From dd6ba075ea5ec86936900adaf71d69ae374d1794 Mon Sep 17 00:00:00 2001
From: Calvin <calvin@EESI>
Date: Thu, 14 Feb 2013 16:21:47 -0500
Subject: quikr and quikr_train

---
 quikr_train.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 quikr_train.py

(limited to 'quikr_train.py')

diff --git a/quikr_train.py b/quikr_train.py
new file mode 100644
index 0000000..2076f5a
--- /dev/null
+++ b/quikr_train.py
@@ -0,0 +1,50 @@
+#from scipy.sparse import * 
+import numpy as np
+import sys
+from subprocess import *
+import platform
+
+# You can call this script independently, and will save the 
+# trained matrix as a numpy file.
+# example: python quikr-train.py input.fasta 6 trained_matrix.npy
+
+def main(argv):
+  input_file_location = argv[1]
+  kmer = argv[2]
+  output_file_location = argv[3]
+
+  # call the quikr train function, save the output with np.save
+  matrix = quikr_train(argv[1], argv[2])
+  np.save(output_file_location, matrix)
+
+  return 0
+
+def quikr_train(input_file_location, kmer):
+
+  
+  print "input fasta training file: " + input_file_location
+  print "kmer: " + kmer
+
+  kmer_file_name = kmer + "mers.txt"
+  print kmer_file_name
+
+  
+  uname = platform.uname()[0]
+
+  if uname == "Linux": 
+    print "Detected Linux"
+    input_file = Popen(["./probabilities-by-read-linux", kmer, input_file_location, kmer_file_name], stdout=PIPE) 
+  elif uname == "Darwin":
+    print "Detected Mac OS X" 
+    input_file = Popen(["./probabilities-by-read-osx", kmer, input_file_location, kmer_file_name]) 
+
+  # load and  normalize the matrix by dividing each element by the sum of it's column.
+  matrix  = np.loadtxt(input_file.stdout)
+  normalized = matrix / matrix.sum(0)
+
+  return normalized
+  
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv))
-- 
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