*/
#include "unifracunweightedcommand.h"
+#include "treereader.h"
+#include "subsample.h"
+#include "consensus.h"
+//**********************************************************************************************************************
+vector<string> UnifracUnweightedCommand::setParameters(){
+ try {
+ CommandParameter ptree("tree", "InputTypes", "", "", "none", "none", "none","unweighted-uwsummary",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 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 psubsample("subsample", "String", "", "", "", "", "","",false,false); parameters.push_back(psubsample);
+ CommandParameter pconsensus("consensus", "Boolean", "", "F", "", "", "","tree",false,false); parameters.push_back(pconsensus);
+ 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); }
+ return myArray;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "setParameters");
+ exit(1);
+ }
+}
+//**********************************************************************************************************************
+string UnifracUnweightedCommand::getHelpString(){
+ try {
+ string helpString = "";
+ helpString += "The unifrac.unweighted command parameters are tree, group, name, count, groups, iters, distance, processors, root 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 1 valid group.\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. You may set distance to lt, square or column.\n";
+ helpString += "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";
+ helpString += "The root parameter allows you to include the entire root in your calculations. The default is false, meaning stop at the root for this comparision instead of the root of the entire tree.\n";
+ helpString += "The processors parameter allows you to specify the number of processors to use. The default is 1.\n";
+ helpString += "The unifrac.unweighted command should be in the following format: unifrac.unweighted(groups=yourGroups, iters=yourIters).\n";
+ helpString += "The subsample parameter allows you to enter the size pergroup of the sample or you can set subsample=T and mothur will use the size of your smallest group. The subsample parameter may only be used with a group file.\n";
+ helpString += "The consensus parameter allows you to indicate you would like trees built from distance matrices created with the results of the subsampling, as well as a consensus tree built from these trees. Default=F.\n";
+ helpString += "Example unifrac.unweighted(groups=A-B-C, iters=500).\n";
+ helpString += "The default value for groups is all the groups in your groupfile, and iters is 1000.\n";
+ helpString += "The unifrac.unweighted command output two files: .unweighted and .uwsummary their descriptions are in the manual.\n";
+ helpString += "Note: No spaces between parameter labels (i.e. groups), '=' and parameters (i.e.yourGroups).\n";
+ return helpString;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "getHelpString");
+ exit(1);
+ }
+}
+//**********************************************************************************************************************
+string UnifracUnweightedCommand::getOutputPattern(string type) {
+ try {
+ string pattern = "";
+ if (type == "unweighted") { pattern = "[filename],unweighted-[filename],[tag],unweighted"; }
+ else if (type == "uwsummary") { pattern = "[filename],uwsummary"; }
+ 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; }
+
+ return pattern;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "getOutputPattern");
+ exit(1);
+ }
+}
+//**********************************************************************************************************************
+UnifracUnweightedCommand::UnifracUnweightedCommand(){
+ try {
+ abort = true; calledHelp = true;
+ setParameters();
+ vector<string> tempOutNames;
+ outputTypes["unweighted"] = tempOutNames;
+ outputTypes["uwsummary"] = tempOutNames;
+ outputTypes["phylip"] = tempOutNames;
+ outputTypes["column"] = tempOutNames;
+ outputTypes["tree"] = tempOutNames;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "UnifracUnweightedCommand");
+ exit(1);
+ }
+}
/***********************************************************/
UnifracUnweightedCommand::UnifracUnweightedCommand(string option) {
try {
