first commit

README.md

+# recursive_gmconvert
+
+Implements divide-and-conquer gmm computation as in [1].
+
+## dependencies
+
+EMAN2 (blake.bcm.edu/emanwiki/EMAN2), used to compute the FSC.
+
+gnuplot (www.gnuplot.info/)
+
+## instalation
+
+1. clone the repository or download the zip and extract it (we refer to the root of the source tree as `SRC_DIR`).
+2. compile gmconvert: `cd ${SRC_DIR}/gmconvert/src ; make ; cd -`
+3. if you need to run some commands before executing the scripts, place them in modules.sh
+
+## usage:
+```
+${SRC_DIR}/recursive_gmconvert.sh MAP_NAME THRESHOLD n N i0 SERIAL
+```
+
+`MAP_NAME`: the file name of the input EM map
+
+`THRESHOLD`: the density threshold
+
+`n`: number of gaussians per sub-process. 2 or 4 is a good guess.
+
+`N`: number recursion levels.
+
+`i0`: initial recursion level (default: 1)
+
+`SERIAL`: if set to 1, uses the serial implementation (default: 0)
+
+The script creates a sub-directory (called `i`) for each recursion level `i`, and the output files are called `i/i.gmm` and `i_imp.gmm`.
+`i/i.gmm` contains the gmm in `gmconvert` format, and `i_imp.gmm` contains the gmm in `IMP` format (the conversion is handeld by `gmconvert2imp.sh`), which can be read in IMP using `IMP.isd.gmm_tools.decorate_gmm_from_text` function.
+
+Unless you set SERIAL to 1, all the scripts assume that you are running a cluster with the `slurm` queuing system.
+
+## References
+
+[1] Bayesian multi-scale modeling of macromolecular structures based on cryo-electron microscopy density maps
+
+Samuel Hanot, Massimiliano Bonomi, Charles H Greenberg, Andrej Sali, Michael Nilges, Michele Vendruscolo, Riccardo Pellarin
+
+doi: https://doi.org/10.1101/113951

get_maps.sh

+#!/bin/bash
+
+
+bindir=$(dirname $0)
+
+source ${bindir}/modules.sh
+
+for f in $(cat)
+do
+	echo $f
+	rsync -a -v -z --delete rsync.ebi.ac.uk::pub/databases/emdb/structures/EMD-${f}/ $f
+	cd $f
+	links http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-$f/analysis > header/map_analysis.txt
+	cutoff=$(awk '/contour/{print $4}' < header/map_analysis.txt)
+	resolution=$(sed -ne 's/[<>/ ]//g' -ne 's/resolutionByAuthor//gp' header/emd-${f}.xml)
+	echo ${cutoff} > cutoff.txt
+	echo ${resolution} > resolution.txt
+	gunzip -c map/emd_${f}.map.gz > emd_${f}.map
+	${bindir}/recursive_gmconvert.sh emd_${f}.map $cutoff $1 $2
+	cd -
+done

gmconvert/src/3DmapEM.c

+/*
+
+ <3DmapEM.c>
+ for EM fitting for Gaussian Mixture Model for 3D Voxel Map Data
+
+ LastModified: 2015/08/03 
+
+*/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <strings.h>
+#include <math.h>
+#include "globalvar.h"
+#include "pdbstruct.h"
+#include "PdbIO.h"
+#include "Radius.h"
+#include "gauss.h"
+#include "Matrix3D.h"
+#include "Voxel.h"
+#include "MCubeFunc.h"
+#include "PointEM.h"
+#include "GaussIO.h"
+#include "GridMap.h"
+
+/*** Functions (GLOBAL) ***/
