else {
//valid paramters for this command
- string Array[] = {"groups","iters","distance","random","outputdir","inputdir"};
+ string Array[] = {"groups","iters","distance","random","processors","outputdir","inputdir"};
vector<string> myArray (Array, Array+(sizeof(Array)/sizeof(string)));
OptionParser parser(option);
temp = validParameter.validFile(parameters, "random", false); if (temp == "not found") { temp = "F"; }
random = m->isTrue(temp);
+ temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = "1"; }
+ convert(temp, processors);
+
if (!random) { iters = 0; } //turn off random calcs
int start = time(NULL);
- Progress* reading;
- if (random) { reading = new Progress("Comparing to random:", iters); }
-
//get weighted for users tree
userData.resize(numComp,0); //data[0] = weightedscore AB, data[1] = weightedscore AC...
randomData.resize(numComp,0); //data[0] = weightedscore AB, data[1] = weightedscore AC...
//get weighted scores for users trees
for (int i = 0; i < T.size(); i++) {
- if (m->control_pressed) {
- delete randT;
- if (random) { delete reading; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
- }
+ if (m->control_pressed) { delete randT; outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
counter = 0;
rScores.resize(numComp); //data[0] = weightedscore AB, data[1] = weightedscore AC...
outputNames.push_back(outputDir + m->getSimpleName(globaldata->getTreeFile()) + toString(i+1) + ".weighted");
}
- userData = weighted->getValues(T[i]); //userData[0] = weightedscore
+ userData = weighted->getValues(T[i], processors, outputDir); //userData[0] = weightedscore
if (m->control_pressed) {
delete randT;
- if (random) { delete reading; delete output; }
+ if (random) { delete output; }
outSum.close();
for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
return 0;
utreeScores.push_back(userData[s]);
}
- //get scores for random trees
- for (int j = 0; j < iters; j++) {
- int count = 0;
- for (int r=0; r<numGroups; r++) {
- for (int l = r+1; l < numGroups; l++) {
- //copy T[i]'s info.
- randT->getCopy(T[i]);
-
- //create a random tree with same topology as T[i], but different labels
- randT->assembleRandomUnifracTree(globaldata->Groups[r], globaldata->Groups[l]);
-
- if (m->control_pressed) {
- delete randT;
- if (random) { delete reading; delete output; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
+ if (random) {
+ vector<double> sums = weighted->getBranchLengthSums(T[i]);
+
+ //calculate number of comparisons i.e. with groups A,B,C = AB, AC, BC = 3;
+ vector< vector<string> > namesOfGroupCombos;
+ for (int a=0; a<numGroups; a++) {
+ for (int l = a+1; l < numGroups; l++) {
+ vector<string> groups; groups.push_back(globaldata->Groups[a]); groups.push_back(globaldata->Groups[l]);
+ namesOfGroupCombos.push_back(groups);
+ }
+ }
+
+ #if defined (__APPLE__) || (__MACH__) || (linux) || (__linux)
+ if(processors != 1){
+ int numPairs = namesOfGroupCombos.size();
+ int numPairsPerProcessor = numPairs / processors;
+
+ for (int i = 0; i < processors; i++) {
+ int startPos = i * numPairsPerProcessor;
+ if(i == processors - 1){
+ numPairsPerProcessor = numPairs - i * numPairsPerProcessor;
+ }
+ lines.push_back(new linePair(startPos, numPairsPerProcessor));
}
+ }
+ #endif
-
- //get wscore of random tree
- randomData = weighted->getValues(randT, globaldata->Groups[r], globaldata->Groups[l]);
-
- if (m->control_pressed) {
- delete randT;
- if (random) { delete reading; delete output; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
+
+ //get scores for random trees
+ for (int j = 0; j < iters; j++) {
+ int count = 0;
+
+ #if defined (__APPLE__) || (__MACH__) || (linux) || (__linux)
+ if(processors == 1){
+ driver(T[i], randT, namesOfGroupCombos, 0, namesOfGroupCombos.size(), sums, rScores);
+ }else{
+ createProcesses(T[i], randT, namesOfGroupCombos, sums, rScores);
+ for (int i = 0; i < lines.size(); i++) { delete lines[i]; } lines.clear();
}
-
- //save scores
- rScores[count].push_back(randomData[0]);
- count++;
- }
+ #else
+ driver(T[i], randT, namesOfGroupCombos, 0, namesOfGroupCombos.size(), sums, rScores);
+ #endif
+
+ if (m->control_pressed) { delete output; outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
}
- //update progress bar
- reading->update(j);
-
- }
-
- //removeValidScoresDuplicates();
- //find the signifigance of the score for summary file
- if (random) {
+
