During the fits evaluate the expected CLs band and return it as part of the result dict. Generate a results dict as the results come in. After the run is finished write the results dict to disk as a JSON file. This can then be easily consumed for further analysis or visualization.
* During the fits evaluate the expected CLs band and return it as part of the result
dict.
* Generate a results dict as the results come in. After the run is finished write the
results dict to disk as a JSON file.
* Correct filename typo in the README.
During the fits evaluate the expected CLs band and return it as part of the result dict. Generate a results dict as the results come in. After the run is finished write the results
dict
to disk as a JSON file. This can then be easily consumed for further analysis or visualization.Note: This is the code that was used for the statistical inference with pyhf and cabinetry examples at the IRIS-HEP Analysis Grand Challenge Tools 2022 Workshop.