An example of Poisson PDF implementation for Spey. This plug-in implements the following likelihood distribution:

$$ \mathcal{L}(\mu) = \prod_{i\in{\rm bins}}{\rm Poiss}(n^i|\mu n_s^i + n_b^i) $$

where $n, n_s$ and $n_b$ are data, signal and background yields, respectively.

These files are designed to be implemented as a plug-in for the spey interface and can be used as a template. Please see Spey's documentation for details on implementing different features.

This plug-in can be installed from GitHub with pip

python -m pip install --upgrade "git+https://github.com/SpeysideHEP/example-plugin" 

or from the locally cloned repository

python -m pip install --upgrade . 

Once this plug in is installed, the model can be used through Spey as follows

stat_wrapper = spey.get_backend('example.poisson') stat_model = stat_wrapper( signal_yields= np.array([12,15]), background_yields = np.array([50.,48.]), data=np.array([36,33]) )

Notice that the name of the accessor 'example.poisson' is the same as in setup.py and the class' name attribute. The exclusion limit can be computed via the following command:

print(stat_model.exclusion_confidence_level()) # [0.9999807105228611]

Thats it! Have fun!