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draw.py
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draw.py
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import sys, os
from array import array
from MuonAnalysis.Cosmics.ROOTTools import *
from bins import make_bins
tdr_style()
ROOT.gStyle.SetOptStat(111111)
ROOT.gStyle.SetOptFit(1111)
def fit_histo(h, hist_name, draw=False):
if '_test' in hist_name:
factor = 2.5 # mean +/- 1.5 * rms
hist_name = hist_name.replace('_test','')
if '_fixed' in hist_name:
factor = 99.9 # mean +/- 1.5 * rms
hist_name = hist_name.replace('_fixed','')
print h
elif 'P' in hist_name: # pull
factor = 3/(h.GetRMS() if h.GetRMS() > 0 else 1) # fix to mean +/- 3
elif 'R' in hist_name or 'D' in hist_name:
factor = 1.5 # mean +/- 1.5 * rms
else:
raise NotImplementedError('fit_histo with hist_name %s' % hist_name)
opt = 'qrll'
if not draw:
opt += '0'
original_range = None
original_prob = None
original_mu = None
original_sigma = None
refit_count = 0
while 1:
if factor == 99.9:
i=[0]
core_mean = h.GetMean()
core_width = factor*h.GetRMS()
fcn = ROOT.TF1('core%i' % i[0], 'gaus', core_mean - core_width, core_mean + core_width)
i[0] += 1
h.Fit(fcn, 'qrll')
res = {
'fcn': fcn,
'constant': (fcn.GetParameter(0), fcn.GetParError(0)),
'mu': (fcn.GetParameter(1), fcn.GetParError(1)),
'sigma': (fcn.GetParameter(2), fcn.GetParError(2)),
}
else:
res = fit_gaussian(h, factor, opt)
if original_range is None:
original_range = res['fcn'].GetXmin(), res['fcn'].GetXmax()
original_prob = res['fcn'].GetProb()
original_mu = res['mu']
original_sigma = res['sigma']
if res['fcn'].GetProb() > 0.03:
break
refit_count += 1
if refit_count >= 5:
break
factor *= 0.9
res['original_range'] = original_range
res['original_prob'] = original_prob
res['original_mu'] = original_mu
res['original_sigma'] = original_sigma
res['refit_count'] = refit_count
return res
def get_histo_stat(h, hist_name, stat):
if stat == 'rms':
return h.GetRMS(), h.GetRMSError()
elif stat == 'mean':
return h.GetMean(), h.GetMeanError()
elif stat == 'sigma':
return fit_histo(h, hist_name)['sigma']
elif stat == 'under':
return h.GetBinContent(0), 0
elif stat == 'over':
return h.GetBinContent(h.GetNbinsX()+1), 0
elif stat == 'out':
u,ue = get_histo_stat(h, hist_name, 'under')
o,oe = get_histo_stat(h, hist_name, 'over')
return u+o, 0
raise NotImplementedError('get_histo_stat for %s' % stat)
class Drawer:
tracks = [
'Global',
'TPFMS',
'TkOnly',
'Picky',
'DYT',
'OldTuneP',
'TuneP'
]
nice_names = {
'Global': 'Global',
'TkOnly': 'Tracker-only',
'TPFMS': 'TPFMS',
'Picky': 'Picky',
'DYT': 'DYT',
'TuneP': 'Tune P',
'OldTuneP': 'Old Tune P',
'StAlone': 'Standalone'
}
colors = {
'Global': ROOT.kBlue,
'TkOnly': ROOT.kRed,
'TPFMS': ROOT.kGreen+1,
'Picky': ROOT.kOrange+7,
'DYT': 6,
'TuneP': ROOT.kBlack,
'OldTuneP': ROOT.kCyan,
'StAlone':ROOT.kMagenta,
}
markers = {
'Global': 21,
'TkOnly': 20,
'TPFMS': 22,
'Picky': 27,
'DYT': 30,
'TuneP': 23,
'OldTuneP': 25,
'StAlone': 26,
}
x_titles = {
'pt': 'ref. p_{T} (GeV)'
}
y_titles = {
('qinvpt', 'upperR1lower', 'out'): '# over and underflows (q/p_{T} rel. res.)',
('qinvpt', 'upperR1lower', 'rms'): 'rms (q/p_{T} rel. residual)',
('qinvpt', 'upperPlower', 'rms'): 'rms (q/p_{T} pull)',
('qinvpt', 'upperR1lower', 'sigma'): 'Width of q/p_{T} rel. residual',
('qinvpt', 'upperR1lower', 'mean'): 'Mean of q/p_{T} rel. residual',
('qinvpt', 'upperPlower', 'sigma'): 'Width of q/p_{T} pull',
('qinvpt', 'upperPlower', 'mean'): 'mean (q/p_{T} pull)',
}
root_cache = []
@classmethod
def make_legend(cls, pos, tracks):
l = ROOT.TLegend(*pos)
l.SetTextFont(42)
