Space#

class zfit.dimension.Space(obs=None, *args, limits=None, binning=None, axes=None, rect_limits=None, name=None, label=None, lower=None, upper=None)[source]#

Bases: BaseSpace, SerializableMixin

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

Deprecated: SOME ARGUMENTS ARE DEPRECATED: (rect_limits). They will be removed in a future version. Instructions for updating: Use lower and upper instead

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, 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).

Parameters:

obs (ztyping.ObsTypeInput | None) – Observable of the space. Serves as the “variable”.
lower (ztyping.LimitsTypeInputV1 | None) – Lower and upper limits of the space, respectively. Each of them should be a scalar-like object.
upper (ztyping.LimitsTypeInputV1 | None) – Lower and upper limits of the space, respectively. Each of them should be a scalar-like object.
limits (ztyping.LimitsTypeInput | None) – A tuple-like object of the limits of the space. These are the lower and upper limits.
binning (ztyping.BinningTypeInput) – Binning of the space. Currently, only regular and variable binning with a name is supported. If an integer or a list of integers is given with lengths equal to the number of observables, it is interpreted as the number of bins and a regular binning is automatically created using the limits as the start and end points.
name (str | None) – Name of the space. Maybe has implications on the serialization and deserialization of the space. For a human-readable name, use the label.

Raises: TypeError: If the axes in the binning do not have a name. ObsIncompatibleError: If the obs do not agree with the name of the binning. ShapeIncompatibleError: If the shape of the limits or the binnings do not match the shape of the obs.

property rect_limits: tuple[ndarray | Tensor | float, ndarray | Tensor | float]#

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.
Raises:: LimitsNotSpecifiedError – If there are not limits set or they are False.

property rect_limits_np: tuple[ndarray, ndarray]#

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.

Raises:

CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set

property rect_upper: ndarray | Tensor | None | bool#

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

property rect_lower: ndarray | Tensor | float#

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_area()[source]#

Calculate the total rectangular area of all the limits and axes.

Useful, for example, for MC integration.

Return type:: float | np.ndarray | znp.array

property rect_limits_are_tensors: 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:: If the rectangular limits are tensors.

property has_rect_limits: bool#: If there are limits and whether they are rectangular.

property limits_are_false: bool#

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

Returns:: True if limits is False

property has_limits: bool#

Whether there are limits set and they are not false.

Returns:

property n_events: int | None#

Return the number of events, the dimension of the first shape.

Returns:

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

property n_limits: int#

The number of different limits.

Returns:: int >= 1

with_limits(limits=None, rect_limits=None, name=None)[source]#

Return a copy of the space with the new limits (and the new name).

Parameters:

limits (Union[ZfitLimit, Tensor, ndarray, Iterable[float], float, tuple[float], list[float], bool, None]) – Limits to use. Can be rectangular, a function (requires to also specify rect_limits or an instance of ZfitLimit.
rect_limits (Union[Tensor, ndarray, Iterable[float], float, tuple[float], list[float], None]) – Rectangular limits that will be assigned with the instance
name (str | None) – Human readable name

Return type:

ZfitSpace

Returns:

Copy of the current object with the new limits.

reorder_x(x, *, x_obs=None, x_axes=None, func_obs=None, func_axes=None)[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 (Tensor | ndarray) – Tensor to be reordered, last dimension should be n_obs resp. n_axes
x_obs (Union[str, Iterable[str], Space]) – Observables associated with x. If both, x_obs and x_axes are given, this has precedency over the latter.
x_axes (Union[int, Iterable[int]]) – Axes associated with x.
func_obs (Union[str, Iterable[str], Space]) – 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 (Union[int, Iterable[int]]) – Axe associated with a function that x will be given to. Reorders x accordingly and assumes self.axes to be the axes of x.

Return type:

Union[ndarray, Tensor]

Returns:

The reordered array-like object

with_obs(obs, allow_superset=True, allow_subset=True)[source]#

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 (Union[str, Iterable[str], Space, None]) – Observables to sort/associate this instance with
allow_superset (bool) – if False and a strict superset of the own observables is given, an error
raised. (is)
allow_subset (bool) – if False and a strict subset of the own observables is given, an error
raised.

Return type:

ZfitSpace

Returns:

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

with_axes(axes, allow_superset=True, allow_subset=True)[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 as-is and now correspond to the already existing obs in the object.

Parameters:

axes (Union[int, Iterable[int], None]) – Axes to sort/associate this instance with
allow_superset (bool) – if False and a strict superset of the own axeservables is given, an error
raised. (is)
allow_subset (bool) – if False and a strict subset of the own axeservables is given, an error
raised.

Return type:

ZfitSpace

Returns:

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

with_coords(coords, allow_superset=True, allow_subset=True)[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 non-matchin axes results in axes being dropped.

