# Source code for zfit.core.interfaces

#  Copyright (c) 2020 zfit

import abc
from abc import ABCMeta, abstractmethod
from typing import Union, List, Dict, Callable, Tuple, Optional

import numpy as np
import tensorflow as tf

import zfit
from ..util import ztyping

[docs]class ZfitObject(abc.ABC):

# TODO: make abstractmethod?
def __eq__(self, other: object) -> bool:
raise NotImplementedError

[docs]class ZfitDimensional(ZfitObject):

@property
@abstractmethod
def obs(self) -> ztyping.ObsTypeReturn:
"""Return the observables, string identifier for the coordinate system."""
raise NotImplementedError

@property
@abstractmethod
def axes(self) -> ztyping.AxesTypeReturn:
"""Return the axes, integer based identifier(indices) for the coordinate system."""
raise NotImplementedError

@property
@abstractmethod
def n_obs(self) -> int:
"""Return the number of observables, the dimensionality. Corresponds to the last dimension."""
raise NotImplementedError

[docs]class ZfitOrderableDimensional(ZfitDimensional, metaclass=ABCMeta):

[docs]    @abstractmethod
def with_obs(self,
obs: Optional[ztyping.ObsTypeInput],
allow_superset: bool = True,
allow_subset: bool = True) -> "ZfitOrderableDimensional":
"""Create a new instance that has obs; sorted by or set or dropped.

The behavior is as follows:

* input obs are None: the observables will be dropped. If no axes are set, an error
will be raised, as no coordinates will be assigned to this instance anymore.
* input obs are not None: the instance will be sorted by the incoming obs. If axes or other
objects have an associated order (e.g. data, limits,...), they will be reordered as well.
If a strict subset is given (and allow_subset is True), only a subset will be returned.
This can be used to take a subspace of limits, data etc.
If a strict superset is given (and allow_superset is True), the obs will be sorted accordingly as
if the obs not contained in the instances obs were not in the input obs.
* obs are not set:
* if the input obs are None, the same object is returned.
* if the input obs are not None, they will be set as-is and now correspond to the already
existing axes in the object.

Args:
obs: Observables to sort/associate this instance with
allow_superset: if False and a strict superset of the own observables is given, an error
is raised.
allow_subset:if False and a strict subset of the own observables is given, an error
is raised.

Returns:
object: a copy of the object with the new ordering/observables

Raises:
CoordinatesUnderdefinedError: if obs is None and the instance does not have axes
ObsIncompatibleError: if obs is a superset and allow_superset is False or a subset and
allow_allow_subset is False
"""
raise NotImplementedError

[docs]    @abstractmethod
def with_axes(self,
axes: Optional[ztyping.AxesTypeInput],
allow_superset: bool = True,
allow_subset: bool = True) -> "ZfitOrderableDimensional":
"""Create a new instance that has axes; sorted by or set or dropped.

The behavior is as follows:

* input axes are None: the axes will be dropped. If no observables are set, an error
will be raised, as no coordinates will be assigned to this instance anymore.
* input axes are not None: the instance will be sorted by the incoming axes. If obs or other
objects have an associated order (e.g. data, limits,...), they will be reordered as well.
If a strict subset is given (and allow_subset is True), only a subset will be returned. This can
be used to retrieve a subspace of limits, data etc.
If a strict superset is given (and allow_superset is True), the axes will be sorted accordingly as
if the axes not contained in the instances axes were not present in the input axes.
* axes are not set:
* if the input axes are None, the same object is returned.
* if the input axes are not None, they will be set as-is and now correspond to the already
existing obs in the object.

Args:
axes: Axes to sort/associate this instance with
allow_superset: if False and a strict superset of the own axeservables is given, an error
is raised.
allow_subset:if False and a strict subset of the own axeservables is given, an error
is raised.

Returns:
object: a copy of the object with the new ordering/axes

Raises:
CoordinatesUnderdefinedError: if obs is None and the instance does not have axes
AxesIncompatibleError: if axes is a superset and allow_superset is False or a subset and
allow_allow_subset is False
"""

raise NotImplementedError

[docs]    @abstractmethod
def with_autofill_axes(self, overwrite: bool = False) -> "ZfitOrderableDimensional":
"""Overwrite the axes of the current object with axes corresponding to range(len(n_obs)).

This effectively fills with (0, 1, 2,...) and can be used mostly when an object enters a PDF or
similar. overwrite allows to remove the axis first in case there are already some set.

