compara-deep-learning/process_negative.py

import pandas as pd
import numpy as np
import sys
from neighbor_genes import read_genome_maps
from process_data import create_data_homology_ls
from threads import Procerssrunner
from read_get_gene_seq import read_gene_sequences
from access_data_rest import update_rest
from prepare_synteny_matrix import read_data_synteny
from save_data import write_dict_json


def read_database_txt(filename):
    df = pd.read_csv(filename, sep="\t", header=None)
    df = df.drop(0, axis=1)
    df.columns = [
        "gene_stable_id",
        "species",
        "homology_gene_stable_id",
        "homology_species",
        "wga",
        "goc",
        "homology_type"]
    return df


def main():
    arg = sys.argv
    a, d, ld, ldg, cmap, cimap = read_genome_maps()
    print("Genome Maps Loaded.")
    df = read_database_txt(arg[-2])
    nop = int(arg[-1])
    print("Data Read.")
    a_h = []
    d_h = []
    a_h.append(df)
    d_h.append(arg[-2].split(".")[0])
    n = 3
    lsy = create_data_homology_ls(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, 0)
    write_dict_json("neighbor_genes_negative", "processed", lsy)
    print("Neighbor Genes Found and Saved Successfully:)")
    gene_sequences = read_gene_sequences(
        a_h, lsy, "geneseq", "gene_seq_negative")
    gene_sequences = update_rest(gene_sequences, "gene_seq_negative")
    ndir = "processed/synteny_matrices/"
    nf1 = "synteny_matrices_global"
    nf2 = "synteny_matrices_local"
    nf3 = "indexes"
    for i in range(len(a_h)):
        df = a_h[i]
        part = len(df) // nop
        pr = Procerssrunner()
        pr.start_processes(nop, df, gene_sequences, lsy, part, n, d_h[i])
        smg, sml, indexes = read_data_synteny(nop, d_h[i])
        print(len(indexes))
        np.save(ndir + str(d_h[i]) + "_" + nf1, smg)
        np.save(ndir + str(d_h[i]) + "_" + nf2, sml)
        np.save(ndir + str(d_h[i]) + "_" + nf3, indexes)
    print("Synteny Matrices Created Successfully :)")


if __name__ == "__main__":
    main()