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/envs/latest/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(
[0.47630554 0.30305761 0.51373995 0.56309431 0.4537837  0.55345857
 0.26094295 0.4182026  0.56049142 0.29194294 0.21958357 0.56411361
 0.3391022  0.46597562 0.37867419 0.30257469 0.31247592 0.27040228
 0.43782796 0.28640862], 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.