https://github.com/jsollari/popABC
Tip revision: e6a0334445b5755bb52a0d2209120ee4e251e7b4 authored by Joao Sollari Lopes on 13 November 2017, 18:32:56 UTC
First commit
First commit
Tip revision: e6a0334
pop_makeprior.c
/*
@author: joao lopes
@workplace: Reading University
@date: 12th May 2009
*/
#include "interface.h"
int makeprior(char *output,int niter,int genet,int npop,int nloc,double *lplo,
char *ltype,struct prior pr_top,struct prior *pr_Ne,struct prior *pr_tev,
struct prior *pr_mig,struct prior pr_mutSTR,struct prior pr_mutSNP,
struct prior pr_recSTR,struct prior pr_recSNP,struct migweights migw){
int i,cparam,cloc,cpop,ctev,cpop2, //iterators
allTev, //check if Tev are defined with only one prior
nNe, //no. of population (modern and ancient)
nmig, //no. of migration rates (modern and ancient
ntev; //no. of time events
char *prs_name; //name of the output file
FILE *out_prs; //pntr to .prs
prs_name = malloc(5 + strlen(output)*sizeof(char));
strcpy(prs_name,output);
out_prs = fopen(strcat(prs_name,".prs"),"w");
if(out_prs == NULL)
return 1; //couldn't create .prs file
/*write the first line to .prs file*/
fprintf(out_prs,"%d %d %d %d \n\n",niter,genet,npop,nloc);
/*write heredity scalars per locus to .prs file*/
for(cloc=0 ; cloc<nloc ; cloc++)
fprintf(out_prs,"%.2lf ",lplo[cloc]);
fprintf(out_prs,"\n\n");
/*write DNA type per locus to .prs file*/
for(cloc=0 ; cloc<nloc ; cloc++)
fprintf(out_prs,"%c ",ltype[cloc]);
/*write the top priors to .prs file*/
ntev = npop-1;
if(pr_top.type==0){
fprintf(out_prs,"\n\n%d\n",pr_top.type);
}
else if(pr_top.type==1){
fprintf(out_prs,"\n\n%d",pr_top.type);
fprintf(out_prs," %.0lf\n",pr_top.p[0]);
}
else if(pr_top.type==2){
fprintf(out_prs,"\n\n%d",pr_top.type);
for(i=0; i<2*ntev; i++)
fprintf(out_prs," %.0lf",pr_top.p[i]);
fprintf(out_prs,"\n");
}
else if(pr_top.type==3){
fprintf(out_prs,"\n\n%d %.0lf\n",pr_top.type,pr_top.p[0]);
}
else if(pr_top.type==4){
fprintf(out_prs,"\n\n%d",pr_top.type);
fprintf(out_prs," %.0lf %.0lf\n",pr_top.p[0],pr_top.p[1]);
}
else{
fprintf(out_prs,"\n\n%d",pr_top.type);
for(i=0; i<2*ntev+1; i++)
fprintf(out_prs," %.0lf",pr_top.p[i]);
fprintf(out_prs,"\n");
}
/*write the Ne priors to .prs file*/
nNe = npop*2-1;
for(i=0 ; i<nNe ; i++){
fprintf(out_prs,"\n%d",pr_Ne[i].type);
if(pr_Ne[i].type==1){
for(cparam=0 ; cparam<2 ; cparam++)
fprintf(out_prs," %g",pr_Ne[i].p[cparam]);
}
else if(pr_Ne[i].type==2){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs," %g",pr_Ne[i].p[cparam]);
}
if((i+1)==npop)
fprintf(out_prs,"\n");
}
fprintf(out_prs,"\n");
if(npop>1){
/*write the tev priors to .prs file*/
allTev=0;
for(i=0 ; i<ntev && !allTev; i++){
fprintf(out_prs,"\n%d",pr_tev[i].type);
if(pr_tev[i].type==3||pr_tev[i].type==4){
allTev = 1;
}
if(pr_tev[i].type==1||pr_tev[i].type==3){
for(cparam=0 ; cparam<2 ; cparam++)
fprintf(out_prs," %g",pr_tev[i].p[cparam]);
}
else if(pr_tev[i].type==2||pr_tev[i].type==4){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs," %g",pr_tev[i].p[cparam]);
}
}
fprintf(out_prs,"\n");
/*write the mig prior to .prs file*/
nmig = npop*2-2;
for(i=0 ; i<nmig ; i++){
