def doEnzyme(self, fileiterator, datfile):
'''
@brief reads the enzyme section and puts the data into a dictionary, data is passed to the datfile object
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
try:
isbound = self.isBoundary
self.logs.datlog.info('Loading and Parsing Enzyme')
while 1:
allparams = {}
while not isbound(self.cargo):
match = rx_enzymeline.search(self.cargo)
if match:
allparams[match.group('key')] = match.group('value')
elif len(self.cargo) > 2:
allparams['Side'] = self.cargo[:-1]
self.cargo = fileiterator.next()
datfile.hdfMascot.writeParameters(allparams, 'enzyme')
yield ('boundary', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Called from doEnzyme')
raise
def doETsummary(self, fileiterator, datfile):
'''
@brief reads the summary section and puts the data into a dictionary, data is passed to the datfile object
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
try:
isbound = self.isBoundary
self.logs.datlog.info('Loading and Parsing et_summary')
while 1:
allparams = {}
while not isbound(self.cargo):
match = rx_summary.search(self.cargo)
if match:
allparams[match.group('key')] = match.group('value')
self.cargo = fileiterator.next()
datfile.addETsummary(allparams)
yield ('boundary', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Called from doETsummary')
raise
def doIndex(self, fileiterator, datfile):
'''
@brief parses index section of the dat file
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
try:
isbound = self.isBoundary
self.logs.datlog.info('Loading and Parsing index')
while 1:
index = []
while not isbound(self.cargo):
try:
key, value = self.cargo.split('=')
index.append((key, int(value)))
except:
pass
self.cargo = fileiterator.next()
datfile.addIndex(index)
yield ('boundary', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Called from doIndex')
raise
def doQuery(self, fileiterator, datfile):
'''
@brief reads the query sections and puts the data into a dictionary, data is passed to the datfile object
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
query = 'None'
try:
isbound = self.isBoundary
while 1:
allparams = {}
match = rx_query.search(self.cargo)
query = match.group('ID')
# if match.group('ID') == '1':
# self.logs.datlog.info('Loading and Parsing queries')
allparams['query'] = query
while not isbound(self.cargo):
match = rx_paramline.search(self.cargo)
if match:
allparams[match.group('key')] = match.group('value')
self.cargo = fileiterator.next()
datfile.addQuerySpectra(allparams)
qry = int(query)
datfile.spectra[qry]['spec_id'] = int(
datfile.spectra[qry]['msmsid'][1:])
datfile.spectra[qry]['rt'] = float(
datfile.spectra[qry]['start'])
yield ('postquery', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'doQuery: last query = %s' % query)
raise
def __init__(self, filedic, hdf5, cfg, logs, searchID, quantMethod):
'''
@brief initialise the Datfile object
@param filedic <dictionary>: containig the full path to the dat file and other file data
@param hdf5 <hdf5 object>: containing all the writing methods for hdf5 output
@param cfg <cfg object>: containing all the running parameters
@param dbobj <db object>: linking to the meet database
'''
self.filedic = filedic
self.searchid = searchID
self.quantMethod = quantMethod
datfilename = filedic['dat']
if datfilename == str(None):
raise ExHa.FileNotFoundException('no resulting datfile found with search')
self.datfilename = datfilename
self.cfg = cfg
self.logs = logs
self.hdfMascot = hdf5
self.dataDir = filedic['datpath'].parent
self.spectra = {}
self.peptidecounter = 0
self.failedsequences = 0
self.sequences = {}
self.seq2acc = {}
self.hookpeps = []
self.hookppm = []
self.analtimes = dict(first=40, last=0, early=[])
self.stats = dict(numpeps=0, numfailedpeps=0, numspectra_nopeps=0)
def startParsing(self):
'''
startParsing(string) -> integer
Parses the dat file given by datfilepath.
'''
datPath = str(self.datfileobj.filedic['datpath'])
self.logs.datlog.info('Starting parsing file %s' % datPath)
fin = open(datPath, 'r')
try:
self.cargo = fin.next()
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Empty dat file.')
