//**********************************************************************************************************************
-ClassifySeqsCommand::ClassifySeqsCommand(string option){
+ClassifySeqsCommand::ClassifySeqsCommand(string option) {
try {
abort = false;
//check for required parameters
templateFileName = validParameter.validFile(parameters, "template", true);
if (templateFileName == "not found") {
- mothurOut("template is a required parameter for the classify.seqs command.");
- mothurOutEndLine();
+ m->mothurOut("template is a required parameter for the classify.seqs command.");
+ m->mothurOutEndLine();
abort = true;
}
else if (templateFileName == "not open") { abort = true; }
fastaFileName = validParameter.validFile(parameters, "fasta", false);
- if (fastaFileName == "not found") { mothurOut("fasta is a required parameter for the classify.seqs command."); mothurOutEndLine(); abort = true; }
+ if (fastaFileName == "not found") { m->mothurOut("fasta is a required parameter for the classify.seqs command."); m->mothurOutEndLine(); abort = true; }
else {
splitAtDash(fastaFileName, fastaFileNames);
ifstream in;
ableToOpen = openInputFile(fastaFileNames[i], in);
if (ableToOpen == 1) {
- mothurOut(fastaFileNames[i] + " will be disregarded."); mothurOutEndLine();
+ m->mothurOut(fastaFileNames[i] + " will be disregarded."); m->mothurOutEndLine();
//erase from file list
fastaFileNames.erase(fastaFileNames.begin()+i);
i--;
}
//make sure there is at least one valid file left
- if (fastaFileNames.size() == 0) { mothurOut("no valid files."); mothurOutEndLine(); abort = true; }
+ if (fastaFileNames.size() == 0) { m->mothurOut("no valid files."); m->mothurOutEndLine(); abort = true; }
}
taxonomyFileName = validParameter.validFile(parameters, "taxonomy", true);
if (taxonomyFileName == "not found") {
- mothurOut("taxonomy is a required parameter for the classify.seqs command.");
- mothurOutEndLine();
+ m->mothurOut("taxonomy is a required parameter for the classify.seqs command.");
+ m->mothurOutEndLine();
abort = true;
}
else if (taxonomyFileName == "not open") { abort = true; }
int ableToOpen;
ifstream in;
ableToOpen = openInputFile(namefileNames[i], in);
- if (ableToOpen == 1) { mothurOut("Unable to match name file with fasta file."); mothurOutEndLine(); abort = true; }
+ if (ableToOpen == 1) { m->mothurOut("Unable to match name file with fasta file."); m->mothurOutEndLine(); abort = true; }
in.close();
}
}
if (namefile != "") {
- if (namefileNames.size() != fastaFileNames.size()) { abort = true; mothurOut("If you provide a name file, you must have one for each fasta file."); mothurOutEndLine(); }
+ if (namefileNames.size() != fastaFileNames.size()) { abort = true; m->mothurOut("If you provide a name file, you must have one for each fasta file."); m->mothurOutEndLine(); }
}
//check for optional parameter and set defaults
if ((method == "bayesian") && (search != "kmer")) {
- mothurOut("The bayesian method requires the kmer search." + search + "will be disregarded." ); mothurOutEndLine();
+ m->mothurOut("The bayesian method requires the kmer search." + search + "will be disregarded." ); m->mothurOutEndLine();
search = "kmer";
}
}
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "ClassifySeqsCommand");
+ m->errorOut(e, "ClassifySeqsCommand", "ClassifySeqsCommand");
exit(1);
}
}
void ClassifySeqsCommand::help(){
try {
- mothurOut("The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n");
- mothurOut("The classify.seqs command parameters are template, fasta, name, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n");
- mothurOut("The template, fasta and taxonomy parameters are required. You may enter multiple fasta files by separating their names with dashes. ie. fasta=abrecovery.fasta-amzon.fasta \n");
- mothurOut("The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast and distance. The default is kmer.\n");
- mothurOut("The name parameter allows you add a names file with your fasta file, if you enter multiple fasta files, you must enter matching names files for them.\n");
- mothurOut("The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n");
- mothurOut("The ksize parameter allows you to specify the kmer size for finding most similar template to candidate. The default is 8.\n");