- globaldata = GlobalData::getInstance();
- abort = false;
- Groups.clear();
+ abort = false; calledHelp = false;
+
//allow user to run help
- if(option == "help") { help(); abort = true; }
+ if(option == "help") { help(); abort = true; calledHelp = true; }
+ else if(option == "citation") { citation(); abort = true; calledHelp = true;}
else {
- //valid paramters for this command
- string Array[] = {"groups","iters","distance","random", "outputdir","inputdir"};
- vector<string> myArray (Array, Array+(sizeof(Array)/sizeof(string)));
+ vector<string> myArray = setParameters();
OptionParser parser(option);
map<string,string> parameters = parser.getParameters();
+ map<string,string>::iterator it;
ValidParameters validParameter;
if (validParameter.isValidParameter(it->first, myArray, it->second) != true) { abort = true; }
}
- if (globaldata->gTree.size() == 0) {//no trees were read
- m->mothurOut("You must execute the read.tree command, before you may execute the unifrac.unweighted command."); m->mothurOutEndLine(); abort = true; }
+ //initialize outputTypes
+ vector<string> tempOutNames;
+ outputTypes["unweighted"] = tempOutNames;
+ outputTypes["uwsummary"] = tempOutNames;
+ outputTypes["phylip"] = tempOutNames;
+ outputTypes["column"] = tempOutNames;
+ outputTypes["tree"] = tempOutNames;
- //if the user changes the output directory command factory will send this info to us in the output parameter
- outputDir = validParameter.validFile(parameters, "outputdir", false); if (outputDir == "not found"){
- outputDir = "";
- outputDir += hasPath(globaldata->inputFileName); //if user entered a file with a path then preserve it
+ //if the user changes the input directory command factory will send this info to us in the output parameter
+ string inputDir = validParameter.validFile(parameters, "inputdir", false);
+ if (inputDir == "not found"){ inputDir = ""; }
+ else {
+ string path;
+ it = parameters.find("tree");
+ //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["tree"] = inputDir + it->second; }
+ }
+
+ it = parameters.find("group");
+ //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["group"] = inputDir + it->second; }
+ }
+
+ it = parameters.find("name");
+ //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["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
+ treefile = validParameter.validFile(parameters, "tree", true);
+ if (treefile == "not open") { abort = true; }
+ else if (treefile == "not found") { //if there is a current design file, use it
+ treefile = m->getTreeFile();
+ if (treefile != "") { m->mothurOut("Using " + treefile + " as input file for the tree parameter."); m->mothurOutEndLine(); }
+ else { m->mothurOut("You have no current tree file and the tree parameter is required."); m->mothurOutEndLine(); abort = true; }
+ }else { m->setTreeFile(treefile); }
+
+ //check for required parameters
+ groupfile = validParameter.validFile(parameters, "group", true);
+ if (groupfile == "not open") { abort = true; }
+ else if (groupfile == "not found") { groupfile = ""; }
+ else { m->setGroupFile(groupfile); }
+
+ namefile = validParameter.validFile(parameters, "name", true);
+ if (namefile == "not open") { namefile = ""; abort = true; }
+ 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); }
+
//check for optional parameter and set defaults
// ...at some point should added some additional type checking...
groups = validParameter.validFile(parameters, "groups", false);
if (groups == "not found") { groups = ""; }
else {
- splitAtDash(groups, Groups);
- globaldata->Groups = Groups;
+ m->splitAtDash(groups, Groups);
+ m->setGroups(Groups);
}
itersString = validParameter.validFile(parameters, "iters", false); if (itersString == "not found") { itersString = "1000"; }
- convert(itersString, iters);
+ m->mothurConvert(itersString, iters);
+
+ string temp = validParameter.validFile(parameters, "distance", false);
+ if (temp == "not found") { phylip = false; outputForm = ""; }
+ else{
+ if (temp=="phylip") { temp = "lt"; }
+ if ((temp == "lt") || (temp == "column") || (temp == "square")) { phylip = true; outputForm = temp; }
+ else { m->mothurOut("Options for distance are: lt, square, or column. Using lt."); m->mothurOutEndLine(); phylip = true; outputForm = "lt"; }
+ }
+
