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Stop emitting misleading warning when constructing results of a fit with daemon_flux parameters #957

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Stop emitting misleading warning when constructing results of a fit with daemon_flux parameters #957

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34 changes: 23 additions & 11 deletions pisa/analysis/analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,28 +294,35 @@ def detailed_metric_info(self, new_info):
[d[self.metric[0 if not is_detectors else i]]["maps"]["total"] for
i, d in enumerate(self._detailed_metric_info)]
)
# Set off by assuming we're unable to obtain the total prior contribution
metric_val_from_priors = np.nan
if is_detectors:
# 1)
# TODO: We don't have access to the Detectors instance itself here
# -> no straightforward way to correctly determine the total prior
# contribution (cf. Detectors.init_params())
metric_val_from_priors = np.nan
pass
elif not "priors_penalty_total" in self._detailed_metric_info[0][self.metric[0]]:
# 2) Don't attempt to manually compute total prior contribution:
# there may be correlations!
pass
else:
# 2)
# can obtain the total prior contribution from any of the list entries
metric_val_from_priors = np.sum(
self._detailed_metric_info[0][self.metric[0]]["priors"]
)
total_metric_val_from_detailed = metric_val_from_maps + metric_val_from_priors
# 3) Can read out the pre-computed total prior contribution from
# any of the list entries
metric_val_from_priors = self._detailed_metric_info[0][self.metric[0]]["priors_penalty_total"]
else:
self._detailed_metric_info = self.deserialize_detailed_metric_info(new_info)
if self.metric_val is None:
return
# sanity check on metric value
total_metric_val_from_detailed = (
self._detailed_metric_info[self.metric[0]]["maps"]["total"] +
np.sum(self._detailed_metric_info[self.metric[0]]["priors"])
)
metric_val_from_priors = np.nan
if "priors_penalty_total" in self._detailed_metric_info[self.metric[0]]:
metric_val_from_priors = self._detailed_metric_info[self.metric[0]]["priors_penalty_total"]
metric_val_from_maps = self._detailed_metric_info[self.metric[0]]["maps"]["total"]

# Get the total and compare if possible
total_metric_val_from_detailed = metric_val_from_maps + metric_val_from_priors

if not np.isnan(total_metric_val_from_detailed):
if not recursiveEquality(total_metric_val_from_detailed, self.metric_val):
logging.warning(
Expand Down Expand Up @@ -455,6 +462,7 @@ def get_detailed_metric_info(data_dist, hypo_maker, hypo_asimov_dist, params,
if include_maps_binned:
name_vals_d['maps_binned'] = MapSet(maps_binned)
name_vals_d['priors'] = params.priors_penalties(metric=metric)
name_vals_d['priors_penalty_total'] = params.priors_penalty(metric=metric)
detailed_metric_info[m] = name_vals_d
return detailed_metric_info

Expand All @@ -479,6 +487,10 @@ def deserialize_detailed_metric_info(info_dict):
# Deserialize if necessary
name_vals_d["maps_binned"] = MapSet(**info_dict[m]["maps_binned"])
name_vals_d["priors"] = info_dict[m]["priors"]
if "priors_penalty_total" in info_dict[m]:
# Don't assume that this entry exists either, so that old fit
# results can still be deserialized
name_vals_d["priors_penalty_total"] = info_dict[m]["priors_penalty_total"]
detailed_metric_info[m] = name_vals_d
return detailed_metric_info

Expand Down