root-numpy 4.5.1

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 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. 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 a Jupyter notebook.

At the core of root_numpy are powerful and flexible functions for converting ROOT TTrees into structured NumPy arrays as well as converting NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings and basic types such as bool, int, float, double, etc. as well as variable-length and fixed-length multidimensional arrays and 1D or 2D vectors of basic types and strings. root_numpy can also create columns in the output array that are expressions involving the TTree branches similar to TTree::Draw().

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.testdata import get_filepath

filename = 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))
hist.Draw('LEGO2')

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)
hist.FillRandom('gaus')
arr = random_sample(hist, 1000000)