+void Set_Less_Than_Threshold_Voxels_to_Zero();
+void Cal_Mean_and_CovarMat_from_Voxel();
+int  EM_optimize_GaussMix_for_3D_Map_SmallMemory();
+int  EM_optimize_GaussMix_for_3D_Map();
+double Corr_Coeff_Bwn_Two_Voxels();
+double Corr_Coeff_Bwn_Voxel_and_GMM();
+double Log_Likelihood_Of_GaussMix_For_3Dmap();
+void Set_GaussMix_Density_To_3Dmap();
+
+/*** Functions (LOCAL) ***/
+static void change_gdf_to_unit_CovM_center_M_zero_Weight();
+
+/*****************/
+/*** FUNCTIONS ***/
+/*****************/
+
+
+
+void Set_Less_Than_Threshold_Voxels_to_Zero(vox,threshold)
+ struct VOXEL *vox;
+ float threshold;  /* normally set to 0.0 */
+{
+ int x,y,z,Nback,Nfore,Nvoxel;
+ double Vvoxel;
+
+ /*
+ Modified By T.K. (2015/08/03) 
+ */
+ Nfore = 0;
+ Nback = 0;
+ Nvoxel = vox->N[0]*vox->N[1]*vox->N[2];
+ Vvoxel = vox->grid_width *  vox->grid_width * vox->grid_width;
+
+ for (x=0;x<vox->N[0];++x){
+   for (y=0;y<vox->N[1];++y){
+     for (z=0;z<vox->N[2];++z){ 
+       if (vox->dat[x][y][z] < threshold){ 
+        vox->dat[x][y][z] = 0.0; 
+        Nback += 1;
+       }
+       else{
+         Nfore += 1;
+       }
+     }
+   } 
+ }
+ printf("#Set_Less_Than_Threshold_Voxels_to_Zero(threshold %f Nzero %d/%d %.2lf %%)\n",threshold,
+   Nback,Nvoxel,100.0*(double)Nback/(double)Nvoxel);
+ printf("THRESHOLD         %lf\n",threshold); 
+ printf("NVOXEL_FOREGROUND %d\n",Nfore); 
+ printf("NVOXEL_BACKGROUND %d\n",Nback); 
+ printf("VOLUME_FOREGROUND %lf\n",Vvoxel * Nfore); 
+ printf("VOLUME_BACKGROUND %lf\n",Vvoxel * Nback); 
+  
+ 
+} /* end of Set_Less_Than_Threshold_Voxels_to_Zero() */
+
+
+                                                                                                               
+void Cal_Mean_and_CovarMat_from_Voxel(vox,M,Cov,Min,Max)
+ struct VOXEL *vox;
+ double  M[3];
+ double Cov[3][3];
+ double Min[3],Max[3];
+{
+ int x,y,z,i,j;
+ double sumDensity,X[3],SD,mag;
+ 
+ /** Cal Mean Vector **/
+ sumDensity = 0.0;
+ M[0] = M[1] = M[2] = 0.0;
+ for (x=0;x<vox->N[0];++x){  
+   X[0] = vox->OrigPos[0] + vox->grid_width *x;
+   for (y=0;y<vox->N[1];++y){ 
+     X[1] = vox->OrigPos[1] + vox->grid_width *y;
+     for (z=0;z<vox->N[2];++z) { 
+       if (vox->dat[x][y][z] > 0.0){
+         X[2] = vox->OrigPos[2] + vox->grid_width *z;
+         sumDensity  += vox->dat[x][y][z];
+         for (i=0;i<3;++i){
+            M[i] += vox->dat[x][y][z] * X[i];
+         }
+       }
+     }
+   }
+ }
+
+ printf("#sumDensity %lf\n",sumDensity);
+
+ for (i=0;i<3;++i){ M[i] /= sumDensity;}
+
+ /** Cal Covariance Matrix **/
+
+  for (i=0;i<3;++i){
+    for (j=0;j<3;++j){
+      Cov[i][j] = 0.0;
+    }
+  }
+
+  for (x=0;x<vox->N[0];++x){  
+    X[0] = vox->OrigPos[0] + vox->grid_width *x;
+    for (y=0;y<vox->N[1];++y){ 
+      X[1] = vox->OrigPos[1] + vox->grid_width *y;
+      for (z=0;z<vox->N[2];++z){ 
+        if (vox->dat[x][y][z] > 0.0){
