//**********************************************************************************************************************
vector<string> UnifracWeightedCommand::setParameters(){
try {
- CommandParameter ptree("tree", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(ptree);
- CommandParameter pname("name", "InputTypes", "", "", "NameCount", "none", "none",false,false); parameters.push_back(pname);
- CommandParameter pcount("count", "InputTypes", "", "", "NameCount-CountGroup", "none", "none",false,false); parameters.push_back(pcount);
- CommandParameter pgroup("group", "InputTypes", "", "", "CountGroup", "none", "none",false,false); parameters.push_back(pgroup);
- CommandParameter pgroups("groups", "String", "", "", "", "", "",false,false); parameters.push_back(pgroups);
- CommandParameter piters("iters", "Number", "", "1000", "", "", "",false,false); parameters.push_back(piters);
- CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
- CommandParameter psubsample("subsample", "String", "", "", "", "", "",false,false); parameters.push_back(psubsample);
- CommandParameter pconsensus("consensus", "Boolean", "", "F", "", "", "",false,false); parameters.push_back(pconsensus);
- CommandParameter prandom("random", "Boolean", "", "F", "", "", "",false,false); parameters.push_back(prandom);
- CommandParameter pdistance("distance", "Multiple", "column-lt-square-phylip", "column", "", "", "",false,false); parameters.push_back(pdistance);
- CommandParameter proot("root", "Boolean", "F", "", "", "", "",false,false); parameters.push_back(proot);
- CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
- CommandParameter poutputdir("outputdir", "String", "", "", "", "", "",false,false); parameters.push_back(poutputdir);
+ CommandParameter ptree("tree", "InputTypes", "", "", "none", "none", "none","weighted-wsummary",false,true,true); parameters.push_back(ptree);
+ CommandParameter pname("name", "InputTypes", "", "", "NameCount", "none", "none","",false,false,true); parameters.push_back(pname);
+ CommandParameter pcount("count", "InputTypes", "", "", "NameCount-CountGroup", "none", "none","",false,false,true); parameters.push_back(pcount);
+ CommandParameter pgroup("group", "InputTypes", "", "", "CountGroup", "none", "none","",false,false,true); parameters.push_back(pgroup);
+ CommandParameter pgroups("groups", "String", "", "", "", "", "","",false,false); parameters.push_back(pgroups);
+ CommandParameter piters("iters", "Number", "", "1000", "", "", "","",false,false); parameters.push_back(piters);
+ CommandParameter pprocessors("processors", "Number", "", "1", "", "", "","",false,false,true); parameters.push_back(pprocessors);
+ CommandParameter psubsample("subsample", "String", "", "", "", "", "","",false,false); parameters.push_back(psubsample);
+ CommandParameter pconsensus("consensus", "Boolean", "", "F", "", "", "","tree",false,false); parameters.push_back(pconsensus);
+ CommandParameter prandom("random", "Boolean", "", "F", "", "", "","",false,false); parameters.push_back(prandom);
+ CommandParameter pdistance("distance", "Multiple", "column-lt-square-phylip", "column", "", "", "","phylip-column",false,false); parameters.push_back(pdistance);
+ CommandParameter proot("root", "Boolean", "F", "", "", "", "","",false,false); parameters.push_back(proot);
+ CommandParameter pinputdir("inputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(pinputdir);
+ CommandParameter poutputdir("outputdir", "String", "", "", "", "", "","",false,false); parameters.push_back(poutputdir);
vector<string> myArray;
for (int i = 0; i < parameters.size(); i++) { myArray.push_back(parameters[i].name); }
}
}
//**********************************************************************************************************************
-string UnifracWeightedCommand::getOutputFileNameTag(string type, string inputName=""){
- try {
- string outputFileName = "";
- map<string, vector<string> >::iterator it;
+string UnifracWeightedCommand::getOutputPattern(string type) {
+ try {
+ string pattern = "";
+ if (type == "weighted") { pattern = "[filename],weighted-[filename],[tag],weighted"; }
+ else if (type == "wsummary") { pattern = "[filename],wsummary"; }
+ else if (type == "phylip") { pattern = "[filename],[tag],[tag2],dist"; }
+ else if (type == "column") { pattern = "[filename],[tag],[tag2],dist"; }
+ else if (type == "tree") { pattern = "[filename],[tag],[tag2],tre"; }
+ else { m->mothurOut("[ERROR]: No definition for type " + type + " output pattern.\n"); m->control_pressed = true; }
- //is this a type this command creates
- it = outputTypes.find(type);
- if (it == outputTypes.end()) { m->mothurOut("[ERROR]: this command doesn't create a " + type + " output file.\n"); }
- else {
- if (type == "weighted") { outputFileName = "weighted"; }
- else if (type == "wsummary") { outputFileName = "wsummary"; }
- else if (type == "phylip") { outputFileName = "dist"; }
- else if (type == "column") { outputFileName = "dist"; }
- else if (type == "tree") { outputFileName = "tre"; }
- else { m->mothurOut("[ERROR]: No definition for type " + type + " output file tag.\n"); m->control_pressed = true; }
- }
- return outputFileName;
- }
- catch(exception& e) {
- m->errorOut(e, "UnifracWeightedCommand", "getOutputFileNameTag");
- exit(1);
- }
+ return pattern;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracWeightedCommand", "getOutputPattern");
+ exit(1);
+ }
}
//**********************************************************************************************************************
UnifracWeightedCommand::UnifracWeightedCommand(){
delete reader;
if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } return 0; }
-
- sumFile = outputDir + m->getSimpleName(treefile) + getOutputFileNameTag("wsummary");
+
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getSimpleName(treefile);
+ sumFile = getOutputFileName("wsummary",variables);
m->openOutputFile(sumFile, outSum);
outputNames.push_back(sumFile); outputTypes["wsummary"].push_back(sumFile);
vector<double> randomData; randomData.resize(numComp,0); //weighted score info for random trees. data[0] = weightedscore AB, data[1] = weightedscore AC...
