1 files changed, 0 insertions, 48 deletions
diff --git a/quikr_train b/quikr_train
deleted file mode 100755
index 6e599c9..0000000
--- a/quikr_train
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/python
-import numpy as np
-import quikr
-import os
-import sys
-import gzip
-from subprocess import *
-import platform
-import argparse
-
-def main():
-  """
-  You can call this script independently, and will save the 
-  trained matrix as a numpy file.
-
-  example: python quikr-train.py -i input.fasta -k 6 -o trained_matrix.npy
- 
-  """
-  parser = argparse.ArgumentParser(description=
-  " quikr_train returns a custom trained matrix that can be used with \
-    the quikr function. \n You must supply a kmer. \n ")
-
-  parser.add_argument("-i", "--input", help="training database of sequences (fasta format)", required=True)
-  parser.add_argument("-o", "--output", help="sensing matrix (text file)", required=True)
-  parser.add_argument("-k", "--kmer", help="kmer size (integer)", 
-                      type=int, required=False )
-  parser.add_argument("-z", "--compress", help="compress output (integer)", 
-                      action='store_true', required=False)
-
-  args = parser.parse_args()
-
-  if not os.path.isfile(args.input):
-    parser.error( "Input database not found")
-
-  # call the quikr train function, save the output with np.save
-  matrix = quikr.train_matrix(args.input, args.kmer)
-
-  if args.compress: 
-    output_file = gzip.open(args.output, "wb")
-  else:
-    output_file = open(args.output, "wb")
-
-  np.save(output_file, matrix)
-
-  return 0
-  
-if __name__ == "__main__":
-    sys.exit(main())