select_good.py

#!/usr/bin/env python
from __future__ import print_function
import IMP
import os
from IMP import ArgumentParser


__doc__ = "List/extract good-scoring models from a set of sampling runs."""

def parse_args():
    parser = ArgumentParser(description="List and extract good-scoring models from a set of sampling runs. Example of usage: %(prog)s -rd <run_directory_for_sampling> -rp <run_prefix> -sl ExcludedVolumeSphere_None GaussianEMRestraint_None -pl CrossLinkingMassSpectrometryDataScore|XLDSS CrossLinkingMassSpectrometryDataScore|XLEDC -agl -9999999.0 -99999.0 -aul 99999999.0 999999.0 -mlt 0 0 -mut 0 0. Flag -h for more details.")

    parser.add_argument("-rd", "--run_directory", dest="run_dir",
            help="directory in which sampling results are stored",
            required=True)

    parser.add_argument("-rp", "--run_prefix", dest="run_prefix",
            help="prefix of runs", required=True)

    parser.add_argument("-sl","--selection_keywords_list",nargs='+',type=str,dest="selection_keywords_list",help="list of stat file keywords corresponding to selection criteria")
    parser.add_argument("-pl","--printing_keywords_list",nargs='+',type=str,dest="printing_keywords_list",help="list of stat file keywords whose values are printed out for selected models")

    # thresholds only apply to selection keywords
    parser.add_argument("-alt","--aggregate_lower_thresholds",nargs='+',type=float,dest="aggregate_lower_thresholds",help="aggregate lower thresholds")
    parser.add_argument("-aut","--aggregate_upper_thresholds",nargs='+',type=float,dest="aggregate_upper_thresholds",help="aggregate upper thresholds")
    parser.add_argument("-mlt","--member_lower_thresholds",nargs='+',type=float,dest="member_lower_thresholds",help="member lower thresholds")
    parser.add_argument("-mut","--member_upper_thresholds",nargs='+',type=float,dest="member_upper_thresholds",help="member upper thresholds")

    parser.add_argument("-e","--extract",default=False,dest="extract",action='store_true',help="Type -e to extract all good scoring model RMFs from the trajectory files")
    parser.add_argument("-sf","--score_file",default="scores", type=str, dest="score_file_prefix",help="Score file prefix for samples A and B. Default is %(default)r")
    result = parser.parse_args()

    return result

def select_good_scoring_models():
    from IMP.sampcon.good_scoring_model_selector import GoodScoringModelSelector

    # process input
    arg=parse_args()

    gsms=GoodScoringModelSelector(arg.run_dir,arg.run_prefix)

    subsets = gsms.get_good_scoring_models(selection_keywords_list=arg.selection_keywords_list,printing_keywords_list=arg.printing_keywords_list,
    aggregate_lower_thresholds=arg.aggregate_lower_thresholds,aggregate_upper_thresholds=arg.aggregate_upper_thresholds,
    member_lower_thresholds=arg.member_lower_thresholds,member_upper_thresholds=arg.member_upper_thresholds,extract=arg.extract)
    return subsets, arg.extract

def create_score_files(subsets, field="Total_Score"):
    arg=parse_args()

    score_dir = os.path.join(arg.run_dir,
                             "good_scoring_models")
    scoreA = open(os.path.join(score_dir, arg.score_file_prefix + "A.txt"), "w")
    scoreB = open(os.path.join(score_dir, arg.score_file_prefix + "B.txt"), "w")
    model_file = open(os.path.join(score_dir, "model_ids_scores.txt"), "r")

    print("Creating input files for Total_Score convergence test")

    for line_index,each_model_line in enumerate(model_file.readlines()):

        # Find index of the field we want to use for model score convergence
        if line_index==0:
            field_headers = each_model_line.strip().split()

            try:
                ts_ix = field_headers.index(field)
                run_ix = field_headers.index("Run_id")
                model_ix = field_headers.index("Model_index")

            except ValueError:
                print("create_scores_file: model_ids_scores.txt file has an incorrect format.")
                exit()

        else:
            fields = each_model_line.strip().split()

            score=fields[ts_ix]

            model = int(fields[model_ix])
            print(score, file=scoreA if model in subsets[0] else scoreB)

    scoreA.close()
    scoreB.close()


def main():
    subsets, extract = select_good_scoring_models()

    print("Filtered model scores ready")

    if extract:

        # Create Score Files
        create_score_files(subsets)

        print("Ready to calculate sampling precision with Master_Sampling_Exhaustiveness_Analysis.py")


if __name__ == "__main__":
    main()