+ temp = validParameter.validFile(parameters, "random", false); if (temp == "not found") { temp = "f"; }
+ random = m->isTrue(temp);
- string temp = validParameter.validFile(parameters, "distance", false); if (temp == "not found") { temp = "false"; }
- phylip = isTrue(temp);
+ temp = validParameter.validFile(parameters, "root", false); if (temp == "not found") { temp = "F"; }
+ includeRoot = m->isTrue(temp);
- temp = validParameter.validFile(parameters, "random", false); if (temp == "not found") { temp = "true"; }
- random = isTrue(temp);
+ temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = m->getProcessors(); }
+ m->setProcessors(temp);
+ m->mothurConvert(temp, processors);
+ temp = validParameter.validFile(parameters, "subsample", false); if (temp == "not found") { temp = "F"; }
+ if (m->isNumeric1(temp)) { m->mothurConvert(temp, subsampleSize); subsample = true; }
+ else {
+ if (m->isTrue(temp)) { subsample = true; subsampleSize = -1; } //we will set it to smallest group later
+ else { subsample = false; }
+ }
+
+ if (!subsample) { subsampleIters = 0; }
+ else { subsampleIters = iters; }
+
+ temp = validParameter.validFile(parameters, "consensus", false); if (temp == "not found") { temp = "F"; }
+ consensus = m->isTrue(temp);
+
+ if (subsample && random) { m->mothurOut("[ERROR]: random must be false, if subsample=t.\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 (!random) { iters = 0; } //turn off random calcs
//if user selects distance = true and no groups it won't calc the pairwise
if ((phylip) && (Groups.size() == 0)) {
groups = "all";
- splitAtDash(groups, Groups);
- globaldata->Groups = Groups;
- }
-
- if (abort == false) {
- T = globaldata->gTree;
- tmap = globaldata->gTreemap;
- sumFile = outputDir + getSimpleName(globaldata->getTreeFile()) + ".uwsummary";
- outputNames.push_back(sumFile);
- openOutputFile(sumFile, outSum);
-
- util = new SharedUtil();
- util->setGroups(globaldata->Groups, tmap->namesOfGroups, allGroups, numGroups, "unweighted"); //sets the groups the user wants to analyze
- util->getCombos(groupComb, globaldata->Groups, numComp);
-
- if (numGroups == 1) { numComp++; groupComb.push_back(allGroups); }
-
- unweighted = new Unweighted(tmap);
-
+ m->splitAtDash(groups, Groups);
+ m->setGroups(Groups);
}
+ if (countfile=="") {
+ if (namefile == "") {
+ vector<string> files; files.push_back(treefile);
+ parser.getNameFile(files);
+ }
+ }
}
}
}
}
-//**********************************************************************************************************************
-
-void UnifracUnweightedCommand::help(){
- try {
- m->mothurOut("The unifrac.unweighted command can only be executed after a successful read.tree command.\n");
- m->mothurOut("The unifrac.unweighted command parameters are groups, iters, distance and random. No parameters are required.\n");
- 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 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");
- m->mothurOut("The unifrac.unweighted command output two files: .unweighted and .uwsummary their descriptions are in the manual.\n");
- m->mothurOut("Note: No spaces between parameter labels (i.e. groups), '=' and parameters (i.e.yourGroups).\n\n");
- }
- catch(exception& e) {
- m->errorOut(e, "UnifracUnweightedCommand", "help");
- exit(1);
- }
-}
-
-
/***********************************************************/
int UnifracUnweightedCommand::execute() {
try {
- if (abort == true) { return 0; }
+ if (abort == true) { if (calledHelp) { return 0; } return 2; }
+
+ m->setTreeFile(treefile);
+
+ TreeReader* reader;
+ if (countfile == "") { reader = new TreeReader(treefile, groupfile, namefile); }
+ else { reader = new TreeReader(treefile, countfile); }
+ T = reader->getTrees();
+ ct = T[0]->getCountTable();
+ delete reader;
+
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ sumFile = getOutputFileName("uwsummary",variables);
+ outputNames.push_back(sumFile); outputTypes["uwsummary"].push_back(sumFile);
+ m->openOutputFile(sumFile, outSum);
+
+ SharedUtil util;
+ Groups = m->getGroups();
+ vector<string> namesGroups = ct->getNamesOfGroups();
+ util.setGroups(Groups, namesGroups, allGroups, numGroups, "unweighted"); //sets the groups the user wants to analyze
- userData.resize(numComp,0); //data[0] = unweightedscore
