Allowing inline plots



In [1]:

    
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib

Creating ROOT file using root_numpy



In [2]:

    
import numpy
import root_numpy
# generating random data
data = numpy.random.normal(size=[10000, 2])
# adding names of columns
data = data.view([('first', float), ('second', float)])
# 
root_numpy.array2root(data, filename='./toy_datasets/random.root', treename='tree', mode='recreate')



In [3]:

    
!cd ./toy_datasets/ ; ls









    



README.md    magic04.data random.root

Plot function using ROOT



In [4]:

    
import ROOT
from rep.plotting import default_canvas
canvas = default_canvas()
fun1 = ROOT.TF1( 'fun1', 'abs(sin(x)/x)', 0, 10)
canvas.SetGridx()
canvas.SetGridy()
fun1.Draw()
# Drawing output (last line is considered as output of cell)
canvas









    Out[4]:

Plot histogram using ROOT for branch in root file



In [5]:

    
File = ROOT.TFile("toy_datasets/random.root")
Tree = File.Get("tree")
Tree.Draw("first")
canvas









    Out[5]:

use histogram settings



In [6]:

    
# we need to keep histogram in any variable, otherwise it will be deleted automatically
h1 = ROOT.TH1F("h1","hist from tree",50, -0.25, 0.25)
Tree.Draw("min_DOCA>>h1")
canvas









    Out[6]:

root_numpy way

There are two libraries to work with ROOT files

rootpy http://www.rootpy.org - direct wrapper to ROOT methods.
root_numpy http://rootpy.github.io/root_numpy/ - new-style, efficient and simple library to deal with ROOT files from python

Let's show how to use the second library.



In [7]:

    
data = root_numpy.root2array("toy_datasets/random.root", 
                             treename='tree', 
                             branches=['first', 'second', 'sin(first) * exp(second)'], 
                             selection='first > 0')

in example above we selected three branches (one of which is an expression and will be computed on-the-fly) and selections



In [8]:

    
# taking, i.e. first 10 elements:
data2 = data[:10]

convert to pandas



In [9]:

    
import pandas
dataframe = pandas.DataFrame(data)
# looking ar first elements
dataframe.head()









    Out[9]:






  
    
      
      first
      second
      sin(first) * exp(second)
    
  
  
    
      0
       0.527168
       0.744906
       1.059625
    
    
      1
       0.916685
      -0.052861
       0.752729
    
    
      2
       0.790873
      -0.355231
       0.498396
    
    
      3
       0.955387
       0.013831
       0.827909
    
    
      4
       0.853532
       0.217922
       0.937102

Histograms in python



In [10]:

    
figure(figsize=(9, 7))
hist(data['first'], bins=50)
xlabel('first')









    Out[10]:





<matplotlib.text.Text at 0x110f821d0>



In [11]:

    
figure(figsize=(9, 7))
hist(data['second'], bins=50)
xlabel('second')









    Out[11]:





<matplotlib.text.Text at 0x1110adbd0>

	first	second	sin(first) * exp(second)
0	0.527168	0.744906	1.059625
1	0.916685	-0.052861	0.752729
2	0.790873	-0.355231	0.498396
3	0.955387	0.013831	0.827909
4	0.853532	0.217922	0.937102