def StorageLifetime(ex, FitResult=None):
print('\nAnalyzing TCN{0} in runs {1}'.format(ex['TCN'], ex['runs']))
if len(ex['start']) == 0:
print('Found no cycles with run numbers {0}!'.format(ex['runs']))
return
# report start time
print(datetime.datetime.fromtimestamp(min(ex['start'])))
# report range of beam current
print('Beam current from {0:.3} to {1:.3} uA'.format(
min(min(c) for c in ex['beamcurrent']),
max(max(c) for c in ex['beamcurrent'])))
# report range of temperature and vapor pressure
print('Temperatures from {0:.3} to {1:.3} K'.format(
min(ex['mintemperature']), max(ex['maxtemperature'])))
print('Vapor pressure from {0:.3} to {1:.3} torr'.format(
min(ex['minvaporpressure']), max(ex['maxvaporpressure'])))
beam = [numpy.mean(cur) for cur in ex['beamcurrent']
], [numpy.std(cur) for cur in ex['beamcurrent']]
canvas = ROOT.TCanvas('c1', 'c1')
canvas.SetLogy()
for det in ['li6', 'he3']:
ex[det +
'backgroundrate'], ex[det +
'backgroundrateerr'] = UCN.BackgroundRate(
ex[det + 'background'],
ex['backgroundduration'])
ex[det + 'irradiationrate'], ex[
det +
'irradiationrateerr'] = UCN.SubtractBackgroundAndNormalizeRate(
ex[det + 'irradiation'], ex['irradiationduration'], det,
beam[0], beam[1])
print(det + ' detector background rate: {0:.4} +/- {1:.2} 1/s'.format(
ex[det + 'backgroundrate'], ex[det + 'backgroundrateerr']))
x = numpy.array(ex['storageduration'])
xerr = numpy.array([0. for _ in x])
# subtract background from UCN counts
ex[det + 'counts_normalized'], ex[
det +
'counts_normalized_err'] = UCN.SubtractBackgroundAndNormalize(
ex[det + 'counts'], ex['countduration'], det, beam[0], beam[1])
y = numpy.array(ex[det + 'counts_normalized'])
yerr = numpy.array(ex[det + 'counts_normalized_err'])
# plot normalized Li6 counts vs storage time
graph = ROOT.TGraphErrors(len(x), x, y, xerr, yerr)
graph.SetTitle(
'TCN{0} ({1} detector, single exponential fit, background subtracted, normalized to beam current)'
.format(ex['TCN'], det))
graph.GetXaxis().SetTitle('Storage time (s)')
graph.GetYaxis().SetTitle('UCN count (#muA^{-1})')
# graph.SetMarkerStyle(20)
# do single exponential fit
f = graph.Fit(UCN.SingleExpo(), 'SQB')
if sum(y) > 0 and f.Error(1) < 5.:
ex[det + 'tau'] = f.Parameter(1)
ex[det + 'tauerr'] = f.Error(1) * max(f.Chi2() / f.Ndf(), 1.)
else:
print(
'SKIPPING lifetime measurement from {0} detector in run(s) {1} because there were no counts detected or the error is larger than 5 s.'
.format(det, ex['runs']))
ex[det + 'tau'] = 0.
ex[det + 'tauerr'] = 0.
print(
'{0:.4} +/- {1:.2} ({2} detector, single exponential fit, background subtracted, normalized to beam current)'
.format(ex[det + 'tau'], ex[det + 'tauerr'], det))
graph.Draw('AP')
pdf = 'TCN{0}_{1}.pdf'.format(ex['TCN'], ex['runs'][0])
if det == 'li6':
canvas.Print(pdf + '(')
else:
canvas.Print(pdf)
if FitResult:
r = DoCombinedFit([ex], pdf, FitResult)
print('{0:.4} +/- {1:.2} (wall-storage lifetime from combined fit)'.