Parameters:

coords (ZfitOrderableDimensional) – An instance of Coordinates
allow_superset (bool) – If False and a strict superset is given, an error is raised
allow_subset (bool) – If False and a strict subset is given, an error is raised

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_autofill_axes(overwrite=False)[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.
Return type:: Space
Returns:: The object with the new axes
Raises:: AxesIncompatibleError – if the axes are already set and overwrite is False.

get_subspace(obs=None, axes=None, name=None)[source]#

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

Parameters:

obs (Union[str, Iterable[str], Space]) – Observables of the subspace to return.
axes (Union[int, Iterable[int]]) – Axes of the subspace to return.
name (str | None) – Human readable names

Return type:

Space

Returns:

A space containing only a subspace (and sublimits etc.)

copy(**overwrite_kwargs)[source]#

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

Parameters:

name – The new name. If not given, the new instance will be named the same as the current one.
**overwrite_kwargs

Return type:

Space

Returns:

Space

classmethod from_axes(cls, axes, limits=None, rect_limits=None, name=None)[source]#

Create a space from axes instead of from obs.

Parameters:

rect_limits
axes (Union[int, Iterable[int]])
limits (Union[ZfitLimit, Tensor, ndarray, Iterable[float], float, tuple[float], list[float], bool, None])
name (str | None)

Return type:

Space

Returns:

Space

__eq__(other)#

Compares two Limits for equality without graph mode allowed.

Returns:

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

__le__(other)#

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.

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

add(*other)#

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 (Union[Space, Iterable[Space]])
Return type:: Space

property axes: tuple[int] | None#

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

Returns:

combine(*other)#

Combine spaces with different obs (but consistent limits).

Parameters:: other (Union[Space, Iterable[Space]])
Return type:: Space

equal(other, allow_graph)#

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.

Return type:: array
Returns:: Result of the comparison
Raises:: IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

filter(x, guarantee_limits=False, axis=None)#

Filter x by removing the elements along axis that are not inside the limits.

This is similar to tf.boolean_mask.

Parameters:

x (Union[ndarray, Tensor, Data]) – 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 (bool) – Guarantee that the values are already inside the rectangular limits.
axis (int | None) – The axis to remove the elements from. Defaults to 0.

Return type:

ndarray | Tensor | DataFrame | Any

Returns:

Return an object with the same shape as x except that along axis elements have been: removed.

classmethod from_asdf(asdf_obj, *, reuse_params=None)#

Load an object from an asdf file.

Parameters:

asdf_obj – Object
reuse_params – If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.

classmethod from_dict(dict_, *, reuse_params=None)#

Creates an object from a dictionary structure as generated by to_dict.

Parameters:

dict – Dictionary structure.
reuse_params – If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.

Returns:

The deserialized object.

classmethod from_json(cls, json, *, reuse_params=None)#

Load an object from a json string.

Parameters:

json (str) – Serialized object in a JSON string.
reuse_params – If parameters, the parameters will be reused if they are given. If a parameter is given, it will be used as the parameter with the same name. If a parameter is not given, a new parameter will be created.

Return type:

object

Returns:

The deserialized object.

get_reorder_indices(obs=None, axes=None)#

Indices that would order the instances obs as obs respectively the instances axes as axes.

Parameters:

obs (Union[str, Iterable[str], Space]) – Observables that the instances obs should be ordered to. Does not reorder, but just return the indices that could be used to reorder.
axes (Union[int, Iterable[int]]) – Axes that the instances obs should be ordered to. Does not reorder, but just return the indices that could be used to reorder.

Return type:

tuple[int]

Returns:

New indices that would reorder the instances obs to be obs respectively axes.

Raises:

CoordinatesUnderdefinedError – If neither obs nor axes is given

classmethod get_repr()#

Abstract representation of the object for serialization.

This objects knows how to serialize and deserialize the object and is used by the to_json, from_json, to_dict and from_dict methods.

Returns:: The representation of the object.
Return type:: pydantic.BaseModel

inside(x, guarantee_limits=False)#

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 (Union[ndarray, Tensor, Data]) – 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 (bool) – Guarantee that the values are already inside the rectangular limits.

Return type:

Union[ndarray, Tensor, Data]

Returns:

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

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

Raises:

IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

property n_obs: int#

Return the number of observables/axes.

Returns: :returns: int >= 1

property name: str#: The name of the object.

property obs: tuple[str, ...] | None#

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

Returns:

to_asdf()#: Convert the object to an asdf file.

to_dict()#

Convert the object to a nested dictionary structure.

Returns:: The dictionary structure.
Return type:: dict

to_json()#

Convert the object to a json string.

Returns:: The json string.
Return type:: str

to_yaml()#

Convert the object to a yaml string.

Returns:: The yaml string.
Return type:: str

Space#

This Page