def MDtest_plot_rhohist(varname='rho',Ndesbins=[2,3],lmin=3,lmax=80,getrhopred=True,firstNreal=-1,rhofiletag='',nvss=True,plottag=''):
plotdir='output/MDchecks/plots/'
rhogrid=MDtest_read_rho_wfiles(varname,Ndesbins,lmin,lmax,rhofiletag,nvss=nvss)
#output order are nvss, then the des-like surveys in given Ndesbins order
Nreal=rhogrid.shape[1]
if firstNreal>0 and firstNreal<Nreal:
Nreal=firstNreal
rhogrid=rhogrid[:,:Nreal]
testname='MDchecks'
if getrhopred:
rhopred=MDtest_get_expected_rho(varname,Ndesbins,lmin,lmax,nvss=nvss)
else:
rhopred=[]
if plottag:
tag='_'+plottag
else:
tag=''
plotname ='MDtest_{1:s}hist_r{0:05d}{2:s}'.format(Nreal,varname,tag)
reclabels=[m.tag for m in MDtest_get_maptypelist(Ndesbins=Ndesbins,nvss=nvss)]
# if nvss:
# reclabels.append('NVSS')
# for n in Ndesbins:
# reclabels.append('DES {0:d} bins'.format(n))
if varname=='rho':
au.plot_rhohist(rhogrid,reclabels,testname,plotdir,plotname,rhopred)
elif varname=='s':
au.plot_shist(rhogrid,reclabels,testname,plotdir,plotname,rhopred)
def analyze_stage4():
''' resolve indirect calls '''
global isKernel
if not isKernel:
AnalysisUtils.forward_analysis_intra_defined_funcs()
AnalysisUtils.forward_analysis_intra_defined_funcs()
keepAllConsistency()
def test_userentries(outputFilePath=None):
if not containsUserClient():
return
rebuildAllInternalDataWOParseModInitFunc(True)
AnalysisUtils.forward_analysis_intra_defined_funcs()
binPath = getOriginalBinaryPath()
outputFile = None
if not None is outputFilePath:
outputFile = open(outputFilePath, "a")
foundEntryPoints = findEntryPoints()
if len(foundEntryPoints) == 0:
if not None is outputFile:
outputFile.write("\n\n************\nNo Entry Points in {}:\n".format(binPath))
else:
if not None is outputFile:
outputFile.write("\n\n============\nEntry Points in {}:\n".format(binPath) )
for entryType in foundEntryPoints:
entries = foundEntryPoints[entryType]
for entry in entries:
outputStr = "[{}] 0x{:016X}: {}".format(entryType, entry, getName(entry))
print outputStr
if not None is outputFile:
outputFile.write(outputStr + "\n")
if not None is outputFile:
outputFile.close()
def test_indirectcalls(outputFilePath=None, listAll=False):
#importDepsTils()
modInitFuncs = getAllModInitFuncs()
if len(modInitFuncs) == 0:
return
parseGOTNames()
#if not haveSymbol():
# return
commonSeg = getSegByName("__common")
if not None is commonSeg:
for ea in Heads(commonSeg.start_ea, commonSeg.end_ea):
deName = getDeNameAtEA(ea)
if None is deName:
continue
if deName.endswith("::gMetaClass"):
SetType(ea, deName[:-len("::gMetaClass")] + "::MetaClass")
parseKernelHeadersAndSetType()
rebuildAllInternalDataWOParseModInitFunc(True)
AnalysisUtils.forward_analysis_intra_defined_funcs()
solvedIndirectCalls, unsolvedIndirectCalls, allIndirectCalls, solveRate = AnalysisUtils.checkResolutionRate()
result = {"solvedIndirectCalls": {x: list(solvedIndirectCalls[x]) for x in solvedIndirectCalls}, "unsolvedIndirectCalls":list(unsolvedIndirectCalls), "allIndirectCalls": list(allIndirectCalls)}
if not None is outputFilePath:
with open(outputFilePath, "w") as f:
json.dump(result, f)
print "solved {}, unsolved {}, rate {}".format(len(solvedIndirectCalls), len(unsolvedIndirectCalls), solveRate)
if listAll:
print ", ".join(["0x{:X}".format(x) for x in unsolvedIndirectCalls])
return solvedIndirectCalls, unsolvedIndirectCalls
def main(args):
