BinnedData#

class zfit.data.BinnedData(*, holder)[source]#

Bases: ZfitBinnedData

Create a binned data object from a BinnedHolder.

Prefer to use the constructors from_* of BinnedData like from_hist(), from_tensor() or from_unbinned().

Parameters: holder –

classmethod from_tensor(space, values, variances=None)[source]#

Create a binned dataset defined in space where values are considered to be the counts.

Parameters

space (ZfitSpace) – Binned space of the data. The space is used to define the binning and the limits of the data.
values (znp.array) – Corresponds to the counts of the histogram. Follows the definition of the Unified Histogram Interface (UHI).
variances (znp.array | None) –
Corresponds to the uncertainties of the histogram. If True, the uncertainties are created assuming that values have been drawn from a Poisson distribution. Follows the definition of the Unified Histogram Interface (UHI).

Return type

BinnedData

classmethod from_unbinned(space, data)[source]#

Convert an unbinned dataset to a binned dataset.

Parameters

space (ZfitSpace) – Binned space of the data. The space is used to define the binning and the limits of the data.
data (ZfitData) – Unbinned data to be converted to binned data

Returns

The binned data

Return type

ZfitBinnedData

classmethod from_hist(h)[source]#

Create a binned dataset from a hist histogram.

A histogram (following the UHI definition) with named axes.

Parameters: h (NamedHist) – A NamedHist. The axes will be used as the binning in zfit.
Return type: BinnedData

with_obs(obs)[source]#

Return a subset of the data in the ordering of obs.

Parameters: obs (Union[str, Iterable[str], Space]) – Which obs to return
Return type: BinnedData

to_hist()[source]#

Convert the binned data to a NamedHist.

While a binned data object can be used inside zfit (PDFs,…), it lacks many convenience features that the hist library offers, such as plots.

Return type: Hist

values()[source]#

Values of the histogram as an ndim array.

Compared to hist, zfit does not make a difference between a view and a copy; tensors are immutable. This distinction is made in the traced function by the compilation backend.

Return type: array
Returns: Tensor of shape (nbins0, nbins1, …) with nbins the number of bins in each observable.

variances()[source]#

Variances, if available, of the histogram as an ndim array.

Compared to hist, zfit does not make a difference between a view and a copy; tensors are immutable. This distinction is made in the traced function by the compilation backend.

Return type: None | znp.array
Returns: Tensor of shape (nbins0, nbins1, …) with nbins the number of bins in each observable.

counts()[source]#

Effective counts of the histogram as a ndim array.

Compared to hist, zfit does not make a difference between a view and a copy; tensors are immutable. This distinction is made in the traced function by the compilation backend.

Returns: Tensor of shape (nbins0, nbins1, …) with nbins the number of bins in each observable.

to_unbinned()[source]#

Use the bincenters as unbinned data with values as counts.

Returns: Unbinned data
Return type: ZfitData

classmethod __class_getitem__(params)#

Parameterizes a generic class.

At least, parameterizing a generic class is the main thing this method does. For example, for some generic class Foo, this is called when we do Foo[int] - there, with cls=Foo and params=int.

However, note that this method is also called when defining generic classes in the first place with class Foo(Generic[T]): ….