def add_statistics(self, histo, pkl_name = None, fit = None) :
print 'histo %f .. %f with %d bins' % (histo.GetBinLowEdge(1), histo.GetXaxis().GetBinUpEdge(histo.GetNbinsX()), histo.GetNbinsX())
if pkl_name == None : pkl_name = self.name
histos = {}
histos['data'] = histo
res = {}
res['mean' ] = histo.GetMean()
res['mean_err'] = histo.GetMeanError()
print 'Mean: %f' % res['mean']
histos['stat'] = ROOT.TH1F('%s_stat' % pkl_name, 'stat', histo.GetNbinsX(), 0., 1.)
histos['stat'].SetBinContent(1, self.run_no )
histos['stat'].SetBinError (1, 0 )
histos['stat'].SetBinContent(2, res['mean' ])
histos['stat'].SetBinError (2, res['mean_err'])
histos['stat'].SetBinContent(3, histo.GetRMS ())
histos['stat'].SetBinError (3, histo.GetRMSError())
histos['stat'].SetBinContent(4, histo.Integral())
histos['stat'].SetBinContent(7, self.nstrips)
if self.run_config_file != '' and self.run_config.has_section('%d' % self.run_no) and self.det_type == 'Dia' :
histos['stat'].SetBinContent(5, eval(self.run_config.get('%d' % self.run_no, 'calibration' )))
histos['stat'].SetBinError (5, eval(self.run_config.get('%d' % self.run_no, 'calibration_err')))
histos['stat'].SetBinContent(6, eval(self.run_config.get('%d' % self.run_no, 'fluence' ))/1e15)
histos['stat'].SetBinError (6, eval(self.run_config.get('%d' % self.run_no, 'fluence_err'))/1e15)
if fit != None :
for i in [0, 1, 2] :
histos['stat'].SetBinContent(i+10, fit.GetParameter(i))
histos['stat'].SetBinError (i+10, fit.GetParError(i))
res['sigma' ] = fit.GetParameter(2)
res['sigma_err'] = fit.GetParError(2)
mean = (fit.GetParameter(1), fit.GetParError(1))
sigma = (fit.GetParameter(2), fit.GetParError(2))
print 'Mean : %s +- %s' % rn.get_roundedNumber(*mean )
print 'Sigma: %s +- %s' % rn.get_roundedNumber(*sigma)
helper.save_object(res, '%s%s_stat.pkl' % (self.output_path, pkl_name))
return histos
def plot(self) :
print '[status] plotting %s..' % self.name
rd42Style()
if self.histo_type == 'PulseHeight' :
ROOT.gStyle.SetOptStat('m')
if self.histo_type == 'Noise' :
ROOT.gStyle.SetOptFit(01111)
# ROOT.gStyle.SetDrawOption('colz')
# ROOT.gStyle.SetCanvasDefW(1200)
canvas = ROOT.TCanvas('%s_%s' % (self.name, self.rand.Integer(10000)), 'canvas')
histo = self.get_histo()
if 'PulseHeight_Cluster1-' in self.histo_type :
for i in range(1, self.nstrips) :
if self.position != '' :
path_tmp = self.path.rstrip('%d/%s/' % (self.run_no, self.position))
else :
path_tmp = self.path.rstrip('%d/' % self.run_no)
pl = plotter(self.config_file, path_tmp, self.output_path.rstrip('%d' % self.run_no), self.run_no, self.position, 'PulseHeight_Cluster%d_%s' % (i, self.histo_type.split('_')[-1]), self.run_config_file)
histo_tmp = pl.get_histo()
histo.Add(histo_tmp)
canvas.cd()
histo.Draw(self.draw_opt)
histo.GetXaxis().SetTitle(self.xTitle)
histo.GetYaxis().SetTitle(self.yTitle)
self.draw_rd42Line()
canvas.UseCurrentStyle()
if histo.GetListOfFunctions().FindObject('palette') == None :
histo.SetMaximum(1.4 * histo.GetMaximum())
else :
# canvas.SetWindowSize(1200, 600)
canvas.SetRightMargin(canvas.GetRightMargin() + 0.08)
pal = histo.GetListOfFunctions().FindObject('palette')
pal_offset = 0.012
pal.SetX1NDC(1. - canvas.GetRightMargin() + pal_offset)
pal.SetX2NDC(1. - canvas.GetRightMargin() + pal_offset + 0.82*histo.GetZaxis().GetTickLength())
pal.SetY1NDC(canvas.GetBottomMargin())
pal.SetY2NDC(1. - canvas.GetTopMargin())
if self.histo_type == 'FidCut' or self.histo_type == 'TrackPos' :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.name), rebinx = 4, rebiny = 4, xmin = 48., ymin = 48., sfx = 0.05, sfy = 0.05)
fid_cut = self.get_fidCut()
fid_cut.SetLineColor(ROOT.kRed)
# fid_cut.Dump()
canvas.cd()
fid_cut.Draw('same')
if 'Alignment_Plane2_YPred_DeltaX' in self.histo_type :
if 'Post' in self.histo_type :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.name), rebinx = 3, rebiny = 5, xmin = 3000., ymin = -15., nxbins = 30, nybins = 96, sfx = 0.001, sfy = 1.)