sims = os.path.sep.join([pathname, '..', 'sims', 'doi_10.1101_025387', args.species])
rdict = {}
corrs = {}
for i in xrange(1, 7):
path = os.path.sep.join([sims, str(i)])
print("reading results from {}".format(path))
tdf = ParsingUtils.readThreeColumnTruth(os.path.sep.join([path, "truth.tsv"]) , "_true")
kdf = ParsingUtils.readKallisto(os.path.sep.join([path, "abundance.tsv"]), "_kallisto")
sdf = ParsingUtils.readSailfish(os.path.sep.join([path, "quant.sf"]), "_sailfish")
df = tdf.join(kdf, rsuffix="_K").join(sdf, rsuffix="_S")
for method in ["sailfish", "kallisto"]:
for ct in ["spearman", "pearson"]:
corrKey = "{}_{}".format(method, ct)
corr = df["TPM_true"].corr(df["TPM_{}".format(method)], method=ct)
if corrKey in corrs:
corrs[corrKey].append(corr)
else:
corrs[corrKey] = [corr]
rdict[i] = df
corrStrs = []
for k, v in corrs.iteritems():
corrStrs.append("{}: {}".format(k, ','.join(["{:0.2}".format(c) for c in v])))
corrStrs = sorted(corrStrs)
print('\n'.join(corrStrs))
relDiffs = {}
for k,v in rdict.iteritems():
rds = AnalysisUtils.relDiff("TPM_true", "TPM_sailfish", v, verbose=False)
rdk = AnalysisUtils.relDiff("TPM_true", "TPM_kallisto", v, verbose=False)
for method, rd in {"sailfish" : rds, "kallisto" : rdk}.iteritems():
for summaryName, summaryFunc in {"median" : AnalysisUtils.getMedian, "mean" : AnalysisUtils.getMean}.iteritems():
signedKey = "{}_{}_{}".format(method, summaryName, "signed")
absKey = "{}_{}_{}".format(method, summaryName, "abs")
if signedKey in relDiffs:
relDiffs[signedKey].append(summaryFunc(rd[0]))
else:
relDiffs[signedKey] = [summaryFunc(rd[0])]
if absKey in relDiffs:
relDiffs[absKey].append(summaryFunc(rd[0].abs()))
else:
relDiffs[absKey] = [summaryFunc(rd[0].abs())]
for signedness in ["signed", "abs"]:
for stat in ["median", "mean"]:
if stat == "median":
print("mean of medians of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.mean(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.mean(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
elif stat == "mean":
print("median of means of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.median(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.median(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
def MDtest_get_glm(Nreal=1,minreal=0,Ndesbins=[2,3],nvss=True):
cldat=MDtest_get_Cl(justread=True,Ndesbins=Ndesbins,nvss=nvss)
print 'cldat.bintaglist:',cldat.bintaglist
#cldat seems to be associated with correct crosspairs
# also, manipulating cl data has expected effects on rho hist
rlzns=np.arange(minreal,minreal+Nreal)
#leaving reclist empty means just gen gal and isw maps
au.getmaps_fromCl(cldat,rlzns=rlzns)
def MDtest_get_glm_and_rec(Nreal=1,minreal=0,justgetrho=0,dorho=1,Ndesbins=[2,3],lmin=3,lmax=80,rhofiletag='',nvss=True):
#adding this block of text fixed bug when passing two sets of analysis. why?
maptypes=MDtest_get_maptypelist(Ndesbins=Ndesbins,nvss=nvss)
mapsfor=[mt.tag for mt in maptypes] #tags for maps we want to make
mapsfor.append('isw')
allcldat=MDtest_get_Cl(justread=True,Ndesbins=Ndesbins,nvss=nvss)
#cldat=get_reduced_cldata(allcldat,dothesemaps=mapsfor)#basically just reorders
cldat=allcldat
makeplots = Nreal==1
rlzns=np.arange(minreal,minreal+Nreal)
reclist=MDtest_get_reclist(Ndesbins=Ndesbins,lmin=lmin,lmax=lmax,nvss=nvss)
au.getmaps_fromCl(cldat,rlzns=rlzns,reclist=reclist,justgetrho=justgetrho,dorho=dorho,dos=False,dochisq=False,rhofiletag=rhofiletag)
