SimpleFuncV1#

class zfit.func.SimpleFuncV1(obs, func, name='Function', **params)[source]#

Bases: BaseFuncV1

Create a simple function out of of func with the observables obs depending on parameters.

Parameters:
add_cache_deps(cache_deps, allow_non_cachable=True)#

Add dependencies that render the cache invalid if they change.

Parameters:

  • cache_deps (ztyping.CacherOrCachersType) –

  • allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitGraphCachable will raise an error.

Raises:

TypeError – if one of the cache_dependents is not a ZfitGraphCachable _and_ allow_non_cachable if False.

analytic_integrate(limits, norm=None, *, norm_range=None)#

Analytical integration over function and raise Error if not possible.

Parameters:
Return type:

Union[float, Tensor]

Returns:

The integral value

Raises:

  • AnalyticIntegralNotImplementedError – If no analytical integral is available (for this limits).

  • NormRangeNotImplementedError – if the norm argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm is not available.

as_pdf()#

Create a PDF out of the function.

Return type:

ZfitPDF

Returns:

A PDF with the current function as the unnormalized probability.

create_sampler(n=None, limits=None, fixed_params=True)#

Create a Sampler that acts as Data but can be resampled, also with changed parameters and n.

If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.

Parameters:

  • n (Union[int, Tensor, str]) –

    The number of samples to be generated. Can be a Tensor that will be or a valid string. Currently implemented:

    • ’extended’: samples poisson(yield) from each pdf that is extended.

  • limits (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space]) – From which space to sample.

  • fixed_params (bool | list[ZfitParameter] | tuple[ZfitParameter]) – A list of Parameters that will be fixed during several resample calls. If True, all are fixed, if False, all are floating. If a Parameter is not fixed and its value gets updated (e.g. by a Parameter.set_value() call), this will be reflected in resample. If fixed, the Parameter will still have the same value as the Sampler has been created with when it resamples.

Return type:

Sampler

Returns:

Sampler

Raises:

  • NotExtendedPDFError – if ‘extended’ is chosen (implicitly by default or explicitly) as an option for n but the pdf itself is not extended.

  • ValueError – if n is an invalid string option.

  • InvalidArgumentError – if n is not specified and pdf is not extended.

property dtype: DType#

The dtype of the object.

func(x, name='value')#

The function evaluated at x.

Parameters:
Returns:

or dataset? Update: rather not, what would obs be?

Return type:

# TODO(Mayou36)

get_cache_deps(only_floating=True)#

Return a set of all independent Parameter that this object depends on.

Parameters:

only_floating (bool) – If True, only return floating Parameter

Return type:

OrderedSet

get_dependencies(only_floating: bool = True) → ztyping.DependentsType#

DEPRECATED FUNCTION

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).

Return type:

OrderedSet

get_params(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)#

Recursively collect parameters that this object depends on according to the filter criteria.

Which parameters should be included can be steered using the arguments as a filter.
  • None: do not filter on this. E.g. floating=None will return parameters that are floating as well as

    parameters that are fixed.

  • True: only return parameters that fulfil this criterion

  • False: only return parameters that do not fulfil this criterion. E.g. floating=False will return

    only parameters that are not floating.

Parameters:

  • floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True

  • is_yield (Optional[bool]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also non-yields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.

  • extract_independent (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.

Return type:

set[ZfitParameter]

property name: str#

The name of the object.

numeric_integrate(limits, norm=None, *, options=None, norm_range=None)#

Numerical integration over the model.

Parameters:

  • limits (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space]) – ​Limits of the integration.​

  • norm (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space]) – ​Normalization of the integration. By default, this is the same as the default space of the PDF. False means no normalization and returns the unnormed integral.​

  • options –

    ​Options for the integration. Additional options for the integration. Currently supported options are: - type: one of (bins)

    This hints that bins are integrated. A method that is vectorizable, non-dynamic and therefore less suitable for complicated functions is chosen.​

Return type:

Union[float, Tensor]

Returns:

The integral value

classmethod register_additional_repr(**kwargs)#

Register an additional attribute to add to the repr.

Parameters:

  • an (any keyword argument. The value has to be gettable from the instance (has to be) –

  • self. (attribute or callable method of) –

classmethod register_analytic_integral(cls, func, limits=None, priority=50, *, supports_norm=None, supports_norm_range=None, supports_multiple_limits=None)#

Register an analytic integral with the class. (deprecated arguments)

Deprecated: SOME ARGUMENTS ARE DEPRECATED: (supports_norm_range). They will be removed in a future version. Instructions for updating: Use supports_norm instead.

Parameters:

  • func (Callable) –

    A function that calculates the (partial) integral over the axes limits. The signature has to be the following:

    • x (ZfitData, None): the data for the remaining axes in a partial

      integral. If it is not a partial integral, this will be None.

    • limits (ZfitSpace): the limits to integrate over.

    • norm_range (ZfitSpace, None): Normalization range of the integral.

      If not supports_supports_norm_range, this will be None.

    • params (Dict[param_name, zfit.Parameters]): The parameters of the model.

    • model (ZfitModel):The model that is being integrated.

  • limits (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space]) – If a Space is given, it is used as limits. Otherwise arguments to instantiate a Range class can be given as follows.|limits_init|

  • priority (int | float) – Priority of the function. If multiple functions cover the same space, the one with the highest priority will be used.

  • supports_multiple_limits (bool) – If True, the ``limits` given to the integration function can have multiple limits. If False, only simple limits will pass through and multiple limits will be auto-handled.

  • supports_norm (bool) – If True, norm argument to the function may not be None. If False, norm will always be None and care is taken of the normalization automatically.

Return type:

None

register_cacher(cacher)#

Register a cacher that caches values produces by this instance; a dependent.

Parameters:

cacher (ztyping.CacherOrCachersType) –

classmethod register_inverse_analytic_integral(func)#

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

Parameters:

func (Callable) – A function with the signature func(x, params), where x is a Data object and params is a dict.

Return type:

None

reset_cache_self()#

Clear the cache of self and all dependent cachers.

sample(n=None, limits=None, x=None)#

Sample n points within limits from the model.

If limits is not specified, space is used (if the space contains limits). If n is None and the model is an extended pdf, ‘extended’ is used by default.

Parameters:

  • n (Union[int, Tensor, str]) –

    The number of samples to be generated. Can be a Tensor that will be or a valid string. Currently implemented:

    • ’extended’: samples poisson(yield) from each pdf that is extended.

  • limits (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space]) – In which region to sample in

Return type:

SampleData

Returns:

SampleData(n_obs, n_samples)

Raises:

  • NotExtendedPDFError – if ‘extended’ is (implicitly by default or explicitly) chosen as an option for n but the pdf itself is not extended.

  • ValueError – if n is an invalid string option.

  • InvalidArgumentError – if n is not specified and pdf is not extended.

update_integration_options(draws_per_dim=None, mc_sampler=None, tol=None, max_draws=None, draws_simpson=None)#

Set the integration options.

Parameters:

  • max_draws (default ~1'000'000) – Maximum number of draws when integrating . Typically 500’000 - 5’000’000.

  • tol – Tolerance on the error of the integral. typically 1e-4 to 1e-8

  • draws_per_dim – The draws for MC integration to do per iteration. Can be set to 'auto’.

  • draws_simpson – Number of points in one dimensional Simpson integration. Can be set to 'auto'.