def __init__(self, path, sample_name, n_spectra=None, deconvoluted=True):
if n_spectra is None:
n_spectra = 2e5
super(MzMLScanStorageHandler, self).__init__()
self.path = path
self.handle = open(path, 'wb')
self.serializer = MzMLScanSerializer(self.handle,
n_spectra,
sample_name=sample_name,
deconvoluted=deconvoluted)
def __init__(self, path, sample_name, n_spectra=None, deconvoluted=True):
if n_spectra is None:
n_spectra = 2e5
super(MzMLScanCacheHandler, self).__init__()
self.path = path
self.handle = open(path, 'wb')
self.serializer = MzMLScanSerializer(
self.handle, n_spectra, sample_name=sample_name,
deconvoluted=deconvoluted)
class MzMLScanCacheHandler(ScanCacheHandlerBase):
def __init__(self, path, sample_name, n_spectra=None, deconvoluted=True):
if n_spectra is None:
n_spectra = 2e5
super(MzMLScanCacheHandler, self).__init__()
self.path = path
self.handle = open(path, 'wb')
self.serializer = MzMLScanSerializer(
self.handle, n_spectra, sample_name=sample_name,
deconvoluted=deconvoluted)
def _get_sample_run(self):
return self.serializer.sample_run
def register_parameter(self, name, value):
self.serializer.add_processing_parameter(name, value)
@classmethod
def configure_storage(cls, path=None, name=None, source=None):
if path is not None:
if name is None:
sample_name = os.path.basename(path)
else:
sample_name = name
else:
path = "processed.mzML"
if source is not None:
reader = MSFileLoader(source.scan_source)
n_spectra = len(reader.index)
deconvoluting = source.deconvoluting
inst = cls(path, sample_name, n_spectra=n_spectra, deconvoluted=deconvoluting)
try:
description = reader.file_description()
except AttributeError:
description = FileInformation()
source_file_metadata = MetadataSourceFile.from_path(source.scan_source)
inst.serializer.add_file_information(description)
try:
inst.serializer.remove_file_contents("profile spectrum")
except KeyError:
pass
inst.serializer.add_file_contents("centroid spectrum")
if source_file_metadata not in description.source_files:
inst.serializer.add_source_file(source_file_metadata)
try:
instrument_configs = reader.instrument_configuration()
for config in instrument_configs:
inst.serializer.add_instrument_configuration(config)
except Exception as e:
log_handle.error(
"An error occurred while writing instrument configuration", e)
for trans in source.ms1_peak_picking_args.get("transforms"):
inst.register_parameter("parameter: ms1-%s" % trans.__class__.__name__, repr(trans))
if deconvoluting:
if source.ms1_deconvolution_args.get("averagine"):
inst.register_parameter(
"parameter: ms1-averagine", repr(source.ms1_deconvolution_args.get("averagine")))
if source.ms1_deconvolution_args.get("scorer"):
inst.register_parameter(
"parameter: ms1-scorer", repr(source.ms1_deconvolution_args.get("scorer")))
if source.ms1_averaging > 0:
inst.register_parameter("parameter: ms1-averaging", repr(source.ms1_averaging))
if source.ignore_tandem_scans:
inst.register_parameter("parameter: ignore-tandem-scans", "")
if source.extract_only_tandem_envelopes:
inst.register_parameter("parameter: extract-only-tandem-envelopes", "")
if source.msn_peak_picking_args is not None:
for trans in source.msn_peak_picking_args.get("transforms"):
inst.register_parameter("parameter: msn-%s" % trans.__class__.__name__, repr(trans))
if deconvoluting:
if source.msn_deconvolution_args.get("averagine"):
inst.register_parameter(
"parameter: msn-averagine", repr(source.msn_deconvolution_args.get("averagine")))
if source.msn_deconvolution_args.get("scorer"):
inst.register_parameter(
"parameter: msn-scorer", repr(source.msn_deconvolution_args.get("scorer")))
data_processing = inst.serializer.build_processing_method()
inst.serializer.add_data_processing(data_processing)
else:
n_spectra = 2e5
inst = cls(path, sample_name, n_spectra=n_spectra)