l.SetShadowColor(ROOT.kWhite)
for t in tracks:
e = ROOT.TLegendEntry()
cls.root_cache.append(e)
e.SetMarkerStyle(cls.markers[t])
e.SetMarkerColor(cls.colors[t])
e.SetLineColor(cls.colors[t])
l.AddEntry(e, cls.nice_names[t], 'lpe')
return l
@classmethod
def draw_legend(cls, pos, tracks):
l = cls.make_legend(pos, tracks)
cls.root_cache.append(l)
l.Draw()
def __init__(self, filename):
self.file = ROOT.TFile(filename)
def get_histo(self, bin_name, track, quantity, hist_name):
return self.file.histos.Get(bin_name).Get(track).Get(quantity).Get(hist_name)
def draw_histos(self, track, quantity, hist_name, bin_by=('pt',)):
hs = []
for bin in make_bins(*bin_by):
hist = self.get_histo(bin.name, track, quantity, hist_name)
if track == 'Global' and quantity == 'qinvpt' and hist_name == 'upperR1lower' and bin.name == 'pT350500':
hist_name = hist_name + '_test'
if track == 'TuneP' and quantity == 'qinvpt' and hist_name == 'upperR1lower' and bin.name == 'pT5002000':
hist_name = hist_name + '_fixed'
res = fit_histo(hist, hist_name, draw=True)
hist_name = hist_name.replace('_test','')
hist_name = hist_name.replace('_fixed','')
hs.append((bin, hist, res))
return hs
def get_curve(self, track, quantity, hist_name, stat, bin_by=('pt',)):
x, y, exl, exh, ey = [], [], [], [], []
for bin in make_bins(*bin_by):
if not bin.use_by_bin:
continue
lower, upper, abscissa = bin.main_var_range()
x.append(abscissa)
exl.append(abscissa - lower)
exh.append(upper - abscissa)
h = self.get_histo(bin.name, track, quantity, hist_name)
if track == 'Global' and quantity == 'qinvpt' and hist_name == 'upperR1lower' and bin.name == 'pT350500':
hist_name = hist_name + '_test'
if track == 'TuneP' and quantity == 'qinvpt' and hist_name == 'upperR1lower' and bin.name == 'pT5002000':
hist_name = hist_name + '_fixed'
value, error = get_histo_stat(h, hist_name, stat)
hist_name = hist_name.replace('_test','')
hist_name = hist_name.replace('_fixed','')
y.append(value)
ey.append(error)
g = ROOT.TGraphAsymmErrors(len(x), *[array('d', z) for z in (x,y,exl,exh,ey,ey)])
g.SetTitle(';%s;%s' % (self.x_titles.get(bin_by[0], 'FIXME'), self.y_titles.get((quantity, hist_name, stat), 'FIXME')))
g.GetYaxis().SetLabelSize(0.04)
g.GetXaxis().SetLabelOffset(0.002)
g.GetYaxis().SetTitleOffset(1.1)
return g
def overlay_curves(self, tracks, quantity, hist_name, stat, ymin, ymax, bin_by=('pt',)):
curves = [(track, self.get_curve(track, quantity, hist_name, stat, bin_by=bin_by)) for track in tracks]
drawopt = 'AP'
for track, curve in curves:
curve.SetLineColor(self.colors[track])
curve.SetMarkerColor(self.colors[track])
curve.SetMarkerStyle(self.markers[track])
curve.SetMinimum(ymin)
curve.SetMaximum(ymax)
curve.Draw(drawopt)
drawopt = 'P same'
return curves
if __name__ == '__main__':
fn = sys.argv[1]
print fn
fn_name = os.path.basename(fn.replace('.histos', '').replace('.root', ''))
plot_path = os.path.join('plots/cosmicres', fn_name)
drawer = Drawer(fn)
for hist_name in ['upperR1lower', 'upperPlower']:
for track in drawer.tracks:
ps = plot_saver(os.path.join(plot_path, hist_name, track))
for bin, hist, res in drawer.draw_histos(track, 'qinvpt', hist_name):
hist.Draw()
if bin.use_by_bin and res['fcn'].GetProb() < 0.03:
print 'check fit: prob for %s %s %s is %s' % (hist_name, track, bin.name, res['fcn'].GetProb())
if bin.use_by_bin and res['refit_count'] > 0:
print 'check fit: refit count for %s %s %s is %s;\n old range: (%10.2g, %10.2g) prob: %10.2g mu: %10.2g sigma: %10.2g\n new range: (%10.2g, %10.2g) prob: %10.2g mu: %10.2g sigma: %10.2g' % (hist_name, track, bin.name, res['refit_count'], res['original_range'][0], res['original_range'][1], res['original_prob'], res['original_mu'][0], res['original_sigma'][0], res['fcn'].GetXmin(), res['fcn'].GetXmax(), res['fcn'].GetProb(), res['mu'][0], res['sigma'][0])
