special¶
Special PDFs are provided in this module. One example is a normal function Function that allows to simply define a non-normalizable function.
-
class
zfit.models.special.
SimpleFunctorPDF
(obs, pdfs, func, name='SimpleFunctorPDF', **params)[source]¶ Bases:
zfit.models.functor.BaseFunctor
,zfit.models.special.SimplePDF
-
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.
-
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.
-
apply_yield
(value: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = False, log: bool = False) → Union[float, tensorflow.python.framework.ops.Tensor]¶ If a norm_range is given, the value will be multiplied by the yield.
Parameters: - value (numerical) –
- () (norm_range) –
- log (bool) –
Returns: numerical
-
as_func
(norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = False)¶ Return a Function with the function model(x, norm_range=norm_range).
Parameters: () (norm_range) –
-
axes
¶ Return the axes.
-
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:
-
copy
(**override_parameters) → zfit.core.basepdf.BasePDF¶ Creates a copy of the model.
Note: the copy model may continue to depend on the original initialization arguments.
Parameters: **override_parameters – String/value dictionary of initialization arguments to override with new value. Returns: - A new instance of type(self) initialized from the union
- of self.parameters and override_parameters, i.e., dict(self.parameters, **override_parameters).
Return type: model
-
create_extended
(yield_: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], name_addition='_extended') → zfit.core.interfaces.ZfitPDF¶ Return an extended version of this pdf with yield yield_. The parameters are shared.
Parameters: Returns:
-
create_projection_pdf
(limits_to_integrate: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool]) → zfit.core.interfaces.ZfitPDF¶ Create a PDF projection by integrating out some of the dimensions.
The new projection pdf is still fully dependent on the pdf it was created with.
Parameters: limits_to_integrate ( Space
) –Returns: a pdf without the dimensions from limits_to_integrate. Return type: ZfitPDF
-
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) –
-
dtype
¶ The dtype of the object
-
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
-
get_models
(names=None) → List[zfit.core.interfaces.ZfitModel]¶
-
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)
-
get_yield
() → Optional[zfit.core.parameter.Parameter]¶ Return the yield (only for extended models).
Returns: the yield of the current model or None Return type: Parameter
-
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)¶
-
graph_caching_methods
= []¶
-
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 ()
-
log_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'log_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Log probability density function normalized over norm_range.
Parameters: Returns: a Tensor of type self.dtype.
Return type: log_pdf
-
models
¶ Return the models of this Functor. Can be pdfs or funcs.
-
n_obs
¶ Return the number of observables.
-
name
¶ The name of the object.
-
norm_range
¶ Return the current normalization range. If None and the `obs`have limits, they are returned.
Returns: The current normalization range Return type: Space
or None
-
normalization
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], name: str = 'normalization') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Return the normalization of the function (usually the integral over limits).
Parameters: Returns: the normalization value
Return type: Tensor
-
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
-
obs
¶ Return the observables.
-
old_graph_caching_methods
= []¶
-
params
¶
-
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.
-
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
-
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
-
pdf
(**kwargs)¶
-
pdfs_extended
¶
-
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) –
-
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) –
-
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) –
-
classmethod
register_inverse_analytic_integral
(func: Callable) → None¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
Parameters: () (func) –
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
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.
-
set_norm_range
(norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool])¶ Set the normalization range (temporarily if used with contextmanager).
Parameters: norm_range (tuple, Space
) –
-
unnormalized_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], component_norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None, name: str = 'unnormalized_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ PDF “unnormalized”. Use functions for unnormalized pdfs. this is only for performance in special cases.
Parameters: Returns: 1-dimensional
tf.Tensor
containing the unnormalized pdf.Return type: tf.Tensor
-
-
class
zfit.models.special.
SimplePDF
(obs, func, name='SimplePDF', **params)[source]¶ Bases:
zfit.core.basepdf.BasePDF
-
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.
-
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.
-
apply_yield
(value: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = False, log: bool = False) → Union[float, tensorflow.python.framework.ops.Tensor]¶ If a norm_range is given, the value will be multiplied by the yield.
Parameters: - value (numerical) –
- () (norm_range) –
- log (bool) –
Returns: numerical
-
as_func
(norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = False)¶ Return a Function with the function model(x, norm_range=norm_range).
Parameters: () (norm_range) –
-
axes
¶ Return the axes.
-
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:
-
copy
(**override_parameters) → zfit.core.basepdf.BasePDF[source]¶ Creates a copy of the model.
Note: the copy model may continue to depend on the original initialization arguments.
Parameters: **override_parameters – String/value dictionary of initialization arguments to override with new value. Returns: - A new instance of type(self) initialized from the union
- of self.parameters and override_parameters, i.e., dict(self.parameters, **override_parameters).
Return type: model
-
create_extended
(yield_: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], name_addition='_extended') → zfit.core.interfaces.ZfitPDF¶ Return an extended version of this pdf with yield yield_. The parameters are shared.
Parameters: Returns:
-
create_projection_pdf
(limits_to_integrate: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool]) → zfit.core.interfaces.ZfitPDF¶ Create a PDF projection by integrating out some of the dimensions.
The new projection pdf is still fully dependent on the pdf it was created with.