+ //find the signifigance of the score for summary file
for (int f = 0; f < numComp; f++) {
//sort random scores
sort(rScores[f].begin(), rScores[f].end());
printWeightedFile();
delete output;
+
}
//clear data
}
- if (m->control_pressed) {
- delete randT;
- if (random) { delete reading; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
- }
-
- //finish progress bar
- if (random) { reading->finish(); delete reading; }
+ if (m->control_pressed) { delete randT; outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
printWSummaryFile();
exit(1);
}
}
+/**************************************************************************************************/
+
+int UnifracWeightedCommand::createProcesses(Tree* t, Tree* randT, vector< vector<string> > namesOfGroupCombos, vector<double>& sums, vector< vector<double> >& scores) {
+ try {
+#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux)
+ int process = 0;
+ int num = 0;
+ vector<int> processIDS;
+
+ EstOutput results;
+
+ //loop through and create all the processes you want
+ while (process != processors) {
+ int pid = fork();
+
+ if (pid > 0) {
+ processIDS.push_back(pid); //create map from line number to pid so you can append files in correct order later
+ process++;
+ }else if (pid == 0){
+ driver(t, randT, namesOfGroupCombos, lines[process]->start, lines[process]->num, sums, scores);
+
+ m->mothurOut("Merging results."); m->mothurOutEndLine();
+
+ //pass numSeqs to parent
+ ofstream out;
+ string tempFile = outputDir + toString(getpid()) + ".results.temp";
+ m->openOutputFile(tempFile, out);
+ out << results.size() << endl;
+ for (int i = lines[process]->start; i < (lines[process]->start + lines[process]->num); i++) { out << results[i] << '\t'; } out << endl;
+ out.close();
+
+ exit(0);
+ }else { m->mothurOut("unable to spawn the necessary processes."); m->mothurOutEndLine(); exit(0); }
+ }
+
+ //force parent to wait until all the processes are done
+ for (int i=0;i<processors;i++) {
+ int temp = processIDS[i];
+ wait(&temp);
+ }
+
+ //get data created by processes
+ for (int i=0;i<processors;i++) {
+ ifstream in;
+ string s = outputDir + toString(processIDS[i]) + ".results.temp";
+ m->openInputFile(s, in);
+
+ vector<double> r;
+
+ //get quantiles
+ while (!in.eof()) {
+ int num;
+ in >> num;
+
+ m->gobble(in);
+
+ double w;
+ for (int j = 0; j < num; j++) {
+ in >> w;
+ r.push_back(w);
+ }
+ m->gobble(in);
+ }
+ in.close();
+ remove(s.c_str());
+
+ //save quan in quantiles
+ for (int j = 0; j < r.size(); j++) {
+ //put all values of r into results
+ results.push_back(r[j]);
+ }
+ }
+
+ m->mothurOut("DONE."); m->mothurOutEndLine(); m->mothurOutEndLine();
+
+ return 0;
+#endif
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracWeightedCommand", "createProcesses");
+ exit(1);
+ }
+}
+
+/**************************************************************************************************/
+int UnifracWeightedCommand::driver(Tree* t, Tree* randT, vector< vector<string> > namesOfGroupCombos, int start, int num, vector<double>& sums, vector< vector<double> >& scores) {
+ try {
+ int count = 0;
+ int total = start+num;
+ int twentyPercent = (total * 0.20);
+
+ for (int h = start; h < (start+num); h++) {
+
+ if (m->control_pressed) { return 0; }
+
+ //initialize weighted score
+ string groupA = namesOfGroupCombos[h][0];
+ string groupB = namesOfGroupCombos[h][1];
+
+ //copy T[i]'s info.
+ randT->getCopy(t);
+
+ //create a random tree with same topology as T[i], but different labels
+ randT->assembleRandomUnifracTree(groupA, groupB);
+
+ if (m->control_pressed) { delete randT; return 0; }
+
+
+ //get wscore of random tree
+ EstOutput randomData = weighted->getValues(randT, groupA, groupB, sums);
+
+ if (m->control_pressed) { delete randT; return 0; }
+
+ //save scores
+ scores[h].push_back(randomData[0]);
+
+ count++;
+
+ //report progress
+ if((h) % twentyPercent == 0){ m->mothurOut("Random comparison percentage complete: " + toString(int((h / (float)total) * 100.0))); m->mothurOutEndLine(); }
+ }
+
+ m->mothurOut("Random comparison percentage complete: 100"); m->mothurOutEndLine();
+
+ return 0;
+
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracWeightedCommand", "driver");
+ exit(1);
+ }
+}
/***********************************************************/
void UnifracWeightedCommand::printWeightedFile() {
try {
projects / mothur.git / blobdiff