doc/master/rf402__datahandling_8py.html

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")

type
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Definition TGWin32VirtualXProxy.cxx:249

Print
void Print(GNN_Data &d, std::string txt="")
Definition TMVA_SOFIE_GNN_Application.C:59