initial commit, playing around with threading

1 files changed, 68 insertions, 0 deletions

diff --git a/multifasta_to_otu.py b/multifasta_to_otu.py
new file mode 100644
index 0000000..110328e
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+++ b/multifasta_to_otu.py
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+#!/usr/bin/python
+from multiprocessing import Pool
+import multiprocessing
+import os
+import quikr_train as qt
+import quikr as q
+import sys
+import numpy as np
+import argparse
+import platform
+
+# our defaults
+kmer = 6
+lamb = 10000
+trained_matrix = ""
+output_directory = ""
+
+
+def main():
+
+
+    #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()
+    
+    # our defaults
+    trained_matrix = args.trained_matrix
+
+    # 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):
+        os.path.mkdir(args,output_directory)
+
+    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.jobs is not None:
+        jobs = args.jobs
+
+    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
+
+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")
+  return 0
+
+ if __name__ == "__main__":
+     sys.exit(main())
+