DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

  1) 0x1a2a770 RooRealVar:: x = 9  L(-10 - 10)  "x"
  2) 0x4ed65a0 RooRealVar:: y = 31.607  L(0 - 40)  "y"
  3) 0x2600270 RooCategory:: c = Plus(idx = 1)
  "c"

  1) 0x1a2a770 RooRealVar:: x = 8  L(-10 - 10)  "x"
  2) 0x4ed65a0 RooRealVar:: y = 30  L(0 - 40)  "y"
  3) 0x2600270 RooCategory:: c = Minus(idx = -1)
  "c"


>> d1 has only columns x,c
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

>> d2 has only column y
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  y = 31.607  L(0 - 40)  "y"

>> d3 has only the points with y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  y = 31.607  L(0 - 40)  "y"
    3)  c = Plus(idx = 1)
  "c"

>> d4 has only columns x,c for data points with y>5.17
DataStore d (d)
  Contains 973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"

>> merge d2(y) with d1(x,c) to form d1(x,c,y)
DataStore d (d)
  Contains 1000 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"

>> append data points of d3 to d1
DataStore d (d)
  Contains 1973 entries
  Observables: 
    1)  x = 9  L(-10 - 10)  "x"
    2)  c = Plus(idx = 1)
  "c"
    3)  y = 31.607  L(0 - 40)  "y"
>> construct dh (binned) from d(unbinned) but only take the x and y dimensions,
>> the category 'c' will be projected in the filling process
DataStore dh (binned version of d)
  Contains 100 entries
  Observables: 
    1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 38  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 100 bins with a total weight of 1000
  Observables:     1)  x = 9  L(-10 - 10) B(10)  "x"
    2)  y = 38  L(0 - 40) B(10)  "y"
>> number of bins in dh   : 100
>> sum of weights in dh   : 1000
>> integral over histogram: 8000
>> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) 
 bin center:
  1) 0x4e62610 RooRealVar:: x = 1  L(-10 - 10) B(10)  "x"
  2) 0x4ed48d0 RooRealVar:: y = 22  L(0 - 40) B(10)  "y"
 weight = 76
>> Creating 1-dimensional projection on y of dh for bins with x>0
DataStore dh (binned version of d)
  Contains 10 entries
  Observables: 
    1)  y = 38  L(0 - 40) B(10)  "y"
Binned Dataset dh (binned version of d)
  Contains 10 bins with a total weight of 500
  Observables:     1)  y = 38  L(0 - 40) B(10)  "y"
[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 500 will supersede previous event count of 1000 for normalization of PDF projections

>> Persisting d via ROOT I/O
TFile**		rf402_datahandling.root	
 TFile*		rf402_datahandling.root	
  KEY: RooDataSet	d;1	d
  KEY: TProcessID	ProcessID0;1	d10637b7-53b7-11f1-979f-0200590abeef