- mothurOut("The processors parameter allows you to specify the number of processors to use. The default is 1.\n");
- mothurOut("The match parameter allows you to specify the bonus for having the same base. The default is 1.0.\n");
- mothurOut("The mistmatch parameter allows you to specify the penalty for having different bases. The default is -1.0.\n");
- mothurOut("The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -2.0.\n");
- mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n");
- mothurOut("The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n");
- mothurOut("The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n");
- mothurOut("The probs parameter shut off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be run.\n");
- mothurOut("The iters parameter allows you to specify how many iterations to do when calculating the bootstrap confidence score for your taxonomy with the bayesian method. The default is 100.\n");
- mothurOut("The classify.seqs command should be in the following format: \n");
- mothurOut("classify.seqs(template=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n");
- mothurOut("Example classify.seqs(fasta=amazon.fasta, template=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n");
- mothurOut("The .taxonomy file consists of 2 columns: 1 = your sequence name, 2 = the taxonomy for your sequence. \n");
- mothurOut("The .tax.summary is a summary of the different taxonomies represented in your fasta file. \n");
- mothurOut("Note: No spaces between parameter labels (i.e. fasta), '=' and parameters (i.e.yourFastaFile).\n\n");
+ m->mothurOut("The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n");
+ m->mothurOut("The classify.seqs command parameters are template, fasta, name, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n");
+ m->mothurOut("The template, fasta and taxonomy parameters are required. You may enter multiple fasta files by separating their names with dashes. ie. fasta=abrecovery.fasta-amzon.fasta \n");
+ m->mothurOut("The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast and distance. The default is kmer.\n");
+ m->mothurOut("The name parameter allows you add a names file with your fasta file, if you enter multiple fasta files, you must enter matching names files for them.\n");
+ m->mothurOut("The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n");
+ m->mothurOut("The ksize parameter allows you to specify the kmer size for finding most similar template to candidate. The default is 8.\n");
+ m->mothurOut("The processors parameter allows you to specify the number of processors to use. The default is 1.\n");
+ m->mothurOut("The match parameter allows you to specify the bonus for having the same base. The default is 1.0.\n");
+ m->mothurOut("The mistmatch parameter allows you to specify the penalty for having different bases. The default is -1.0.\n");
+ m->mothurOut("The gapopen parameter allows you to specify the penalty for opening a gap in an alignment. The default is -2.0.\n");
+ m->mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n");
+ m->mothurOut("The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n");
+ m->mothurOut("The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n");
+ m->mothurOut("The probs parameter shut off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be run.\n");
+ m->mothurOut("The iters parameter allows you to specify how many iterations to do when calculating the bootstrap confidence score for your taxonomy with the bayesian method. The default is 100.\n");
+ m->mothurOut("The classify.seqs command should be in the following format: \n");
+ m->mothurOut("classify.seqs(template=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n");
+ m->mothurOut("Example classify.seqs(fasta=amazon.fasta, template=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n");
+ m->mothurOut("The .taxonomy file consists of 2 columns: 1 = your sequence name, 2 = the taxonomy for your sequence. \n");
+ m->mothurOut("The .tax.summary is a summary of the different taxonomies represented in your fasta file. \n");
+ m->mothurOut("Note: No spaces between parameter labels (i.e. fasta), '=' and parameters (i.e.yourFastaFile).\n\n");
