interfaces — zfit 0.4.1 documentation

class zfit.core.interfaces.ZfitConstraint[source]¶

Bases: abc.ABC

value()[source]¶

class zfit.core.interfaces.ZfitData[source]¶

Bases: zfit.core.interfaces.ZfitDimensional

axes¶: Return the axes.

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

n_obs¶: Return the number of observables.

name¶: Name prepended to all ops created by this model.

obs¶: Return the observables.

sort_by_axes(axes, allow_superset: bool = False)[source]¶

sort_by_obs(obs, allow_superset: bool = False)[source]¶

space¶: Return the Space object that defines the dimensionality of the object.

value(obs: List[str] = None) → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

weights¶

class zfit.core.interfaces.ZfitDependentsMixin[source]¶

Bases: object

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

axes¶: Return the axes.

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

n_obs¶: Return the number of observables.

name¶: Name prepended to all ops created by this model.

obs¶: Return the observables.

space¶: Return the Space object that defines the dimensionality of the object.

class zfit.core.interfaces.ZfitFunc[source]¶

Bases: zfit.core.interfaces.ZfitModel

as_pdf()[source]¶

axes¶: Return the axes.

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

dtype¶: The DType of Tensor`s handled by this `model.

func(x: Union[float, tensorflow.python.framework.ops.Tensor], name: str = 'value') → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

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

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

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

n_obs¶: Return the number of observables.

name¶: Name prepended to all ops created by this model.

obs¶: Return the observables.

params¶

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, name: str = 'partial_integrate') → 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 name (str) –
Returns:	the value of the partially integrated function evaluated at x.
Return type:	Tensor

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:	() (limits) – () – \|limits_arg_descr\| priority (int) – supports_multiple_limits (bool) – supports_norm_range (bool) –

Returns:

classmethod register_inverse_analytic_integral(func: Callable)¶

Register an inverse analytical integral, the inverse (unnormalized) cdf.

Parameters:	() (func) –

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)

space¶: Return the Space object that defines the dimensionality of the object.

update_integration_options(*args, **kwargs)¶

class zfit.core.interfaces.ZfitFunctorMixin[source]¶

Bases: object

get_models() → List[zfit.core.interfaces.ZfitModel][source]¶

models¶

class zfit.core.interfaces.ZfitLoss[source]¶

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

add_constraints(constraints: List[tensorflow.python.framework.ops.Tensor])[source]¶

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

data¶

errordef¶

fit_range¶

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

gradients(params: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor] = None) → List[tensorflow.python.framework.ops.Tensor][source]¶

model¶

name¶: Name prepended to all ops created by this model.

value() → Union[tensorflow.python.framework.ops.Tensor, numpy.array][source]¶

class zfit.core.interfaces.ZfitModel[source]¶

Bases: zfit.core.interfaces.ZfitNumeric, zfit.core.interfaces.ZfitDimensional

axes¶: Return the axes.

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

dtype¶: The DType of Tensor`s handled by this `model.

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

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

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

n_obs¶: Return the number of observables.

name¶: Name prepended to all ops created by this model.

obs¶: Return the observables.

params¶

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, name: str = 'partial_integrate') → 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 name (str) –
Returns:	the value of the partially integrated function evaluated at x.
Return type:	Tensor

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:	() (limits) – () – \|limits_arg_descr\| priority (int) – supports_multiple_limits (bool) – supports_norm_range (bool) –

Returns:

classmethod register_inverse_analytic_integral(func: Callable)[source]¶

Register an inverse analytical integral, the inverse (unnormalized) cdf.

Parameters:	() (func) –

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)

space¶: Return the Space object that defines the dimensionality of the object.

update_integration_options(*args, **kwargs)[source]¶

class zfit.core.interfaces.ZfitNumeric[source]¶

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

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

dtype¶: The DType of Tensor`s handled by this `model.

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

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter][source]¶

name¶: Name prepended to all ops created by this model.

params¶

class zfit.core.interfaces.ZfitObject[source]¶

Bases: abc.ABC

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject[source]¶

name¶: Name prepended to all ops created by this model.

class zfit.core.interfaces.ZfitPDF[source]¶

Bases: zfit.core.interfaces.ZfitModel

as_func(norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = False)[source]¶

axes¶: Return the axes.

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

create_extended(yield_: Union[zfit.core.interfaces.ZfitParameter, int, float, complex, tensorflow.python.framework.ops.Tensor]) → zfit.core.interfaces.ZfitPDF[source]¶

dtype¶: The DType of Tensor`s handled by this `model.

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

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

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

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

is_extended¶

n_obs¶: Return the number of observables.

name¶: Name prepended to all ops created by this model.

normalization(limits: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool], name: str = 'normalization') → Union[tensorflow.python.framework.ops.Tensor, numpy.array][source]¶

obs¶: Return the observables.

params¶

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, name: str = 'partial_integrate') → 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 name (str) –
Returns:	the value of the partially integrated function evaluated at x.
Return type:	Tensor

pdf(x: Union[float, tensorflow.python.framework.ops.Tensor], norm_range: Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool] = None, name: str = 'model') → Union[float, tensorflow.python.framework.ops.Tensor][source]¶

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:	() (limits) – () – \|limits_arg_descr\| priority (int) – supports_multiple_limits (bool) – supports_norm_range (bool) –

Returns:

classmethod register_inverse_analytic_integral(func: Callable)¶

Register an inverse analytical integral, the inverse (unnormalized) cdf.

Parameters:	() (func) –

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)

set_norm_range()[source]¶

space¶: Return the Space object that defines the dimensionality of the object.

update_integration_options(*args, **kwargs)¶

class zfit.core.interfaces.ZfitParameter[source]¶

Bases: zfit.core.interfaces.ZfitNumeric

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

dtype¶: The DType of Tensor`s handled by this `model.

floating¶

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

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[zfit.core.interfaces.ZfitParameter]¶

independent¶

name¶: Name prepended to all ops created by this model.

params¶

value() → tensorflow.python.framework.ops.Tensor[source]¶

class zfit.core.interfaces.ZfitSpace[source]¶

Bases: zfit.core.interfaces.ZfitObject

area() → float[source]¶: Return the total area of all the limits and axes. Useful, for example, for MC integration.

axes¶

copy(deep: bool = False, **overwrite_params) → zfit.core.interfaces.ZfitObject¶

get_axes(obs: Union[str, Tuple[str, ...]] = None, as_dict: bool = True)[source]¶

Return the axes number of the observable if available (set by axes_by_obs).

Raises:	`AxesNotUnambiguousError` – In case

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

iter_areas(rel: bool = False) → Tuple[float, ...][source]¶: Return the areas of each limit.

iter_limits()[source]¶: Iterate through the limits by returning several observables/(lower, upper)-tuples.

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

lower¶: Return the lower limits.

n_limits¶: Return the number of limits.

n_obs¶: Return the number of observables (axis).

name¶: Name prepended to all ops created by this model.

obs¶: Return a list of the observable names.

upper¶: Return the upper limits.

with_autofill_axes(overwrite: bool)[source]¶

Return a Space with filled axes corresponding to range(len(n_obs)).

Parameters:	overwrite (bool) – If self.axes is not None, replace the axes with the autofilled ones. If axes is already set, don’t do anything if overwrite is False.
Returns:	`Space`

with_axes(axes)[source]¶

Sort by obs and return the new instance.

Parameters:	() (axes) –
Returns:	`Space`

with_limits(limits, name)[source]¶

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

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

with_obs(obs)[source]¶

Sort by obs and return the new instance.

Parameters:	() (obs) –
Returns:	Space

interfaces¶