format(ex['tau_wall'], ex['tau_wallerr']))
canvas.SetLogy(0)
UCN.PrintTemperatureVsCycle(ex, pdf)
ex['channels'].Draw()
canvas.Print(pdf + ')')
# return result from primary detector
if max(ex['li6counts']) > max(ex['he3counts']):
ex['tau'] = ex['li6tau']
ex['tauerr'] = ex['li6tauerr']
else:
ex['tau'] = ex['he3tau']
ex['tauerr'] = ex['he3tauerr']
def StorageLifetime(ex):
print('\nAnalyzing TCN' + ex['TCN'])
if len(ex['start']) == 0:
print('Found no cycles with run numbers {0}!'.format(ex['runs']))
return
ex['li6backgroundrate'], ex['li6backgroundrateerr'] = UCN.BackgroundRate(
ex['li6background'], ex['backgroundduration'])
print('Li6 detector background rate: {0} +/- {1} 1/s'.format(
ex['li6backgroundrate'], ex['li6backgroundrateerr']))
beam = [numpy.mean(cur) for cur in ex['beamcurrent']
], [numpy.std(cur) for cur in ex['beamcurrent']]
ex['li6irradiationrate'], ex[
'li6irradiationrateerr'] = UCN.SubtractBackgroundAndNormalizeRate(
ex['li6irradiation'], ex['irradiationduration'], 'li6', beam[0],
beam[1])
# report average monitor counts, range of beam current, range of He-II temperature
monitoravg = numpy.average(ex['monitorcounts'], None,
[1. / m for m in ex['monitorcounts']], True)
print 'Monitor counts: {0} +/- {1}'.format(monitoravg[0],
1. / math.sqrt(monitoravg[1]))
print('Beam current from {0} to {1} uA'.format(
min(min(c) for c in ex['beamcurrent']),
max(max(c) for c in ex['beamcurrent'])))
print 'Temperatures from {0} to {1} K'.format(min(ex['mintemperature']),
max(ex['maxtemperature']))
x = ex['storageduration']
xerr = [0. for _ in ex['storageduration']]
y, yerr = UCN.SubtractBackgroundAndNormalize(
ex['li6counts'], ex['countduration'], 'li6', ex['monitorcounts'],
[math.sqrt(m) for m in ex['monitorcounts']])
# plot normalized, background corrected counts vs storage time
graph = ROOT.TGraphErrors(len(x), numpy.array(x), numpy.array(y),
numpy.array(xerr), numpy.array(yerr))
graph.SetTitle(
'TCN{0} (single exponential fit, with background subtracted, normalized to monitor detector)'
.format(ex['TCN']))
graph.GetXaxis().SetTitle('Storage time (s)')
graph.GetYaxis().SetTitle('UCN-count-to-monitor ratio')
# graph.SetMarkerStyle(20)
# do single exponential fit
f = graph.Fit(UCN.SingleExpo(), 'SQB', '', 0., 1000.)
canvas = ROOT.TCanvas('c', 'c')
canvas.SetLogy()
graph.Draw('AP')
pdf = 'TCN{0}.pdf'.format(ex['TCN'])
canvas.Print(pdf + '(')
ex['tau'] = f.GetParams()[1]
ex['tauerr'] = f.GetErrors()[1]
print(
'{0} +/- {1} (single exponential fit, with background subtracted, normalized to monitor detector)'
.format(ex['tau'], ex['tauerr']))
#do single exponential fit with data point at 0 excluded
f = graph.Fit(UCN.SingleExpo(), 'SQB', '', 1., 1000.)
graph.SetTitle(
'TCN{0} (single exponential fit, with background subtracted, normalized to monitor detector, 0s excluded)'
.format(ex['TCN']))
graph.Draw('AP')
canvas.Print(pdf)
# do double exponential fit
graph.SetTitle(
'TCN{0} (double exponential fit, with background subtracted, normalized to monitor detector)'
.format(ex['TCN']))
f = graph.Fit(UCN.DoubleExpo(), 'SQB')
graph.Draw('AP')
canvas.Print(pdf)
print(
'{0} +/- {1}, {2} +/- {3} (double exponential fit, with background subtracted, normalized to monitor detector)'
.format(f.GetParams()[1],
f.GetErrors()[1],
