interfaces¶

class
zfit.core.interfaces.
ZfitData
[source]¶ Bases:
zfit.core.interfaces.ZfitDimensional

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract
value
(obs: List[str] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

abstract property
weights
¶

abstract property

class
zfit.core.interfaces.
ZfitDimensional
[source]¶ Bases:
zfit.core.interfaces.ZfitObject

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract property

class
zfit.core.interfaces.
ZfitFunc
[source]¶ Bases:
zfit.core.interfaces.ZfitModel

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

abstract
func
(x: Union[float, tensorflow.python.framework.ops.Tensor], name: str = 'value') → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

abstract
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).

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract property
params
¶

abstract
partial_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) → Union[float, tensorflow.python.framework.ops.Tensor]¶ Partially integrate the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
 Parameters
 Returns
the value of the partially integrated function evaluated at x.
 Return type
Tensor

abstract classmethod
register_analytic_integral
(func: Callable, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, priority: int = 50, *, supports_norm_range: bool = False, supports_multiple_limits: bool = False)¶ Register an analytic integral with the class.
 Parameters
() (limits) –
() – limits_arg_descr
priority (int) –
supports_multiple_limits (bool) –
supports_norm_range (bool) –
Returns:

abstract classmethod
register_inverse_analytic_integral
(func: Callable)¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
 Parameters
() (func) –

abstract
sample
(n: int, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'sample') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Sample n points within limits from the model.

abstract
update_integration_options
(*args, **kwargs)¶

abstract property

class
zfit.core.interfaces.
ZfitIndependentParameter
[source]¶ Bases:
zfit.core.interfaces.ZfitParameter

abstract property
at_limit
¶ If the value is at the limit (or over it).
 Returns
Boolean tf.Tensor that tells whether the value is at the limits.
 Return type
tf.Tensor

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

abstract property
floating
¶

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

abstract property
has_limits
¶ If the parameter has limits set or not
 Returns
bool

abstract property
independent
¶

abstract property
name
¶

abstract property
params
¶

abstract
randomize
(minval, maxval, sampler)[source]¶ Update the parameter with a randomised value between minval and maxval and return it.
 Parameters
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
The sampled value
 Return type
tf.Tensor

abstract
read_value
() → tensorflow.python.framework.ops.Tensor¶

abstract
set_value
(value)[source]¶ Set the
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.
 Parameters
value (float) – The value the parameter will take on.

property
shape
¶

property
step_size
¶ 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
the step size
 Return type
tf.Tensor

abstract
value
() → tensorflow.python.framework.ops.Tensor¶

abstract property

class
zfit.core.interfaces.
ZfitLimit
[source]¶ Bases:
abc.ABC

abstract
__eq__
(other: object) → bool[source]¶ Compares two Limits for equality without graph mode allowed.
 Returns
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
__le__
(other: object) → bool[source]¶ Setlike 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
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
equal
(other: object, allow_graph: bool) → Union[bool, tensorflow.python.framework.ops.Tensor][source]¶ 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
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
filter
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], guarantee_limits: bool = False, axis: Optional[int] = None) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor][source]¶ Filter x by removing the elements along axis that are not inside the limits.
This is similar to tf.boolean_mask.
 Parameters
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
 Return an object with the same shape as x except that along axis elements have been
removed.
 Return type
tensorlike

abstract
get_sublimits
()[source]¶ Splits itself into multiple sublimits with smaller n_obs.
If this is not possible, if the limits are not rectangular, just returns itself.
 Returns
The sublimits if it was able to split.
 Return type
Iterable[ZfitLimits]

abstract property
has_limits
¶ Whether there are limits set and they are not false.
 Returns
 Return type

abstract property
has_rect_limits
¶ If there are limits and whether they are rectangular.

abstract
inside
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], guarantee_limits: bool = False) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.core.data.Data][source]¶ 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
 Parameters
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
 Return a boolean tensorlike object with the same shape as the input x except of the
last dimension removed.
 Return type
tensorlike

abstract
less_equal
(other: object, allow_graph: bool = True) → Union[bool, tensorflow.python.framework.ops.Tensor][source]¶ Setlike 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.
 Parameters
other – Any other object to compare with
allow_graph – If False and the function returns a symbolic tensor, raise IllegalInGraphModeError instead.
 Returns
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract property
limits_are_false
¶ If the limits have been set to False, so the object on purpose does not contain limits.
 Returns
 Return type

abstract property
limits_are_set
¶ If the limits have been set to a limit or are False.
 Returns
Whether the limits have been set or not.
 Return type

property
n_events
¶ Shape of the first dimension, usually reflects the number of events.

abstract property
n_obs
¶ Dimensionality, the number of observables, of the limits. Equals to the last axis in rectangular limits.
 Returns
Dimensionality of the limits.
 Return type

abstract
rect_area
() → Union[float, numpy.ndarray, tensorflow.python.framework.ops.Tensor][source]¶ Calculate the total rectangular area of all the limits and axes. Useful, for example, for MC integration.

