HistogramPDF

class zfit.pdf.HistogramPDF(data, extended=None, norm=None, name='HistogramPDF')

Bases: zfit.core.binnedpdf.BaseBinnedPDFV1

Binned PDF resembling a histogram.

Simple histogram PDF that can be used to model a histogram as a PDF.

Parameters

• data (Union[PlottableHistogram, Iterable[PlottableHistogram]]) – Histogram to be used as PDF.

• 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 the ext_* methods and the counts (for binned PDFs).​​If True, the PDF will be extended automatically if the PDF is extended using the total number of events in the histogram. This is the default.​

• norm (Optional[zfit.Space]) – ​Normalization of the PDF. By default, this is the same as the default space of the PDF.​

• name (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 (Union[zfit.core.interfaces.ZfitGraphCachable, Iterable[zfit.core.interfaces.ZfitGraphCachable]]) –

• 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.

property dtype: tensorflow.python.framework.dtypes.DType

The dtype of the object.

Return type

DType

ext_integrate(limits, norm=None, *, norm_range=None, options=None)

Extended integral of the PDF, i.e. the expected counts.

Parameters

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

• norm (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space, None]) – ​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.​

Returns:

Return type

Union[float, Tensor]

ext_pdf(x, norm=None, *, norm_range=None)

DEPRECATED FUNCTION ARGUMENTS

Warning: SOME ARGUMENTS ARE DEPRECATED: (norm_range). They will be removed in a future version. Instructions for updating: Use norm instead.

Return type

Union[float, Tensor]

get_cache_deps(only_floating=True)

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

Parameters

only_floating – If True, only return floating Parameter

get_dependencies(only_floating=True)

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(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 (bool | None) – if a parameter is floating, e.g. if floating() returns True

• is_yield (bool | None) – 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 (bool | None) – 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.

Return type

str

property norm_range

DEPRECATED FUNCTION

Warning: 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 of x (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (norm_range). They will be removed in a future version. Instructions for updating: Use norm instead.

Parameters

• x (Union[float, Tensor]) – values to evaluate the PDF at. If this is a ZfitBinnedData-like object, a histogram of densities will be returned. If x is a ZfitUnbinnedData-like object, the densities will be evaluated at the points of x.

• norm (Union[Tuple[Tuple[float, ...]], Tuple[float, ...], bool, Space, None]) – ​Normalization of the function. By default, this is the norm 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 (Union[zfit.core.interfaces.ZfitGraphCachable, Iterable[zfit.core.interfaces.ZfitGraphCachable]]) –

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 (Optional[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, None]) – ​Limits of the sampling. By default, this is the same as the default space of the PDF.​

Returns

Sampled dataset

Return type

ZfitBinnedData

to_binneddata(**kwargs)

Create an Asimov dataset as BinnedData using either counts (for extended) or rel_counts

Parameters

() (**kwargs) – arguments to counts or rel_counts.

Returns

Binned data representing the Asimov dataset of this PDF.

Return type

BinnedData

to_hist(**kwargs)

Create an Asimov histogram as Hist using either counts (for extended) or rel_counts.

Parameters

() (**kwargs) – arguments to counts or rel_counts.

Returns

Histogram representing the Asimov dataset of this PDF.

Return type

hist.Hist

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

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