interfaces

class zfit.core.interfaces.ZfitConstraint[source]

Bases: abc.ABC

abstract value()[source]
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 sort_by_axes(axes, allow_superset: bool = True)[source]
abstract sort_by_obs(obs, allow_superset: bool = True)[source]
abstract value(obs: List[str] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]
abstract property weights
class zfit.core.interfaces.ZfitDependentsMixin[source]

Bases: object

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

class zfit.core.interfaces.ZfitFunc[source]

Bases: zfit.core.interfaces.ZfitModel

abstract as_pdf()[source]
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).

Parameters
  • limits (tuple, Space) – the limits to integrate over

  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability

  • name (str) –

Returns

the integral value

Return type

Tensor

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
  • x (numerical) – The value at which the partially integrated function will be evaluated

  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes

  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes

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

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.

Parameters
  • n (int) – The number of samples to be generated

  • limits (tuple, Space) – In which region to sample in

  • name (str) –

Returns

Tensor(n_obs, n_samples)

abstract update_integration_options(*args, **kwargs)
class zfit.core.interfaces.ZfitFunctorMixin[source]

Bases: object

abstract get_models() → List[zfit.core.interfaces.ZfitModel][source]
abstract property models
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
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

bool

Raises

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

abstract __le__(other: object) → bool[source]

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.

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

bool

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

tensor-like

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]

get_subspace(*_, **__)[source]
abstract property has_limits

Whether there are limits set and they are not false.

Returns

Return type

bool

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 tensor-like object with the same shape as the input x except of the

last dimension removed.

Return type

tensor-like

abstract less_equal(other: object, allow_graph: bool = True) → Union[bool, tensorflow.python.framework.ops.Tensor][source]

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.

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

bool

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

bool

property n_events

Shape of the first dimension, usually reflects the number of events.

Returns

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

it’s vectorized.

Return type

int, None

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

int

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

bool

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
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

class zfit.core.interfaces.ZfitLoss[source]

Bases: zfit.core.interfaces.ZfitObject, zfit.core.interfaces.ZfitDependentsMixin

abstract add_constraints(constraints: List[tensorflow.python.framework.ops.Tensor])[source]
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 value() → Union[tensorflow.python.framework.ops.Tensor, numpy.array][source]
abstract value_gradients(params)[source]
abstract value_gradients_hessian(params, hessian=None)[source]
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).

Parameters
  • limits (tuple, Space) – the limits to integrate over

  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability

  • name (str) –

Returns

the integral value

Return type

Tensor

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
  • x (numerical) – The value at which the partially integrated function will be evaluated

  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes

  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes

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

Returns:

abstract classmethod register_inverse_analytic_integral(func: Callable)[source]

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][source]

Sample n points within limits from the model.

Parameters
  • n (int) – The number of samples to be generated

  • limits (tuple, Space) – In which region to sample in

  • name (str) –

Returns

Tensor(n_obs, n_samples)

abstract update_integration_options(*args, **kwargs)[source]
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
class zfit.core.interfaces.ZfitObject[source]

Bases: abc.ABC

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

tuple(int)

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 (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

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

object

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 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

object

Raises
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 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

object

Raises
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 get_yield() → Optional[zfit.core.interfaces.ZfitParameter][source]
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).

Parameters
  • limits (tuple, Space) – the limits to integrate over

  • norm_range (tuple, Space) – the limits to normalize over or False to integrate the unnormalized probability

  • name (str) –

Returns

the integral value

Return type

Tensor

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
  • x (numerical) – The value at which the partially integrated function will be evaluated

  • limits (tuple, Space) – the limits to integrate over. Can contain only some axes

  • norm_range (tuple, Space, False) – the limits to normalize over. Has to have all axes

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

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.

Parameters
  • n (int) – The number of samples to be generated

  • limits (tuple, Space) – In which region to sample in

  • name (str) –

Returns

Tensor(n_obs, n_samples)

abstract set_norm_range()[source]
abstract update_integration_options(*args, **kwargs)
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
abstract read_value() → tensorflow.python.framework.ops.Tensor[source]
property shape
abstract value() → tensorflow.python.framework.ops.Tensor[source]
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

bool

Raises

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

abstract __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.

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

bool

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

tensor-like

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

tuple(int)

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).

Parameters
  • obs (str, Tuple[str]) –

  • axes (int, Tuple[int]) –

  • () (name) –

Returns:

abstract property has_limits

Whether there are limits set and they are not false.

Returns

Return type

bool

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 tensor-like object with the same shape as the input x except of the

last dimension removed.

Return type

tensor-like

abstract less_equal(other: object, allow_graph: bool = True) → Union[bool, tensorflow.python.framework.ops.Tensor]

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.

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

bool

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

bool

abstract property lower

Return the lower limits.

property n_events

Shape of the first dimension, usually reflects the number of events.

Returns

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

it’s vectorized.

Return type

int, None

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

int

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

bool

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
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 (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

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

object

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 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

object

Raises
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 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
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

Space

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 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

object

Raises