abstract property
rect_limits
¶ Return the rectangular limits as np.ndarray``tf.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
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
The lower and upper limits.
 Return type
tuple(np.ndarray/tf.Tensor, np.ndarray/tf.Tensor) or bool or None
 Raises
LimitsNotSpecifiedError – If there are not limits set or they are False.

property
rect_limits_are_tensors
¶ Return True if the rectangular limits are tensors.
If a limit with tensors is evaluated inside a graph context, comparison operations will fail.
 Returns
if the rectangular limits are tensors.
 Return type

abstract property
rect_limits_np
¶ 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
 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.
 Return type
(lower, upper)
 Raises
CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set or are false

abstract property
rect_lower
¶ 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

abstract property
rect_upper
¶ 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

abstract

class
zfit.core.interfaces.
ZfitLoss
[source]¶ Bases:
zfit.core.interfaces.ZfitObject
,zfit.core.interfaces.ZfitDependentsMixin

abstract property
data
¶

abstract property
errordef
¶

abstract property
fit_range
¶

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

abstract
gradients
(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor] = None) → List[tensorflow.python.framework.ops.Tensor][source]¶

abstract property
model
¶

abstract property

class
zfit.core.interfaces.
ZfitModel
[source]¶ Bases:
zfit.core.interfaces.ZfitNumeric
,zfit.core.interfaces.ZfitDimensional

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

abstract
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][source]¶ Integrate the function over limits (normalized over norm_range if not False).

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract property
params
¶

abstract
partial_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) → Union[float, tensorflow.python.framework.ops.Tensor][source]¶ Partially integrate the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
 Parameters
 Returns
the value of the partially integrated function evaluated at x.
 Return type
Tensor

abstract classmethod
register_analytic_integral
(func: Callable, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, priority: int = 50, *, supports_norm_range: bool = False, supports_multiple_limits: bool = False)[source]¶ Register an analytic integral with the class.
 Parameters
() (limits) –
() – limits_arg_descr
priority (int) –
supports_multiple_limits (bool) –
supports_norm_range (bool) –
Returns:

abstract classmethod
register_inverse_analytic_integral
(func: Callable)[source]¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
 Parameters
() (func) –

abstract property

class
zfit.core.interfaces.
ZfitNumeric
[source]¶ Bases:
zfit.core.interfaces.ZfitDependentsMixin
,zfit.core.interfaces.ZfitObject

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter][source]¶

abstract property
params
¶

abstract property

class
zfit.core.interfaces.
ZfitOrderableDimensional
[source]¶ Bases:
zfit.core.interfaces.ZfitDimensional

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract
get_reorder_indices
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None) → Tuple[int][source]¶ Indices that would order the instances obs as obs respectively the instances axes as axes.
 Parameters
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
New indices that would reorder the instances obs to be obs respectively axes.
 Return type
 Raises
CoordinatesUnderdefinedError – If neither obs nor axes is given

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract
reorder_x
(x: Union[tensorflow.python.framework.ops.Tensor, numpy.ndarray], *, x_obs: Union[str, Iterable[str], zfit.Space] = None, x_axes: Union[int, Iterable[int]] = None, func_obs: Union[str, Iterable[str], zfit.Space] = None, func_axes: Union[int, Iterable[int]] = None) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor][source]¶ 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.
 Parameters
x (tensorlike) – 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
the reordered arraylike object
 Return type
tensorlike

abstract
with_autofill_axes
(overwrite: bool = False) → zfit.core.interfaces.ZfitOrderableDimensional[source]¶ 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.
object.obs > ('x', 'z', 'y') object.axes > None object.with_autofill_axes() object.obs > ('x', 'z', 'y') object.axes > (0, 1, 2)
 Parameters
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
the object with the new axes
 Return type
 Raises
AxesIncompatibleError – if the axes are already set and overwrite is False.

abstract
with_axes
(axes: Union[int, Iterable[int], None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitOrderableDimensional[source]¶ Create a new instance that has axes; sorted by or set or dropped.
The behavior is as follows:
axes are already set: * 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 asis and now correspond to the already
existing obs in the object.
 Parameters
axes – Axes to sort/associate this instance with
allow_superset – if False and a strict superset of the own axeservables is given, an error
raised. (is) –
allow_subset – if False and a strict subset of the own axeservables is given, an error
raised. –
 Returns
a copy of the object with the new ordering/axes
 Return type
 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

abstract
with_obs
(obs: Union[str, Iterable[str], zfit.Space, None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitOrderableDimensional[source]¶ Create a new instance that has obs; sorted by or set or dropped.
The behavior is as follows:
obs are already set: * 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 asis and now correspond to the already
existing axes in the object.
 Parameters
obs – Observables to sort/associate this instance with
allow_superset – if False and a strict superset of the own observables is given, an error
raised. (is) –
allow_subset – if False and a strict subset of the own observables is given, an error
raised. –
 Returns
a copy of the object with the new ordering/observables
 Return type
 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

abstract property

class
zfit.core.interfaces.
ZfitPDF
[source]¶ Bases:
zfit.core.interfaces.ZfitModel

abstract
as_func
(norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = False)[source]¶

