def __init__(self, config, bin_center, bin_content, opt_t0, bkg_stat_noise, fit_boundary1, fit_boundary2, n_positron_hits):
super(Fourier, self).__init__(config)
self.bin_center = bin_center
self.bin_content = bin_content
self.opt_t0 = opt_t0
self.fit_boundary1 = fit_boundary1
self.fit_boundary2 = fit_boundary2
self.bkg_stat_noise = bkg_stat_noise
self.n_positron_hits = n_positron_hits
# create the TCanvas for all the plots
self.canvas_fourier = r.TCanvas('c_fourier', 'c_fourier', 900, 600)
style.setTCanvasStyle(self.canvas_fourier)
# create the TH1 to store the cosine and sine Fourier transforms
self.cosine_histogram = r.TH1D(
'cosine', 'cosine', self.n_freq_step, self.lower_freq, self.upper_freq)
self.sine_histogram = r.TH1D(
'sine', 'sine', self.n_freq_step, self.lower_freq, self.upper_freq)
# create the TFile that will store the various results
self.out_file2 = r.TFile(
'results/' + self.tag + '/results.root', 'UPDATE')
# when performing scan and wanting to keep in separate ROOT files
# the results from each scan point
if (self.print_plot >= 2):
self.out_file = r.TFile('results/' + self.tag + '/results_t0_{0:.5f}_tS_{1}_tM_{2}_df_{3}.root'.format(
self.opt_t0, self.tS, self.tM, self.freq_step_size), 'RECREATE')
def __init__(self, config):
super(FastRotation, self).__init__(config)
# define the time ranges to plot the various signal from tS t0 tS+self.times_to_plot in micro-seconds
self.times_to_plot = [1, 10, 50, 100, 200, self.tM]
# create the TCanvas for all the plots
canvas_fastrotation = r.TCanvas(
'c_fastrotation', 'c_fastrotation', 900, 600)
style.setTCanvasStyle(canvas_fastrotation)
# create output ROOT file to store the fast rotation signal
self.out_file = r.TFile(
'results/' + self.tag + '/fastrotation.root', 'RECREATE')
colorList = [
600, 601, 602, 603, 604, 599, 632, 633, 634, 635, 636, 631, 416, 417, 418,
419, 420, 415, 616, 617, 618, 619, 620, 615
]
statCeMean = []
statXeMean = []
statWMean = []
statT0Mean = []
statCeStd = []
statXeStd = []
statWStd = []
statT0Std = []
c = r.TCanvas('c', 'c', 900, 600)
style.setTCanvasStyle(c)
#leg = r.TLegend(0.895,0.28,0.95,0.925)
leg = r.TLegend(0.15, 0.25, 0.23, 0.925)
for i in range(0, 24):
fileList.append(
r.TFile('../results/60h_nominal_calo' + str(i + 1) + '/results.root'))
histoList.append(fileList[i].Get('rad'))
style.setTH1Style(histoList[i], '', 'Radius [mm]', 'Arbitrary units')
histoList[i].SetLineColor(colorList[i])
histoList[i].SetMarkerColor(colorList[i])
leg.AddEntry(histoList[i], ' calo ' + str(i + 1), "l")
if (i == 0):
histoList[i].Draw()
else:
def produce(self):
print(' ### Step 2/4: produce fast rotation signal\n')
# open input ROOT file containing the input positron counts histogram
in_file = r.TFile(self.root_file)
# retrive and style input histogram
self.histogram = in_file.Get(self.histo_name)
style.setTH1Style(self.histogram, '', 'Time [#mus]', 'Intensity')
# save input histogram to ROOT file
self.out_file.cd()
self.histogram.Write('positron_spectrum')
# define and style canvas (canvas local to 'produce' method because not needed elsewhere)
canvas_fastrotation = r.TCanvas(
'c_fastrotation', 'c_fastrotation', 900, 600)
style.setTCanvasStyle(canvas_fastrotation)
# vary statistics in each bin of the input histogram if option specified
if (self.stat_fluctuation):
self.histogram = self.apply_stat_fluctutation()
# rebin fast rotation histogram
if (self.n_fit_param == 0):
self.histogram.Rebin(self.rebin_frs_factor)
# plot input histogram if option specified
if (self.print_plot >= 2):