def MDtest_get_expected_rho(varname='rho',Ndesbins=[2,3],lmin=3,lmax=80,nvss=True):
Nrec=len(Ndesbins)+nvss
cldat=MDtest_get_Cl(Ndesbins=Ndesbins,nvss=nvss)
cldat=MDtest_boostNVSSnoise(cldat)
reclist=MDtest_get_reclist(Ndesbins,lmin,lmax,nvss=nvss)
rhopred=np.zeros(Nrec)
for i in xrange(Nrec):
rhopred[i]=au.compute_rho_fromcl(cldat,reclist[i],varname=varname)
return rhopred
def launchCheckerRacedemo(checkerResultFileName, kext):
checkerArgs = AnalysisUtils.CheckerArgs(\
checkerName="checker_racedemo", \
checkerResultFileName=checkerResultFileName, \
preCheckers=[checker_racedemo_pre], \
resultHandler=checkerRacedemoResultHandler, \
crossKEXT = False, \
onlyUserClients=True)
launchChecker(checkerArgs, kext, q_ea_and_lockretain=[])
def launchCheckerAtIndex(checkerIdx, kext=None):
if checkerIdx >= len(Checkers):
print "checkerIdx {} out of {}".format(checkerIdx, len(Checkers))
return
checker = Checkers[checkerIdx]
if not None is checker:
checker_name = checker["name"]
checker_launcher = checker["launcher"]
allUCInfos = getAllUCInfos()
if checker["requireUC"] and len(allUCInfos) == 0:
print "[!] {} requires accessible userclient, but kext does not have".format(
checker_name)
return
AnalysisUtils.forward_analysis_intra_defined_funcs(kext)
print "[+] Launching checker {}".format(checker_name)
checker_launcher("abc", kext)
def main():
sims = os.path.sep.join([pathname, '..', 'sims', 'rsem'])
rdict = {}
for i in xrange(1, 21):
path = os.path.sep.join([sims, str(i)])
print("reading results from {}".format(path))
tdf = ParsingUtils.readRSEMTruth(os.path.sep.join([path, "truth.tsv"]) , "_true")
kdf = ParsingUtils.readKallisto(os.path.sep.join([path, "abundance.tsv"]), "_kallisto")
sdf = ParsingUtils.readSailfish(os.path.sep.join([path, "quant.sf"]), "_sailfish")
df = tdf.join(kdf, rsuffix="_K").join(sdf, rsuffix="_S")
rdict[i] = df
relDiffs = {}
for k,v in rdict.iteritems():
rds = AnalysisUtils.relDiff("TPM_true", "TPM_sailfish", v, verbose=False)
rdk = AnalysisUtils.relDiff("TPM_true", "TPM_kallisto", v, verbose=False)
for method, rd in {"sailfish" : rds, "kallisto" : rdk}.iteritems():
for summaryName, summaryFunc in {"median" : AnalysisUtils.getMedian, "mean" : AnalysisUtils.getMean}.iteritems():
signedKey = "{}_{}_{}".format(method, summaryName, "signed")
absKey = "{}_{}_{}".format(method, summaryName, "abs")
if signedKey in relDiffs:
relDiffs[signedKey].append(summaryFunc(rd[0]))
else:
relDiffs[signedKey] = [summaryFunc(rd[0])]
if absKey in relDiffs:
relDiffs[absKey].append(summaryFunc(rd[0].abs()))
else:
relDiffs[absKey] = [summaryFunc(rd[0].abs())]
for signedness in ["signed", "abs"]:
for stat in ["median", "mean"]:
if stat == "median":
print("mean of medians of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.mean(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.mean(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
elif stat == "mean":
print("median of means of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.median(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.median(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
def launchCheckerInFunc(funcEA, className, resultFile, checkerArgs, **kwargs):
if None is resultFile:
resultFile = sys.stdout
print "\n[+] Launch {} in func {} at {:016X}".format(
checkerArgs.checkerName, getName(funcEA), funcEA)
