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One of the builds in a metadata-only PR (#219) just failed with:
> ll = lf.limit('er_rate_multiplier', bestfit, confidence_level=0.9, kind='lower') tests/test_inference.py:88: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ flamedisx/likelihood.py:707: in limit res = opt( flamedisx/inference.py:374: in minimize result, llval = self.parse_result(result) flamedisx/inference.py:461: in parse_result self.fail(f"Scipy optimizer failed: " _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <flamedisx.inference.ScipyIntervalObjective object at 0x7f83a450feb0> message = 'Scipy optimizer failed: status = 0: The maximum number of function evaluations is exceeded.' def fail(self, message): if self.allow_failure: warnings.warn(message, OptimizerWarning) else: > raise OptimizerFailure(message) E flamedisx.inference.OptimizerFailure: Scipy optimizer failed: status = 0: The maximum number of function evaluations is exceeded. flamedisx/inference.py:396: OptimizerFailure
but succeeded on a rerun. Apparently our tests are not fully deterministic.
The text was updated successfully, but these errors were encountered:
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One of the builds in a metadata-only PR (#219) just failed with:
but succeeded on a rerun. Apparently our tests are not fully deterministic.
The text was updated successfully, but these errors were encountered: