BinnedData#
- class zfit.data.BinnedData(*, holder)[source]#
Bases:
ZfitBinnedData
Create a binned data object from a
BinnedHolder
.Prefer to use the constructors
from_*
ofBinnedData
likefrom_hist()
,from_tensor()
orfrom_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 thatvalues
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:
- 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. :rtype:
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 doFoo[int]
- there, withcls=Foo
andparams=int
.However, note that this method is also called when defining generic classes in the first place with
class Foo(Generic[T]): ...
.