How to use the pyopenms.TargetedExperiment function in pyopenms

def main(options):

    # load chromatograms
    chromatograms = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(options.infile, chromatograms)

    # load TraML file
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(options.traml_in, targeted);

    trafo_out = algorithm(chromatograms, targeted)

    pyopenms.TransformationXMLFile().store(options.outfile, trafo_out);

product_tolerance = options.product_tolerance
    out = options.outfile
    chromat_in = options.infile
    traml_in = options.traml_in

    # precursor_tolerance = 0.05
    # product_tolerance = 0.05
    # out = "/tmp/out.mzML"
    # chromat_in = "../source/TEST/TOPP/MRMMapping_input.chrom.mzML"
    # traml_in = "../source/TEST/TOPP/MRMMapping_input.TraML"

    ff = pyopenms.MRMFeatureFinderScoring()
    chromatogram_map = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(chromat_in, chromatogram_map)
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(traml_in, targeted);

    output = algorithm(chromatogram_map, targeted, precursor_tolerance, product_tolerance)

    pyopenms.MzMLFile().store(out, output);

def main(options):

    # load chromatograms
    chromatograms = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(options.infile, chromatograms)

    # load TraML file
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(options.traml_in, targeted);

    trafo_out = algorithm(chromatograms, targeted)

    pyopenms.TransformationXMLFile().store(options.outfile, trafo_out);

pp_params.setValue("PeakPickerMRM:gauss_width", 0.1, '')
        pp_params.setValue("resample_boundary", 0.05, '')
        pp_params.setValue("compute_peak_quality", "true", '')
    pp.setParameters(pp_params)

    scorer = pyopenms.MRMFeatureFinderScoring()
    scoring_params = scorer.getDefaults();
    # Only report the top 5 features
    scoring_params.setValue("stop_report_after_feature", 5, '')
    scoring_params.setValue("rt_normalization_factor", rt_normalization_factor, '')
    scorer.setParameters(scoring_params);

    chromatograms = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(chromat_in, chromatograms)
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(traml_in, targeted);

    trafoxml = pyopenms.TransformationXMLFile()
    trafo = pyopenms.TransformationDescription()
    if trafo_in is not None:
        model_params = pyopenms.Param()
        model_params.setValue("symmetric_regression", "false", "", [])
        model_type = "linear"
        trafoxml.load(trafo_in, trafo, True)
        trafo.fitModel(model_type, model_params);


    light_targeted = pyopenms.LightTargetedExperiment();
    pyopenms.OpenSwathDataAccessHelper().convertTargetedExp(targeted, light_targeted)
    output = algorithm(chromatograms, light_targeted, pp, scorer, trafo)

# load TraML file
    targeted = pyopenms.TargetedExperiment();
    pyopenms.TraMLFile().load(options.traml_in, targeted);

    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.MSExperiment();

    # load input
    for infile in options.infiles:
        exp = pyopenms.MSExperiment()
        pyopenms.FileHandler().loadExperiment(infile, exp)

        transition_exp_used = pyopenms.TargetedExperiment();

        do_continue = True
        if options.is_swath:
            do_continue = pyopenms.OpenSwathHelper().checkSwathMapAndSelectTransitions(exp, targeted, transition_exp_used, options.min_upper_edge_dist)
        else:
            transition_exp_used = targeted

        if do_continue:
            # set up extractor and run
            tmp_out = pyopenms.MSExperiment();
            extractor = pyopenms.ChromatogramExtractor()
            extractor.extractChromatograms(exp, tmp_out, targeted, options.extraction_window, options.ppm, trafo, options.rt_extraction_window, options.extraction_function)
            # add all chromatograms to the output
            for chrom in tmp_out.getChromatograms():
                output.addChromatogram(chrom)

import pyopenms
    except ImportError as e:
        print("\nError!")
        print("Could not import pyOpenMS while trying to load a TraML file - please make sure pyOpenMS is installed.")
        print("pyOpenMS is available from https://pypi.python.org/pypi/pyopenms")
        print()
        raise e

    assert len(aligned_pg_files) == 1, "There should only be one file in simple mode"
    f = aligned_pg_files[0]

    # Produce simple mapping between runs and files (assume each file is one run)
    for i,raw in enumerate(rawdata_files):
        mapping[str(i)] = [ raw ]

    targexp = pyopenms.TargetedExperiment()
    pyopenms.TraMLFile().load(f, targexp)

    for peptide_precursor in targexp.getPeptides():

        # Fill the protein mapping
        protein_id = peptide_precursor.protein_refs
        if len(protein_id) > 0:
            protein_id = protein_id[0] # take the first one ... 

        tmp = protein_mapping.get(protein_id, [])
        if peptide_precursor.sequence not in tmp:
            tmp.append(peptide_precursor.sequence)
        protein_mapping[protein_id] = tmp

        # Fill the sequence mapping
        tmp = sequences_mapping.get(peptide_precursor.sequence, [])