# print TrackerHits,len(RecoTracks)
#get track efficiency
eff = hf.GetTrackEfficiency(stripTolerance[0] * pitch, XY, mXmY,
RecoTracks, TrackerZ[0:2], pitch)
trackEfficiency.append(100 * eff)
#calculate track ambiguity rate
nFakeTracks = len(RecoTracks) - (nProton * eff)
if len(RecoTracks) > 0:
trackAmbiguity.append(100 * (len(RecoTracks) - nProton) /
len(RecoTracks))
correctedTrackAmbiguity.append(100 * nFakeTracks / len(RecoTracks))
if saveStripMaps:
hf.DrawTrackMap("TrackMap", RecoTracks, XY, xmax)
################### REAR TRACKERS #######################################################
phantomdepth = 30.0 #cm
energy = 230.0 #MeV
XY_MS, mXmY_MS = hf.ApplyMultipleScattering(
XY, mXmY, phantomdepth, energy, (ZMeans[2] + ZMeans[1]) / 2)
#XY_MS,mXmY_MS=XY,mXmY
#reconstruct hits for each tracker module
TrackerHits = []
MaxNStrips = []
for module in [2, 3, 4]:
#convert simulated protons to strips
nTracks.append(len(RecoTracks))
#get track efficiency
eff = hf.GetTrackEfficiency(stripTolerance * pitch, XY, mXmY,
RecoTracks, TrackerZ, pitch)
trackEfficiency.append(100 * eff)
#calculate track ambiguity rate
nFakeTracks = len(RecoTracks) - (nProton * eff)
if len(RecoTracks) > 0:
trackAmbiguity.append(100 * (len(RecoTracks) - nProton) /
len(RecoTracks))
correctedTrackAmbiguity.append(100 * nFakeTracks / len(RecoTracks))
if saveStripMaps and len(TrackerAngles) > 1:
hf.DrawTrackMap("TrackMap", RecoTracks, XY, xmax)
for module in range(len(TrackerAngles)):
print "Module ", module, ": Hits= ", sum(
nTrackerHits[module]), " Ambiguity= ", np.mean(
ambiguity[module]), "%", " Efficiency=", np.mean(
trackerEffs[module]), "Purity= ", 100 - np.mean(
correctedAmbiguity[module]), "%"
print "Combined: Tracks= ", sum(nTracks), " Ambiguity= ", np.mean(
trackAmbiguity), "%", " Efficiency=", np.mean(
trackEfficiency
), "Purity= ", 100 - np.mean(correctedTrackAmbiguity), "%"
print "Errors: Eff. Error=", np.std(trackEfficiency) / math.sqrt(
NLoops), "Purity Err", np.std(correctedTrackAmbiguity) / math.sqrt(
NLoops), "%"
