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main.cpp
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main.cpp
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/***************************************************************************
© Champak Beeravolu Reddy 2015-now
This software is governed by the CeCILL license under French law and
abiding by the rules of distribution of free software. You can use,
modify and/ or redistribute the software under the terms of the CeCILL
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info".
As a counterpart to the access to the source code and rights to copy,
modify and redistribute granted by the license, users are provided only
with a limited warranty and the software's author, the holder of the
economic rights, and the successive licensors have only limited
liability.
In this respect, the user's attention is drawn to the risks associated
with loading, using, modifying and/or developing or reproducing the
software by the user in light of its specific status of free software,
that may mean that it is complicated to manipulate, and that also
therefore means that it is reserved for developers and experienced
professionals having in-depth computer knowledge. Users are therefore
encouraged to load and test the software's suitability as regards their
requirements in conditions enabling the security of their systems and/or
data to be ensured and, more generally, to use and operate it in the
same conditions as regards security.
The fact that you are presently reading this means that you have had
knowledge of the CeCILL license and that you accept its terms.
***************************************************************************/
/*
* main.cpp
*
* Created on: 3 Jun 2015
* Author: champost
*/
#include <ctime>
#include <string>
#include <fstream>
#include <iostream>
#include <sstream>
#include <cstdlib>
#include <cmath>
#include <cstdio>
#include <map>
#include <vector>
#include <algorithm>
#include <numeric>
#include <limits>
#include <iomanip>
#include <set>
#include <nlopt.hpp>
#include <gsl/gsl_rng.h>
#include <omp.h>
#include "main.h"
#include "utils.h"
using namespace std;
//************ EXTERN **************
int brClass = 0, mutClass = 0, foldBrClass = 0, allBrClasses = 0, sampledPopsSize = 0, poisTableSize = 0, procs = 1;
int *poisTableScaleOrder;
int **blockLengthsMat;
//**********************************
gsl_rng * PRNG;
map<vector<int>, int> intVec2BrConfig;
map<int, vector<int> > tbiMsCmdIdx;
map<int, double> tbiUserVal;
map<int, vector<double> > tbiSearchBounds, tbiProfilesGrid;
map<int, int> parConstraints, tbi2ParVec, ParVecToTbi;
map<int, bool> setRandomPars;
map<double, vector<double> > bestParsMap;
//map<int, int> trackSelectConfigs;
//------------------------------------------------------
vector<string> dataFile;
int mbSFSLen = 0;
vector<int> dataScales;
set<int> uniqueScales;
vector<vector<int> > data2PoisTableIdx;
vector<vector<vector<int> > > dataConfigs;
vector<vector<double> > dataConfigFreqs, selectConfigFreqs;
vector<vector<int> > trackSelectConfigsForInf;
//------------------------------------------------------
string dataFileFormat = "bSFS", alleleType = "genotype", configFile, globalSearchAlg, bSFSFile = "bSFS.txt", data2bSFSFile;
//ofstream testLik, testConfig;
vector<int> allConfigs, sampledPops, allPops, profileVarKey, subSamplePops;
vector<double> allConfigFreqs, upperBounds, lowerBounds, hardUpperBounds, hardLowerBounds, bestGlobalSPars, bestLocalSPars, profileVars;
vector<string> cmdLine;
vector<gsl_rng *> PRNGThreadVec;
nlopt::opt opt, local_opt, AUGLAG;
char **ms_argv;
int ms_argc = 0, ms_crash_flag = 0;
int npops = 0, kmax = 0, nsubpops = 0;
int estimate = 0, evalCount = 0, crash_counter = 0, sampledTrees = 0, recLen = 0;
int globalTrees = 0, localTrees = 0, globalEvals = 0, localEvals = 0, refineLikTrees = 0,
profileLikTrees = 0, ms_trees = 1,
reportEveryEvals = 0, set_threads = 0, numGlobalSearches = 1, outputDigits = 6;
size_t bestParsMapSize = 0, ploidy = 1;
double globalUpper = 5, globalLower = 1e-3, dataLnL = 0.0, bestGlobalSlLnL = -1000000.0,
bestLocalSlLnL = -1000000.0, userLnL = 0.0, localSearchAbsTol = 1e-3;
bool skipGlobalSearch = false, bSFSmode = false, profileLikBool = false, abortNLopt = false,
cmdLineInConfigFile = false, progressiveBounds = false, seedPRNGBool = false,
nobSFSFile = false, printLikCorrFactor = false, startRandom = false, dataConvert = false,
skipLocalSearch = false, outputSNPfile = true;
unsigned long int finalTableSize = 0, seedPRNG = 123456;
enum SearchStates {GLOBAL, LOCAL, OTHER};
SearchStates currState = OTHER;
double ran1()
{
return gsl_rng_uniform(PRNGThreadVec[omp_get_thread_num()]);
}
void free_objects() {
for(int i = 0; i < ms_argc; i++)
free(ms_argv[i]);
free(ms_argv);
gsl_rng_free(PRNG);
for (size_t i = 0; i < PRNGThreadVec.size(); i++)
gsl_rng_free(PRNGThreadVec[i]);
if (poisTableSize) {
free(poisTableScaleOrder);
freed2int_matrix(blockLengthsMat, poisTableSize);
}
}
// Temporarily deactivated code for likelihood profiles (not to be confused with profile likelihoods!)
