constraint¶

zfit.constraint.nll_gaussian(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], observation: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, zfit.core.interfaces.ZfitParameter], uncertainty: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, zfit.core.interfaces.ZfitParameter]) → tensorflow.python.framework.ops.Tensor[source]¶

Return negative log likelihood graph for gaussian constraints on a list of parameters. :param params: The parameters to constraint :type params: list(zfit.Parameter) :param observation: observed values of the parameter :type observation: numerical, list(numerical) :param uncertainty: Uncertainties or covariance/error

matrix of the observed values. Can either be a single value, a list of values, an array or a tensor

Returns:	the constraint object
Return type:	GaussianConstraint
Raises:	`ShapeIncompatibleError` – if params, mu and sigma don’t have the same size

class zfit.constraint.SimpleConstraint(func: Callable, params: Optional[Dict[str, zfit.core.interfaces.ZfitParameter]])[source]¶

Bases: zfit.core.constraint.BaseConstraint

Constraint from a (function returning a) Tensor.

The parameters are named “param_{i}” with i starting from 0 and corresponding to the index of params.

Parameters:	func – Callable that constructs the constraint and returns a tensor. params – The dependents (independent zfit.Parameter) of the loss. If not given, the dependents are figured out automatically.

add_cache_deps(cache_deps: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶

Add dependencies that render the cache invalid if they change.

Parameters:	cache_deps (ZfitGraphCachable) – allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
Raises:	`TypeError` – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

dtype¶: The dtype of the object

get_cache_deps(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶

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

Parameters:	only_floating (bool) – If True, only return floating `Parameter`

get_dependencies(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶

DEPRECATED FUNCTION

Warning: 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).

get_params(floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter]¶

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:

Parameters:	floating – if a parameter is floating, e.g. if `floating()` returns True is_yield – 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

floating – if a parameter is floating, e.g. if floating() returns True
is_yield – 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

graph_caching_methods = []¶

instances = <_weakrefset.WeakSet object>¶

name¶: The name of the object.

params¶

register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶

Parameters:	() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitGraphCachable)¶

reset_cache_self()¶: Clear the cache of self and all dependent cachers.

value()¶

class zfit.constraint.GaussianConstraint(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], observation: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, zfit.core.interfaces.ZfitParameter], uncertainty: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, zfit.core.interfaces.ZfitParameter])[source]¶

Bases: zfit.core.constraint.TFProbabilityConstraint

Gaussian constraints on a list of parameters to some observed values with uncertainties.

A Gaussian constraint is defined as the likelihood of params given the observations and uncertainty from a different measurement.

\[constraint = Gauss(observation; params, uncertainty)\]

Parameters:

Parameters:	params (list(zfit.Parameter)) – The parameters to constraint; corresponds to mu in the Gaussian distribution. observation (numerical, list(numerical)) – observed values of the parameter; corresponds to the x argument in the Gaussian distribution. uncertainty (numerical, list(numerical) or array/tensor) – Uncertainties or covariance/error matrix of the observed values. Can either be a single value, a list of values, an array or a tensor. Corresponds to the sigma of the Gaussian distribution.
Raises:	`ShapeIncompatibleError` – if params, mu and sigma don’t have incompatible shapes

params (list(zfit.Parameter)) – The parameters to constraint; corresponds to mu in the Gaussian distribution.
observation (numerical, list(numerical)) – observed values of the parameter; corresponds to the x argument in the Gaussian distribution.
uncertainty (numerical, list(numerical) or array/tensor) – Uncertainties or covariance/error matrix of the observed values. Can either be a single value, a list of values, an array or a tensor. Corresponds to the sigma of the Gaussian distribution.

Raises:

ShapeIncompatibleError – if params, mu and sigma don’t have incompatible shapes

add_cache_deps(cache_deps: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶

Add dependencies that render the cache invalid if they change.

Parameters:	cache_deps (ZfitGraphCachable) – allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
Raises:	`TypeError` – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

covariance¶: Return the covariance matrix of the observed values of the parameters constrained.

distribution¶

dtype¶: The dtype of the object

get_cache_deps(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶

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

Parameters:	only_floating (bool) – If True, only return floating `Parameter`

get_dependencies(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])¶

DEPRECATED FUNCTION

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:

Parameters:	floating – if a parameter is floating, e.g. if `floating()` returns True is_yield – 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

floating – if a parameter is floating, e.g. if floating() returns True
is_yield – 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

graph_caching_methods = []¶

instances = <_weakrefset.WeakSet object>¶

name¶: The name of the object.

observation¶: Return the observed values of the parameters constrained.

params¶

register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶

Parameters:	() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitGraphCachable)¶

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, tf.Tensor) – The number of samples to be generated.
Returns:	n_samples)
Return type:	Dict(Parameter

value()¶