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import numpy as np
import os
import sys
from subprocess import *
import platform
import argparse
def main():
"""
You can call this script independently, and will save the
trained matrix as a numpy file.
example: python quikr-train.py input.fasta 6 trained_matrix.npy
"""
parser = argparse.ArgumentParser(description=
" quikr_train returns a custom trained matrix that can be used with \
the quikr function. \n You must supply a kmer. \n ")
parser.add_argument("-i", "--input", help="path to the database", required=True)
parser.add_argument("-o", "--output", help="path to output", required=True)
parser.add_argument("-k", "--kmer", type=int, help="specifies which kmer to use", required=True)
args = parser.parse_args()
if not os.path.isfile(args.input):
parser.error( "Input database not found")
# call the quikr train function, save the output with np.save
matrix = quikr_train(args.input, args.kmer)
np.save(args.output, matrix)
return 0
def quikr_train(input_file_location, kmer):
"""
Takes a input fasta file, and kmer, returns a custom trained matrix
"""
print "input fasta training file: " + input_file_location
print "kmer: " + kmer
kmer_file_name = kmer + "mers.txt"
print kmer_file_name
uname = platform.uname()[0]
if uname == "Linux":
print "Detected Linux"
input_file = Popen(["./probabilities-by-read-linux", kmer, input_file_location, kmer_file_name], stdout=PIPE)
elif uname == "Darwin":
print "Detected Mac OS X"
input_file = Popen(["./probabilities-by-read-osx", kmer, input_file_location, kmer_file_name])
# load and normalize the matrix by dividing each element by the sum of it's column.
matrix = np.loadtxt(input_file.stdout)
normalized = matrix / matrix.sum(0)
return normalized
if __name__ == "__main__":
sys.exit(main())
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