use sensing matrix

1 files changed, 6 insertions, 10 deletions

diff --git a/src/python/quikr b/src/python/quikr
index bae2b9f..7c9ce25 100755
--- a/src/python/quikr
+++ b/src/python/quikr
@@ -9,15 +9,11 @@ def main():
 
     parser = argparse.ArgumentParser(description=
     "Quikr returns the estimated frequencies of batcteria present when given a \
-    input FASTA file. \n \
-    A default trained matrix will be used if none is supplied \n \
-    You must supply a kmer and default lambda if using a custom trained \
-    matrix.")
-
+    input FASTA file. \n")
     parser.add_argument("-f", "--fasta", help="the sample's fasta file of NGS READS", required=True)
     parser.add_argument("-o", "--output", help="OTU_FRACTION_PRESENT, a vector \ 
     representing the percentage of database sequence's presence in a sequence. (csv output)", required=True)
-    parser.add_argument("-t", "--trained-matrix", help="the trained matrix", required=True)
+    parser.add_argument("-s", "--sensing-matrix", help="the sensing matrix", required=True)
     parser.add_argument("-l", "--lamb", type=int, help="the lambda size. (default is 10,000")
     parser.add_argument("-k", "--kmer", type=int, required=True,
         help="specifies the size of the kmer to use (the default is 6)")
@@ -33,8 +29,8 @@ def main():
     if not os.path.isfile(args.fasta):
         parser.error( "Input fasta file not found")
 
-    if not os.path.isfile(args.trained_matrix):
-        parser.error("Custom trained matrix not found")
+    if not os.path.isfile(args.sensing_matrix):
+        parser.error("Custom sensing matrix not found")
     
     # use alternative lambda
     if args.lamb is not None:
@@ -44,8 +40,8 @@ def main():
     if args.kmer is not None:
         kmer = args.kmer
 
-    trained_matrix = quikr.load_trained_matrix_from_file(args.trained_matrix)
-    xstar = quikr.calculate_estimated_frequencies(args.fasta, trained_matrix, kmer, lamb)
+    sensing_matrix = quikr.load_sensing_matrix_from_file(args.sensing_matrix)
+    xstar = quikr.calculate_estimated_frequencies(args.fasta, sensing_matrix, kmer, lamb)
 
     np.savetxt(args.output, xstar, delimiter=",", fmt="%f")
     return 0