checkerResults = CheckerResults(checkerArgs.checkerName, className, funcEA,
resultFile)
AnalysisUtils.forward_analysis_in_func(funcEA, \
isPureChecker=True, \
isInterProc=checkerArgs.isInterProc, \
crossKEXT = checkerArgs.crossKEXT, \
preCheckers=checkerArgs.preCheckers, \
postCheckers=checkerArgs.postCheckers, \
checkerResults = checkerResults, \
checkerArgs=checkerArgs, \
**kwargs)
if not None is checkerArgs.resultHandler:
checkerArgs.resultHandler(checkerResults, resultFile)
def MDtest_read_rho_wfiles(varname='rho',Ndesbins=[2,3],lmin=3,lmax=80,rhofiletag='',nvss=True):
maptypes=MDtest_get_maptypelist(Ndesbins=Ndesbins,nvss=nvss) #list of LSS survey types
mapdir='output/MDchecks/map_output/'
if not rhofiletag:
tagstr=''
else:
tagstr='_'+rhofiletag
if lmax>0:
files=['iswREC.{0:s}.fid.fullsky-lmin{2:02d}-lmax{3:02d}.MDtest{4:s}.{1:s}.dat'.format(mtype.tag,varname,lmin,lmax,tagstr) for mtype in maptypes]
else:
files=['iswREC.{0:s}.fid.fullsky-lmin{2:02d}.MDtest{3:s}.{1:s}.dat'.format(mtype.tag,varname,lmin,tagstr) for mtype in maptypes]
rhogrid=np.array([au.read_rhodat_wfile(mapdir+f) for f in files])
return rhogrid
def test_indirectcalls(kextPrefix=None, outputFilePath=None, listAll=False):
loadNecessaryDataFromPersistNode()
AnalysisUtils.forward_analysis_intra_defined_funcs(kextPrefix)
solvedIndirectCalls, unsolvedIndirectCalls, allIndirectCalls, solveRate = AnalysisUtils.checkResolutionRate(
kextPrefix)
result = {
"solvedIndirectCalls":
{x: list(solvedIndirectCalls[x])
for x in solvedIndirectCalls},
"unsolvedIndirectCalls": list(unsolvedIndirectCalls),
"allIndirectCalls": allIndirectCalls
}
if not None is outputFilePath:
with open(outputFilePath, "w") as f:
json.dump(result, f)
print "solved {}, unsolved {}, rate {}".format(len(solvedIndirectCalls),
len(unsolvedIndirectCalls),
solveRate)
if listAll:
print ", ".join(
["0x{:X}".format(x) for x in sorted(unsolvedIndirectCalls)])
return solvedIndirectCalls, unsolvedIndirectCalls
def preprocess(self, dst_file):
chain = r.TChain("HPS_Event")
chain.Add(dst_file)
tree = chain.GetTree()
from ROOT import HpsEvent
hps_event = HpsEvent()
branch = chain.SetBranchAddress("Event", r.AddressOf(hps_event))
# Add variables to the ntuple
ft_maker = ft.FlatTupleMaker(self.output_file_name)
ft_maker.add_variable("cluster_0_energy")
ft_maker.add_variable("cluster_1_energy")
ft_maker.add_variable("cluster_0_x")
ft_maker.add_variable("cluster_1_x")
ft_maker.add_variable("cluster_0_y")
ft_maker.add_variable("cluster_1_y")
ft_maker.add_variable("track_0_p")
ft_maker.add_variable("track_0_px")
ft_maker.add_variable("track_0_py")
ft_maker.add_variable("track_0_pz")
ft_maker.add_variable("track_0_theta")
ft_maker.add_variable("track_0_phi0")
ft_maker.add_variable("track_0_omega")
ft_maker.add_variable("track_0_d0")
ft_maker.add_variable("track_0_z0")
ft_maker.add_variable("track_1_p")
ft_maker.add_variable("track_1_px")
ft_maker.add_variable("track_1_py")
ft_maker.add_variable("track_1_pz")
ft_maker.add_variable("track_1_theta")
ft_maker.add_variable("track_1_phi0")
ft_maker.add_variable("track_1_omega")
ft_maker.add_variable("track_1_d0")
ft_maker.add_variable("track_1_z0")
ft_maker.add_variable("track_pair_p_sum")
matcher = tcm.TrackClusterMatcher()
for entry in xrange(0, chain.GetEntries()):
if (entry + 1) % 1000 == 0: print "Event " + str(entry + 1)
chain.GetEntry(entry)
