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rf402_datahandling.py File Reference

Detailed Description

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Data and categories: tools for manipulation of (un)binned datasets

from __future__ import print_function
import ROOT
import math
# WVE Add reduction by range
# Binned (RooDataHist) and unbinned datasets (RooDataSet) share
# many properties and inherit from a common abstract base class
# (RooAbsData), provides an interface for all operations
# that can be performed regardless of the data format
x = ROOT.RooRealVar("x", "x", -10, 10)
y = ROOT.RooRealVar("y", "y", 0, 40)
c = ROOT.RooCategory("c", "c")
c.defineType("Plus", +1)
c.defineType("Minus", -1)
# Basic operations on unbinned datasetss
# --------------------------------------------------------------
# ROOT.RooDataSet is an unbinned dataset (a collection of points in
# N-dimensional space)
d = ROOT.RooDataSet("d", "d", {x, y, c})
# Unlike ROOT.RooAbsArgs (ROOT.RooAbsPdf, ROOT.RooFormulaVar,....) datasets are not attached to
# the variables they are constructed from. Instead they are attached to an internal
# clone of the supplied set of arguments
# Fill d with dummy values
for i in range(1000):
x.setVal(i / 50 - 10)
y.setVal(math.sqrt(1.0 * i))
if i % 2:
c.setLabel("Plus")
else:
c.setLabel("Minus")
# We must explicitly refer to x,y, here to pass the values because
# d is not linked to them (as explained above)
if i < 3:
print(x, y, c)
print(type(x))
d.add({x, y, c})
d.Print("v")
print("")
# The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c)
# supplied in the constructor
row = d.get()
row.Print("v")
print("")
# Get with an argument loads a specific data point in row and returns
# a pointer to row argset. get() always returns the same pointer, unless
# an invalid row number is specified. In that case a null ptr is returned
d.get(900).Print("v")
print("")
# Reducing, appending and merging
# -------------------------------------------------------------
# The reduce() function returns a dataset which is a subset of the
# original
print("\n >> d1 has only columns x,c")
d1 = d.reduce({x, c})
d1.Print("v")
print("\n >> d2 has only column y")
d2 = d.reduce({y})
d2.Print("v")
print("\n >> d3 has only the points with y>5.17")
d3 = d.reduce("y>5.17")
d3.Print("v")
print("\n >> d4 has only columns x, for data points with y>5.17")
d4 = d.reduce({x, c}, "y>5.17")
d4.Print("v")
# The merge() function adds two data set column-wise
print("\n >> merge d2(y) with d1(x,c) to form d1(x,c,y)")
d1.merge(d2)
d1.Print("v")
# The append() function adds two datasets row-wise
print("\n >> append data points of d3 to d1")
d1.append(d3)
d1.Print("v")
# Operations on binned datasets
# ---------------------------------------------------------
# A binned dataset can be constructed empty, an unbinned dataset, or
# from a ROOT native histogram (TH1,2,3)
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")
# The binning of real variables (like x,y) is done using their fit range
# 'get/setRange()' and number of specified fit bins 'get/setBins()'.
# Category dimensions of binned datasets get one bin per defined category
# state
x.setBins(10)
y.setBins(10)
dh = ROOT.RooDataHist("dh", "binned version of d", {x, y}, d)
dh.Print("v")
yframe = y.frame(Bins=10, Title="Operations on binned datasets")
dh.plotOn(yframe) # plot projection of 2D binned data on y
# Examine the statistics of a binned dataset
print(">> number of bins in dh : ", dh.numEntries())
print(">> sum of weights in dh : ", dh.sum(False))
# accounts for bin volume
print(">> integral over histogram: ", dh.sum(True))
# Locate a bin from a set of coordinates and retrieve its properties
x.setVal(0.3)
