PoissonConstraint#

class zfit.constraint.PoissonConstraint(params, observation)[source]#

Bases: TFProbabilityConstraint, SerializableMixin

Poisson constraints on a list of parameters to some observed values.

Constraints parameters that can be counts (i.e. from a histogram) or, more generally, are Poisson distributed. This is often used in the case of histogram templates which are obtained from simulation and have a poisson uncertainty due to limited statistics.

\[\text{constraint} = \text{Poisson}(\text{observation}; \text{params})\]
Parameters:

  • params (TypeVar(ParamTypeInput, zfit.interface.ZfitParameter, Union[int, float, complex, tf.Tensor, zfit.interface.ZfitParameter])) – The parameters to constraint; corresponds to the mu in the Poisson distribution.

  • observation (Union[int, float, complex, tf.Tensor, ZfitParameter]) – observed values of the parameter; corresponds to lambda in the Poisson distribution.

Raises:

ShapeIncompatibleError – If params and observation have incompatible shapes.

__repr__()#

Return a detailed string representation of the probability constraint.

Return type:

str

__str__()#

Return a user-friendly string representation of the probability constraint.

Return type:

str

add_cache_deps(cache_deps, allow_non_cachable=True)#

Add dependencies that render the cache invalid if they change.

Parameters:

  • cache_deps

  • allow_non_cachable – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitGraphCachable will raise an error.

Raises:

TypeError – if one of the cache_dependents is not a ZfitGraphCachable _and_ allow_non_cachable if False.

property dtype: DType#

The dtype of the object.

classmethod from_asdf(asdf_obj, *, reuse_params=None)#

Load an object from an asdf file.

Parameters:

  • asdf_obj – Object

  • reuse_params – ​If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.​

classmethod from_dict(dict_, *, reuse_params=None)#

Creates an object from a dictionary structure as generated by to_dict.

Parameters:

  • dict – Dictionary structure.

  • reuse_params – ​If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.​

Returns:

The deserialized object.

classmethod from_json(cls, json, *, reuse_params=None)#

Load an object from a json string.

Parameters:

  • json (str) – Serialized object in a JSON string.

  • reuse_params – ​If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.​

Return type:

object

Returns:

The deserialized object.

get_params(floating=True, is_yield=None, extract_independent=True, *, autograd=None)#

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.
  • None: do not filter on this. E.g. floating=None will return parameters that are floating as well as

    parameters that are fixed.

  • True: only return parameters that fulfil this criterion

  • False: only return parameters that do not fulfil this criterion. E.g. floating=False will return

    only parameters that are not floating.

Parameters:

  • floating (bool | None) – if a parameter is floating, e.g. if floating() returns True

  • is_yield (bool | None) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.

  • extract_independent (bool | None) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type:

OrderedSet[ZfitParameter]

classmethod get_repr()#

Abstract representation of the object for serialization.

This objects knows how to serialize and deserialize the object and is used by the to_json, from_json, to_dict and from_dict methods.

Returns:

The representation of the object.

Return type:

pydantic.BaseModel

property name: str#

The name of the object.

property observation: tuple#

Return the observed values of the parameters constrained.

register_cacher(cacher)#

Register a cacher that caches values produces by this instance; a dependent.

Parameters:

cacher

reset_cache_self()#

Clear the cache of self and all dependent cachers.

sample(n)#

Sample n points from the probability density function for the observed value of the parameters.

Parameters:

n (int) – The number of samples to be generated.

Return type:

dict[ZfitParameter, tf.Tensor]

Returns:

Dictionary mapping parameters to their sampled values.

to_asdf()#

Convert the object to an asdf file.

to_dict()#

Convert the object to a nested dictionary structure.

Return type:

dict

Returns:

The dictionary structure.

to_json()#

Convert the object to a json string.

Return type:

str

Returns:

The json string.

to_yaml()#

Convert the object to a yaml string.

Return type:

str

Returns:

The yaml string.