt = getparam("format");
strcpy (fmt,pfmt);
if (strchr(fmt,' ')==NULL && strchr(fmt,',')==NULL)
strcat (fmt," "); /* append blank if user did not specify sep */
get_history(instr); /* read history */
for(;;) { /* repeating until first or all times are read */
get_history(instr);
if (!get_tag_ok(instr, SnapShotTag))
break; /* done with work */
get_snap(instr, &btab, &nbody, &tsnap, &bits);
if (!streq(times,"all") && !within(tsnap,times,0.0001))
continue; /* skip work on this snapshot */
if ( (bits & ParticlesBit) == 0)
continue; /* skip work, only diagnostics here */
for (bp = btab, i=0; bp < btab+nbody; bp++, i++) {
for (n=0; n<nopt; n++) {
aux = fopt[n](bp,tsnap,i);
if (i==0) ini_moment(&var[n],2,0);
accum_moment(&var[n], aux, 1.0);
}
}
if (Qtime)
fprintf(tabstr,fmt,tsnap);
if (Qmin) {
for (n=0; n<nopt; n++)
fprintf(tabstr,fmt,min_moment(&var[n]));
}
if (Qmax) {
for (n=0; n<nopt; n++)
fprintf(tabstr,fmt,max_moment(&var[n]));
}
if (Qmean) {
for (n=0; n<nopt; n++)
fprintf(tabstr,fmt,mean_moment(&var[n]));
}
if (Qsig) {
for (n=0; n<nopt; n++)
fprintf(tabstr,fmt,sigma_moment(&var[n]));
}
fprintf(tabstr,"\n");
}
strclose(instr);
}
nemo_main()
{
int i, j, k;
real x, xmin, xmax, mean, sigma, skew, kurt, median, bad, w, *data;
real sum, sov;
Moment m;
bool Qmin, Qmax, Qbad, Qw, Qmedian, Qmmcount = getbparam("mmcount");
real nu, nppb = getdparam("nppb");
int npar = getiparam("npar");
int ngood = 0;
int min_count, max_count;
instr = stropen (getparam("in"), "r");
read_image (instr,&iptr);
strclose(instr);
if (hasvalue("win")) {
instr = stropen (getparam("win"), "r");
read_image (instr,&wptr);
strclose(instr);
if (Nx(iptr) != Nx(wptr)) error("X sizes of in/win don't match");
if (Ny(iptr) != Ny(wptr)) error("X sizes of in/win don't match");
if (Nz(iptr) != Nz(wptr)) error("X sizes of in/win don't match");
Qw = TRUE;
} else
Qw = FALSE;
nx = Nx(iptr);
ny = Ny(iptr);
nz = Nz(iptr);
Qmin = hasvalue("min");
if (Qmin) xmin = getdparam("min");
Qmax = hasvalue("max");
if (Qmax) xmax = getdparam("max");
Qbad = hasvalue("bad");
if (Qbad) bad = getdparam("bad");
Qmedian = getbparam("median");
if (Qmedian)
data = (real *) allocate(nx*ny*nz*sizeof(real));
sov = Dx(iptr)*Dy(iptr)*Dz(iptr); /* voxel volume; TODO: should we do 2D vs. 3D ? */
ini_moment(&m,4,0);
for (i=0; i<nx; i++) {
for (j=0; j<ny; j++) {
for (k=0; k<nz; k++) {
x = CubeValue(iptr,i,j,k);
if (Qmin && x<xmin) continue;
if (Qmax && x>xmax) continue;
if (Qbad && x==bad) continue;
w = Qw ? CubeValue(wptr,i,j,k) : 1.0;
accum_moment(&m,x,w);
if (Qmedian) data[ngood++] = x;
}
}
}
if (npar > 0) {
nu = n_moment(&m)/nppb - npar;
if (nu < 1) error("%g: No degrees of freedom",nu);
printf("chi2= %g\n", show_moment(&m,2)/nu/nppb);
printf("df= %g\n", nu);
} else {
nsize = nx * ny * nz;
mean = mean_moment(&m);
sigma = sigma_moment(&m);
skew = skewness_moment(&m);
kurt = kurtosis_moment(&m);
sum = show_moment(&m,1);
printf ("Min=%f Max=%f\n",min_moment(&m), max_moment(&m));
printf ("Number of points : %d\n",n_moment(&m));
printf ("Mean and dispersion : %f %f\n",mean,sigma);
printf ("Skewness and kurtosis : %f %f\n",skew,kurt);