# Loop through all clusters in the event and find a 'good' pair.
# For now, a 'good' pair is defined as two clusters whose cluster
# time difference is less than 1.6 ns and greater than -1.6 ns
cluster_pair = au.get_good_cluster_pair(hps_event)
if len(cluster_pair) != 2: continue
matcher.find_all_matches(hps_event)
tracks = [
matcher.get_track(cluster_pair[0]),
matcher.get_track(cluster_pair[1])
]
if (tracks[0] is None) or (tracks[1] is None): continue
ft_maker.set_variable_value("cluster_0_energy",
cluster_pair[0].getEnergy())
ft_maker.set_variable_value("cluster_1_energy",
cluster_pair[1].getEnergy())
ft_maker.set_variable_value("cluster_0_x",
cluster_pair[0].getPosition()[0])
ft_maker.set_variable_value("cluster_1_x",
cluster_pair[1].getPosition()[0])
ft_maker.set_variable_value("cluster_0_y",
cluster_pair[0].getPosition()[1])
ft_maker.set_variable_value("cluster_1_y",
cluster_pair[1].getPosition()[1])
ft_maker.set_variable_value(
"track_0_p",
np.linalg.norm(np.asarray(tracks[0].getMomentum())))
ft_maker.set_variable_value("track_0_px",
tracks[0].getMomentum()[0])
ft_maker.set_variable_value("track_0_py",
tracks[0].getMomentum()[1])
ft_maker.set_variable_value("track_0_pz",
tracks[0].getMomentum()[2])
track_0_theta = math.fabs(math.pi / 2 -
math.acos(te.get_cos_theta(tracks[0])))
ft_maker.set_variable_value("track_0_theta", track_0_theta)
ft_maker.set_variable_value("track_0_phi0", tracks[0].getPhi0())
ft_maker.set_variable_value("track_0_omega", tracks[0].getOmega())
ft_maker.set_variable_value("track_0_d0", tracks[0].getD0())
ft_maker.set_variable_value("track_0_z0", tracks[0].getZ0())
ft_maker.set_variable_value(
"track_1_p",
np.linalg.norm(np.asarray(tracks[1].getMomentum())))
ft_maker.set_variable_value("track_1_px",
tracks[1].getMomentum()[0])
ft_maker.set_variable_value("track_1_py",
tracks[1].getMomentum()[1])
ft_maker.set_variable_value("track_1_pz",
tracks[1].getMomentum()[2])
track_1_theta = math.fabs(math.pi / 2 -
math.acos(te.get_cos_theta(tracks[1])))
ft_maker.set_variable_value("track_1_theta", track_1_theta)
ft_maker.set_variable_value("track_1_phi0", tracks[1].getPhi0())
ft_maker.set_variable_value("track_1_omega", tracks[1].getOmega())
ft_maker.set_variable_value("track_1_d0", tracks[1].getD0())
ft_maker.set_variable_value("track_1_z0", tracks[1].getZ0())
p_sum = np.linalg.norm(np.asarray(
tracks[0].getMomentum())) + np.linalg.norm(
np.asarray(tracks[1].getMomentum()))
ft_maker.set_variable_value("track_pair_p_sum", p_sum)
ft_maker.fill()
ft_maker.close()
def perform_checker_on_kext(kextname, checker_id):
if not kextname in getAllKEXTPrefixes():
return
AnalysisUtils.forward_analysis_intra_defined_funcs(kextname)
PolicyChecker.launchCheckerAtIndex(checker_id, kextname)
def preprocess(self, dst_file):
chain = r.TChain("HPS_Event")
chain.Add(dst_file)
tree = chain.GetTree()
from ROOT import HpsEvent
hps_event = HpsEvent()
branch = chain.SetBranchAddress("Event", r.AddressOf(hps_event))
# Create the file name for the preprocessed ROOT file
#output_file_name = root_file.GetName()[str(root_file.GetName()).rindex("/") + 1:-5]
output_file_name = "beam_tri_preprocessed_signal.root"
# Add variables to the ntuple
ft_maker = ft.FlatTupleMaker(output_file_name)
ft_maker.add_variable("cluster_0_energy")
ft_maker.add_variable("cluster_1_energy")
ft_maker.add_variable("cluster_0_x")
ft_maker.add_variable("cluster_1_x")
ft_maker.add_variable("cluster_0_y")
ft_maker.add_variable("cluster_1_y")
ft_maker.add_variable("track_0_p")
ft_maker.add_variable("track_0_px")
ft_maker.add_variable("track_0_py")
ft_maker.add_variable("track_0_pz")
ft_maker.add_variable("track_0_tanlambda")
ft_maker.add_variable("track_0_phi0")
ft_maker.add_variable("track_0_omega")
ft_maker.add_variable("track_0_d0")
ft_maker.add_variable("track_0_z0")
ft_maker.add_variable("track_0_charge")
ft_maker.add_variable("track_1_p")
ft_maker.add_variable("track_1_px")
ft_maker.add_variable("track_1_py")
ft_maker.add_variable("track_1_pz")
ft_maker.add_variable("track_1_tanlambda")
ft_maker.add_variable("track_1_phi0")
ft_maker.add_variable("track_1_omega")
ft_maker.add_variable("track_1_d0")
ft_maker.add_variable("track_1_z0")
ft_maker.add_variable("track_1_charge")
matcher = tcm.TrackClusterMatcher()
for entry in xrange(0, chain.GetEntries()):
if (entry + 1) % 1000 == 0: print "Event " + str(entry + 1)
chain.GetEntry(entry)
