BinwiseScaleModifier#
- class zfit.pdf.BinwiseScaleModifier(pdf, modifiers=None, extended=None, norm=None, name='BinnedTemplatePDF')[source]#
Bases:
BaseBinnedFunctorPDF
Modifier that scales each bin separately of the pdf.
Binwise modification can be used to account for uncorrelated or correlated uncertainties.
- Parameters
pdf (
ZfitBinnedPDF
) – Binned pdf to be modified.modifiers (
bool
|Mapping
[str
,TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter])]) – Modifiers for each bin.extended (
Union
[bool
,TypeVar
(ParamTypeInput
, zfit.core.interfaces.ZfitParameter,Union
[int
,float
,complex
,Tensor
, zfit.core.interfaces.ZfitParameter]),None
]) – The overall yield of the PDF. If this is parameter-like, it will be used as the yield, the expected number of events, and the PDF will be extended. An extended PDF has additional functionality, such as theext_*
methods and thecounts
(for binned PDFs).norm (
Optional
[Space
]) – Normalization of the PDF. By default, this is the same as the default space of the PDF.name (
Optional
[str
]) – Human-readable name or label of the PDF for better identification. Has no programmatical functional purpose as identification.
- 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.
- counts(x=None, norm=None)#
Calculate the number of events in each bin.
This is the integrals of the PDF in each bin.
- Parameters
- Returns
A histogram with the number of events in each bin.
- Return type
ZfitBinnedData
- 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
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 aParameter
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
BinnedSampler
- Returns
BinnedSampler
- 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.
- ext_integrate(limits, norm=None, *, options=None, norm_range=None)#
Extended integral of the PDF, i.e. the expected counts or the integral scaled by the yield.
- 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
- Returns
Scalar integration value.
- ext_pdf(x, norm=None, *, norm_range=None)#
Probability density function scaled by yield, evaluated at
x
or in the bins ofx
- Parameters
x (
Union
[float
,Tensor
]) – Values to evaluate the PDF at. If this is aZfitBinnedData
-like object, a histogram of densities will be returned. If x is aZfitUnbinnedData
-like object, the densities will be evaluated at the points ofx
.norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the function. By default, this is thenorm
of the PDF (which by default is the same as the space of the PDF).
- Returns
- binnedpdf.out.problike| If the input was unbinned, it returns an array
of shape (nevents,). If the input was binned, the dimensions and ordering of the axes corresponds to the input axes.
- Return type
|@doc
- get_cache_deps(only_floating=True)#
Return a set of all independent
Parameter
that this object depends on.- Parameters
only_floating (
bool
) – IfTrue
, only return floatingParameter
- 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.
- None: do not filter on this. E.g.
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.
- False: only return parameters that do not fulfil this criterion. E.g.
- Parameters
floating (
Optional
[bool
]) – if a parameter is floating, e.g. iffloating()
returns Trueis_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 aZfitIndependentParameter
.
- Return type
set
[ZfitParameter
]
- property models: list[zfit.core.interfaces.ZfitModel]#
Return the models of this Functor.
Can be pdfs or funcs.
- property norm_range#
DEPRECATED FUNCTION
Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: use norm instead.
- pdf(x, norm=None, *, norm_range=None)#
Probability density function, evaluated at
x
or in the bins ofx
- Parameters
x (
Union
[float
,Tensor
]) – Values to evaluate the PDF at. If this is aZfitBinnedData
-like object, a histogram of densities will be returned. If x is aZfitUnbinnedData
-like object, the densities will be evaluated at the points ofx
.norm (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Normalization of the function. By default, this is thenorm
of the PDF (which by default is the same as the space of the PDF).
- Returns
probability density
- Return type
Array-like
- register_cacher(cacher)#
Register a cacher that caches values produces by this instance; a dependent.
- Parameters
cacher (ztyping.CacherOrCachersType) –
- rel_counts(x=None, norm=None)#
Calculate the relative number of events in each bin.
This is the integrals of the PDF in each bin divided by the integral of the PDF over the whole space. It is not equal to the density but rather a histogram scaled to 1.
- Parameters
- Returns
A histogram with the relative number of events in each bin.
- Return type
ZfitBinnedData
- reset_cache_self()#
Clear the cache of self and all dependent cachers.
- sample(n=None, limits=None)#
Draw a random binned sample from the PDF.
- Parameters
n (
int
) – Number of samples to draw. For an extended PDF, the argument is optional and will be the poisson-fluctuated expected number of events, i.e. the yield.limits (
Union
[Tuple
[Tuple
[float
,...
]],Tuple
[float
,...
],bool
,Space
]) – Limits of the sampling. By default, this is the same as the default space of the PDF.
- Returns
Sampled dataset
- Return type
ZfitBinnedData
- to_binned(space, *, extended=None, norm=None)#
Convert the PDF to a binned PDF, returns self.
For compatibility with unbinned PDFs.
- to_binneddata(**kwargs)#
Create an Asimov dataset as
BinnedData
using eithercounts
(for extended) orrel_counts
.- Parameters
() (**kwargs) – arguments to
counts
orrel_counts
.- Returns
Binned data representing the Asimov dataset of this PDF.
- Return type
- to_hist(**kwargs)#
Create an Asimov histogram as
Hist
using eithercounts
(for extended) orrel_counts
.- Parameters
() (**kwargs) – arguments to
counts
orrel_counts
.- Returns
Histogram representing the Asimov dataset of this PDF.
- Return type
hist.Hist
- to_unbinned()#
Convert the PDF to an unbinned PDF.
- values(*, var=None)#
Histogram values that are either the counts or the normalized counts.
If the PDF is extended, the counts are returned, otherwise the normalized counts are returned.
- Returns
Histogram values
- Return type
ZfitBinnedData