diff options
| -rwxr-xr-x | multifasta_to_otu.py | 35 |
1 files changed, 26 insertions, 9 deletions
diff --git a/multifasta_to_otu.py b/multifasta_to_otu.py index ef7f5f6..ac883ca 100755 --- a/multifasta_to_otu.py +++ b/multifasta_to_otu.py @@ -29,7 +29,8 @@ def main(): parser.add_argument("-i", "--input-directory", help="directory containing fasta files", required=True) parser.add_argument("-o", "--otu-table", help="otu_table", required=True) - parser.add_argument("-t", "--trained-matrix", help="otu_table", required=True) + parser.add_argument("-t", "--trained-matrix", help="your trained matrix ", required=True) + parser.add_argument("-f", "--trained-fasta", help="the fasta file used to train your matrix", required=True) parser.add_argument("-d", "--output-directory", help="quikr output directory", required=True) parser.add_argument("-l", "--lamb", type=int, help="the default lambda value is 10,000") parser.add_argument("-k", "--kmer", type=int, help="specifies which kmer to use, default=6") @@ -72,22 +73,38 @@ def main(): fasta_list = os.listdir(args.input_directory) # Queue up and run our quikr functions. - pool = Pool(processes=jobs) - results = pool.map(quikr_call, fasta_list) +# pool = Pool(processes=jobs) +# results = pool.map(quikr_call, fasta_list) - # Create a dictionary and load up our keys + # Create an array of headers records = [] - - trained_matrix_headers = open(args.trained_matrix, "rU") + trained_matrix_headers = open(args.trained_fasta, "rU") for record in SeqIO.parse(trained_matrix_headers, "fasta"): - records.append((record.id, 0)) + records.append(record.id) + trained_matrix_headers.close() - records = dict(records) + final_output = np.zeros((len(records), len(fasta_list))) + print len(fasta_list) + # load the keys with values from each fasta result + for fasta, fasta_it in map(None, fasta_list, range(len(fasta_list))): + fasta_file = open(input_directory + fasta, "rU") + sequences = list(SeqIO.parse(fasta_file, "fasta")) + number_of_sequences = len(sequences) + fasta_file.close() + print number_of_sequences + + proportions = np.loadtxt(output_directory + fasta); + for proportion, proportion_it in map(None, proportions, range(len(proportions))): + if(round(proportion * number_of_sequences) is not 0): + print str(fasta_it) + " " + str(proportion_it) + final_output[fasta_it, proportion_it] = proportion * number_of_sequences + + np.savetxt(args.otu_table, final_output, delimiter=",", fmt="%d") + - # load the keys with values from each fasta result # Write the otu table return 0 |