fprintf(out_prs,"\n%d",pr_mig[i].type);
if(pr_mig[i].type==1||pr_mig[i].type==3){
for(cparam=0 ; cparam<2 ; cparam++)
fprintf(out_prs," %g",pr_mig[i].p[cparam]);
}
else if(pr_mig[i].type==2||pr_mig[i].type==4){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs," %g",pr_mig[i].p[cparam]);
}
if((i+1)==npop)
fprintf(out_prs,"\n");
}
fprintf(out_prs,"\n");
}
/*write the mut prior to .prs file*/
fprintf(out_prs,"\n%d ",pr_mutSTR.type);
if(pr_mutSTR.type==1||pr_mutSTR.type==2){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs,"%g ",pr_mutSTR.p[cparam]);
}
fprintf(out_prs,"\n%d ",pr_mutSNP.type);
if(pr_mutSNP.type==1||pr_mutSNP.type==2){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs,"%g ",pr_mutSNP.p[cparam]);
}
fprintf(out_prs,"\n");
/*write the rec prior to .prs file*/
fprintf(out_prs,"\n%d ",pr_recSTR.type);
if(pr_recSTR.type==1||pr_recSTR.type==2){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs,"%g ",pr_recSTR.p[cparam]);
}
fprintf(out_prs,"\n%d ",pr_recSNP.type);
if(pr_recSNP.type==1||pr_recSNP.type==2){
for(cparam=0 ; cparam<4 ; cparam++)
fprintf(out_prs,"%g ",pr_recSNP.p[cparam]);
}
/*write the mig weights to .prs file*/
fprintf(out_prs,"\n");
if(npop>2){
if(migw.type==0)
fprintf(out_prs,"\n0");
else{
fprintf(out_prs,"\n1");
for(cpop=0;cpop<npop;cpop++){
fprintf(out_prs,"\n");
for(ctev=0;ctev<ntev;ctev++){
for(cpop2=0;cpop2<npop;cpop2++){
fprintf(out_prs,"%g ",migw.m[cpop][ctev][cpop2]);
}
fprintf(out_prs,"\n");
}
}
}
}
/*write the legend*/
if(npop==1){
fprintf(out_prs,"----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 0 - uniform distribution;\n");
fprintf(out_prs," 3 - uniform distribution (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n\n");
}
else if(npop==2){
fprintf(out_prs,"\n----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 0 - uniform distribution;\n");
fprintf(out_prs," 3 - uniform distribution (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs,">ne2 params\n\n");
fprintf(out_prs,">neanc1 params\n\n");
fprintf(out_prs,">t1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n\n");
fprintf(out_prs,">mig1 params: 0 - zero migration;\n");
fprintf(out_prs," 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution;\n");
fprintf(out_prs," 3 - uniform distribution (on number of migrations);\n");
fprintf(out_prs," 4 - generalized gamma distribution (on number of migrations).\n");
fprintf(out_prs," [for 3 and 4 real mig rate is calculated as nmig/Ne]\n");
fprintf(out_prs,">mig2 params\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n");
fprintf(out_prs,"----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"Tree topology:\n\n");
fprintf(out_prs," || PopA1\n");
fprintf(out_prs," || |\n");
fprintf(out_prs," || |\n");
fprintf(out_prs," t1|| ---------\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," \\/ Pop1 Pop2\n\n");
}
else if(npop==3){
fprintf(out_prs,"\n----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 0 - uniform distribution;\n");
fprintf(out_prs," 1 - choose topology from a list;\n");
fprintf(out_prs," 2 - specify topology manually [e.g. ((Pop1,Pop2)Pop3) -> 1 2 2 3];\n");
