//**********************************************************************************************************************
vector<string> ClusterSplitCommand::setParameters(){
try {
- CommandParameter ptaxonomy("taxonomy", "InputTypes", "", "", "none", "none", "FastaTaxName",false,false); parameters.push_back(ptaxonomy);
- CommandParameter pphylip("phylip", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "none",false,false); parameters.push_back(pphylip);
- CommandParameter pfasta("fasta", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "FastaTaxName",false,false); parameters.push_back(pfasta);
- CommandParameter pname("name", "InputTypes", "", "", "NameCount", "none", "ColumnName-FastaTaxName",false,false); parameters.push_back(pname);
- CommandParameter pcount("count", "InputTypes", "", "", "NameCount", "none", "",false,false); parameters.push_back(pcount);
- CommandParameter pcolumn("column", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "ColumnName",false,false); parameters.push_back(pcolumn);
- CommandParameter ptaxlevel("taxlevel", "Number", "", "3", "", "", "",false,false); parameters.push_back(ptaxlevel);
- CommandParameter psplitmethod("splitmethod", "Multiple", "classify-fasta-distance", "distance", "", "", "",false,false); parameters.push_back(psplitmethod);
- CommandParameter plarge("large", "Boolean", "", "F", "", "", "",false,false); parameters.push_back(plarge);
- CommandParameter pshowabund("showabund", "Boolean", "", "T", "", "", "",false,false); parameters.push_back(pshowabund);
- CommandParameter pcluster("cluster", "Boolean", "", "T", "", "", "",false,false); parameters.push_back(pcluster);
- CommandParameter ptiming("timing", "Boolean", "", "F", "", "", "",false,false); parameters.push_back(ptiming);
- CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
- CommandParameter pcutoff("cutoff", "Number", "", "0.25", "", "", "",false,false); parameters.push_back(pcutoff);
- CommandParameter pprecision("precision", "Number", "", "100", "", "", "",false,false); parameters.push_back(pprecision);
- CommandParameter pmethod("method", "Multiple", "furthest-nearest-average-weighted", "average", "", "", "",false,false); parameters.push_back(pmethod);
- CommandParameter phard("hard", "Boolean", "", "T", "", "", "",false,false); parameters.push_back(phard);
- CommandParameter pclassic("classic", "Boolean", "", "F", "", "", "",false,false); parameters.push_back(pclassic);
- CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
- CommandParameter poutputdir("outputdir", "String", "", "", "", "", "",false,false); parameters.push_back(poutputdir);
+ CommandParameter ptaxonomy("taxonomy", "InputTypes", "", "", "none", "none", "FastaTaxName","",false,false,true); parameters.push_back(ptaxonomy);
+ CommandParameter pphylip("phylip", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "none","list",false,false,true); parameters.push_back(pphylip);
+ CommandParameter pfasta("fasta", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "FastaTaxName","list",false,false,true); parameters.push_back(pfasta);
+ CommandParameter pname("name", "InputTypes", "", "", "NameCount", "none", "ColumnName-FastaTaxName","rabund-sabund",false,false,true); parameters.push_back(pname);
+ CommandParameter pcount("count", "InputTypes", "", "", "NameCount", "none", "","",false,false,true); parameters.push_back(pcount);
+ CommandParameter pcolumn("column", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "ColumnName","list",false,false,true); parameters.push_back(pcolumn);
+ CommandParameter ptaxlevel("taxlevel", "Number", "", "3", "", "", "","",false,false,true); parameters.push_back(ptaxlevel);
+ CommandParameter psplitmethod("splitmethod", "Multiple", "classify-fasta-distance", "distance", "", "", "","",false,false,true); parameters.push_back(psplitmethod);
+ CommandParameter plarge("large", "Boolean", "", "F", "", "", "","",false,false); parameters.push_back(plarge);
+ CommandParameter pshowabund("showabund", "Boolean", "", "T", "", "", "","",false,false); parameters.push_back(pshowabund);
+ CommandParameter pcluster("cluster", "Boolean", "", "T", "", "", "","",false,false); parameters.push_back(pcluster);
+ CommandParameter ptiming("timing", "Boolean", "", "F", "", "", "","",false,false); parameters.push_back(ptiming);
+ CommandParameter pprocessors("processors", "Number", "", "1", "", "", "","",false,false,true); parameters.push_back(pprocessors);