raise
def doPostQuery(self, fileiterator, datfile):
'''
@brief perfoms tasks after all the query data is loaded
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
try:
datfile.doPostQuery()
while 1:
# Used to trigger the QC of the spectrum if required.
yield ('boundary', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Called from doPostQuery')
raise
def doETpeptides(self, fileiterator, datfile):
'''
@brief reads the peptide section and puts the data into a dictionary, data is passed to the datfile object
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
key = 'None'
try:
isbound = self.isBoundary
self.logs.datlog.info('Loading and Parsing et_peptides')
collection = {}
lastquery = ''
# create the etpeptides table in the HDF5 file
datfile.createETpeptidesTable()
while 1:
while not isbound(self.cargo):
match = rx_peptide.search(self.cargo)
if match:
value = match.group('value')
key = match.group('key')
if value == '-1':
# no matching peptide
datfile.stats['numspectra_nopeps'] += 1
else:
query = key.split('_')[0]
if query != lastquery:
lastquery = query
if collection:
# add the query number to addpeptides
datfile.addETpeptides(collection)
collection = {}
collection[key] = value
self.cargo = fileiterator.next()
# add last peptide
if collection:
# add the query number to addpeptides
datfile.addETpeptides(collection)
yield ('postpeptides', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'doETpeptides, last peptide = %s' % key)
raise
def doUnimod(self, fileiterator, datfile):
'''
@brief reads the masses section and puts the data into a dictionary, data is passed to the datfile object
@param fileiterator <file object>: linked to the dat file
@param datfile <datfile object>: containing all the dat file data and processing methods
'''
try:
isbound = self.isBoundary
self.logs.datlog.info('Loading and Parsing Unimod data')
while 1:
xml = []
while not isbound(self.cargo):
xml.append(self.cargo)
self.cargo = fileiterator.next()
datfile.addUnimod(xml)
yield ('boundary', self.cargo)
except Exception, genEx:
# catch exceptions and add some context data
ExHa.addContext(genEx, 'Called from doUnimod')
raise
ret = cfg.evaluateCommandLineArgs(sys.argv)
try:
cfg.scalePpmMda()
dataDir = cfg.parameters['runtime']['datadir']
logParam = cfg.parameters['logging']
logPath = Path(dataDir.joinpath(logParam['logdir']))
if not logPath.exists():
logPath.mkdir(parents=True)
logFile = logPath.joinpath(logParam['logfile'])
logger = Logger(logFile, logParam['loglevel'], logParam['screenlevel'],
False)
logger.setMascotParserLogs()
jobcontrol(cfg, logger)
except ExHa.UsageError as useEx:
ExHa.reformatException(useEx)
print useEx.context
except Exception as genEx:
ExHa.reformatException(genEx)
errorFile = Path(cfg.parameters['runtime']['hdf5file']).stem + '.error'
ExHa.exportError2File(
genEx, cfg.parameters['runtime']['datadir'].joinpath(errorFile))
if logs:
logs.datlog.warning(ExHa.oneLineRepr(genEx))
else:
print ExHa.multiLineRepr(genEx)
def run(self, jobObj):
"""
@brief run the analysis
@param job <object>: containing all the job data
"""
# needs to catch exceptions here so that LUX doesn't recieve an exception
hdf = ''
xraw = ''
tempFile = ''
config = self.cfg
logs = self.logs
try:
hdf = ''
maxspec = jobObj.args['maxspec']
logs.log.info("Starting PyMSSafe runner")
# assign objects to the file interfaces
rawpath = jobObj.srcpth.absolute()
namebase = rawpath.stem
hdfpath = jobObj.dstpth.absolute().joinpath(namebase + '.hdf5')
runname = namebase
tempFile = jobObj.dstpth.absolute().joinpath(
str(config.parameters['runtime']['pid']))
if tempFile.exists():
tempFile.unlink()
logs.log.info('started job: %s' % rawpath.name)
watch = Stopwatch()
if not rawpath.exists():
logs.log.info('could not find file: %s' % str(rawpath))
logs.log.info('Stopped')
return {
'code': 1,
'error': 'could not find file: %s' % str(rawpath)
}
# get quant information if file is quan type
quantMeth = self.extractQuant(jobObj.srcpth.name.upper())
if quantMeth == -1:
