return rankingModel

            

if __name__ == '__main__':
    print 'This program generates a results file containing Raw lads output postscored with the algorithm of choice. The discmodel is a supplied model, if necessary for the postscoring algorithm'
    options = ArgLib.parse(['init', 'ppmstd', 'dtadir', 'lads', 'sequest', 'config', 'model', 'output', 'symbolmap'], optArgs=[{'opts': ('-D', '--discmodel'), 'attrs': {'type': 'string', 'dest': 'discmodel', 'help': 'Model used to calculate discriminant score'}}, {'opts': ('-P', '--pairconfig'), 'attrs': {'type': 'string', 'dest': 'pairconfig', 'help': 'Name of LADS Pair Configuration'}}, {'opts': ('-F', '--featurelist'), 'attrs': {'type': 'string', 'dest': 'featurelist', 'help': 'File containing pickled list of desired features (optional)'}}])
    parent = os.path.abspath(os.pardir)
                           
    PNet = PN.ProbNetwork(options.config, options.model)
    
    paramsDict = ArgLib.parseInitFile(options.init, options)
    pairConfigurations = paramsDict['Pair Configurations']

    LADSSeqInfo = GLFD.parseSequenceDTAsLogfile(options.lads)

    with open(options.symbolmap, 'r') as fin:
        symbolMap = pickle.load(fin)
    seqMap = DataFile.generateSeqMap({'LADS Unit Test': 'LADS'}, symbolMap, paramsDict)
    seqMap = seqMap['LADS Unit Test']

    if options.featurelist:
        with open(options.featurelist) as fin:
            desired_feats = pickle.load(fin)
    else:
        desired_feats = None

    heavySeqMaps = {}
    for confName in pairConfigurations:
        heavySeqMaps[confName] = copy.deepcopy(seqMap)