printf ("Sum and Sum*Dx*Dy*Dz : %f %f\n",sum, sum*sov);
if (Qmedian)
printf ("Median : %f\n",get_median(ngood,data));
if (Qmmcount) {
min_count = max_count = 0;
xmin = min_moment(&m);
xmax = max_moment(&m);
for (i=0; i<nx; i++) {
for (j=0; j<ny; j++) {
for (k=0; k<nz; k++) {
x = CubeValue(iptr,i,j,k);
if (x==xmin) min_count++;
if (x==xmax) max_count++;
}
}
} /* i */
printf("Min_Max_count : %d %d\n",min_count,max_count);
}
printf ("%d/%d out-of-range points discarded\n",nsize-n_moment(&m), nsize);
}
}
nemo_main()
{
int i, j, k, ki;
real x, y, z, xmin, xmax, mean, sigma, skew, kurt, bad, w, *data;
real dmin, dmax;
real sum, sov, q1, q2, q3;
Moment m;
bool Qmin, Qmax, Qbad, Qw, Qmedian, Qrobust, Qtorben, Qmmcount = getbparam("mmcount");
bool Qx, Qy, Qz, Qone, Qall, Qign = getbparam("ignore");
bool Qhalf = getbparam("half");
bool Qmaxpos = getbparam("maxpos");
real nu, nppb = getdparam("nppb");
int npar = getiparam("npar");
int ngood;
int ndat = 0;
int nplanes;
int min_count, max_count;
int maxmom = getiparam("maxmom");
int maxpos[2];
char slabel[32];
instr = stropen (getparam("in"), "r");
read_image (instr,&iptr);
strclose(instr);
nx = Nx(iptr);
ny = Ny(iptr);
nz = Nz(iptr);
dprintf(1,"# data order debug: %f %f\n",Frame(iptr)[0], Frame(iptr)[1]);
if (hasvalue("tab")) tabstr = stropen(getparam("tab"),"w");
planes = (int *) allocate((nz+1)*sizeof(int));
nplanes = nemoinpi(getparam("planes"),planes,nz+1);
Qall = (planes[0]==-1);
if (planes[0]==0) {
Qone = FALSE;
nplanes = nz;
for (k=0; k<nz; k++)
planes[k] = k;
} else if (!Qall) {
Qone = (!Qall && nplanes==1);
for (k=0; k<nplanes; k++) {
if (planes[k]<1 || planes[k]>nz) error("%d is an illegal plane [1..%d]",planes[k],nz);
planes[k]--;
}
}
if (hasvalue("win")) {
instr = stropen (getparam("win"), "r");
read_image (instr,&wptr);
strclose(instr);
if (Nx(iptr) != Nx(wptr)) error("X sizes of in=/win= don't match");
if (Ny(iptr) != Ny(wptr)) error("Y sizes of in=/win= don't match");
if (Nz(iptr) != Nz(wptr)) error("Z sizes of in=/win= don't match");
Qw = TRUE;
} else
Qw = FALSE;
Qmin = hasvalue("min");
if (Qmin) xmin = getdparam("min");
Qmax = hasvalue("max");
if (Qmax) xmax = getdparam("max");
Qbad = hasvalue("bad");
if (Qbad) bad = getdparam("bad");
Qmedian = getbparam("median");
Qrobust = getbparam("robust");
Qtorben = getbparam("torben");
if (Qtorben) Qmedian = TRUE;
if (Qmedian || Qrobust || Qtorben) {
ndat = nx*ny*nz;
data = (real *) allocate(ndat*sizeof(real));
}
sov = 1.0; /* volume of a pixel/voxel */
Qx = Qign && Nx(iptr)==1;
Qy = Qign && Ny(iptr)==1;
Qz = Qign && Nz(iptr)==1;
sov *= Qx ? 1.0 : Dx(iptr);
sov *= Qy ? 1.0 : Dy(iptr);
sov *= Qz ? 1.0 : Dz(iptr);
strcpy(slabel,"*");
if (!Qx) strcat(slabel,"Dx*");
if (!Qy) strcat(slabel,"Dy*");
if (!Qz) strcat(slabel,"Dz*");
if (maxmom < 0) {
warning("No work done, maxmom<0");
stop(0);
}
if (Qall) { /* treat cube as one data block */
ini_moment(&m,maxmom,ndat);
ngood = 0;
for (k=0; k<nz; k++) {
for (j=0; j<ny; j++) {
for (i=0; i<nx; i++) {
x = CubeValue(iptr,i,j,k);
if (Qhalf && x>=0.0) continue;
if (Qmin && x<xmin) continue;
if (Qmax && x>xmax) continue;
