from glycresoft_ms2_classification.structure import sequence
from glycresoft_ms2_classification.structure import glycans
from glycresoft_ms2_classification.structure import modification
from glycresoft_ms2_classification.prediction_tools.false_discovery_rate import random_glycopeptide
from glycresoft_ms2_classification.utils import config_loader
config_file = "test.config"
config = ConfigParser()
config.read(config_file)
#multiprocessing_util.log_to_stderr()
config_loader.load("base.config")
def try_type(obj):
try:
return int(obj)
except:
try:
return float(obj)
except:
return str(obj)
# class DebugPipeline(unittest.TestCase):
# db_file_name = "test_data/Phil-82-Chemotrypsin/ResultOf20150428_04_isos.db"
# ms1_matching_output_file = "test_data/Phil-82-Chemotrypsin/ResultOf20150428_04_isos.csv"
# ms2_decon_file = "test_data/Phil-82-Chemotrypsin/20150428_04_isos_individual_scans_processed.yaml"
# glycosylation_sites_file = "test_data/Phil-82-Chemotrypsin/Phil82-glycosites.txt"
def main():
import argparse
app = argparse.ArgumentParser()
subparsers = app.add_subparsers()
app.add_argument("-c", "--config", type=str, default=None, required=False, help="Path to configuration file")
app.add_argument(
"-d", "--debug", action='store_true', default=False, required=False)
app.add_argument("-n", "--n-processes", type=int, action="store",
default=4, help="Number of procresses to use")
# BUILD MODEL
build_model_app = subparsers.add_parser(
"build-model", help="Build a model and prepare it to be labeled")
build_model_app.add_argument(
"--parameter-file", action="store", default=None)
build_model_app.add_argument(
"--ms1-results-file", action="store", required=True)
build_model_app.add_argument(
"--glycosylation-sites-file", action="store", required=True)
build_model_app.add_argument(
"-e", "--enzyme", action="store", help="Name of the enzyme used")
build_model_app.add_argument("-p", "--protein-prospector-xml", action="store", help="path to msdgist XML file.\
Instead of --enzyme,--constant_modifications and --variable_modifications")
build_model_app.add_argument(
"--deconvoluted-spectra-file", action="store", required=True)
build_model_app.add_argument("--method", action="store", default="full_random_forest",
choices=set(PrepareModelTask.method_table),
help="Select the model method to use for classification")
build_model_app.add_argument(
"--ms1-match-tolerance", type=float, action="store",
default=match_ions2.ms1_tolerance_default,
help="Mass Error Tolerance for matching MS1 masses in PPM")
build_model_app.add_argument(
"--ms2-match-tolerance", type=float, action="store",
default=match_ions2.ms2_tolerance_default,
help="Mass Error Tolerance for matching MS2 masses in PPM")
build_model_app.add_argument(
"--constant-modification-list", type=str, action="append", default=None,
help="Pass the list of constant modifications to include in the sequence search space")
build_model_app.add_argument(
"--variable-modification-list", type=str, action="append", default=None,
help="Pass the list of variable modifications to include in the sequence search space")
build_model_app.add_argument("--out", action="store", default=None)
build_model_app.set_defaults(func=build_model_app_function)
# CLASSIFY WITH MODEL
classify_with_model_app = subparsers.add_parser(
"classify-with-model", help="Classify a data set using a labeled model")
classify_with_model_app.add_argument(
"--parameter-file", action="store", default=None)
classify_with_model_app.add_argument(
"--ms1-results-file", action="store", required=True)
classify_with_model_app.add_argument(
"--glycosylation-sites-file", action="store", required=True)
classify_with_model_app.add_argument(
"--deconvoluted-spectra-file", action="store", required=True)
classify_with_model_app.add_argument(
"--method", action="store", default="full_random_forest",
choices=set(ClassifyTargetWithModelTask.method_table),
help="Select the model method to use for classification")
classify_with_model_app.add_argument("--model-file",
action="store", default="naive", required=True)
classify_with_model_app.add_argument(
"--ms1-match-tolerance", type=float, action="store",
default=match_ions2.ms1_tolerance_default,
help="Mass Error Tolerance for matching MS1 masses in PPM")
classify_with_model_app.add_argument(
"--ms2-match-tolerance", type=float, action="store",
default=match_ions2.ms2_tolerance_default,
help="Mass Error Tolerance for matching MS2 masses in PPM")
classify_with_model_app.add_argument(
"--constant-modification-list", type=str, action="append", default=None,
help="Pass the list of constant modifications to include in the sequence search space")
classify_with_model_app.add_argument(
"--variable-modification-list", type=str, action="append", default=None,
help="Pass the list of variable modifications to include in the sequence search space")
classify_with_model_app.add_argument(
"-e", "--enzyme", action="store", help="Name of the enzyme used")
classify_with_model_app.add_argument("-p", "--protein-prospector-xml", action="store", help="path to msdgist\
XML file. Instead of --enzyme,--constant_modifications and --variable_modifications")
classify_with_model_app.add_argument("--out", action="store", default=None)
classify_with_model_app.set_defaults(func=classify_with_model_app_function)
classify_with_model_app.add_argument("--decoy-to-real-ratio", action="store", default=1, type=int, help="Number of\
decoys per prediction sequence")
classify_with_model_app.add_argument("--random-only", action="store_true", default=False, help="Don't\
generate shuffled decoys, only randomized sequences")
classify_with_model_app.add_argument("--prefix-length", default=0, required=False, type=int,
help="Length of peptide prefix to preserve when generating\
random glycopeptides by shuffling.")
