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)