raise ExHa.QuantificationMethodError(
'Unable to find "%s" quantification method' %
self.cfg.parameters['runtime']['quant'].upper())
elif quantMeth == -2:
raise ExHa.QuantificationMethodError(
'Unable to find valid quantification method in file name (%s)'
% jobObj.srcpth.name.upper())
xraw = XRawFile(str(rawpath))
if config.parameters['general']['skipscanevents']:
xraw.skipScanEvents = config.parameters['general'][
'skipscanevents']
# opens hdf5 file for writing
hdf = hdf5Base(hdfpath, True, True)
hdf.appendOpen()
self.createHDFtables(hdf)
# save the config entries
hdf.appendRows('/rawdata/config', config.convertConfig())
# create datamanager object
config.rawfile = jobObj.srcpth
dataman = Datamanager(xraw, config, logs, hdf, quantMeth,
str(tempFile))
dataman.maxspec = maxspec
dataman.addLUXdata(jobObj, config.parameters['runtime']['pid'])
# run the analysis in the datamanager
ok = dataman.processSpectra(quantMeth, watch, runname)
if ok['code'] != 0:
raise ExHa.SpectraProcessingError(ok['error'])
# run the XIC generation
logs.log.info('Processing XIC data for %d MS/MS events' %
len(dataman.specs))
ok = dataman.processXICs()
if ok['code'] != 0:
raise ExHa.XICprocessingError(ok['error'])
watch.rec('Processing XIC')
logs.log.info('Writing HDF5 indexes')
hdf.indexTable('/rawdata/msmsheader', ['spec_id'])
hdf.close()
xraw = ''
hdf = ''
watch.stop()
logs.log.info('job took: %s' % watch.oneLineFormat())
tempFile.unlink()
except ExHa.MSdataConsistancyError, msg:
self.shutdown(hdf, xraw, tempFile)
logs.log.warning('error: pyMSsafe Data error: %s' % msg)
return {'code': 2, 'error': 'pyMSsafe Data error: %s' % msg}
def jobcontrol(cfg, logs):
'''
@brief takes filepath, executes parse ... import
@param cfg <ConfigManager>: all the configuartion parameters including the sample data
@param logs <loggingManager>: to control the logging of events
'''
try:
msg = 'Setting files'
datFileName = cfg.parameters['runtime']['datfile']
hdf5FileName = cfg.parameters['runtime']['hdf5file']
logs.setuplog.info('started job: %s' % datFileName)
watch = Stopwatch()
filedic = dict(dat=datFileName,
datpath=dataDir.joinpath(datFileName),
hdf5=hdf5FileName,
hdf5path=dataDir.joinpath(hdf5FileName))
searchID = int(filedic['datpath'].stem[1:])
hdfMascot = HDF5Mascot(hdfFilePath=filedic['hdf5path'])
hdfMascot.appendOpen()
importGroup = hdfMascot.checkDatFilePresence(filedic['dat'])
# test the quantification method already in the hdf5 file
msg = 'Setting quantification method'
quantMethID = hdfMascot.getH5DFQuantMeth()
quantHandler = QuantMethods()
quantMethod = quantHandler.getMethodByID(quantMethID)
except Exception as genEx:
ExHa.addContext(genEx, 'jobcontrol Error: %s' % msg)
raise
try:
# control the deletion of existing data
msg = 'Deleting existing HDF5 data'
hdfMascot.deleteAllMascotImports(0)
# if overwrite:
# # delte all Mascot data whatever the source
# hdfMascot.deleteAllMascotImports(0)
# elif importGroup:
# # delete previous version of this data
# hdfMascot.deleteMascotImport(importGroup)
importGroup = filedic['dat'].replace('.', '_')
hdfMascot.createTables(importGroup, searchID, 0)
hdfMascot.writeConfig(cfg.convertConfig())
datfile = Datfile(filedic, hdfMascot, cfg, logs, searchID, quantMethod)
logs.setuplog.info('searchid: %d, dat file: %s, hdf5 file: %s ' %
(searchID, datfile.datfilename, filedic['hdf5']))
msg = 'Parsing data'
datparser = DatParser(datfile, cfg, logs)
datparser.startParsing()
watch.rec('parser')
# post parsing processing
logs.qclog.info('Effective Run-time analysis')
datfile.doTimeAnalysis()
msg = 'Find top protein hit'
logs.qclog.info(msg)
# tophit = db.getMascotTopHit(searchID)
if datfile.seq2acc:
datfile.findBestProtein()
except Exception as genEx:
ExHa.addContext(genEx, 'jobcontrol Error: %s' % msg)
raise
finally:
hdfMascot.close()
logs.setuplog.info('Closing HDF5')
hdfMascot.close()
watch.rec('processing')
watch.stop()
logs.setuplog.info('job took %s' % watch.format())
return
class mgftools:
def __init__(self, hdf5file):
"""
