LogNormalConstraint#

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

Bases: TFProbabilityConstraint, SerializableMixin

Log-normal 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 LogNormal distributed. This is often used in the case of histogram templates which are obtained from simulation and have a log-normal uncertainty due to a multiplicative uncertainty.

\[\text{constraint} = \text{LogNormal}(\text{observation}; \text{params})\]

Parameters:

params (ztyping.ParamTypeInput) – The parameters to constraint; corresponds to the mu in the Poisson distribution.
observation (ztyping.NumericalScalarType) – observed values of the parameter; corresponds to lambda in the Poisson distribution.
uncertainty (ztyping.NumericalScalarType) – uncertainty of the observed values of the parameter; corresponds to sigma in the Poisson distribution.

Raises:

ShapeIncompatibleError – If params, mu and sigma have incompatible shapes.

add_cache_deps(cache_deps, allow_non_cachable=True)#

Add dependencies that render the cache invalid if they change.

Parameters:

cache_deps (ztyping.CacherOrCachersType) –
allow_non_cachable (bool) – 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.

Args#

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_cache_deps(only_floating=True)#

Return a set of all independent Parameter that this object depends on.

Parameters:: only_floating (bool) – If True, only return floating Parameter
Return type:: OrderedSet

get_dependencies(only_floating: bool = True) → ztyping.DependentsType#

DEPRECATED FUNCTION

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type:: OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)#

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:

set[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#: 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 (ztyping.CacherOrCachersType) –

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 – The number of samples to be generated.

Returns:

to_asdf()#: Convert the object to an asdf file.

to_dict()#

Convert the object to a nested dictionary structure.

Returns:: The dictionary structure.
Return type:: dict

to_json()#

Convert the object to a json string.

Returns:: The json string.
Return type:: str

to_yaml()#

Convert the object to a yaml string.

Returns:: The yaml string.
Return type:: str

LogNormalConstraint#

Args#