void profileLik(vector<double> MLEparVec) {
double parLowerBound, parUpperBound, MLEparVal, MLEparLik;
size_t parIdx = 0;
// Likelihood profiles for all parameters to be inferred (tbi)
for (map<int, vector<int> >::iterator it = tbiMsCmdIdx.begin(); it != tbiMsCmdIdx.end(); it++) {
stringstream tbiFileName;
MLEparVal = MLEparVec[tbi2ParVec[it->first]];
/*
double quarterRange = (upperBounds[parIdx] - lowerBounds[parIdx]) / 4;
parUpperBound = MLEparVal + quarterRange;
parLowerBound = MLEparVal - quarterRange;
if (parLowerBound < lowerBounds[parIdx])
parLowerBound = lowerBounds[parIdx];
if (parUpperBound > upperBounds[parIdx])
parUpperBound = upperBounds[parIdx];
*/
parUpperBound = upperBounds[parIdx];
parLowerBound = lowerBounds[parIdx];
tbiFileName << "tbi" << it->first;
printf("\nCalculating profiles of the likelihood surface for %s\n", tbiFileName.str().c_str());
tbiFileName << ".txt";
ofstream outFile(tbiFileName.str().c_str(),ios::out);
// Calculating the LnL at the MLE
for (size_t i = 0; i < it->second.size(); i++) {
stringstream ststMLEPar;
ststMLEPar << MLEparVal;
ststMLEPar >> ms_argv[it->second[i]];
}
MLEparLik = computeLik();
outFile << scientific << MLEparVal << "\t" << MLEparLik << endl;
// Likelihood profile intervals for this parameter
vector<double> parRange;
if (parLowerBound > 0)
parRange = logspaced(parLowerBound, parUpperBound, 10);
else if (parUpperBound < 0)
parRange = negLogspaced(parLowerBound, parUpperBound, 10);
else
parRange = linspaced(parLowerBound, parUpperBound, 10);
for (size_t j = 0; j < parRange.size(); j++) {
if (parRange[j] == MLEparVal)
outFile << scientific << MLEparVal << "\t" << MLEparLik << endl;
else {
for (size_t i = 0; i < it->second.size(); i++) {
stringstream ststProfilePar;
ststProfilePar << parRange[j];
ststProfilePar >> ms_argv[it->second[i]];
}
double loglik = computeLik();
if (ms_crash_flag)
loglik = 2*dataLnL;
outFile << scientific << parRange[j] << "\t" << loglik << endl;
}
}
outFile.close();
// Resetting the MLE for this parameter
for (size_t i = 0; i < it->second.size(); i++) {
stringstream ststMLEPar;
ststMLEPar << MLEparVal;
ststMLEPar >> ms_argv[it->second[i]];
}
++parIdx;
}
}
// conversion from decimal to base-mutClass
vector<int> getMutConfigVec(unsigned long int i) {
int quo = i;
int rem = 0;
vector<int> MutConfig;
for (int j = 0; j < brClass; j++) {
if (quo) {
rem = quo % mutClass;
quo /= mutClass;
MutConfig.push_back(rem);
}
else
MutConfig.push_back(0);
}
reverse(MutConfig.begin(),MutConfig.end());
return MutConfig;
}
// conversion from decimal to base-mutClass
string getMutConfigStr(unsigned long int i) {
int quo = i;
int rem = 0;
stringstream stst;
stst << ")";
for (int j = 0; j < brClass; j++) {
if (quo) {
rem = quo % mutClass;
quo /= mutClass;
/*
if (rem == mutClass-1)
stst << rem-1 << ">";
else
*/
stst << rem;
}
else
stst << "0";
if (j < brClass - 1)
stst << ",";
}
stst << "(";
string config = stst.str();
reverse(config.begin(),config.end());
return config;
}
string getMutConfigStr(vector<int> configVec) {
int size = configVec.size();
stringstream stst;
stst << "(";
for (int j = 0; j < size-1; j++)
stst << configVec[j] << ",";
stst << configVec[size-1] << ")";
return stst.str();
}
int getPopSampleStatus(int pop) { return allPops[pop]; }
int getBrConfigNum(int *brConfVec) {
if (accumulate(brConfVec, brConfVec+sampledPops.size(), 0)) {
vector<int> vec(brConfVec, brConfVec+sampledPops.size());
return intVec2BrConfig[vec];
}
return -1;
}
int getBrConfigNum(vector<int> brConfVec) {