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "help");
+ m->errorOut(e, "ClassifySeqsCommand", "help");
exit(1);
}
}
if(method == "bayesian"){ classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters); }
else if(method == "knn"){ classify = new Knn(taxonomyFileName, templateFileName, search, kmerSize, gapOpen, gapExtend, match, misMatch, numWanted); }
else {
- mothurOut(search + " is not a valid method option. I will run the command using bayesian.");
- mothurOutEndLine();
+ m->mothurOut(search + " is not a valid method option. I will run the command using bayesian.");
+ m->mothurOutEndLine();
classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters);
}
+ vector<string> outputNames;
for (int s = 0; s < fastaFileNames.size(); s++) {
inNames.close();
}
- mothurOut("Classifying sequences from " + fastaFileNames[s] + " ..." ); mothurOutEndLine();
+ m->mothurOut("Classifying sequences from " + fastaFileNames[s] + " ..." ); m->mothurOutEndLine();
if (outputDir == "") { outputDir += hasPath(fastaFileNames[s]); }
string newTaxonomyFile = outputDir + getRootName(getSimpleName(fastaFileNames[s])) + getRootName(getSimpleName(taxonomyFileName)) + "taxonomy";
string tempTaxonomyFile = outputDir + getRootName(getSimpleName(fastaFileNames[s])) + "taxonomy.temp";
string taxSummary = outputDir + getRootName(getSimpleName(fastaFileNames[s])) + getRootName(getSimpleName(taxonomyFileName)) + "tax.summary";
+ outputNames.push_back(newTaxonomyFile);
+ outputNames.push_back(taxSummary);
+
int start = time(NULL);
int numFastaSeqs = 0;
for (int i = 0; i < lines.size(); i++) { delete lines[i]; } lines.clear();
itNames = nameMap.find(name);
if (itNames == nameMap.end()) {
- mothurOut(name + " is not in your name file please correct."); mothurOutEndLine(); exit(1);
+ m->mothurOut(name + " is not in your name file please correct."); m->mothurOutEndLine(); exit(1);
}else{
for (int i = 0; i < itNames->second; i++) {
taxaBrowser.addSeqToTree(name+toString(i), taxon); //add it as many times as there are identical seqs
remove(newTaxonomyFile.c_str());
rename(unclass.c_str(), newTaxonomyFile.c_str());
- mothurOutEndLine();
- mothurOut("It took " + toString(time(NULL) - start) + " secs to classify " + toString(numFastaSeqs) + " sequences."); mothurOutEndLine(); mothurOutEndLine();
+ m->mothurOutEndLine();
+ m->mothurOut("Output File Names: "); m->mothurOutEndLine();
+ for (int i = 0; i < outputNames.size(); i++) { m->mothurOut(outputNames[i]); m->mothurOutEndLine(); }
+ m->mothurOutEndLine();
+
+
+ m->mothurOutEndLine();
+ m->mothurOut("It took " + toString(time(NULL) - start) + " secs to classify " + toString(numFastaSeqs) + " sequences."); m->mothurOutEndLine(); m->mothurOutEndLine();
}
delete classify;
return 0;
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "execute");
+ m->errorOut(e, "ClassifySeqsCommand", "execute");
exit(1);
}
}
return newTax;
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "addUnclassifieds");
+ m->errorOut(e, "ClassifySeqsCommand", "addUnclassifieds");
exit(1);
}
}
}else if (pid == 0){
driver(lines[process], taxFileName + toString(getpid()) + ".temp", tempTaxFile + toString(getpid()) + ".temp", filename);
exit(0);
- }else { mothurOut("unable to spawn the necessary processes."); mothurOutEndLine(); exit(0); }
+ }else { m->mothurOut("unable to spawn the necessary processes."); m->mothurOutEndLine(); exit(0); }
}
//force parent to wait until all the processes are done
#endif
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "createProcesses");
+ m->errorOut(e, "ClassifySeqsCommand", "createProcesses");
exit(1);
}
}
output.close();
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "appendTaxFiles");
+ m->errorOut(e, "ClassifySeqsCommand", "appendTaxFiles");
exit(1);
}
}
delete candidateSeq;
if((i+1) % 100 == 0){
- mothurOut("Classifying sequence " + toString(i+1)); mothurOutEndLine();
+ m->mothurOut("Classifying sequence " + toString(i+1)); m->mothurOutEndLine();
}
}
return 1;
}
catch(exception& e) {
- errorOut(e, "ClassifySeqsCommand", "driver");
+ m->errorOut(e, "ClassifySeqsCommand", "driver");
exit(1);
}
}
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