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import os
import sys
import scipy.optimize.nnls
import scipy.sparse
import numpy as np
import scipy as sp
from scipy.sparse import lil_matrix
from scipy.sparse.linalg import spsolve
from subprocess import *
import argparse
import platform
def main(args):
parser = argparse.ArgumentParser(description="An argparse example")
parser.add_argument("--trained", help="The trained matrix")
parser.add_argument("--fasta", help="input fasta file")
args = parser.parse_args()
if args.trained== "install":
print "You asked for installation"
else:
print "You asked for something other than installation"
return 0
def quikr(input_fasta_location, trained_matrix_location, 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 " + kmer, "-1", "-u", input_fasta], stdout=PIPE)
elif uname == "Darwin" and os.path.isfile("./count-osx"):
print "Detected Mac OS X"
count_input = Popen(["count-osx", "-r 6", "-1", "-u", input_fasta], stdout=PIPE)
counts = np.loadtxt(count_input.stdout) # create a ndarray
counts = counts / np.sum(counts) # form a probablility vector from our counts
counts = default_lambda * counts
trained_matrix = np.loadtxt(trained_matrix_location)
xstar = nnls(trained_matrix, counts)
return 1
if __name__ == "__main__":
sys.exit(main(sys.argv))
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