f.GetParams()[3],
f.GetErrors()[3]))
# plot uncorrected UCN counts
y = [float(c) for c in ex['li6counts']]
yerr = [math.sqrt(c) for c in ex['li6counts']]
graph = ROOT.TGraphErrors(len(x), numpy.array(x), numpy.array(y),
numpy.array(xerr), numpy.array(yerr))
graph.SetTitle(
'TCN{0} (single exponential fit + background, unnormalized)'.format(
ex['TCN']))
graph.GetXaxis().SetTitle('Storage time (s)')
graph.GetYaxis().SetTitle('UCN count')
# do single exponential fit with background
f = graph.Fit(UCN.SingleExpoWithBackground(), 'SQB')
graph.Draw('AP')
canvas.Print(pdf)
print(
'{0} +/- {1} (single exponential fit with {2} +/- {3} background, unnormalized)'
.format(f.GetParams()[1],
f.GetErrors()[1],
f.GetParams()[2],
f.GetErrors()[2]))
mtau = []
mtauerr = []
for he3rate, m, s in zip(ex['he3rate'], ex['monitorduration'],
ex['storageduration']):
fitstart = m + 5
fitend = m + s
if fitend > fitstart + 10 and he3rate.Integral(
he3rate.FindBin(fitstart), he3rate.FindBin(fitend)) > 0:
f = he3rate.Fit(UCN.SingleExpo(), 'SQB', '', fitstart, fitend)
he3rate.Draw()
canvas.Print(pdf) # print fitted He3 rate to pdf
mtau.append(f.GetParams()[1])
mtauerr.append(f.GetErrors()[1])
# print average storage lifetime from He3 fits to pdf
canvas = ROOT.TCanvas('c', 'c')
if len(mtau) > 0:
tauavg = numpy.average(mtau, None, [1. / dt**2 for dt in mtauerr],
True)
label = ROOT.TLatex(0.1, 0.6, 'Average lifetime from He3 data:')
label.Draw()
label2 = ROOT.TLatex(
0.1, 0.5, '#tau = {0} +/- {1} s'.format(tauavg[0],
1. / tauavg[1]**2))
label2.Draw()
print(
'{0} +/- {1} (single exponential fit to rate in monitor detector during storage period)'
.format(tauavg[0], 1. / tauavg[1]**2))
canvas.Print(pdf + ')')
def StorageLifetime(ex):
print('\nAnalyzing TCN' + ex['TCN'])
if len(ex['start']) == 0:
print('Found no cycles with run numbers {0}!'.format(ex['runs']))
return
canvas = ROOT.TCanvas('c', 'c')
pdf = 'TCN{0}.pdf'.format(ex['TCN'])
# subtract background from Li6 counts and normalize to monitor counts
y, yerr = UCN.SubtractBackgroundAndNormalize(ex['li6counts'], ex['countduration'], 'li6', ex['monitorcounts2'], [math.sqrt(m) for m in ex['monitorcounts2']])
x = ex['storageduration']
xerr = [0. for _ in ex['storageduration']]
# plot normalized, background corrected counts vs storage time
graph = ROOT.TGraphErrors(len(x), numpy.array(x), numpy.array(y), numpy.array(xerr), numpy.array(yerr))
graph.SetTitle('TCN{0} (single exponential fit, with background subtracted, normalized to monitor detector)'.format(ex['TCN']))
graph.GetXaxis().SetTitle('Storage time (s)')
graph.GetYaxis().SetTitle('UCN-count-to-monitor ratio')
graph.SetMarkerStyle(20)
canvas.SetLogy()
#do single exponential fit with data point at 0 excluded
f = graph.Fit(UCN.SingleExpo(), 'SQB', '', 1., 1000.)
graph.SetTitle('TCN{0} (single exponential fit, with background subtracted, normalized to monitor detector, 0s excluded)'.format(ex['TCN']))
graph.Draw('AP')
canvas.Print(pdf + '(')
ex['tau'] = f.GetParams()[1]
ex['tauerr'] = f.GetErrors()[1]*max(math.sqrt(f.Chi2()/f.Ndf()), 1.0)
# do double exponential fit
graph.SetTitle('TCN{0} (double exponential fit, with background subtracted, normalized to monitor detector, 0s excluded)'.format(ex['TCN']))
f = graph.Fit(UCN.DoubleExpo(), 'SQB', '', 1., 1000.)