fprintf(out_prs," 3 - uniform distribution (and choose a Model marker);\n");
fprintf(out_prs," 4 - choose topology from a list (and choose a Model marker);\n");
fprintf(out_prs," 5 - specify topology manually (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs,">ne2 params\n");
fprintf(out_prs,">ne3 params\n\n");
fprintf(out_prs,">neanc1 params\n\n");
fprintf(out_prs,">neanc2 params\n");
fprintf(out_prs,">t1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs," 3 - uniform distribtuion (for all time events);\n");
fprintf(out_prs," 4 - generalized gamma distribution (for all time events).\n");
fprintf(out_prs," [for 1 and 2 t(n) is added to t(n+1)]\n");
fprintf(out_prs," [for 3 and 4 set only one priors for all t(n)]\n");
fprintf(out_prs,">t2 params\n\n");
fprintf(out_prs,">mig1 params: 0 - zero migration;\n");
fprintf(out_prs," 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution\n");
fprintf(out_prs," 3 - uniform distribution (on number of migrations);\n");
fprintf(out_prs," 4 - generalized gamma distribution (on number of migrations).\n");
fprintf(out_prs," [for 3 and 4 real mig rate is calculated as nmig/Ne]\n");
fprintf(out_prs,">mig2 params\n");
fprintf(out_prs,">mig3 params\n\n");
fprintf(out_prs,">miganc1 params\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n\n");
fprintf(out_prs,">migweight: 0 - do not use migweights matrix;\n");
fprintf(out_prs," 1 - use migweights matrix as following:\n\n");
fprintf(out_prs," 0 mw112 mw113\n");
fprintf(out_prs," 0 mw122 mw123\n\n");
fprintf(out_prs," mw211 0 mw213\n");
fprintf(out_prs," mw221 0 mw223\n\n");
fprintf(out_prs," mw311 mw312 0\n");
fprintf(out_prs," mw321 mw322 0\n\n");
fprintf(out_prs," , where mwitj is the prob that the fraction of migrantes in pop i comes\n");
fprintf(out_prs," from pop j at a period of time before time event t. Sum of prob should\n");
fprintf(out_prs," be equal to 1.\n");
fprintf(out_prs," [only use migweight if the topology is specified (option 1,2,4 or 5)]\n");
fprintf(out_prs,"----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"Tree topology:\n\n");
fprintf(out_prs," || PopA2\n");
fprintf(out_prs," || |\n");
fprintf(out_prs," t2|| ------------\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," || PopA1 |\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," t1|| --------- |\n");
fprintf(out_prs," || | | |\n");
fprintf(out_prs," || | | |\n");
fprintf(out_prs," \\/ Pop Pop Pop\n\n");
}
else if(npop==4){
fprintf(out_prs,"\n----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 0 - uniform distribution;\n");
fprintf(out_prs," 1 - choose topology from a list;\n");
fprintf(out_prs," 2 - specify topology manually [e.g. ((Pop1,Pop2)Pop3) -> 1 2 2 3];\n");
fprintf(out_prs," 3 - uniform distribution (and choose a Model marker);\n");
fprintf(out_prs," 4 - choose topology from a list (and choose a Model marker);\n");
fprintf(out_prs," 5 - specify topology manually (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs,">ne2 params\n");
fprintf(out_prs,">ne3 params\n");
fprintf(out_prs,">ne4 params\n\n");