if (Qbad && x==bad) continue;
w = Qw ? CubeValue(wptr,i,j,k) : 1.0;
accum_moment(&m,x,w);
if (Qhalf && x<0) accum_moment(&m,-x,w);
if (Qmedian) data[ngood++] = x;
if (tabstr) fprintf(tabstr,"%g\n",x);
}
}
}
if (npar > 0) {
nu = n_moment(&m)/nppb - npar;
if (nu < 1) error("%g: No degrees of freedom",nu);
printf("chi2= %g\n", show_moment(&m,2)/nu/nppb);
printf("df= %g\n", nu);
} else {
nsize = nx * ny * nz;
sum = mean = sigma = skew = kurt = 0;
if (maxmom > 0) {
mean = mean_moment(&m);
sum = show_moment(&m,1);
}
if (maxmom > 1)
sigma = sigma_moment(&m);
if (maxmom > 2)
skew = skewness_moment(&m);
if (maxmom > 3)
kurt = kurtosis_moment(&m);
printf ("Number of points : %d\n",n_moment(&m));
printf ("Min and Max : %f %f\n",min_moment(&m), max_moment(&m));
printf ("Mean and dispersion : %f %f\n",mean,sigma);
printf ("Skewness and kurtosis : %f %f\n",skew,kurt);
printf ("Sum and Sum*%s : %f %f\n",slabel, sum, sum*sov);
if (Qmedian) {
if (Qtorben) {
printf ("Median Torben : %f (%d)\n",median_torben(ngood,data,min_moment(&m),max_moment(&m)),ngood);
} else {
printf ("Median : %f\n",get_median(ngood,data));
q2 = median(ngood,data);
q1 = median_q1(ngood,data);
q3 = median_q3(ngood,data);
printf ("Q1,Q2,Q3 : %f %f %f\n",q1,q2,q3);
}
#if 1
if (ndat>0)
printf ("MedianL : %f\n",median_moment(&m));
#endif
}
if (Qrobust) {
compute_robust_moment(&m);
printf ("Mean Robust : %f\n",mean_robust_moment(&m));
printf ("Sigma Robust : %f\n",sigma_robust_moment(&m));
printf ("Median Robust : %f\n",median_robust_moment(&m));
}
if (Qmmcount) {
min_count = max_count = 0;
xmin = min_moment(&m);
xmax = max_moment(&m);
for (i=0; i<nx; i++) {
for (j=0; j<ny; j++) {
for (k=0; k<nz; k++) {
x = CubeValue(iptr,i,j,k);
if (x==xmin) min_count++;
if (x==xmax) max_count++;
}
}
} /* i */
printf("Min_Max_count : %d %d\n",min_count,max_count);
}
printf ("%d/%d out-of-range points discarded\n",nsize-n_moment(&m), nsize);
}
} else { /* treat each plane seperately */
/* tabular output, one line per (selected) plane */
printf("# iz z min max N mean sigma skew kurt sum sumsov ");
if (Qmedian) printf(" [med med]");
if (Qrobust) printf(" robust[N mean sig med]");
if (Qmaxpos) printf(" maxposx maxposy");
printf("\n");
ini_moment(&m,maxmom,ndat);
for (ki=0; ki<nplanes; ki++) {
reset_moment(&m);
k = planes[ki];
z = Zmin(iptr) + k*Dz(iptr);
ngood = 0;
for (j=0; j<ny; j++) {
for (i=0; i<nx; i++) {
x = CubeValue(iptr,i,j,k);
if (Qmin && x<xmin) continue;
if (Qmax && x>xmax) continue;
if (Qbad && x==bad) continue;
if (Qmaxpos) {
if (i==0 && j==0) { dmax = x; maxpos[0] = 0; maxpos[1] = 0;}
else if (x>dmax) { dmax = x; maxpos[0] = i; maxpos[1] = j;}
}
w = Qw ? CubeValue(wptr,i,j,k) : 1.0;
accum_moment(&m,x,w);
if (Qmedian) data[ngood++] = x;
}
}
nsize = nx * ny * nz;
sum = mean = sigma = skew = kurt = 0;
if (maxmom > 0) {
mean = mean_moment(&m);
sum = show_moment(&m,1);
}
if (maxmom > 1)
sigma = sigma_moment(&m);
if (maxmom > 2)
skew = skewness_moment(&m);
if (maxmom > 3)
kurt = kurtosis_moment(&m);
if (n_moment(&m) == 0) {
printf("# %d no data\n",k+1);
continue;
}
printf("%d %f %f %f %d %f %f %f %f %f %f",