@brief initialises the mgftools class
@param hdf5file <string/path>: path for the hdf5 file to analyse
"""
self.cfgFilters = cfg.parameters['msmsfilters']
self.maxint = 0
self.proton = cfg.parameters['general']['proton']
self.neutron = cfg.parameters['general']['neutron']
self.hdf5 = hdf5Base(hdf5file)
self.hdf5.readOpen()
self.filters = dict(ten=self.tenpercent,
zone=self.zonefilter,
repion=self.repionfilter,
deconv=self.deconvolute,
mascot=self.mascot,
neutralloss=self.neutrallossfilter,
multi=self.multichargefilt,
immonium=self.immoniumfilter,
none='')
self.isos = self.hdf5.readTable('/rawdata/isotopes')
frags = self.getActivationTypes()
if len(frags) == 1:
if frags == ['HCD']:
# hcd olny method
usefilts = self.cfgFilters['filt_hcd']
self.remove = self.cfgFilters['rem_hcd']
else:
# CID/PQD only method
usefilts = self.cfgFilters['filt_other']
self.remove = self.cfgFilters['rem_other']
else:
# mixed method
usefilts = self.cfgFilters['filt_mixed_hcd_other']
self.remove = self.cfgFilters['rem_mixed_hcd_other']
if len(self.isos) == 0:
repionIdx = -1
for idx in range(len(usefilts)):
if usefilts[idx] == 'repion':
repionIdx = idx
break
if repionIdx != -1:
usefilts.pop(repionIdx)
self.usefilts = usefilts
self.hdf5.close()
def close(self):
self.hdf5.close()
def getActivationTypes(self):
data = self.hdf5.getDataEqual('/rawdata/parameters', 'parameter',
'Activation Type')
if len(data) == 0:
data = self.hdf5.getDataEqual('/rawdata/parameters', 'parameter',
'activation')
types = {}
for act in data.flat:
activation = act['value'].upper()
types[activation] = types.get(activation, 0) + 1
return types.keys()
def export(self, hcdonly=0):
"""
@brief creates an mgf file for the MS/MS spectra in the hdf5 file
@param hcdonly <integer>: flag to switch output to only HCD spectra bypassing the normal export filters
"""
if hcdonly:
remove = ['CID']
filters = ['none']
else:
remove = self.remove
filters = self.usefilts
hdf = self.hdf5
mgfFile = hdf.filePath.parent.joinpath(hdf.filePath.stem + '.mgf')
# extra = path(hdf.filepath.splitext()[0] + '.txt')
# self.fextra = extra.open('w')
# self.fextra.write('spec_id\tmz\tinten\trel_inten\tion\texp_mz\n')
mgfOut = open(str(mgfFile), 'w')
mgfOut.write('#Removed spectra = %s, filtering = %s\n' %
(remove, filters))
spec = 0
# read parameters from hdf5 file
try:
hdf.appendOpen()
headers = hdf.readTable('/rawdata/msmsheader')
runTimeEntry = hdf.getDataEqual('/rawdata/parameters', 'parameter',
'MS Run Time (min)')
if len(runTimeEntry) == 0:
raise ExHa.MGFprocessingError(
'MGF Error: Could not find "MS Run Time (min)" parameter in HDF5 file.'
)
runtime = runTimeEntry[0]['value']
units = self.readUnitsOK()
# add new table for the deconvoluted spectrum data
hdf.removeTable('/rawdata/deconvions')
hdf.createTable('rawdata', 'deconvions', 'DeconvIons')
ident = []
for frag in units[1]:
# find all the frag methods to be used in identification
if 'I' in frag['use']:
ident.append(frag['order'])
logger.log.info('Reading %d spectra from %s' %
(len(headers), hdf.filePath.name))
if 'deconv' in filters:
deconv = 1
else:
deconv = 0
pBar = progBar.ProgressBar(widgets=progBar.name_widgets,
maxval=len(headers),
name='Create .mgf').start()
for idx, h in enumerate(headers):
if hcdonly:
if h['fragmeth'] != 'HCD':
continue
elif not h['order'] in ident:
continue
pBar.update(idx)
# get spectrum data
spec = h['spec_id']
spectrum = hdf.getDataEqual('/rawdata/ions', 'spec_id', spec)
if deconv:
# need extra column for charge information
spectrum = self.addChargeColumn(spectrum)
data = hdf.getDataGeneral(
'/rawdata/specparams',
'(spec_id == %i) & (parameter == "%s")' %
(spec, 'setmass1'))
setmass = data[0]['value']
data = hdf.getDataGeneral(
'/rawdata/specparams',
'(spec_id == %i) & (parameter == "%s")' % (spec, 'frag1'))
frag = data[0]['value']
try:
self.maxint = max(spectrum['inten'])
except:
self.maxint = 0
# construct title values list
rt = '%.3f' % h['rt']
use = units[1][h['order'] - 1]['use']
pretitle = ''
if use == 'IQ':
