"epsilon", "ambiguous edges", "num ambig edges", ] if options.output: outFile = open(options.output, "w") outFile.write("\t".join([col.upper() for col in cols]) + "\n") t1 = time.time() print "Configuring LADS for sequencing..." ETDPNet = PN.ProbNetwork(paramsDict["Models"]["etd"]["config"], paramsDict["Models"]["etd"]["model"]) HCDPNet = PN.ProbNetwork(paramsDict["Models"]["hcd"]["config"], paramsDict["Models"]["hcd"]["model"]) dtaList = glob.glob(options.dtadir + "/*.dta") scanFDict = getScanFDict(dtaList) aas = Constants.addPepsToAADict(options.minedge) hashedAAs = Constants.hashAAsEpsilonRange(aas, epStep, maxEp) ambigOpenPenalty = 0 ambigPenaltyFun = DNS.getAmbigEdgePenaltyFunction(options.minedge, ambigOpenPenalty, options.ambigpenalty) ppmPenaltyFun = DNS.getPPMPenaltyFun( options.ppmstd, hashedAAs, options.minedge, options.ppmpenalty, options.ppmsyserror, epStep ) addEnds = DNS.getSpectrumGraphEndpointInitFunction( np.array(Constants.NTermMods.values()), np.array(Constants.CTermMods.values()), paramsDict["Enzyme"]["specificity"], ) termModHash = Constants.createTermModHashAAs( N=copy.deepcopy(Constants.NTermMods), C=copy.deepcopy(Constants.CTermMods)
with open(options.columns) as fin: cols = pickle.load(fin) else: print 'Using default cols' cols = ['light scan', 'heavy scan', 'pair configuration', 'M+H', 'score', 'seq', 'epsilon', 'ambiguous edges', 'num ambig edges'] if options.output: outFile = open(options.output, 'w') outFile.write('\t'.join([col.upper() for col in cols]) + '\n') PNet = PN.ProbNetwork(options.config, options.model) dtaList = glob.glob(options.dtadir + '/*.dta') scanFDict = getScanFDict(dtaList) aas = Constants.addPepsToAADict(300) hashedAAs = Constants.hashAAsEpsilonRange(aas, epStep, maxEp) ambigOpenPenalty = 0 ambigPenaltyFun = DNS.getAmbigEdgePenaltyFunction(options.minedge, ambigOpenPenalty, options.ambigpenalty) ppmPenaltyFun = DNS.getPPMPenaltyFun(options.ppmstd, hashedAAs, options.minedge, options.ppmpenalty, options.ppmsyserror, epStep) print 'Getting Clusters' parent = os.path.abspath(os.pardir) clusterSVMModel = svmutil.svm_load_model(parent + paramsDict['Cluster Configuration']['model']) clusterSVMRanges = svmutil.load_ranges(parent + os.path.splitext((paramsDict['Cluster Configuration']['model']))[0] + '.range') precMassClusters = Analytics.findSamePrecMassClusters(dtaList, ppm=options.ppmstd) # print 'precMassClusters', precMassClusters samePeptideClusters = Analytics.getSamePeptideClusters(precMassClusters, scanFDict, clusterSVMModel, clusterSVMRanges, ppmSTD=options.ppmstd, cutOff=float(paramsDict['Cluster Configuration']['cutoff'])) # samePeptideClusters = Analytics.getSamePeptideClusters(precMassClusters, scanFDict, clusterSVMModel, clusterSVMRanges, ppmSTD=options.ppmstd, cutOff=4)
def loadInit(self): self._paramsDict = DataFile.parseParams(self._selectedInitFile.get()) with open('../Misc/symbolmap.txt', 'r') as fin: symbolMap = pickle.load(fin) self._seqMap = DataFile.generateSeqMap({'LADS Unit Test': 'LADS'}, symbolMap, self._paramsDict)['LADS Unit Test'] self._aas = Constants.addPepsToAADict(self._minedge)