k+1, z, min_moment(&m), max_moment(&m), n_moment(&m),
mean,sigma,skew,kurt,sum,sum*sov);
if (Qmedian) {
printf (" %f",get_median(ngood,data));
if (ndat>0) printf (" %f",median_moment(&m));
}
if (Qrobust) {
compute_robust_moment(&m);
printf (" %d %f %f %f",n_robust_moment(&m), mean_robust_moment(&m),
sigma_robust_moment(&m), median_robust_moment(&m));
}
if (Qmaxpos) {
printf(" %d %d",maxpos[0],maxpos[1]);
}
#if 0
if (Qmmcount) {
min_count = max_count = 0;
xmin = min_moment(&m);
xmax = max_moment(&m);
for (i=0; i<nx; i++) {
for (j=0; j<ny; j++) {
x = CubeValue(iptr,i,j,k);
if (x==xmin) min_count++;
if (x==xmax) max_count++;
}
} /* i,j */
printf(" %d %d",min_count,max_count);
}
printf ("%d/%d out-of-range points discarded\n",nsize-n_moment(&m), nsize);
#endif
printf("\n");
} /* ki */
}
}
local void histogram(void)
{
int i,j,k, l, kmin, kmax, lcount = 0;
real count[MAXHIST], under, over;
real xdat,ydat,xplt,yplt,dx,r,sum,sigma2, q, qmax;
real mean, sigma, mad, skew, kurt, h3, h4, lmin, lmax, median;
real rmean, rsigma, rrange[2];
Moment m;
dprintf (0,"read %d values\n",npt);
dprintf (0,"min and max value in column(s) %s: %g %g\n",getparam("xcol"),xmin,xmax);
if (!Qauto) {
xmin = xrange[0];
xmax = xrange[1];
dprintf (0,"min and max value reset to : %g %g\n",xmin,xmax);
lmin = xmax;
lmax = xmin;
for (i=0; i<npt; i++) {
if (x[i]>xmin && x[i]<=xmax) {
lmin = MIN(lmin, x[i]);
lmax = MAX(lmax, x[i]);
}
}
dprintf (0,"min and max value in range : %g %g\n",lmin,lmax);
}
for (k=0; k<nsteps; k++)
count[k] = 0; /* init histogram */
under = over = 0;
ini_moment(&m, 4, Qrobust||Qmad ? npt : 0);
for (i=0; i<npt; i++) {
if (Qbin) {
k=ring_index(nsteps,bins,x[i]);
} else {
if (xmax != xmin)
k = (int) floor((x[i]-xmin)/(xmax-xmin)*nsteps);
else
k = 0;
dprintf(2,"%d k=%d %g\n",i,k,x[i]);
}
if (k==nsteps && x[i]==xmax) k--; /* include upper edge */
if (k<0) { under++; continue; }
if (k>=nsteps) { over++; continue; }
count[k] = count[k] + 1;
dprintf (4,"%d : %f %d\n",i,x[i],k);
accum_moment(&m,x[i],1.0);
}
if (under > 0) error("bug: under = %d",under);
if (over > 0) error("bug: over = %d",over);
under = Nunder;
over = Nover;
mean = mean_moment(&m);
sigma = sigma_moment(&m);
skew = skewness_moment(&m);
kurt = kurtosis_moment(&m);
h3 = h3_moment(&m);
h4 = h4_moment(&m);
if (Qmad) mad = mad_moment(&m);
if (nsigma > 0) { /* remove outliers iteratively, starting from the largest */
iq = (int *) allocate(npt*sizeof(int));
for (i=0; i<npt; i++) {
#if 1
iq[i] = x[i] < xmin || x[i] > xmax;
#else
iq[i] = 0;
#endif
}
lcount = 0;
do { /* loop to remove outliers one by one */
qmax = -1.0;
for (i=0, l=-1; i<npt; i++) { /* find largest deviation from current mean */
if (iq[i]) continue; /* but skip previously flagged points */
q = (x[i]-mean)/sigma;
q = ABS(q);
if (q > qmax) {
qmax = q;
l = i;
}
}
if (qmax > nsigma) {
lcount++;
iq[l] = 1;
decr_moment(&m,x[l],1.0);
mean = mean_moment(&m);
sigma = sigma_moment(&m);
skew = skewness_moment(&m);
kurt = kurtosis_moment(&m);
h3 = h3_moment(&m);
h4 = h4_moment(&m);
if (Qmad) mad = mad_moment(&m);
dprintf(1,"%d/%d: removing point %d, m/s=%g %g qmax=%g\n",