+          X[2] = vox->OrigPos[2] + vox->grid_width *z;
+          for (i=0;i<3;++i){ 
+            for (j=0;j<3;++j){ 
+              Cov[i][j] += vox->dat[x][y][z]*(X[i]-M[i])*(X[j]-M[j]);
+            }
+          }
+        }
+      }
+    }
+  }
+
+
+  for (i=0;i<3;++i){
+    for (j=0;j<3;++j){
+      Cov[i][j] /= sumDensity;
+    }
+  }
+
+
+  mag = 2.0;
+  for (i=0;i<3;++i){
+    SD = sqrt(Cov[i][i]);
+    Min[i] = M[i] - mag * SD;
+    Max[i] = M[i] + mag * SD;
+  }
+
+} /* end of Cal_Mean_and_CovarMat_from_Voxel() */
+
+
+
+
+
+int EM_optimize_GaussMix_for_3D_Map_SmallMemory(Nrepeat,Ngauss,Garray,vox,logLike_final)
+ int Nrepeat;
+ int Ngauss;
+ struct GAUSS3D *Garray;
+ struct VOXEL  *vox;
+ double *logLike_final;
+{
+ int r,g,h,i,j,x,y,z;
+ struct VOXEL Hvox;
+ double sumG,val,Hg_sum_a,logLike,sumDensity;
+ float X[3];
+
+ printf("#EM_optimize_GaussMix_for_3D_Map_SmallMemory(Nrepeat,Ngauss,Garray,vox,logLike_final)\n");
+
+ /*** [1] Malloc matrix Hvox ***/
+ Malloc_Voxel(&Hvox,vox->N[0],vox->N[1],vox->N[2]);
+ sumDensity = 0.0;
+ for (x=0;x<vox->N[0];++x){
+   for (y=0;y<vox->N[1];++y){
+     for (z=0;z<vox->N[2];++z){ sumDensity  += vox->dat[x][y][z];}
+   }
+ }
+ /*** [3] Iteration(r) for EM  ***/
+ 
+ r = 0;
+ while (r<Nrepeat){
+  for (g=0;g<Ngauss;++g){ 
+    /** (1) Calculate Hvox **/ 
+    for (x=0;x<vox->N[0];++x){  
+      X[0] = vox->OrigPos[0] + vox->grid_width *x;
+      for (y=0;y<vox->N[1];++y){ 
+        X[1] = vox->OrigPos[1] + vox->grid_width *y;
+        for (z=0;z<vox->N[2];++z){ 
+          X[2] = vox->OrigPos[2] + vox->grid_width *z;
+          sumG = 0.0;
+          for (h=0;h<Ngauss;++h){ 
+            val = Garray[h].Weight * Gaussian3D_Value_Table(&(Garray[h]),X);
+            sumG += val;
+            if (h==g){Hvox.dat[x][y][z] = val;}
+          } 
+          Hvox.dat[x][y][z] /= sumG;
+       } 
+     } 
+   } 
+
+   /** (2) Calculate Weight[g] **/ 
+   Hg_sum_a =  0.0;
+   for (x=0;x<vox->N[0];++x){
+     for (y=0;y<vox->N[1];++y){
+       for (z=0;z<vox->N[2];++z){Hg_sum_a += vox->dat[x][y][z] * Hvox.dat[x][y][z];}
+     }
+   }
+
+   Garray[g].Weight = Hg_sum_a/sumDensity;
+
+   for (i=0;i<3;++i) Garray[g].M[i] = 0.0;
+    
+    /* (3) Calculate Mean M[] */
+    for (x=0;x<vox->N[0];++x){
+      X[0] = vox->OrigPos[0] + vox->grid_width *x;
+      for (y=0;y<vox->N[1];++y){ 
+        X[1] = vox->OrigPos[1] + vox->grid_width *y;
+        for (z=0;z<vox->N[2];++z){ 
+          X[2] = vox->OrigPos[2] + vox->grid_width *z;
+          for (i=0;i<3;++i) { Garray[g].M[i] += vox->dat[x][y][z] * Hvox.dat[x][y][z] * X[i];}
+        }
+      }
+    } 
+
+   for (i=0;i<3;++i){Garray[g].M[i]  /=  Hg_sum_a;}
+
+    /* (4) Caluclate Covariance Matrix CovM[] */
+    for (i=0;i<3;++i){ 
+     for (j=0;j<3;++j){Garray[g].CovM[i][j] = 0.0;}
+    }
+
+   for (x=0;x<vox->N[0];++x){
