Add error bars to charts#77
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ezrakilty
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May 27, 2026
| # First we want the standard deviations as percentages of the mean: | ||
| data_stddev_percent = np.divide(data_stddev, data) | ||
| # Then we apply the formula for error propagation for division | ||
| ratio_stddev = ratio * np.sqrt(np.square(data_stddev_percent[:, 0]) + np.square(data_stddev_percent[:, 1])) |
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Rather than using all the colon/comma sort of notation, can we break out the different pieces and name them? That'll help me keep track.
ezrakilty
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May 27, 2026
| for i, engine in enumerate(engines): | ||
| # Get array of data from this engine | ||
| engine_data = data[(i)*(len(benches)):(i+1)*(len(benches))].flatten() | ||
| engine_data_stddev = data_stddev[(i)*(len(benches)):(i+1)*(len(benches))].flatten() |
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These lines are a lot to chew on. Any combinator (or refactoring) you could use to make them easier to read?
ezrakilty
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May 27, 2026
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Looks pretty good. I think we'll want to do some refactoring to make this clearer, but it's a great step forward!
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