return intVec2BrConfig[brConfVec];
}
void process_tree_cond_bSFS (double ***onetreePoisTable) {
// TODO: need to look for alternatives to speed up mbSFS traversal
for (int data = 0; data < mbSFSLen; data++) {
for (size_t i = 0; i < dataConfigs[data].size(); i++) {
for (int scale = 0; scale < dataScales[data]; scale++) {
double jointPoisson = 1.0;
for (int j = 0; j < brClass; j++)
jointPoisson *= onetreePoisTable[data2PoisTableIdx[data][scale]][j][dataConfigs[data][i][j]];
if (jointPoisson > (numeric_limits<double>::min()*ms_trees)) {
selectConfigFreqs[data][i] += jointPoisson;
trackSelectConfigsForInf[data][i] = 1;
}
}
}
}
}
void process_tree_exact_bSFS (double ***onetreePoisTable) {
for (unsigned long int i = 0; i < finalTableSize; i++) {
double jointPoisson = 1.0;
vector<int> vec = getMutConfigVec(i);
for (int j = 0; j < brClass; j++)
jointPoisson *= onetreePoisTable[0][j][vec[j]];
if (jointPoisson > (numeric_limits<double>::min()*ms_trees)) {
allConfigs[i] = i;
allConfigFreqs[i] += jointPoisson;
}
}
}
void calcBSFSTable() {
ms_crash_flag = 0;
{
stringstream stst;
stst << 1;
stst >> ms_argv[2];
}
if (estimate == 1) {
/*
int treesSampled = 0;
#pragma omp parallel for
for (int trees = 0; trees < ms_trees; trees++) {
if (!ms_crash_flag) {
double **onetreePoisTable;
onetreePoisTable = d2matrix(brClass, mutClass);
// calling ms for sampling genealogies
main_ms_ABLE(ms_argc, ms_argv, onetreePoisTable);
#pragma omp critical
{
++treesSampled;
double loglik = 0.0;
for (size_t i = 0; i < dataConfigs.size(); i++) {
double jointPoisson = 1.0;
for (int j = 0; j < brClass; j++)
jointPoisson *= onetreePoisTable[j][dataConfigs[i][j]];
if (jointPoisson > (numeric_limits<double>::min()*ms_trees))
selectConfigFreqs[i] += jointPoisson;
if (selectConfigFreqs[i] != 0.0) {
loglik += log(selectConfigFreqs[i] / treesSampled) * dataConfigFreqs[i];
trackSelectConfigs[i] = 1;
}
}
testLik << scientific << loglik << endl;
testConfig << scientific << (double) trackSelectConfigs.size()/dataConfigs.size() << endl;
}
freed2matrix(onetreePoisTable, brClass);
}
}
*/
}
else {
if (estimate == 0) {
allConfigs = vector<int>(finalTableSize,-1);
allConfigFreqs = vector<double>(finalTableSize,0.0);
}
int sim_trees = ms_trees, crashLimit;
crash_counter = sampledTrees = 0;
if (ms_trees < 100)
crashLimit = 1;
else
crashLimit = ms_trees/100;
do {
#pragma omp parallel for shared(ms_crash_flag, crash_counter, sampledTrees)
for (int trees = 0; trees < sim_trees; trees++) {
if (!ms_crash_flag) {
double ***onetreePoisTable = d3matrix(poisTableSize,brClass, mutClass);
// calling ms for sampling genealogies
if (main_ms_ABLE(ms_argc, ms_argv, onetreePoisTable)) {
#pragma omp atomic
++crash_counter;
}
else {
if (bSFSmode || (estimate > 0))
process_tree_cond_bSFS(onetreePoisTable);
else if (estimate == 0)
process_tree_exact_bSFS(onetreePoisTable);
#pragma omp atomic
++sampledTrees;
}
if (crash_counter >= crashLimit)
ms_crash_flag = 1;
freed3matrix(onetreePoisTable, poisTableSize, brClass);
}
}
if (sampledTrees == ms_trees)
break;
else if ((sampledTrees < ms_trees) && !ms_crash_flag)
sim_trees = ms_trees - sampledTrees;
} while(!ms_crash_flag);
}
}
double computeLik() {
double loglik = 0.0;
if (bSFSmode || (estimate > 0)) {
for (int data = 0; data < mbSFSLen; data++) {
selectConfigFreqs.push_back(vector<double>(dataConfigFreqs[data].size(),0.0));
trackSelectConfigsForInf.push_back(vector<int>(dataConfigFreqs[data].size(),0));
}
}
// calculating the bSFS config. probs.
