//**********************************************************************************************************************
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 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); }
string ClusterSplitCommand::getHelpString(){
try {
string helpString = "";
- helpString += "The cluster.split command parameter options are fasta, phylip, column, name, count, cutoff, precision, method, splitmethod, taxonomy, taxlevel, showabund, timing, hard, large, processors. Fasta or Phylip or column and name are required.\n";
+ helpString += "The cluster.split command parameter options are fasta, phylip, column, name, count, cutoff, precision, method, splitmethod, taxonomy, taxlevel, showabund, timing, hard, large, cluster, processors. Fasta or Phylip or column and name are required.\n";
helpString += "The cluster.split command can split your files in 3 ways. Splitting by distance file, by classification, or by classification also using a fasta file. \n";
helpString += "For the distance file method, you need only provide your distance file and mothur will split the file into distinct groups. \n";
helpString += "For the classification method, you need to provide your distance file and taxonomy file, and set the splitmethod to classify. \n";
helpString += "The fasta parameter allows you to enter your aligned fasta file. \n";
helpString += "The name parameter allows you to enter your name file. \n";
helpString += "The count parameter allows you to enter your count file. \n A count or name file is required if your distance file is in column format";
+ 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(){
showabund = validParameter.validFile(parameters, "showabund", false);
if (showabund == "not found") { showabund = "T"; }
+
+ temp = validParameter.validFile(parameters, "cluster", false); if (temp == "not found") { temp = "T"; }
+ runCluster = m->isTrue(temp);
timing = validParameter.validFile(parameters, "timing", false);
if (timing == "not found") { timing = "F"; }
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) {
+
+ 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(); }
+ 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);
//Above fork() will clone, so memory is separate, but that's not the case with windows,
//Taking advantage of shared memory to allow both threads to add labels.
//////////////////////////////////////////////////////////////////////////////////////////////////////
-
+ /*
vector<clusterData*> pDataArray;
DWORD dwThreadIdArray[processors-1];
HANDLE hThreadArray[processors-1];
CloseHandle(hThreadArray[i]);
delete pDataArray[i];
}
-
+*/
#endif
return listFiles;
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); }
list = read->getListVector();
oldList = *list;
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);
projects / mothur.git / blobdiff