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract
create_extended
(yield_: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor]) → zfit.core.interfaces.ZfitPDF[source]¶

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

abstract
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).

abstract property
is_extended
¶

abstract property
n_obs
¶ Return the number of observables, the dimensionality. Corresponds to the last dimension.

abstract
normalization
(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool]) → Union[tensorflow.python.framework.ops.Tensor, numpy.array][source]¶

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract property
params
¶

abstract
partial_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) → Union[float, tensorflow.python.framework.ops.Tensor]¶ Partially integrate the function over the limits and evaluate it at x.
Dimension of limits and x have to add up to the full dimension and be therefore equal to the dimensions of norm_range (if not False)
 Parameters
 Returns
the value of the partially integrated function evaluated at x.
 Return type
Tensor

abstract
pdf
(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

abstract classmethod
register_analytic_integral
(func: Callable, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, priority: int = 50, *, supports_norm_range: bool = False, supports_multiple_limits: bool = False)¶ Register an analytic integral with the class.
 Parameters
() (limits) –
() – limits_arg_descr
priority (int) –
supports_multiple_limits (bool) –
supports_norm_range (bool) –
Returns:

abstract classmethod
register_inverse_analytic_integral
(func: Callable)¶ Register an inverse analytical integral, the inverse (unnormalized) cdf.
 Parameters
() (func) –

abstract
sample
(n: int, limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'sample') → Union[float, tensorflow.python.framework.ops.Tensor]¶ Sample n points within limits from the model.

abstract
update_integration_options
(*args, **kwargs)¶

abstract

class
zfit.core.interfaces.
ZfitParameter
[source]¶ Bases:
zfit.core.interfaces.ZfitNumeric

abstract property
dtype
¶ The DType of Tensor`s handled by this `model.

abstract property
floating
¶

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

abstract
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

abstract property
independent
¶

abstract property
name
¶

abstract property
params
¶

property
shape
¶

abstract property

class
zfit.core.interfaces.
ZfitSpace
[source]¶ Bases:
zfit.core.interfaces.ZfitLimit
,zfit.core.interfaces.ZfitOrderableDimensional
,zfit.core.interfaces.ZfitObject

abstract
__eq__
(other: object) → bool¶ Compares two Limits for equality without graph mode allowed.
 Returns
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
__le__
(other: object) → bool¶ Setlike 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
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
area
() → float[source]¶ Return the total area of all the limits and axes. Useful, for example, for MC integration.

abstract property
axes
¶ Return the axes, integer based identifier(indices) for the coordinate system.

abstract
equal
(other: object, allow_graph: bool) → Union[bool, tensorflow.python.framework.ops.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
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract
filter
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], guarantee_limits: bool = False, axis: Optional[int] = None) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]¶ Filter x by removing the elements along axis that are not inside the limits.
This is similar to tf.boolean_mask.
 Parameters
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
 Return an object with the same shape as x except that along axis elements have been
removed.
 Return type
tensorlike

abstract
get_reorder_indices
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None) → Tuple[int]¶ Indices that would order the instances obs as obs respectively the instances axes as axes.
 Parameters
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
New indices that would reorder the instances obs to be obs respectively axes.
 Return type
 Raises
CoordinatesUnderdefinedError – If neither obs nor axes is given

abstract
get_sublimits
()¶ Splits itself into multiple sublimits with smaller n_obs.
If this is not possible, if the limits are not rectangular, just returns itself.
 Returns
The sublimits if it was able to split.
 Return type
Iterable[ZfitLimits]

abstract
get_subspace
(obs, axes, name)[source]¶ Create a
Space
consisting of only a subset of the obs/axes (only one allowed).Returns:

abstract property
has_limits
¶ Whether there are limits set and they are not false.
 Returns
 Return type

abstract property
has_rect_limits
¶ If there are limits and whether they are rectangular.

abstract
inside
(x: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.Data], guarantee_limits: bool = False) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, zfit.core.data.Data]¶ 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
 Parameters
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
 Return a boolean tensorlike object with the same shape as the input x except of the
last dimension removed.
 Return type
tensorlike

abstract
less_equal
(other: object, allow_graph: bool = True) → Union[bool, tensorflow.python.framework.ops.Tensor]¶ Setlike 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.
 Parameters
other – Any other object to compare with
allow_graph – If False and the function returns a symbolic tensor, raise IllegalInGraphModeError instead.
 Returns
result of the comparison
 Return type
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

abstract property
limits
¶ Return the tuple(lower, upper).

