diff options
| author | Calvin <calvin@EESI> | 2013-03-06 16:58:47 -0500 | 
|---|---|---|
| committer | Calvin <calvin@EESI> | 2013-03-06 16:58:47 -0500 | 
| commit | f1f0df1e32e1892fa07ae616bc76ac215d3c5dec (patch) | |
| tree | 1be5bfb3e41a049ed9e131449a03f1181765abc9 | |
| parent | 0fc232a2f5347b9b2577c2597c9344ae7f4be540 (diff) | |
removed debugging statements
| -rwxr-xr-x | quikr.py | 5 | ||||
| -rwxr-xr-x | quikr_train.py | 2 | 
2 files changed, 1 insertions, 6 deletions
| @@ -37,7 +37,7 @@ def main():          parser.error( "Input fasta file not found")      if not os.path.isfile(args.trained_matrix): -        parser.error("custom trained matrix not found") +        parser.error("Custom trained matrix not found")      # use alternative lambda      if args.lamb is not None: @@ -72,10 +72,8 @@ def quikr(input_fasta_location, trained_matrix, kmer, default_lambda):    # We use the count program to count ____    if uname == "Linux" and os.path.isfile("./count-linux"): -    print "Detected Linux"      count_input = Popen(["./count-linux", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE)     elif uname == "Darwin" and os.path.isfile("./count-osx"): -    print "Detected Mac OS X"       count_input = Popen(["count-osx", "-r", str(kmer), "-1", "-u", input_fasta_location], stdout=PIPE)  @@ -91,7 +89,6 @@ def quikr(input_fasta_location, trained_matrix, kmer, default_lambda):    xstar, rnorm = scipy.optimize.nnls(trained_matrix, counts)  -    xstar = xstar / xstar.sum(0)     return xstar diff --git a/quikr_train.py b/quikr_train.py index b86afb9..5089e2a 100755 --- a/quikr_train.py +++ b/quikr_train.py @@ -48,10 +48,8 @@ def quikr_train(input_file_location, kmer):    uname = platform.uname()[0]    if uname == "Linux":  -    print "Detected Linux"      input_file = Popen(["./probabilities-by-read-linux", str(kmer), input_file_location, kmer_file_name], stdout=PIPE)     elif uname == "Darwin": -    print "Detected Mac OS X"       input_file = Popen(["./probabilities-by-read-osx", str(kmer), input_file_location, kmer_file_name])     # load and  normalize the matrix by dividing each element by the sum of it's column. | 