for time in self.times_to_plot:
plotting.plot(canvas_fastrotation, self.histogram, self.tag +
'/Intensity', self.tS, self.tS + time, self.tM)
# check number of hits in positron spectrum if option specified
if (self.check_positron_hits):
n_positron_hits = self.histogram.Integral(
self.histogram.FindBin(self.tS), self.histogram.FindBin(self.tM))
if (self.verbose > 1):
print(' Number of positron analyzed: ', n_positron_hits)
if (n_positron_hits < self.positron_hits_threshold):
print(' Warning -- low positron statistics: program exiting')
sys.exit(1)
else:
n_positron_hits = -1
# return histogram if no need for fit (typically for simulated fast rotation signals)
if (self.n_fit_param == 0):
# optimize tS and tM
opt_tS, opt_tM = self.optimize_tS_tM()
bin_center, bin_content = self.return_frs_np_array(self.histogram)
# save signal to ROOT file
self.out_file.cd()
self.histogram.Write('fast_rotation')
return opt_tS, opt_tM, n_positron_hits, bin_center, bin_content
# clone input histogram to produce wiggle plot (histogram local to 'produce' method because not needed elsewhere)
wiggle_histogram = self.histogram.Clone()
# rebin cloned histograms
wiggle_histogram.Rebin(self.rebin_wiggle_factor)
# plot wiggle plot if option specified
if (self.print_plot >= 2):
for time in self.times_to_plot:
plotting.plot(canvas_fastrotation, wiggle_histogram, self.tag +
'/Wiggle', self.tS, self.start_fit_time + time, self.tM)
# fit wiggle plot (fit local to 'produce' method because not needed elsewhere)
wiggle_fit = self.fit_wiggle(wiggle_histogram)
# plot fitted wiggle plot if option specified
if (self.print_plot >= 2):
for time in self.times_to_plot:
plotting.plot(canvas_fastrotation, wiggle_histogram, self.tag +
'/FittedWiggle', self.tS, self.start_fit_time + time, self.tM, wiggle_fit)
# create histogram of the fit residuals
residuals = wiggle_histogram
residuals.GetYaxis().SetTitle('Residual [%]')
for i in range(wiggle_histogram.GetNbinsX()):
if (wiggle_histogram.GetBinContent(i+1) != 0):
residuals.SetBinContent(i+1, (wiggle_histogram.GetBinContent(i+1)-wiggle_fit.Eval(
wiggle_histogram.GetBinCenter(i+1)))/wiggle_histogram.GetBinContent(i+1)*100)
# plot histogram of residuals if option specified
if (self.print_plot >= 2):
for time in self.times_to_plot:
plotting.plot(canvas_fastrotation, residuals, self.tag + '/Residuals',
self.tS, self.start_fit_time + time, self.tM)
plotting.plot(canvas_fastrotation, residuals, self.tag + '/Residuals',
self.start_fit_time, self.start_fit_time + time, self.tM)
# create fast rotation histogram
for i in range(self.histogram.GetNbinsX()):
self.histogram.SetBinContent(i+1, self.histogram.GetBinContent(i+1)/(
wiggle_fit.Eval(self.histogram.GetBinCenter(i+1))/self.rebin_wiggle_factor))
# rebin fast rotation histogram if option specified
self.histogram.Rebin(self.rebin_frs_factor)
# optimize tS and tM
opt_tS, opt_tM = self.optimize_tS_tM()
# plot frs plot if option specified
if (self.print_plot >= 1):
plotting.plot(canvas_fastrotation, self.histogram,
self.tag + '/FRS', 0, 10, self.tM)
for time in self.times_to_plot:
plotting.plot(canvas_fastrotation, self.histogram, self.tag +
'/FRS', round(opt_tS, 6), round(opt_tS + time, 6), round(opt_tM, 6))
# save signal to ROOT file
self.out_file.cd()
self.histogram.Write('fast_rotation')
# retrieve arrays of bin contents and bin centers
bin_center, bin_content = self.return_frs_np_array(self.histogram)
# return results
return opt_tS, opt_tM, n_positron_hits, bin_center, bin_content