import ROOT
import math
if i % 2:
else:
if i < 3:
print(x, y, c)
print("")
print("")
print("")
print("\n >> d1 has only columns x,c")
print("\n >> d2 has only column y")
print("\n >> d3 has only the points with y>5.17")
print("\n >> d4 has only columns x, for data points with y>5.17")
print("\n >> merge d2(y) with d1(x,c) to form d1(x,c,y)")
print("\n >> append data points of d3 to d1")
print(">> construct dh (binned) from d(unbinned) but only take the x and y dimensions, ")
print(">> the category 'c' will be projected in the filling process")
yframe =
y.frame(Bins=10, Title=
"Operations on binned datasets")
print(
">> sum of weights in dh : ",
dh.sum(
False))
print(
">> integral over histogram: ",
dh.sum(
True))
print(">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:")
print(">> Creating 1-dimensional projection on y of dh for bins with x>0")
dh2.plotOn(yframe, LineColor=
"r", MarkerColor=
"r")
print("\n >> Persisting d via ROOT I/O")
f =
ROOT.TFile(
"rf402_datahandling.root",
"RECREATE")
c =
ROOT.TCanvas(
"rf402_datahandling",
"rf402_datahandling", 600, 600)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
void Print(GNN_Data &d, std::string txt="")
RooRealVar::x = -10 L(-10 - 10)
RooRealVar::y = 0 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x5584f6768cd0>
RooRealVar::x = -9.98 L(-10 - 10)
RooRealVar::y = 1 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x5584f6768cd0>
RooRealVar::x = -9.96 L(-10 - 10)
RooRealVar::y = 1.41421 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x5584f6768cd0>
DataStore d (d)
Contains 1000 entries
Observables:
1) y = 31.607 L(0 - 40) "y"
2) x = 9.98 L(-10 - 10) "x"
3) c = Plus(idx = 1)
"c"
1) 0x5584f6872ac0 RooRealVar:: y = 31.607 L(0 - 40) "y"
2) 0x5584f6800e80 RooRealVar:: x = 9.98 L(-10 - 10) "x"
3) 0x5584f6c9bbd0 RooCategory:: c = Plus(idx = 1)
"c"
1) 0x5584f6872ac0 RooRealVar:: y = 30 L(0 - 40) "y"
2) 0x5584f6800e80 RooRealVar:: x = 8 L(-10 - 10) "x"
3) 0x5584f6c9bbd0 RooCategory:: c = Minus(idx = -1)
"c"
>> d1 has only columns x,c
DataStore d (d)
Contains 1000 entries
Observables:
1) x = 9.98 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) y = 31.607 L(0 - 40) "y"
2) x = 9.98 L(-10 - 10) "x"
3) c = Plus(idx = 1)
"c"
>> d4 has only columns x, for data points with y>5.17
DataStore d (d)
Contains 973 entries
Observables:
1) x = 9.98 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 = 9.98 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 = 9.98 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) y = 38 L(0 - 40) B(10) "y"
2) x = 9 L(-10 - 10) B(10) "x"
Binned Dataset dh (binned version of d)
Contains 100 bins with a total weight of 1000
Observables: 1) y = 38 L(0 - 40) B(10) "y"
2) x = 9 L(-10 - 10) B(10) "x"
>> number of bins in dh : 100
>> sum of weights in dh : 1000.0
>> integral over histogram: 8000.0
>> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:
1) 0x5584f707d0e0 RooRealVar:: y = 22 L(0 - 40) B(10) "y"
2) 0x5584f6ea2f20 RooRealVar:: x = 1 L(-10 - 10) B(10) "x"
weight = 76.0
>> 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 e0e3e0ba-f2b1-11ef-a968-0200590abeef