graph.Draw('AP')
canvas.Print(pdf)
print('{0} +/- {1}, {2} +/- {3} (double exponential fit, with background subtracted, normalized to monitor detector, 0s excluded)'.format(f.GetParams()[1], f.GetErrors()[1], f.GetParams()[3], f.GetErrors()[3]))
# plot uncorrected UCN counts
# y = [float(c) for c in ex['li6counts']]
# yerr = [math.sqrt(c) for c in ex['li6counts']]
# graph = ROOT.TGraphErrors(len(x), numpy.array(x), numpy.array(y), numpy.array(xerr), numpy.array(yerr))
# graph.SetTitle('TCN{0} (single exponential fit + background, unnormalized)'.format(ex['TCN']))
# graph.GetXaxis().SetTitle('Storage time (s)')
# graph.GetYaxis().SetTitle('UCN count')
# do single exponential fit with background
# f = graph.Fit(UCN.SingleExpoWithBackground(), 'SQB')
# graph.Draw('AP')
# canvas.Print(pdf)
# print('{0} +/- {1} (single exponential fit with {2} +/- {3} background, unnormalized)'.format(f.GetParams()[1], f.GetErrors()[1], f.GetParams()[2], f.GetErrors()[2]))
# draw plot of temperature during each cycle
UCN.PrintTemperatureVsCycle(ex, pdf)
mtau = []
mtauerr = []
# fit single exponential to He3 count-rate histogram during storage period (pinhole method)
for he3rate, m, s, c in zip(ex['he3rate'], ex['monitorduration'], ex['storageduration'], ex['countduration']):
fitstart = m + 5
fitend = m + s
# if not ex['pinhole']:
# fitend = fitend + c
if fitend > fitstart + 10 and he3rate.Integral(he3rate.FindBin(fitstart), he3rate.FindBin(fitend)) > 0:
f = he3rate.Fit(UCN.SingleExpo(), 'SQB', '', fitstart, fitend)
he3rate.SetTitle('TCN{0} (He3 rate)'.format(ex['TCN']))
he3rate.Draw()
canvas.Print(pdf) # print fitted He3 rate to pdf
mtau.append(f.GetParams()[1])
mtauerr.append(f.GetErrors()[1]*max(math.sqrt(f.Chi2()/f.Ndf()), 1.0))
# print average storage lifetime from He3 fits to pdf
canvas = ROOT.TCanvas('c','c')
if len(mtau) > 0:
he3tau = ROOT.TGraphErrors(len(mtau), numpy.array(ex['cyclenumber']), numpy.array(mtau), numpy.array([0. for _ in mtau]), numpy.array(mtauerr))
fit = he3tau.Fit('pol0', 'SQ')
ex['pinholetau'] = fit.Parameter(0)
ex['pinholetauerr'] = fit.ParError(0)*max(math.sqrt(f.Chi2()/f.Ndf()), 1.0)
he3tau.SetMarkerStyle(20)
he3tau.GetXaxis().SetTitle('Cycle')
he3tau.GetYaxis().SetTitle('Pinhole storage lifetime (s)')
he3tau.SetTitle('')
he3tau.Draw('AP')
print('{0} +/- {1} (single exponential fit to rate in monitor detector during storage period)'.format(fit.Parameter(0), fit.ParError(0)))
else:
ex['pinholetau'] = 0.
ex['pinholetauerr'] = 0.
canvas.Print(pdf)
# draw plot of Li6 background rate during each cycle
ex['li6backgroundrate'], ex['li6backgroundrateerr'] = UCN.PrintBackgroundVsCycle(ex, pdf, 'li6')
print('Li6 detector background rate: {0} +/- {1} 1/s'.format(ex['li6backgroundrate'], ex['li6backgroundrateerr']))
beam = [numpy.mean(cur) for cur in ex['beamcurrent']], [numpy.std(cur) for cur in ex['beamcurrent']]
# subtract background from Li6 counts during irradiation and normalize to beam current, draw plot for each cycle
ex['li6irradiationrate'], ex['li6irradiationrateerr'] = UCN.SubtractBackgroundAndNormalizeRate(ex['li6irradiation'], ex['irradiationduration'], 'li6', beam[0], beam[1])
UCN.PrintIrradiationBackgroundVsCycle(ex, pdf, 'li6')
# report average monitor counts, range of beam current, range of He-II temperature
monitoravg = numpy.average(ex['monitorcounts2'], None, [1./m for m in ex['monitorcounts2']], True)
print('Monitor counts: {0} +/- {1}'.format(monitoravg[0], 1./math.sqrt(monitoravg[1])))
print('Beam current from {0} to {1} uA'.format(min(min(c) for c in ex['beamcurrent']), max(max(c) for c in ex['beamcurrent'])))
print('Temperatures from {0} to {1} K'.format(min(ex['mintemperature']), max(ex['maxtemperature'])))
# draw Li6 channel histogram
ex['channels'].Draw()
canvas.Print(pdf + ')')