Comments (2)
ainoam
commented on September 4, 2024
Thanks for suggesting @tensorpro.
Sounds like this becomes an issue where a run of interest contains significantly less data points than the others. If one were to naively adjust the sampling to match the run with the smallest number of points, this could incur an unnecessary load as significantly more data points would be retrieved from the other runs.
Perhaps a better approach for this use case could be to equalize the x-axis range so that only the points being compared would be retrieved (and as a result, the same sampling would be applied)?
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