if (random) {
- output = new ColumnFile(outputDir + m->getSimpleName(treefile) + toString(i+1) + "." + getOutputFileNameTag("weighted"), itersString);
- outputNames.push_back(outputDir + m->getSimpleName(treefile) + toString(i+1) + "." + getOutputFileNameTag("weighted"));
- outputTypes["weighted"].push_back(outputDir + m->getSimpleName(treefile) + toString(i+1) + "." + getOutputFileNameTag("weighted"));
+ variables["[filename]"] = outputDir + m->getSimpleName(treefile);
+ variables["[tag]"] = toString(i+1);
+ string wFileName = getOutputFileName("weighted", variables);
+ output = new ColumnFile(wFileName, itersString);
+ outputNames.push_back(wFileName); outputTypes["weighted"].push_back(wFileName);
}
userData = weighted.getValues(T[i], processors, outputDir); //userData[0] = weightedscore
delete newCt;
delete subSampleTree;
- if((thisIter+1) % 100 == 0){ m->mothurOut(toString(thisIter+1)); m->mothurOutEndLine(); }
+ if((thisIter+1) % 100 == 0){ m->mothurOutJustToScreen(toString(thisIter+1)+"\n"); }
}
if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } if (random) { delete output; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
//we need to find the average distance and standard deviation for each groups distance
//finds sum
- vector<double> averages; averages.resize(numComp, 0);
- for (int thisIter = 0; thisIter < subsampleIters; thisIter++) {
- for (int i = 0; i < dists[thisIter].size(); i++) {
- averages[i] += dists[thisIter][i];
- }
- }
-
- //finds average.
- for (int i = 0; i < averages.size(); i++) { averages[i] /= (float) subsampleIters; }
+ vector<double> averages = m->getAverages(dists);
//find standard deviation
- vector<double> stdDev; stdDev.resize(numComp, 0);
-
- for (int thisIter = 0; thisIter < iters; thisIter++) { //compute the difference of each dist from the mean, and square the result of each
- for (int j = 0; j < dists[thisIter].size(); j++) {
- stdDev[j] += ((dists[thisIter][j] - averages[j]) * (dists[thisIter][j] - averages[j]));
- }
- }
- for (int i = 0; i < stdDev.size(); i++) {
- stdDev[i] /= (float) subsampleIters;
- stdDev[i] = sqrt(stdDev[i]);
- }
+ vector<double> stdDev = m->getStandardDeviation(dists, averages);
//make matrix with scores in it
- vector< vector<double> > avedists; avedists.resize(m->getNumGroups());
+ vector< vector<double> > avedists; //avedists.resize(m->getNumGroups());
for (int i = 0; i < m->getNumGroups(); i++) {
- avedists[i].resize(m->getNumGroups(), 0.0);
+ vector<double> temp;
+ for (int j = 0; j < m->getNumGroups(); j++) { temp.push_back(0.0); }
+ avedists.push_back(temp);
}
//make matrix with scores in it
- vector< vector<double> > stddists; stddists.resize(m->getNumGroups());
+ vector< vector<double> > stddists; //stddists.resize(m->getNumGroups());
for (int i = 0; i < m->getNumGroups(); i++) {
- stddists[i].resize(m->getNumGroups(), 0.0);
+ vector<double> temp;
+ for (int j = 0; j < m->getNumGroups(); j++) { temp.push_back(0.0); }
+ //stddists[i].resize(m->getNumGroups(), 0.0);
+ stddists.push_back(temp);
}
+
//flip it so you can print it
int count = 0;
}
}
- string aveFileName = outputDir + m->getSimpleName(treefile) + toString(treeNum+1) + ".weighted.ave." + getOutputFileNameTag("phylip");
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getSimpleName(treefile);
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "weighted.ave";
+ string aveFileName = getOutputFileName("phylip",variables);
if (outputForm != "column") { outputNames.push_back(aveFileName); outputTypes["phylip"].push_back(aveFileName); }
else { outputNames.push_back(aveFileName); outputTypes["column"].push_back(aveFileName); }