calcBSFSTable();
if (!ms_crash_flag) {
vector<double> loglikData(mbSFSLen, 0.0);
for (int data = 0; data < mbSFSLen; data++) {
vector<int> trackedConfigs (mbSFSLen, 0);
if (bSFSmode || (estimate > 1))
trackedConfigs[data] = accumulate(trackSelectConfigsForInf[data].begin(),trackSelectConfigsForInf[data].end(),0);
// else if (estimate == 1)
// trackedConfigs = trackSelectConfigs.size();
if (bSFSmode) {
if (nobSFSFile || profileLikBool) {
for (size_t i = 0; i < dataConfigs[data].size(); i++) {
if (selectConfigFreqs[data][i] != 0.0)
loglikData[data] += log(selectConfigFreqs[data][i] / (ms_trees * dataScales[data])) * dataConfigFreqs[data][i];
}
}
else {
ofstream ofs(bSFSFile.c_str(),ios::out);
for (size_t i = 0; i < dataConfigs[data].size(); i++) {
if (selectConfigFreqs[data][i] != 0.0) {
ofs << getMutConfigStr(dataConfigs[data][i]) << " : " << scientific << selectConfigFreqs[data][i] / (ms_trees * dataScales[data]) << endl;
loglikData[data] += log(selectConfigFreqs[data][i] / (ms_trees * dataScales[data])) * dataConfigFreqs[data][i];
}
}
ofs.close();
}
// correct for the LnL if there are any data bSFS configs are unvisited
if (trackedConfigs[data])
loglikData[data] *= (double) dataConfigFreqs[data].size() / trackedConfigs[data];
if (printLikCorrFactor)
printf("Likelihood correction factor : %.*f\n", outputDigits, (double) dataConfigFreqs[data].size() / trackedConfigs[data]);
}
else if (estimate > 0) {
for (size_t i = 0; i < dataConfigs[data].size(); i++) {
if (selectConfigFreqs[data][i] != 0.0)
loglikData[data] += log(selectConfigFreqs[data][i] / (ms_trees * dataScales[data])) * dataConfigFreqs[data][i];
}
// correct for the LnL if there are any data bSFS configs are unvisited
if (trackedConfigs[data])
loglikData[data] *= (double) dataConfigFreqs[data].size() / trackedConfigs[data];
}
}
if (!mbSFSLen && (estimate == 0)) {
ofstream ofs(bSFSFile.c_str(),ios::out);
for (size_t i = 0; i < allConfigs.size(); i++)
if (allConfigs[i] >= 0)
ofs << getMutConfigStr(allConfigs[i]) << " : " << scientific << allConfigFreqs[allConfigs[i]] / ms_trees << endl;
ofs.close();
allConfigs.clear();
allConfigFreqs.clear();
}
loglik = accumulate(loglikData.begin(), loglikData.end(), 0.0);
}
trackSelectConfigsForInf.clear();
// trackSelectConfigs.clear();
selectConfigFreqs.clear();
return loglik;
}
// conversion from decimal to base-(maxPopSize+1)
void evalBranchConfigs(vector<int> popsVec) {
int quo, rem, maxPopSize, totPopSum, count = 0, sumConfig;
bool skipConfig;
maxPopSize = totPopSum = popsVec[0];
if (kmax == 0)
mutClass = 0;
else
mutClass = kmax+2;
brClass = popsVec[0]+1;
for (size_t i = 1; i < popsVec.size(); i++)
brClass *= (popsVec[i]+1);
brClass -= 2;
allBrClasses = brClass;
// fold the branch classes
if (foldBrClass) {
if (brClass % 2)
brClass = (brClass+1) / 2;
else
brClass = brClass / 2;
}
for (size_t i = 1; i < popsVec.size(); i++) {
totPopSum += popsVec[i];
if (popsVec[i] > maxPopSize)
maxPopSize = popsVec[i];
}
++maxPopSize;
for (unsigned long int i = 1; i <= (unsigned long int) pow(maxPopSize,popsVec.size()); i++) {
quo = i;
rem = 0;
/*
stringstream stst;
stst << ")";
*/
sumConfig = 0;
