sample¶

class zfit.core.sample.EventSpace(obs: Union[str, Iterable[str], zfit.Space], limits: Union[zfit.core.interfaces.ZfitLimit, tensorflow.python.framework.ops.Tensor, numpy.ndarray, Iterable[float], float, Tuple[float], List[float], bool, None], factory=None, dtype=tf.float64, name: Optional[str] = 'Space')[source]¶

Bases: zfit.core.space.Space

EXPERIMENTAL SPACE CLASS!

ANY = <Any>¶

ANY_LOWER = <Any Lower Limit>¶

ANY_UPPER = <Any Upper Limit>¶

AUTO_FILL = <object object>¶

__eq__(other: object) → bool¶

Compares two Limits for equality without graph mode allowed.

Returns:
Return type:	bool
Raises:	`IllegalInGraphModeError` – it the comparison happens with tensors in a graph context.

__le__(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:	result of the comparison
Return type:	bool
Raises:	`IllegalInGraphModeError` – it the comparison happens with tensors in a graph context.

add(other: Union[zfit.Space, Iterable[zfit.Space]])[source]¶

Add the limits of the spaces. Only works for the same obs.

In case the observables are different, the order of the first space is taken.

Parameters:	other (`Space`) –
Returns:
Return type:	`Space`

area(**kwargs)¶

axes¶

The axes (“obs with int”) the space is defined in.

Returns:

combine(other: Union[zfit.Space, Iterable[zfit.Space]])[source]¶

Combine spaces with different obs (but consistent limits).

Parameters:	other (`Space`) –
Returns:
Return type:	`Space`

copy(**overwrite_kwargs) → zfit.core.space.Space¶

Create a new Space using the current attributes and overwriting with overwrite_overwrite_kwargs.

Parameters:	name (str) – The new name. If not given, the new instance will be named the same as the current one. () (**overwrite_kwargs) –
Returns:	`Space`

create_limits(n)[source]¶

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:	bool
Raises:	`IllegalInGraphModeError` – it the comparison happens with tensors in a graph context.

factory¶

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:

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:	tensor-like

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:

tensor-like

classmethod from_axes(axes: Union[int, Iterable[int]], 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=None, name: str = None) → zfit.Space¶

Create a space from axes instead of from obs. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use directly the class to create a Space. E.g. zfit.Space(axes=(0, 1), …)

Parameters:	rect_limits – () (limits) – () – name (str) –
Returns:	`Space`

get_limits(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None) → Union[Dict[str, Union[Dict[Tuple[int], zfit.core.interfaces.ZfitLimit], Dict[Tuple[str], zfit.core.interfaces.ZfitLimit]]], Dict[Tuple[int], zfit.core.interfaces.ZfitLimit], Dict[Tuple[str], zfit.core.interfaces.ZfitLimit]]¶

get_obs_axes(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None)¶

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:	tuple(int)
Raises:	`CoordinatesUnderdefinedError` – If neither obs nor axes is given

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]

get_subspace(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, name: Optional[str] = None) → zfit.core.space.Space¶

Create a Space consisting of only a subset of the obs/axes (only one allowed).

Parameters:	obs (str, Tuple[str]) – Observables of the subspace to return. axes (int, Tuple[int]) – Axes of the subspace to return. name – Human readable names
Returns:	A space containing only a subspace (and sublimits etc.)
Return type:	`Space`

has_limits¶

Whether there are limits set and they are not false.

Returns:
Return type:	bool

has_rect_limits¶: If there are limits and whether they are rectangular.

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:

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 tensor-like object with the same shape as the input x except of the last dimension removed.
Return type:	tensor-like

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 tensor-like object with the same shape as the input x except of the: last dimension removed.

Return type:

tensor-like

is_generator¶

iter_areas(rel: bool = False) → Tuple[float, ...][source]¶

iter_limits¶

REMOVED.Return the limits, either as Space objects or as pure limits-tuple. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Iterate over the space directly and use the limits from the spaces.

This makes iterating over limits easier: for limit in space.iter_limits() allows to, for example, pass limit to a function that can deal with simple limits only or if as_tuple is True the limit can be directly used to calculate something.

Example

for lower, upper in space.iter_limits(as_tuple=True):
    integrals = integrate(lower, upper)  # calculate integral
integral = sum(integrals)

Returns:
Return type:	List[`Space`] or List[limit,…]

less_equal(other, allow_graph)¶

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.

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:	bool
Raises:	`IllegalInGraphModeError` – it the comparison happens with tensors in a graph context.

limit1d¶

limit2d¶

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Depreceated, use .rect_limits or .inside to check if a value is inside or userect_limits to receive the rectangular limits.

limits¶: Return the tuple(lower, upper).

limits1d¶

return the tuple(low_1, …, low_n, up_1, …, up_n).

Returns:	so low_1, low_2, up_1, up_2 = space.limits1d for several, 1 obs limits. low_1 to up_1 is the first interval, low_2 to up_2 is the second interval etc.
Return type:	tuple(float, float, ..)
Raises:	`RuntimeError` – if the conditions (n_obs or n_limits) are not satisfied.
Type:	Simplified .limits for exactly 1 obs, n limits

limits_are_false¶

If the limits have been set to False, so the object on purpose does not contain limits.

Returns:	True if limits is False
Return type:	bool

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:	bool

lower¶

n_events¶

int, None: Return the number of events, the dimension of the first shape. If this is > 1 or None,: it’s vectorized.

Type:	Returns

n_limits¶

The number of different limits.

Returns:	int >= 1

n_obs¶

Return the number of observables/axes.

Returns:	int >= 1

name¶: The name of the object.

obs¶

The observables (“axes with str”)the space is defined in.

Returns:

obs_axes¶

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.

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.

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:	bool

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:

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

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

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

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

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 (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:	the reordered array-like object
Return type:	tensor-like

upper¶

with_autofill_axes(overwrite: bool = False) → zfit.core.space.Space¶

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.

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:	ZfitSpace
Raises:	`AxesIncompatibleError` – if the axes are already set and overwrite is False.

with_axes(axes: Union[int, Iterable[int], None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitSpace¶

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 as-is 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:	`Space`
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

with_coords(coords: zfit.core.interfaces.ZfitOrderableDimensional, allow_superset: bool = True, allow_subset: bool = True) → zfit.core.space.Space¶

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 non-matchin 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:	`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

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¶

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:	`Space`

with_obs(obs: Union[str, Iterable[str], zfit.Space, None], allow_superset: bool = True, allow_subset: bool = True) → zfit.core.interfaces.ZfitSpace¶

Create a new Space 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 as-is 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:	`Space`
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

with_obs_axes(**kwargs)¶

class zfit.core.sample.UniformSampleAndWeights[source]¶: Bases: object

zfit.core.sample.extended_sampling(pdfs: Union[Iterable[zfit.core.interfaces.ZfitPDF], zfit.core.interfaces.ZfitPDF], limits: zfit.core.space.Space) → tensorflow.python.framework.ops.Tensor[source]¶

Create a sample from extended pdfs by sampling poissonian using the yield.

Parameters:	pdfs (iterable[ZfitPDF]) – limits (`Space`) –
Returns:
Return type:	Union[tf.Tensor]

zfit.core.sample.extract_extended_pdfs(pdfs: Union[Iterable[zfit.core.interfaces.ZfitPDF], zfit.core.interfaces.ZfitPDF]) → List[zfit.core.interfaces.ZfitPDF][source]¶

Return all extended pdfs that are daughters.

Parameters:	pdfs (Iterable[pdfs]) –
Returns:
Return type:	List[pdfs]