ps.save(bin.name)
ps = plot_saver(plot_path, log=False)
d = drawer.file.histos.Get('copied_histograms')
d.Get('track_multiplicity').Draw('hist text00')
ps.save('track_multiplicity', log=True)
d.Get('muon_multiplicity').Draw('hist text00')
ps.save('muon_multiplicity', log=True)
d.Get('errors').Draw('hist text00')
ps.save('ntuple_errors')
drawer.file.histos.Get('errors').Draw('hist text00')
ps.save('histo_errors')
for x in ['pT010', 'pT1020', 'pT2030', 'pT3040', 'pT4050', 'pT5075', 'pT75100', 'pT100150', 'pT150200', 'pT200350', 'pT350500', 'pT5002000', 'pTall', 'pTabove500', 'pTabove750']:
y = drawer.file.histos.Get(x)
if y.Get('choice_tunep_upper').GetEntries() != y.Get('choice_tunep_upper').GetBinContent(2) and y.Get('choice_tunep_lower').GetEntries() != y.Get('choice_tunep_lower').GetBinContent(2):
for b,n in enumerate(['Global','TkOnly','TPFMS','Picky','DYT']):
y.Get('choice_tunep_upper').GetXaxis().SetBinLabel(b+1,n)
ROOT.gStyle.SetOptStat(0)
y.Get('choice_tunep_upper').Draw('hist text00')
y.Get('choice_tunep_lower').SetLineColor(ROOT.kRed)
y.Get('choice_tunep_lower').Draw('hist text00 sames')
leg = ROOT.TLegend( 0.78, 0.50, 0.98, 0.65 ) ;
leg.AddEntry( y.Get('choice_tunep_upper'), "upper" )
leg.AddEntry( y.Get('choice_tunep_lower'), "lower" )
if x == 'pT150200':
leg.Draw()
ps.save('choice_tunep_'+x, log=True)
if y.Get('choice_tunep_old_upper').GetEntries() != y.Get('choice_tunep_old_upper').GetBinContent(2) or y.Get('choice_tunep_old_lower').GetEntries() != y.Get('choice_tunep_old_lower').GetBinContent(2):
for b,n in enumerate(['Global','TkOnly','TPFMS','Picky','DYT']):
y.Get('choice_tunep_old_upper').GetXaxis().SetBinLabel(b+1,n)
ROOT.gStyle.SetOptStat(0)
y.Get('choice_tunep_old_upper').Draw('hist text00')
y.Get('choice_tunep_old_lower').SetLineColor(ROOT.kRed)
y.Get('choice_tunep_old_lower').Draw('hist text00 sames')
leg = ROOT.TLegend( 0.78, 0.50, 0.98, 0.65 ) ;
leg.AddEntry( y.Get('choice_tunep_old_upper'), "upper" )
leg.AddEntry( y.Get('choice_tunep_old_lower'), "lower" )
if x == 'pT150200':
leg.Draw()
ps.save('choice_tunep_old_'+x, log=True)
for proj in ['xy', 'rz']:
for which in ['upper', 'lower']:
for end in ['inner', 'outer']:
n = 'h_global_%s_%s_pos_%s' % (which, end, proj)
h = drawer.file.histos.Get(n)
h.SetStats(0)
h.SetMarkerStyle(6)
h.Draw()
ps.save(n)
ps.save_dir('upperR1lower')
ps.save_dir('upperPlower')
ps.make_canvas((700,700))
ps.c.SetLogx(1)
tracks = ['Global', 'TkOnly', 'TPFMS', 'Picky', 'DYT', 'OldTuneP', 'TuneP']
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperR1lower', 'out', 0, 60)
drawer.draw_legend((0.61,0.70,0.91,0.91), tracks)
ps.save('res_out')
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperR1lower', 'rms', 0, 0.2)
drawer.draw_legend((0.21,0.70,0.49,0.91), tracks)
ps.save('res_rms')
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperR1lower', 'sigma', 0, 0.15)
drawer.draw_legend((0.21,0.70,0.49,0.91), tracks)
ps.save('res_sigma')
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperR1lower', 'mean', -0.02, 0.03)
drawer.draw_legend((0.21,0.70,0.49,0.91), tracks)
ps.save('res_mean')
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperPlower', 'sigma', 0.6, 1.8)
drawer.draw_legend((0.21,0.70,0.49,0.91), tracks)
ps.save('pull_sigma')
curves = drawer.overlay_curves(tracks, 'qinvpt', 'upperPlower', 'mean', -0.6, 0.6)
drawer.draw_legend((0.21,0.70,0.49,0.91), tracks)
ps.save('pull_mean')
print