skipConfig = false;
vector<int> vec;
for (size_t j = 0; j < popsVec.size(); j++) {
if (quo) {
rem = quo % (maxPopSize);
quo /= (maxPopSize);
if (rem > popsVec[popsVec.size()-1-j]) {
skipConfig = true;
break;
}
// stst << rem;
sumConfig += rem;
vec.push_back(rem);
}
else {
// stst << "0";
vec.push_back(0);
}
/*
if (j < popsVec.size() - 1)
stst << ",";
*/
}
if (sumConfig == totPopSum)
break;
if (!skipConfig) {
/*
stst << "(";
string config = stst.str();
reverse(config.begin(),config.end());
*/
reverse(vec.begin(),vec.end());
intVec2BrConfig[vec] = count;
++count;
/*
printf("%d\t%d\t%s\n", i, count, config.c_str());
printf("%d\t%s\n", count, config.c_str());
*/
}
}
}
void readConfigFile() {
string line, del, keyWord;
ifstream ifs(configFile.c_str(),ios::in);
while (getline(ifs,line)) {
vector<string> tokens;
del = " ";
if (line.size() > 0) {
Tokenize(line, tokens, del);
for(unsigned int j=0;j<tokens.size();j++)
TrimSpaces(tokens[j]);
if (tokens[0][0] != '#') {
if (tokens[0] == "ABLE") {
cmdLineInConfigFile = true;
cmdLine = tokens;
}
else if (tokens[0] == "pops") {
stringstream stst1(tokens[1]);
stst1 >> npops;
for (int i = 0; i < npops; i++) {
stringstream stst2(tokens[i+2]);
if (stst2.str() != "u") {
int tmp;
stst2 >> tmp;
sampledPops.push_back(tmp);
allPops.push_back(1);
}
else
allPops.push_back(0);
}
sampledPopsSize = sampledPops.size();
}
else if (tokens[0] == "subsample_pops") {
stringstream stst1(tokens[1]);
stst1 >> nsubpops;
for (int i = 0; i < nsubpops; i++) {
stringstream stst2(tokens[i+2]);
if (stst2.str() != "u") {
int tmp;
stst2 >> tmp;
subSamplePops.push_back(tmp);
}
}
}
else if (tokens[0] == "ploidy") {
stringstream stst(tokens[1]);
stst >> ploidy;
}
else if (tokens[0] == "start") {
double val;
if (tokens[1] == "all") {
for(unsigned int j = 2; j < tokens.size(); j++) {
stringstream stst_val(tokens[j]);
stst_val >> val;
tbiUserVal[j-1] = val;
}
}
else if (tokens[1] == "random") {
startRandom = true;
}
else {
stringstream stst(tokens[2]);
stst >> val;
tbiUserVal[atoi(tokens[1].substr(3).c_str())] = val;
}
}
else if (tokens[0] == "datafile") {
mbSFSLen = 0;
for(unsigned int j = 1; j < tokens.size(); j++) {
dataFile.push_back(tokens[j]);
++mbSFSLen;
}
}
else if (tokens[0] == "scale") {
for(unsigned int j = 1; j < tokens.size(); j++) {
dataScales.push_back(atoi(tokens[j].c_str()));
uniqueScales.insert(atoi(tokens[j].c_str()));
}
}
else if (tokens[0] == "datafile_format") {
dataFileFormat = tokens[1];
}
else if (tokens[0] == "convert_data_to_bSFS") {
dataConvert = true;
data2bSFSFile = "data2bSFS.txt";
if (tokens.size() > 1)
data2bSFSFile = tokens[1];
}
else if (tokens[0] == "allele_type") {
alleleType = tokens[1];
}
// this option has been deprecated
else if (tokens[0] == "estimate") {
stringstream stst(tokens[1]);
stst >> estimate;
}
else if (tokens[0] == "task") {
if (tokens[1] == "exact_bSFS")
estimate = 0;
else if (tokens[1] == "conditional_bSFS")
bSFSmode = true;
else if (tokens[1] == "infer")
estimate = 2;
}
else if (tokens[0] == "bSFS") {
if (tokens.size() > 1)
bSFSFile = tokens[1];
}
else if (tokens[0] == "kmax") {
stringstream stst(tokens[1]);
stst >> kmax;
}
