_tf1.pyzdoc

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\pythondoc TF1
 
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
arrays.
 
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)
 
# 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
 
The below example defines a TF1 instance using the ROOT constructor, and sets its parameters using the Pythonized TF1.SetParameters function (i.e. without evaluating).
\code{.py}
import numpy as np
 
# for illustration, a sinusoidal function with six parameters
myFunction = ROOT.TF1("myExampleFunction", "[0] + [1] * x + [2]*sin([3] * x) + [4]*cos([5] * x)", -5, 5)
 
# declare the parameters as a numpy.array or array.array 
myParams = np.array([0.04, 0.4, 3.0, 3.14, 1.5, 3.14])
 
# note: the following line would also work by setting all six parameters equal to 3.0
# myParams = np.array([3.0] * 6) 
 
myFunction.SetParameters(myParams)
\endcode
 
\endpythondoc