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pyroot004_NumbaDeclare.py File Reference

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View in nbviewer Open in SWAN 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.

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;')
# 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({}) = {}'.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.
@ROOT.Numba.Declare(['RVec<float>', 'int'], 'RVec<float>')
def pypowarray(x, y):
return x**y
ROOT.gInterpreter.ProcessLine('''
ROOT::RVec<float> x = {0, 1, 2, 3};
cout << "pypowarray(" << x << ") = " << Numba::pypowarray(x, 3) << endl;
''')
Date
March 2020
Author
Stefan Wunsch

Definition in file pyroot004_NumbaDeclare.py.

ROOT::RDataFrame
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTrees,...
Definition: RDataFrame.hxx:42