Parameters: limits_to_integrate ( Space
) –Returns: a pdf without the dimensions from limits_to_integrate. Return type: ZfitPDF
-
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) –
-
dtype
¶ The dtype of the object
-
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
-
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)
-
get_yield
() → Optional[zfit.core.parameter.Parameter]¶ Return the yield (only for extended models).
Returns: the yield of the current model or None Return type: Parameter
-
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)¶
-
graph_caching_methods
= []¶
-
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 ()
-
log_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'log_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Log probability density function normalized over norm_range.
Parameters: Returns: a Tensor of type self.dtype.
Return type: log_pdf
-
n_obs
¶ Return the number of observables.
-
name
¶ The name of the object.
-
norm_range
¶ Return the current normalization range. If None and the `obs`have limits, they are returned.
Returns: The current normalization range Return type: Space
or None
-
normalization
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], name: str = 'normalization') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Return the normalization of the function (usually the integral over limits).
Parameters: Returns: the normalization value
Return type: Tensor
-
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
-
obs
¶ Return the observables.
-
old_graph_caching_methods
= []¶
-
params
¶
-
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.
-
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
-
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
-
pdf
(**kwargs)¶
-
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) –
-
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) –
-
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) –
-
classmethod
register_inverse_analytic_integral
(func: Callable) → None¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
Parameters: () (func) –
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
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.
-
set_norm_range
(norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool])¶ Set the normalization range (temporarily if used with contextmanager).
Parameters: norm_range (tuple, Space
) –
-
unnormalized_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], component_norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None, name: str = 'unnormalized_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ PDF “unnormalized”. Use functions for unnormalized pdfs. this is only for performance in special cases.
Parameters: Returns: 1-dimensional
tf.Tensor
containing the unnormalized pdf.Return type: tf.Tensor
-
-
class
zfit.models.special.
ZPDF
(obs: Union[str, Iterable[str], zfit.Space], name: str = 'ZPDF', **params)[source]¶ Bases:
zfit.core.basemodel.SimpleModelSubclassMixin
,zfit.core.basepdf.BasePDF
-
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.
-
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.
-
apply_yield
(value: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = False, log: bool = False) → Union[float, tensorflow.python.framework.ops.Tensor]¶ If a norm_range is given, the value will be multiplied by the yield.
Parameters: - value (numerical) –
- () (norm_range) –
- log (bool) –
Returns: numerical
-
as_func
(norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = False)¶ Return a Function with the function model(x, norm_range=norm_range).
Parameters: () (norm_range) –
-
axes
¶ Return the axes.
-
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:
-
copy
(**override_parameters) → zfit.core.basepdf.BasePDF¶ Creates a copy of the model.
Note: the copy model may continue to depend on the original initialization arguments.
Parameters: **override_parameters – String/value dictionary of initialization arguments to override with new value. Returns: - A new instance of type(self) initialized from the union
- of self.parameters and override_parameters, i.e., dict(self.parameters, **override_parameters).
Return type: model
-
create_extended
(yield_: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], name_addition='_extended') → zfit.core.interfaces.ZfitPDF¶ Return an extended version of this pdf with yield yield_. The parameters are shared.
Parameters: Returns:
-
create_projection_pdf
(limits_to_integrate: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool]) → zfit.core.interfaces.ZfitPDF¶ Create a PDF projection by integrating out some of the dimensions.
The new projection pdf is still fully dependent on the pdf it was created with.
Parameters: limits_to_integrate ( Space
) –Returns: a pdf without the dimensions from limits_to_integrate. Return type: ZfitPDF
-
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) –
-
dtype
¶ The dtype of the object
-
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
-
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)
-
get_yield
() → Optional[zfit.core.parameter.Parameter]¶ Return the yield (only for extended models).
Returns: the yield of the current model or None Return type: Parameter
-
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)¶
-
graph_caching_methods
= []¶
-
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 ()
-
log_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'log_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Log probability density function normalized over norm_range.
Parameters: Returns: a Tensor of type self.dtype.
Return type: log_pdf
-
n_obs
¶ Return the number of observables.
-
name
¶ The name of the object.
-
norm_range
¶ Return the current normalization range. If None and the `obs`have limits, they are returned.
Returns: The current normalization range Return type: Space
or None
-
normalization
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], name: str = 'normalization') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Return the normalization of the function (usually the integral over limits).
Parameters: Returns: the normalization value
Return type: Tensor
-
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
-
obs
¶ Return the observables.
-
old_graph_caching_methods
= []¶
-
params
¶
-
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.
-
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
-
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
-
pdf
(**kwargs)¶
-
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) –
-
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) –
-
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) –
-
classmethod
register_inverse_analytic_integral
(func: Callable) → None¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
Parameters: () (func) –
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
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.
-
set_norm_range
(norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool])¶ Set the normalization range (temporarily if used with contextmanager).
Parameters: norm_range (tuple, Space
) –
-
unnormalized_pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], component_norm_range: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None, name: str = 'unnormalized_pdf') → Union[float, tensorflow.python.framework.ops.Tensor]¶ PDF “unnormalized”. Use functions for unnormalized pdfs. this is only for performance in special cases.
Parameters: Returns: 1-dimensional
tf.Tensor
containing the unnormalized pdf.Return type: tf.Tensor
-