# spec is both ID and Quan so us normal msms ID
titles = ['msmsid:F%06d' % h['spec_id']]
elif use == 'I':
if h['quan_spec'] == 0:
# no quant data so use spec_id
titles = ['msmsid:F%06d' % h['spec_id']]
else:
# spec is only for ident find the quan spec
titles = ['msmsid:F%06d' % h['quan_spec']]
pretitle = '#CID=F%06d\n' % h['id_spec']
elif use == 'Q':
titles = ['msmsid:F%06d' % h['quan_spec']]
pretitle = '#CID=F%06d\n' % h['id_spec']
titles.append('rt:' + rt)
titles.append('survey:S%06d' % h['survey_spec'])
titles.append('parent:' + setmass)
titles.append('AnalTime:' + runtime)
titles.append('Activation:' + frag.upper())
titleline = 'TITLE=%s\n' % ','.join(titles)
if h['precmz'] > 0:
pepmass = h['precmz']
elif h['precmz_surv'] > 0:
pepmass = h['precmz_surv']
else:
pepmass = h['monomz']
if pepmass == 0:
continue
for filt in filters:
if len(spectrum) > 5 and self.filters[filt]:
spectrum = self.filters[filt](h, spectrum)
# filter for mascot interference
ionList = []
if len(spectrum) > 2:
mgfOut.write(pretitle)
mgfOut.write('BEGIN IONS\n')
mgfOut.write(titleline)
mgfOut.write('PEPMASS=%f\n' % pepmass)
mgfOut.write('CHARGE=%d+\n' % h['charge'])
if deconv:
for pt in spectrum:
if pt['inten'] == 0:
continue
mgfOut.write('%f %f %s\n' %
(pt['mz'], pt['inten'], pt['charge']))
ionList.append(
dict(spec_id=pt['spec_id'],
mz=pt['mz'],
inten=pt['inten'],
charge=pt['charge']))
else:
for pt in spectrum:
if pt['inten'] == 0:
continue
mgfOut.write('%f %f\n' % (pt['mz'], pt['inten']))
ionList.append(
dict(spec_id=pt['spec_id'],
mz=pt['mz'],
inten=pt['inten']))
mgfOut.write('END IONS\n\n')
if len(ionList) > 0:
hdf.appendRows('/rawdata/deconvions', ionList)
pBar.finish()
except ExHa.MGFprocessingError, czEx:
if spec:
ExHa.addContext(czEx,
'Raised whist processing spectrum %i' % spec)
raise
except Exception as genEx:
ExHa.reformatException(genEx)
if spec:
ExHa.addContext(genEx,
'Raised whist processing spectrum %i' % spec)
raise
def export(self, hcdonly=0):
"""
@brief creates an mgf file for the MS/MS spectra in the hdf5 file
@param hcdonly <integer>: flag to switch output to only HCD spectra bypassing the normal export filters
"""
if hcdonly:
remove = ['CID']
filters = ['none']
else:
remove = self.remove
filters = self.usefilts
hdf = self.hdf5
mgfFile = hdf.filePath.parent.joinpath(hdf.filePath.stem + '.mgf')
# extra = path(hdf.filepath.splitext()[0] + '.txt')
# self.fextra = extra.open('w')
# self.fextra.write('spec_id\tmz\tinten\trel_inten\tion\texp_mz\n')
mgfOut = open(str(mgfFile), 'w')
mgfOut.write('#Removed spectra = %s, filtering = %s\n' %
(remove, filters))
spec = 0
# read parameters from hdf5 file
try:
hdf.appendOpen()
headers = hdf.readTable('/rawdata/msmsheader')
runTimeEntry = hdf.getDataEqual('/rawdata/parameters', 'parameter',
'MS Run Time (min)')
if len(runTimeEntry) == 0:
raise ExHa.MGFprocessingError(
'MGF Error: Could not find "MS Run Time (min)" parameter in HDF5 file.'
)
runtime = runTimeEntry[0]['value']
units = self.readUnitsOK()
# add new table for the deconvoluted spectrum data
hdf.removeTable('/rawdata/deconvions')
hdf.createTable('rawdata', 'deconvions', 'DeconvIons')
ident = []
for frag in units[1]:
# find all the frag methods to be used in identification
if 'I' in frag['use']:
ident.append(frag['order'])
logger.log.info('Reading %d spectra from %s' %
(len(headers), hdf.filePath.name))
if 'deconv' in filters:
deconv = 1
else:
deconv = 0
pBar = progBar.ProgressBar(widgets=progBar.name_widgets,
maxval=len(headers),
name='Create .mgf').start()
for idx, h in enumerate(headers):
if hcdonly:
if h['fragmeth'] != 'HCD':
continue
elif not h['order'] in ident:
continue
pBar.update(idx)
# get spectrum data
spec = h['spec_id']
spectrum = hdf.getDataEqual('/rawdata/ions', 'spec_id', spec)
if deconv:
# need extra column for charge information
spectrum = self.addChargeColumn(spectrum)
data = hdf.getDataGeneral(
'/rawdata/specparams',
'(spec_id == %i) & (parameter == "%s")' %
(spec, 'setmass1'))
setmass = data[0]['value']
data = hdf.getDataGeneral(