else if (tokens[0] == "folded")
foldBrClass = 1;
else if (tokens[0] == "global_search")
globalSearchAlg = tokens[1];
else if (tokens[0] == "global_search_trees") {
stringstream stst(tokens[1]);
stst >> globalTrees;
}
else if (tokens[0] == "local_search_trees") {
stringstream stst(tokens[1]);
stst >> localTrees;
}
else if (tokens[0] == "global_search_evals") {
stringstream stst(tokens[1]);
stst >> globalEvals;
}
else if (tokens[0] == "local_search_evals") {
stringstream stst(tokens[1]);
stst >> localEvals;
}
else if (tokens[0] == "global_upper_bound") {
stringstream stst(tokens[1]);
stst >> globalUpper;
}
else if (tokens[0] == "global_lower_bound") {
stringstream stst(tokens[1]);
stst >> globalLower;
}
else if (tokens[0] == "skip_global_search") {
skipGlobalSearch = true;
}
else if (tokens[0] == "skip_local_search") {
skipLocalSearch = true;
}
else if (tokens[0] == "bounds") {
int paramID = atoi(tokens[1].substr(3).c_str());
for(unsigned int j = 2; j < tokens.size(); j++) {
double val;
stringstream stst(tokens[j]);
stst >> val;
tbiSearchBounds[paramID].push_back(val);
}
}
else if (tokens[0] == "constrain") {
int paramID1 = atoi(tokens[1].substr(3).c_str()), paramID2 = atoi(tokens[2].substr(3).c_str());
parConstraints[paramID1] = paramID2;
}
else if (tokens[0] == "retain_best_points") {
if (tokens.size() > 1) {
stringstream stst(tokens[1]);
stst >> bestParsMapSize;
}
else
bestParsMapSize = 10;
}
else if (tokens[0] == "global_search_iterations") {
if (tokens.size() > 1) {
stringstream stst(tokens[1]);
stst >> numGlobalSearches;
}
}
else if (tokens[0] == "global_bounds_refinement") {
if (tokens.size() > 1) {
if (tokens[1] == "progressive")
progressiveBounds = true;
else if (tokens[1] == "overall")
progressiveBounds = false;
}
}
else if (tokens[0] == "seed_PRNG") {
stringstream stst(tokens[1]);
stst >> seedPRNG;
seedPRNGBool = true;
}
else if (tokens[0] == "refine_likelihoods") {
if (tokens.size() > 1) {
stringstream stst(tokens[1]);
stst >> refineLikTrees;
}
}
else if (tokens[0] == "profile_likelihoods") {
bSFSmode = profileLikBool = true;
if (tokens.size() > 1) {
stringstream stst(tokens[1]);
stst >> profileLikTrees;
}
}
else if (tokens[0] == "report_likelihoods") {
stringstream stst(tokens[1]);
stst >> reportEveryEvals;
}
else if (tokens[0] == "start_likelihood") {
stringstream stst(tokens[1]);
stst >> userLnL;
}
else if (tokens[0] == "no_bSFS_file") {
nobSFSFile = true;
}
else if (tokens[0] == "print_correction_factor") {
printLikCorrFactor = true;
}
else if (tokens[0] == "set_ftol_abs") {
stringstream stst(tokens[1]);
stst >> localSearchAbsTol;
}
else if (tokens[0] == "set_threads") {
stringstream stst(tokens[1]);
stst >> set_threads;
}
else if (tokens[0] == "profile") {
int paramID = atoi(tokens[1].substr(3).c_str());
for(unsigned int j = 2; j < tokens.size(); j++) {
double val;
stringstream stst(tokens[j]);
stst >> val;
tbiProfilesGrid[paramID].push_back(val);
}
}
else if (tokens[0] == "output_precision") {
stringstream stst(tokens[1]);
stst >> outputDigits;
}
else if (tokens[0] == "no_SNP_file") {
outputSNPfile = false;
}
else {
cerr << "Unrecognised keyword \"" << tokens[0] << "\" found in the config file!" << endl;