fprintf(out_prs,">neanc1 params\n");
fprintf(out_prs,">neanc2 params\n");
fprintf(out_prs,">neanc3 params\n\n");
fprintf(out_prs,">t1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs," 3 - uniform distribtuion (for all time events);\n");
fprintf(out_prs," 4 - generalized gamma distribution (for all time events).\n");
fprintf(out_prs," [for 1 and 2 t(n) is added to t(n+1)]\n");
fprintf(out_prs," [for 3 and 4 set only one priors for all t(n)]\n");
fprintf(out_prs,">t2 params\n");
fprintf(out_prs,">t3 params\n\n");
fprintf(out_prs,">mig1 params: 0 - zero migration;\n");
fprintf(out_prs," 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution\n");
fprintf(out_prs," 3 - uniform distribution (on number of migrations);\n");
fprintf(out_prs," 4 - generalized gamma distribution (on number of migrations).\n");
fprintf(out_prs," [for 3 and 4 real mig rate is calculated as nmig/Ne]\n");
fprintf(out_prs,">mig2 params\n");
fprintf(out_prs,">mig3 params\n");
fprintf(out_prs,">mig4 params\n\n");
fprintf(out_prs,">miganc1 params\n");
fprintf(out_prs,">miganc2 params\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n\n");
fprintf(out_prs,">migweight: 0 - do not use migweights matrix;\n");
fprintf(out_prs," 1 - use migweights matrix as following:\n\n");
fprintf(out_prs," 0 mw112 mw113 mw114\n");
fprintf(out_prs," 0 mw122 mw123 mw124\n");
fprintf(out_prs," 0 mw132 mw133 mw134\n\n");
fprintf(out_prs," mw211 0 mw213 mw214\n");
fprintf(out_prs," mw221 0 mw223 mw224\n");
fprintf(out_prs," mw231 0 mw233 mw234\n\n");
fprintf(out_prs," mw311 mw312 0 mw314\n");
fprintf(out_prs," mw321 mw322 0 mw324\n");
fprintf(out_prs," mw331 mw332 0 mw334\n\n");
fprintf(out_prs," , where mwitj is the prob that the fraction of migrantes in pop i comes\n");
fprintf(out_prs," from pop j at a period of time before time event t. Sum of prob should\n");
fprintf(out_prs," be equal to 1.\n");
fprintf(out_prs," [only use migweight if the topology is specified (option 1,2,4 or 5)]\n");
fprintf(out_prs,"----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"Tree topology:\n\n");
fprintf(out_prs," || PopA3 PopA3\n");
fprintf(out_prs," || | |\n");
fprintf(out_prs," t3|| ------------- ----------------\n");
fprintf(out_prs," || | | | |\n");
fprintf(out_prs," || PopA2 | PopA2 |\n");
fprintf(out_prs," || | | | |\n");
fprintf(out_prs," t2|| ----------- | OR -------- |\n");
fprintf(out_prs," || | | | | | |\n");
fprintf(out_prs," || PopA1 | | | | PopA1\n");
fprintf(out_prs," || | | | | | |\n");
fprintf(out_prs," t1|| -------- | | | | --------\n");
fprintf(out_prs," || | | | | | | | |\n");
fprintf(out_prs," \\/ Pop Pop Pop Pop Pop Pop Pop Pop\n\n");
}
else if(npop==5){
fprintf(out_prs,"\n----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 0 - uniform distribution;\n");
fprintf(out_prs," 1 - choose topology from a list;\n");
fprintf(out_prs," 2 - specify topology manually [e.g. ((Pop1,Pop2)Pop3) -> 1 2 2 3];\n");
fprintf(out_prs," 3 - uniform distribution (and choose a Model marker);\n");
fprintf(out_prs," 4 - choose topology from a list (and choose a Model marker);\n");