.. code-block::

object.obs -> ('x', 'z', 'y')
object.axes -> None

object.with_autofill_axes()

object.obs -> ('x', 'z', 'y')
object.axes -> (0, 1, 2)

Args:
overwrite (bool): If axes are already set, replace the axes with the autofilled ones.
If axes is already set and overwrite is False, raise an error.

Returns:
object: the object with the new axes

Raises:
AxesIncompatibleError: if the axes are already set and overwrite is False.
"""
raise NotImplementedError

[docs]    @abstractmethod
def reorder_x(self, x: Union[tf.Tensor, np.ndarray],
*,
x_obs: ztyping.ObsTypeInput = None,
x_axes: ztyping.AxesTypeInput = None,
func_obs: ztyping.ObsTypeInput = None,
func_axes: ztyping.AxesTypeInput = None
) -> ztyping.XTypeReturnNoData:
"""Reorder x in the last dimension either according to its own obs or assuming a function ordered with func_obs.

There are two obs or axes around: the one associated with this Coordinate object and the one associated with x.
If x_obs or x_axes is given, then this is assumed to be the obs resp. the axes of x and x will be reordered
according to self.obs resp. self.axes.

If func_obs resp. func_axes is given, then x is assumed to have self.obs resp. self.axes and will be
reordered to align with a function ordered with func_obs resp. func_axes.

Switching func_obs for x_obs resp. func_axes for x_axes inverts the reordering of x.

Args:
x (tensor-like): Tensor to be reordered, last dimension should be n_obs resp. n_axes
x_obs: Observables associated with x. If both, x_obs and x_axes are given, this has precedency over the
latter.
x_axes: Axes associated with x.
func_obs: Observables associated with a function that x will be given to. Reorders x accordingly and assumes
self.obs to be the obs of x. If both, func_obs and func_axes are given, this has precedency over the
latter.
func_axes: Axe associated with a function that x will be given to. Reorders x accordingly and assumes
self.axes to be the axes of x.

Returns:
tensor-like: the reordered array-like object
"""
raise NotImplementedError

[docs]    @abstractmethod
def get_reorder_indices(self,
obs: ztyping.ObsTypeInput = None,
axes: ztyping.AxesTypeInput = None
) -> Tuple[int]:
"""Indices that would order the instances obs as obs respectively the instances axes as axes.

Args:
obs: Observables that the instances obs should be ordered to. Does not reorder, but just
return the indices that could be used to reorder.
axes: Axes that the instances obs should be ordered to. Does not reorder, but just
return the indices that could be used to reorder.

Returns:
tuple(int): New indices that would reorder the instances obs to be obs respectively axes.

Raises:
CoordinatesUnderdefinedError: If neither obs nor axes is given
"""
raise NotImplementedError

[docs]class ZfitData(ZfitDimensional):

[docs]    @abstractmethod
def value(self, obs: List[str] = None) -> ztyping.XType:
raise NotImplementedError

[docs]    @abstractmethod
def sort_by_obs(self, obs, allow_superset: bool = True):
raise NotImplementedError

[docs]    @abstractmethod
def sort_by_axes(self, axes, allow_superset: bool = True):
raise NotImplementedError

@property
@abstractmethod
def weights(self):
raise NotImplementedError

[docs]class ZfitLimit(abc.ABC, metaclass=ABCMeta):

@property
@abstractmethod
def rect_limits(self) -> ztyping.RectLimitsReturnType:
"""Return the rectangular limits as np.ndarraytf.Tensor if they are set and not false.

The rectangular limits can be used for sampling. They do not in general represent the limits
of the object as a functional limit can be set and to check if something is inside the limits,
the method :py:meth:~Limit.inside should be used.

In order to test if the limits are False or None, it is recommended to use the appropriate methods
limits_are_false and limits_are_set.

Returns:
tuple(np.ndarray/tf.Tensor, np.ndarray/tf.Tensor) or bool or None: The lower and upper limits.
Raises:
LimitsNotSpecifiedError: If there are not limits set or they are False.
"""
raise NotImplementedError

@property
@abstractmethod
def rect_limits_np(self) -> ztyping.RectLimitsNPReturnType:
"""Return the rectangular limits as np.ndarray. Raise error if not possible.

Rectangular limits are returned as numpy arrays which can be useful when doing checks that do not
need to be involved in the computation later on as they allow direct interaction with Python as
compared to tf.Tensor inside a graph function.

In order to test if the limits are False or None, it is recommended to use the appropriate methods
limits_are_false and limits_are_set.

