void EDTAFile::store(const String& filename, const ConsensusMap& map) const
{
TextFile tf;
// search for maximum number of sub-features (since this determines the number of columns)
Size max_sub(0);
for (Size i = 0; i < map.size(); ++i)
{
max_sub = std::max(max_sub, map[i].getFeatures().size());
}
// write header
String header("RT\tm/z\tintensity\tcharge");
for (Size i = 1; i <= max_sub; ++i)
{
header += "\tRT" + String(i) + "\tm/z" + String(i) + "\tintensity" + String(i) + "\tcharge" + String(i);
}
tf.addLine(header);
for (Size i = 0; i < map.size(); ++i)
{
ConsensusFeature f = map[i];
// consensus
String entry = String(f.getRT()) + "\t" + f.getMZ() + "\t" + f.getIntensity() + "\t" + f.getCharge();
// sub-features
ConsensusFeature::HandleSetType handle = f.getFeatures();
for (ConsensusFeature::HandleSetType::const_iterator it = handle.begin(); it != handle.end(); ++it)
{
entry += String("\t") + it->getRT() + "\t" + it->getMZ() + "\t" + it->getIntensity() + "\t" + it->getCharge();
}
// missing sub-features
for (Size j = handle.size(); j < max_sub; ++j)
{
entry += "\tNA\tNA\tNA\tNA";
}
tf.addLine(entry);
}
tf.store(filename);
}
ConsensusFeature::MapsLess ml; TEST_EQUAL(ml(c1,c1), 0); TEST_EQUAL(ml(c1,c2), 1); TEST_EQUAL(ml(c2,c1), 0); TEST_EQUAL(ml(c2,c2), 0); END_SECTION START_SECTION((ConsensusFeature& operator=(const ConsensusFeature &rhs))) ConsensusFeature cons(tmp_feature); cons.insert(1,tmp_feature); ConsensusFeature cons_copy; cons_copy = cons; TEST_REAL_SIMILAR(cons_copy.getRT(),1.0) TEST_REAL_SIMILAR(cons_copy.getMZ(),2.0) TEST_REAL_SIMILAR(cons_copy.getIntensity(),200.0) TEST_EQUAL((cons_copy.begin())->getMapIndex(),1) TEST_EQUAL((cons_copy.begin())->getUniqueId(),3) TEST_EQUAL((cons_copy.begin())->getIntensity(),200) END_SECTION START_SECTION((ConsensusFeature(const ConsensusFeature &rhs))) ConsensusFeature cons(tmp_feature); cons.insert(1,tmp_feature); ConsensusFeature cons_copy(cons); TEST_REAL_SIMILAR(cons_copy.getRT(),1.0) TEST_REAL_SIMILAR(cons_copy.getMZ(),2.0)
TEST_EQUAL(map[0].getPeptideIdentifications()[1].getHits()[1].getSequence(), "D")
TEST_EQUAL(map[1].getPeptideIdentifications().size(), 1)
TEST_EQUAL(map[1].getPeptideIdentifications()[0].getHits().size(), 1)
TEST_EQUAL(map[1].getPeptideIdentifications()[0].getHits()[0].getSequence(), "E")
//unassigned peptide identifications
TEST_EQUAL(map.getUnassignedPeptideIdentifications().size(), 2)
TEST_EQUAL(map.getUnassignedPeptideIdentifications()[0].getHits().size(), 1)
TEST_EQUAL(map.getUnassignedPeptideIdentifications()[0].getHits()[0].getSequence(), "F")
TEST_EQUAL(map.getUnassignedPeptideIdentifications()[1].getHits().size(), 2)
TEST_EQUAL(map.getUnassignedPeptideIdentifications()[1].getHits()[0].getSequence(), "G")
TEST_EQUAL(map.getUnassignedPeptideIdentifications()[1].getHits()[1].getSequence(), "H")
//features
TEST_EQUAL(map.size(), 6)
ConsensusFeature cons_feature = map[0];
TEST_REAL_SIMILAR(cons_feature.getRT(), 1273.27)
TEST_REAL_SIMILAR(cons_feature.getMZ(), 904.47)
TEST_REAL_SIMILAR(cons_feature.getIntensity(), 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[0], 1273.27)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[0], 1273.27)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[1], 904.47)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[1], 904.47)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().minPosition()[0], 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().maxPosition()[0], 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getQuality(), 1.1)
TEST_EQUAL(cons_feature.getMetaValue("peptide_id") == DataValue("RefSeq:NC_1234"), true)
ConsensusFeature::HandleSetType::const_iterator it = cons_feature.begin();
TEST_REAL_SIMILAR(it->getIntensity(), 3.12539e+07)
cons_feature = map[5];
TEST_REAL_SIMILAR(cons_feature.getRT(), 1194.82)