fprintf(out_prs," 5 - specify topology manually (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs,">ne2 params\n");
fprintf(out_prs,">ne3 params\n");
fprintf(out_prs,">ne4 params\n");
fprintf(out_prs,">ne5 params\n\n");
fprintf(out_prs,">neanc1 params\n");
fprintf(out_prs,">neanc2 params\n");
fprintf(out_prs,">neanc3 params\n");
fprintf(out_prs,">neanc4 params\n\n");
fprintf(out_prs,">t1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs," 3 - uniform distribtuion (for all time events);\n");
fprintf(out_prs," 4 - generalized gamma distribution (for all time events).\n");
fprintf(out_prs," [for 1 and 2 t(n) is added to t(n+1)]\n");
fprintf(out_prs," [for 3 and 4 set only one priors for all t(n)]\n");
fprintf(out_prs,">t2 params\n");
fprintf(out_prs,">t3 params\n");
fprintf(out_prs,">t4 params\n\n");
fprintf(out_prs,">mig1 params: 0 - zero migration;\n");
fprintf(out_prs," 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution\n");
fprintf(out_prs," 3 - uniform distribution (on number of migrations);\n");
fprintf(out_prs," 4 - generalized gamma distribution (on number of migrations).\n");
fprintf(out_prs," [for 3 and 4 real mig rate is calculated as nmig/Ne]\n");
fprintf(out_prs,">mig2 params\n");
fprintf(out_prs,">mig3 params\n");
fprintf(out_prs,">mig4 params\n");
fprintf(out_prs,">mig5 params\n\n");
fprintf(out_prs,">miganc1 params\n");
fprintf(out_prs,">miganc2 params\n");
fprintf(out_prs,">miganc3 params\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n\n");
fprintf(out_prs,">migweight: 0 - do not use migweights matrix;\n");
fprintf(out_prs," 1 - use migweights matrix as following:\n\n");
fprintf(out_prs," 0 mw112 mw113 mw114 mw115\n");
fprintf(out_prs," 0 mw122 mw123 mw124 mw125\n");
fprintf(out_prs," 0 mw132 mw133 mw134 mw135\n");
fprintf(out_prs," 0 mw142 mw143 mw144 mw145\n\n");
fprintf(out_prs," mw211 0 mw213 mw214 mw215\n");
fprintf(out_prs," mw221 0 mw223 mw224 mw225\n");
fprintf(out_prs," mw231 0 mw233 mw234 mw235\n");
fprintf(out_prs," mw241 0 mw243 mw244 mw245\n\n");
fprintf(out_prs," mw311 mw312 0 mw314 mw315\n");
fprintf(out_prs," mw321 mw322 0 mw324 mw325\n");
fprintf(out_prs," mw331 mw332 0 mw334 mw335\n");
fprintf(out_prs," mw341 mw342 0 mw344 mw345\n\n");
fprintf(out_prs," mw411 mw412 mw413 0 mw415\n");
fprintf(out_prs," mw421 mw422 mw423 0 mw425\n");
fprintf(out_prs," mw431 mw432 mw433 0 mw435\n");
fprintf(out_prs," mw441 mw442 mw443 0 mw445\n\n");
fprintf(out_prs," mw511 mw512 mw513 mw514 0\n");
fprintf(out_prs," mw521 mw522 mw523 mw524 0\n");
fprintf(out_prs," mw531 mw532 mw533 mw534 0\n");
fprintf(out_prs," mw541 mw542 mw543 mw544 0\n\n");
fprintf(out_prs," , where mwitj is the prob that the fraction of migrantes in pop i comes\n");
fprintf(out_prs," from pop j at a period of time before time event t. Sum of prob should\n");
fprintf(out_prs," be equal to 1.\n");
fprintf(out_prs," [only use migweight if the topology is specified (option 1,2,4 or 5)]\n");
fprintf(out_prs,"----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"Tree topology:\n\n");
fprintf(out_prs," || Neanc Neanc Neanc\n");
fprintf(out_prs," || | | |\n");