else :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.name), rebinx = 4, rebiny = 2, xmin = 3000., ymin = -260., nxbins = 32, nybins = 146, sfx = 0.001, sfy = 1.)
return
if 'Alignment_Plane2_XPred_DeltaY' in self.histo_type :
if 'Post' in self.histo_type :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.name), rebinx = 4, rebiny = 13, xmin = 3000., ymin = -6.2, nxbins = 30, nybins = 37, sfx = 0.001, sfy = 1.)
else :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.name), rebinx = 4, rebiny = 2, xmin = 3000., ymin = -540., nxbins = 32, nybins = 148, sfx = 0.001, sfy = 1.)
return
if self.histo_type == 'StripMeanPedestal_Dia' :
profile = histo.ProfileX('profile', 1, -1, 'os')
nbins = profile.GetNbinsX()
sigma_histo = ROOT.TH1F('ped_sigma', 'ped_sigma', nbins, profile.GetBinLowEdge(1), profile.GetXaxis().GetBinUpEdge(nbins))
for ibin in range(1, nbins+1) :
projection = histo.ProjectionY('tmp%d' % ibin, ibin, ibin, 'o')
sigma_histo.SetBinContent(ibin, projection.GetRMS())
sigma_histo.SetBinError (ibin, projection.GetRMSError())
print profile.GetEntries()
print profile.GetBinLowEdge(1)
print profile.GetBinContent(1)
print profile.GetBinError(1)
histos = self.add_statistics(profile)
print histos['data'].GetEntries()
print histos['data'].GetBinLowEdge(1)
print histos['data'].GetBinContent(1)
print histos['data'].GetBinError(1)
histos['sigma'] = sigma_histo
processes = ['data', 'sigma', 'stat']
print profile
self.save_histo2table(histos = histos, processes = processes, path = '%s%s.dat' % (self.output_path, self.name), var = self.variable, bin_width = False)
# sigma spectra
histos = self.add_statistics(sigma_histo)
processes = ['data', 'stat']
self.save_histo2table(histos = histos, processes = processes, path = '%s%s.dat' % (self.output_path, 'StripPedestalSigma_Dia'), var = 'StripPedestalSigma', bin_width = False)
return
if self.histo_type == 'PulseHeight_ClusterSize' or self.histo_type == 'Eta_Dia_Area' :
processes = ['data', 'stat']
slice_name = self.histo_type
if self.histo_type == 'Eta_Dia_Area' :
slice_name = 'Eta_Area'
slices = self.get_histoSlices(histo, path = self.output_path, slice_name = slice_name)
for projection in slices :
if '-' in projection :
self.nstrips = int(projection.lstrip('1-'))
else :
self.nstrips = int(projection)
print self.nstrips
histos = self.add_statistics(slices[projection], slices[projection].GetName())
self.save_histo2table(histos = histos, processes = processes, path = '%s%s.dat' % (self.output_path, slices[projection].GetName()), var = self.variable, bin_width = False)
return
canvas.Update()
