}else {
m->clearGroups();
Groups.clear();
+ m->Treenames.clear();
vector<SharedRAbundVector*> temp;
for (int i = 0; i < lookup.size(); i++) {
if (lookup[i]->getNumSeqs() < subsampleSize) {
}else {
Groups.push_back(lookup[i]->getGroup());
temp.push_back(lookup[i]);
+ m->Treenames.push_back(lookup[i]->getGroup());
}
}
lookup = temp;
for (int i = 0; i < calcDists.size(); i++) { calcDists[i].clear(); }
}
}
-
+
+ if (m->debug) { m->mothurOut("[DEBUG]: done with iters.\n"); }
+
if (iters != 1) {
//we need to find the average distance and standard deviation for each groups distance
vector< vector<seqDist> > calcAverages = m->getAverages(calcDistsTotals);
+ if (m->debug) { m->mothurOut("[DEBUG]: found averages.\n"); }
+
//create average tree for each calc
for (int i = 0; i < calcDists.size(); i++) {
vector< vector<double> > matrix; //square matrix to represent the distance
if (newTree != NULL) { writeTree(outputFile, newTree); }
}
+ if (m->debug) { m->mothurOut("[DEBUG]: done averages trees.\n"); }
+
//create all trees for each calc and find their consensus tree
for (int i = 0; i < calcDists.size(); i++) {
if (m->control_pressed) { break; }
int row = calcDistsTotals[myIter][i][j].seq1;
int column = calcDistsTotals[myIter][i][j].seq2;
double dist = calcDistsTotals[myIter][i][j].dist;
-
+
matrix[row][column] = dist;
matrix[column][row] = dist;
}
outAll.close();
if (m->control_pressed) { for (int k = 0; k < trees.size(); k++) { delete trees[k]; } }
+ if (m->debug) { m->mothurOut("[DEBUG]: done all trees.\n"); }
+
Consensus consensus;
//clear old tree names if any
m->Treenames.clear(); m->Treenames = m->getGroups(); //may have changed if subsample eliminated groups
Tree* conTree = consensus.getTree(trees);
+ if (m->debug) { m->mothurOut("[DEBUG]: done cons tree.\n"); }
+
//create a new filename
variables["[tag]"] = "cons";
string conFile = getOutputFileName("tree",variables);