histos = [ h_background_mc + [h_ttz_mc], [templates['p']], [templates['m']], [templates['L']], [h_fitresults], [h_data]]
plot = Plot.fromHisto(name = "fit_plot_pseudoData_%s_%s_%s"%( self.usePseudoData, channel, region), histos = histos , texX = "cos#theta^{*}", texY = "Events" )
plotting.draw(plot,
plot_directory = os.path.join( plot_directory, 'polFits'),
logX = False, logY = False, sorting = True,
legend = ([0.15,0.7,0.90,0.90], 2)
)
templates['p'].legendText = 'pol(+) %3.1f #pm %3.1f'%( y_p.val, y_p.sigma )
templates['m'].legendText = 'pol(-) %3.1f #pm %3.1f'%( y_m.val, y_m.sigma )
templates['L'].legendText = 'pol(L) %3.1f #pm %3.1f'%( y_L.val, y_L.sigma )
histos = [ [h_ttz_mc], [templates['p']], [templates['m']], [templates['L']], [h_fitresults], [h_data_subtracted]]
plot = Plot.fromHisto(name = "fit_plot_subtracted_pseudoData_%s_%s_%s"%( self.usePseudoData, channel, region), histos = histos , texX = "cos#theta^{*}", texY = "Events" )
plotting.draw(plot,
plot_directory = os.path.join( plot_directory, 'polFits'),
logX = False, logY = False, sorting = False,
legend = ([0.15,0.7,0.90,0.90], 2),
yRange = (0, 30),
)
if __name__=='__main__':
setup = Setup()
estimator_mc = PolarisationEstimate( "polarisation_data", usePseudoData = True)
estimator_mc._estimate( Region("Z_pt", (0, 100)), 'all', setup )
estimator_data = PolarisationEstimate( "polarisation_data" )
estimator_data._estimate( Region("Z_pt", (0, 100)), 'all', setup )
regions_varOne = getRegionsFromThresholds(varOne, varOneThresholds)
regions_varTwo = getRegionsFromThresholds(varTwo, varTwoThresholds)
regions2D = []
for r1 in regions_varOne:
for r2 in regions_varTwo:
regions2D.append(r1+r2)
return regions2D
#Put all sets of regions that are used in the analysis, closure, tables, etc.
## 3l signal regions
regionsA = [ Region("Z_pt", (0, 200)), Region("Z_pt", (200, 400)), Region("Z_pt", (400, -1)) ] # first sensitivity study
regionsB = getRegions2D("Z_pt", [0,100,200,-1], "cosThetaStar", [-1,-0.6, 0.6, 1])
regionsC = getRegions2D("Z_pt", [0,100,200,-1], "abs(cosThetaStar)", [0, 0.6, 1])
regionsD = [ Region("Z_pt", (0, 100)), Region("Z_pt", (100, 200)), Region("Z_pt", (200, -1)) ]
regionsE = getRegions2D("Z_pt", [0,100,200,400,-1], "cosThetaStar", [-1,-0.6, 0.6, 1]) # best results
regionsF = getRegions2D("Z_pt", [0,100,200,400], "cosThetaStar", [-1,-0.6, 0.6, 1]) + [Region("Z_pt", (400, -1))]
regionsG = getRegions2D("Z_pt", [0,100,200,400,-1], "lep_pt[2]", [0,30, 60, -1])
regionsH = getRegions2D("Z_pt", [0,100,200,400,-1], "lep_pt[Z_l2_index]", [0, 30, 60, -1])
regionsXSec = getRegions2D("nBTag", [1, 2, -1], "nJetSelected", [2,3,4,5,-1])
regionsXSecB = getRegions2D("nBTag", [1, 2, -1], "nJetSelected", [1,2,3,-1])
regionsXSecC = getRegions2D("nBTag", [1, -1], "nJetSelected", [3,4,5,-1])
## 4l signal regions
regions4l = getRegions2D("Z1_pt_4l", [0,-1], "Z1_cosThetaStar_4l", [-1,-0.6, 0.6, 1])
regions4lB = getRegions2D("Z1_pt_4l", [0,100,200,-1], "Z1_cosThetaStar_4l", [-1, 1])
def getRegionsFromThresholds(var, vals, gtLastThreshold = True):
return [Region(var, (vals[i], vals[i+1])) for i in range(len(vals)-1)]
def getRegions2D(varOne, varOneThresholds, varTwo, varTwoThresholds):
regions_varOne = getRegionsFromThresholds(varOne, varOneThresholds)
regions_varTwo = getRegionsFromThresholds(varTwo, varTwoThresholds)
regions2D = []
for r1 in regions_varOne:
for r2 in regions_varTwo:
regions2D.append(r1 + r2)
return regions2D
#Put all sets of regions that are used in the analysis, closure, tables, etc.
regionsA = [
Region("Z_pt", (0, 200)),
Region("Z_pt", (200, 400)),
Region("Z_pt", (400, -1))
] # first sensitivity study
regionsB = getRegions2D("Z_pt", [0, 100, 200, -1], "cosThetaStar",
[-1, -0.6, 0.6, 1])
regionsC = getRegions2D("Z_pt", [0, 100, 200, -1], "abs(cosThetaStar)",
[0, 0.6, 1])
regionsD = [
Region("Z_pt", (0, 100)),
Region("Z_pt", (100, 200)),
Region("Z_pt", (200, -1))
]
regionsE = getRegions2D("Z_pt", [0, 100, 200, 400, -1], "cosThetaStar",
[-1, -0.6, 0.6, 1]) # best results
regionsF = getRegions2D("Z_pt", [0, 100, 200, 400], "cosThetaStar",