#if not au.is_good_data_event(hps_event) : continue
# Loop through all clusters in the event and find a 'good' pair.
# For now, a 'good' pair is defined as two clusters whose cluster
# time difference is less than 1.7 ns and greater than -1.6 ns
cluster_pair = au.get_good_cluster_pair(hps_event)
if not self.is_good_cluster_pair(cluster_pair): continue
matcher.find_all_matches(hps_event)
tracks = [
matcher.get_track(cluster_pair[0]),
matcher.get_track(cluster_pair[1])
]
if (tracks[0] is None) or (tracks[1] is None): continue
ft_maker.set_variable_value("cluster_0_energy",
cluster_pair[0].getEnergy())
ft_maker.set_variable_value("cluster_1_energy",
cluster_pair[1].getEnergy())
ft_maker.set_variable_value("cluster_0_x",
cluster_pair[0].getPosition()[0])
ft_maker.set_variable_value("cluster_1_x",
cluster_pair[1].getPosition()[0])
ft_maker.set_variable_value("cluster_0_y",
cluster_pair[0].getPosition()[1])
ft_maker.set_variable_value("cluster_1_y",
cluster_pair[1].getPosition()[1])
ft_maker.set_variable_value(
"track_0_p",
np.linalg.norm(np.asarray(tracks[0].getMomentum())))
ft_maker.set_variable_value("track_0_px",
tracks[0].getMomentum()[0])
ft_maker.set_variable_value("track_0_py",
tracks[0].getMomentum()[1])
ft_maker.set_variable_value("track_0_pz",
tracks[0].getMomentum()[2])
ft_maker.set_variable_value("track_0_tanlambda",
tracks[0].getTanLambda())
ft_maker.set_variable_value("track_0_phi0", tracks[0].getPhi0())
ft_maker.set_variable_value("track_0_omega", tracks[0].getOmega())
ft_maker.set_variable_value("track_0_d0", tracks[0].getD0())
ft_maker.set_variable_value("track_0_z0", tracks[0].getZ0())
ft_maker.set_variable_value("track_0_charge",
tracks[0].getCharge())
ft_maker.set_variable_value(
"track_1_p",
np.linalg.norm(np.asarray(tracks[1].getMomentum())))
ft_maker.set_variable_value("track_1_px",
tracks[1].getMomentum()[0])
ft_maker.set_variable_value("track_1_py",
tracks[1].getMomentum()[1])
ft_maker.set_variable_value("track_1_pz",
tracks[1].getMomentum()[2])
ft_maker.set_variable_value("track_1_tanlambda",
tracks[1].getTanLambda())
ft_maker.set_variable_value("track_1_phi0", tracks[1].getPhi0())
ft_maker.set_variable_value("track_1_omega", tracks[1].getOmega())
ft_maker.set_variable_value("track_1_d0", tracks[1].getD0())
ft_maker.set_variable_value("track_1_z0", tracks[1].getZ0())
ft_maker.set_variable_value("track_1_charge",
tracks[1].getCharge())
ft_maker.fill()
ft_maker.close()
def preprocess(self, dst_file):
chain = r.TChain("HPS_Event")
chain.Add(dst_file)
tree = chain.GetTree()
from ROOT import HpsEvent
hps_event = HpsEvent()
branch = chain.SetBranchAddress("Event", r.AddressOf(hps_event))
# Create the file name for the preprocessed ROOT file
#output_file_name = root_file.GetName()[str(root_file.GetName()).rindex("/") + 1:-5]
output_file_name = "beam_tri_preprocessed_signal.root"
# Add variables to the ntuple
ft_maker = ft.FlatTupleMaker(output_file_name)
ft_maker.add_variable("cluster_0_energy")
ft_maker.add_variable("cluster_1_energy")
ft_maker.add_variable("cluster_0_x")
ft_maker.add_variable("cluster_1_x")
ft_maker.add_variable("cluster_0_y")
ft_maker.add_variable("cluster_1_y")
ft_maker.add_variable("track_0_p")
ft_maker.add_variable("track_0_px")
ft_maker.add_variable("track_0_py")
ft_maker.add_variable("track_0_pz")
ft_maker.add_variable("track_0_tanlambda")
ft_maker.add_variable("track_0_phi0")
ft_maker.add_variable("track_0_omega")
ft_maker.add_variable("track_0_d0")
ft_maker.add_variable("track_0_z0")
ft_maker.add_variable("track_0_charge")
ft_maker.add_variable("track_1_p")
ft_maker.add_variable("track_1_px")
ft_maker.add_variable("track_1_py")
ft_maker.add_variable("track_1_pz")
ft_maker.add_variable("track_1_tanlambda")
ft_maker.add_variable("track_1_phi0")
ft_maker.add_variable("track_1_omega")
ft_maker.add_variable("track_1_d0")
ft_maker.add_variable("track_1_z0")
ft_maker.add_variable("track_1_charge")
matcher = tcm.TrackClusterMatcher()
for entry in xrange(0, chain.GetEntries()):
if (entry+1)%1000 == 0 : print "Event " + str(entry+1)
chain.GetEntry(entry)