fprintf(out_prs," t4|| -------- ---------- -----------\n");
fprintf(out_prs," || | | | | | |\n");
fprintf(out_prs," || Neanc | | Neanc Neanc |\n");
fprintf(out_prs," || | | | | | |\n");
fprintf(out_prs," t3|| -------- | | ------- --------- |\n");
fprintf(out_prs," || | | | | | | | | |\n");
fprintf(out_prs," || Neanc | | OR | | Neanc OR | Neanc |\n");
fprintf(out_prs," || | | | | | | | | |\n");
fprintf(out_prs," t2|| ------- | | | | ----- | ----- |\n");
fprintf(out_prs," || | | | | | | | | | | | |\n");
fprintf(out_prs," || Neanc | | | | | | | Neanc | | |\n");
fprintf(out_prs," || | | | | | | | | | | | |\n");
fprintf(out_prs," t1|| ----- | | | ----- | | | ----- | | |\n");
fprintf(out_prs," || | | | | | | | | | | | | | | |\n");
fprintf(out_prs," \\/ Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne Ne\n\n");
}
else{
fprintf(out_prs,"\n----------------------------------------------------------------------------------------\n");
fprintf(out_prs,"PopABC - Mark Beaumont & Joao Lopes 01/05/09\n\n");
fprintf(out_prs,">no_iterations, generation_time, no_populations, no_loci\n\n");
fprintf(out_prs,">escalar per locus (autosome - 1; X-linked - 0.75; Y-linked or mitDNA - 0.25)\n\n");
fprintf(out_prs,">type of DNA data (s - sequence; m - microssatelites)\n\n");
fprintf(out_prs,">topology: 2 - specify topology manually [e.g. ((Pop1,Pop2)Pop3) -> 1 2 2 3];\n");
fprintf(out_prs," 5 - specify topology manually (and choose a Model marker).\n\n");
fprintf(out_prs,">ne1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs,">ne2 params\n");
fprintf(out_prs,">ne3 params\n");
fprintf(out_prs,">ne4 params\n");
fprintf(out_prs,">ne5 params\n");
fprintf(out_prs,">(...)\n\n");
fprintf(out_prs,">neanc1 params\n");
fprintf(out_prs,">neanc2 params\n");
fprintf(out_prs,">neanc3 params\n");
fprintf(out_prs,">neanc4 params\n");
fprintf(out_prs,">(...)\n\n");
fprintf(out_prs,">t1 params: 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution.\n");
fprintf(out_prs," 3 - uniform distribtuion (for all time events);\n");
fprintf(out_prs," 4 - generalized gamma distribution (for all time events).\n");
fprintf(out_prs," [for 1 and 2 t(n) is added to t(n+1)]\n");
fprintf(out_prs," [for 3 and 4 set only one priors for all t(n)]\n");
fprintf(out_prs,">t2 params\n");
fprintf(out_prs,">t3 params\n");
fprintf(out_prs,">t4 params\n");
fprintf(out_prs,">(...)\n\n");
fprintf(out_prs,">mig1 params: 0 - zero migration;\n");
fprintf(out_prs," 1 - uniform distribtuion;\n");
fprintf(out_prs," 2 - generalized gamma distribution\n");
fprintf(out_prs," 3 - uniform distribution (on number of migrations);\n");
fprintf(out_prs," 4 - generalized gamma distribution (on number of migrations).\n");
fprintf(out_prs," [for 3 and 4 real mig rate is calculated as nmig/Ne]\n");
fprintf(out_prs,">mig2 params\n");
fprintf(out_prs,">mig3 params\n");
fprintf(out_prs,">mig4 params\n");
fprintf(out_prs,">mig5 params\n");
fprintf(out_prs,">(...)\n\n");
fprintf(out_prs,">miganc1 params\n");
fprintf(out_prs,">miganc2 params\n");
fprintf(out_prs,">miganc3 params\n");
fprintf(out_prs,">(...)\n\n");