+ CommandParameter pcutoff("cutoff", "Number", "", "0.25", "", "", "","",false,false,true); parameters.push_back(pcutoff);
+ CommandParameter pprecision("precision", "Number", "", "100", "", "", "","",false,false); parameters.push_back(pprecision);
+ CommandParameter pmethod("method", "Multiple", "furthest-nearest-average-weighted", "average", "", "", "","",false,false); parameters.push_back(pmethod);
+ CommandParameter phard("hard", "Boolean", "", "T", "", "", "","",false,false); parameters.push_back(phard);
+ CommandParameter pclassic("classic", "Boolean", "", "F", "", "", "","",false,false); parameters.push_back(pclassic);
+ 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); }
helpString += "The cluster parameter allows you to indicate whether you want to run the clustering or just split the distance matrix, default=t";
helpString += "The cutoff parameter allow you to set the distance you want to cluster to, default is 0.25. \n";
helpString += "The precision parameter allows you specify the precision of the precision of the distances outputted, default=100, meaning 2 decimal places. \n";
- helpString += "The method allows you to specify what clustering algorythm you want to use, default=average, option furthest, nearest, or average. \n";
+ helpString += "The method allows you to specify what clustering algorithm you want to use, default=average, option furthest, nearest, or average. \n";
helpString += "The splitmethod parameter allows you to specify how you want to split your distance file before you cluster, default=distance, options distance, classify or fasta. \n";
helpString += "The taxonomy parameter allows you to enter the taxonomy file for your sequences, this is only valid if you are using splitmethod=classify. Be sure your taxonomy file does not include the probability scores. \n";
helpString += "The taxlevel parameter allows you to specify the taxonomy level you want to use to split the distance file, default=3, meaning use the first taxon in each list. \n";
}
}
//**********************************************************************************************************************
-string ClusterSplitCommand::getOutputFileNameTag(string type, string inputName=""){
- try {
- string outputFileName = "";
- map<string, vector<string> >::iterator it;
+string ClusterSplitCommand::getOutputPattern(string type) {
+ try {
+ string pattern = "";
- //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 == "list") { outputFileName = "list"; }
- else if (type == "rabund") { outputFileName = "rabund"; }
- else if (type == "sabund") { outputFileName = "sabund"; }
- else if (type == "column") { outputFileName = "dist"; }
- 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, "ClusterSplitCommand", "getOutputFileNameTag");
- exit(1);
- }
+ if (type == "list") { pattern = "[filename],[clustertag],list-[filename],[clustertag],[tag2],list"; }
+ else if (type == "rabund") { pattern = "[filename],[clustertag],rabund"; }
+ else if (type == "sabund") { pattern = "[filename],[clustertag],sabund"; }
+ else if (type == "column") { pattern = "[filename],dist"; }
+ else { m->mothurOut("[ERROR]: No definition for type " + type + " output pattern.\n"); m->control_pressed = true; }
+
+ return pattern;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "ClusterSplitCommand", "getOutputPattern");
+ exit(1);
+ }
}
//**********************************************************************************************************************
ClusterSplitCommand::ClusterSplitCommand(){
vector< map<string, string> > distName = split->getDistanceFiles(); //returns map of distance files -> namefile sorted by distance file size
delete split;
+ if (m->debug) { m->mothurOut("[DEBUG]: distName.size() = " + toString(distName.size()) + ".\n"); }
+
//output a merged distance file
- if (splitmethod == "fasta") { createMergedDistanceFile(distName); }
-
+ //if (splitmethod == "fasta") { createMergedDistanceFile(distName); }
if (m->control_pressed) { return 0; }
m->mothurOut("It took " + toString(time(NULL) - estart) + " seconds to split the distance file."); m->mothurOutEndLine();
estart = time(NULL);
-
+
if (!runCluster) {
-#ifdef USE_MPI
- }
-#endif
+
m->mothurOutEndLine();
m->mothurOut("Output File Names: "); m->mothurOutEndLine();
for (int i = 0; i < distName.size(); i++) { m->mothurOut(distName[i].begin()->first); m->mothurOutEndLine(); m->mothurOut(distName[i].begin()->second); m->mothurOutEndLine(); }
return 0;