lcount,npt,l,mean,sigma,qmax);
if (sigma <= 0) {
/* RELATED TO presetting MINMAX */
warning("BUG");
accum_moment(&m,x[l],1.0);
mean = mean_moment(&m);
sigma = sigma_moment(&m);
skew = skewness_moment(&m);
kurt = kurtosis_moment(&m);
h3 = h3_moment(&m);
h4 = h4_moment(&m);
dprintf(1,"%d/%d: LAST removing point %d, m/s=%g %g qmax=%g\n",
lcount,npt,l,mean,sigma,qmax);
break;
}
} else
dprintf(1,"%d/%d: keeping point %d, m/s=%g %g qmax=%g\n",
lcount,npt,l,mean,sigma,qmax);
/* if (lcount > npt/2) break; */
} while (qmax > nsigma);
dprintf(0,"Removed %d/%d points for nsigma=%g\n",lcount,npt,nsigma);
/* @algorithm left shift array values from mask array */
/* now shift all points into the array, decreasing npt */
/* otherwise the median is not correctly computed */
for (i=0, k=0; i<npt; i++) {
dprintf(1,"iq->%d\n",iq[i]);
if (iq[i]) k++;
if (k==0) continue; /* ?? */
if (i-k < 0) continue;
dprintf(1,"SHIFT: %d <= %d\n",i-k,i);
x[i-k] = x[i];
}
npt -= lcount; /* correct for outliers */
free(iq);
} /* nsigma > 0 */
if (npt != n_moment(&m))
error("Counting error, probably in removing outliers...");
dprintf (0,"Number of points : %d\n",npt);
if (npt>1)
dprintf (0,"Mean and dispersion : %g %g %g\n",mean,sigma,sigma/sqrt(npt-1.0));
else
dprintf (0,"Mean and dispersion : %g %g 0.0\n",mean,sigma);
if (Qmad) dprintf (0,"MAD : %g\n",mad);
dprintf (0,"Skewness and kurtosis: %g %g\n",skew,kurt);
dprintf (0,"h3 and h4 : %g %g\n", h3, h4);
if (Qmedian) {
if (npt % 2)
median = x[(npt-1)/2];
else
median = 0.5 * (x[npt/2] + x[npt/2-1]);
dprintf (0,"Median : %g\n",median);
} else if (Qtorben) {
median = median_torben(npt,x,xmin,xmax);
dprintf (0,"Median_torben : %g\n",median);
}
dprintf (0,"Sum : %g\n",show_moment(&m,1));
if (Qrobust) {
compute_robust_moment(&m);
rmean = mean_robust_moment(&m);
rsigma = sigma_robust_moment(&m);
robust_range(&m, rrange);
dprintf (0,"Robust N : %d\n",n_robust_moment(&m));
dprintf (0,"Robust Mean Disp : %g %g\n",rmean,rsigma);
dprintf (0,"Robust Range : %g %g\n",rrange[0],rrange[1]);
if (outstr) {
for (i=0; i<npt; i++) {
if (x[i]<rrange[0] || x[i]>rrange[1]) continue;
fprintf(outstr,"%g %d\n",x[i],i+1);
}
}
}
if (lcount > 0) {
warning("Recompute histogram because of outlier removals");
/* recompute histogram if we've lost some outliers */
for (k=0; k<nsteps; k++)
count[k] = 0; /* init histogram */
under = over = 0;
for (i=0; i<npt; i++) {
if (xmax != xmin)
k = (int) floor((x[i]-xmin)/(xmax-xmin)*nsteps);
else
k = 0;
if (k==nsteps && x[i]==xmax) k--; /* include upper edge */
if (k<0) { under++; continue; }
if (k>=nsteps) { over++; continue; }
count[k] = count[k] + 1;
dprintf (4,"%d : %f %d\n",i,x[i],k);
}
if (under > 0 || over > 0) error("under=%d over=%d in recomputed histo",under,over);
}
dprintf (3,"Histogram values : \n");
dx=(xmax-xmin)/nsteps;
kmax=0;
sum=0.0;
for (k=0; k<nsteps; k++) {
sum = sum + dx*count[k];
if (ylog) {
if (count[k]>0.0)
count[k] = log10(count[k]);
else
count[k] = -1.0;
}
if (count[k]>kmax)
kmax=count[k];
dprintf (3,"%f ",count[k]);
if (Qcumul) {
if (k==0)
count[k] += under;
else
count[k] += count[k-1];
}
}