- randomData.resize(numComp,0); //data[0] = unweightedscore
- //create new tree with same num nodes and leaves as users
+ Unweighted unweighted(includeRoot);
- outSum << "Tree#" << '\t' << "Groups" << '\t' << "UWScore" <<'\t' << "UWSig" << endl;
- m->mothurOut("Tree#\tGroups\tUWScore\tUWSig"); m->mothurOutEndLine();
+ int start = time(NULL);
+
+ //set or check size
+ if (subsample) {
+ //user has not set size, set size = smallest samples size
+ if (subsampleSize == -1) {
+ vector<string> temp; temp.push_back(Groups[0]);
+ subsampleSize = ct->getGroupCount(Groups[0]); //num in first group
+ for (int i = 1; i < Groups.size(); i++) {
+ int thisSize = ct->getGroupCount(Groups[i]);
+ if (thisSize < subsampleSize) { subsampleSize = thisSize; }
+ }
+ m->mothurOut("\nSetting subsample size to " + toString(subsampleSize) + ".\n\n");
+ }else { //eliminate any too small groups
+ vector<string> newGroups = Groups;
+ Groups.clear();
+ for (int i = 0; i < newGroups.size(); i++) {
+ 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"); }
+ }
+ m->setGroups(Groups);
+ }
+ }
+ util.getCombos(groupComb, Groups, numComp);
+ m->setGroups(Groups);
+
+ if (numGroups == 1) { numComp++; groupComb.push_back(allGroups); }
+
+ if (numComp < processors) { processors = numComp; }
+
+ if (consensus && (numComp < 2)) { m->mothurOut("consensus can only be used with numComparisions greater than 1, setting consensus=f.\n"); consensus=false; }
+
+ outSum << "Tree#" << '\t' << "Groups" << '\t' << "UWScore" <<'\t';
+ m->mothurOut("Tree#\tGroups\tUWScore\t");
+ if (random) { outSum << "UWSig"; m->mothurOut("UWSig"); }
+ outSum << endl; m->mothurOutEndLine();
+
//get pscores for users trees
for (int i = 0; i < T.size(); i++) {
- counter = 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;
if (random) {
- output = new ColumnFile(outputDir + getSimpleName(globaldata->getTreeFile()) + toString(i+1) + ".unweighted", itersString);
- outputNames.push_back(outputDir + getSimpleName(globaldata->getTreeFile()) + toString(i+1) + ".unweighted");
+ variables["[filename]"] = outputDir + m->getSimpleName(treefile);
+ variables["[tag]"] = toString(i+1);
+ string unFileName = getOutputFileName("unweighted", variables);
+ output = new ColumnFile(unFileName, itersString);
+ outputNames.push_back(unFileName); outputTypes["unweighted"].push_back(unFileName);
}
+
//get unweighted for users tree
rscoreFreq.resize(numComp);
rCumul.resize(numComp);
utreeScores.resize(numComp);
UWScoreSig.resize(numComp);
+
+ vector<double> userData; userData.resize(numComp,0); //weighted score info for user tree. data[0] = weightedscore AB, data[1] = weightedscore AC...
- userData = unweighted->getValues(T[i]); //userData[0] = unweightedscore
+ userData = unweighted.getValues(T[i], processors, outputDir); //userData[0] = unweightedscore
+
+ 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; }
//output scores for each combination
for(int k = 0; k < numComp; k++) {
//add users score to validscores
validScores[userData[k]] = userData[k];
+
+ if (!random) { UWScoreSig[k].push_back(0.0); }
}
+
+ if (random) { runRandomCalcs(T[i], userData); }
- //get unweighted scores for random trees
- 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], "", "");
-
- for(int k = 0; k < numComp; k++) {
- //add trees unweighted score to map of scores
- map<float,float>::iterator it = rscoreFreq[k].find(randomData[k]);
- if (it != rscoreFreq[k].end()) {//already have that score
- rscoreFreq[k][randomData[k]]++;
- }else{//first time we have seen this score
- rscoreFreq[k][randomData[k]] = 1;
- }
-
- //add randoms score to validscores
- validScores[randomData[k]] = randomData[k];
- }
- }
-
- 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); }
- }
-
- //print output files
+ 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; }
+
+ int startSubsample = time(NULL);
+
+ //subsample loop
+ vector< vector<double> > calcDistsTotals; //each iter, each groupCombos dists. this will be used to make .dist files
+ for (int thisIter = 0; thisIter < subsampleIters; thisIter++) { //subsampleIters=0, if subsample=f.
+ if (m->control_pressed) { break; }
+
+ //copy to preserve old one - would do this in subsample but memory cleanup becomes messy.
