1 files changed, 0 insertions, 154 deletions
diff --git a/src/multifasta_to_otu.py b/src/multifasta_to_otu.py
deleted file mode 100755
index c6fb562..0000000
--- a/src/multifasta_to_otu.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#!/usr/bin/python
-
-from multiprocessing import Pool
-from Bio import SeqIO
-import multiprocessing
-from subprocess import *
-import os
-import gzip
-import quikr as q
-import sys
-import numpy as np
-import argparse
-import platform
-import csv
-
-# our defaults
-kmer = 6
-lamb = 10000
-output_directory = ""
-input_directory = ""
-
-
-def main():
-
-  global kmer
-  global input_directory 
-  global output_directory 
-  global lamb
-  global trained_matrix 
-
-  parser = argparse.ArgumentParser(description="MultifastaOTU")
-
-  parser.add_argument("-i", "--input-directory", help="directory containing fasta files", required=True)
-  parser.add_argument("-o", "--otu-table", help="otu_table", required=True)
-  parser.add_argument("-t", "--trained-matrix", help="your trained matrix ", required=True)
-  parser.add_argument("-f", "--trained-fasta", help="the fasta file used to train your matrix", required=True)
-  parser.add_argument("-d", "--output-directory", help="quikr output directory", required=True)
-  parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000")
-  parser.add_argument("-k", "--kmer", type=int, help="specifies which kmer to use, default=6")
-  parser.add_argument("-j", "--jobs", type=int, help="specifies how many jobs to run at once, default=number of CPUs")
-  args = parser.parse_args()
-    
-  # our defaults
-  jobs=multiprocessing.cpu_count()
-  trained_matrix = args.trained_matrix
-  input_directory = args.input_directory
-  output_directory = args.output_directory
-
-  # Make sure our input exist
-  if not os.path.isdir(args.input_directory):
-    parser.error("Input directory not found")
-
-  if not os.path.isdir(args.output_directory):
-    print "Output directory not found, creating directory"
-    os.path.mkdir(args, output_directory)
-
-  if not os.path.isfile(args.trained_matrix):
-    parser.error("Custom trained matrix not found")
-    
-  if not os.path.isfile(args.trained_fasta):
-    parser.error("Fasta file of trained matrix not found")
-
-  # use alternative lambda
-  if args.lamb is not None:
-    lamb = args.lamb
-    
-  if args.jobs is not None:
-    jobs = args.jobs
-
-  if args.kmer is not None:
-    kmer = args.kmer
-
-  # Load trained matrix
-  if qu.isCompressed(args.trained_matrix):
-    trained_matrix_file = gzip.open(args.trained_matrix, "rb")
-  else:
-    trained_matrix_file = open(args.trained_matrix, "rb")
-  
-  trained_matrix = np.load(trained_matrix_file)
-
-  # Return a list of the input directory
-  fasta_list = os.listdir(args.input_directory)
-
-  # Queue up and run our quikr functions.
-  pool = Pool(processes=jobs)
-  results = pool.map(quikr_call, fasta_list)
-
-  # Create an array of headers
-  headers = []
-  trained_matrix_headers = open(args.trained_fasta, "rU")
-  for header in SeqIO.parse(trained_matrix_headers, "fasta"):
-    headers.append(header.id)
-  trained_matrix_headers.close()
-
-  # create our number of reads matrix
-  number_of_reads = np.zeros((len(headers), len(fasta_list)))
-
-  # load the keys with values from each fasta result
-  for fasta, fasta_it in map(None, fasta_list, range(len(fasta_list))):
-
-    count_sequences = Popen(["grep", "-c" , "^>", args.input_directory + fasta], stdout=PIPE) 
-    number_of_sequences = int(count_sequences.stdout.readline())
-
-    proportions = np.loadtxt(output_directory + fasta);
-    
-    for proportion, proportion_it in map(None, proportions, range(len(proportions))):
-      number_of_reads[proportion_it, fasta_it] = round(proportion * number_of_sequences)
-
-  # remove empty rows from our matrix
-  final_headers = list()
-  final_data = list()
-  for row, header in map(None, number_of_reads, headers):
-    if row.sum() != 0:
-      final_headers.append(header)
-      final_data.append(row)
-
-  # convert from a list back into a numpy array
-  final_data = np.array(final_data, dtype=int)
-
-  # stack our final header and our number_of_reads matrix
-  number_of_reads = np.column_stack((final_headers, final_data))
-
-  # write our OTU table
-  output_file = open(args.otu_table, "wb") 
-  writer = csv.writer(output_file, delimiter="\t")
-
-  #write out our fasta file row
-  writer.writerow(['# QIIME vGail OTU table'])
-
-  fasta_row = ['#OTU_ID']
-  fasta_row.append(' '.join(fasta_list))
-  fasta_row = [' '.join(fasta_row)]
-  writer.writerow(fasta_row)
-
-  # write out our results
-  for i in range(0, np.shape(number_of_reads)[0]):
-      writer.writerow(list(number_of_reads[i]))
-
-  output_file.close()
-
-  return 0
-
-
-def quikr_call(fasta_file):
-  input_location = input_directory + fasta_file
-  output_location = output_directory + os.path.basename(fasta_file)
-
-  xstar = q.quikr(input_location, trained_matrix, kmer, lamb)
-  np.savetxt(output_location, xstar, delimiter=",", fmt="%f")
-  return xstar
-
-if __name__ == "__main__":
-  sys.exit(main())
-