'/rawdata/specparams',
'(spec_id == %i) & (parameter == "%s")' % (spec, 'frag1'))
frag = data[0]['value']
try:
self.maxint = max(spectrum['inten'])
except:
self.maxint = 0
# construct title values list
rt = '%.3f' % h['rt']
use = units[1][h['order'] - 1]['use']
pretitle = ''
if use == 'IQ':
# spec is both ID and Quan so us normal msms ID
titles = ['msmsid:F%06d' % h['spec_id']]
elif use == 'I':
if h['quan_spec'] == 0:
# no quant data so use spec_id
titles = ['msmsid:F%06d' % h['spec_id']]
else:
# spec is only for ident find the quan spec
titles = ['msmsid:F%06d' % h['quan_spec']]
pretitle = '#CID=F%06d\n' % h['id_spec']
elif use == 'Q':
titles = ['msmsid:F%06d' % h['quan_spec']]
pretitle = '#CID=F%06d\n' % h['id_spec']
titles.append('rt:' + rt)
titles.append('survey:S%06d' % h['survey_spec'])
titles.append('parent:' + setmass)
titles.append('AnalTime:' + runtime)
titles.append('Activation:' + frag.upper())
titleline = 'TITLE=%s\n' % ','.join(titles)
if h['precmz'] > 0:
pepmass = h['precmz']
elif h['precmz_surv'] > 0:
pepmass = h['precmz_surv']
else:
pepmass = h['monomz']
if pepmass == 0:
continue
for filt in filters:
if len(spectrum) > 5 and self.filters[filt]:
spectrum = self.filters[filt](h, spectrum)
# filter for mascot interference
ionList = []
if len(spectrum) > 2:
mgfOut.write(pretitle)
mgfOut.write('BEGIN IONS\n')
mgfOut.write(titleline)
mgfOut.write('PEPMASS=%f\n' % pepmass)
mgfOut.write('CHARGE=%d+\n' % h['charge'])
if deconv:
for pt in spectrum:
if pt['inten'] == 0:
continue
mgfOut.write('%f %f %s\n' %
(pt['mz'], pt['inten'], pt['charge']))
ionList.append(
dict(spec_id=pt['spec_id'],
mz=pt['mz'],
inten=pt['inten'],
charge=pt['charge']))
else:
for pt in spectrum:
if pt['inten'] == 0:
continue
mgfOut.write('%f %f\n' % (pt['mz'], pt['inten']))
ionList.append(
dict(spec_id=pt['spec_id'],
mz=pt['mz'],
inten=pt['inten']))
mgfOut.write('END IONS\n\n')
if len(ionList) > 0:
hdf.appendRows('/rawdata/deconvions', ionList)
pBar.finish()
except ExHa.MGFprocessingError, czEx:
if spec:
ExHa.addContext(czEx,
'Raised whist processing spectrum %i' % spec)
raise
for f in dataDir.glob(fileFilter):
if not f.is_file():
# skip any directories
continue
# if f.name[:4] in ['6528', '1814', '2032']: continue
mgf = mgftools(f)
logger.log.info('Filename: %s' % f.name)
if hcdOnly:
logger.log.info('Export HCD data only')
else:
logger.log.info('Using filters: %s' % str(mgf.usefilts))
rtn = mgf.export(hcdOnly)
mgf.close()
if f == 0:
raise ExHa.MGFprocessingError('no files found for: %s' %
str(dataDir / fileFilter))
except ExHa.UsageError as useEx:
ExHa.reformatException(useEx)
logger.log.info(useEx.context)
except Exception, genEx:
ExHa.reformatException(genEx)
if f:
ExHa.exportError2File(genEx, f.parent.joinpath(f.stem + '.error'))
else:
ExHa.exportError2File(genEx, dataDir.joinpath('errors.error'))
logger.log.info(ExHa.multiLineRepr(genEx))
logger.log.info('finished')
def updateHDF5(self):
"""
@brief controls the updating of the data to the hdf5 results file
@return finalMessage <string>: constructed from the protein data this is the RESULT stored in the DB
"""
pep2unique = self.pep2unique
baseContext = 'updateHDF5: '
context = 'updateHDF5'
try:
# find the peptide sequences that are being imported
usedPeps = self.setsManager.findUsedPeptides()
logger.log.info('there are %s usedPeps' % len(usedPeps))
context = baseContext + 'Retrieving sample IDs'
sample_ids = range(1, len(self.hdfFiles) + 1)
# create proteinset and proteinhit data
starting_protein_group_no = 1
self.setsManager.setProteinGroupNo(starting_protein_group_no)
logger.log.info('adding protein group data to HDF5')
logger.log.debug(str(self.hdfFiles.keys()))
spectrum_id = 0
peptide_id = 0
hdfFileList = self.hdfFiles.keys()
hdfFileList.sort()
for key in hdfFileList:
baseContext += '%s: ' % key
logger.log.log(
logger.PROCESS,
'Integrating Spectrum, Peptide & Quantification data from %s'
% key)
# collect fileData
hdf = self.hdfFiles[key]
hdfObj = hdf.hdfObject
