_tf1.pyzdoc

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/**
\class TF1
\brief \parblock \endparblock
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## PyROOT
 
The TF1 class has several additions for its use from Python, which are also
available in its subclasses TF2 and TF3.
 
First, TF1 instance can be initialized with user-defined Python functions. Given a generic Python callable,
the following can performed:
 
\code{.py}
def func(x: numpy.ndarray, pars: numpy.ndarray) -> float:
    return pars[0] * x[0] * x[0] + x[1] * pars[0]
 
my_func = ROOT.TF1("my_func", func, -10, 10, npar=2, ndim=2)
\endcode
 
Second, after performing the initialisation with a Python functor, the TF1 instance can be evaluated using the Pythonized
`TF1::EvalPar` function. The pythonization allows passing in 1D(single set of x variables) or 2D(a dataset) NumPy arrays.
 
The following example shows how we can create a TF1 instance with a Python function and evaluate it on a dataset:
 
\code{.py}
import ROOT
import math
import numpy as np
 
def pyf_tf1_coulomb(x, p):
    return p[1] * x[0] * x[1] / (p[0]**2) * math.exp(-p[2] / p[0])
 
rtf1_coulomb = ROOT.TF1("my_func", pyf_tf1_coulomb, -10, 10, ndims = 2, npars = 3)
 
# x dataset: 5 pairs of particle charges
x = np.array([
    [1.0, 10, 2.0],
    [1.5, 10, 2.5],
    [2.0, 10, 3.0],
    [2.5, 10, 3.5],
    [3.0, 10, 4.0]
])
 
params = np.array([
    [1.0],       # Distance between charges r
    [8.99e9],    # Coulomb constant k (in N·m²/C²)
    [0.1]        # Additional factor for modulation
])
 
# Slice to avoid the dummy column of 10's
res = rtf1_coulomb.EvalPar(x[:, ::2], params)
        
\endcode
 
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*/