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https://github.com/Priyatham-sai-chand/compara-deep-learning.git
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63 lines
2 KiB
Python
63 lines
2 KiB
Python
import pandas as pd
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import numpy as np
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import sys
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from neighbor_genes import read_genome_maps
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from process_data import create_data_homology_ls
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from threads import Procerssrunner
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from read_get_gene_seq import read_gene_sequences
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from access_data_rest import update_rest
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from prepare_synteny_matrix import read_data_synteny
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from save_data import write_dict_json
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def read_database_txt(filename):
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df = pd.read_csv(filename, sep="\t", header=None)
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df = df.drop(0, axis=1)
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df.columns = [
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"gene_stable_id",
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"species",
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"homology_gene_stable_id",
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"homology_species",
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"wga",
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"goc",
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"homology_type"]
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return df
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def main():
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arg = sys.argv
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a, d, ld, ldg, cmap, cimap = read_genome_maps()
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print("Genome Maps Loaded.")
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df = read_database_txt(arg[-2])
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nop = int(arg[-1])
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print("Data Read.")
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a_h = []
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d_h = []
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a_h.append(df)
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d_h.append(arg[-2].split(".")[0])
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n = 3
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lsy = create_data_homology_ls(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, 0)
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write_dict_json("neighbor_genes_negative", "processed", lsy)
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print("Neighbor Genes Found and Saved Successfully:)")
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gene_sequences = read_gene_sequences(
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a_h, lsy, "geneseq", "gene_seq_negative")
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gene_sequences = update_rest(gene_sequences, "gene_seq_negative")
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ndir = "processed/synteny_matrices/"
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nf1 = "synteny_matrices_global"
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nf2 = "synteny_matrices_local"
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nf3 = "indexes"
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for i in range(len(a_h)):
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df = a_h[i]
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part = len(df) // nop
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pr = Procerssrunner()
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pr.start_processes(nop, df, gene_sequences, lsy, part, n, d_h[i])
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smg, sml, indexes = read_data_synteny(nop, d_h[i])
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print(len(indexes))
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np.save(ndir + str(d_h[i]) + "_" + nf1, smg)
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np.save(ndir + str(d_h[i]) + "_" + nf2, sml)
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np.save(ndir + str(d_h[i]) + "_" + nf3, indexes)
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print("Synteny Matrices Created Successfully :)")
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if __name__ == "__main__":
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main()
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