Fix test_priors_to_measurements #336
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## develop #336 +/- ##
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Coverage 74.36% 74.36%
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| x_scaled_dict = dict( | ||
| zip( | ||
| original_problem.x_free_ids, | ||
| original_problem.x_nominal_free_scaled, | ||
| strict=True, | ||
| ) | ||
| ) |
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Should it really be scaled values here? I assume the prior is either (1) unscaled or (2) on parameterScale. The former means this should be unscaled, and the latter is already handled in the observable formulae for these dummy prior observables, so no scaling is required here?
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So far, this test only handles PARAMETER_SCALE_NORMAL (to be extended in #329). Yes, the scaling is handled in the observable formulae, but here, we need to write the simulation table. For that, we do not evaluate the observable formula using the given parameters, but write the result directly. Therefore, we need the scaled ones.
Fixes an issue in `test_priors_to_measurements` which led to evaluating the prior at the wrong parameter values (using the location parameter of the prior instead of the actually estimated parameters). The problem was in the test code, not the tested code.
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Co-authored-by: Dilan Pathirana <[email protected]>
Fixes an issue in
test_priors_to_measurementswhich led to evaluating the prior at the wrong parameter values (using the location parameter of the prior instead of the actually estimated parameters). The problem was in the test code, not the tested code.