Space¶
- class zfit.dimension.Space(obs=None, limits=None, binning=None, axes=None, rect_limits=None, name='Space')[source]¶
Bases:
zfit.core.space.BaseSpace
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 defaultnorm_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).
- Parameters
obs (
Union
[str
,Iterable
[str
],ForwardRef
,None
]) – Observable of the space.limits (
Union
[ForwardRef
,Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],bool
,None
]) – A tuple-like object of the limits of the space. These are the lower and upper limits.binning (
Union
[Iterable
[ForwardRef
],ForwardRef
,int
]) – Binning of the space. Currently, only regular and variable binning with a name is supported. If an integer is given, 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 (
Optional
[str
]) – Human-readable name of the space.
- property rect_limits: Tuple[Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, float], Union[numpy.ndarray, tensorflow.python.framework.ops.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
andlimits_are_set
.
- property rect_limits_np: Tuple[numpy.ndarray, numpy.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
andlimits_are_set
.- Return type
- 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 withz.unstack_x()
as can be done with data.
- A tuple of two
- Raises
CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set
- property rect_lower: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, float]¶
The lower, rectangular limits, equivalent to
rect_limits[0]
with shape (…, n_obs)
- property rect_upper: Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, None, bool]¶
The upper, rectangular limits, equivalent to
rect_limits[1]
with shape (…, n_obs)
- rect_area()[source]¶
Calculate the total rectangular area of all the limits and axes.
Useful, for example, for MC integration.
- Return type
Union
[float
,ndarray
,Tensor
]
- 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.
- Return type
bool
- Returns
If the rectangular limits are tensors.
- property has_rect_limits: bool¶
If there are limits and whether they are rectangular.
- Return type
bool
- property limits_are_false: bool¶
If the limits have been set to False, so the object on purpose does not contain limits.
- Return type
bool
- Returns
True if limits is False
- property has_limits: bool¶
Whether there are limits set and they are not false.
Returns:
- Return type
bool
- property n_events: Optional[int]¶
Return the number of events, the dimension of the first shape.
- Return type
Optional
[int
]- Returns
- Number of events, the dimension of the first shape. If this is > 1 or None,
it’s vectorized.
- property limit2d¶
DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use
limits
instead.
- property limits1d: Tuple[float]¶
return the tuple(low_1, …, low_n, up_1, …, up_n).
- Return type
Tuple
[float
]- 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.
- So
- Raises
RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.
- Type
Simplified
limits
for exactly 1 obs, n limits
- property n_limits: int¶
The number of different limits.
- Return type
int
- Returns
int >= 1
- property iter_limits: Union[Tuple[zfit.core.space.Space], Tuple[Tuple[Tuple[float]]], Tuple[Tuple[float]]]¶
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, passlimit
to a function that can deal with simple limits only or ifas_tuple
is True thelimit
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,…]
- with_limits(limits=None, rect_limits=None, name=None)[source]¶
Return a copy of the space with the new
limits
(and the newname
).- 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 specifyrect_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 instancename (
Optional
[str
]) – 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 withfunc_obs
resp.func_axes
.Switching
func_obs
forx_obs
resp.func_axes
forx_axes
inverts the reordering of x.- Parameters
x (
Union
[Tensor
,ndarray
]) – Tensor to be reordered, last dimension should be n_obs resp. n_axesx_obs (
Union
[str
,Iterable
[str
],Space
,None
]) – 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
],None
]) – Axes associated with x.func_obs (
Union
[str
,Iterable
[str
],Space
,None
]) – 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
andfunc_axes
are given, this has precedency over the latter.func_axes (
Union
[int
,Iterable
[int
],None
]) – 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 withallow_superset (
bool
) – if False and a strict superset of the own observables is given, an errorraised. (is) –
allow_subset (
bool
) – if False and a strict subset of the own observables is given, an errorraised. –
- 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 withallow_superset (
bool
) – if False and a strict superset of the own axeservables is given, an errorraised. (is) –
allow_subset (
bool
) – if False and a strict subset of the own axeservables is given, an errorraised. –
- 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 ofCoordinates
allow_superset (
bool
) – IfFalse
and a strict superset is given, an error is raisedallow_subset (
bool
) – IfFalse
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_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 andoverwrite
is False, raise an error.- Return type
- 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 theobs
/axes
(only one allowed).
- copy(**overwrite_kwargs)[source]¶
Create a new
Space
using the current attributes and overwriting withoverwrite_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
- Returns
Space
- classmethod from_axes(cls, axes, limits=None, rect_limits=None, name=None)[source]¶
Create a space from
axes
instead of fromobs
. (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), …)
- __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.
- property axes: Optional[Tuple[int]]¶
The axes (“obs with int”) the space is defined in.
Returns:
- Return type
Optional
[Tuple
[int
]]
- combine(*other)¶
Combine spaces with different obs (but consistent limits).
- 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
Union
[bool
,Tensor
]- 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 alongaxis
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 specifiedaxis
) 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 (
Optional
[int
]) – The axis to remove the elements from. Defaults to 0.
- Return type
Union
[ndarray
,Tensor
]- Returns
- Return an object with the same shape as
x
except that alongaxis
elements have been removed.
- Return an object with the same shape as
- get_reorder_indices(obs=None, axes=None)¶
Indices that would order the instances obs as
obs
respectively the instances axes asaxes
.- Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) – 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
],None
]) – 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
noraxes
is given
- 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
- Return type
- Returns
- Return a boolean tensor-like object with the same shape as the input
x
except of the last dimension removed.
- Return a boolean tensor-like object with the same shape as the input
- 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.
- Return type
int
- Returns
int >= 1
- property name: str¶
The name of the object.
- Return type
str
- property obs: Optional[Tuple[str, ...]]¶
The observables (“axes with str”)the space is defined in.
Returns:
- Return type
Optional
[Tuple
[str
, …]]