From 8fe20384c10cff03f4c9a4613499bf2b711c9bab Mon Sep 17 00:00:00 2001
From: Calvin <calvin@EESI>
Date: Tue, 26 Feb 2013 10:48:18 -0500
Subject: some upodates

---
 multifasta_to_otu.py | 83 +++++++++++++++++++++++++++++++---------------------
 1 file changed, 49 insertions(+), 34 deletions(-)
 mode change 100644 => 100755 multifasta_to_otu.py

(limited to 'multifasta_to_otu.py')

diff --git a/multifasta_to_otu.py b/multifasta_to_otu.py
old mode 100644
new mode 100755
index 34af060..cca2e89
--- a/multifasta_to_otu.py
+++ b/multifasta_to_otu.py
@@ -14,55 +14,70 @@ kmer = 6
 lamb = 10000
 trained_matrix = ""
 output_directory = ""
+input_directory = ""
 
 
 def main():
 
+  global kmer
+  global input_directory 
+  global output_directory 
+  global lamb
+  global trained_matrix 
+  #do: write up the description
+  parser = argparse.ArgumentParser(description="MultifastaOTU")
 
-    #do: write up the description
-    parser = argparse.ArgumentParser(description="MultifastaOTU")
-
-    parser.add_argument("-i", "--input", 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="otu_table", 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()
+  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="otu_table", 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
-    trained_matrix = args.trained_matrix
+  # 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")
 
-    # Make sure our input exist
-    if not os.path.isdir(args.input):
-        parser.error( "Input directory not found")
+  if not os.path.isdir(args.output_directory):
+    parser.error( "Input directory not found")
 
-    if not os.path.isdir(args.output_directory):
-        os.path.mkdir(args,output_directory)
+  if not os.path.isdir(args.output_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_matrix):
+    parser.error("custom trained matrix not found")
     
     # use alternative lambda
-    if args.lamb is not None:
-        lamb = args.lamb
+  if args.lamb is not None:
+    lamb = args.lamb
     
-    if args.jobs is not None:
-        jobs = args.jobs
+  if args.jobs is not None:
+    jobs = args.jobs
+
+  if args.kmer is not None:
+    kmer = args.kmer
 
-    if args.kmer is not None:
-        kmer = args.kmer
-    fasta_list = os.listdir(args.
-    pool = Pool(processes=jobs)
-    result = pool.map(quikr_call, fasta_list)
-    return 0
+  fasta_list = os.listdir(args.input_directory)
+  pool = Pool(processes=jobs)
+  result = pool.map(quikr_call, fasta_list)
+
+  return 0
 
 def quikr_call(fasta_file):
-  xstar = q.quikr(fasta_file, training_matrix, kmer, lamb)
-  np.savetxt(output_directory + os.path.basename(fasta_file), xstar, delimiter=",", fmt="%f")
+  inp = input_directory + fasta_file
+  output = output_directory + os.path.basename(fasta_file)
+  xstar = q.quikr(inp, trained_matrix, kmer, lamb)
+  np.savetxt(output, xstar, delimiter=",", fmt="%f")
   return 0
 
- if __name__ == "__main__":
-     sys.exit(main())
+if __name__ == "__main__":
+  sys.exit(main())
  
-- 
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