basefunctor¶
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class
zfit.models.basefunctor.
FunctorMixin
(models, obs, **kwargs)[source]¶ Bases:
zfit.core.interfaces.ZfitFunctorMixin
,zfit.core.basemodel.BaseModel
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add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- 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.
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analytic_integrate
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'analytic_integrate') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Analytical integration over function and raise Error if not possible.
Parameters: Returns: the integral value
Return type: Tensor
Raises: NotImplementedError
– If no analytical integral is available (for this limits).NormRangeNotImplementedError
– if the norm_range argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm_range is not available.
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axes
¶ Return the axes.
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convert_sort_space
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None) → Optional[zfit.core.limits.Space]¶ Convert the inputs (using eventually obs, axes) to
Space
and sort them according to own obs.Parameters: - () (limits) –
- () –
- () –
Returns:
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copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
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create_sampler
(n: Union[int, tensorflow.python.framework.ops.Tensor, str] = None, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, fixed_params: Union[bool, List[zfit.core.interfaces.ZfitParameter], Tuple[zfit.core.interfaces.ZfitParameter]] = True, name: str = 'create_sampler') → zfit.core.data.Sampler¶ Create a
Sampler
that acts as Data but can be resampled, also with changed parameters and n.If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.Parameters: - n (int, tf.Tensor, str) –
The number of samples to be generated. Can be a Tensor that will be or a valid string. Currently implemented:
- ’extended’: samples poisson(yield) from each pdf that is extended.
- () (name) – From which space to sample.
- () – A list of Parameters that will be fixed during several resample calls.
If True, all are fixed, if False, all are floating. If a
Parameter
is not fixed and its value gets updated (e.g. by a Parameter.set_value() call), this will be reflected in resample. If fixed, the Parameter will still have the same value as the Sampler has been created with when it resamples. - () –
Returns: py:class:~`zfit.core.data.Sampler`
Raises: NotExtendedPDFError
– if ‘extended’ is chosen (implicitly by default or explicitly) as an option for n but the pdf itself is not extended.ValueError
– if n is an invalid string option.InvalidArgumentError
– if n is not specified and pdf is not extended.
- n (int, tf.Tensor, str) –
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dtype
¶ The dtype of the object
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get_dependents
(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
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get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
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gradients
(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], params: Optional[Iterable[zfit.core.interfaces.ZfitParameter]] = None)¶
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graph_caching_methods
= []¶
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integrate
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'integrate') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Integrate the function over limits (normalized over norm_range if not False).
Parameters: Returns: py:class`tf.Tensor`: the integral value as a scalar with shape ()
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models
¶ Return the models of this Functor. Can be pdfs or funcs.
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n_obs
¶ Return the number of observables.
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name
¶ The name of the object.
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numeric_integrate
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'numeric_integrate') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Numerical integration over the model.
Parameters: Returns: the integral value
Return type: Tensor
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obs
¶ Return the observables.
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old_graph_caching_methods
= []¶
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params
¶
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partial_analytic_integrate
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'partial_analytic_integrate') → Union[tensorflow.python.framework.ops.Tensor, zfit.Data]¶ Do analytical partial integration of the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
Parameters: Returns: the value of the partially integrated function evaluated at x.
Return type: Tensor
Raises: NotImplementedError
– if the analytic integral (over this limits) is not implementedNormRangeNotImplementedError
– if the norm_range argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm_range is not available.
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partial_integrate
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'partial_integrate') → Union[tensorflow.python.framework.ops.Tensor, zfit.Data]¶ Partially integrate the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
Parameters: Returns: the value of the partially integrated function evaluated at x.
Return type: Tensor
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partial_numeric_integrate
(x: Union[float, tensorflow.python.framework.ops.Tensor], limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'partial_numeric_integrate') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Force numerical partial integration of the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
Parameters: Returns: the value of the partially integrated function evaluated at x.
Return type: Tensor
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classmethod
register_additional_repr
(**kwargs)¶ Register an additional attribute to add to the repr.
Parameters: - keyword argument. The value has to be gettable from the instance (has to be an (any) –
- or callable method of self. (attribute) –
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classmethod
register_analytic_integral
(func: Callable, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, priority: Union[int, float] = 50, *, supports_norm_range: bool = False, supports_multiple_limits: bool = False) → None¶ Register an analytic integral with the class.
Parameters: - func (callable) –
A function that calculates the (partial) integral over the axes limits. The signature has to be the following:
- x (
ZfitData
, None): the data for the remaining axes in a partial - integral. If it is not a partial integral, this will be None.
- x (
- limits (
Space
): the limits to integrate over. - norm_range (
Space
, None): Normalization range of the integral. - If not supports_supports_norm_range, this will be None.
- norm_range (
- params (Dict[param_name,
zfit.Parameters
]): The parameters of the model. - model (
ZfitModel
):The model that is being integrated.
- () (limits) – |limits_arg_descr|
- priority (int) – Priority of the function. If multiple functions cover the same space, the one with the highest priority will be used.
- supports_multiple_limits (bool) – If True, the limits given to the integration function can have multiple limits. If False, only simple limits will pass through and multiple limits will be auto-handled.
- supports_norm_range (bool) – If True, norm_range argument to the function may not be None. If False, norm_range will always be None and care is taken of the normalization automatically.
- func (callable) –
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register_cacher
(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶ Register a cacher that caches values produces by this instance; a dependent.
Parameters: () (cacher) –
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classmethod
register_inverse_analytic_integral
(func: Callable) → None¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
Parameters: () (func) –
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reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
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reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
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sample
(n: Union[int, tensorflow.python.framework.ops.Tensor, str] = None, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'sample') → zfit.core.data.SampleData¶ Sample n points within limits from the model.
If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.
Parameters: Returns: SampleData(n_obs, n_samples)
Raises: NotExtendedPDFError
– if ‘extended’ is (implicitly by default or explicitly) chosen as an option for n but the pdf itself is not extended.ValueError
– if n is an invalid string option.InvalidArgumentError
– if n is not specified and pdf is not extended.
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