pyhf.simplemodels.uncorrelated_background

pyhf.simplemodels.uncorrelated_background(signal, bkg, bkg_uncertainty, batch_size=None, validate=True)[source]

Construct a simple single channel Model with a shapesys modifier representing an uncorrelated background uncertainty.

Example

>>> import pyhf
>>> pyhf.set_backend("numpy")
>>> model = pyhf.simplemodels.uncorrelated_background(
...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
... )
>>> model.schema
'model.json'
>>> model.config.channels
['singlechannel']
>>> model.config.samples
['background', 'signal']
>>> model.config.parameters
['mu', 'uncorr_bkguncrt']
>>> model.expected_data(model.config.suggested_init())
array([ 62.        ,  63.        , 277.77777778,  55.18367347])
Parameters

  • signal (list) – The data in the signal sample

  • bkg (list) – The data in the background sample

  • bkg_uncertainty (list) – The statistical uncertainty on the background sample counts

  • 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.

Returns

The statistical model adhering to the model.json schema

Return type

Model