pdf

class zfit.pdf.BasePDF(obs: Union[str, Iterable[str], zfit.Space], params: Dict[str, zfit.core.interfaces.ZfitParameter] = None, dtype: Type[CT_co] = tf.float64, name: str = 'BasePDF', **kwargs)[source]

Bases: zfit.core.interfaces.ZfitPDF, zfit.core.basemodel.BaseModel

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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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][source]

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)[source]

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[source]

Return an extended version of this pdf with yield yield_. The parameters are shared.

Parameters:
Returns:

ZfitPDF

create_projection_pdf(limits_to_integrate: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool]) → zfit.core.interfaces.ZfitPDF[source]

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.
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][source]

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)[source]
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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][source]

Log probability density function normalized over norm_range.

Parameters:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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][source]

Return the normalization of the function (usually the integral over limits).

Parameters:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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])[source]

Set the normalization range (temporarily if used with contextmanager).

Parameters:norm_range (tuple, Space) –
space

Return the Space object that defines the dimensionality of the object.

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][source]

PDF “unnormalized”. Use functions for unnormalized pdfs. this is only for performance in special cases.

Parameters:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.BaseFunctor(pdfs, name='BaseFunctor', **kwargs)[source]

Bases: zfit.models.basefunctor.FunctorMixin, 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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Exponential(lambda_, obs: Union[str, Iterable[str], zfit.Space], name: str = 'Exponential', **kwargs)[source]

Bases: zfit.core.basepdf.BasePDF

Exponential function exp(lambda * x).

The function is normalized over a finite range and therefore a pdf. So the PDF is precisely defined as \(\frac{ e^{\lambda \cdot x}}{ \int_{lower}^{upper} e^{\lambda \cdot x} dx}\)

Parameters:
  • lambda (Parameter) – Accessed as parameter “lambda”.
  • obs (Space) – The Space the pdf is defined in.
  • name (str) – Name of the pdf.
  • dtype (DType) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.CrystalBall(mu: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], sigma: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], alpha: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], n: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], name: str = 'CrystalBall', dtype: Type[CT_co] = tf.float64)[source]

Bases: zfit.core.basepdf.BasePDF

`Crystal Ball shaped PDF`__. A combination of a Gaussian with an powerlaw tail.

The function is defined as follows:

\[f(x;\mu, \sigma, \alpha, n) = \begin{cases} \exp(- \frac{(x - \mu)^2}{2 \sigma^2}), & \mbox{for}\frac{x - \mu}{\sigma} \geqslant -\alpha \newline A \cdot (B - \frac{x - \mu}{\sigma})^{-n}, & \mbox{for }\frac{x - \mu}{\sigma} < -\alpha \end{cases}\]

with

\[ \begin{align}\begin{aligned}A = \left(\frac{n}{\left| \alpha \right|}\right)^n \cdot \exp\left(- \frac {\left|\alpha \right|^2}{2}\right)\\B = \frac{n}{\left| \alpha \right|} - \left| \alpha \right|\end{aligned}\end{align} \]
Parameters:
  • mu (zfit.Parameter) – The mean of the gaussian
  • sigma (zfit.Parameter) – Standard deviation of the gaussian
  • alpha (zfit.Parameter) – parameter where to switch from a gaussian to the powertail
  • n (zfit.Parameter) – Exponent of the powertail
  • obs (Space) –
  • name (str) –
  • dtype (tf.DType) –

__CBShape_

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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.DoubleCB(mu: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], sigma: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], alphal: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], nl: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], alphar: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], nr: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], name: str = 'DoubleCB', dtype: Type[CT_co] = tf.float64)[source]

Bases: zfit.core.basepdf.BasePDF

`Double sided Crystal Ball shaped PDF`__. A combination of two CB using the mu (not a frac). on each side.

