def generate_decoy_match_results(scored_matches_path, decon_data, model_file_path,
prefix_len=0, suffix_len=0, ms1_tolerance=1e-5,
ms2_tolerance=2e-5, num_decoys_per_real_mass=1.0,
method="full_random_forest", random_only=False,
method_init_args=None, method_fit_args=None,
n_processes=6, outfile_path=None):
logger.info("Creating Decoys")
predictions = classify_matches.prepare_model_file(scored_matches_path)
decoy_file_name = make_decoys_from_search_space.taskmain(
predictions.metadata["db_file_name"],
prefix_len=prefix_len, suffix_len=suffix_len,
n_processes=n_processes, out=outfile_path)
logger.info("Decoy Ion Space: %s", decoy_file_name)
match_ions2.match_frags(
decoy_file_name, decon_data,
ms1_tolerance, ms2_tolerance,
n_processes=n_processes)
logger.info("Decoy Matches Done")
postprocess_file, postprocess_data = postprocess2.main(decoy_file_name)
logger.info("Decoys Postprocessed: %s", postprocess_file)
classifier = classify_matches.ClassifyTargetWithModelTask(model_file_path, postprocess_file,
method=method,
method_init_args=method_init_args,
method_fit_args=method_fit_args)
decoy_matches_path = classifier.run()
return decoy_matches_path
def match_deconvoluted_ions(theoretical_ion_space, deconvoluted_spectra,
ms1_match_tolerance, ms2_match_tolerance, n_processes):
try:
path = match_ions2.match_frags(
theoretical_ion_space, deconvoluted_spectra,
ms1_match_tolerance, ms2_match_tolerance, split_decon_data=True,
n_processes=n_processes,
outfile=None)
return path
except match_ions2.NoIonsMatchedException, e:
raise NoIonsMatchedException(str(e))