return predictions def add_options(option_parser): "Add options for MEME to the option parser." pass if '__main__' == __name__: # # Set up the logging and options # import sys options, args = parse_options(add_options) # fasta = '/home/john/Data/NTNU-TF-search-dataset/datasets/model_real/M00724.fas' # fasta = os.path.abspath(os.path.join(os.path.dirname(__file__), '../fasta/T00759trimRM-test-x2.fa')) # fasta = os.path.abspath(os.path.join(os.path.dirname(__file__), '../fasta/T00759-tiny.fa')) fasta = '/home/john/Data/Tompa-data-set/Real/hm22r.fasta' # fasta = '/home/john/Data/GappedPssms/apr-2009/T99006trimRM.fa' # fasta = '/home/john/Data/GappedPssms/apr-2009/T99004trimRM.fa' options.output_dir = os.path.abspath(os.path.join('output', 'MEME')) # del options.max_num_sites # del options.min_num_sites method = Algorithm(options) predictions = method(fasta) from Bio.Motif.Parsers.MEME import MEMEParser parser = MEMEParser()
# # Parse the options # from optparse import OptionParser option_parser = OptionParser() option_parser.add_option( "--num-threads", dest="num_threads", default=3, type='int', help="Number of threads to run jobs on." ) option_parser.add_option("--data-sets", action="append") stem.add_options(option_parser) meme.add_options(option_parser) options = stem.parse_options(option_parser=option_parser) stem.turn_on_google_profiling_if_asked_for(options) # for each method and suite for method_name in method_names: for suite_name in suite_names: suite = suite_for_name(suite_name) method = method_for_name(method_name) predictions_by_dataset = [] import cookbook.function_as_task as F def do_work(task): method_name, suite_name, data_set, options = task logging.info('Running %s', task)
# # Set up the logging # import logging import sys from cookbook.script_basics import setup_logging setup_logging(__file__, level=logging.INFO) # show_environment() # # Set up options # import stempy options, args = stempy.parse_options(stempy.add_options) if len(args) != 0: raise RuntimeError('USAGE: %s <options>', sys.argv[0]) W = 8 fasta_file = '/home/john/Data/GappedPssms/apr-2009/T99006trimRM.fa' algorithm = stempy.Algorithm(options) algorithm.initialise(fasta_file) model = algorithm.create_default_model(W) model.prior_num_sites = 18.276144706645898 model.lambda_ = 0.00037315491488373951 model.bs.pssm.log_probs.values()[:] = [ [0.012967, 0.884511, 0.064057, 0.038465], [0.021795, 0.875048, 0.023177, 0.079979], [0.031394, 0.912065, 0.018154, 0.038387], [0.22118, 0.486244, 0.067231, 0.225346],
def time_per_iteration_per_base(timings): return timings['duration'] / timings['niters'] / test_data.get_num_w_mers(timings['dataset'], len(timings['seed'])) if '__main__' == __name__: def add_options(parser): parser.add_option("-f", "--force", action="store_true", help="Always run even if already have results.") # # parse options # options, args = stempy.parse_options( lambda parser: (add_options(parser), stempy.add_options( parser), test_data.add_options(parser)) ) if len(args) != 0: raise RuntimeError('USAGE: %s <options>', sys.argv[0]) run(options) # # Slice and dice data # h5file = tables.openFile(filename) timings_table = h5file.root.EtaStability.timings nsite_values = list(set(row['nsites'] for row in timings_table)) nsite_values.sort() time_per_iteration_per_base = [