diff options
Diffstat (limited to 'src/python/quikr')
| -rwxr-xr-x | src/python/quikr | 20 | 
1 files changed, 13 insertions, 7 deletions
| diff --git a/src/python/quikr b/src/python/quikr index 979f647..bae2b9f 100755 --- a/src/python/quikr +++ b/src/python/quikr @@ -14,18 +14,20 @@ def main():      You must supply a kmer and default lambda if using a custom trained \      matrix.") -    parser.add_argument("-f", "--fasta", help="fasta file", required=True) -    parser.add_argument("-o", "--output", help="output path (csv output)", required=True) -    parser.add_argument("-t", "--trained-matrix", help="trained matrix", required=True) -    parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000") +    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("-l", "--lamb", type=int, help="the lambda size. (default is 10,000")      parser.add_argument("-k", "--kmer", type=int, required=True, -        help="specifies which kmer to use, must be used with a custom trained database") - +        help="specifies the size of the kmer to use (the default is 6)")      args = parser.parse_args()      # our default lambda is 10,000      lamb = 10000 +    # our default kmer size is 6 +    kmer = 6      # Make sure our input exist      if not os.path.isfile(args.fasta): @@ -38,8 +40,12 @@ def main():      if args.lamb is not None:          lamb = args.lamb +    # use alternative kmer +    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, args.kmer, lamb) +    xstar = quikr.calculate_estimated_frequencies(args.fasta, trained_matrix, kmer, lamb)      np.savetxt(args.output, xstar, delimiter=",", fmt="%f")      return 0 | 