ofstream out;
m->openOutputFile(aveFileName, out);
- string stdFileName = outputDir + m->getSimpleName(treefile) + toString(treeNum+1) + ".weighted.std." + getOutputFileNameTag("phylip");
+ variables["[tag2]"] = "weighted.std";
+ string stdFileName = getOutputFileName("phylip",variables);
if (outputForm != "column") { outputNames.push_back(stdFileName); outputTypes["phylip"].push_back(stdFileName); }
else { outputNames.push_back(stdFileName); outputTypes["column"].push_back(stdFileName); }
ofstream outStd;
Tree* conTree = con.getTree(newTrees);
//create a new filename
- string conFile = outputDir + m->getRootName(m->getSimpleName(treefile)) + toString(treeNum+1) + ".weighted.cons." + getOutputFileNameTag("tree");
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "weighted.cons";
+ string conFile = getOutputFileName("tree",variables);
outputNames.push_back(conFile); outputTypes["tree"].push_back(conFile);
ofstream outTree;
m->openOutputFile(conFile, outTree);
vector<Tree*> trees;
//create a new filename
- string outputFile = outputDir + m->getRootName(m->getSimpleName(treefile)) + toString(treeNum+1) + ".weighted.all." + getOutputFileNameTag("tree");
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "weighted.all";
+ string outputFile = getOutputFileName("tree",variables);
outputNames.push_back(outputFile); outputTypes["tree"].push_back(outputFile);
ofstream outAll;
lines.clear();
-#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
- if(processors != 1){
- int numPairs = namesOfGroupCombos.size();
- int numPairsPerProcessor = numPairs / processors;
+ //breakdown work between processors
+ 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(linePair(startPos, numPairsPerProcessor));
- }
+ for (int i = 0; i < processors; i++) {
+ int startPos = i * numPairsPerProcessor;
+ if(i == processors - 1){ numPairsPerProcessor = numPairs - i * numPairsPerProcessor; }
+ lines.push_back(linePair(startPos, numPairsPerProcessor));
}
-#endif
//get scores for random trees
for (int j = 0; j < iters; j++) {
- cout << j << endl;
-#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
- if(processors == 1){
- driver(thisTree, namesOfGroupCombos, 0, namesOfGroupCombos.size(), rScores);
- }else{
+//#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
+ //if(processors == 1){
+ // driver(thisTree, namesOfGroupCombos, 0, namesOfGroupCombos.size(), rScores);
+ // }else{
createProcesses(thisTree, namesOfGroupCombos, rScores);
- }
-#else
- driver(thisTree, namesOfGroupCombos, 0, namesOfGroupCombos.size(), rScores);
-#endif
+ // }
+//#else
+ //driver(thisTree, namesOfGroupCombos, 0, namesOfGroupCombos.size(), rScores);
+//#endif
+
if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } delete output; outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
- //report progress
- // m->mothurOut("Iter: " + toString(j+1)); m->mothurOutEndLine();
}
lines.clear();
int UnifracWeightedCommand::createProcesses(Tree* t, vector< vector<string> > namesOfGroupCombos, vector< vector<double> >& scores) {
try {
-#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
- int process = 1;
+ int process = 1;
vector<int> processIDS;
-
EstOutput results;
-
+
+#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
//loop through and create all the processes you want
while (process != processors) {
int pid = fork();
in.close();
m->mothurRemove(s);
}
+#else
+ //fill in functions
+ vector<weightedRandomData*> pDataArray;
+ DWORD dwThreadIdArray[processors-1];
+ HANDLE hThreadArray[processors-1];
+ vector<CountTable*> cts;
+ vector<Tree*> trees;
- return 0;
-#endif
+ //Create processor worker threads.