The function is defined as follows:

\[f(x;\mu, \sigma, \alpha_{L}, n_{L}, \alpha_{R}, n_{R}) = \begin{cases} A_{L} \cdot (B_{L} - \frac{x - \mu}{\sigma})^{-n}, & \mbox{for }\frac{x - \mu}{\sigma} < -\alpha_{L} \newline \exp(- \frac{(x - \mu)^2}{2 \sigma^2}), & -\alpha_{L} \leqslant \mbox{for}\frac{x - \mu}{\sigma} \leqslant \alpha_{R} \newline A_{R} \cdot (B_{R} - \frac{x - \mu}{\sigma})^{-n}, & \mbox{for }\frac{x - \mu}{\sigma} > \alpha_{R} \end{cases}\]

with

\[ \begin{align}\begin{aligned}A_{L/R} = \left(\frac{n_{L/R}}{\left| \alpha_{L/R} \right|}\right)^n_{L/R} \cdot \exp\left(- \frac {\left|\alpha_{L/R} \right|^2}{2}\right)\\B_{L/R} = \frac{n_{L/R}}{\left| \alpha_{L/R} \right|} - \left| \alpha_{L/R} \right|\end{aligned}\end{align} \]
Parameters:
  • mu (zfit.Parameter) – The mean of the gaussian
  • sigma (zfit.Parameter) – Standard deviation of the gaussian
  • alphal (zfit.Parameter) – parameter where to switch from a gaussian to the powertail on the left
  • side
  • nl (zfit.Parameter) – Exponent of the powertail on the left side
  • alphar (zfit.Parameter) – parameter where to switch from a gaussian to the powertail on the right
  • side
  • nr (zfit.Parameter) – Exponent of the powertail on the right side
  • obs (Space) –
  • name (str) –
  • dtype (tf.DType) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Gauss(mu: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], sigma: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], name: str = 'Gauss')[source]

Bases: zfit.models.dist_tfp.WrapDistribution

Gaussian or Normal distribution with a mean (mu) and a standartdevation (sigma).

The gaussian shape is defined as

\[f(x \mid \mu, \sigma^2) = e^{ -\frac{(x - \mu)^{2}}{2\sigma^2} }\]

with the normalization over [-inf, inf] of

\[\frac{1}{\sqrt{2\pi\sigma^2} }\]

The normalization changes for different normalization ranges

Parameters:
  • mu (Parameter) – Mean of the gaussian dist
  • sigma (Parameter) – Standard deviation or spread of the gaussian
  • obs (Space) – Observables and normalization range the pdf is defined in
  • name (str) – Name of the pdf
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
distribution
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Uniform(low: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], high: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], name: str = 'Uniform')[source]

Bases: zfit.models.dist_tfp.WrapDistribution

Uniform distribution which is constant between low, high and zero outside.

Parameters:
  • low (Parameter) – Below this value, the pdf is zero.
  • high (Parameter) – Above this value, the pdf is zero.
  • obs (Space) – Observables and normalization range the pdf is defined in
  • name (str) – Name of the pdf
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
distribution
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.TruncatedGauss(mu: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], sigma: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], low: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], high: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], name: str = 'TruncatedGauss')[source]

Bases: zfit.models.dist_tfp.WrapDistribution

Gaussian distribution that is 0 outside of low, high. Equivalent to the product of Gauss and Uniform.

Parameters:
  • mu (Parameter) – Mean of the gaussian dist
  • sigma (Parameter) – Standard deviation or spread of the gaussian
  • low (Parameter) – Below this value, the pdf is zero.
  • high (Parameter) – Above this value, the pdf is zero.
  • obs (Space) – Observables and normalization range the pdf is defined in
  • name (str) – Name of the pdf
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
distribution
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.WrapDistribution(distribution, dist_params, obs, params=None, dist_kwargs=None, dtype=tf.float64, name=None, **kwargs)[source]

Bases: zfit.core.basepdf.BasePDF

Baseclass to wrap tensorflow-probability distributions automatically.

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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
distribution
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Chebyshev(obs, coeffs: list, apply_scaling: bool = True, coeff0: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, name: str = 'Chebyshev')[source]

Bases: zfit.models.polynomials.RecursivePolynomial

Linear combination of Chebyshev (first kind) polynomials of order len(coeffs), coeffs are scaling factors.

The 0th coefficient is set to 1 by default but can be explicitly set with coeff0. Since the PDF normalization removes a degree of freedom, the 0th coefficient is redundant and leads to an arbitrary overall scaling of all parameters.

Notice that this is already a sum of polynomials and the coeffs are simply scaling the individual orders of the polynomials.

The recursive definition of _a single order_ of the polynomial is

\[ \begin{align}\begin{aligned}T_{n+1}(x) = 2 x T_{n}(x) - T_{n-1}(x)\\with T_{0} = 1 T_{1} = x\end{aligned}\end{align} \]

Notice that \(T_1\) is x as opposed to 2x in Chebyshev polynomials of the second kind.