Returns:
(lower, upper): A tuple of two np.ndarray with shape (1, n_obs) typically. The last
dimension is always n_obs, the first can be vectorized. This allows unstacking
with z.unstack_x() as can be done with data.

Raises:
CannotConvertToNumpyError: In case the conversion fails.
LimitsNotSpecifiedError: If the limits are not set or are false
"""
raise NotImplementedError

@property
@abstractmethod
def rect_lower(self) -> ztyping.RectLowerReturnType:
"""The lower, rectangular limits, equivalent to rect_limits[0] with shape (..., n_obs)

Returns:
The lower, rectangular limits as np.ndarray or tf.Tensor
Raises:
LimitsNotSpecifiedError: If the limits are not set or are false
"""
raise NotImplementedError

@property
@abstractmethod
def rect_upper(self) -> ztyping.RectUpperReturnType:
"""The upper, rectangular limits, equivalent to rect_limits[1] with shape (..., n_obs)

Returns:
The upper, rectangular limits as np.ndarray or tf.Tensor
Raises:
LimitsNotSpecifiedError: If the limits are not set or are false
"""
raise NotImplementedError

[docs]    @abstractmethod
def rect_area(self) -> Union[float, np.ndarray, tf.Tensor]:
"""Calculate the total rectangular area of all the limits and axes. Useful, for example, for MC integration."""
raise NotImplementedError

[docs]    @abstractmethod
def inside(self, x: ztyping.XTypeInput, guarantee_limits: bool = False) -> ztyping.XTypeReturn:
"""Test if x is inside the limits.

This function should be used to test if values are inside the limits. If the given x is already inside
the rectangular limits, e.g. because it was sampled from within them

Args:
x: Values to be checked whether they are inside of the limits. The shape is expected to have the last
dimension equal to n_obs.
guarantee_limits: Guarantee that the values are already inside the rectangular limits.

Returns:
tensor-like: Return a boolean tensor-like object with the same shape as the input x except of the
last dimension removed.
"""
raise NotImplementedError

[docs]    @abstractmethod
def filter(self, x: ztyping.XTypeInput,
guarantee_limits: bool = False,
axis: Optional[int] = None
) -> ztyping.XTypeReturnNoData:
"""Filter x by removing the elements along axis that are not inside the limits.

This is similar to tf.boolean_mask.

Args:
x: Values to be checked whether they are inside of the limits. If not, the corresonding element (in the
specified axis) is removed. The shape is expected to have the last dimension equal to n_obs.
guarantee_limits: Guarantee that the values are already inside the rectangular limits.
axis: The axis to remove the elements from. Defaults to 0.

Returns:
tensor-like: Return an object with the same shape as x except that along axis elements have been
removed.
"""

@property
@abstractmethod
def has_rect_limits(self) -> bool:
"""If there are limits and whether they are rectangular."""
raise NotImplementedError

@property
def rect_limits_are_tensors(self) -> bool:
"""Return True if the rectangular limits are tensors.

If a limit with tensors is evaluated inside a graph context, comparison operations will fail.

Returns:
bool: if the rectangular limits are tensors.
"""
raise NotImplementedError

@property
@abstractmethod
def limits_are_set(self) -> bool:
"""If the limits have been set to a limit or are False.

Returns:
bool: Whether the limits have been set or not.
"""
raise NotImplementedError

@property
@abstractmethod
def limits_are_false(self) -> bool:
"""If the limits have been set to False, so the object on purpose does not contain limits.

Returns:
bool:
"""
raise NotImplementedError

@property
@abstractmethod
def has_limits(self) -> bool:
"""Whether there are limits set and they are not false.

Returns:
bool:
"""
raise NotImplementedError

# TODO: remove from API?
[docs]    def get_subspace(self, *_, **__):
from zfit.util.exception import InvalidLimitSubspaceError
raise InvalidLimitSubspaceError("ZfitLimits does not suppoert subspaces")

@property
@abstractmethod
def n_obs(self) -> int:
"""Dimensionality, the number of observables, of the limits. Equals to the last axis in rectangular limits.

Returns:
int: Dimensionality of the limits.
"""
raise NotImplementedError

@property
def n_events(self) -> Union[int, None]:
"""Shape of the first dimension, usually reflects the number of events.

Returns:
int, None: Return the number of events, the dimension of the first shape. If this is > 1 or None,
it's vectorized.
"""
raise NotImplementedError

[docs]    @abstractmethod
def equal(self, other: object, allow_graph: bool) -> Union[bool, tf.Tensor]:
"""Compare the limits on equality. For ANY objects, this also returns true.