+ for( int i=1; i<processors; i++ ){
+ CountTable* copyCount = new CountTable();
+ copyCount->copy(ct);
+ Tree* copyTree = new Tree(copyCount);
+ copyTree->getCopy(t);
+
+ cts.push_back(copyCount);
+ trees.push_back(copyTree);
+
+ vector< vector<double> > copyScores = rScores;
+
+ weightedRandomData* tempweighted = new weightedRandomData(m, lines[i].start, lines[i].num, namesOfGroupCombos, copyTree, copyCount, includeRoot, copyScores);
+ pDataArray.push_back(tempweighted);
+ processIDS.push_back(i);
+
+ hThreadArray[i-1] = CreateThread(NULL, 0, MyWeightedRandomThreadFunction, pDataArray[i-1], 0, &dwThreadIdArray[i-1]);
+ }
+
+ driver(t, namesOfGroupCombos, lines[0].start, lines[0].num, scores);
+
+ //Wait until all threads have terminated.
+ WaitForMultipleObjects(processors-1, hThreadArray, TRUE, INFINITE);
+
+ //Close all thread handles and free memory allocations.
+ for(int i=0; i < pDataArray.size(); i++){
+ for (int j = pDataArray[i]->start; j < (pDataArray[i]->start+pDataArray[i]->num); j++) {
+ scores[j].push_back(pDataArray[i]->scores[j][pDataArray[i]->scores[j].size()-1]);
+ }
+ delete cts[i];
+ delete trees[i];
+ CloseHandle(hThreadArray[i]);
+ delete pDataArray[i];
+ }
+
+
+#endif
+
+ return 0;
}
catch(exception& e) {
m->errorOut(e, "UnifracWeightedCommand", "createProcesses");
for(int a = 0; a < numComp; a++) {
output->initFile(groupComb[a], tags);
//print each line
- for (map<float,float>::iterator it = validScores.begin(); it != validScores.end(); it++) {
+ for (map<double,double>::iterator it = validScores.begin(); it != validScores.end(); it++) {
data.push_back(it->first); data.push_back(rScoreFreq[a][it->first]); data.push_back(rCumul[a][it->first]);
output->output(data);
data.clear();
//for each tree
for (int i = 0; i < T.size(); i++) {
- string phylipFileName;
- if ((outputForm == "lt") || (outputForm == "square")) {
- phylipFileName = outputDir + m->getSimpleName(treefile) + toString(i+1) + ".weighted.phylip." + getOutputFileNameTag("phylip");
- outputNames.push_back(phylipFileName); outputTypes["phylip"].push_back(phylipFileName);
- }else { //column
- phylipFileName = outputDir + m->getSimpleName(treefile) + toString(i+1) + ".weighted.column." + getOutputFileNameTag("column");
- outputNames.push_back(phylipFileName); outputTypes["column"].push_back(phylipFileName);
- }
+ string phylipFileName;
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getSimpleName(treefile);
+ variables["[tag]"] = toString(i+1);
+ if ((outputForm == "lt") || (outputForm == "square")) {
+ variables["[tag2]"] = "weighted.phylip";
+ phylipFileName = getOutputFileName("phylip",variables);
+ outputNames.push_back(phylipFileName); outputTypes["phylip"].push_back(phylipFileName);
+ }else { //column
+ variables["[tag2]"] = "weighted.column";
+ phylipFileName = getOutputFileName("column",variables);
+ outputNames.push_back(phylipFileName); outputTypes["column"].push_back(phylipFileName);
+ }
+
ofstream out;
m->openOutputFile(phylipFileName, out);
for (int f = 0; f < numComp; f++) {
for (int i = 0; i < rScores[f].size(); i++) { //looks like 0,0,1,1,1,2,4,7... you want to make a map that say rScoreFreq[0] = 2, rScoreFreq[1] = 3...
validScores[rScores[f][i]] = rScores[f][i];
- map<float,float>::iterator it = rScoreFreq[f].find(rScores[f][i]);
+ map<double,double>::iterator it = rScoreFreq[f].find(rScores[f][i]);
if (it != rScoreFreq[f].end()) {
rScoreFreq[f][rScores[f][i]]++;
}else{
for(int a = 0; a < numComp; a++) {
float rcumul = 1.0000;
//this loop fills the cumulative maps and put 0.0000 in the score freq map to make it easier to print.
- for (map<float,float>::iterator it = validScores.begin(); it != validScores.end(); it++) {
+ for (map<double,double>::iterator it = validScores.begin(); it != validScores.end(); it++) {
//make rscoreFreq map and rCumul
- map<float,float>::iterator it2 = rScoreFreq[a].find(it->first);
+ map<double,double>::iterator it2 = rScoreFreq[a].find(it->first);
rCumul[a][it->first] = rcumul;
//get percentage of random trees with that info
if (it2 != rScoreFreq[a].end()) { rScoreFreq[a][it->first] /= iters; rcumul-= it2->second; }
projects / mothur.git / blobdiff