dprintf (3,"\n");
sigma2 = 2.0 * sigma * sigma; /* gaussian */
sum /= sigma * sqrt(2*PI); /* scaling factor for equal area gauss */
if (ylog && over>0) over = log10(over);
if (ylog && under>0) under = log10(under);
kmax *= 1.1; /* add 10% */
if (Qcumul) kmax = npt;
if (maxcount>0) /* force scaling by user ? */
kmax=maxcount;
if (Qtab) {
maxcount = 0;
for (k=0; k<nsteps; k++)
maxcount = MAX(maxcount,count[k]);
if (maxcount>0)
r = 29.0/maxcount;
else
r = 1.0;
printf(" Bin Value Number\n");
printf(" Underflow %d\n",Nunder);
for (k=0; k<nsteps; k++) {
j = (int) (r*count[k]) + 1;
if (ylog) printf("%3d %13.6g %13.6g ",
k+1, xmin+(k+0.5)*dx, count[k]);
else printf("%3d %13.6g %8d ",
k+1, xmin+(k+0.5)*dx, (int)count[k]);
while (j-- > 0) printf("*");
printf("\n");
}
printf(" Overflow %d\n",Nover);
stop(0);
}
#ifdef YAPP
/* PLOTTING */
plinit("***",0.0,20.0,0.0,20.0);
xplot[0] = xmin;
xplot[1] = xmax;
yplot[0] = 0.0;
yplot[1] = (real) kmax;
xaxis (2.0, 2.0, 16.0, xplot, -7, xtrans, xlab);
xaxis (2.0, 18.0,16.0, xplot, -7, xtrans, NULL);
yaxis (2.0, 2.0, 16.0, yplot, -7, ytrans, ylab);
yaxis (18.0, 2.0, 16.0, yplot, -7, ytrans, NULL);
pljust(-1); /* set to left just */
pltext(input,2.0,18.2,0.32,0.0); /* filename */
pljust(1);
pltext(headline,18.0,18.2,0.24,0.0); /* headline */
pljust(-1); /* return to left just */
xdat=xmin;
dx=(xmax-xmin)/nsteps;
plmove(xtrans(xmin),ytrans(0.0));
for (k=0; k<nsteps; k++) { /* nsteps= */
xplt = xtrans(xdat);
yplt = ytrans((real)count[k]);
plline (xplt,yplt);
xdat += dx;
xplt = xtrans(xdat);
plline (xplt,yplt);
}
plline(xplt,ytrans(0.0));
for (i=0; i<nxcoord; i++) {
plmove(xtrans(xcoord[i]),ytrans(yplot[0]));
plline(xtrans(xcoord[i]),ytrans(yplot[1]));
}
if (Qgauss) { /* plot model and residuals */
if (ylog)
plmove(xtrans(xmin),ytrans(-1.0));
else
plmove(xtrans(xmin),ytrans(0.0));
for (k=0; k<100; k++) {
xdat = xmin + (k+0.5)*(xmax-xmin)/100.0;
ydat = sum * exp( -sqr(xdat-mean)/sigma2);
if (ylog) ydat = log10(ydat);
plline(xtrans(xdat), ytrans(ydat));
}
}
if (Qresid) {
plltype(0,2); /* dotted residuals */
xdat = xmin+0.5*dx;
dprintf(1,"# residuals from gauss\n");
for (k=0; k<nsteps; k++, xdat +=dx) {
ydat = sum * exp( -sqr(xdat-mean)/sigma2);
dprintf(1,"%g %g %g\n",xdat,count[k],ydat);
if (ylog) ydat = log10(ydat);
ydat = count[k] - ydat;
if (k==0)
plmove(xtrans(xdat),ytrans(ydat));
else
plline(xtrans(xdat),ytrans(ydat));
}
plltype(0,1); /* back to normal line type */
}
plstop();
#endif
}
int
box::split_recurse(int *t, int n)
{
// The orientation for the parent box is already assigned to this->pR.
// The axis along which to split will be column 0 of this->pR.
// The mean point is passed in on this->pT.
// When this routine completes, the position and orientation in model
// space will be established, as well as its dimensions. Child boxes
// will be constructed and placed in the parent's CS.
if (n == 1)
{
return split_recurse(t);
}
// walk along the tris for the box, and do the following:
// 1. collect the max and min of the vertices along the axes of <or>.
// 2. decide which group the triangle goes in, performing appropriate swap.