If called inside a graph context *and* the limits are tensors, this will return a symbolic tf.Tensor.

Returns:
bool: result of the comparison
Raises:
IllegalInGraphModeError: it the comparison happens with tensors in a graph context.
"""
raise NotImplementedError

[docs]    @abstractmethod
def __eq__(self, other: object) -> bool:
"""Compares two Limits for equality without graph mode allowed.

Returns:
bool:
Raises:
IllegalInGraphModeError: it the comparison happens with tensors in a graph context.
"""
raise NotImplementedError

[docs]    @abstractmethod
def less_equal(self,
other: object,
allow_graph: bool = True
) -> Union[bool, tf.Tensor]:
"""Set-like comparison for compatibility. If an object is less_equal to another, the limits are combatible.

This can be used to determine whether a fitting range specification can handle another limit.

If called inside a graph context *and* the limits are tensors, this will return a symbolic tf.Tensor.

Args:
other: Any other object to compare with
allow_graph: If False and the function returns a symbolic tensor, raise IllegalInGraphModeError instead.

Returns:
bool: result of the comparison
Raises:
IllegalInGraphModeError: it the comparison happens with tensors in a graph context.

"""
raise NotImplementedError

[docs]    @abstractmethod
def __le__(self, other: object) -> bool:
"""Set-like comparison for compatibility. If an object is less_equal to another, the limits are combatible.

This can be used to determine whether a fitting range specification can handle another limit.

Returns:
bool: result of the comparison
Raises:
IllegalInGraphModeError: it the comparison happens with tensors in a graph context.
"""
raise NotImplementedError

[docs]    @abstractmethod
def get_sublimits(self):
"""Splits itself into multiple sublimits with smaller n_obs.

If this is not possible, if the limits are not rectangular, just returns itself.

Returns:
Iterable[ZfitLimits]: The sublimits if it was able to split.
"""
raise NotImplementedError

[docs]class ZfitSpace(ZfitLimit, ZfitOrderableDimensional, ZfitObject, metaclass=ABCMeta):

@property
@abstractmethod
def n_limits(self) -> int:
"""Return the number of limits."""
raise NotImplementedError

# TODO: legacy?
@property
@abstractmethod
def limits(self) -> Tuple[ztyping.LowerTypeReturn, ztyping.UpperTypeReturn]:
"""Return the tuple(lower, upper)."""
raise NotImplementedError

# TODO: legacy?
@property
@abstractmethod
def lower(self) -> ztyping.LowerTypeReturn:
"""Return the lower limits.

"""
raise NotImplementedError

# TODO: legacy?
@property
@abstractmethod
def upper(self) -> ztyping.UpperTypeReturn:
"""Return the upper limits.

"""
raise NotImplementedError

# TODO: legacy?
[docs]    @abstractmethod
def area(self) -> float:
"""Return the total area of all the limits and axes. Useful, for example, for MC integration."""
raise NotImplementedError

[docs]    @abstractmethod
def with_limits(self,
limits: ztyping.LimitsTypeInput = None,
rect_limits: Optional[ztyping.RectLimitsInputType] = None,
name: Optional[str] = None
) -> "ZfitSpace":
"""Return a copy of the space with the new limits (and the new name).

Args:
limits: Limits to use. Can be rectangular, a function (requires to also specify rect_limits
or an instance of ZfitLimit.
rect_limits: Rectangular limits that will be assigned with the instance

Returns:
:py:class:~zfit.Space: Copy of the current object with the new limits.
"""
raise NotImplementedError

[docs]    @abstractmethod
def get_subspace(self, obs, axes, name):
"""Create a :py:class:~zfit.Space consisting of only a subset of the obs/axes (only one allowed).

Args:
obs (str, Tuple[str]):
axes (int, Tuple[int]):
name ():

Returns:

"""
raise NotImplementedError

[docs]    @abstractmethod
def with_coords(self,
coords: ZfitOrderableDimensional,
allow_superset: bool = True,
allow_subset: bool = True) -> object:
"""Create a new :py:class:~zfit.Space with reordered observables and/or axes.

The behavior is that _at least one coordinate (obs or axes) has to be set in both instances
(the space itself or in coords). If both match, observables is taken as the defining coordinate.
The space is sorted according to the defining coordinate and the other coordinate is sorted as well.
If either the space did not have the "weaker coordinate" (e.g. both have observables, but only coords
has axes), then the resulting Space will have both.
If both have both coordinates, obs and axes, and sorting for obs results in non-matchin axes results
in axes being dropped.