+ CountTable* newCt = new CountTable();
+
+ //uses method of setting groups to doNotIncludeMe
+ int sampleTime = 0;
+ if (m->debug) { sampleTime = time(NULL); }
+ SubSample sample;
+ Tree* subSampleTree = sample.getSample(T[i], ct, newCt, subsampleSize);
+
+ if (m->debug) { m->mothurOut("[DEBUG]: iter " + toString(thisIter) + " took " + toString(time(NULL) - sampleTime) + " seconds to sample tree.\n"); }
+
+ //call new weighted function
+ vector<double> iterData; iterData.resize(numComp,0);
+ Unweighted thisUnweighted(includeRoot);
+ iterData = thisUnweighted.getValues(subSampleTree, processors, outputDir); //userData[0] = weightedscore
+
+ //save data to make ave dist, std dist
+ calcDistsTotals.push_back(iterData);
+
+ delete newCt;
+ delete subSampleTree;
+
+ if((thisIter+1) % 100 == 0){ m->mothurOutJustToScreen(toString(thisIter+1)+"\n"); }
+ }
+ if (subsample) { m->mothurOut("It took " + toString(time(NULL) - startSubsample) + " secs to run the subsampling."); m->mothurOutEndLine(); }
+
+ 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); }
+
+ //print output files
printUWSummaryFile(i);
if (random) { printUnweightedFile(); delete output; }
if (phylip) { createPhylipFile(i); }
UWScoreSig.clear();
}
- //reset groups parameter
- globaldata->Groups.clear();
+
outSum.close();
+ 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; }
+
+ m->mothurOut("It took " + toString(time(NULL) - start) + " secs to run unifrac.unweighted."); m->mothurOutEndLine();
+
+ //set phylip file as new current phylipfile
+ string current = "";
+ itTypes = outputTypes.find("phylip");
+ if (itTypes != outputTypes.end()) {
+ if ((itTypes->second).size() != 0) { current = (itTypes->second)[0]; m->setPhylipFile(current); }
+ }
+
+ //set column file as new current columnfile
+ itTypes = outputTypes.find("column");
+ if (itTypes != outputTypes.end()) {
+ if ((itTypes->second).size() != 0) { current = (itTypes->second)[0]; m->setColumnFile(current); }
+ }
m->mothurOutEndLine();
m->mothurOut("Output File Names: "); m->mothurOutEndLine();
exit(1);
}
}
+/**************************************************************************************************/
+int UnifracUnweightedCommand::getAverageSTDMatrices(vector< vector<double> >& dists, int treeNum) {
+ try {
+ //we need to find the average distance and standard deviation for each groups distance
+ //finds sum
+ vector<double> averages = m->getAverages(dists);
+
+ //find standard deviation
+ vector<double> stdDev = m->getStandardDeviation(dists, averages);
+
+ //make matrix with scores in it
+ vector< vector<double> > avedists; //avedists.resize(m->getNumGroups());
+ for (int i = 0; i < m->getNumGroups(); i++) {
+ 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());
+ for (int i = 0; i < m->getNumGroups(); i++) {
+ 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);
+ }
+
+ if (m->debug) { m->mothurOut("[DEBUG]: about to fill matrix.\n"); }
+
+ //flip it so you can print it
+ int count = 0;
+ for (int r=0; r<m->getNumGroups(); r++) {
+ for (int l = 0; l < r; l++) {
+ avedists[r][l] = averages[count];
+ avedists[l][r] = averages[count];
+ stddists[r][l] = stdDev[count];
+ stddists[l][r] = stdDev[count];
+ count++;
+ }
+ }
+
+ if (m->debug) { m->mothurOut("[DEBUG]: done filling matrix.\n"); }
+
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "unweighted.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);
+
+ variables["[tag2]"] = "unweighted.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;
+ m->openOutputFile(stdFileName, outStd);
+
+ if ((outputForm == "lt") || (outputForm == "square")) {
+ //output numSeqs
+ out << m->getNumGroups() << endl;
+ outStd << m->getNumGroups() << endl;
+ }
+
+ //output to file
+ for (int r=0; r<m->getNumGroups(); r++) {
+ //output name
+ string name = (m->getGroups())[r];
+ if (name.length() < 10) { //pad with spaces to make compatible
+ while (name.length() < 10) { name += " "; }
+ }
+
+ if (outputForm == "lt") {
