Reduce number of printed warnings
@@ -1467,13 +1467,13 @@ class SampleAugmenter(DataAnalyzer):
n_too_large_weights = np.sum(p_sampling > 1./n_eff_forced)
if n_too_large_weights > 0:
n_too_large_weights_warnings += 1
- if n_too_large_weights_warnings <= 3:
+ if n_too_large_weights_warnings <= 1:
logger.warning(
"For this value of theta, %s / %s events have too large weight and will be ignored",
n_too_large_weights,
p_sampling.size,
)
- if n_too_large_weights_warnings == 3:
+ if n_too_large_weights_warnings == 1:
logger.warning("Skipping warnings about too large weights in the future...")
p_sampling[p_sampling > 1./n_eff_forced] = 0.0
#####
