Parameter

Several objects in zfit, most importantly models, have one or more parameter which typically parametrise a function or distribution. There are two different kinds of parameters in zfit:

• Independent: can be changed in a fit (or explicitly be set to fixed).

• Dependent: cannot be directly changed but may depend on independent parameters.

Independent Parameter

To create a parameter that can be changed, e.g., to fit a model, a Parameter has to be instantiated.

The syntax is as follows:

param1 = zfit.Parameter("unique_param_name", start_value[, lower_limit, upper_limit])

Furthermore, a step_size can be specified. If not, it is set to a default value around 0.1. Parameter can have limits (tested with has_limits()), which will clip the value to the limits given by lower_limit() and upper_limit().

Note

Comparison to RooFit

While this closely follows the RooFit syntax, it is very important to note that the optional limits of the parameter behave differently: if not given, the parameter will be “unbounded”, not fixed (as in RooFit). Parameters are therefore floating by default, but their value can be fixed by setting the attribute floating to False or already specifying it in the init.

The value of the parameter can be changed with the set_value() method. Using this method as a context manager, the value can also temporarily changed. However, be aware that anything dependent on the parameter will have a value with the parameter evaluated with the new value at run-time:

import zfit

mu = zfit.Parameter("mu_one", 1)  # no limits, but FLOATING (!)
with mu.set_value(3):
    print(f'before {mu}')

# here, mu is again 1
print(f'after {mu}')

before mu_one=3

after mu_one=1

Dependent Parameter

A parameter can be composed of several other parameters. They can be used equivalently to Parameter.

mu2 = zfit.Parameter("mu_two", 7)

def dependent_func(mu, mu2):
    return mu * 5 + mu2  # or any kind of computation
dep_param = zfit.ComposedParameter("dependent_param", dependent_func, params=[mu, mu2])

print(dep_param.get_params())

OrderedSet([<zfit.Parameter 'mu_one' floating=True value=1>, <zfit.Parameter 'mu_two' floating=True value=7>])

A special case of the above is ComplexParameter: it provides a few special methods (like real(), conj() etc.) to easier deal with complex numbers. Additionally, the from_cartesian() and from_polar() methods can be used to initialize polar parameters from floats, avoiding the need of creating complex tf.Tensor objects.

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