+     X[0] = vox->OrigPos[0] + vox->grid_width *x;
+     for (y=0;y<vox->N[1];++y){ 
+       X[1] = vox->OrigPos[1] + vox->grid_width *y;
+       for (z=0;z<vox->N[2];++z){ 
+         X[2] = vox->OrigPos[2] + vox->grid_width *z;
+         for (i=0;i<3;++i){ 
+           for (j=0;j<3;++j){ 
+             Garray[g].CovM[i][j] +=  
+             vox->dat[x][y][z] * Hvox.dat[x][y][z]  * (X[i]- Garray[g].M[i]) * (X[j] - Garray[g].M[j]);
+           }
+         }
+       }
+      }
+    }
+
+   for (i=0;i<3;++i){ 
+     for (j=0;j<3;++j){ Garray[g].CovM[i][j] /= Hg_sum_a;}
+   }
+   
+  /* (5) Set up iCovM */
+   Cal_Inverse_Matrix3D_by_Cramer_Rule(Garray[g].iCovM, Garray[g].CovM, &(Garray[g].det));
+   Garray[g].Cons = 1.0/(two_pi_32*sqrt(Garray[g].det));
+
+  } /* g */
+
+  logLike = Log_Likelihood_Of_GaussMix_For_3Dmap(Ngauss,Garray,vox);
+
+  printf("Step %d logLike %lf\n",r,logLike);
+  *logLike_final = logLike;
+  ++r;
+ } /* r */
+
+ Free_Voxel(&Hvox,vox->N[0],vox->N[1],vox->N[2]);
+ return(1);
+} /* end of EM_optimize_GaussMix_for_3D_Map_SmallMemory() */
+
+
+
+
+
+
+
+
+int EM_optimize_GaussMix_for_3D_Map(Nrepeat,Ngauss,Garray,vox,logLike_final,ConvThre)
+ int Nrepeat;
+ int Ngauss;
+ struct GAUSS3D *Garray;
+ struct VOXEL  *vox;
+ double *logLike_final;
+ double ConvThre;  /* Threshold value for logLike convergence */
+{
+ struct FLOAT4DMAP Ppost; /* prob(g/x,y,z) [x][y][z][g] */
+ /* struct DOUBLE4DMAP Ppost; */ /* prob(g/x,y,z) [x][y][z][g] */
+ float *X,*Y,*Z;          /* X,Y,Z coordinates [x], [y], [z] */
+ double sumDensity;             /* sum of density vox->dat[][][] */
+ double Sxyz_vox_hpost;    /* sum of density x hpos */
+ int r,g,i,j,x,y,z;
+ double sumWG,logLike,logLikePre,DiffLogLike,minVar;
+ float Pos[3];
+
+ minVar = (0.5*vox->grid_width)*(0.5*vox->grid_width);
+ 
+ printf("#EM_optimize_GaussMix_for_3D_Map(N %d %d %d Ngauss %d Nrep %d ConvThre %lf grid_width %lf minVar %lf)\n",
+  vox->N[0],vox->N[1],vox->N[2],Ngauss,Nrepeat,ConvThre,vox->grid_width,minVar);
+
+ if (PAR.ologfile[0]!='\0'){
+   fprintf(PAR.fp_olog,"#EM_optimize_GaussMix_for_3D_Map(N %d %d %d Ngauss %d Nrep %d ConvThre %lf)\n",
+    vox->N[0],vox->N[1],vox->N[2],Ngauss,Nrepeat,ConvThre);
+ }
+  
+ logLike = Log_Likelihood_Of_GaussMix_For_3Dmap(Ngauss,Garray,vox);
+ logLikePre = logLike - 10000.0*ConvThre;
+
+ if (PAR.ologfile[0]!='\0'){
+   fprintf(PAR.fp_olog,"#Initial_logLike %lf %e\n",logLike,logLike);
+   fprintf(PAR.fp_olog,"#[Step] [logLike]  [Diff] [Voxel_sizeX]:[Y]:[Z]\n");
+ }
+
+
+
+
+ /*** [1] Malloc matrix Ppost[][][][] and X[],Y[],Z[] ***/
+ X = (float *)malloc(sizeof(float)*vox->N[0]);
+ Y = (float *)malloc(sizeof(float)*vox->N[1]);
+ Z = (float *)malloc(sizeof(float)*vox->N[2]);
+
+ Malloc_FLOAT4DMAP(&Ppost,vox->N[0],vox->N[1],vox->N[2],Ngauss);
+/* 
+ Malloc_DOUBLE4DMAP(&Ppost,vox->N[0],vox->N[1],vox->N[2],Ngauss);
