CommandParameter pfasta("fasta", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "FastaTaxName",false,false); parameters.push_back(pfasta);
CommandParameter pname("name", "InputTypes", "", "", "none", "none", "ColumnName-FastaTaxName",false,false); parameters.push_back(pname);
CommandParameter pcolumn("column", "InputTypes", "", "", "PhylipColumnFasta", "PhylipColumnFasta", "ColumnName",false,false); parameters.push_back(pcolumn);
- CommandParameter ptaxlevel("taxlevel", "Number", "", "1", "", "", "",false,false); parameters.push_back(ptaxlevel);
+ 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", "", "10", "", "", "",false,false); parameters.push_back(pcutoff);
+ 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);
helpString += "The phylip and column parameter allow you to enter your distance file. \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 and is required if your distance file is in column format. \n";
- helpString += "The cutoff parameter allow you to set the distance you want to cluster to, default is 10.0. \n";
+ 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 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=1, meaning use the first taxon in each list. \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";
helpString += "The large parameter allows you to indicate that your distance matrix is too large to fit in RAM. The default value is false.\n";
#ifdef USE_MPI
helpString += "When using MPI, the processors parameter is set to the number of MPI processes running. \n";
else { distfile = fastafile; splitmethod = "fasta"; m->setFastaFile(fastafile); }
taxFile = validParameter.validFile(parameters, "taxonomy", true);
- if (taxFile == "not open") { abort = true; }
+ if (taxFile == "not open") { taxFile = ""; abort = true; }
else if (taxFile == "not found") { taxFile = ""; }
else { m->setTaxonomyFile(taxFile); }
if (temp == "not found") { temp = "100"; }
//saves precision legnth for formatting below
length = temp.length();
- convert(temp, precision);
+ m->mothurConvert(temp, precision);
temp = validParameter.validFile(parameters, "hard", false); if (temp == "not found") { temp = "T"; }
hard = m->isTrue(temp);
temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = m->getProcessors(); }
m->setProcessors(temp);
- convert(temp, processors);
+ m->mothurConvert(temp, processors);
temp = validParameter.validFile(parameters, "splitmethod", false);
if (splitmethod != "fasta") {
else { splitmethod = temp; }
}
- temp = validParameter.validFile(parameters, "cutoff", false); if (temp == "not found") { temp = "10"; }
- convert(temp, cutoff);
+ temp = validParameter.validFile(parameters, "cutoff", false); if (temp == "not found") { temp = "0.25"; }
+ m->mothurConvert(temp, cutoff);
cutoff += (5 / (precision * 10.0));
- temp = validParameter.validFile(parameters, "taxlevel", false); if (temp == "not found") { temp = "1"; }
- convert(temp, taxLevelCutoff);
+ temp = validParameter.validFile(parameters, "taxlevel", false); if (temp == "not found") { temp = "3"; }
+ m->mothurConvert(temp, taxLevelCutoff);
method = validParameter.validFile(parameters, "method", false); if (method == "not found") { method = "average"; }
listFileNames = cluster(distName, labels); //clusters individual files and returns names of list files
}else{
- cout << processors << '\t' << distName.size() << endl;
+ //cout << processors << '\t' << distName.size() << endl;
vector < vector < map<string, string> > > dividedNames; //distNames[1] = vector of filenames for process 1...
dividedNames.resize(processors);
//for each file group figure out which process will complete it
//want to divide the load intelligently so the big files are spread between processes
for (int i = 0; i < distName.size(); i++) {
- cout << i << endl;
+ //cout << i << endl;
int processToAssign = (i+1) % processors;
if (processToAssign == 0) { processToAssign = processors; }
//not 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;
+ //cout << i << endl;
int remainder = ((i+1) % processors);
if (remainder) { reverse(dividedNames[i].begin(), dividedNames[i].end()); }
}
projects / mothur.git / blobdiff