fprintf(out_prs,">mutM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">mutS params\n\n");
fprintf(out_prs,">recM params: 0 - zero mutation;\n");
fprintf(out_prs," 1 - lognormal distribution: (mean of mean(log10); stdev of mean(log10);\n");
fprintf(out_prs," mean of Sdev(log10); stdev of stdev(log10). Stdev truncated at 0.\n");
fprintf(out_prs," 2 - normal distribution: (mean of mean; stdev of mean; mean of Sdev;\n");
fprintf(out_prs," stdev of stdev. Stdev truncated at 0.\n");
fprintf(out_prs,">recS params\n\n");
fprintf(out_prs,">migweight: 0 - do not use migweights matrix;\n");
fprintf(out_prs," 1 - use migweights matrix as following:\n\n");
fprintf(out_prs," 0 mw112 mw113 mw114 mw115 ...\n");
fprintf(out_prs," 0 mw122 mw123 mw124 mw125 ...\n");
fprintf(out_prs," 0 mw132 mw133 mw134 mw135 ...\n");
fprintf(out_prs," 0 mw142 mw143 mw144 mw145 ...\n");
fprintf(out_prs," ... ... ... ... ... ...\n\n");
fprintf(out_prs," mw211 0 mw213 mw214 mw215 ...\n");
fprintf(out_prs," mw221 0 mw223 mw224 mw225 ...\n");
fprintf(out_prs," mw231 0 mw233 mw234 mw235 ...\n");
fprintf(out_prs," mw241 0 mw243 mw244 mw245 ...\n");
fprintf(out_prs," ... ... ... ... ... ...\n\n");
fprintf(out_prs," mw311 mw312 0 mw314 mw315 ...\n");
fprintf(out_prs," mw321 mw322 0 mw324 mw325 ...\n");
fprintf(out_prs," mw331 mw332 0 mw334 mw335 ...\n");
fprintf(out_prs," mw341 mw342 0 mw344 mw345 ...\n");
fprintf(out_prs," ... ... ... ... ... ...\n\n");
fprintf(out_prs," mw411 mw412 mw413 0 mw415 ...\n");
fprintf(out_prs," mw421 mw422 mw423 0 mw425 ...\n");
fprintf(out_prs," mw431 mw432 mw433 0 mw435 ...\n");
fprintf(out_prs," mw441 mw442 mw443 0 mw445 ...\n");
fprintf(out_prs," ... ... ... ... ... ...\n\n");
fprintf(out_prs," mw511 mw512 mw513 mw514 0 ...\n");
fprintf(out_prs," mw521 mw522 mw523 mw524 0 ...\n");
fprintf(out_prs," mw531 mw532 mw533 mw534 0 ...\n");
fprintf(out_prs," mw541 mw542 mw543 mw544 0 ...\n");
fprintf(out_prs," ... ... ... ... ... ...\n\n");
fprintf(out_prs," ...\n\n");
fprintf(out_prs," , where mwitj is the prob that the fraction of migrantes in pop i comes\n");
fprintf(out_prs," from pop j at a period of time before time event t. Sum of prob should\n");
fprintf(out_prs," be equal to 1.\n");
fprintf(out_prs," [only use migweight if the topology is specified (option 1,2,4 or 5)]\n");
}
/*free stuff*/
free(prs_name);
free(lplo);
free(ltype);
if(pr_top.type!=0)
free(pr_top.p);
for(cpop=0; cpop<npop*2-1; cpop++)
free(pr_Ne[cpop].p);
free(pr_Ne);
if(npop>1){
if(pr_tev[0].type!=3 && pr_tev[0].type!=4){
for(ctev=0; ctev<ntev; ctev++)
free(pr_tev[ctev].p);
free(pr_tev);
}
else{
free(pr_tev[0].p);
free(pr_tev);
}
for(cpop=0; cpop<npop*2-2; cpop++)
if(pr_mig[cpop].type!=0)
free(pr_mig[cpop].p);
free(pr_mig);
}
if(pr_mutSTR.type!=0)
free(pr_mutSTR.p);
if(pr_mutSNP.type!=0)
free(pr_mutSNP.p);
if(pr_recSTR.type!=0)
free(pr_recSTR.p);
if(pr_recSNP.type!=0)
free(pr_recSNP.p);
if(migw.type==1){
for(cpop=0;cpop<npop;cpop++){
for(ctev=0;ctev<ntev;ctev++)
free(migw.m[cpop][ctev]);
free(migw.m[cpop]);
}
free(migw.m);
}
//close files
fclose(out_prs);
//no errors
return 0;
} //end of main
