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Hmm, do you have the resulting dataframe output? I'd expect such warnings will result in some NaN measurements (which is the typical behavior of the measurement libraries when a measurement fails for some objects).
Would be nice if there is a way that the user can look at such warnings, but it's not critical information we need to highlight or something that should make us fail a workflow. If a user defines a workflow that results in many NaNs, then they may want to change their workflow (=> check relevant logs to understand what happened or just test their workflow better) or they may just choose to ignore measurements with many NaNs.
While running tests within #167 (see for instance https://github.com/fractal-analytics-platform/fractal-tasks-core/actions/runs/3394321877/jobs/5642784994), we have warnings like
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