ExitCodes common_main_(FeatureGroupingAlgorithm * algorithm,
bool labeled = false)
{
//-------------------------------------------------------------
// parameter handling
//-------------------------------------------------------------
StringList ins;
if (labeled) ins.push_back(getStringOption_("in"));
else ins = getStringList_("in");
String out = getStringOption_("out");
//-------------------------------------------------------------
// check for valid input
//-------------------------------------------------------------
// check if all input files have the correct type
FileTypes::Type file_type = FileHandler::getType(ins[0]);
for (Size i = 0; i < ins.size(); ++i)
{
if (FileHandler::getType(ins[i]) != file_type)
{
writeLog_("Error: All input files must be of the same type!");
return ILLEGAL_PARAMETERS;
}
}
//-------------------------------------------------------------
// set up algorithm
//-------------------------------------------------------------
Param algorithm_param = getParam_().copy("algorithm:", true);
writeDebug_("Used algorithm parameters", algorithm_param, 3);
algorithm->setParameters(algorithm_param);
//-------------------------------------------------------------
// perform grouping
//-------------------------------------------------------------
// load input
ConsensusMap out_map;
StringList ms_run_locations;
if (file_type == FileTypes::FEATUREXML)
{
vector<ConsensusMap > maps(ins.size());
FeatureXMLFile f;
FeatureFileOptions param = f.getOptions();
// to save memory don't load convex hulls and subordinates
param.setLoadSubordinates(false);
param.setLoadConvexHull(false);
f.setOptions(param);
Size progress = 0;
setLogType(ProgressLogger::CMD);
startProgress(0, ins.size(), "reading input");
for (Size i = 0; i < ins.size(); ++i)
{
FeatureMap tmp;
f.load(ins[i], tmp);
out_map.getFileDescriptions()[i].filename = ins[i];
out_map.getFileDescriptions()[i].size = tmp.size();
out_map.getFileDescriptions()[i].unique_id = tmp.getUniqueId();
// copy over information on the primary MS run
const StringList& ms_runs = tmp.getPrimaryMSRunPath();
ms_run_locations.insert(ms_run_locations.end(), ms_runs.begin(), ms_runs.end());
// to save memory, remove convex hulls, subordinates:
for (FeatureMap::Iterator it = tmp.begin(); it != tmp.end();
++it)
{
it->getSubordinates().clear();
it->getConvexHulls().clear();
it->clearMetaInfo();
}
MapConversion::convert(i, tmp, maps[i]);
maps[i].updateRanges();
setProgress(progress++);
}
endProgress();
// exception for "labeled" algorithms: copy file descriptions
if (labeled)
{
out_map.getFileDescriptions()[1] = out_map.getFileDescriptions()[0];
out_map.getFileDescriptions()[0].label = "light";
out_map.getFileDescriptions()[1].label = "heavy";
}
// group
algorithm->group(maps, out_map);
}
else
{
vector<ConsensusMap> maps(ins.size());
ConsensusXMLFile f;
for (Size i = 0; i < ins.size(); ++i)
{
f.load(ins[i], maps[i]);
maps[i].updateRanges();
// copy over information on the primary MS run
const StringList& ms_runs = maps[i].getPrimaryMSRunPath();
ms_run_locations.insert(ms_run_locations.end(), ms_runs.begin(), ms_runs.end());
}
// group
algorithm->group(maps, out_map);
// set file descriptions:
bool keep_subelements = getFlag_("keep_subelements");
if (!keep_subelements)
{
for (Size i = 0; i < ins.size(); ++i)
{
out_map.getFileDescriptions()[i].filename = ins[i];
out_map.getFileDescriptions()[i].size = maps[i].size();
out_map.getFileDescriptions()[i].unique_id = maps[i].getUniqueId();
}
}
else
{
// components of the output map are not the input maps themselves, but
// the components of the input maps:
algorithm->transferSubelements(maps, out_map);
}
}
// assign unique ids
out_map.applyMemberFunction(&UniqueIdInterface::setUniqueId);
// annotate output with data processing info
addDataProcessing_(out_map,
getProcessingInfo_(DataProcessing::FEATURE_GROUPING));
// set primary MS runs
out_map.setPrimaryMSRunPath(ms_run_locations);
// write output
ConsensusXMLFile().store(out, out_map);
// some statistics
map<Size, UInt> num_consfeat_of_size;
for (ConsensusMap::const_iterator cmit = out_map.begin();
cmit != out_map.end(); ++cmit)
{
++num_consfeat_of_size[cmit->size()];
}
LOG_INFO << "Number of consensus features:" << endl;
for (map<Size, UInt>::reverse_iterator i = num_consfeat_of_size.rbegin();
i != num_consfeat_of_size.rend(); ++i)
{
LOG_INFO << " of size " << setw(2) << i->first << ": " << setw(6)
<< i->second << endl;
}
LOG_INFO << " total: " << setw(6) << out_map.size() << endl;
return EXECUTION_OK;
}
m1.getDataProcessing().resize(1);
m1.getProteinIdentifications().resize(1);
m1.getUnassignedPeptideIdentifications().resize(1);
m1.ensureUniqueId();
m2.setIdentifier ("321");
m2.getDataProcessing().resize(2);
m2.getProteinIdentifications().resize(2);
m2.getUnassignedPeptideIdentifications().resize(2);
m2.push_back(Feature());
m2.push_back(Feature());
m1+=m2;
TEST_EQUAL(m1.getIdentifier(), "");
TEST_EQUAL(UniqueIdInterface::isValid(m1.getUniqueId()), false);
TEST_EQUAL(m1.getDataProcessing().size(), 3);
TEST_EQUAL(m1.getProteinIdentifications().size(),3);
TEST_EQUAL(m1.getUnassignedPeptideIdentifications().size(),3);
TEST_EQUAL(m1.size(),3);
END_SECTION
START_SECTION((void sortByIntensity(bool reverse=false)))
FeatureMap<> to_be_sorted;
Feature f1;
f1.setIntensity(10.0f);
to_be_sorted.push_back(f1);