+ out << name << '\t';
+ outStd << name << '\t';
+
+ //output distances
+ for (int l = 0; l < r; l++) { out << avedists[r][l] << '\t'; outStd << stddists[r][l] << '\t';}
+ out << endl; outStd << endl;
+ }else if (outputForm == "square") {
+ out << name << '\t';
+ outStd << name << '\t';
+
+ //output distances
+ for (int l = 0; l < m->getNumGroups(); l++) { out << avedists[r][l] << '\t'; outStd << stddists[r][l] << '\t'; }
+ out << endl; outStd << endl;
+ }else{
+ //output distances
+ for (int l = 0; l < r; l++) {
+ string otherName = (m->getGroups())[l];
+ if (otherName.length() < 10) { //pad with spaces to make compatible
+ while (otherName.length() < 10) { otherName += " "; }
+ }
+
+ out << name << '\t' << otherName << avedists[r][l] << endl;
+ outStd << name << '\t' << otherName << stddists[r][l] << endl;
+ }
+ }
+ }
+ out.close();
+ outStd.close();
+
+ return 0;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "getAverageSTDMatrices");
+ exit(1);
+ }
+}
+
+/**************************************************************************************************/
+int UnifracUnweightedCommand::getConsensusTrees(vector< vector<double> >& dists, int treeNum) {
+ try {
+
+ //used in tree constructor
+ m->runParse = false;
+
+ //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, newCt); //also creates .all.tre file containing the trees created
+
+ if (m->control_pressed) { return 0; }
+
+ Consensus con;
+ Tree* conTree = con.getTree(newTrees);
+
+ //create a new filename
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "unweighted.cons";
+ string conFile = getOutputFileName("tree",variables);
+ outputNames.push_back(conFile); outputTypes["tree"].push_back(conFile);
+ ofstream outTree;
+ m->openOutputFile(conFile, outTree);
+
+ if (conTree != NULL) { conTree->print(outTree, "boot"); delete conTree; }
+ outTree.close();
+
+ return 0;
+
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "getConsensusTrees");
+ exit(1);
+ }
+}
+/**************************************************************************************************/
+
+vector<Tree*> UnifracUnweightedCommand::buildTrees(vector< vector<double> >& dists, int treeNum, CountTable& myct) {
+ try {
+
+ vector<Tree*> trees;
+
+ //create a new filename
+ map<string, string> variables;
+ variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(treefile));
+ variables["[tag]"] = toString(treeNum+1);
+ variables["[tag2]"] = "unweighted.all";
+ string outputFile = getOutputFileName("tree",variables);
+ outputNames.push_back(outputFile); outputTypes["tree"].push_back(outputFile);
+
+ ofstream outAll;
+ m->openOutputFile(outputFile, outAll);
+
+
+ for (int i = 0; i < dists.size(); i++) { //dists[0] are the dists for the first subsampled tree.
+
+ if (m->control_pressed) { break; }
+
+ //make matrix with scores in it
+ vector< vector<double> > sims; sims.resize(m->getNumGroups());
+ for (int j = 0; j < m->getNumGroups(); j++) {
+ sims[j].resize(m->getNumGroups(), 0.0);
+ }
+
+ int count = 0;
+ for (int r=0; r<m->getNumGroups(); r++) {
+ for (int l = 0; l < r; l++) {
+ double sim = -(dists[i][count]-1.0);
+ sims[r][l] = sim;
+ sims[l][r] = sim;
+ count++;
+ }
+ }
+
+ //create tree
+ Tree* tempTree = new Tree(&myct, sims);
+ tempTree->assembleTree();
+
+ trees.push_back(tempTree);
+
+ //print tree
+ tempTree->print(outAll);
+ }
+
+ outAll.close();
+
+ if (m->control_pressed) { for (int i = 0; i < trees.size(); i++) { delete trees[i]; trees[i] = NULL; } m->mothurRemove(outputFile); }
+
+ return trees;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "buildTrees");
+ exit(1);
+ }
+}
+/**************************************************************************************************/
+
+int UnifracUnweightedCommand::runRandomCalcs(Tree* thisTree, vector<double> usersScores) {
+ try {
+ vector<double> randomData; randomData.resize(numComp,0); //weighted score info for random trees. data[0] = weightedscore AB, data[1] = weightedscore AC...
