Run

  1. Run the fitting

    fit.py efitter_params.py
    

    The fitting runs will execute in the background and take from a few dozens of minutes to several hours depending on the case.

    In the output, you should have got the following directory structure:

    master_outdir/ # Directory specified with "master_outdir" parameter in params.py
        config_prefix1/ # named after config_prefix specifications in fitmap_args in params.py
            map1.mrc/   # named after the filenames of the EM maps used
                map1.mrc  # symbolic link to the reference map
                pdb_file_name1.pdb/     # named after the pdb file names used for fitting
                    solutions.csv   # the list of solutions and their scores
                    log_err.txt     # standard error log
                    log_out.txt     # standeard output log
                    run.sh          # sbatch script used for running the job
                    ori_pdb.pdb     # symbolic link to the original query file
                    map1.mrc        # symbolic link to the reference map
                pdb_file_name2.pdb/
                pdb_file_name3.pdb/
                config.txt          # A config file for fitting, saved FYI.
            map2.mrc/
        config_prefix2/
        config_prefix3/
    

    Note

    The fitting is complete when each of the .pdb directories contains solutions.csv file.

    Inspect the log_out.txt files for status and log_err.txt for error messages.

  2. Upon completion, calculate p-values:

    genpval.py <fitting directory/master_outdir>
    

    This should create additional files in each .pdb directory:

    Rplots.pdf
    solutions_pvalues.csv
    

    The solutions_pvalues.csv is crucial for the global optimization step.