classify_with_model_app.add_argument("--suffix-length", default=1, required=False, type=int,
help="Length of peptide suffix to preserve when generating\
random glycopeptides by shuffling.")
reclassify_with_model_app = subparsers.add_parser(
"reclassify-with-model", help="Rerun classification of an matched ion data file")
reclassify_with_model_app.add_argument("--target-file", action="store", default=None, required=True,
help="Matched ion data file to re-classify")
reclassify_with_model_app.add_argument(
"--method", action="store", default="full_random_forest",
choices=set(ModelDiagnosticsTask.method_table),
help="Select the model method to use for classification")
reclassify_with_model_app.add_argument("--model-file",
action="store", default="naive", required=True)
reclassify_with_model_app.set_defaults(
func=reclassify_with_model_app_function)
# Simple Diagnostic plots for testing a model on itself
model_diagnostics_app = subparsers.add_parser(
"model-diagnostics", help="Given a labeled model, calculate model diagnostics")
model_diagnostics_app.add_argument(
"--method", action="store", default="full_random_forest",
choices=set(ModelDiagnosticsTask.method_table),
help="Select the model method to use for classification")
model_diagnostics_app.add_argument("--model-file",
action="store", default="naive", required=True)
model_diagnostics_app.set_defaults(func=model_diagnostics_app_function)
# Stand alone False Discovery Rate Calculations. Either reuse decoys or create them anew. May also
# include re-scoring for the predictions and decoys
calculate_fdr_app = subparsers.add_parser("calculate-fdr", help="Given a set of predictions from a collection\
of data, estimate the false discovery rate")
calculate_fdr_app.add_argument("--predictions-file", required=True, help="Path to predictions file generated by\
classify-with-model, build-model, or reclassify-with-model")
calculate_fdr_app.add_argument(
"--deconvoluted-spectra-file", action="store", default=None)
calculate_fdr_app.add_argument(
"--method", action="store", default="full_random_forest",
choices=set(ModelDiagnosticsTask.method_table),
help="Select the model method to use for classification")
calculate_fdr_app.add_argument(
"--decoys-file", default=None, help="A file containing precomputed decoy sequence matches")
calculate_fdr_app.add_argument("--decoy-to-real-ratio", action="store", default=1, type=int, help="Number of\
decoys per prediction sequence")
calculate_fdr_app.add_argument("--random-only", action="store_true", default=False, help="Don't\
generate shuffled decoys, only randomized sequences")
calculate_fdr_app.add_argument("--prefix-length", default=0, required=False, type=int,
help="Length of peptide prefix to preserve when generating\
random glycopeptides by shuffling.")
calculate_fdr_app.add_argument("--suffix-length", default=1, required=False, type=int,
help="Length of peptide suffix to preserve when generating\
random glycopeptides by shuffling.")
calculate_fdr_app.add_argument("--model-file",
action="store", default="naive", required=False)
calculate_fdr_app.add_argument("--out", action="store", default=None)
calculate_fdr_app.set_defaults(func=calculate_fdr_app_function)
try:
args = app.parse_args()
args = args.__dict__
func = args.pop("func")
debug = args.pop("debug", os.environ.get("GLYCRESOFT_DEBUG", False))
config_path = args.pop("config")
if config_path is not None:
config_loader.load(config_path)
logger.debug("Config: %r", json.dumps(config_loader.gather(), indent=4))
if 'constant_modification_list' in args:
args['constant_modification_list'] = uri_decode_list(
args['constant_modification_list'])
if 'variable_modification_list' in args:
args['variable_modification_list'] = uri_decode_list(
args['variable_modification_list'])
if 'protein_prospector_xml' in args and args["protein_prospector_xml"] is not None:
ms_digest = MSDigestParameters.parse(args["protein_prospector_xml"])
args[
"constant_modification_list"] = ms_digest.constant_modifications
args[
"variable_modification_list"] = ms_digest.variable_modifications
args["enzyme"] = ms_digest.enzyme
args.pop("protein_prospector_xml", None)
param_file = args.pop("parameter_file", None)
if param_file is not None:
params = load_parameters_from_json(param_file)
args.update(params)
if args['out'] is not None and args['out'][0] != os.sep:
args['out'] = ".{0}{1}".format(os.sep, args['out'])
logger.info(json.dumps(args, indent=4))
if debug:
pass
else:
#atexit.register(lambda: clean_up_files(*intermediary_files))
pass
logger.debug("Entering main program")
func(**args)
except GlycReSoftInterprocessCommunicationException, e:
logger.debug("An error occurred", exc_info=e)
exit(e.errcode)
