root-numpy 3.0.2

root_numpy is a Python extension module that provides an efficient interface between ROOT and NumPy. root_numpy’s internals are compiled C++ and can therefore handle large amounts of data much faster than equivalent pure Python implementations.

With your ROOT data in NumPy form, make use of NumPy’s broad library, including fancy indexing, slicing, broadcasting, random sampling, sorting, shape transformations, linear algebra operations, and more. See this introductory tutorial to get started. NumPy is the fundamental library of the scientific Python ecosystem. Using NumPy arrays opens up many new possibilities beyond what ROOT offers. For example, convert your TTrees into NumPy arrays and use SciPy for numerical integration and optimization, matplotlib for plotting, pandas for data analysis, statsmodels for statistical modelling, scikit-learn for machine learning, and perform quick exploratory analysis in interactive environments like IPython, especially IPython’s popular notebook feature.

At the core of root_numpy are powerful and flexible functions for converting ROOT TTrees into NumPy recarrays or structured arrays as well as converting NumPy arrays back into ROOT TTrees. root_numpy can convert branches of basic types such as bool, int, float, double, etc. as well as variable and fixed-length arrays of basic types. std::vector of basic types are also supported.

For example, get a structured or record array from a TTree in a ROOT file (you should be able to copy and paste the following examples into a Python session):

from root_numpy import root2array, root2rec
from root_numpy.testdata import get_filepath

filename = get_filepath('test.root')

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

# Convert a tree into a numpy record array
rec = root2rec(filename, 'tree')

or directly from a TTree:

import ROOT
from root_numpy import tree2rec

file = ROOT.TFile(filename)
intree = file.Get('tree')
rec = tree2rec(intree)

Include only certain branches and entries:

rec = tree2rec(intree, branches=['x', 'y'], selection='z > 0',
               start=0, stop=10, step=2)

The above conversion creates an array with two 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

tree = array2tree(rec, name='tree')

# or dump directly into a ROOT file without using PyROOT
array2root(rec, 'selected_tree.root', 'tree')

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

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

hist = TH2D('name', 'title', 20, -3, 3, 20, -3, 3)
fill_hist(hist, np.random.randn(1E6, 2))
canvas = TCanvas()
hist.Draw('LEGO2')

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

from ROOT import TF2
from root_numpy import random_sample

func = TF2('func', 'sin(x)*sin(y)/(x*y)')
arr = random_sample(func, 1E6)

Also see the root2hdf5 script in the rootpy package that uses root_numpy and PyTables to convert all TTrees in a ROOT file into the HDF5 format.