Args:
coords: An instance of :py:class:Coordinates
allow_superset: If false and a strict superset is given, an error is raised
allow_subset: If false and a strict subset is given, an error is raised

Returns:
:py:class:~zfit.Space:
Raises:
CoordinatesUnderdefinedError: if neither both obs or axes are specified.
CoordinatesIncompatibleError: if coords is a superset and allow_superset is False or a subset and
allow_allow_subset is False
"""
raise NotImplementedError

@abstractmethod
def __iter__(self):
raise NotImplementedError

@abstractmethod
def __len__(self):
raise NotImplementedError

[docs]class ZfitDependentsMixin:
[docs]    @abstractmethod
def get_dependents(self, only_floating: bool = True) -> ztyping.DependentsType:
raise NotImplementedError

[docs]class ZfitNumeric(ZfitDependentsMixin, ZfitObject):
[docs]    @abstractmethod
def get_params(self, only_floating: bool = False,
names: ztyping.ParamsNameOpt = None) -> List["ZfitParameter"]:
raise NotImplementedError

@property
@abstractmethod
def dtype(self) -> tf.DType:
"""The DType of Tensors handled by this model."""
raise NotImplementedError

@property
@abstractmethod
def params(self) -> ztyping.ParametersType:
raise NotImplementedError

[docs]class ZfitParameter(ZfitNumeric):

@property
@abstractmethod
def name(self) -> str:
raise NotImplementedError

# TODO: maybe add to numerics?
@property
def shape(self):
raise NotImplementedError

@property
@abstractmethod
def floating(self) -> bool:
raise NotImplementedError

@floating.setter
@abstractmethod
def floating(self, value: bool):
raise NotImplementedError

[docs]    @abstractmethod
def value(self) -> tf.Tensor:
raise NotImplementedError

[docs]    @abstractmethod
raise NotImplementedError

@property
@abstractmethod
def independent(self) -> bool:
raise NotImplementedError

# TODO: shape to numeric?
# @property
# @abc.abstractmethod
# def shape(self):
#     raise NotImplementedError

[docs]class ZfitIndependentParameter(ZfitParameter, metaclass=ABCMeta):
[docs]    @abstractmethod
def randomize(self, minval, maxval, sampler):
"""Update the parameter with a randomised value between minval and maxval and return it.

Args:
minval (Numerical): The lower bound of the sampler. If not given, lower_limit is used.
maxval (Numerical): The upper bound of the sampler. If not given, upper_limit is used.
sampler (): A sampler with the same interface as tf.random.uniform

Returns:
tf.Tensor: The sampled value
"""
raise NotImplementedError

[docs]    @abstractmethod
def set_value(self, value):
"""Set the :py:class:~zfit.Parameter to value (temporarily if used in a context manager).

This operation won't, compared to the assign, return the read value but an object that *can* act as a context
manager.

Args:
value (float): The value the parameter will take on.
"""
raise NotImplementedError

@property
@abstractmethod
def has_limits(self) -> bool:
"""If the parameter has limits set or not

Returns:
bool
"""
raise NotImplementedError

@property
@abstractmethod
def at_limit(self) -> tf.Tensor:
"""If the value is at the limit (or over it).

Returns:
tf.Tensor: Boolean tf.Tensor that tells whether the value is at the limits.

"""
raise NotImplementedError

@property
def step_size(self) -> tf.Tensor:
"""Step size of the parameter, the estimated order of magnitude of the uncertainty.

This can be crucial to tune for the minimization. A too large step_size can produce NaNs, a too small won't
converge.

If the step size is not set, the DEFAULT_STEP_SIZE is used.

Returns:
:py:class:tf.Tensor: the step size
"""
raise NotImplementedError

[docs]class ZfitLoss(ZfitObject, ZfitDependentsMixin, metaclass=ABCMeta):

[docs]    @abstractmethod
def gradients(self, params: ztyping.ParamTypeInput = None) -> List[tf.Tensor]:
raise NotImplementedError

[docs]    @abstractmethod
def value(self) -> ztyping.NumericalTypeReturn:
raise NotImplementedError

@property
@abstractmethod
def errordef(self) -> Union[float, int]:
raise NotImplementedError

@property
@abstractmethod
def model(self) -> List["ZfitModel"]:
raise NotImplementedError

