vector<string> UnifracWeightedCommand::setParameters(){
try {
CommandParameter ptree("tree", "InputTypes", "", "", "none", "none", "none",false,true); parameters.push_back(ptree);
- CommandParameter pgroup("group", "InputTypes", "", "", "none", "none", "none",false,false); parameters.push_back(pgroup);
- CommandParameter pname("name", "InputTypes", "", "", "none", "none", "none",false,false); parameters.push_back(pname);
+ 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);
string UnifracWeightedCommand::getHelpString(){
try {
string helpString = "";
- helpString += "The unifrac.weighted command parameters are tree, group, name, groups, iters, distance, processors, root, subsample, consensus and random. tree parameter is required unless you have valid current tree file.\n";
+ helpString += "The unifrac.weighted command parameters are tree, group, name, count, groups, iters, distance, processors, root, subsample, consensus and random. tree parameter is required unless you have valid current tree file.\n";
helpString += "The groups parameter allows you to specify which of the groups in your groupfile you would like analyzed. You must enter at least 2 valid groups.\n";
helpString += "The group names are separated by dashes. The iters parameter allows you to specify how many random trees you would like compared to your tree.\n";
helpString += "The distance parameter allows you to create a distance file from the results. The default is false.\n";
//if the user has not given a path then, add inputdir. else leave path alone.
if (path == "") { parameters["name"] = inputDir + it->second; }
}
+
+ it = parameters.find("count");
+ //user has given a template file
+ if(it != parameters.end()){
+ path = m->hasPath(it->second);
+ //if the user has not given a path then, add inputdir. else leave path alone.
+ if (path == "") { parameters["count"] = inputDir + it->second; }
+ }
}
//check for required parameters
else if (namefile == "not found") { namefile = ""; }
else { m->setNameFile(namefile); }
+ countfile = validParameter.validFile(parameters, "count", true);
+ if (countfile == "not open") { countfile = ""; abort = true; }
+ else if (countfile == "not found") { countfile = ""; }
+ else { m->setCountTableFile(countfile); }
+
+ if ((namefile != "") && (countfile != "")) {
+ m->mothurOut("[ERROR]: you may only use one of the following: name or count."); m->mothurOutEndLine(); abort = true;
+ }
+
+ if ((groupfile != "") && (countfile != "")) {
+ m->mothurOut("[ERROR]: you may only use one of the following: group or count."); m->mothurOutEndLine(); abort=true;
+ }
+
outputDir = validParameter.validFile(parameters, "outputdir", false); if (outputDir == "not found"){ outputDir = m->hasPath(treefile); }
consensus = m->isTrue(temp);
if (subsample && random) { m->mothurOut("[ERROR]: random must be false, if subsample=t.\n"); abort=true; }
- if (subsample && (groupfile == "")) { m->mothurOut("[ERROR]: if subsample=t, a group file must be provided.\n"); abort=true; }
+ if (countfile == "") { if (subsample && (groupfile == "")) { m->mothurOut("[ERROR]: if subsample=t, a group file must be provided.\n"); abort=true; } }
+ else {
+ CountTable testCt;
+ if ((!testCt.testGroups(countfile)) && (subsample)) {
+ m->mothurOut("[ERROR]: if subsample=t, a count file with group info must be provided.\n"); abort=true;
+ }
+ }
if (subsample && (!phylip)) { phylip=true; outputForm = "lt"; }
if (consensus && (!subsample)) { m->mothurOut("[ERROR]: you cannot use consensus without subsample.\n"); abort=true; }
- if (namefile == "") {
- vector<string> files; files.push_back(treefile);
- parser.getNameFile(files);
- }
+ if (countfile=="") {
+ if (namefile == "") {
+ vector<string> files; files.push_back(treefile);
+ parser.getNameFile(files);
+ }
+ }
}
m->setTreeFile(treefile);
- TreeReader* reader = new TreeReader(treefile, groupfile, namefile);
+ TreeReader* reader;
+ if (countfile == "") { reader = new TreeReader(treefile, groupfile, namefile); }
+ else { reader = new TreeReader(treefile, countfile); }
T = reader->getTrees();
- tmap = T[0]->getTreeMap();
- map<string, string> nameMap = reader->getNames();
- map<string, string> unique2Dup = reader->getNameMap();
+ ct = T[0]->getCountTable();
delete reader;
-
- if (m->control_pressed) { delete tmap; for (int i = 0; i < T.size(); i++) { delete T[i]; } return 0; }
+
+ 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");
m->openOutputFile(sumFile, outSum);
SharedUtil util;
string s; //to make work with setgroups
Groups = m->getGroups();
- vector<string> nameGroups = tmap->getNamesOfGroups();
+ vector<string> nameGroups = ct->getNamesOfGroups();
util.setGroups(Groups, nameGroups, s, numGroups, "weighted"); //sets the groups the user wants to analyze
m->setGroups(Groups);
- if (m->control_pressed) { delete tmap; for (int i = 0; i < T.size(); i++) { delete T[i]; } return 0; }
+ if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } return 0; }
Weighted weighted(includeRoot);
//user has not set size, set size = smallest samples size
if (subsampleSize == -1) {
vector<string> temp; temp.push_back(Groups[0]);
- subsampleSize = (tmap->getNamesSeqs(temp)).size(); //num in first group
+ subsampleSize = ct->getGroupCount(Groups[0]); //num in first group
for (int i = 1; i < Groups.size(); i++) {
- temp.clear(); temp.push_back(Groups[i]);
- int thisSize = (tmap->getNamesSeqs(temp)).size();
+ int thisSize = ct->getGroupCount(Groups[i]);
if (thisSize < subsampleSize) { subsampleSize = thisSize; }
}
m->mothurOut("\nSetting subsample size to " + toString(subsampleSize) + ".\n\n");
vector<string> newGroups = Groups;
Groups.clear();
for (int i = 0; i < newGroups.size(); i++) {
- vector<string> thisGroup; thisGroup.push_back(newGroups[i]);
- vector<string> thisGroupsSeqs = tmap->getNamesSeqs(thisGroup);
- int thisSize = thisGroupsSeqs.size();
+ int thisSize = ct->getGroupCount(newGroups[i]);
if (thisSize >= subsampleSize) { Groups.push_back(newGroups[i]); }
- else { m->mothurOut("You have selected a size that is larger than "+newGroups[i]+" number of sequences, removing "+newGroups[i]+".\n"); }
+ else { m->mothurOut("You have selected a size that is larger than "+newGroups[i]+" number of sequences, removing "+newGroups[i]+".\n"); }
}
m->setGroups(Groups);
}
//get weighted scores for users trees
for (int i = 0; i < T.size(); i++) {
- if (m->control_pressed) { delete tmap; for (int i = 0; i < T.size(); i++) { delete T[i]; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
+ if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
counter = 0;
rScores.resize(numComp); //data[0] = weightedscore AB, data[1] = weightedscore AC...
