pyhf.simplemodels.correlated_background#

pyhf.simplemodels.correlated_background(signal, bkg, bkg_up, bkg_down, batch_size=None, validate=True, poi_name='mu')[source]#

Construct a simple single channel Model with a histosys modifier representing a background with a fully correlated bin-by-bin uncertainty.

Parameters:

signal (list) – The data in the signal sample.
bkg (list) – The data in the background sample.
bkg_up (list) – The background sample under an upward variation corresponding to \(\alpha=+1\).
bkg_down (list) – The background sample under a downward variation corresponding to \(\alpha=-1\).
batch_size (None or int) – Number of simultaneous (batched) Models to compute.
validate (bool) – If True, validate the model before returning. Only set this to False if you have an experimental use case and know what you’re doing.
poi_name (str) – The Model parameter of interest name. Defaults to "mu".

Returns:

The statistical model adhering to the model.json schema.

Return type:

Model

Changed in version 0.8.0: Added poi_name argument.

Example

>>> import pyhf
>>> pyhf.set_backend("numpy")
>>> model = pyhf.simplemodels.correlated_background(
...     signal=[12.0, 11.0],
...     bkg=[50.0, 52.0],
...     bkg_up=[45.0, 57.0],
...     bkg_down=[55.0, 47.0],
... )
>>> model.schema
'model.json'
>>> model.config.channels
['single_channel']
>>> model.config.samples
['background', 'signal']
>>> model.config.parameters
['correlated_bkg_uncertainty', 'mu']
>>> model.expected_data(model.config.suggested_init())
array([62., 63.,  0.])