Parameters:
  • obs – The default space the PDF is defined in.
  • coeffs (list[params]) – A list of the coefficients for the polynomials of order 1+ in the sum.
  • apply_scaling (bool) – Rescale the data so that the actual limits represent (-1, 1).
  • coeff0 (param) – The scaling factor of the 0th order polynomial. If not given, it is set to 1.
  • name (str) – Name of the polynomial
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Legendre(obs: Union[str, Iterable[str], zfit.Space], coeffs: List[Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor]], apply_scaling: bool = True, coeff0: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, name: str = 'Legendre')[source]

Bases: zfit.models.polynomials.RecursivePolynomial

Linear combination of Legendre polynomials of order len(coeffs), the coeffs are overall scaling factors.

The 0th coefficient is set to 1 by default but can be explicitly set with coeff0. Since the PDF normalization removes a degree of freedom, the 0th coefficient is redundant and leads to an arbitrary overall scaling of all parameters.

Notice that this is already a sum of polynomials and the coeffs are simply scaling the individual orders of the polynomials.

The recursive definition of _a single order_ of the polynomial is

\[ \begin{align}\begin{aligned}(n+1) P_{n+1}(x) = (2n + 1) x P_{n}(x) - n P_{n-1}(x)\\with P_0 = 1 P_1 = x\end{aligned}\end{align} \]
Parameters:
  • obs – The default space the PDF is defined in.
  • coeffs (list[params]) – A list of the coefficients for the polynomials of order 1+ in the sum.
  • apply_scaling (bool) – Rescale the data so that the actual limits represent (-1, 1).
  • coeff0 (param) – The scaling factor of the 0th order polynomial. If not given, it is set to 1.
  • name (str) – Name of the polynomial
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Chebyshev2(obs, coeffs: list, apply_scaling: bool = True, coeff0: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, name: str = 'Chebyshev2')[source]

Bases: zfit.models.polynomials.RecursivePolynomial

Linear combination of Chebyshev (second kind) polynomials of order len(coeffs), coeffs are scaling factors.

The 0th coefficient is set to 1 by default but can be explicitly set with coeff0. Since the PDF normalization removes a degree of freedom, the 0th coefficient is redundant and leads to an arbitrary overall scaling of all parameters.

Notice that this is already a sum of polynomials and the coeffs are simply scaling the individual orders of the polynomials.

The recursive definition of _a single order_ of the polynomial is

\[ \begin{align}\begin{aligned}T_{n+1}(x) = 2 x T_{n}(x) - T_{n-1}(x)\\with T_{0} = 1 T_{1} = 2x\end{aligned}\end{align} \]

Notice that \(T_1\) is 2x as opposed to x in Chebyshev polynomials of the first kind.

Parameters:
  • obs – The default space the PDF is defined in.
  • coeffs (list[params]) – A list of the coefficients for the polynomials of order 1+ in the sum.
  • apply_scaling (bool) – Rescale the data so that the actual limits represent (-1, 1).
  • coeff0 (param) – The scaling factor of the 0th order polynomial. If not given, it is set to 1.
  • name (str) – Name of the polynomial
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Hermite(obs, coeffs: list, apply_scaling: bool = True, coeff0: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, name: str = 'Hermite')[source]

Bases: zfit.models.polynomials.RecursivePolynomial

Linear combination of Hermite polynomials (for physics) of order len(coeffs), with coeffs as scaling factors.

The 0th coefficient is set to 1 by default but can be explicitly set with coeff0. Since the PDF normalization removes a degree of freedom, the 0th coefficient is redundant and leads to an arbitrary overall scaling of all parameters.

Notice that this is already a sum of polynomials and the coeffs are simply scaling the individual orders of the polynomials.

The recursive definition of _a single order_ of the polynomial is

\[H_{n+1}(x) = 2x H_{n}(x) - 2n H_{n-1}(x)\]

with P_0 = 1 P_1 = 2x

Parameters:
  • obs – The default space the PDF is defined in.
  • coeffs (list[params]) – A list of the coefficients for the polynomials of order 1+ in the sum.
  • apply_scaling (bool) – Rescale the data so that the actual limits represent (-1, 1).
  • coeff0 (param) – The scaling factor of the 0th order polynomial. If not given, it is set to 1.
  • name (str) – Name of the polynomial
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.Laguerre(obs, coeffs: list, apply_scaling: bool = True, coeff0: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, name: str = 'Laguerre')[source]

Bases: zfit.models.polynomials.RecursivePolynomial

Linear combination of Laguerre polynomials of order len(coeffs), the coeffs are overall scaling factors.

