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);
string temp = validParameter.validFile(parameters, "distance", false); if (temp == "not found") { temp = "false"; }
phylip = m->isTrue(temp);
- temp = validParameter.validFile(parameters, "random", false); if (temp == "not found") { temp = "true"; }
+ 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
//if user selects distance = true and no groups it won't calc the pairwise
m->mothurOut("The groups parameter allows you to specify which of the groups in your groupfile you would like analyzed. You must enter at least 1 valid group.\n");
m->mothurOut("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");
m->mothurOut("The distance parameter allows you to create a distance file from the results. The default is false.\n");
- m->mothurOut("The random parameter allows you to shut off the comparison to random trees. The default is true, meaning compare your trees with randomly generated trees.\n");
+ m->mothurOut("The random parameter allows you to shut off the comparison to random trees. The default is false, meaning compare don't your trees with randomly generated trees.\n");
m->mothurOut("The unifrac.unweighted command should be in the following format: unifrac.unweighted(groups=yourGroups, iters=yourIters).\n");
m->mothurOut("Example unifrac.unweighted(groups=A-B-C, iters=500).\n");
m->mothurOut("The default value for groups is all the groups in your groupfile, and iters is 1000.\n");
utreeScores.resize(numComp);
UWScoreSig.resize(numComp);
- userData = unweighted->getValues(T[i]); //userData[0] = unweightedscore
-
- if (m->control_pressed) {
- if (random) { delete output; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
- }
+ userData = unweighted->getValues(T[i], processors, outputDir); //userData[0] = unweightedscore
+
+ if (m->control_pressed) { if (random) { delete output; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }return 0; }
//output scores for each combination
for(int k = 0; k < numComp; k++) {
//add users score to validscores
validScores[userData[k]] = userData[k];
}
-
+
//get unweighted scores for random trees - if random is false iters = 0
for (int j = 0; j < iters; j++) {
+
//we need a different getValues because when we swap the labels we only want to swap those in each pairwise comparison
- randomData = unweighted->getValues(T[i], "", "");
+ randomData = unweighted->getValues(T[i], "", "", processors, outputDir);
- if (m->control_pressed) {
- if (random) { delete output; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
- }
+ if (m->control_pressed) { if (random) { delete output; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
for(int k = 0; k < numComp; k++) {
//add trees unweighted score to map of scores
//add randoms score to validscores
validScores[randomData[k]] = randomData[k];
}
+
+ //report progress
+ m->mothurOut("Iter: " + toString(j+1)); m->mothurOutEndLine();
}
-
+
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++) {
- //make rscoreFreq map and rCumul
- map<float,float>::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; }
- else { rscoreFreq[a][it->first] = 0.0000; } //no random trees with that score
- }
- if (random) { UWScoreSig[a].push_back(rCumul[a][userData[a]]); }
- else { UWScoreSig[a].push_back(0.0); }
+ if (random) {
+ //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++) {
+ //make rscoreFreq map and rCumul
+ map<float,float>::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; }
+ else { rscoreFreq[a][it->first] = 0.0000; } //no random trees with that score
+ }
+ UWScoreSig[a].push_back(rCumul[a][userData[a]]);
+ }else { UWScoreSig[a].push_back(0.0); }
+
}
-
- if (m->control_pressed) {
- if (random) { delete output; }
- outSum.close();
- for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); }
- return 0;
- }
+ if (m->control_pressed) { if (random) { delete output; } outSum.close(); for (int i = 0; i < outputNames.size(); i++) { remove(outputNames[i].c_str()); } return 0; }
//print output files
printUWSummaryFile(i);
if (UWScoreSig[a][0] > (1/(float)iters)) {
outSum << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << setprecision(itersString.length()) << UWScoreSig[a][0] << endl;
cout << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << setprecision(itersString.length()) << UWScoreSig[a][0] << endl;
- m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t" + toString(UWScoreSig[a][0])); m->mothurOutEndLine();
+ m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t" + toString(UWScoreSig[a][0])+ "\n");
}else {
outSum << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << setprecision(itersString.length()) << "<" << (1/float(iters)) << endl;
cout << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << setprecision(itersString.length()) << "<" << (1/float(iters)) << endl;
- m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t<" + toString((1/float(iters)))); m->mothurOutEndLine();
+ m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t<" + toString((1/float(iters))) + "\n");
}
}else{
outSum << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << "0.00" << endl;
cout << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << '\t' << "0.00" << endl;
- m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t0.00"); m->mothurOutEndLine();
+ m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\t0.00\n");
}
}
projects / mothur.git / blobdiff