1 files changed, 0 insertions, 115 deletions
diff --git a/quikr.py b/quikr.py
deleted file mode 100755
index 3f8221e..0000000
--- a/quikr.py
+++ /dev/null
@@ -1,115 +0,0 @@
-#!/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
-import gzip
-import itertools
-
-def generate_kmers(kmer):
-  """ This will return a list of kmers seperated by newlines """
-  return '\n'.join(''.join(x) for x in itertools.product('acgt', repeat=kmer))
-
-def isCompressed(filename):
-  """ This function checks to see if the file is gzipped """ 
-
-  f = open(filename, "rb")
-
-  # The first two bytes of a gzipped file are always '1f 8b'
-  if f.read(2) == '\x1f\x8b':
-    f.close()
-    return True
-  else:
-    f.close()
-    return False
-
-  
-def train_matrix(input_file_location, kmer):
-  """
-  Takes a input fasta file, and kmer, returns a custom trained matrix
-  """
-
-  kmer_file_name = str(kmer) + "mers.txt"
-
-  if not os.path.isfile(kmer_file_name):
-    print "could not find kmer file"
-    exit()
-  
-  uname = platform.uname()[0]
-
-  if uname == "Linux": 
-    input_file = Popen(["./probabilities-by-read-linux", str(kmer), input_file_location, kmer_file_name], stdout=PIPE) 
-  elif uname == "Darwin":
-    input_file = Popen(["./probabilities-by-read-osx", str(kmer), input_file_location, kmer_file_name]) 
-
-  # load and  normalize the matrix by dividing each element by the sum of it's column.
-  # also do some fancy rotations so that it works properly with quikr
-  matrix  = np.loadtxt(input_file.stdout)
-  
-  matrix = np.rot90(matrix)
-  matrix = matrix / matrix.sum(0)
-  matrix = np.flipud(matrix);
-  return matrix
-
-
-def load_trained_matrix_from_file(trained_matrix_location):
-  """ This is a helper function to load our trained matrix and run quikr """
-  
-  if isCompressed(trained_matrix_location):
-    trained_matrix_file = gzip.open(trained_matrix_location, "rb")
-  else:
-    trained_matrix_file = open(trained_matrix_location, "rb")
-  
-  trained_matrix = np.load(trained_matrix_file)
-
-  return trained_matrix
-
-
-def calculate_estimated_frequencies(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"):
-    count_input = Popen(["./count-linux", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE) 
-  elif uname == "Darwin" and os.path.isfile("./count-osx"):
-    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])
-
-  #form the k-mer sensing matrix
-  trained_matrix = trained_matrix * default_lambda;
-  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())