+ */
+
+ /*** [2] Calculate sumDensity and X[],Y[],Z[] ***/
+ sumDensity = 0.0;
+ for (x=0;x<vox->N[0];++x){
+   for (y=0;y<vox->N[1];++y){
+     for (z=0;z<vox->N[2];++z){
+       if (vox->dat[x][y][z]>0.0){
+         sumDensity  += vox->dat[x][y][z];
+       }
+     }
+   }
+ }
+
+ for (x=0;x<vox->N[0];++x){ X[x] = vox->OrigPos[0] + vox->grid_width * x;}
+ for (y=0;y<vox->N[1];++y){ Y[y] = vox->OrigPos[1] + vox->grid_width * y;}
+ for (z=0;z<vox->N[2];++z){ Z[z] = vox->OrigPos[2] + vox->grid_width * z;}
+
+ /*** [3] Iteration(r) for EM  ***/
+ 
+ r = 0;
+
+ do {
+   
+   /** [E-step] Calculate Ppost **/ 
+   logLike = 0.0;
+   for (x=0;x<vox->N[0];++x){
+     Pos[0] = X[x];
+     for (y=0;y<vox->N[1];++y){
+       Pos[1] = Y[y];
+       for (z=0;z<vox->N[2];++z){
+         Pos[2] = Z[z];
+         if (vox->dat[x][y][z]>0.0){
+           sumWG = 0.0;
+           for (g=0;g<Ngauss;++g){  
+             if (Garray[g].Weight > 0.0){ 
+               Ppost.dat[x][y][z][g] = Garray[g].Weight * Gaussian3D_Value_Table(&(Garray[g]),Pos);
+               sumWG += Ppost.dat[x][y][z][g];
+              }
+              else {
+                Ppost.dat[x][y][z][g] = 0.0;
+              }
+           } 
+
+           if (sumWG > 0.0){ 
+             for (g=0;g<Ngauss;++g){
+                Ppost.dat[x][y][z][g] /= sumWG;
+             }
+             logLike += vox->dat[x][y][z] * log(sumWG);
+           }
+         }
+       }
+     }
+   } 
+ 
+   DiffLogLike = logLike - logLikePre; 
+   logLikePre = logLike;
+
+   printf("#step %d %lf %lf %d:%d:%d\n",r,logLike,DiffLogLike,vox->N[0],vox->N[1],vox->N[2]);
+   if (PAR.ologfile[0]!='\0'){
+     fprintf(PAR.fp_olog,"%d %lf  %lf %d:%d:%d\n",r,logLike,DiffLogLike,vox->N[0],vox->N[1],vox->N[2]);
+   }
+ 
+   /** [M-step] Calculate Weight[],M[], Sig[][] by g-loop  **/ 
+   for (g=0;g<Ngauss;++g){
+    if (Garray[g].Weight > 0.0){
+      /** (2-0) Initialize **/ 
+      Sxyz_vox_hpost = 0.0; 
+      for (i=0;i<3;++i){ 
+        Garray[g].M[i] = 0.0;
+        for (j=0;j<3;++j){Garray[g].CovM[i][j] = 0.0;}
+      }
+  
+      /** (2-1) Summation for x,y,z to get Sxyz_vox_hpost, M[],CovM **/ 
+      for (x=0;x<vox->N[0];++x){
+        Pos[0] = X[x];
+        for (y=0;y<vox->N[1];++y){
+          Pos[1] = Y[y];
+          for (z=0;z<vox->N[2];++z){ 
+            Pos[2] = Z[z];
+            if (vox->dat[x][y][z]>0.0){   
+              Sxyz_vox_hpost  += vox->dat[x][y][z] * Ppost.dat[x][y][z][g]; 
+          
+              for (i=0;i<3;++i){   
+                Garray[g].M[i] += vox->dat[x][y][z] * Ppost.dat[x][y][z][g] * Pos[i]; 
+                for (j=i;j<3;++j){
+                  Garray[g].CovM[i][j] += vox->dat[x][y][z] * Ppost.dat[x][y][z][g] * Pos[i]*Pos[j]; 
+                }
+              }
+            }
+          }
+        }
+      }
+
+        Garray[g].Weight = Sxyz_vox_hpost/sumDensity;