// 3. accumulate the mean point and covariance data for that triangle.
accum M1, M2;
double C[3][3];
double c[3];
double minval[3], maxval[3];
int rc; // for return code on procedure calls.
int in;
tri *ptr;
int i;
double axdmp;
int n1 = 0; // The number of tris in group 1.
// Group 2 will have n - n1 tris.
// project approximate mean point onto splitting axis, and get coord.
axdmp = (pR[0][0] * pT[0] + pR[1][0] * pT[1] + pR[2][0] * pT[2]);
clear_accum(M1);
clear_accum(M2);
MTxV(c, pR, RAPID_tri[t[0]].p1);
minval[0] = maxval[0] = c[0];
minval[1] = maxval[1] = c[1];
minval[2] = maxval[2] = c[2];
for(i=0; i<n; i++)
{
in = t[i];
ptr = RAPID_tri + in;
MTxV(c, pR, ptr->p1);
minmax(minval[0], maxval[0], c[0]);
minmax(minval[1], maxval[1], c[1]);
minmax(minval[2], maxval[2], c[2]);
MTxV(c, pR, ptr->p2);
minmax(minval[0], maxval[0], c[0]);
minmax(minval[1], maxval[1], c[1]);
minmax(minval[2], maxval[2], c[2]);
MTxV(c, pR, ptr->p3);
minmax(minval[0], maxval[0], c[0]);
minmax(minval[1], maxval[1], c[1]);
minmax(minval[2], maxval[2], c[2]);
// grab the mean point of the in'th triangle, project
// it onto the splitting axis (1st column of pR) and
// see where it lies with respect to axdmp.
mean_from_moment(c, RAPID_moment[in]);
if (((pR[0][0]*c[0] + pR[1][0]*c[1] + pR[2][0]*c[2]) < axdmp)
&& ((n!=2)) || ((n==2) && (i==0)))
{
// accumulate first and second order moments for group 1
accum_moment(M1, RAPID_moment[in]);
// put it in group 1 by swapping t[i] with t[n1]
int temp = t[i];
t[i] = t[n1];
t[n1] = temp;
n1++;
}
else
{
// accumulate first and second order moments for group 2
accum_moment(M2, RAPID_moment[in]);
// leave it in group 2
// do nothing...it happens by default
}
}
// done using this->pT as a mean point.
// error check!
if ((n1 == 0) || (n1 == n))
{
// our partitioning has failed: all the triangles fell into just
// one of the groups. So, we arbitrarily partition them into
// equal parts, and proceed.
n1 = n/2;
// now recompute accumulated stuff
reaccum_moments(M1, t, n1);
reaccum_moments(M2, t + n1, n - n1);
}
// With the max and min data, determine the center point and dimensions
// of the parent box.
c[0] = (minval[0] + maxval[0])*0.5;
c[1] = (minval[1] + maxval[1])*0.5;
c[2] = (minval[2] + maxval[2])*0.5;
pT[0] = c[0] * pR[0][0] + c[1] * pR[0][1] + c[2] * pR[0][2];
pT[1] = c[0] * pR[1][0] + c[1] * pR[1][1] + c[2] * pR[1][2];
pT[2] = c[0] * pR[2][0] + c[1] * pR[2][1] + c[2] * pR[2][2];
d[0] = (maxval[0] - minval[0])*0.5;
d[1] = (maxval[1] - minval[1])*0.5;
d[2] = (maxval[2] - minval[2])*0.5;
// allocate new boxes
P = RAPID_boxes + RAPID_boxes_inited++;
N = RAPID_boxes + RAPID_boxes_inited++;
// Compute the orienations for the child boxes (eigenvectors of
// covariance matrix). Select the direction of maximum spread to be
// the split axis for each child.
double tR[3][3];
if (n1 > 1)
{
mean_from_accum(P->pT, M1);
covariance_from_accum(C, M1);
if (eigen_and_sort1(tR, C) > 30)
{
// unable to find an orientation. We'll just pick identity.
Midentity(tR);
}
McM(P->pR, tR);
if ((rc = P->split_recurse(t, n1)) != RAPID_OK) return rc;
}
else
{
if ((rc = P->split_recurse(t)) != RAPID_OK) return rc;
}
McM(C, P->pR); MTxM(P->pR, pR, C); // and F1
VmV(c, P->pT, pT); MTxV(P->pT, pR, c);
if ((n-n1) > 1)
{
mean_from_accum(N->pT, M2);
covariance_from_accum (C, M2);
if (eigen_and_sort1(tR, C) > 30)
{
// unable to find an orientation. We'll just pick identity.
