Pyroot 0 0 4_ Numba Declare

This tutorial illustrates how PyROOT supports declaring C++ callables from Python callables making them, for example, usable with RDataFrame. The feature uses the numba Python package for just-in-time compilation of the Python callable and supports fundamental types and ROOT::RVec thereof.

Author: Stefan Wunsch
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Thursday, June 24, 2021 at 08:27 AM.

In [ ]:
import ROOT

To mark a Python callable to be used from C++, you have to use the decorator provided by PyROOT passing the C++ types of the input arguments and the return value.

In [ ]:
@ROOT.Numba.Declare(['float', 'int'], 'float')
def pypow(x, y):
    return x**y

The Python callable is now available from C++ in the Numba namespace. For example, we can use it from the interpreter.

In [ ]:
ROOT.gInterpreter.ProcessLine('cout << "2^3 = " << Numba::pypow(2, 3) << endl;')

Or we can use the callable as well within a RDataFrame workflow.

In [ ]:
data = ROOT.RDataFrame(4).Define('x', '(float)rdfentry_')\
                         .Define('x_pow3', 'Numba::pypow(x, 3)')\

print('pypow({}) = {}'.format(data['x'], data['x_pow3']))

ROOT uses the numba Python package to create C++ functions from python ones. We support as input and return types of the callable fundamental types and ROOT::RVec thereof. See the following callable computing the power of the elements in an array.

In [ ]:
@ROOT.Numba.Declare(['RVec<float>', 'int'], 'RVec<float>')
def pypowarray(x, y):
    return x**y

ROOT::RVec<float> x = {0, 1, 2, 3};
cout << "pypowarray(" << x << ") =  " << Numba::pypowarray(x, 3) << endl;