zfit.func.
SumFunc
Bases: zfit.models.functions.BaseFunctorFunc
zfit.models.functions.BaseFunctorFunc
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
Union
ForwardRef
Iterable
allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
bool
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
analytic_integrate
Analytical integration over function and raise Error if not possible.
limits (Union[Tuple[Tuple[float, …]], Tuple[float, …], bool, ForwardRef]) – the limits to integrate over
Tuple
float
norm_range (Union[Tuple[Tuple[float, …]], Tuple[float, …], bool, ForwardRef]) – the limits to normalize over
Union[float, Tensor]
Tensor
The integral value
AnalyticIntegralNotImplementedError – If no analytical integral is available (for this limits).
NormRangeNotImplementedError – if the norm_range argument is not supported. This means that no analytical normalization is available, explicitly: the analytical integral over the limits = norm_range is not available.
as_pdf
Create a PDF out of the function
ZfitPDF
A PDF with the current function as the unnormalized probability.
convert_sort_space
Convert the inputs (using eventually obs, axes) to ZfitSpace and sort them according to own obs.
ZfitSpace
obs (Union[str, Iterable[str], Space, ZfitLimit, Tensor, ndarray, Iterable[float], float, Tuple[float], List[float], bool, None]) –
str
Space
ZfitLimit
ndarray
List
None
axes (Union[int, Iterable[int], None]) –
int
limits (Union[ZfitLimit, Tensor, ndarray, Iterable[float], float, Tuple[float], List[float], bool, None]) –
Returns:
Optional[ZfitSpace]
Optional
create_sampler
Create a Sampler that acts as Data but can be resampled, also with changed parameters and n.
Sampler
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.
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, ForwardRef]) – From which space to sample.
fixed_params (Union[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.
ZfitParameter
Parameter
py:class:~`zfit.core.data.Sampler`
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.
dtype
The dtype of the object
DType
func
The function evaluated at x.
x (Union[float, Tensor]) –
name (str) –
or dataset? Update: rather not, what would obs be?
# TODO(Mayou36)
get_cache_deps
Return a set of all independent Parameter that this object depends on.
only_floating (bool) – If True, only return floating Parameter
OrderedSet
get_dependencies
DEPRECATED FUNCTION
Warning: 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).
get_params
Recursively collect parameters that this object depends on according to the filter criteria.
parameters that are fixed.
True: only return parameters that fulfil this criterion
only parameters that are not floating.
floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True
floating()
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.
Set[ZfitParameter]
Set
models
Return the models of this Functor. Can be pdfs or funcs.
List[ZfitModel]
ZfitModel
name
The name of the object.
numeric_integrate
Numerical integration over the model.
register_additional_repr
Register an additional attribute to add to the repr.
keyword argument. The value has to be gettable from the instance (has to be an (any) –
or callable method of self. (attribute) –
register_analytic_integral
Register an analytic integral with the class.
func (Callable) –
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 partialintegral. 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.
ZfitData
integral. If it is not a partial integral, this will be None.
limits (ZfitSpace): the limits to integrate over.
If not supports_supports_norm_range, this will be None.
params (Dict[param_name, zfit.Parameters]): The parameters of the model.
zfit.Parameters
model (ZfitModel):The model that is being integrated.
limits (Union[Tuple[Tuple[float, …]], Tuple[float, …], bool, ForwardRef]) – If a :py:class:~`zfit.Space` is given, it is used as limits. Otherwise arguments to instantiate a Range class can be given as follows.|limits_init|
priority (Union[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_range (bool) – If True, norm_range argument to the function may not be None. If False, norm_range will always be None and care is taken of the normalization automatically.
register_cacher
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
register_inverse_analytic_integral
Register an inverse analytical integral, the inverse (unnormalized) cdf.
func (Callable) – A function with the signature func(x, params), where x is a Data object and params is a dict.
reset_cache_self
Clear the cache of self and all dependent cachers.
sample
Sample n points within limits from the model.
limits (Union[Tuple[Tuple[float, …]], Tuple[float, …], bool, ForwardRef]) – In which region to sample in
SampleData
SampleData(n_obs, n_samples)
NotExtendedPDFError – if ‘extended’ is (implicitly by default or explicitly) chosen as an option for n but the pdf itself is not extended.
update_integration_options
Set the integration options.
draws_per_dim – The draws for MC integration to do
mc_sampler –