Functions
def	getEff (var, cut, n=40, limits=(0., 2500.))

Variables
string	InputFile = "cosmic.0008.03420_03427.root"
	Input root file, produced by AsicBackgroundLibraryCreator.

	df = read_root(InputFile, columns=["Channel", "ADC", "Board", "Nhit", "Asic"])
	Dataframe, containing relevant variables from the root file.

def	u1 = getEff(df[(df.asic % 3 == 1)].ADC_ADC_Sig, df.Nhit > 1, 128, (0, 1024.))
	auxiliary variable, split ADC range in two < 1024 and above (u1: below)

def	u2 = getEff(df[(df.asic % 3 == 1)].ADC_ADC_Sig, df.Nhit > 1, 16, (1024, 7800.))
	auxiliary variable, split ADC range in two < 1024 and above (u2: above)

	u = np.append(u1, u2, axis=1)
	merged u1 and u2

	x = np.nan_to_num(u[0])
	aux. More...

	e = np.nan_to_num(u[1])
	aux. More...

	ee = np.where(np.nan_to_num(u[2]) == 0, 1000., u[2])
	aux. More...

	f = UnivariateSpline(x, e, 1/ee)
	spline parameterisation of efficiency

	xp = np.arange(-0.5, 8197.5, 1)
	location of points for the plot

	fmt
	use dots

list	names = ["Board", "Channel"]
	variables to write out

	index
	do not write index

	fi = TFile("t.root", "update")
	open root file to store x-talk probability histogram

	t = TH1F("ProbXTalk", "Prob xTalk", 8196, 0, 8196)
	histogram to store x-talk probability

	INPUT = FileSystem.findFile("t.root")
	prepared payload:

int	expFirst = 0
	starting experiment for payload

int	expLast = -1
	last experiment for payload

int	runFirst = 0
	first run for payload

int	runLast = -1
	last run for payload

	main = basf2.create_path()
	path

	eventinfosetter = basf2.register_module('EventInfoSetter')
	Event info setter - execute single event.

	dbImporter = CDCDatabaseImporter(expFirst, runFirst, expLast, runLast)
	get the importer module

Detailed Description

Prepare CDC x-talk simulation  DB object

Function Documentation

◆ getEff()

def prepareAsicCrosstalkSimDB.getEff	(	var,
		cut,
		n = `40`,
		limits = `(0., 2500.)`
	)

Simple efficiency estimator
   var: pandas series/np array of variable to study
   cut: bool series/np array pass/fail
   n  : number of bins
   limits : histogram limits

Definition at line 15 of file prepareAsicCrosstalkSimDB.py.

 def getEff(var, cut, n=40, limits=(0., 2500.)):
     ''' Simple efficiency estimator
        var: pandas series/np array of variable to study
        cut: bool series/np array pass/fail
        n  : number of bins
        limits : histogram limits
     '''
     a = np.histogram(var, n, limits)
     b = np.histogram(var[cut], n, limits)
  
     eff = b[0]/a[0]
     # simple binomial formula:
     effErr = 1/a[0]*np.sqrt(a[0]*eff*(1-eff))
     x = 0.5*(a[1][1:]+a[1][:-1])
     return x, eff, effErr
  
 #
 # Specify file name here:
 #
  