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#!/usr/bin/python
import os
import sys
import scipy.optimize.nnls
import scipy.sparse
import numpy as np
from subprocess import *
import argparse
import platform
def main():
parser = argparse.ArgumentParser(description=
"Quikr returns the estimated frequencies of batcteria present when given a \
input FASTA file. \n \
A default trained matrix will be used if none is supplied \n \
You must supply a kmer and default lambda if using a custom trained \
matrix.")
parser.add_argument("-f", "--fasta", help="fasta file", required=True)
parser.add_argument("-o", "--output", help="output path (csv output)", required=True)
parser.add_argument("-t", "--trained-matrix", help="trained matrix", required=True)
parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000")
parser.add_argument("-k", "--kmer", type=int, required=True,
help="specifies which kmer to use, must be used with a custom trained database")
args = parser.parse_args()
# our default lambda is 10,000
lamb = 10000
# Make sure our input exist
if not os.path.isfile(args.fasta):
parser.error( "Input fasta file not found")
if not os.path.isfile(args.trained_matrix):
parser.error("custom trained matrix not found")
# use alternative lambda
if args.lamb is not None:
lamb = args.lamb
xstar = quikr(args.fasta, args.trained_matrix, args.kmer, lamb)
np.savetxt(args.output, xstar, delimiter=",", fmt="%f")
return 0
def quikr_load_trained_matrix_from_file(input_fasta_location, trained_matrix_location, kmer, default_lambda):
trained_matrix = np.load(trained_matrix_location)
xstar = quikr(input_fasta_location, trained_matrix, kmer, default_lambda)
return xstar
def quikr(input_fasta_location, trained_matrix, kmer, default_lambda):
"""
input_fasta is the input fasta file to find the estimated frequencies of
trained_matrix is the trained matrix we are using to estimate the species
kmer is the desired k-mer to use
default_lambda is inp
returns the estimated requencies of bacteria present when given an input
FASTA file of amplicon (454) reads. A k-mer based, L1 regularized, sparsity
promoting algorthim is utilized.
In practice reconstruction is accurate only down to the genus level (not
species or strain).
"""
uname = platform.uname()[0]
# We use the count program to count ____
if uname == "Linux" and os.path.isfile("./count-linux"):
print "Detected Linux"
count_input = Popen(["./count-linux", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE)
elif uname == "Darwin" and os.path.isfile("./count-osx"):
print "Detected Mac OS X"
count_input = Popen(["count-osx", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE)
# load the output of our count program and form a probability vector from the counts
counts = np.loadtxt(count_input.stdout)
counts = counts / counts.sum(0)
counts = default_lambda * counts
counts = np.concatenate([np.zeros(1), counts])
# load our trained matrix
#form the k-mer sensing matrix
trained_matrix = trained_matrix * default_lambda;
trained_matrix = np.flipud(trained_matrix);
trained_matrix = np.vstack((np.ones(trained_matrix.shape[1]), trained_matrix))
xstar, rnorm = scipy.optimize.nnls(trained_matrix, counts)
xstar = xstar / xstar.sum(0)
return xstar
if __name__ == "__main__":
sys.exit(main())
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