# ROOT.gPad.Update()
# canvas.Dump()
# raw_input('ok?')
processes = ['data',]
if self.return_value == 'mean' :
histos = self.add_statistics(histo)
processes.append('stat')
elif self.return_value == 'sigma' :
fit = histo.GetListOfFunctions().FindObject('histofitx')
histos = self.add_statistics(histo, fit = fit)
processes.append('stat')
mean = (fit.GetParameter(1), fit.GetParError(1))
sigma = (fit.GetParameter(2), fit.GetParError(2))
print 'Mean : %s +- %s' % rn.get_roundedNumber(*mean )
print 'Sigma: %s +- %s' % rn.get_roundedNumber(*sigma)
entries = []
entries.append(('$\\chi^2 / \\ndof$', '{%.0f / %d}' % (fit.GetChisquare(), fit.GetNDF())))
entries.append(('Mean' , '%s +- %s' % rn.get_roundedNumber(fit.GetParameter(1), fit.GetParError(1))))
entries.append(('Sigma', '%s +- %s' % rn.get_roundedNumber(fit.GetParameter(2), fit.GetParError(2))))
print entries
# ROOT.gStyle.SetOptFit(0)
# self.draw_statbox(entries)
if self.histo_type != 'FidCut' and self.histo_type != 'TrackPos' :
self.save_histo2table(histos = histos, processes = processes, path = '%s%s.dat' % (self.output_path, self.name), var = self.variable, bin_width = False)
canvas.Print('%s%s.pdf' % (self.output_path, self.name))
canvas.Print('%s%s.tex' % (self.output_path, self.name))
return -1.
def plot(self) :
rd42Style()
if self.histo_type == 'PulseHeight' :
ROOT.gStyle.SetOptStat('m')
if self.histo_type == 'Noise' :
ROOT.gStyle.SetOptFit(01111)
# ROOT.gStyle.SetDrawOption('colz')
# ROOT.gStyle.SetCanvasDefW(1200)
canvas = ROOT.TCanvas('%s_%s' % (self.histo_name, self.rand.Integer(10000)), 'canvas')
histo = self.get_histo()
histos = {}
histos['data'] = histo
canvas.cd()
histo.Draw(self.draw_opt)
histo.GetXaxis().SetTitle(self.xTitle)
histo.GetYaxis().SetTitle(self.yTitle)
self.draw_rd42Line()
canvas.UseCurrentStyle()
if histo.GetListOfFunctions().FindObject('palette') == None :
histo.SetMaximum(1.4 * histo.GetMaximum())
else :
# canvas.SetWindowSize(1200, 600)
canvas.SetRightMargin(canvas.GetRightMargin() + 0.08)
pal = histo.GetListOfFunctions().FindObject('palette')
pal_offset = 0.012
pal.SetX1NDC(1. - canvas.GetRightMargin() + pal_offset)
pal.SetX2NDC(1. - canvas.GetRightMargin() + pal_offset + 0.82*histo.GetZaxis().GetTickLength())
pal.SetY1NDC(canvas.GetBottomMargin())
pal.SetY2NDC(1. - canvas.GetTopMargin())
if self.histo_type == 'FidCut' :
self.save_TH2histo2table(histo, path = '%s%s.dat' % (self.output_path, self.histo_name))
fid_cut = self.get_fidCut()
fid_cut.SetLineColor(ROOT.kRed)
# fid_cut.Dump()
canvas.cd()
fid_cut.Draw('same')
canvas.Update()
# ROOT.gPad.Update()
# canvas.Dump()
# raw_input('ok?')
processes = ['data',]
if self.return_value == 'mean' :
mean = histo.GetMean()
print 'Mean: %f' % mean
elif self.return_value == 'sigma' :
fit = histo.GetListOfFunctions().FindObject('histofitx')
histos['fit'] = ROOT.TH1F('%s_fit' % self.histo_type, 'fit', histo.GetNbinsX(), 0., 1.)
for i in [0, 1, 2] :
histos['fit'].SetBinContent(i+1, fit.GetParameter(i))
histos['fit'].SetBinError (i+1 , fit.GetParError(i))
processes.append('fit')
mean = (fit.GetParameter(1), fit.GetParError(1))
sigma = (fit.GetParameter(2), fit.GetParError(2))
print 'Mean : %s +- %s' % rn.get_roundedNumber(*mean )
print 'Sigma: %s +- %s' % rn.get_roundedNumber(*sigma)
entries = []
entries.append(('$\\chi^2 / \\ndof$', '{%.0f / %d}' % (fit.GetChisquare(), fit.GetNDF())))
entries.append(('Mean' , '%s +- %s' % rn.get_roundedNumber(fit.GetParameter(1), fit.GetParError(1))))
entries.append(('Sigma', '%s +- %s' % rn.get_roundedNumber(fit.GetParameter(2), fit.GetParError(2))))
print entries
# ROOT.gStyle.SetOptFit(0)
# self.draw_statbox(entries)
if self.histo_type != 'FidCut' :
self.save_histo2table(histos = histos, processes = processes, path = '%s%s.dat' % (self.output_path, self.histo_name), var = self.histo_type, bin_width = False)
canvas.Print('%s%s.pdf' % (self.output_path, self.histo_name))
canvas.Print('%s%s.tex' % (self.output_path, self.histo_name))
return -1.