Midentity(tR);
}
McM(N->pR, tR);
if ((rc = N->split_recurse(t + n1, n - n1)) != RAPID_OK) return rc;
}
else
{
if ((rc = N->split_recurse(t+n1)) != RAPID_OK) return rc;
}
McM(C, N->pR); MTxM(N->pR, pR, C);
VmV(c, N->pT, pT); MTxV(N->pT, pR, c);
return RAPID_OK;
}
int
RAPID_model::build_hierarchy()
{
// allocate the boxes and set the box list globals
num_boxes_alloced = num_tris * 2;
b = new box[num_boxes_alloced];
if (b == 0) return RAPID_ERR_MODEL_OUT_OF_MEMORY;
RAPID_boxes = b;
RAPID_boxes_inited = 1; // we are in process of initializing b[0].
// Determine initial orientation, mean point, and splitting axis.
int i;
accum M;
// double F1[3];
// double S1[6];
double C[3][3];
RAPID_moment = new moment[num_tris];
if (RAPID_moment == 0)
{
delete [] b;
return RAPID_ERR_MODEL_OUT_OF_MEMORY;
}
compute_moments(RAPID_moment, tris, num_tris);
clear_accum(M);
for(i=0; i<num_tris; i++)
accum_moment(M, RAPID_moment[i]);
mean_from_accum(b[0].pT, M);
covariance_from_accum(C, M);
eigen_and_sort1(b[0].pR, C);
// create the index list
int *t = new int[num_tris];
if (t == 0)
{
delete [] b;
delete [] RAPID_moment;
return RAPID_ERR_MODEL_OUT_OF_MEMORY;
}
for(i=0; i<num_tris; i++) t[i] = i;
// set the tri pointer
RAPID_tri = tris;
// do the build
int rc = b[0].split_recurse(t, num_tris);
if (rc != RAPID_OK)
{
delete [] b;
delete [] RAPID_moment;
delete [] t;
return RAPID_ERR_MODEL_OUT_OF_MEMORY;
}
// free the moment list
delete [] RAPID_moment; RAPID_moment = 0;
// null the tri pointer
RAPID_tri = 0;
// free the index list
delete [] t;
return RAPID_OK;
}
int
build_recurse(PQP_Model *m, int bn, int first_tri, int num_tris)
{
BV *b = m->child(bn);
// compute a rotation matrix
PQP_REAL C[3][3], E[3][3], R[3][3], s[3], axis[3], mean[3], coord;
#if RAPID2_FIT
moment *tri_moment = new moment[num_tris];
compute_moments(tri_moment, &(m->tris[first_tri]), num_tris);
accum acc;
clear_accum(acc);
for(int i = 0; i < num_tris; i++) accum_moment(acc, tri_moment[i]);
delete [] tri_moment;
covariance_from_accum(C,acc);
#else
get_covariance_triverts(C,&m->tris[first_tri],num_tris);
#endif
Meigen(E, s, C);
// place axes of E in order of increasing s
int min, mid, max;
if (s[0] > s[1]) { max = 0; min = 1; }
else { min = 0; max = 1; }
if (s[2] < s[min]) { mid = min; min = 2; }
else if (s[2] > s[max]) { mid = max; max = 2; }
else { mid = 2; }
McolcMcol(R,0,E,max);
McolcMcol(R,1,E,mid);
R[0][2] = E[1][max]*E[2][mid] - E[1][mid]*E[2][max];
R[1][2] = E[0][mid]*E[2][max] - E[0][max]*E[2][mid];
R[2][2] = E[0][max]*E[1][mid] - E[0][mid]*E[1][max];
// fit the BV
b->FitToTris(R, &m->tris[first_tri], num_tris);
if (num_tris == 1)
{
// BV is a leaf BV - first_child will index a triangle
b->first_child = -(first_tri + 1);
}
else if (num_tris > 1)
{
// BV not a leaf - first_child will index a BV
b->first_child = m->num_bvs;
m->num_bvs+=2;
// choose splitting axis and splitting coord
McolcV(axis,R,0);
#if RAPID2_FIT
mean_from_accum(mean,acc);
#else
get_centroid_triverts(mean,&m->tris[first_tri],num_tris);
#endif
coord = VdotV(axis, mean);
// now split
int num_first_half = split_tris(&m->tris[first_tri], num_tris,
axis, coord);
// recursively build the children
build_recurse(m, m->child(bn)->first_child, first_tri, num_first_half);
build_recurse(m, m->child(bn)->first_child + 1,
first_tri + num_first_half, num_tris - num_first_half);
}
return PQP_OK;
}