compara-deep-learning/pfam_parser.py

import pandas as pd 
import progressbar
import gc
import sys

def pfam_parse(filename):
    rlist=[]
    try:
        with open(filename) as file:
            file.seek(0,0)
            for line in progressbar.progressbar(file):
                if line.startswith("#"):
                    continue
                temp_dict={}
                x=[y for y in line.split(" ") if y !='']
                if x[9] !="1":
                    continue
                temp_dict["gene_stable_id"]=x[3]
                temp_dict["accession"]=x[1]
                temp_dict["tlen"]=x[2]
                temp_dict["qlen"]=x[5]
                temp_dict["domain"]=x[0]
                temp_dict["hmm_from"]=x[15]
                temp_dict["hmm_to"]=x[16]
                temp_dict["ali_from"]=x[17]
                temp_dict["ali_to"]=x[18]
                temp_dict["env_from"]=x[19]
                temp_dict["env_to"]=x[20]
                rlist.append(temp_dict)
    except Exception as e:
        print(e)
        return pd.DataFrame()
    print(len(rlist))
    tdf=pd.DataFrame(rlist)
    return tdf

def main():
    arg=sys.argv
    fname_1=arg[-2]
    fname_2=arg[-1]
    df=pfam_parse(fname_1)
    df.to_hdf("pfam_db_positive.h5",key="pfam_db_positive",mode="w")
    df=""
    gc.collect()
    df=pfam_parse(fname_2)
    df.to_hdf("pfam_db_negative.h5",key="pfam_db_negative",mode="w")
    print("Pfam Databases Written Successfully :)")

if __name__ == "__main__":
    main()