+
+
+        for (i=0;i<3;++i){Garray[g].M[i]  /=  Sxyz_vox_hpost;}
+      
+        for (i=0;i<3;++i){ 
+          for (j=i;j<3;++j){ 
+            Garray[g].CovM[i][j] /= Sxyz_vox_hpost;
+            Garray[g].CovM[i][j] -= Garray[g].M[i]*Garray[g].M[j];
+          } 
+        }
+
+        Garray[g].CovM[1][0] = Garray[g].CovM[0][1]; 
+        Garray[g].CovM[2][0] = Garray[g].CovM[0][2]; 
+        Garray[g].CovM[2][1] = Garray[g].CovM[1][2]; 
+
+       /** Enforce Minimal Variance (grid_width/2)^2 for CovM[0][0], CovM[1][1], CovM[2][2].**/
+       for (i=0;i<3;++i){
+         if (Garray[g].CovM[i][i]<minVar){ Garray[g].CovM[i][i]=minVar;}
+       }
+/*
+       if ((Garray[g].CovM[0][0]<minVar) || (Garray[g].CovM[1][1]<minVar) || (Garray[g].CovM[2][2]<minVar)){ 
+         change_gdf_to_unit_CovM_center_M_zero_Weight(Garray,g,vox);
+       }
+*/
+       /* NaN GDF check */
+        if (Check_NaN_Inf_Gaussian3D(&Garray[g])==1){
+          printf("#WARNING:Non-normal value is observed in %d-th GMM parameters.\n",g+1);
+          show_Gaussian3D(&Garray[g]);
+          printf("#Forced into unit CovM[][] with the center M[]  with the zero weight.\n");
+          change_gdf_to_unit_CovM_center_M_zero_Weight(Garray,g,vox);
+        } 
+ 
+   
+      /* (2-4) Set up iCovM */
+      Cal_Inverse_Matrix3D_by_Cramer_Rule_Symmetric(Garray[g].iCovM, Garray[g].CovM, &(Garray[g].det));
+      Garray[g].Cons = 1.0/(two_pi_32*sqrt(Garray[g].det));
+    } 
+  } /* g */
+
+  /*
+  printf("#loglike %e\n",Log_Likelihood_Of_GaussMix_For_3Dmap(Ngauss,Garray,vox));
+  */
+ 
+  ++r;
+
+ } while ((r<Nrepeat)&&(DiffLogLike>ConvThre));
+
+  printf("#DiffLogLike %lf %e\n",DiffLogLike,DiffLogLike);
+
+
+ *logLike_final = logLike;
+
+ logLike = Log_Likelihood_Of_GaussMix_For_3Dmap(Ngauss,Garray,vox);
+ printf("#logLike_final %lf %e\n",logLike,logLike);
+ if (PAR.ologfile[0]!='\0'){
+   fprintf(PAR.fp_olog,"#logLike_final %lf %e\n",logLike,logLike);
+ }
+ /*** [4] Free  Variables (Ppost,fixg,X,Y,Z) ***/
+ Free_FLOAT4DMAP(&Ppost); 
+ /* Free_DOUBLE4DMAP(&Ppost);  */
+ free(X); free(Y); free(Z);
+ return(1);
+} /* end of EM_optimize_GaussMix_for_3D_Map() */
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+void change_gdf_to_unit_CovM_center_M_zero_Weight(Garray,g,vox)
+  struct GAUSS3D *Garray;
+  int g;
+  struct VOXEL *vox;
+{
+  int i,j;
+
+  Garray[g].Weight = 0.0;
+  for (i=0;i<3;++i){
+     Garray[g].M[i] = vox->OrigPos[i] + vox->grid_width * vox->N[i]/2;
+     for (j=0;j<3;++j){ 
+       Garray[g].CovM[i][j] = 0.0;
+       if (i==j) {Garray[g].CovM[i][j] = 1.0;}
+     }
+  } 
+  Cal_Inverse_Matrix3D_by_Cramer_Rule_Symmetric(Garray[g].iCovM, Garray[g].CovM, &(Garray[g].det));
+} /* end of change_to_unit_CovM_center_M_zero_Weight() */
+
+
+
+
+double Corr_Coeff_Bwn_Two_Voxels(voxA,voxB)
+ struct VOXEL  *voxA,*voxB;
+{