def dataGenUnitTest(): print "dataGenUnitTest" dummyParameters = logging.parameterObj() dummyParameters.N, dummyParameters.G, dummyParameters.L, dummyParameters.p, typeOfGen, detail = 100, 10000,100, 0.01, 't', "500" dummyParameters.indel = True motherGen, reads, noisyReads = dataGen.generateData(typeOfGen,detail,dummyParameters)
def testWhole(): af_dict = { 'first_name': 'first_names.csv', 'last_name': 'last_names.csv', 'street_name': 'street_names.csv', 'street_number': 'street_numbers.csv' } noise_functions = { 'swapNames': nf.swapNames, 'initialFirstName': nf.initialFirstName, 'initialLastName': nf.initialLastName, 'deleteAttribute': nf.deleteAttribute, 'shareAttributes': nf.shareAttributes, 'sameProfiles': nf.sameProfiles, } noise_function_percentages = { 'swapNames': 0.05, 'initialFirstName': 0.1, 'initialLastName': 0.05, 'deleteAttribute': 0.05, 'shareAttributes': 0.3, 'sameProfiles': 0.8 } profiles = dg.generateData(af_dict, 200) dg.addNoise(profiles, noise_functions, noise_function_percentages) dg.writeCSV(profiles, "noisyv2.csv")
def runAssembler(snpRate, typeOfGen, detail, parameterRobot): #N, G, L, p,K,snpRate, typeOfGen, detail = 100, 100, 10000, 0.01,30,0.001, 'r', "1000" motherGen, reads, noisyReads = dataGen.generateData( typeOfGen, detail, parameterRobot) motherGen, reads, noisyReads = logging.rawDataLoad( parameterRobot.defaultFolder + "UnitTest", parameterRobot.G, parameterRobot.N, parameterRobot.L, "dn") f1 = cluster.groupIndelNoisyKmers(noisyReads, parameterRobot) G1, startList, f1 = graphForm.getSeqGraph(f1, noisyReads, parameterRobot) f2, G2 = branchClear.clearResidual(f1, G1, parameterRobot) G3 = bridgeResolve.resolveRepeats(f2, G2, parameterRobot) G4 = alignmentBridge.MSAresolve(f2, G3, noisyReads, snpRate, parameterRobot) #G4 = G3 recovSeq = eulerCycle.findEC(G4) recovGen = readAns.reportRecovSeq(recovSeq, f2, noisyReads, parameterRobot) numMistakes, success = compare.subAlignCompare(recovGen, motherGen, parameterRobot) #numMistakes, success = 0 , 0 return numMistakes, success # Target of the new code : Fast Assemble and assembly in optimal amount of information #t0 = time.time() #N, G, L, p,snpRate, typeOfGen, detail = 1000, 10000,200, 0.015, 0.001 ,'m', "500-300-50" #runAssembler(N, G, L, p,snpRate, typeOfGen, detail) #print "Time (sec) :", time.time() - t0
def runAssembler(snpRate, typeOfGen, detail,parameterRobot): #N, G, L, p,K,snpRate, typeOfGen, detail = 100, 100, 10000, 0.01,30,0.001, 'r', "1000" motherGen, reads, noisyReads = dataGen.generateData(typeOfGen, detail,parameterRobot) motherGen, reads, noisyReads = logging.rawDataLoad(parameterRobot.defaultFolder+"UnitTest",parameterRobot.G,parameterRobot.N,parameterRobot.L, "dn") f1 = cluster.groupIndelNoisyKmers(noisyReads,parameterRobot) G1,startList, f1 = graphForm.getSeqGraph(f1,noisyReads, parameterRobot) f2, G2 = branchClear.clearResidual(f1, G1,parameterRobot) G3 = bridgeResolve.resolveRepeats(f2, G2,parameterRobot) G4 = alignmentBridge.MSAresolve(f2, G3, noisyReads, snpRate,parameterRobot ) #G4 = G3 recovSeq = eulerCycle.findEC(G4) recovGen = readAns.reportRecovSeq(recovSeq, f2, noisyReads,parameterRobot) numMistakes, success = compare.subAlignCompare(recovGen, motherGen,parameterRobot) #numMistakes, success = 0 , 0 return numMistakes, success # Target of the new code : Fast Assemble and assembly in optimal amount of information #t0 = time.time() #N, G, L, p,snpRate, typeOfGen, detail = 1000, 10000,200, 0.015, 0.001 ,'m', "500-300-50" #runAssembler(N, G, L, p,snpRate, typeOfGen, detail) #print "Time (sec) :", time.time() - t0
def dataGenUnitTest(): print "dataGenUnitTest" dummyParameters = logging.parameterObj() dummyParameters.N, dummyParameters.G, dummyParameters.L, dummyParameters.p, typeOfGen, detail = 100, 10000, 100, 0.01, 't', "500" dummyParameters.indel = True motherGen, reads, noisyReads = dataGen.generateData( typeOfGen, detail, dummyParameters)