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https://github.com/Priyatham-sai-chand/compara-deep-learning.git
synced 2026-10-05 08:11:34 -07:00
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import pandas as pd
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import numpy as np
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import os
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import sys
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from selector import select, create_map_reverse
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def read_db_homology(dir_name, filename):
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df = pd.read_csv(dir_name + "/" + filename, compression='gzip', sep='\t')
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n = filename.split(".")[0]
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n = n.split(" ")[0]
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return df, n
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def get_selection_data():
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with open("dist_matrix", "r") as file:
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matrix = file.readlines()
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matrix = [x.split("\t") for x in matrix]
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matrix = [[float(y) for y in x] for x in matrix]
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matrix = np.array(matrix)
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with open("sp_names", "r") as file:
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dname = file.readlines()
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dname = [x.split("\n")[0] for x in dname]
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spnmap, nspmap = create_map_reverse(dname)
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return matrix, spnmap, nspmap
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def read_select_data(dirname, matrix, spnmap, nspmap, nos):
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lf = os.listdir(dirname)
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if len(lf) == 0:
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print("No Files in the Directory!!!!!!!")
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sys.exit(1)
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if not os.path.isdir("processed"):
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os.mkdir("processed")
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for x in lf:
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df, n = read_db_homology(dirname, x)
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n = n.split(" ")[0]
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df = select(df, nos, matrix, spnmap, nspmap, n)
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(len(df) == nos)
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indexes = np.array(list(df.index.values))
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np.save("processed/" + n + "_selected_indexes", indexes)
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def main():
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arg = sys.argv
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nos = int(arg[-1])
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matrix, spnmap, nspmap = get_selection_data()
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read_select_data("data_homology", matrix, spnmap, nspmap, nos)
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if __name__ == "__main__":
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main()
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