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!

Define a space with the name (obs) of the axes (and it’s number) and possibly it’s limits.

A space can be thought of as coordinates, possibly with the definition of a range (limits). For most use-cases, it is sufficient to specify a Space via observables; simple string identifiers. They can be multidimensional.

Observables are like the columns of a spreadsheet/dataframe, and are therefore needed for any object that does numerical operations or holds data in order to match the right axes. On object creation, the observables are assigned using a Space. This is often used as the default space of an object and can be used as the default norm_range, sampling limits etc.

Axes are the same concept as observables, but numbers, indexes, and are used inside an object. There, axes 0 corresponds to the 0th data column we get (which corresponds to a certain observable).

Every space can have limits; they are either rectangular or an arbitrary function (together with rectangular limits). Spaces can be combined (multiplied) to create higher dimensional spaces. Spaces can be added, which combines them into one Space consisting of two disconnected limits.

So integrating over the space consisting of the two added disconnected ranges, e.g. 0 to 1 and 2 to 3 will return the sum of the two separate integrals.

lower_band = zfit.Space('obs1', (0, 1))
upper_band = zfit.Space('obs1', (2, 3))
combined_obs = lower_band + upper_band
integral_comb = model.integrate(limits=combined_obs)
# which is equivalent to the lower
integral_sep = model.integrate(limits=lower_band) + model.integrate(limits=upper_band)
assert integral_comb == integral_sep

In principle, the same behavior could also be achieved by specifying an arbitrary function. Using the addition allows for certain optimizations inside.

Parameters
  • obs (str, List[str,..]) –

  • () (limits) –

  • name (str) –

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)
property 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.

property 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
  • 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

property has_limits

Whether there are limits set and they are not false.

Returns

Return type

bool

property 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
  • 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

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

property limit1d
property 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.

property limits

Return the tuple(lower, upper).

property 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

property 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

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

bool

property lower
property n_events

Returns: int, None: Return the number of events, the dimension of the first shape. If this is > 1 or None,

it’s vectorized.

property n_limits

The number of different limits.

Returns

int >= 1

property n_obs

Return the number of observables/axes.

Returns

int >= 1

property name

The name of the object.

property obs

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

Returns:

property 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.

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

bool

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
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

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

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

property 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.

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

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
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
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
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
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]