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 Wednesday, April 17, 2024 at 11:16 AM.
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.
@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.
ROOT.gInterpreter.ProcessLine('cout << "2^3 = " << Numba::pypow(2, 3) << endl;')
140549259224192
2^3 = 8
Or we can use the callable as well within a RDataFrame workflow.
data = ROOT.RDataFrame(4).Define('x', '(float)rdfentry_')\
.Define('x_pow3', 'Numba::pypow(x, 3)')\
.AsNumpy()
print('pypow({}, 3) = {}'.format(data['x'], data['x_pow3']))
pypow([0. 1. 2. 3.], 3) = [ 0. 1. 8. 27.]
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.
@ROOT.Numba.Declare(['RVecF', 'int'], 'RVecF')
def pypowarray(x, y):
return x**y
ROOT.gInterpreter.ProcessLine('''
ROOT::RVecF x = {0, 1, 2, 3};
cout << "pypowarray(" << x << ", 3) = " << Numba::pypowarray(x, 3) << endl;
''')
140549259224192
pypowarray({ 0, 1, 2, 3 }, 3) = { 0, 1, 8, 27 }
and now with RDataFrame
s = ROOT.RDataFrame(1).Define('x', 'ROOT::RVecF{1,2,3}')\
.Define('x2', 'Numba::pypowarray(x, 2)')\
.Sum('x2') # 1 + 4 + 9 == 14
print('sum(pypowarray({ 1, 2, 3 }, 2)) = ', s.GetValue())
sum(pypowarray({ 1, 2, 3 }, 2)) = 14.0