Getting Started

If you have access to CERN’s CVMFS then you can activate an environment with compatible builds of python, ROOT, numpy and root_numpy with the following:

export LCGENV_PATH=/cvmfs/
/cvmfs/ -p LCG_85swan2 --ignore Grid x86_64-slc6-gcc49-opt root_numpy >
echo 'export PATH=$HOME/.local/bin:$PATH' >>

In new terminal sessions, only the last line above will be required.

If you want to instead use your own installation of root_numpy along with any other packages you need, then continue with setting up a virtualenv. First install pip and virtualenv:

curl -O
python --user
pip install --user virtualenv

Then create and activate a virtualenv (change my_env at your will):

virtualenv my_env
source my_env/bin/activate

Now install NumPy and root_numpy:

pip install numpy
pip install root_numpy

Note that neither sudo nor --user is used, because we are in a virtualenv.

root_numpy should now be ready to use:

>>> from root_numpy import root2array, testdata
>>> root2array(testdata.get_filepath('single1.root'))[:20] 
rec.array([(1, 1.0, 1.0), (2, 3.0, 4.0), (3, 5.0, 7.0), (4, 7.0, 10.0),
       (5, 9.0, 13.0), (6, 11.0, 16.0), (7, 13.0, 19.0), (8, 15.0, 22.0),
       (9, 17.0, 25.0), (10, 19.0, 28.0), (11, 21.0, 31.0),
       (12, 23.0, 34.0), (13, 25.0, 37.0), (14, 27.0, 40.0),
       (15, 29.0, 43.0), (16, 31.0, 46.0), (17, 33.0, 49.0),
       (18, 35.0, 52.0), (19, 37.0, 55.0), (20, 39.0, 58.0)],
      dtype=[('n_int', '<i4'), ('f_float', '<f4'), ('d_double', '<f8')])

A Quick Tutorial

For example, get a structured NumPy array from a TTree (copy and paste the following examples into your Python prompt):

from root_numpy import root2array, tree2array
from root_numpy import testdata

filename = testdata.get_filepath('test.root')

# Convert a TTree in a ROOT file into a NumPy structured array
arr = root2array(filename, 'tree')
# The TTree name is always optional if there is only one TTree in the file

# Or first get the TTree from the ROOT file
import ROOT
rfile = ROOT.TFile(filename)
intree = rfile.Get('tree')

# and convert the TTree into an array
array = tree2array(intree)

Include specific branches or expressions and only entries passing a selection:

array = tree2array(intree,
    branches=['x', 'y', 'sqrt(y)', 'TMath::Landau(x)', 'cos(x)*sin(y)'],
    selection='z > 0',
    start=0, stop=10, step=2)

The above conversion creates an array with five columns from the branches x and y where z is greater than zero and only looping on the first ten entries in the tree while skipping every second entry.

Now convert our array back into a TTree:

from root_numpy import array2tree, array2root

# Rename the fields
array.dtype.names = ('x', 'y', 'sqrt_y', 'landau_x', 'cos_x_sin_y')

# Convert the NumPy array into a TTree
tree = array2tree(array, name='tree')

# Or write directly into a ROOT file without using PyROOT
array2root(array, 'selected_tree.root', 'tree')

root_numpy also provides a function for filling a ROOT histogram from a NumPy array:

from ROOT import TH2D
from root_numpy import fill_hist
import numpy as np

# Fill a ROOT histogram from a NumPy array
hist = TH2D('name', 'title', 20, -3, 3, 20, -3, 3)
fill_hist(hist, np.random.randn(1000000, 2))

and a function for creating a random NumPy array by sampling a ROOT function or histogram:

from ROOT import TF2, TH1D
from root_numpy import random_sample

# Sample a ROOT function
func = TF2('func', 'sin(x)*sin(y)/(x*y)')
arr = random_sample(func, 1000000)

# Sample a ROOT histogram
hist = TH1D('hist', 'hist', 10, -3, 3)
arr = random_sample(hist, 1000000)