+
+ Unweighted unweighted(includeRoot);
+
+ //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(thisTree, "", "", processors, outputDir);
+
+ if (m->control_pressed) { return 0; }
+
+ for(int k = 0; k < numComp; k++) {
+ //add trees unweighted score to map of scores
+ map<float,float>::iterator it = rscoreFreq[k].find(randomData[k]);
+ if (it != rscoreFreq[k].end()) {//already have that score
+ rscoreFreq[k][randomData[k]]++;
+ }else{//first time we have seen this score
+ rscoreFreq[k][randomData[k]] = 1;
+ }
+
+ //add randoms score to validscores
+ validScores[randomData[k]] = randomData[k];
+ }
+ }
+
+ 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
+ }
+ UWScoreSig[a].push_back(rCumul[a][usersScores[a]]);
+ }
+
+ return 0;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "UnifracUnweightedCommand", "runRandomCalcs");
+ exit(1);
+ }
+}
/***********************************************************/
void UnifracUnweightedCommand::printUnweightedFile() {
try {
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();
+ outSum << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << endl;
+ cout << setprecision(6) << groupComb[a] << '\t' << utreeScores[a][0] << endl;
+ m->mothurOutJustToLog(groupComb[a] + "\t" + toString(utreeScores[a][0]) + "\n");
}
}
/***********************************************************/
void UnifracUnweightedCommand::createPhylipFile(int i) {
try {
- string phylipFileName = outputDir + getSimpleName(globaldata->getTreeFile()) + toString(i+1) + ".unweighted.dist";
- outputNames.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]"] = "unweighted.phylip";
+ phylipFileName = getOutputFileName("phylip",variables);
+ outputNames.push_back(phylipFileName); outputTypes["phylip"].push_back(phylipFileName);
+ }else { //column
+ variables["[tag2]"] = "unweighted.column";
+ phylipFileName = getOutputFileName("column",variables);
+ outputNames.push_back(phylipFileName); outputTypes["column"].push_back(phylipFileName);
+ }
ofstream out;
- openOutputFile(phylipFileName, out);
-
- //output numSeqs
- out << globaldata->Groups.size() << endl;
-
+ m->openOutputFile(phylipFileName, out);
+
+ if ((outputForm == "lt") || (outputForm == "square")) {
+ //output numSeqs
+ out << m->getNumGroups() << endl;
+ }
+
//make matrix with scores in it
- vector< vector<float> > dists; dists.resize(globaldata->Groups.size());
- for (int i = 0; i < globaldata->Groups.size(); i++) {
- dists[i].resize(globaldata->Groups.size(), 0.0);
+ vector< vector<float> > dists; dists.resize(m->getNumGroups());
+ for (int i = 0; i < m->getNumGroups(); i++) {
+ dists[i].resize(m->getNumGroups(), 0.0);
}
//flip it so you can print it
int count = 0;
- for (int r=0; r<globaldata->Groups.size(); r++) {
- for (int l = r+1; l < globaldata->Groups.size(); l++) {
- dists[r][l] = (1.0 - utreeScores[count][0]);
- dists[l][r] = (1.0 - utreeScores[count][0]);
+ for (int r=0; r<m->getNumGroups(); r++) {
+ for (int l = 0; l < r; l++) {
+ dists[r][l] = utreeScores[count][0];
+ dists[l][r] = utreeScores[count][0];
count++;
}
}
//output to file
- for (int r=0; r<globaldata->Groups.size(); r++) {
+ for (int r=0; r<m->getNumGroups(); r++) {
//output name
- string name = globaldata->Groups[r];
+ string name = (m->getGroups())[r];
if (name.length() < 10) { //pad with spaces to make compatible
while (name.length() < 10) { name += " "; }
}
- out << name << '\t';
- //output distances
- for (int l = 0; l < r; l++) { out << dists[r][l] << '\t'; }
- out << endl;
+ if (outputForm == "lt") {
+ out << name << '\t';
+
+ //output distances
+ for (int l = 0; l < r; l++) { out << dists[r][l] << '\t'; }
+ out << endl;
+ }else if (outputForm == "square") {
+ out << name << '\t';
+
+ //output distances
+ for (int l = 0; l < m->getNumGroups(); l++) { out << dists[r][l] << '\t'; }
+ out << endl;
+ }else{
+ //output distances
+ for (int l = 0; l < r; l++) {
+ string otherName = (m->getGroups())[l];
+ if (otherName.length() < 10) { //pad with spaces to make compatible
+ while (otherName.length() < 10) { otherName += " "; }
+ }
+
+ out << name << '\t' << otherName << dists[r][l] << endl;
+ }
+ }
}
out.close();
}
+
projects / mothur.git / blobdiff