# Force marshalling of controlled vocabularies early.
inst.serializer.writer.param("32-bit float")
return inst
def save_bunch(self, precursor, products):
self.serializer.save_scan_bunch(ScanBunch(precursor, products))
def complete(self):
self.save()
self.serializer.complete()
try:
self.serializer.format()
except OSError as e:
if e.errno == 32:
log_handle.log("Could not reformat the file in-place")
except Exception:
import traceback
traceback.print_exc()
class MzMLScanStorageHandler(ScanStorageHandlerBase):
def __init__(self, path, sample_name, n_spectra=None, deconvoluted=True):
if n_spectra is None:
n_spectra = 2e5
super(MzMLScanStorageHandler, self).__init__()
self.path = path
self.handle = open(path, 'wb')
self.serializer = MzMLScanSerializer(self.handle,
n_spectra,
sample_name=sample_name,
deconvoluted=deconvoluted)
def _get_sample_run(self):
return self.serializer.sample_run
def register_parameter(self, name, value):
self.serializer.add_processing_parameter(name, value)
@classmethod
def configure_storage(cls, path=None, name=None, source=None):
if path is not None:
if name is None:
sample_name = os.path.basename(path)
else:
sample_name = name
else:
path = "processed.mzML"
if source is not None:
reader = MSFileLoader(source.scan_source)
n_spectra = len(reader.index)
deconvoluting = source.deconvoluting
inst = cls(path,
sample_name,
n_spectra=n_spectra,
deconvoluted=deconvoluting)
try:
description = reader.file_description()
except AttributeError:
description = FileInformation()
source_file_metadata = MetadataSourceFile.from_path(
source.scan_source)
inst.serializer.add_file_information(description)
try:
inst.serializer.remove_file_contents("profile spectrum")
except KeyError:
pass
inst.serializer.add_file_contents("centroid spectrum")
if source_file_metadata not in description.source_files:
inst.serializer.add_source_file(source_file_metadata)
try:
instrument_configs = reader.instrument_configuration()
for config in instrument_configs:
inst.serializer.add_instrument_configuration(config)
except Exception as e:
inst.error(
"An error occurred while writing instrument configuration",
e)
for trans in source.ms1_peak_picking_args.get("transforms", []):
inst.register_parameter(
"parameter: ms1-%s" % trans.__class__.__name__,
repr(trans))
if deconvoluting:
if source.ms1_deconvolution_args.get("averagine"):
inst.register_parameter(
"parameter: ms1-averagine",
repr(source.ms1_deconvolution_args.get("averagine")))
if source.ms1_deconvolution_args.get("scorer"):
inst.register_parameter(
"parameter: ms1-scorer",
repr(source.ms1_deconvolution_args.get("scorer")))
if source.ms1_averaging > 0:
inst.register_parameter("parameter: ms1-averaging",
repr(source.ms1_averaging))
if source.ignore_tandem_scans:
inst.register_parameter("parameter: ignore-tandem-scans",
"")
if source.extract_only_tandem_envelopes:
inst.register_parameter(
"parameter: extract-only-tandem-envelopes", "")
if source.msn_peak_picking_args is not None:
for trans in source.msn_peak_picking_args.get(
"transforms", []):
inst.register_parameter(
"parameter: msn-%s" % trans.__class__.__name__,
repr(trans))
if deconvoluting:
if source.msn_deconvolution_args.get("averagine"):
inst.register_parameter(
"parameter: msn-averagine",
repr(source.msn_deconvolution_args.get("averagine")))
if source.msn_deconvolution_args.get("scorer"):
inst.register_parameter(
"parameter: msn-scorer",
repr(source.msn_deconvolution_args.get("scorer")))
data_processing = inst.serializer.build_processing_method()
inst.serializer.add_data_processing(data_processing)
else:
n_spectra = 2e5
inst = cls(path, sample_name, n_spectra=n_spectra)
# Force marshalling of controlled vocabularies early.
inst.serializer.writer.param("32-bit float")
return inst
def save_bunch(self, precursor, products):
self.serializer.save_scan_bunch(ScanBunch(precursor, products))
def complete(self):
self.save()
self.serializer.close()