# set the current sample_id from the list of IDs extracted from the DB
current_sample_id = sample_ids.pop()
hdf.acquired_spectra, hdf.mascot_matched_spectra, numIsotopes, runTime = hdfObj.getNumbers(
)
# read the Mascot data
context = baseContext + 'Reading Mascot data'
tmp = hdfObj.readImporterData(usedPeps, hdf)
peptides = tmp[0]
queryDict = tmp[1]
headerArray = tmp[2]
quanArray = tmp[3]
hdf.spectra_in_qc_proteins = len(peptides)
logger.log.debug('getting spectrum_ids')
context = baseContext + 'Retrieving spectrum IDs'
acqTime, hdf.idAct, hdf.quanAct = hdfObj.getTimeAndActivation()
# create blank lists to hold data for writing to hdf5 file
spectrum_list = []
peptide_list = []
quant_list = []
logger.log.info('collating spectrum, peptide & quant data')
pBar = progBar.ProgressBar(widgets=progBar.name_widgets,
maxval=len(queryDict),
name='collate data').start()
for idx, q in enumerate(queryDict):
# loop round all the required spectra
pBar.nextPrimary()
context = baseContext + 'query %i: Setting spectrum data' % q
# extract a spectrum_id from the list
spectrum_id += 1
query = queryDict[q]
spec = int(query['spec_id'])
context = baseContext + 'spectrum %i: Updating DB with spectrum data' % spec
# add spectrum data to spectrum_list
header = self.filterArrayEqual(headerArray, 'spec_id',
spec)
spectrum_list.append(
self.makeSpectrumDict(spectrum_id, current_sample_id,
query, acqTime, header))
# find the appropriate peptides
pepList = peptides[q]
logger.log.debug('there are %s in peplist %s' %
(len(pepList), str(pepList)))
quantFound = 0
# this list will hold all peptides returned from makePeptideDictList and then filter
# those non-rank1 equivalents based on the score of the rank 1 peptide
tmplist = []
for pep in pepList:
# find the sets that the peptide belongs to and add to the peptide_list
sets = self.setsManager.peptide2set[pep['peptide']]
context = baseContext + 'spectrum %i: Creating peptide data entries for hdf5' % spec
tmp, qf = self.makePeptideDictList(
spectrum_id, pep, query, sets, hdf, pep2unique)
tmplist.extend(tmp)
peptide_list += tmp
quantFound += qf
# only keep rank1 equivalent peptides (based on score)
tmplist.sort(key=lambda x: x['rank'])
toprankscore = tmplist[0]['score']
tmplist = [
x for x in tmplist if x['score'] == toprankscore
]
if quantMethID and quantFound:
# extract quantification data for the spectrum
context = baseContext + 'spectrum %i: Creating quantitation data entries for DB' % spec
newquant, deltas = self.makeQuantDictLists(
spectrum_id, spec, tmplist, header, quanArray, hdf)
quant_list += newquant
if quantSource == 'ms2':
context = baseContext + 'spectrum %i: Adding reporter ion delta data' % spec
hdf.addReporterDeltas(deltas)
pBar.finish()
# calculate statistics
context = baseContext + 'Calculating statistics'
hdf.calcReporterStats()
context = baseContext + 'Calculating delta m/z for fragment ions'
context = baseContext + 'Updating sample table (%i)' % current_sample_id
sample_data = hdf.getSampleDataDict(current_sample_id, key,
runTime)
hdf5results.writeSample(sample_data)
self.importData.combineStatistics(hdf)
# write data to HDF5
context = baseContext + 'Updating spectrum table'
logger.log.info('updating HDF5 with spectrum data')
hdf5results.writeSpectrum(spectrum_list)
if quantMethID:
context = baseContext + 'Updating specquant table'
logger.log.info('updating HDF5 with quant data')
hdf5results.writeSpecQuant(quant_list)
context = baseContext + 'Retrieving peptide IDs'
logger.log.info('updating HDF5 with peptide data')
for pepdata in peptide_list:
pepdata['peptide_id'] = peptide_id
peptide_id += 1
context = baseContext + 'Updating peptide table'
hdf5results.writePeptide(peptide_list)
hdf5results.createIndexes()
logger.log.info('finalising HDF5 entries')
hdf5results.writeFDRdata(self.importData.score2fdr, 'peptide')
hdf5results.writeFDRdata(self.importData.proteinscore2fdr,
'protein')
topScoringProteinInfo = self.setsManager.addPeptideSetDBdata(
hdf5results, self.importData.proteinscore2fdr)
