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)
