Creating your own pdf#

A core feature of zfit is the ability to create custom pdfs and functions in an simple and straightforward way.

There are two main possibilities to create a custom pdf, an easier for most use-cases and an advanced way..

The simple way#

While the same works for functions, an example with a PDF is shown here.

import numpy as np
import zfit
from zfit import z
/home/docs/checkouts/readthedocs.org/user_builds/zfit/checkouts/latest/.venv/lib/python3.12/site-packages/zfit/__init__.py:59: UserWarning: TensorFlow warnings are by default suppressed by zfit. In order to show them, set the environment variable ZFIT_DISABLE_TF_WARNINGS=0. In order to suppress the TensorFlow warnings AND this warning, set ZFIT_DISABLE_TF_WARNINGS=1.
  warnings.warn(

The first way is the most simple and should only be used for the trivial cases, i.e. if you’re not familiar with Python classes (especially not with the __init__ method).

class MyGauss(zfit.pdf.ZPDF):
    _N_OBS = 1  # dimension, can be omitted
    _PARAMS = ['mean', 'std']  # the name of the parameters

    @zfit.supports()
    def _unnormalized_pdf(self, x, params):
        x0 = x[0]  # using the 0th axis
        mean = params['mean']
        std = params['std']
        return z.exp(- ((x0 - mean) / std) ** 2)

Done. Now we can use our pdf already!

The slightly more general way involves overwritting the __init__ and gives you all the possible flexibility: to use custom parameters, to preprocess them etc.

Here we inherit from BasePDF

class MyGauss(zfit.pdf.BasePDF):

    def __init__(self, mean, std, obs, extended=None, norm=None, name=None, label=None):
        params = {'mean': mean,  # 'mean' is the name as it will be named in the PDF, mean is just the parameter to create the PDF
                  'std': std
                  }
        super().__init__(obs=obs, params=params, extended=extended, norm=norm,
                         name=name, label=label)

    @zfit.supports()
    def _unnormalized_pdf(self, x, params):
        x0 = x[0]  # using the 0th axis
        mean = params['mean']
        std = params['std']
        return z.exp(- ((x0 - mean) / std) ** 2)
obs = zfit.Space('obs1', -3, 6)

data_np = np.random.random(size=1000)
data = zfit.Data(data_np, obs=obs)

Create two parameters and an instance of your own pdf

mean = zfit.Parameter("mean", 1.)
std = zfit.Parameter("std", 1.)
my_gauss = MyGauss(obs=obs, mean=mean, std=std)
probs = my_gauss.pdf(data)
print(probs[:20])
tf.Tensor(
[0.2205262  0.27421676 0.36053994 0.55443544 0.51321848 0.26648678
 0.51636342 0.41515494 0.26366824 0.4487316  0.41894563 0.54279156
 0.38410997 0.55658002 0.24423407 0.49353533 0.56410481 0.44889765
 0.46065624 0.22814219], shape=(20,), dtype=float64)

If we want to make sure it’s a numpy array, we can use zfit.run

We could improve our PDF by registering an integral

def gauss_integral_from_any_to_any(limits, params, model):
    lower, upper = limits.v1.limits
    mean = params['mean']
    std = params['std']
    # write your integral here
    return 42.  # dummy integral, must be a scalar!
limits = zfit.Space(axes=0, lower=zfit.Space.ANY_LOWER, upper=zfit.Space.ANY_UPPER)
MyGauss.register_analytic_integral(func=gauss_integral_from_any_to_any, limits=limits)

More advanced custom PDFs are introduced in the guide on custom PDFs.