runtimedata = self.importData.getSummaryStatisticsDict()
hdf5results.writeStatistics(runtimedata)
finalMessage = 'queries matched: %i / %s (%.1f%%) ' % (
runtimedata['spectra_in_qc_proteins'],
runtimedata['mascot_matched_spectra'],
(runtimedata['spectra_in_qc_proteins'] /
float(runtimedata['mascot_matched_spectra'])) * 100)
finalMessage += 'spectra quantified: %i top hit %s (%s) ' % (
runtimedata['quantified_spectra'], '', '')
finalMessage += 'with total score %f and %i matched peptides (hook AND non hook)' % \
(topScoringProteinInfo[0], topScoringProteinInfo[2])
baseContext = 'updateHDF5: '
context = baseContext + 'Finalising HDF5 entries'
except Exception, genEx:
# make sure that there aren't any permanent changes
ExHa.addContext(genEx, context)
finalMessage = 'Error: %s' % ExHa.oneLineRepr(genEx)
raise
msg = 'merged sample'
else:
isMerged = False
msg = 'single sample'
importer = DATimporter(cfg, logger)
logger.log.info(msg)
searchDict = {}
for idx, hdf5file in enumerate(searches):
searchData = {}
searchData['hdf5name'] = hdf5file
searchData['archivepath'] = cfg.parameters['runtime']['datadir']
fullPath = searchData['archivepath']
fi = fullPath.joinpath(searchData['hdf5name'])
if not fi.exists():
raise ExHa.FileNotFoundException('Missing file: %s' % str(fi))
logger.log.info('Reading: %s' % str(fi))
importer.addHDFfile(fi, idx + 1, logger)
sw.rec('file loading')
fdrthreshold = cfg.parameters['general']['fdrthreshold']
peptidescoreatthreshold, score2fdr = importer.calculateFDR(
importer.setsManager.FDRdata, fdrthreshold)
importer.importData.score2fdr = score2fdr
importer.finaliseProteins(peptidescoreatthreshold)
proteinscoreatthreshold, score2fdr = importer.calculateFDR(
importer.setsManager.proteinFDRdata, fdrthreshold)
importer.importData.proteinscore2fdr = score2fdr
finalMessage = importer.updateHDF5()
allfractionbgratios = {}
for isotopelabel_id, data in allsumionratiodata.iteritems():
allfractionbgratios[isotopelabel_id] = np.median(data)
# second normalization so that the bg-ratios all add to 1
for isotopelabel_id, data in allfractionbgratios.iteritems():
allfractionbgratios[isotopelabel_id] = data / sum(allfractionbgratios.values())
logger.log.debug(('allfractionbgratios are %s' % str(allfractionbgratios)))
for corrects2iquantob in corrects2iquantoblist:
# perform correction for each of the analyzed .hdf5 files.
s2icorrecteddata = corrects2iquantob.performS2Icorrection(allfractionbgratios)
corrects2iquantob.hdf5corrects2iquant.updates2ivalues(s2icorrecteddata)
hdf5corrects2iquant.close()
except ExHa.czException as czEx:
ExHa.reformatException(czEx)
ExHa.addContext(czEx, 'Error during corrects2iquant run')
ExHa.exportError2File(czEx, cfg.parameters['runtime']['datadir'] / Path('errors.error'))
if logger:
logger.log.warning(ExHa.oneLineRepr(czEx))
else:
print ExHa.multiLineRepr(czEx)
except Exception as genEx:
ExHa.reformatException(genEx)
ExHa.addContext(genEx, 'Error during corrects2iquant run')
ExHa.exportError2File(genEx, cfg.parameters['runtime']['datadir'] / 'errors.error')
if logger:
logger.log.warning(ExHa.oneLineRepr(genEx))
else:
from pathlib import Path
# cellzome CommonUtils
sys.path.insert(0, '..')
from CommonUtils.tools import *
from CommonUtils.ConfigManager import pyMSsafeConfigManager
from CommonUtils.LoggingManager import Logger
from CommonUtils.hdf5Base import hdf5Base
from CommonUtils.QuantMethodHandler import QuantMethods
import CommonUtils.ExceptionHandler as ExHa
# pyMSsafe modules
try:
from xRawFile import XRawFile
except ImportError, ieEx:
ExHa.reformatException(ieEx)
ExHa.addContext(ieEx, 'Xcalibur not set up properly')
configPath = './pymssafe.cfg'
cfg = pyMSsafeConfigManager(configPath)
ret = cfg.evaluateCommandLineArgs(sys.argv)
dataDir = cfg.parameters['runtime']['datadir']
ExHa.exportError2File(ieEx, dataDir.joinpath('errors.error'))
from datastore import Datamanager
class pymssafe:
def __init__(self, config):
"""
@brief initiaise pyMSsafe controller
@param mode <string>: operational mode of the application