cerr << "Aborting ABLE..." << endl;
exit(-1);
}
}
}
}
ifs.close();
}
void parseCmdLine(char* argv[]) {
if (cmdLineInConfigFile)
ms_argc = cmdLine.size();
ms_argv = (char **)malloc( ms_argc*sizeof(char *) ) ;
for(int i =0; i < ms_argc; i++)
ms_argv[i] = (char *)malloc(30*sizeof(char) ) ;
for (int i = 0; i < ms_argc; i++) {
string param;
if (cmdLineInConfigFile)
param = cmdLine[i];
else
param = string(argv[i]);
stringstream stst;
if (param.substr(0,3) == "tbi") {
if ((estimate == 2) || profileLikBool) {
int paramID = atoi(param.substr(3).c_str());
tbiMsCmdIdx[paramID].push_back(i);
if (tbiUserVal.find(paramID) != tbiUserVal.end()) {
stst << tbiUserVal[paramID];
stst >> ms_argv[i];
}
else {
// check for tbi start values which are needed for a local search
// N.B. Likelihood profile code needs to re reviewed when activated in the future
if (skipGlobalSearch && !startRandom) {
cerr << "For this task you need to specify values for the \"tbi\" keywords using the \"start\" keyword" << endl;
cerr << "Exiting ABLE...\n" << endl;
free_objects();
exit(-1);
}
double tmpPar = gsl_rng_uniform(PRNG);
tbiUserVal[paramID] = tmpPar;
stst << tmpPar;
stst >> ms_argv[i];
setRandomPars[paramID] = true;
}
}
else {
cerr << "You cannot use \"tbi\" keywords for this task!" << endl;
cerr << "Exiting ABLE...\n" << endl;
free_objects();
exit(-1);
}
}
else {
if (cmdLineInConfigFile)
stst << cmdLine[i];
else
stst << argv[i];
stst >> ms_argv[i];
if (param == "-r") {
stringstream length;
if (cmdLineInConfigFile)
length << cmdLine[i+2];
else
length << argv[i+2];
length >> recLen;
}
}
}
/*
for(int i = 1; i < ms_argc; i++)
printf("%s ",ms_argv[i]);
printf("\n");
free_objects();
exit(-1);
*/
}
void checkConfigOptions() {
if ((estimate == 2) || profileLikBool) {
if (!tbiMsCmdIdx.size()) {
cerr << "\nCannot proceed with inference" << endl;
cerr << "\"tbi\" (To be Inferred) keywords are expected in the \"task infer\" mode" << endl;
cerr << "Exiting ABLE...\n" << endl;
free_objects();
exit(-1);
}
else if (estimate == 2) {
stringstream stst;
if (globalTrees == 0)
globalTrees = 10000;
if (localTrees == 0)
localTrees = 15000;
}
}
if ((dataFile.size() == 1) && (dataScales.size() == 0)) {
dataScales.push_back(1);
uniqueScales.insert(1);
}
if (dataFile.size() != dataScales.size()) {
cerr << "The respective \"scale\" of each specified dataset must be specified for using the mbSFS!" << endl;
cerr << "Exiting ABLE...\n" << endl;
free_objects();
exit(-1);
}
else if ((estimate > 1) || bSFSmode) {
// enumerate and label the number of onetreePoisTables needed
poisTableSize = accumulate(uniqueScales.begin(), uniqueScales.end(), 0);
poisTableScaleOrder = (int *) malloc(poisTableSize * sizeof(int));
// calculate the block lengths for each "SUB"-onetreePoisTable of the mbSFS
blockLengthsMat = d2int_matrix(poisTableSize, 3);
int count = 0;
for (set<int>::iterator it = uniqueScales.begin(); it != uniqueScales.end(); it++) {
int prev = -1;
for (int i = 0; i < *it; i++) {
poisTableScaleOrder[count] = *it;
if (recLen) {
blockLengthsMat[count][0] = recLen / *it;
blockLengthsMat[count][1] = prev + 1;