abstract property
limits_are_false
¶ If the limits have been set to False, so the object on purpose does not contain limits.
 Returns
 Return type

abstract property
limits_are_set
¶ If the limits have been set to a limit or are False.
 Returns
Whether the limits have been set or not.
 Return type

abstract property
lower
¶ Return the lower limits.

property
n_events
¶ Shape of the first dimension, usually reflects the number of events.

abstract property
n_limits
¶ Return the number of limits.

abstract property
n_obs
¶ Dimensionality, the number of observables, of the limits. Equals to the last axis in rectangular limits.
 Returns
Dimensionality of the limits.
 Return type

abstract property
obs
¶ Return the observables, string identifier for the coordinate system.

abstract
rect_area
() → Union[float, numpy.ndarray, tensorflow.python.framework.ops.Tensor]¶ Calculate the total rectangular area of all the limits and axes. Useful, for example, for MC integration.

abstract property
rect_limits
¶ Return the rectangular limits as np.ndarray``tf.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
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
The lower and upper limits.
 Return type
tuple(np.ndarray/tf.Tensor, np.ndarray/tf.Tensor) or bool or None
 Raises
LimitsNotSpecifiedError – If there are not limits set or they are False.

property
rect_limits_are_tensors
¶ Return True if the rectangular limits are tensors.
If a limit with tensors is evaluated inside a graph context, comparison operations will fail.
 Returns
if the rectangular limits are tensors.
 Return type

abstract property
rect_limits_np
¶ 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
 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.
 Return type
(lower, upper)
 Raises
CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set or are false

abstract property
rect_lower
¶ 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

abstract property
rect_upper
¶ 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

abstract
reorder_x
(x: Union[tensorflow.python.framework.ops.Tensor, numpy.ndarray], *, x_obs: Union[str, Iterable[str], zfit.Space] = None, x_axes: Union[int, Iterable[int]] = None, func_obs: Union[str, Iterable[str], zfit.Space] = None, func_axes: Union[int, Iterable[int]] = None) → Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor]¶ 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.
 Parameters
x (tensorlike) – 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
the reordered arraylike object
 Return type
tensorlike

abstract property
upper
¶ Return the upper limits.

abstract
with_autofill_axes
(overwrite: bool = False) → zfit.core.interfaces.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.
object.obs > ('x', 'z', 'y') object.axes > None object.with_autofill_axes() object.obs > ('x', 'z', 'y') object.axes > (0, 1, 2)
 Parameters
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
the object with the new axes
 Return type
 Raises
AxesIncompatibleError – if the axes are already set and overwrite is False.

abstract
with_axes
(axes: Union[int, Iterable[int], None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitOrderableDimensional¶ Create a new instance that has axes; sorted by or set or dropped.
The behavior is as follows:
axes are already set: * 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 asis and now correspond to the already
existing obs in the object.
 Parameters
axes – Axes to sort/associate this instance with
allow_superset – if False and a strict superset of the own axeservables is given, an error
raised. (is) –
allow_subset – if False and a strict subset of the own axeservables is given, an error
raised. –
 Returns
a copy of the object with the new ordering/axes
 Return type
 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

abstract
with_coords
(coords: zfit.core.interfaces.ZfitOrderableDimensional, allow_superset: bool = True, allow_subset: bool = True) → object[source]¶ Create a new
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 nonmatchin axes results in axes being dropped.
 Parameters
coords – An instance of
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
 Return type
 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

abstract
with_limits
(limits: Union[zfit.core.interfaces.ZfitLimit, tensorflow.python.framework.ops.Tensor, numpy.ndarray, Iterable[float], float, Tuple[float], List[float], bool, None] = None, rect_limits: Union[tensorflow.python.framework.ops.Tensor, numpy.ndarray, Iterable[float], float, Tuple[float], List[float], None] = None, name: Optional[str] = None) → zfit.core.interfaces.ZfitSpace[source]¶ Return a copy of the space with the new limits (and the new name).
 Parameters
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
name – Human readable name
 Returns
Copy of the current object with the new limits.
 Return type

abstract
with_obs
(obs: Union[str, Iterable[str], zfit.Space, None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitOrderableDimensional¶ Create a new instance that has obs; sorted by or set or dropped.
The behavior is as follows:
obs are already set: * 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 asis and now correspond to the already
existing axes in the object.
 Parameters
obs – Observables to sort/associate this instance with
allow_superset – if False and a strict superset of the own observables is given, an error
raised. (is) –
allow_subset – if False and a strict subset of the own observables is given, an error
raised. –
 Returns
a copy of the object with the new ordering/observables
 Return type
 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

abstract