y.setVal(20.5)
print(">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:")
# load bin center coordinates in internal buffer
dh.get({x, y}).Print("v")
print(" weight = ", dh.weight()) # return weight of last loaded coordinates
# Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset
#
# All reduce() methods are interfaced in RooAbsData. All reduction techniques
# demonstrated on unbinned datasets can be applied to binned datasets as
# well.
print(">> Creating 1-dimensional projection on y of dh for bins with x>0")
dh2 = dh.reduce({y}, "x>0")
dh2.Print("v")
# Add dh2 to yframe and redraw
dh2.plotOn(yframe, LineColor="r", MarkerColor="r")
# Saving and loading from file
# -------------------------------------------------------
# Datasets can be persisted with ROOT I/O
print("\n >> Persisting d via ROOT I/O")
f = ROOT.TFile("rf402_datahandling.root", "RECREATE")
d.Write()
f.ls()
# To read back in future session:
# > ROOT.TFile f("rf402_datahandling.root")
# > d = (ROOT.RooDataSet*) f.FindObject("d")
c = ROOT.TCanvas("rf402_datahandling", "rf402_datahandling", 600, 600)
ROOT.gPad.SetLeftMargin(0.15)
yframe.GetYaxis().SetTitleOffset(1.4)
yframe.Draw()
c.SaveAs("rf402_datahandling.png")
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="")
DataStore d (d)
Contains 1000 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
3) y = 31.607 L(0 - 40) "y"
1) 0x77867d0 RooCategory:: c = Plus(idx = 1)
"c"
2) 0x77d83c0 RooRealVar:: x = 9.98 L(-10 - 10) "x"
3) 0x77d7a10 RooRealVar:: y = 31.607 L(0 - 40) "y"
1) 0x77867d0 RooCategory:: c = Minus(idx = -1)
"c"
2) 0x77d83c0 RooRealVar:: x = 8 L(-10 - 10) "x"
3) 0x77d7a10 RooRealVar:: y = 30 L(0 - 40) "y"
DataStore d (d)
Contains 1000 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
DataStore d (d)
Contains 1000 entries
Observables:
1) y = 31.607 L(0 - 40) "y"
DataStore d (d)
Contains 973 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
3) y = 31.607 L(0 - 40) "y"
DataStore d (d)
Contains 973 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
DataStore d (d)
Contains 1000 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
3) y = 31.607 L(0 - 40) "y"
DataStore d (d)
Contains 1973 entries
Observables:
1) c = Plus(idx = 1)
"c"
2) x = 9.98 L(-10 - 10) "x"
3) y = 31.607 L(0 - 40) "y"
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"
1) 0x7f34970 RooRealVar:: x = 1 L(-10 - 10) B(10) "x"
2) 0x81d3700 RooRealVar:: y = 22 L(0 - 40) B(10) "y"
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
TFile** rf402_datahandling.root
TFile* rf402_datahandling.root
KEY: RooDataSet d;1 d
KEY: TProcessID ProcessID0;1 00fa6f86-2e18-11ef-bb10-942c8a89beef
RooRealVar::x = -10 L(-10 - 10)
RooRealVar::y = 0 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x742fc00>
RooRealVar::x = -9.98 L(-10 - 10)
RooRealVar::y = 1 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x742fc00>
RooRealVar::x = -9.96 L(-10 - 10)
RooRealVar::y = 1.41421 L(0 - 40)
{ {"Minus" , -1}, {"Plus" , 1} }
<class cppyy.gbl.RooRealVar at 0x742fc00>
>> d1 has only columns x,c
>> d2 has only column y
>> d3 has only the points with y>5.17
>> d4 has only columns x, for data points with y>5.17
>> merge d2(y) with d1(x,c) to form d1(x,c,y)
>> append data points of d3 to d1
>> construct dh (binned) from d(unbinned) but only take the x and y dimensions,
>> the category 'c' will be projected in the filling process
>> 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:
weight = 76.0
>> Creating 1-dimensional projection on y of dh for bins with x>0
>> Persisting d via ROOT I/O
Date
February 2018
Authors
Clemens Lange, Wouter Verkerke (C++ version)

Definition in file rf402_datahandling.py.