Esempio n. 1
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        template_settings["params"], minimizer_settings,
        args.save_steps, check_octant)

    output[data_tag]["true_h_fiducial"] = trials

    # If we do not run the alt_fit, then we simply continue in the for
    # loop and the LLR distributions will be interpreted as the
    # ability to discriminate between hierarchies, without fitting for
    # the false hierarchy parameters that match best to the True
    # hierarchy fiducial model.

    if not args.no_alt_fit:
        logging.info("Running false hierarchy best fit...")
        output[data_tag]["false_h_best_fit"] = {}

        false_h_params = fix_non_atm_params(template_settings['params'])
        false_h_settings, llh_data = getAltHierarchyBestFit(
            asimov_data, template_maker, false_h_params, minimizer_settings,
            (not data_normal), check_octant)

        asimov_data_null = get_asimov_fmap(
            template_maker=template_maker,
            fiducial_params=false_h_settings,
            channel=false_h_settings['channel'])

        # Store all data tag related inputs:
        output[data_tag]['false_h_best_fit']['false_h_settings'] = false_h_settings
        output[data_tag]['false_h_best_fit']['llh_null'] = llh_data

        trials = get_llh_hypothesis(
            data_tag, asimov_data_null, args.ntrials, template_maker,
Esempio n. 2
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    'minimizer_settings': minimizer_settings
}

asimov_data = {}
asimov_data_null = {}
alt_mh_settings = {}
for data_tag, data_normal in [('true_NMH', True), ('true_IMH', False)]:
    tprofile.info("Assuming: %s" % data_tag)

    output[data_tag] = {}

    # Get Asimov data set for assuming true: data_tag
    asimov_data = getAsimovData(template_maker, template_settings['params'],
                                data_normal)

    alt_params = fix_non_atm_params(template_settings['params'])
    alt_mh_settings, llh_data = getAltHierarchyBestFit(
        asimov_data, template_maker, alt_params, minimizer_settings,
        (not data_normal), check_octant)

    asimov_data_null = get_asimov_fmap(template_maker=template_maker,
                                       fiducial_params=alt_mh_settings,
                                       channel=alt_mh_settings['channel'])

    # Store all data tag related inputs:
    output[data_tag]['asimov_data'] = asimov_data
    output[data_tag]['asimov_data_null'] = asimov_data_null
    output[data_tag]['alt_mh_settings'] = alt_mh_settings
    output[data_tag]['llh_null'] = llh_data

    # If we are not taking the best fit of the asimov data to the