@property
@abstractmethod
def data(self) -> List["ZfitData"]:
raise NotImplementedError

@property
@abstractmethod
def fit_range(self) -> List["ZfitSpace"]:
raise NotImplementedError

[docs]    @abstractmethod
raise NotImplementedError

@property
@abstractmethod
def errordef(self) -> float:
raise NotImplementedError

[docs]    @abstractmethod
pass

[docs]    @abstractmethod
pass

[docs]class ZfitModel(ZfitNumeric, ZfitDimensional):

[docs]    @abstractmethod
def update_integration_options(self, *args, **kwargs):  # TODO: handling integration properly
raise NotImplementedError

[docs]    @abstractmethod
def integrate(self, limits: ztyping.LimitsType, norm_range: ztyping.LimitsType = None,
name: str = "integrate") -> ztyping.XType:
"""Integrate the function over limits (normalized over norm_range if not False).

Args:
limits (tuple, :py:class:~zfit.Space): the limits to integrate over
norm_range (tuple, :py:class:~zfit.Space): the limits to normalize over or False to integrate the
unnormalized probability
name (str):

Returns:
Tensor: the integral value
"""
raise NotImplementedError

[docs]    @classmethod
@abstractmethod
def register_analytic_integral(cls, func: Callable, limits: ztyping.LimitsType = None,
priority: int = 50, *,
supports_norm_range: bool = False,
supports_multiple_limits: bool = False):
"""Register an analytic integral with the class.

Args:
func ():
limits (): |limits_arg_descr|
priority (int):
supports_multiple_limits (bool):
supports_norm_range (bool):

Returns:

"""
raise NotImplementedError

[docs]    @abstractmethod
def partial_integrate(self, x: ztyping.XType, limits: ztyping.LimitsType,
norm_range: ztyping.LimitsType = None) -> ztyping.XType:
"""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)

Args:
x (numerical): The value at which the partially integrated function will be evaluated
limits (tuple, :py:class:~zfit.Space): the limits to integrate over. Can contain only some axes
norm_range (tuple, :py:class:~zfit.Space, False): the limits to normalize over. Has to have all axes

Returns:
Tensor: the value of the partially integrated function evaluated at x.
"""
raise NotImplementedError

[docs]    @classmethod
@abstractmethod
def register_inverse_analytic_integral(cls, func: Callable):
"""Register an inverse analytical integral, the inverse (unnormalized) cdf.

Args:
func ():
"""
raise NotImplementedError

[docs]    @abstractmethod
def sample(self, n: int, limits: ztyping.LimitsType = None, name: str = "sample") -> ztyping.XType:
"""Sample n points within limits from the model.

Args:
n (int): The number of samples to be generated
limits (tuple, :py:class:~zfit.Space): In which region to sample in
name (str):

Returns:
Tensor(n_obs, n_samples)
"""
raise NotImplementedError

[docs]class ZfitFunc(ZfitModel):
[docs]    @abstractmethod
def func(self, x: ztyping.XType, name: str = "value") -> ztyping.XType:
raise NotImplementedError

[docs]    @abstractmethod
def as_pdf(self):
raise NotImplementedError

[docs]class ZfitPDF(ZfitModel):

[docs]    @abstractmethod
def pdf(self, x: ztyping.XType, norm_range: ztyping.LimitsType = None) -> ztyping.XType:
raise NotImplementedError

@property
@abstractmethod
def is_extended(self) -> bool:
raise NotImplementedError

[docs]    @abstractmethod
def set_norm_range(self):
raise NotImplementedError

[docs]    @abstractmethod
def create_extended(self, yield_: ztyping.ParamTypeInput) -> "ZfitPDF":
raise NotImplementedError

[docs]    @abstractmethod
def get_yield(self) -> Union[ZfitParameter, None]:
raise NotImplementedError

[docs]    @abstractmethod
def normalization(self, limits: ztyping.LimitsType) -> ztyping.NumericalTypeReturn:
raise NotImplementedError

[docs]    @abstractmethod
def as_func(self, norm_range: ztyping.LimitsType = False):
raise NotImplementedError

[docs]class ZfitFunctorMixin:

@property
@abstractmethod
def models(self) -> Dict[Union[float, int, str], ZfitModel]:
raise NotImplementedError

[docs]    @abstractmethod
def get_models(self) -> List[ZfitModel]:
raise NotImplementedError

[docs]class ZfitConstraint(abc.ABC):
[docs]    @abstractmethod
def value(self):
raise NotImplementedError