#if not au.is_good_data_event(hps_event) : continue
# Loop through all clusters in the event and find a 'good' pair.
# For now, a 'good' pair is defined as two clusters whose cluster
# time difference is less than 1.7 ns and greater than -1.6 ns
cluster_pair = au.get_good_cluster_pair(hps_event)
if not self.is_good_cluster_pair(cluster_pair) : continue
matcher.find_all_matches(hps_event)
tracks = [matcher.get_track(cluster_pair[0]), matcher.get_track(cluster_pair[1])]
if (tracks[0] is None) or (tracks[1] is None) : continue
ft_maker.set_variable_value("cluster_0_energy", cluster_pair[0].getEnergy())
ft_maker.set_variable_value("cluster_1_energy", cluster_pair[1].getEnergy())
ft_maker.set_variable_value("cluster_0_x", cluster_pair[0].getPosition()[0])
ft_maker.set_variable_value("cluster_1_x", cluster_pair[1].getPosition()[0])
ft_maker.set_variable_value("cluster_0_y", cluster_pair[0].getPosition()[1])
ft_maker.set_variable_value("cluster_1_y", cluster_pair[1].getPosition()[1])
ft_maker.set_variable_value("track_0_p", np.linalg.norm(np.asarray(tracks[0].getMomentum())))
ft_maker.set_variable_value("track_0_px", tracks[0].getMomentum()[0])
ft_maker.set_variable_value("track_0_py", tracks[0].getMomentum()[1])
ft_maker.set_variable_value("track_0_pz", tracks[0].getMomentum()[2])
ft_maker.set_variable_value("track_0_tanlambda", tracks[0].getTanLambda())
ft_maker.set_variable_value("track_0_phi0", tracks[0].getPhi0())
ft_maker.set_variable_value("track_0_omega", tracks[0].getOmega())
ft_maker.set_variable_value("track_0_d0", tracks[0].getD0())
ft_maker.set_variable_value("track_0_z0", tracks[0].getZ0())
ft_maker.set_variable_value("track_0_charge", tracks[0].getCharge())
ft_maker.set_variable_value("track_1_p", np.linalg.norm(np.asarray(tracks[1].getMomentum())))
ft_maker.set_variable_value("track_1_px", tracks[1].getMomentum()[0])
ft_maker.set_variable_value("track_1_py", tracks[1].getMomentum()[1])
ft_maker.set_variable_value("track_1_pz", tracks[1].getMomentum()[2])
ft_maker.set_variable_value("track_1_tanlambda", tracks[1].getTanLambda())
ft_maker.set_variable_value("track_1_phi0", tracks[1].getPhi0())
ft_maker.set_variable_value("track_1_omega", tracks[1].getOmega())
ft_maker.set_variable_value("track_1_d0", tracks[1].getD0())
ft_maker.set_variable_value("track_1_z0", tracks[1].getZ0())
ft_maker.set_variable_value("track_1_charge", tracks[1].getCharge())
ft_maker.fill()
ft_maker.close()
def preprocess(self, dst_file):
chain = r.TChain("HPS_Event")
chain.Add(dst_file)
tree = chain.GetTree()
from ROOT import HpsEvent
hps_event = HpsEvent()
branch = chain.SetBranchAddress("Event", r.AddressOf(hps_event))
# Add variables to the ntuple
ft_maker = ft.FlatTupleMaker(self.output_file_name)
ft_maker.add_variable("cluster_0_energy")
ft_maker.add_variable("cluster_1_energy")
ft_maker.add_variable("cluster_0_x")
ft_maker.add_variable("cluster_1_x")
ft_maker.add_variable("cluster_0_y")
ft_maker.add_variable("cluster_1_y")
ft_maker.add_variable("track_0_p")
ft_maker.add_variable("track_0_px")
ft_maker.add_variable("track_0_py")
ft_maker.add_variable("track_0_pz")
ft_maker.add_variable("track_0_theta")
ft_maker.add_variable("track_0_phi0")
ft_maker.add_variable("track_0_omega")
ft_maker.add_variable("track_0_d0")
ft_maker.add_variable("track_0_z0")
ft_maker.add_variable("track_1_p")
ft_maker.add_variable("track_1_px")
ft_maker.add_variable("track_1_py")
ft_maker.add_variable("track_1_pz")
ft_maker.add_variable("track_1_theta")
ft_maker.add_variable("track_1_phi0")
ft_maker.add_variable("track_1_omega")
ft_maker.add_variable("track_1_d0")
ft_maker.add_variable("track_1_z0")
ft_maker.add_variable("track_pair_p_sum")
matcher = tcm.TrackClusterMatcher()
for entry in xrange(0, chain.GetEntries()):
if (entry+1)%1000 == 0 : print "Event " + str(entry+1)
chain.GetEntry(entry)
