def __init__( self, name, title, source, expression, cuts, labels,
**kwargs ):
self.xTitle = 's_{true} = E_{RECO}/E_{GEN} - 1'
self.nBins = 40
self.xRange = (-20, 30)
self.fitRange = (-20, 30)
PlotData.__init__( self, name, title, source, expression, cuts,
labels, **kwargs )
def __init__(self, name, title, source, expression, cuts, labels):
PlotData.__init__(self, name, title, source, expression, cuts, labels)
self.xTitle = 's = E_{RECO}/E_{KIN} - 1'
self.nBins = 40
self.xRange = (-30, 50)
from JPsi.MuMu.common.roofit import *
from JPsi.MuMu.common.plotData import PlotData
_chains = esChains.getChains('v7')
_baseCuts = [
'abs(1/kRatio - 1) < 0.5',
'abs(mmgMass-91.2) < 4',
]
## ----------------------------------------------------------------------------
## Customize below
leftPlot = PlotData(
name='EB_lowR9_data_new',
title='Barrel, R9 < 0.94, data, new corrections',
labels=['Barrel', 'R9 < 0.94', 'data', 'new corr.'],
source=_chains['data'],
expression='100 * (1/newCorrKRatio - 1)',
cuts=_baseCuts + ['phoIsEB', 'phoR9 < 0.94'],
)
rightPlot = PlotData(
name='EB_lowR9_data_old',
title='Barrel, R9 < 0.94, data, default corrections',
labels=['Barrel', 'R9 < 0.94', 'data', 'default corr.'],
source=_chains['data'],
expression='100 * (1/kRatio - 1)',
cuts=_baseCuts + ['phoIsEB', 'phoR9 < 0.94'],
)
ws1 = RooWorkspace('ws1', 'mmg energy scale')
def __init__( self, name, title, source, xExpression, cuts, labels,
**kwargs ):
self.xName = 's'
self.xTitle = 's = E_{RECO}/E_{KIN} - 1'
self.nBins = 40
self.nBinsZoom = 20
self.xRange = (-30, 50)
self.xRangeZoom = (-10, 10)
self.xUnit = '%'
self.fitRange = (-30, 30)
self.massWindow = None
self.massWindowScale = 2
self.fitResults = []
self.canvases = []
self.pads = []
self.pdf = 'model'
self.chi2s = []
self.definitions = []
self.paramLayout = (.57, 0.92, 0.92)
self.labelsLayout = (0.61, 0.6)
self.canvasStyle = 'compact'
## Chi2 statistic follows the chi2 PDF (and one can trust the p-value
## from ROOT if int(f(x), x in bin_i) = nu_i > 5, see explanation
## near (33.34) on page 13 of the 2011 PDG Statistics Review
## http://pdg.lbl.gov/2011/reviews/rpp2011-rev-statistics.pdf
## Use bin content n_i >= 10 to be on the safe side (nu_i != n_i)
self.binContentMin = 10
self.binContentMax = 100
self.doAutoBinning = False
self.xRangeMode = 'SigmaLevel'
self.xRangeSigmaLevel = 5
self.xRangeFraction = 1.
self.xRangeNumberOfEntries = 5000
self.xRangeModeZoom = 'SigmaLevel'
self.xRangeSigmaLevelZoom = 2
self.xRangeFractionZoom = .9
self.xRangeNumberOfEntries = 5000
self.fitRangeMode = 'SigmaLevel'
self.fitRangeSigmaLevel = 5
self.fitRangeFraction = 1.
self.fitRangeNumberOfEntries = 5000
self.doAutoXRange = False
self.doAutoXRangeZoom = False
self.doAutoFitRange = False
self.useCustomChi2Calculator = False
self.pullRangeMode = 'fitRange'
self.residRangeMode = 'fitRange'
PlotData.__init__( self, name, title, source, xExpression, cuts,
labels, **kwargs )
## Initialize latex label
latexLabel = TLatex()
latexLabel.SetNDC()
## Font size in pixels
latexLabel.SetTextFont( 10*(latexLabel.GetTextFont()/10) + 3)
latexLabel.SetTextSize(18)
self.latex = latexLabel
def __init__( self, name, title, source, xExpression, cuts, labels,
**kwargs ):
self.xName = 's'
self.xTitle = 's = E_{RECO}/E_{KIN} - 1'
self.nBins = 40
self.nBinsZoom = 20
self.xRange = (-30, 50)
self.xRangeZoom = (-10, 10)
self.xUnit = '%'
self.fitRange = (-30, 30)
self.massWindow = None
self.massWindowScale = 2
self.fitResults = []
self.canvases = []
self.pads = []
self.pdf = 'model'
self.chi2s = []
self.definitions = []
self.paramLayout = (.57, 0.92, 0.92)
self.labelsLayout = (0.61, 0.6)
self.canvasStyle = 'compact'
## Chi2 statistic follows the chi2 PDF (and one can trust the p-value
## from ROOT if int(f(x), x in bin_i) = nu_i > 5, see explanation
## near (33.34) on page 13 of the 2011 PDG Statistics Review
## http://pdg.lbl.gov/2011/reviews/rpp2011-rev-statistics.pdf
## Use bin content n_i >= 10 to be on the safe side (nu_i != n_i)
self.binContentMin = 10
self.binContentMax = 100
self.doAutoBinning = False
self.xRangeMode = 'SigmaLevel'
self.xRangeSigmaLevel = 5
self.xRangeFraction = 1.
self.xRangeNumberOfEntries = 5000
self.xRangeModeZoom = 'SigmaLevel'
self.xRangeSigmaLevelZoom = 2
self.xRangeFractionZoom = .9
self.xRangeNumberOfEntries = 5000
self.fitRangeMode = 'SigmaLevel'
self.fitRangeSigmaLevel = 5
self.fitRangeFraction = 1.
self.fitRangeNumberOfEntries = 5000
self.doAutoXRange = False
self.doAutoXRangeZoom = False
self.doAutoFitRange = False
self.useCustomChi2Calculator = False
PlotData.__init__( self, name, title, source, xExpression, cuts,
labels, **kwargs )
## Initialize latex label
latexLabel = TLatex()
latexLabel.SetNDC()
## Font size in pixels
latexLabel.SetTextFont( 10*(latexLabel.GetTextFont()/10) + 3)
latexLabel.SetTextSize(18)
self.latex = latexLabel
