Setting a better visualization for vertex probability histograms

[aboletti]

cmssw/DQMOffline/Trigger/python/BPHMonitor_cfi.py

Lines 72 to 76 in 0636e2e

	hltBPHmonitoring.histoPSet.probPSet = cms.PSet(
	nbins = cms.int32 ( 10),
	xmin = cms.double( 0),
	xmax = cms.double(1),
	)

The present binning structure of the vertex-probability histograms is suboptimal, since there is a single bin containing all the events in the 0-0.1 range, which is a region containing fine structures (like a peak of background events at 0).

I propose two possible solutions:

• using a logarithmic x-axis visualization (I currently do not have idea if it is possible or not) and set a logarithmic binning (such that it appear with constant widths)
• setting a variable binning, with narrower bins for small values

[aboletti]

@anigamova
can you find out if it is possible to set a logarithmic scale in an histogram?

[anigamova]

Yes, I have found this:
binsLogX_vtxProb = []
nBinsLogX_vtxProb = 100
powerLo_vtxProb = -1
powerHi_vtxProb = 0
binPowerWidth_vtxProb = (powerHi_vtxProb - powerLo_vtxProb) / nBinsLogX_vtxProb
for ibin in range(nBinsLogX_vtxProb + 1):
binsLogX_vtxProb.append( pow(10, powerLo_vtxProb + ibin * binPowerWidth_vtxProb) )
hltBPHmonitoring.histoPSet.probPSet = cms.vdouble(binsLogX_vtxProb)
Will check it and add it to the code

[anigamova]

Variable binning was set in commit b8e7b77