}
-
+
//****************** break up files between processes and cluster each file set ******************************//
#ifdef USE_MPI
////you are process 0 from above////
if (outputDir == "") { outputDir += m->hasPath(distfile); }
fileroot = outputDir + m->getRootName(m->getSimpleName(distfile));
- string sabundFileName = fileroot+ tag + "." + getOutputFileNameTag("sabund");
- string rabundFileName = fileroot+ tag + "." + getOutputFileNameTag("rabund");
- string listFileName = fileroot+ tag + ".";
- if (countfile != "") { listFileName += "unique_"; }
- listFileName += getOutputFileNameTag("list");
+ map<string, string> variables;
+ variables["[filename]"] = fileroot;
+ variables["[clustertag]"] = tag;
+ string sabundFileName = getOutputFileName("sabund", variables);
+ string rabundFileName = getOutputFileName("rabund", variables);
+ if (countfile != "") { variables["[tag2]"] = "unique_list"; }
+ string listFileName = getOutputFileName("list", variables);
if (countfile == "") {
m->openOutputFile(sabundFileName, outSabund);
if (itLabel->first == -1) { thisLabel = "unique"; }
else { thisLabel = toString(itLabel->first, length-1); }
- outList << thisLabel << '\t' << itLabel->second << '\t';
+ //outList << thisLabel << '\t' << itLabel->second << '\t';
RAbundVector* rabund = NULL;
+ ListVector completeList;
+ completeList.setLabel(thisLabel);
+
if (countfile == "") {
rabund = new RAbundVector();
rabund->setLabel(thisLabel);
//add in singletons
if (listSingle != NULL) {
for (int j = 0; j < listSingle->getNumBins(); j++) {
- outList << listSingle->get(j) << '\t';
+ //outList << listSingle->get(j) << '\t';
+ completeList.push_back(listSingle->get(j));
if (countfile == "") { rabund->push_back(m->getNumNames(listSingle->get(j))); }
}
}
if (list == NULL) { m->mothurOut("Error merging listvectors in file " + listNames[k]); m->mothurOutEndLine(); }
else {
for (int j = 0; j < list->getNumBins(); j++) {
- outList << list->get(j) << '\t';
+ //outList << list->get(j) << '\t';
+ completeList.push_back(list->get(j));
if (countfile == "") { rabund->push_back(m->getNumNames(list->get(j))); }
}
delete list;
sabund.print(outSabund);
rabund->print(outRabund);
}
- outList << endl;
+ //outList << endl;
+ completeList.print(outList);
if (rabund != NULL) { delete rabund; }
}
if ((processToAssign-1) == 1) { m->mothurOut(distName[i].begin()->first + "\n"); }
}
- //not lets reverse the order of ever other process, so we balance big files running with little ones
+ //now lets reverse the order of ever other process, so we balance big files running with little ones
for (int i = 0; i < processors; i++) {
//cout << i << endl;
int remainder = ((i+1) % processors);
cluster->readPhylipFile(thisDistFile, nameMap);
}else if (countfile != "") {
ct = new CountTable();
- ct->readTable(thisNamefile);
+ ct->readTable(thisNamefile, false);
cluster->readPhylipFile(thisDistFile, ct);
}
tag = cluster->getTag();
read->read(nameMap);
}else if (countfile != "") {
ct = new CountTable();
- ct->readTable(thisNamefile);
+ ct->readTable(thisNamefile, false);
read->read(ct);
}else { read->read(nameMap); }
m->mothurOutEndLine(); m->mothurOut("Clustering " + thisDistFile); m->mothurOutEndLine();
//create cluster
- if (method == "furthest") { cluster = new CompleteLinkage(rabund, list, matrix, cutoff, method); }
- else if(method == "nearest"){ cluster = new SingleLinkage(rabund, list, matrix, cutoff, method); }
- else if(method == "average"){ cluster = new AverageLinkage(rabund, list, matrix, cutoff, method); }
+ float adjust = -1.0;
+ if (method == "furthest") { cluster = new CompleteLinkage(rabund, list, matrix, cutoff, method, adjust); }
+ else if(method == "nearest"){ cluster = new SingleLinkage(rabund, list, matrix, cutoff, method, adjust); }
+ else if(method == "average"){ cluster = new AverageLinkage(rabund, list, matrix, cutoff, method, adjust); }
tag = cluster->getTag();
if (outputDir == "") { outputDir += m->hasPath(thisDistFile); }
string thisOutputDir = outputDir;
if (outputDir == "") { thisOutputDir = m->hasPath(fastafile); }
- string outputFileName = thisOutputDir + m->getRootName(m->getSimpleName(fastafile)) + getOutputFileNameTag("column");
+ map<string, string> variables;
+ variables["[filename]"] = thisOutputDir + m->getRootName(m->getSimpleName(fastafile));
+ string outputFileName = getOutputFileName("column", variables);
m->mothurRemove(outputFileName);
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