The 0th coefficient is set to 1 by default but can be explicitly set with coeff0. Since the PDF normalization removes a degree of freedom, the 0th coefficient is redundant and leads to an arbitrary overall scaling of all parameters.

Notice that this is already a sum of polynomials and the coeffs are simply scaling the individual orders of the polynomials.

The recursive definition of _a single order_ of the polynomial is

\[(n+1) L_{n+1}(x) = (2n + 1 + lpha - x) L_{n}(x) - (n + lpha) L_{n-1}(x)\]

with P_0 = 1 P_1 = 1 - x

Parameters:
  • obs – The default space the PDF is defined in.
  • coeffs (list[params]) – A list of the coefficients for the polynomials of order 1+ in the sum.
  • apply_scaling (bool) – Rescale the data so that the actual limits represent (-1, 1).
  • coeff0 (param) – The scaling factor of the 0th order polynomial. If not given, it is set to 1.
  • name (str) – Name of the polynomial
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.RecursivePolynomial(obs, coeffs: list, apply_scaling: bool = True, coeff0: Optional[tensorflow.python.framework.ops.Tensor] = None, name: str = 'Polynomial')[source]

Bases: zfit.core.basepdf.BasePDF

1D polynomial generated via three-term recurrence.

Base class to create 1 dimensional recursive polynomials that can be rescaled. Overwrite _poly_func.

Parameters:
  • coeffs (list) – Coefficients for each polynomial. Used to calculate the degree.
  • apply_scaling (bool) –

    Rescale the data so that the actual limits represent (-1, 1).

    \[x_{n+1} = recurrence(x_{n}, x_{n-1}, n)\]
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
degree

degree of the polynomial, starting from 0.

Type:int
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.ProductPDF(pdfs: List[zfit.core.interfaces.ZfitPDF], obs: Union[str, Iterable[str], zfit.Space] = None, name='ProductPDF')[source]

Bases: zfit.models.functor.BaseFunctor

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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.SumPDF(pdfs: List[zfit.core.interfaces.ZfitPDF], fracs: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, obs: Union[str, Iterable[str], zfit.Space] = None, name: str = 'SumPDF')[source]

Bases: zfit.models.functor.BaseFunctor

Create the sum of the pdfs with fracs as coefficients.

Parameters:
  • pdfs (pdf) – The pdfs to add.
  • fracs (iterable) –

    coefficients for the linear combination of the pdfs. If pdfs are extended, this throws an error.

    • len(frac) == len(basic) - 1 results in the interpretation of a non-extended pdf. The last coefficient will equal to 1 - sum(frac)
    • len(frac) == len(pdf) each pdf in pdfs will become an extended pdf with the given yield.
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
dtype

The dtype of the object

fracs
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –
class zfit.pdf.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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:

ZfitPDF

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.
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability
  • name (str) – name of the operation shown in the tf.Graph
Returns:

py:class`tf.Tensor`: the integral value as a scalar with shape ()

is_extended

Flag to tell whether the model is extended or not.

Returns:
Return type:bool
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:
  • x (numerical) – float or double Tensor.
  • norm_range (tuple, Space) – Space to normalize over
  • name (str) – Prepended to names of ops created by this function.
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:
  • limits (tuple, Space) – The limits on where to normalize over
  • name (str) –
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:
  • limits (tuple, Space) – the limits to integrate over
  • norm_range (tuple, Space, False) – the limits to normalize over
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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 implemented
  • 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.
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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:
  • x (numerical) – The value at which the partially integrated function will be evaluated
  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes
  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes
  • name (str) –
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.
    • 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.
    • 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.
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:
  • 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.
  • limits (tuple, Space) – In which region to sample in
  • name (str) –
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) –
space

Return the Space object that defines the dimensionality of the object.

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:
  • x (numerical) – The value, have to be convertible to a Tensor
  • component_norm_range (Space) – The normalization range for the components. Needed for
  • composition (certain) – pdfs.
  • name (str) –
Returns:

1-dimensional tf.Tensor containing the unnormalized pdf.

Return type:

tf.Tensor

update_integration_options(draws_per_dim=None, mc_sampler=None)

Set the integration options.

Parameters:
  • draws_per_dim (int) – The draws for MC integration to do
  • () (mc_sampler) –