How to use the pyopenms.MSExperiment function in pyopenms

options = f_fmxl.getOptions()
        options.setLoadConvexHull(False)
        options.setLoadSubordinates(False)
        f_fmxl.setOptions(options)

    if align_features:
        map_ref = pms.FeatureMap()
        f_fxml_tmp = pms.FeatureXMLFile()
        options = f_fmxl.getOptions()
        options.setLoadConvexHull(False)
        options.setLoadSubordinates(False)
        f_fxml_tmp.setOptions(options)
        f_fxml_tmp.load(file_, map_ref)
        algorithm.setReference(map_ref)
    else:
        map_ref = pms.MSExperiment()
        pms.MzMLFile().load(file_, map_ref)
        algorithm.setReference(map_ref)

    plog.startProgress(0, len(in_files), "Align input maps")
    for i, in_file in enumerate(in_files):
        trafo = pms.TransformationDescription()
        if align_features:
            map_ = pms.FeatureMap()
            f_fxml_tmp = pms.FeatureXMLFile()
            f_fxml_tmp.setOptions(f_fmxl.getOptions())
            f_fxml_tmp.load(in_file, map_)
            if in_file == file_:
                trafo.fitModel("identity")
            else:
                algorithm.align(map_, trafo)
            if out_files:

def addDataProcessing(obj, params, action):
    if isinstance(obj, pms.MSExperiment):
        result = pms.MSExperiment()
        for spec in obj:
            spec = _addDataProcessing(spec, params, action)
            result.addSpectrum(spec)
    else:
        result = _addDataProcessing(obj, params, action)
    return result

ok = run_mode or write_mode
    if not ok:
        parser.error("either specify -in, -out and -(dict)ini for running "
                     "the peakpicker\nor -write(dict)ini for creating std "
                     "ini file")

    defaults = pms.PeakPickerHiRes().getDefaults()

    write_requested = writeParamsIfRequested(args, defaults)

    if not write_requested:
        updateDefaults(args, defaults)

        fh = pms.MzMLFile()
        fh.setLogType(pms.LogType.CMD)
        input_map = pms.MSExperiment()
        fh.load(args.in_, input_map)

        run_peak_picker(input_map, defaults, args.out)

def algorithm(chromatograms, targeted):
    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.FeatureMap();

    # set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected]

# 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)

    dp = pyopenms.DataProcessing()
    pa = pyopenms.ProcessingAction().SMOOTHING
    dp.setProcessingActions(set([pa]))

    chromatograms = output.getChromatograms();
    for chrom in chromatograms:
        this_dp = chrom.getDataProcessing()
        this_dp.append(dp)
        chrom.setDataProcessing(this_dp)

def main(options):
    precursor_tolerance = options.precursor_tolerance
    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 algorithm(chromatograms, targeted):
    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.FeatureMap();

    # set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected]

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);

def _acquisition_grid(self):
        ms_experiment = ms.MSExperiment()
        file_handler = ms.FileHandler()
        file_handler.loadExperiment(self.ms_file_path, ms_experiment)
        pixel_coords = [(spec.getRT(), 0.0) for spec in ms_experiment]
        return {
            ACQ_GEOMETRY_KEYS.AcqGridSection.REGULAR_GRID: False,
            ACQ_GEOMETRY_KEYS.AcqGridSection.PIXEL_CORRD_LIST: pixel_coords
        }

def main(options):

    # 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