# Loop through all clusters in the event and find a 'good' pair.
# For now, a 'good' pair is defined as two clusters whose cluster
# time difference is less than 1.6 ns and greater than -1.6 ns
cluster_pair = au.get_good_cluster_pair(hps_event)
if len(cluster_pair) != 2 : continue
matcher.find_all_matches(hps_event)
tracks = [matcher.get_track(cluster_pair[0]), matcher.get_track(cluster_pair[1])]
if (tracks[0] is None) or (tracks[1] is None) : continue
ft_maker.set_variable_value("cluster_0_energy", cluster_pair[0].getEnergy())
ft_maker.set_variable_value("cluster_1_energy", cluster_pair[1].getEnergy())
ft_maker.set_variable_value("cluster_0_x", cluster_pair[0].getPosition()[0])
ft_maker.set_variable_value("cluster_1_x", cluster_pair[1].getPosition()[0])
ft_maker.set_variable_value("cluster_0_y", cluster_pair[0].getPosition()[1])
ft_maker.set_variable_value("cluster_1_y", cluster_pair[1].getPosition()[1])
ft_maker.set_variable_value("track_0_p", np.linalg.norm(np.asarray(tracks[0].getMomentum())))
ft_maker.set_variable_value("track_0_px", tracks[0].getMomentum()[0])
ft_maker.set_variable_value("track_0_py", tracks[0].getMomentum()[1])
ft_maker.set_variable_value("track_0_pz", tracks[0].getMomentum()[2])
track_0_theta = math.fabs(math.pi/2 - math.acos(te.get_cos_theta(tracks[0])))
ft_maker.set_variable_value("track_0_theta", track_0_theta)
ft_maker.set_variable_value("track_0_phi0", tracks[0].getPhi0())
ft_maker.set_variable_value("track_0_omega", tracks[0].getOmega())
ft_maker.set_variable_value("track_0_d0", tracks[0].getD0())
ft_maker.set_variable_value("track_0_z0", tracks[0].getZ0())
ft_maker.set_variable_value("track_1_p", np.linalg.norm(np.asarray(tracks[1].getMomentum())))
ft_maker.set_variable_value("track_1_px", tracks[1].getMomentum()[0])
ft_maker.set_variable_value("track_1_py", tracks[1].getMomentum()[1])
ft_maker.set_variable_value("track_1_pz", tracks[1].getMomentum()[2])
track_1_theta = math.fabs(math.pi/2 - math.acos(te.get_cos_theta(tracks[1])))
ft_maker.set_variable_value("track_1_theta", track_1_theta)
ft_maker.set_variable_value("track_1_phi0", tracks[1].getPhi0())
ft_maker.set_variable_value("track_1_omega", tracks[1].getOmega())
ft_maker.set_variable_value("track_1_d0", tracks[1].getD0())
ft_maker.set_variable_value("track_1_z0", tracks[1].getZ0())
p_sum = np.linalg.norm(np.asarray(tracks[0].getMomentum())) + np.linalg.norm(np.asarray(tracks[1].getMomentum()))
ft_maker.set_variable_value("track_pair_p_sum", p_sum)
ft_maker.fill()
ft_maker.close()
def MDtest_iswrec(Nreal,minreal=0,justgetrho=0,dorho=1,Ndesbins=[2,3],lmin=3,lmax=80,rhofiletag='',nvss=True,fitbias=True):
rlzns=np.arange(minreal,minreal+Nreal)
cldat=MDtest_get_Cl(justread=False,Ndesbins=Ndesbins,nvss=nvss)
reclist=MDtest_get_reclist(Ndesbins=Ndesbins,lmin=lmin,lmax=lmax,nvss=nvss)
dummyglm=gmc.get_glm(cldat,Nreal=0,runtag=cldat.rundat.tag)
au.doiswrec_formaps(dummyglm,cldat,rlzns=rlzns,reclist=reclist,rhofiletag=rhofiletag,dos=False,fitbias=fitbias)