}
userData = weighted.getValues(T[i], processors, outputDir); //userData[0] = weightedscore
- if (m->control_pressed) { delete tmap; 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; }
+ 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; }
//save users score
for (int s=0; s<numComp; s++) {
if (m->control_pressed) { break; }
//copy to preserve old one - would do this in subsample but memory cleanup becomes messy.
- TreeMap* newTmap = new TreeMap();
- //newTmap->getCopy(*tmap);
-
- //SubSample sample;
- //Tree* subSampleTree = sample.getSample(T[i], newTmap, nameMap, subsampleSize);
+ CountTable* newCt = new CountTable();
//uses method of setting groups to doNotIncludeMe
SubSample sample;
- Tree* subSampleTree = sample.getSample(T[i], tmap, newTmap, subsampleSize, unique2Dup);
-
+ Tree* subSampleTree = sample.getSample(T[i], ct, newCt, subsampleSize);
+
//call new weighted function
vector<double> iterData; iterData.resize(numComp,0);
Weighted thisWeighted(includeRoot);
//save data to make ave dist, std dist
calcDistsTotals.push_back(iterData);
- delete newTmap;
+ delete newCt;
delete subSampleTree;
if((thisIter+1) % 100 == 0){ m->mothurOut(toString(thisIter+1)); m->mothurOutEndLine(); }
}
- if (m->control_pressed) { delete tmap; 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; }
+ 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; }
if (subsample) { getAverageSTDMatrices(calcDistsTotals, i); }
if (consensus) { getConsensusTrees(calcDistsTotals, i); }
}
- if (m->control_pressed) { delete tmap; for (int i = 0; i < T.size(); i++) { delete T[i]; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
+ if (m->control_pressed) { delete ct; for (int i = 0; i < T.size(); i++) { delete T[i]; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
if (phylip) { createPhylipFile(); }
//clear out users groups
m->clearGroups();
- delete tmap;
+ delete ct;
for (int i = 0; i < T.size(); i++) { delete T[i]; }
if (m->control_pressed) { for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } return 0; }
//used in tree constructor
m->runParse = false;
- //create treemap class from groupmap for tree class to use
- TreeMap newTmap;
- newTmap.makeSim(m->getGroups());
+ ///create treemap class from groupmap for tree class to use
+ CountTable newCt;
+ set<string> nameMap;
+ map<string, string> groupMap;
+ set<string> gps;
+ for (int i = 0; i < m->getGroups().size(); i++) {
+ nameMap.insert(m->getGroups()[i]);
+ gps.insert(m->getGroups()[i]);
+ groupMap[m->getGroups()[i]] = m->getGroups()[i];
+ }
+ newCt.createTable(nameMap, groupMap, gps);
//clear old tree names if any
m->Treenames.clear();
//fills globaldatas tree names
m->Treenames = m->getGroups();
- vector<Tree*> newTrees = buildTrees(dists, treeNum, newTmap); //also creates .all.tre file containing the trees created
+ vector<Tree*> newTrees = buildTrees(dists, treeNum, newCt); //also creates .all.tre file containing the trees created
if (m->control_pressed) { return 0; }
}
/**************************************************************************************************/
-vector<Tree*> UnifracWeightedCommand::buildTrees(vector< vector<double> >& dists, int treeNum, TreeMap& mytmap) {
+vector<Tree*> UnifracWeightedCommand::buildTrees(vector< vector<double> >& dists, int treeNum, CountTable& myct) {
try {
vector<Tree*> trees;
}
//create tree
- Tree* tempTree = new Tree(&mytmap, sims);
- map<string, string> empty;
- tempTree->assembleTree(empty);
+ Tree* tempTree = new Tree(&myct, sims);
+ tempTree->assembleTree();
trees.push_back(tempTree);
//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);
driver(thisTree, namesOfGroupCombos, 0, namesOfGroupCombos.size(), rScores);
#endif
- if (m->control_pressed) { delete tmap; 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; }
+ 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();
/**************************************************************************************************/
int UnifracWeightedCommand::driver(Tree* t, vector< vector<string> > namesOfGroupCombos, int start, int num, vector< vector<double> >& scores) {
try {
- Tree* randT = new Tree(tmap);
+ Tree* randT = new Tree(ct);
Weighted weighted(includeRoot);
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